update

correlation/NAS_Bench_201.py

@@ -11,24 +11,60 @@ from models import get_cell_based_tiny_net
 import pickle
 
 
+def get_score(net, x, device, measure='meco'):
+    result_list = []
+
+    def forward_hook(module, data_input, data_output):
+        fea = data_output[0].clone().detach()
+        n = torch.tensor(fea.shape[0])
+        fea = fea.reshape(n, -1)
+        if measure == 'meco':
+            corr = torch.corrcoef(fea)
+            corr[torch.isnan(corr)] = 0
+            corr[torch.isinf(corr)] = 0
+            values = torch.linalg.eig(corr)[0]
+            result = torch.min(torch.real(values))
+        elif measure == 'meco_opt':
+            idxs = random.sample(range(n), 8)
+            fea = fea[idxs, :]
+            corr = torch.corrcoef(fea)
+            corr[torch.isnan(corr)] = 0
+            corr[torch.isinf(corr)] = 0
+            values = torch.linalg.eig(corr)[0]
+            result = torch.min(torch.real(values)) * n / 8
+        result_list.append(result)
+    for name, modules in net.named_modules():
+        modules.register_forward_hook(forward_hook)
+    x = x.to(device)
+    net(x)
+    results = torch.tensor(result_list)
+    results = results[torch.logical_not(torch.isnan(results))]
+    results = results[torch.logical_not(torch.isinf(results))]
+    res = torch.sum(results)
+    result_list.clear()
+
+    return res.item()
+
 def get_num_classes(args):
     return 100 if args.dataset == 'cifar100' else 10 if args.dataset == 'cifar10' else 120
 
 
 def parse_arguments():
     parser = argparse.ArgumentParser(description='Zero-cost Metrics for NAS-Bench-201')
-    parser.add_argument('--api_loc', default='../data/NAS-Bench-201-v1_0-e61699.pth',
-                        type=str, help='path to API')
+    # parser.add_argument('--api_loc', default='../data/NAS-Bench-201-v1_0-e61699.pth',
+    #                     type=str, help='path to API')
     parser.add_argument('--outdir', default='./',
                         type=str, help='output directory')
+    parser.add_argument('--search_space', type=str, default='tss', choices=['tss', 'sss'])
     parser.add_argument('--init_w_type', type=str, default='none',
                         help='weight initialization (before pruning) type [none, xavier, kaiming, zero, one]')
     parser.add_argument('--init_b_type', type=str, default='none',
                         help='bias initialization (before pruning) type [none, xavier, kaiming, zero, one]')
-    parser.add_argument('--batch_size', default=64, type=int)
-    parser.add_argument('--dataset', type=str, default='ImageNet16-120',
+    parser.add_argument('--measure', type=str, default='meco', choices=['meco', 'meco_opt'])
+    parser.add_argument('--batch_size', default=1, type=int)
+    parser.add_argument('--dataset', type=str, default='cifar10',
                         help='dataset to use [cifar10, cifar100, ImageNet16-120]')
-    parser.add_argument('--gpu', type=int, default=5, help='GPU index to work on')
+    parser.add_argument('--gpu', type=int, default=0, help='GPU index to work on')
     parser.add_argument('--data_size', type=int, default=32, help='data_size')
     parser.add_argument('--num_data_workers', type=int, default=2, help='number of workers for dataloaders')
     parser.add_argument('--dataload', type=str, default='appoint', help='random, grasp, appoint supported')
@@ -49,11 +85,9 @@ if __name__ == '__main__':
     args = parse_arguments()
     print(args.device)
 
-    if args.noacc:
-        api = pickle.load(open(args.api_loc,'rb'))
-    else:
-        from nas_201_api import NASBench201API as API
-        api = API(args.api_loc)
+    from nats_bench import create
+
+    api = create(None, args.search_space, fast_mode=True, verbose=False)
 
     torch.manual_seed(args.seed)
     torch.backends.cudnn.deterministic = True
@@ -61,9 +95,6 @@ if __name__ == '__main__':
 
     train_loader, val_loader = get_cifar_dataloaders(args.batch_size, args.batch_size, args.dataset, args.num_data_workers, resize=args.data_size)
     x, y = next(iter(train_loader))
-    # random data
-    # x = torch.rand((args.batch_size, 3, args.data_size, args.data_size))
-    # y = 0
 
     cached_res = []
     pre = 'cf' if 'cifar' in args.dataset else 'im'
@@ -81,7 +112,6 @@ if __name__ == '__main__':
             break
 
         res = {'i': i, 'arch': arch_str}
-        # print(arch_str)
         if args.search_space == 'tss':
             net = nasbench2.get_model_from_arch_str(arch_str, get_num_classes(args))
             arch_str2 = nasbench2.get_arch_str_from_model(net)
@@ -91,21 +121,22 @@ if __name__ == '__main__':
                 raise ValueError
         elif args.search_space == 'sss':
             config = api.get_net_config(i, args.dataset)
-            # print(config)
             net = get_cell_based_tiny_net(config)
         net.to(args.device)
-        # print(net)
 
         init_net(net, args.init_w_type, args.init_b_type)
 
-        # print(x.size(), y)
-        measures = get_score(net, x, i, args.device)
+        measures = get_score(net, x, args.device, measure=args.measure)
 
-        res['meco'] = measures
+        res[f'{args.measure}'] = measures
 
         if not args.noacc:
-            info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None,
-                                     hp='200', is_random=False)
+            if args.search_space == 'tss':
+                info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None,
+                                         hp='200', is_random=False)
+            else:
+                info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None,
+                                         hp='90', is_random=False)
 
             trainacc = info['train-accuracy']
             valacc = info['valid-accuracy']

correlation/compute_rho.py

@@ -0,0 +1,24 @@
+import pandas as pd
+import pickle
+
+# path = 'result/sss_cf10_meco.p'
+path = 'nb2_sss_cf10_seed42_dlappoint_dlinfo1_initwnone_initbnone_1.p'
+meco = []
+accs = []
+with open(path, 'rb') as f:
+    while True:
+        try:
+            fl = pickle.load(f)
+            meco.append(fl['meco'])
+            accs.append(fl['testacc'])
+        except:
+            break
+
+N = len(meco)
+print(N)
+df = pd.DataFrame({
+    'meco':meco[:N],
+    'acc': accs[:N]
+    })
+print(df.corr(method='spearman'))
+

correlation/foresight/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from .version import *

correlation/foresight/dataset.py

@@ -0,0 +1,133 @@
+
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+
+from torchvision.datasets import MNIST, CIFAR10, CIFAR100, SVHN
+from torchvision.transforms import Compose, ToTensor, Normalize
+from torchvision import transforms
+from torch.utils.data import TensorDataset, DataLoader
+import torch
+
+from .imagenet16 import *
+
+
+def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_workers, resize=None, datadir='_dataset'):
+    # print(dataset)
+    if 'ImageNet16' in dataset:
+        mean = [x / 255 for x in [122.68, 116.66, 104.01]]
+        std  = [x / 255 for x in [63.22,  61.26 , 65.09]]
+        size, pad = 16, 2
+    elif 'cifar' in dataset:
+        mean = (0.4914, 0.4822, 0.4465)
+        std = (0.2023, 0.1994, 0.2010)
+        size, pad = 32, 4
+    elif 'svhn' in dataset:
+        mean = (0.5, 0.5, 0.5)
+        std = (0.5, 0.5, 0.5)
+        size, pad = 32, 0
+    elif dataset == 'ImageNet1k':
+        from .h5py_dataset import H5Dataset
+        size,pad = 224,2
+        mean = (0.485, 0.456, 0.406)
+        std  = (0.229, 0.224, 0.225)
+        #resize = 256
+    elif 'random' in dataset:
+        mean = (0.5, 0.5, 0.5)
+        std = (1, 1, 1)
+        size, pad = 32, 0
+
+    if resize is None:
+        resize = size
+
+    train_transform = transforms.Compose([
+        transforms.RandomCrop(size, padding=pad),
+        transforms.Resize(resize),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean,std),
+    ])
+
+    test_transform = transforms.Compose([
+        transforms.Resize(resize),
+        transforms.ToTensor(),
+        transforms.Normalize(mean,std),
+    ])
+
+    if dataset == 'cifar10':
+        train_dataset = CIFAR10(datadir, True, train_transform, download=True)
+        test_dataset = CIFAR10(datadir, False, test_transform, download=True)
+    elif dataset == 'cifar100':
+        train_dataset = CIFAR100(datadir, True, train_transform, download=True)
+        test_dataset = CIFAR100(datadir, False, test_transform, download=True)
+    elif dataset == 'svhn':
+        train_dataset = SVHN(datadir, split='train', transform=train_transform, download=True)
+        test_dataset = SVHN(datadir, split='test', transform=test_transform, download=True)
+    elif dataset == 'ImageNet16-120':
+        train_dataset = ImageNet16(os.path.join(datadir, 'ImageNet16'), True , train_transform, 120)
+        test_dataset  = ImageNet16(os.path.join(datadir, 'ImageNet16'), False, test_transform , 120)
+    elif dataset == 'ImageNet1k':
+        train_dataset = H5Dataset(os.path.join(datadir, 'imagenet-train-256.h5'), transform=train_transform)
+        test_dataset  = H5Dataset(os.path.join(datadir, 'imagenet-val-256.h5'),   transform=test_transform)
+            
+    else:
+        raise ValueError('There are no more cifars or imagenets.')
+
+    train_loader = DataLoader(
+        train_dataset,
+        train_batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        pin_memory=True)
+    test_loader = DataLoader(
+        test_dataset,
+        test_batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=True)
+
+
+    return train_loader, test_loader
+
+
+def get_mnist_dataloaders(train_batch_size, val_batch_size, num_workers):
+
+    data_transform = Compose([transforms.ToTensor()])
+
+    # Normalise? transforms.Normalize((0.1307,), (0.3081,))
+
+    train_dataset = MNIST("_dataset", True, data_transform, download=True)
+    test_dataset = MNIST("_dataset", False, data_transform, download=True)
+
+    train_loader = DataLoader(
+        train_dataset,
+        train_batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        pin_memory=True)
+    test_loader = DataLoader(
+        test_dataset,
+        val_batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=True)
+
+    return train_loader, test_loader
+
+if __name__ == '__main__':
+    tr, te = get_cifar_dataloaders(64, 64, 'random', 2, resize=None, datadir='_dataset')
+    for x, y in tr:
+        print(x.size(), y.size())
+        break

correlation/foresight/h5py_dataset.py

@@ -0,0 +1,55 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import h5py
+import numpy as np
+from PIL import Image
+
+
+import torch
+from torch.utils.data import Dataset, DataLoader
+
+class H5Dataset(Dataset):
+    def __init__(self, h5_path, transform=None):
+        self.h5_path = h5_path
+        self.h5_file = None
+        self.length = len(h5py.File(h5_path, 'r'))
+        self.transform = transform
+
+    def __getitem__(self, index):
+
+        #loading in getitem allows us to use multiple processes for data loading
+        #because hdf5 files aren't pickelable so can't transfer them across processes
+        # https://discuss.pytorch.org/t/hdf5-a-data-format-for-pytorch/40379
+        # https://discuss.pytorch.org/t/dataloader-when-num-worker-0-there-is-bug/25643/16
+        # TODO possible look at __getstate__ and __setstate__ as a more elegant solution
+        if self.h5_file is None:
+            self.h5_file = h5py.File(self.h5_path, 'r')
+
+        record = self.h5_file[str(index)]
+
+        if self.transform:
+            x = Image.fromarray(record['data'][()])
+            x = self.transform(x)
+        else:
+            x = torch.from_numpy(record['data'][()])
+
+        y = record['target'][()]
+        y = torch.from_numpy(np.asarray(y))
+
+        return (x,y)
+
+    def __len__(self):
+        return self.length

correlation/foresight/imagenet16.py

@@ -0,0 +1,142 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import hashlib
+import os
+import sys
+
+import numpy as np
+import torch.utils.data as data
+from PIL import Image
+
+if sys.version_info[0] == 2:
+    import cPickle as pickle
+else:
+    import pickle
+
+
+def calculate_md5(fpath, chunk_size=1024 * 1024):
+    md5 = hashlib.md5()
+    with open(fpath, 'rb') as f:
+        for chunk in iter(lambda: f.read(chunk_size), b''):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
+def check_md5(fpath, md5, **kwargs):
+    return md5 == calculate_md5(fpath, **kwargs)
+
+
+def check_integrity(fpath, md5=None):
+    if not os.path.isfile(fpath):
+        print(fpath)
+        return False
+    if md5 is None:
+        return True
+    else:
+        return check_md5(fpath, md5)
+
+
+class ImageNet16(data.Dataset):
+    # http://image-net.org/download-images
+    # A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
+    # https://arxiv.org/pdf/1707.08819.pdf
+
+    train_list = [
+        ['train_data_batch_1', '27846dcaa50de8e21a7d1a35f30f0e91'],
+        ['train_data_batch_2', 'c7254a054e0e795c69120a5727050e3f'],
+        ['train_data_batch_3', '4333d3df2e5ffb114b05d2ffc19b1e87'],
+        ['train_data_batch_4', '1620cdf193304f4a92677b695d70d10f'],
+        ['train_data_batch_5', '348b3c2fdbb3940c4e9e834affd3b18d'],
+        ['train_data_batch_6', '6e765307c242a1b3d7d5ef9139b48945'],
+        ['train_data_batch_7', '564926d8cbf8fc4818ba23d2faac7564'],
+        ['train_data_batch_8', 'f4755871f718ccb653440b9dd0ebac66'],
+        ['train_data_batch_9', 'bb6dd660c38c58552125b1a92f86b5d4'],
+        ['train_data_batch_10', '8f03f34ac4b42271a294f91bf480f29b'],
+    ]
+    valid_list = [
+        ['val_data', '3410e3017fdaefba8d5073aaa65e4bd6'],
+    ]
+
+    def __init__(self, root, train, transform, use_num_of_class_only=None):
+        self.root = root
+        self.transform = transform
+        self.train = train  # training set or valid set
+        if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.')
+
+        if self.train:
+            downloaded_list = self.train_list
+        else:
+            downloaded_list = self.valid_list
+        self.data = []
+        self.targets = []
+
+        # now load the picked numpy arrays
+        for i, (file_name, checksum) in enumerate(downloaded_list):
+            file_path = os.path.join(self.root, file_name)
+            # print ('Load {:}/{:02d}-th : {:}'.format(i, len(downloaded_list), file_path))
+            with open(file_path, 'rb') as f:
+                if sys.version_info[0] == 2:
+                    entry = pickle.load(f)
+                else:
+                    entry = pickle.load(f, encoding='latin1')
+                self.data.append(entry['data'])
+                self.targets.extend(entry['labels'])
+        self.data = np.vstack(self.data).reshape(-1, 3, 16, 16)
+        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
+        if use_num_of_class_only is not None:
+            assert isinstance(use_num_of_class_only,
+                              int) and use_num_of_class_only > 0 and use_num_of_class_only < 1000, 'invalid use_num_of_class_only : {:}'.format(
+                use_num_of_class_only)
+            new_data, new_targets = [], []
+            for I, L in zip(self.data, self.targets):
+                if 1 <= L <= use_num_of_class_only:
+                    new_data.append(I)
+                    new_targets.append(L)
+            self.data = new_data
+            self.targets = new_targets
+        #    self.mean.append(entry['mean'])
+        # self.mean = np.vstack(self.mean).reshape(-1, 3, 16, 16)
+        # self.mean = np.mean(np.mean(np.mean(self.mean, axis=0), axis=1), axis=1)
+        # print ('Mean : {:}'.format(self.mean))
+        # temp      = self.data - np.reshape(self.mean, (1, 1, 1, 3))
+        # std_data  = np.std(temp, axis=0)
+        # std_data  = np.mean(np.mean(std_data, axis=0), axis=0)
+        # print ('Std  : {:}'.format(std_data))
+
+    def __getitem__(self, index):
+        img, target = self.data[index], self.targets[index] - 1
+
+        img = Image.fromarray(img)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+    def _check_integrity(self):
+        root = self.root
+        for fentry in (self.train_list + self.valid_list):
+            filename, md5 = fentry[0], fentry[1]
+            fpath = os.path.join(root, filename)
+            if not check_integrity(fpath, md5):
+                return False
+        return True
+
+
+#
+if __name__ == '__main__':
+    train = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True, None)
+    valid = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False, None)
+
+    print(len(train))
+    print(len(valid))
+    image, label = train[111]
+    trainX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True, None, 200)
+    validX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False, None, 200)
+    print(len(trainX))
+    print(len(validX))
+    # import pdb; pdb.set_trace()

correlation/foresight/models/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from os.path import dirname, basename, isfile, join
+import glob
+modules = glob.glob(join(dirname(__file__), "*.py"))
+__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

correlation/foresight/models/nasbench1.py

@@ -0,0 +1,251 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Builds the Pytorch computational graph.
+Tensors flowing into a single vertex are added together for all vertices
+except the output, which is concatenated instead. Tensors flowing out of input
+are always added.
+If interior edge channels don't match, drop the extra channels (channels are
+guaranteed non-decreasing). Tensors flowing out of the input as always
+projected instead.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import math
+
+from .nasbench1_ops import *
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Network(nn.Module):
+    def __init__(self, spec, stem_out, num_stacks, num_mods, num_classes, bn=True):
+        super(Network, self).__init__()
+
+        self.spec=spec
+        self.stem_out=stem_out 
+        self.num_stacks=num_stacks 
+        self.num_mods=num_mods
+        self.num_classes=num_classes
+
+        self.layers = nn.ModuleList([])
+
+        in_channels = 3
+        out_channels = stem_out
+
+        # initial stem convolution
+        stem_conv = ConvBnRelu(in_channels, out_channels, 3, 1, 1, bn=bn)
+        self.layers.append(stem_conv)
+
+        in_channels = out_channels
+        for stack_num in range(num_stacks):
+            if stack_num > 0:
+                downsample = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.layers.append(downsample)
+
+                out_channels *= 2
+
+            for _ in range(num_mods):
+                cell = Cell(spec, in_channels, out_channels, bn=bn)
+                self.layers.append(cell)
+                in_channels = out_channels
+
+        self.classifier = nn.Linear(out_channels, num_classes)
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        for _, layer in enumerate(self.layers):
+            x = layer(x)
+        out = torch.mean(x, (2, 3))
+        out = self.classifier(out)
+
+        return out
+    
+    def get_prunable_copy(self, bn=False):
+
+        model_new = Network(self.spec, self.stem_out, self.num_stacks, self.num_mods, self.num_classes, bn=bn)
+        
+        #TODO this is quite brittle and doesn't work with nn.Sequential when bn is different
+        # it is only required to maintain initialization -- maybe init after get_punable_copy?
+        model_new.load_state_dict(self.state_dict(), strict=False)
+        model_new.train()
+
+        return model_new
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2.0 / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                n = m.weight.size(1)
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+class Cell(nn.Module):
+    """
+    Builds the model using the adjacency matrix and op labels specified. Channels
+    controls the module output channel count but the interior channels are
+    determined via equally splitting the channel count whenever there is a
+    concatenation of Tensors.
+    """
+    def __init__(self, spec, in_channels, out_channels, bn=True):
+        super(Cell, self).__init__()
+
+        self.spec = spec
+        self.num_vertices = np.shape(self.spec.matrix)[0]
+
+        # vertex_channels[i] = number of output channels of vertex i
+        self.vertex_channels = ComputeVertexChannels(in_channels, out_channels, self.spec.matrix)
+        #self.vertex_channels = [in_channels] + [out_channels] * (self.num_vertices - 1)
+
+        # operation for each node
+        self.vertex_op = nn.ModuleList([None])
+        for t in range(1, self.num_vertices-1):
+            op = OP_MAP[spec.ops[t]](self.vertex_channels[t], self.vertex_channels[t], bn=bn)
+            self.vertex_op.append(op)
+
+        # operation for input on each vertex
+        self.input_op = nn.ModuleList([None])
+        for t in range(1, self.num_vertices):
+            if self.spec.matrix[0, t]:
+                self.input_op.append(Projection(in_channels, self.vertex_channels[t], bn=bn))
+            else:
+                self.input_op.append(None)
+
+    def forward(self, x):
+        tensors = [x]
+
+        out_concat = []
+        for t in range(1, self.num_vertices-1):
+            fan_in = [Truncate(tensors[src], self.vertex_channels[t]) for src in range(1, t) if self.spec.matrix[src, t]]
+
+            if self.spec.matrix[0, t]:
+                fan_in.append(self.input_op[t](x))
+
+            # perform operation on node
+            #vertex_input = torch.stack(fan_in, dim=0).sum(dim=0)
+            vertex_input = sum(fan_in)
+            #vertex_input = sum(fan_in) / len(fan_in)
+            vertex_output = self.vertex_op[t](vertex_input)
+
+            tensors.append(vertex_output)
+            if self.spec.matrix[t, self.num_vertices-1]:
+                out_concat.append(tensors[t])
+
+        if not out_concat:
+            assert self.spec.matrix[0, self.num_vertices-1]
+            outputs = self.input_op[self.num_vertices-1](tensors[0])
+        else:
+            if len(out_concat) == 1:
+                outputs = out_concat[0]
+            else:
+                outputs = torch.cat(out_concat, 1)
+
+            if self.spec.matrix[0, self.num_vertices-1]:
+                outputs += self.input_op[self.num_vertices-1](tensors[0])
+
+            #if self.spec.matrix[0, self.num_vertices-1]:
+            #    out_concat.append(self.input_op[self.num_vertices-1](tensors[0]))
+            #outputs = sum(out_concat) / len(out_concat)
+
+        return outputs
+
+def Projection(in_channels, out_channels, bn=True):
+    """1x1 projection (as in ResNet) followed by batch normalization and ReLU."""
+    return ConvBnRelu(in_channels, out_channels, 1, bn=bn)
+
+def Truncate(inputs, channels):
+    """Slice the inputs to channels if necessary."""
+    input_channels = inputs.size()[1]
+    if input_channels < channels:
+        raise ValueError('input channel < output channels for truncate')
+    elif input_channels == channels:
+        return inputs   # No truncation necessary
+    else:
+        # Truncation should only be necessary when channel division leads to
+        # vertices with +1 channels. The input vertex should always be projected to
+        # the minimum channel count.
+        assert input_channels - channels == 1
+        return inputs[:, :channels, :, :]
+
+def ComputeVertexChannels(in_channels, out_channels, matrix):
+    """Computes the number of channels at every vertex.
+    Given the input channels and output channels, this calculates the number of
+    channels at each interior vertex. Interior vertices have the same number of
+    channels as the max of the channels of the vertices it feeds into. The output
+    channels are divided amongst the vertices that are directly connected to it.
+    When the division is not even, some vertices may receive an extra channel to
+    compensate.
+    Returns:
+        list of channel counts, in order of the vertices.
+    """
+    num_vertices = np.shape(matrix)[0]
+
+    vertex_channels = [0] * num_vertices
+    vertex_channels[0] = in_channels
+    vertex_channels[num_vertices - 1] = out_channels
+
+    if num_vertices == 2:
+        # Edge case where module only has input and output vertices
+        return vertex_channels
+
+    # Compute the in-degree ignoring input, axis 0 is the src vertex and axis 1 is
+    # the dst vertex. Summing over 0 gives the in-degree count of each vertex.
+    in_degree = np.sum(matrix[1:], axis=0)
+    interior_channels = out_channels // in_degree[num_vertices - 1]
+    correction = out_channels % in_degree[num_vertices - 1]  # Remainder to add
+
+    # Set channels of vertices that flow directly to output
+    for v in range(1, num_vertices - 1):
+      if matrix[v, num_vertices - 1]:
+          vertex_channels[v] = interior_channels
+          if correction:
+              vertex_channels[v] += 1
+              correction -= 1
+
+    # Set channels for all other vertices to the max of the out edges, going
+    # backwards. (num_vertices - 2) index skipped because it only connects to
+    # output.
+    for v in range(num_vertices - 3, 0, -1):
+        if not matrix[v, num_vertices - 1]:
+            for dst in range(v + 1, num_vertices - 1):
+                if matrix[v, dst]:
+                    vertex_channels[v] = max(vertex_channels[v], vertex_channels[dst])
+        assert vertex_channels[v] > 0
+
+    # Sanity check, verify that channels never increase and final channels add up.
+    final_fan_in = 0
+    for v in range(1, num_vertices - 1):
+        if matrix[v, num_vertices - 1]:
+            final_fan_in += vertex_channels[v]
+        for dst in range(v + 1, num_vertices - 1):
+            if matrix[v, dst]:
+                assert vertex_channels[v] >= vertex_channels[dst]
+    assert final_fan_in == out_channels or num_vertices == 2
+    # num_vertices == 2 means only input/output nodes, so 0 fan-in
+
+    return vertex_channels

correlation/foresight/models/nasbench1_ops.py

@@ -0,0 +1,83 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Base operations used by the modules in this search space."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvBnRelu(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, bn=True):
+        super(ConvBnRelu, self).__init__()
+
+        if bn:
+            self.conv_bn_relu = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
+                nn.BatchNorm2d(out_channels),
+                nn.ReLU(inplace=False)
+            )
+        else:
+            self.conv_bn_relu = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
+                nn.ReLU(inplace=False)
+            )
+
+    def forward(self, x):
+        return self.conv_bn_relu(x)
+
+class Conv3x3BnRelu(nn.Module):
+    """3x3 convolution with batch norm and ReLU activation."""
+    def __init__(self, in_channels, out_channels, bn=True):
+        super(Conv3x3BnRelu, self).__init__()
+
+        self.conv3x3 = ConvBnRelu(in_channels, out_channels, 3, 1, 1, bn=bn)
+
+    def forward(self, x):
+        x = self.conv3x3(x)
+        return x
+
+class Conv1x1BnRelu(nn.Module):
+    """1x1 convolution with batch norm and ReLU activation."""
+    def __init__(self, in_channels, out_channels, bn=True):
+        super(Conv1x1BnRelu, self).__init__()
+
+        self.conv1x1 = ConvBnRelu(in_channels, out_channels, 1, 1, 0, bn=bn)
+
+    def forward(self, x):
+        x = self.conv1x1(x)
+        return x
+
+class MaxPool3x3(nn.Module):
+    """3x3 max pool with no subsampling."""
+    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bn=None):
+        super(MaxPool3x3, self).__init__()
+
+        self.maxpool = nn.MaxPool2d(kernel_size, stride, padding)
+
+    def forward(self, x):
+        x = self.maxpool(x)
+        return x
+
+# Commas should not be used in op names
+OP_MAP = {
+    'conv3x3-bn-relu': Conv3x3BnRelu,
+    'conv1x1-bn-relu': Conv1x1BnRelu,
+    'maxpool3x3': MaxPool3x3
+}

correlation/foresight/models/nasbench1_spec.py

@@ -0,0 +1,295 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Model specification for module connectivity individuals.
+This module handles pruning the unused parts of the computation graph but should
+avoid creating any TensorFlow models (this is done inside model_builder.py).
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import hashlib
+import itertools
+import numpy as np
+
+
+# Graphviz is optional and only required for visualization.
+try:
+  import graphviz   # pylint: disable=g-import-not-at-top
+except ImportError:
+  pass
+
+def _ToModelSpec(mat, ops):
+    return ModelSpec(mat, ops)
+
+def gen_is_edge_fn(bits):
+  """Generate a boolean function for the edge connectivity.
+  Given a bitstring FEDCBA and a 4x4 matrix, the generated matrix is
+    [[0, A, B, D],
+     [0, 0, C, E],
+     [0, 0, 0, F],
+     [0, 0, 0, 0]]
+  Note that this function is agnostic to the actual matrix dimension due to
+  order in which elements are filled out (column-major, starting from least
+  significant bit). For example, the same FEDCBA bitstring (0-padded) on a 5x5
+  matrix is
+    [[0, A, B, D, 0],
+     [0, 0, C, E, 0],
+     [0, 0, 0, F, 0],
+     [0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0]]
+  Args:
+    bits: integer which will be interpreted as a bit mask.
+  Returns:
+    vectorized function that returns True when an edge is present.
+  """
+  def is_edge(x, y):
+    """Is there an edge from x to y (0-indexed)?"""
+    if x >= y:
+      return 0
+    # Map x, y to index into bit string
+    index = x + (y * (y - 1) // 2)
+    return (bits >> index) % 2 == 1
+
+  return np.vectorize(is_edge)
+
+
+def is_full_dag(matrix):
+  """Full DAG == all vertices on a path from vert 0 to (V-1).
+  i.e. no disconnected or "hanging" vertices.
+  It is sufficient to check for:
+    1) no rows of 0 except for row V-1 (only output vertex has no out-edges)
+    2) no cols of 0 except for col 0 (only input vertex has no in-edges)
+  Args:
+    matrix: V x V upper-triangular adjacency matrix
+  Returns:
+    True if the there are no dangling vertices.
+  """
+  shape = np.shape(matrix)
+
+  rows = matrix[:shape[0]-1, :] == 0
+  rows = np.all(rows, axis=1)     # Any row with all 0 will be True
+  rows_bad = np.any(rows)
+
+  cols = matrix[:, 1:] == 0
+  cols = np.all(cols, axis=0)     # Any col with all 0 will be True
+  cols_bad = np.any(cols)
+
+  return (not rows_bad) and (not cols_bad)
+
+
+def num_edges(matrix):
+  """Computes number of edges in adjacency matrix."""
+  return np.sum(matrix)
+
+
+def hash_module(matrix, labeling):
+  """Computes a graph-invariance MD5 hash of the matrix and label pair.
+  Args:
+    matrix: np.ndarray square upper-triangular adjacency matrix.
+    labeling: list of int labels of length equal to both dimensions of
+      matrix.
+  Returns:
+    MD5 hash of the matrix and labeling.
+  """
+  vertices = np.shape(matrix)[0]
+  in_edges = np.sum(matrix, axis=0).tolist()
+  out_edges = np.sum(matrix, axis=1).tolist()
+
+  assert len(in_edges) == len(out_edges) == len(labeling)
+  hashes = list(zip(out_edges, in_edges, labeling))
+  hashes = [hashlib.md5(str(h).encode('utf-8')).hexdigest() for h in hashes]
+  # Computing this up to the diameter is probably sufficient but since the
+  # operation is fast, it is okay to repeat more times.
+  for _ in range(vertices):
+    new_hashes = []
+    for v in range(vertices):
+      in_neighbors = [hashes[w] for w in range(vertices) if matrix[w, v]]
+      out_neighbors = [hashes[w] for w in range(vertices) if matrix[v, w]]
+      new_hashes.append(hashlib.md5(
+          (''.join(sorted(in_neighbors)) + '|' +
+           ''.join(sorted(out_neighbors)) + '|' +
+           hashes[v]).encode('utf-8')).hexdigest())
+    hashes = new_hashes
+  fingerprint = hashlib.md5(str(sorted(hashes)).encode('utf-8')).hexdigest()
+
+  return fingerprint
+
+
+def permute_graph(graph, label, permutation):
+  """Permutes the graph and labels based on permutation.
+  Args:
+    graph: np.ndarray adjacency matrix.
+    label: list of labels of same length as graph dimensions.
+    permutation: a permutation list of ints of same length as graph dimensions.
+  Returns:
+    np.ndarray where vertex permutation[v] is vertex v from the original graph
+  """
+  # vertex permutation[v] in new graph is vertex v in the old graph
+  forward_perm = zip(permutation, list(range(len(permutation))))
+  inverse_perm = [x[1] for x in sorted(forward_perm)]
+  edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1
+  new_matrix = np.fromfunction(np.vectorize(edge_fn),
+                               (len(label), len(label)),
+                               dtype=np.int8)
+  new_label = [label[inverse_perm[i]] for i in range(len(label))]
+  return new_matrix, new_label
+
+
+def is_isomorphic(graph1, graph2):
+  """Exhaustively checks if 2 graphs are isomorphic."""
+  matrix1, label1 = np.array(graph1[0]), graph1[1]
+  matrix2, label2 = np.array(graph2[0]), graph2[1]
+  assert np.shape(matrix1) == np.shape(matrix2)
+  assert len(label1) == len(label2)
+
+  vertices = np.shape(matrix1)[0]
+  # Note: input and output in our constrained graphs always map to themselves
+  # but this script does not enforce that.
+  for perm in itertools.permutations(range(0, vertices)):
+    pmatrix1, plabel1 = permute_graph(matrix1, label1, perm)
+    if np.array_equal(pmatrix1, matrix2) and plabel1 == label2:
+      return True
+
+  return False
+
+class ModelSpec(object):
+  """Model specification given adjacency matrix and labeling."""
+
+  def __init__(self, matrix, ops, data_format='channels_last'):
+    """Initialize the module spec.
+    Args:
+      matrix: ndarray or nested list with shape [V, V] for the adjacency matrix.
+      ops: V-length list of labels for the base ops used. The first and last
+        elements are ignored because they are the input and output vertices
+        which have no operations. The elements are retained to keep consistent
+        indexing.
+      data_format: channels_last or channels_first.
+    Raises:
+      ValueError: invalid matrix or ops
+    """
+    if not isinstance(matrix, np.ndarray):
+      matrix = np.array(matrix)
+    shape = np.shape(matrix)
+    if len(shape) != 2 or shape[0] != shape[1]:
+      raise ValueError('matrix must be square')
+    if shape[0] != len(ops):
+      raise ValueError('length of ops must match matrix dimensions')
+    if not is_upper_triangular(matrix):
+      raise ValueError('matrix must be upper triangular')
+
+    # Both the original and pruned matrices are deep copies of the matrix and
+    # ops so any changes to those after initialization are not recognized by the
+    # spec.
+    self.original_matrix = copy.deepcopy(matrix)
+    # print(self.original_matrix)
+    self.original_ops = copy.deepcopy(ops)
+
+    self.matrix = copy.deepcopy(matrix)
+    self.ops = copy.deepcopy(ops)
+    self.valid_spec = True
+    self._prune()
+
+    self.data_format = data_format
+
+  def _prune(self):
+    """Prune the extraneous parts of the graph.
+    General procedure:
+      1) Remove parts of graph not connected to input.
+      2) Remove parts of graph not connected to output.
+      3) Reorder the vertices so that they are consecutive after steps 1 and 2.
+    These 3 steps can be combined by deleting the rows and columns of the
+    vertices that are not reachable from both the input and output (in reverse).
+    """
+    num_vertices = np.shape(self.original_matrix)[0]
+
+    # DFS forward from input
+    visited_from_input = set([0])
+    frontier = [0]
+    while frontier:
+      top = frontier.pop()
+      for v in range(top + 1, num_vertices):
+        if self.original_matrix[top, v] and v not in visited_from_input:
+          visited_from_input.add(v)
+          frontier.append(v)
+
+    # DFS backward from output
+    visited_from_output = set([num_vertices - 1])
+    frontier = [num_vertices - 1]
+    while frontier:
+      top = frontier.pop()
+      for v in range(0, top):
+        if self.original_matrix[v, top] and v not in visited_from_output:
+          visited_from_output.add(v)
+          frontier.append(v)
+
+    # Any vertex that isn't connected to both input and output is extraneous to
+    # the computation graph.
+    extraneous = set(range(num_vertices)).difference(
+        visited_from_input.intersection(visited_from_output))
+
+    # If the non-extraneous graph is less than 2 vertices, the input is not
+    # connected to the output and the spec is invalid.
+    if len(extraneous) > num_vertices - 2:
+      self.matrix = None
+      self.ops = None
+      self.valid_spec = False
+      return
+
+    self.matrix = np.delete(self.matrix, list(extraneous), axis=0)
+    self.matrix = np.delete(self.matrix, list(extraneous), axis=1)
+    for index in sorted(extraneous, reverse=True):
+      del self.ops[index]
+
+  def hash_spec(self, canonical_ops):
+    """Computes the isomorphism-invariant graph hash of this spec.
+    Args:
+      canonical_ops: list of operations in the canonical ordering which they
+        were assigned (i.e. the order provided in the config['available_ops']).
+    Returns:
+      MD5 hash of this spec which can be used to query the dataset.
+    """
+    # Invert the operations back to integer label indices used in graph gen.
+    labeling = [-1] + [canonical_ops.index(op) for op in self.ops[1:-1]] + [-2]
+    return graph_util.hash_module(self.matrix, labeling)
+
+  def visualize(self):
+    """Creates a dot graph. Can be visualized in colab directly."""
+    num_vertices = np.shape(self.matrix)[0]
+    g = graphviz.Digraph()
+    g.node(str(0), 'input')
+    for v in range(1, num_vertices - 1):
+      g.node(str(v), self.ops[v])
+    g.node(str(num_vertices - 1), 'output')
+
+    for src in range(num_vertices - 1):
+      for dst in range(src + 1, num_vertices):
+        if self.matrix[src, dst]:
+          g.edge(str(src), str(dst))
+
+    return g
+
+
+def is_upper_triangular(matrix):
+  """True if matrix is 0 on diagonal and below."""
+  for src in range(np.shape(matrix)[0]):
+    for dst in range(0, src + 1):
+      if matrix[src, dst] != 0:
+        return False
+
+  return True

correlation/foresight/models/nasbench2.py

@@ -0,0 +1,140 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import os
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .nasbench2_ops import *
+
+
+def gen_searchcell_mask_from_arch_str(arch_str):
+    nodes = arch_str.split('+') 
+    nodes = [node[1:-1].split('|') for node in nodes]
+    nodes = [[op_and_input.split('~')  for op_and_input in node] for node in nodes]
+
+    keep_mask = []
+    for curr_node_idx in range(len(nodes)):
+            for prev_node_idx in range(curr_node_idx+1): 
+                _op = [edge[0] for edge in nodes[curr_node_idx] if int(edge[1]) == prev_node_idx]
+                assert len(_op) == 1, 'The arch string does not follow the assumption of 1 connection between two nodes.'
+                for _op_name in OPS.keys():
+                    keep_mask.append(_op[0] == _op_name)
+    return keep_mask
+
+
+def get_model_from_arch_str(arch_str, num_classes, use_bn=True, init_channels=16):
+    keep_mask = gen_searchcell_mask_from_arch_str(arch_str)
+    net = NAS201Model(arch_str=arch_str, num_classes=num_classes, use_bn=use_bn, keep_mask=keep_mask, stem_ch=init_channels)
+    return net
+
+
+def get_super_model(num_classes, use_bn=True):
+    net = NAS201Model(arch_str=arch_str, num_classes=num_classes, use_bn=use_bn)
+    return net
+
+
+class NAS201Model(nn.Module):
+
+    def __init__(self, arch_str, num_classes, use_bn=True, keep_mask=None, stem_ch=16):
+        super(NAS201Model, self).__init__()
+        self.arch_str=arch_str
+        self.num_classes=num_classes
+        self.use_bn= use_bn
+
+        self.stem = stem(out_channels=stem_ch, use_bn=use_bn)
+        self.stack_cell1 = nn.Sequential(*[SearchCell(in_channels=stem_ch, out_channels=stem_ch, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)])
+        self.reduction1 = reduction(in_channels=stem_ch, out_channels=stem_ch*2)
+        self.stack_cell2 = nn.Sequential(*[SearchCell(in_channels=stem_ch*2, out_channels=stem_ch*2, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)])
+        self.reduction2 = reduction(in_channels=stem_ch*2, out_channels=stem_ch*4)
+        self.stack_cell3 = nn.Sequential(*[SearchCell(in_channels=stem_ch*4, out_channels=stem_ch*4, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)])
+        # self.top = top(in_dims=stem_ch*4, num_classes=num_classes, use_bn=use_bn)
+        self.top = top(in_dims=stem_ch*4, use_bn=use_bn)
+        self.classifier = nn.Linear(stem_ch*4, num_classes)
+        self.pre_GAP = nn.Sequential(nn.BatchNorm2d(stem_ch * 4), nn.ReLU(inplace=True))
+
+    def forward(self, x):
+        x = self.stem(x)        
+
+        x = self.stack_cell1(x)
+        x = self.reduction1(x)
+
+        x = self.stack_cell2(x)
+        x = self.reduction2(x)
+
+        x = self.stack_cell3(x)
+
+        x = self.top(x)
+        x = self.classifier(x)
+        return x
+
+    def forward_pre_GAP(self, x):
+        x = self.stem(x)
+
+        x = self.stack_cell1(x)
+        x = self.reduction1(x)
+
+        x = self.stack_cell2(x)
+        x = self.reduction2(x)
+
+        x = self.stack_cell3(x)
+        x = self.pre_GAP(x)
+        return x
+
+
+    
+    def get_prunable_copy(self, bn=False):
+        model_new = get_model_from_arch_str(self.arch_str, self.num_classes, use_bn=bn)
+
+        #TODO this is quite brittle and doesn't work with nn.Sequential when bn is different
+        # it is only required to maintain initialization -- maybe init after get_punable_copy?
+        model_new.load_state_dict(self.state_dict(), strict=False)
+        model_new.train()
+
+        return model_new
+    
+
+def get_arch_str_from_model(net):
+    search_cell = net.stack_cell1[0].options
+    keep_mask = net.stack_cell1[0].keep_mask
+    num_nodes = net.stack_cell1[0].num_nodes
+
+    nodes = []
+    idx = 0
+    for curr_node in range(num_nodes -1):
+        edges = []
+        for prev_node in range(curr_node+1): # n-1 prev nodes
+            for _op_name in OPS.keys():
+                if keep_mask[idx]:
+                    edges.append(f'{_op_name}~{prev_node}')
+                idx += 1
+        node_str = '|'.join(edges)
+        node_str = f'|{node_str}|'
+        nodes.append(node_str) 
+    arch_str = '+'.join(nodes)
+    return arch_str
+
+
+if __name__ == "__main__":
+    arch_str = '|nor_conv_3x3~0|+|none~0|none~1|+|avg_pool_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|'
+    
+    n = get_model_from_arch_str(arch_str=arch_str, num_classes=10)
+    print(n.stack_cell1[0])
+    
+    arch_str2 = get_arch_str_from_model(n)
+    print(arch_str)
+    print(arch_str2)
+    print(f'Are the two arch strings same? {arch_str == arch_str2}')

correlation/foresight/models/nasbench2_ops.py

@@ -0,0 +1,166 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import os
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ReLUConvBN(nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats=True, use_bn=True, name='ReLUConvBN'):
+        super(ReLUConvBN, self).__init__()
+        self.name = name
+        if use_bn:
+            self.op = nn.Sequential(
+                nn.ReLU(inplace=False),
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
+                nn.BatchNorm2d(out_channels, affine=affine, track_running_stats=track_running_stats)
+                )
+        else:
+            self.op = nn.Sequential(
+                nn.ReLU(inplace=False),
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine)
+                )
+
+    def forward(self, x):
+        return self.op(x)
+
+class Identity(nn.Module):
+    def __init__(self, name='Identity'):
+        self.name = name
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+class Zero(nn.Module):
+
+  def __init__(self, stride, name='Zero'):
+    self.name = name
+    super(Zero, self).__init__()
+    self.stride = stride
+
+  def forward(self, x):
+    if self.stride == 1:
+      return x.mul(0.)
+    return x[:,:,::self.stride,::self.stride].mul(0.)
+
+class POOLING(nn.Module):
+    def __init__(self, kernel_size, stride, padding, name='POOLING'):
+        super(POOLING, self).__init__()
+        self.name = name
+        self.avgpool = nn.AvgPool2d(kernel_size=kernel_size, stride=1, padding=1, count_include_pad=False)
+
+    def forward(self, x):
+        return self.avgpool(x)
+
+
+class reduction(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(reduction, self).__init__()
+        self.residual = nn.Sequential(
+                            nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False))
+
+        self.conv_a = ReLUConvBN(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1, dilation=1, affine=True, track_running_stats=True)
+        self.conv_b = ReLUConvBN(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, dilation=1, affine=True, track_running_stats=True)
+
+    def forward(self, x):
+        basicblock = self.conv_a(x)
+        basicblock = self.conv_b(basicblock)
+        residual = self.residual(x)
+        return residual + basicblock
+
+class stem(nn.Module):
+    def __init__(self, out_channels, use_bn=True):
+        super(stem, self).__init__()
+        if use_bn:
+            self.net = nn.Sequential(
+                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(out_channels))
+        else:
+            self.net = nn.Sequential(
+                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False)
+            )
+
+    def forward(self, x):
+        return self.net(x)
+
+class top(nn.Module):
+    # def __init__(self, in_dims, num_classes, use_bn=True):
+    def __init__(self, in_dims, use_bn=True):
+        super(top, self).__init__()
+        if use_bn:
+            self.lastact = nn.Sequential(nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True))
+        else:
+            self.lastact = nn.ReLU(inplace=True)
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        # self.classifier = nn.Linear(in_dims, num_classes)
+
+    def forward(self, x):
+        x = self.lastact(x)
+        x = self.global_pooling(x)
+        x = x.view(x.size(0), -1)
+        # logits = self.classifier(x)
+        # return logits
+        return x
+
+
+class SearchCell(nn.Module):
+
+    def __init__(self, in_channels, out_channels, stride, affine, track_running_stats, use_bn=True, num_nodes=4, keep_mask=None):
+        super(SearchCell, self).__init__()
+        self.num_nodes = num_nodes
+        self.options = nn.ModuleList()
+        for curr_node in range(self.num_nodes-1):
+            for prev_node in range(curr_node+1): 
+                for _op_name in OPS.keys():
+                    op = OPS[_op_name](in_channels, out_channels, stride, affine, track_running_stats, use_bn)
+                    self.options.append(op)
+
+        if keep_mask is not None:
+            self.keep_mask = keep_mask
+        else:
+            self.keep_mask = [True]*len(self.options)
+
+    def forward(self, x):
+        outs = [x]
+
+        idx = 0
+        for curr_node in range(self.num_nodes-1):
+            edges_in = []
+            for prev_node in range(curr_node+1): # n-1 prev nodes
+                for op_idx in range(len(OPS.keys())):
+                    if self.keep_mask[idx]:
+                        edges_in.append(self.options[idx](outs[prev_node]))
+                    idx += 1
+            node_output = sum(edges_in)
+            outs.append(node_output)
+        
+        return outs[-1]
+
+
+
+OPS = {
+    'none' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Zero(stride, name='none'),
+    'avg_pool_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: POOLING(3, 1, 1, name='avg_pool_3x3'),
+    'nor_conv_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 3, 1, 1, 1, affine, track_running_stats, use_bn, name='nor_conv_3x3'),
+    'nor_conv_1x1' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 1, 1, 0, 1, affine, track_running_stats, use_bn, name='nor_conv_1x1'),
+    'skip_connect' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Identity(name='skip_connect'),
+}
+
+

correlation/foresight/pruners/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from os.path import dirname, basename, isfile, join
+import glob
+modules = glob.glob(join(dirname(__file__), "*.py"))
+__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

correlation/foresight/pruners/measures/__init__.py

@@ -0,0 +1,69 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+
+available_measures = []
+_measure_impls = {}
+
+
+def measure(name, bn=True, copy_net=True, force_clean=True, **impl_args):
+    def make_impl(func):
+        def measure_impl(net_orig, device, *args, **kwargs):
+            if copy_net:
+                net = net_orig.get_prunable_copy(bn=bn).to(device)
+            else:
+                net = net_orig
+            ret = func(net, *args, **kwargs, **impl_args)
+            if copy_net and force_clean:
+                import gc
+                import torch
+                del net
+                torch.cuda.empty_cache()
+                gc.collect()
+            return ret
+
+        global _measure_impls
+        if name in _measure_impls:
+            raise KeyError(f'Duplicated measure! {name}')
+        available_measures.append(name)
+        _measure_impls[name] = measure_impl
+        return func
+    return make_impl
+
+
+def calc_measure(name, net, device, *args, **kwargs):
+    return _measure_impls[name](net, device, *args, **kwargs)
+
+
+def load_all():
+    # from . import grad_norm
+    # from . import snip
+    # from . import grasp
+    # from . import fisher
+    # from . import jacob_cov
+    # from . import plain
+    # from . import synflow
+    # from . import var
+    # from . import cor
+    # from . import norm
+    from . import meco
+    # from . import zico
+    # from . import gradsign
+    # from . import ntk
+    # from . import zen
+
+
+# TODO: should we do that by default?
+load_all()

correlation/foresight/pruners/measures/cor.py

@@ -0,0 +1,53 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    def forward_hook(module, data_input, data_output):
+        corr = np.mean(np.corrcoef(data_input[0].detach().cpu().numpy()))
+        result_list.append(corr)
+    net.classifier.register_forward_hook(forward_hook)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+    cor = result_list[0].item()
+    result_list.clear()
+    return cor
+
+
+
+@measure('cor', bn=True)
+def compute_norm(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    try:
+        cor= get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        cor= np.nan
+
+    return cor

correlation/foresight/pruners/measures/cova.py

@@ -0,0 +1,67 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import copy
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+from torch import nn
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    result_t = []
+    def forward_hook(module, data_input, data_output):
+        s = time.time()
+        fea = data_output[0].detach().cpu().numpy()
+        fea = fea.reshape(fea.shape[0], -1)
+        result = 1 / np.var(np.corrcoef(fea))
+        e = time.time()
+        t = e - s
+        result_list.append(result)
+        result_t.append(t)
+    for name, modules in net.named_modules():
+        modules.register_forward_hook(forward_hook)
+
+
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+    results = np.array(result_list)
+    results = results[np.logical_not(np.isnan(results))]
+    v = np.sum(results)
+    t = sum(result_t)
+    result_list.clear()
+    result_t.clear()
+    return v, t
+
+
+
+@measure('cova', bn=True)
+def compute_cova(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    try:
+        cova, t = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        cova, t = np.nan, None
+    return cova, t

correlation/foresight/pruners/measures/fisher.py

@@ -0,0 +1,107 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import types
+
+from . import measure
+from ..p_utils import get_layer_metric_array, reshape_elements
+
+
+def fisher_forward_conv2d(self, x):
+    x = F.conv2d(x, self.weight, self.bias, self.stride,
+                    self.padding, self.dilation, self.groups)
+    #intercept and store the activations after passing through 'hooked' identity op
+    self.act = self.dummy(x)
+    return self.act
+
+def fisher_forward_linear(self, x):
+    x = F.linear(x, self.weight, self.bias)
+    self.act = self.dummy(x)
+    return self.act
+
+@measure('fisher', bn=True, mode='channel')
+def compute_fisher_per_weight(net, inputs, targets, loss_fn, mode, split_data=1):
+    
+    device = inputs.device
+
+    if mode == 'param':
+        raise ValueError('Fisher pruning does not support parameter pruning.')
+
+    net.train()
+    all_hooks = []
+    for layer in net.modules():
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            #variables/op needed for fisher computation
+            layer.fisher = None
+            layer.act = 0.
+            layer.dummy = nn.Identity()
+
+            #replace forward method of conv/linear
+            if isinstance(layer, nn.Conv2d):
+                layer.forward = types.MethodType(fisher_forward_conv2d, layer)
+            if isinstance(layer, nn.Linear):
+                layer.forward = types.MethodType(fisher_forward_linear, layer)
+
+            #function to call during backward pass (hooked on identity op at output of layer)
+            def hook_factory(layer):
+                def hook(module, grad_input, grad_output):
+                    act = layer.act.detach()
+                    grad = grad_output[0].detach()
+                    if len(act.shape) > 2:
+                        g_nk = torch.sum((act * grad), list(range(2,len(act.shape))))
+                    else:
+                        g_nk = act * grad
+                    del_k = g_nk.pow(2).mean(0).mul(0.5)
+                    if layer.fisher is None:
+                        layer.fisher = del_k
+                    else:
+                        layer.fisher += del_k
+                    del layer.act #without deleting this, a nasty memory leak occurs! related: https://discuss.pytorch.org/t/memory-leak-when-using-forward-hook-and-backward-hook-simultaneously/27555
+                return hook
+
+            #register backward hook on identity fcn to compute fisher info
+            layer.dummy.register_backward_hook(hook_factory(layer))
+
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+
+        net.zero_grad()
+        outputs = net(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+
+    # retrieve fisher info
+    def fisher(layer):
+        if layer.fisher is not None:
+            return torch.abs(layer.fisher.detach())
+        else:
+            return torch.zeros(layer.weight.shape[0]) #size=ch
+
+    grads_abs_ch = get_layer_metric_array(net, fisher, mode)
+
+    #broadcast channel value here to all parameters in that channel
+    #to be compatible with stuff downstream (which expects per-parameter metrics)
+    #TODO cleanup on the selectors/apply_prune_mask side (?)
+    shapes = get_layer_metric_array(net, lambda l : l.weight.shape[1:], mode)
+
+    grads_abs = reshape_elements(grads_abs_ch, shapes, device)
+
+    return grads_abs

correlation/foresight/pruners/measures/grad_norm.py

@@ -0,0 +1,38 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import torch.nn.functional as F
+
+import copy
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+@measure('grad_norm', bn=True)
+def get_grad_norm_arr(net, inputs, targets, loss_fn, split_data=1, skip_grad=False):
+    net.zero_grad()
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+
+        outputs = net.forward(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+
+        grad_norm_arr = get_layer_metric_array(net, lambda l: l.weight.grad.norm() if l.weight.grad is not None else torch.zeros_like(l.weight), mode='param')
+        
+    return grad_norm_arr

correlation/foresight/pruners/measures/gradsign.py

@@ -0,0 +1,76 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+from torch import nn
+import numpy as np
+
+from . import measure
+
+
+def get_flattened_metric(net, metric):
+    grad_list = []
+    for layer in net.modules():
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            grad_list.append(metric(layer).flatten())
+    flattened_grad = np.concatenate(grad_list)
+
+    return flattened_grad
+
+
+def get_grad_conflict(net, inputs, targets, loss_fn):
+    N = inputs.shape[0]
+    batch_grad = []
+    for i in range(N):
+        net.zero_grad()
+        outputs = net.forward(inputs[[i]])
+        loss = loss_fn(outputs, targets[[i]])
+        loss.backward()
+        flattened_grad = get_flattened_metric(net, lambda
+            l: l.weight.grad.data.clone().cpu().numpy() if l.weight.grad is not None else torch.zeros_like(
+            l.weight).clone().cpu().numpy())
+        batch_grad.append(flattened_grad)
+    batch_grad = np.stack(batch_grad)
+    direction_code = np.sign(batch_grad)
+    direction_code = abs(direction_code.sum(axis=0))
+    score = np.nansum(direction_code)
+    return score
+
+
+def get_gradsign(input, target, net, device, loss_fn):
+    s = []
+    net = net.to(device)
+    x, target = input, target
+    # x2 = torch.clone(x)
+    # x2 = x2.to(device)
+    x, target = x.to(device), target.to(device)
+    s.append(get_grad_conflict(net=net, inputs=x, targets=target, loss_fn=loss_fn))
+    s = np.mean(s)
+    return s
+
+@measure('gradsign', bn=True)
+def compute_gradsign(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+
+    try:
+        gradsign = get_gradsign(inputs, targets, net, device, loss_fn)
+    except Exception as e:
+        print(e)
+        gradsign= np.nan
+
+    return gradsign

correlation/foresight/pruners/measures/grasp.py

@@ -0,0 +1,87 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.autograd as autograd
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+@measure('grasp', bn=True, mode='param')
+def compute_grasp_per_weight(net, inputs, targets, mode, loss_fn, T=1, num_iters=1, split_data=1):
+
+    # get all applicable weights
+    weights = []
+    for layer in net.modules():
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            weights.append(layer.weight)
+            layer.weight.requires_grad_(True) # TODO isn't this already true?
+
+    # NOTE original code had some input/target splitting into 2
+    # I am guessing this was because of GPU mem limit
+    net.zero_grad()
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+
+        #forward/grad pass #1
+        grad_w = None
+        for _ in range(num_iters):
+            #TODO get new data, otherwise num_iters is useless!
+            outputs = net.forward(inputs[st:en])/T
+            loss = loss_fn(outputs, targets[st:en])
+            grad_w_p = autograd.grad(loss, weights, allow_unused=True)
+            if grad_w is None:
+                grad_w = list(grad_w_p)
+            else:
+                for idx in range(len(grad_w)):
+                    grad_w[idx] += grad_w_p[idx]
+
+    
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+
+        # forward/grad pass #2
+        outputs = net.forward(inputs[st:en])/T
+        loss = loss_fn(outputs, targets[st:en])
+        grad_f = autograd.grad(loss, weights, create_graph=True, allow_unused=True)
+        
+        # accumulate gradients computed in previous step and call backwards
+        z, count = 0,0
+        for layer in net.modules():
+            if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+                if grad_w[count] is not None:
+                    z += (grad_w[count].data * grad_f[count]).sum()
+                count += 1
+        z.backward()
+
+    # compute final sensitivity metric and put in grads
+    def grasp(layer):
+        if layer.weight.grad is not None:
+            return -layer.weight.data * layer.weight.grad   # -theta_q Hg
+            #NOTE in the grasp code they take the *bottom* (1-p)% of values
+            #but we take the *top* (1-p)%, therefore we remove the -ve sign
+            #EDIT accuracy seems to be negatively correlated with this metric, so we add -ve sign here!
+        else:
+            return torch.zeros_like(layer.weight)
+    
+    grads = get_layer_metric_array(net, grasp, mode)
+
+    return grads

correlation/foresight/pruners/measures/jacob_cov.py

@@ -0,0 +1,57 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import numpy as np
+
+from . import measure
+
+
+def get_batch_jacobian(net, x, target, device, split_data):
+    x.requires_grad_(True)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+        y = net(x[st:en])
+        y.backward(torch.ones_like(y))
+
+    jacob = x.grad.detach()
+    x.requires_grad_(False)
+    return jacob, target.detach()
+
+def eval_score(jacob, labels=None):
+    corrs = np.corrcoef(jacob)
+    v, _  = np.linalg.eig(corrs)
+    k = 1e-5
+    return -np.sum(np.log(v + k) + 1./(v + k))
+
+@measure('jacob_cov', bn=True)
+def compute_jacob_cov(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    jacobs, labels = get_batch_jacobian(net, inputs, targets, device, split_data=split_data)
+    jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy()
+
+    try:
+        jc = eval_score(jacobs, labels)
+    except Exception as e:
+        print(e)
+        jc = np.nan
+
+    return jc

correlation/foresight/pruners/measures/l2_norm.py

@@ -0,0 +1,22 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+@measure('l2_norm', copy_net=False, mode='param')
+def get_l2_norm_array(net, inputs, targets, mode, split_data=1):
+    return get_layer_metric_array(net, lambda l: l.weight.norm(), mode=mode)

correlation/foresight/pruners/measures/mean.py

@@ -0,0 +1,63 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    def forward_hook(module, data_input, data_output):
+        s = time.time()
+        mean = torch.mean(data_input[0])
+        e = time.time()
+        t = e - s
+        result_list.append(mean)
+        result_list.append(t)
+    net.classifier.register_forward_hook(forward_hook)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        # t1 = time.time()
+        y = net(x[st:en])
+        # t2 = time.time()
+        # print('var:', t2-t1)
+    m = result_list[0].item()
+    t = result_list[1]
+    result_list.clear()
+    return m, t
+
+
+
+@measure('mean', bn=True)
+def compute_mean(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    # print('var:', features.shape)
+    try:
+        mean, t = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        mean, t = np.nan, None
+    # print(jc)
+    # print(f'var time: {t} s')
+    return mean, t

correlation/foresight/pruners/measures/meco.py

@@ -0,0 +1,73 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import copy
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+from torch import nn
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    x = torch.randn(size=(1, 3, 64, 64)).to(device)
+    net.to(device)
+    def forward_hook(module, data_input, data_output):
+
+        fea = data_output[0].detach()
+        fea = fea.reshape(fea.shape[0], -1)
+        n = fea.shape[0]
+        corr = torch.corrcoef(fea)
+        corr[torch.isnan(corr)] = 0
+        corr[torch.isinf(corr)] = 0
+        values = torch.linalg.eig(corr)[0]
+        # result = np.real(np.min(values)) / np.real(np.max(values))
+        result = torch.min(torch.real(values))
+        result_list.append(result)
+
+    for name, modules in net.named_modules():
+        modules.register_forward_hook(forward_hook)
+
+
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+        # break
+    results = torch.tensor(result_list)
+    results = results[torch.logical_not(torch.isnan(results))]
+    v = torch.sum(results)
+    result_list.clear()
+    return v.item()
+
+
+
+@measure('meco', bn=True)
+def compute_meco(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+
+    try:
+        meco = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        meco = np.nan, None
+    return meco

correlation/foresight/pruners/measures/norm.py

@@ -0,0 +1,55 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    def forward_hook(module, data_input, data_output):
+        norm = torch.norm(data_input[0])
+        result_list.append(norm)
+    net.classifier.register_forward_hook(forward_hook)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+    n = result_list[0].item()
+    result_list.clear()
+    return n
+
+
+
+@measure('norm', bn=True)
+def compute_norm(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    # print('var:', feature.shape)
+    try:
+        norm, t = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        norm, t = np.nan, None
+    # print(jc)
+    # print(f'norm time: {t} s')
+    return norm, t

correlation/foresight/pruners/measures/ntk.py

@@ -0,0 +1,94 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import numpy as np
+
+from . import measure
+
+
+def recal_bn(network, inputs, targets, recalbn, device):
+    for m in network.modules():
+        if isinstance(m, torch.nn.BatchNorm2d):
+            m.running_mean.data.fill_(0)
+            m.running_var.data.fill_(0)
+            m.num_batches_tracked.data.zero_()
+            m.momentum = None
+    network.train()
+    with torch.no_grad():
+        for i, (inputs, targets) in enumerate(zip(inputs, targets)):
+            if i >= recalbn: break
+            inputs = inputs.cuda(device=device, non_blocking=True)
+            _, _ = network(inputs)
+    return network
+
+
+def get_ntk_n(inputs, targets, network, device, recalbn=0, train_mode=False, num_batch=1):
+    device = device
+    # if recalbn > 0:
+    #     network = recal_bn(network, xloader, recalbn, device)
+    #     if network_2 is not None:
+    #         network_2 = recal_bn(network_2, xloader, recalbn, device)
+    network.eval()
+    networks = []
+    networks.append(network)
+    ntks = []
+    # if train_mode:
+    #     networks.train()
+    # else:
+    #     networks.eval()
+    ######
+    grads = [[] for _ in range(len(networks))]
+    for i in range(num_batch):
+        if num_batch > 0 and i >= num_batch: break
+        inputs = inputs.cuda(device=device, non_blocking=True)
+        for net_idx, network in enumerate(networks):
+            network.zero_grad()
+            # print(inputs.size())
+            inputs_ = inputs.clone().cuda(device=device, non_blocking=True)
+            logit = network(inputs_)
+            if isinstance(logit, tuple):
+                logit = logit[1]  # 201 networks: return features and logits
+            for _idx in range(len(inputs_)):
+                logit[_idx:_idx + 1].backward(torch.ones_like(logit[_idx:_idx + 1]), retain_graph=True)
+                grad = []
+                for name, W in network.named_parameters():
+                    if 'weight' in name and W.grad is not None:
+                        grad.append(W.grad.view(-1).detach())
+                grads[net_idx].append(torch.cat(grad, -1))
+                network.zero_grad()
+                torch.cuda.empty_cache()
+    ######
+    grads = [torch.stack(_grads, 0) for _grads in grads]
+    ntks = [torch.einsum('nc,mc->nm', [_grads, _grads]) for _grads in grads]
+    for ntk in ntks:
+        eigenvalues, _ = torch.linalg.eigh(ntk)  # ascending
+        conds = np.nan_to_num((eigenvalues[-1] / eigenvalues[0]).item(), copy=True, nan=100000.0)
+    return conds
+
+@measure('ntk', bn=True)
+def compute_ntk(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+
+    try:
+        conds = get_ntk_n(inputs, targets, net, device)
+    except Exception as e:
+        print(e)
+        conds= np.nan
+
+    return conds

correlation/foresight/pruners/measures/param_count.py

@@ -0,0 +1,16 @@
+import time
+import torch
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+
+@measure('param_count', copy_net=False, mode='param')
+def get_param_count_array(net, inputs, targets, mode, loss_fn, split_data=1):
+    s = time.time()
+    count = get_layer_metric_array(net, lambda l: torch.tensor(sum(p.numel() for p in l.parameters() if p.requires_grad)), mode=mode)
+    e = time.time()
+    t = e - s
+    # print(f'param_count time: {t} s')
+    return count, t

correlation/foresight/pruners/measures/pearson.py

@@ -0,0 +1,71 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import copy
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+from torch import nn
+# import pandas as pd
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    result_t = []
+    def forward_hook(module, data_input, data_output):
+        s = time.time()
+        fea = data_output[0].detach().cpu().numpy()
+        fea = fea.reshape(fea.shape[0], -1)
+        # result = 1 / np.var(np.corrcoef(fea))
+        result = np.var(np.corrcoef(fea))
+        e = time.time()
+        t = e - s
+        result_list.append(result)
+        result_t.append(t)
+
+    for name, modules in net.named_modules():
+        modules.register_forward_hook(forward_hook)
+
+
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+        # print(y)
+    results = np.array(result_list)
+    results = results[np.logical_not(np.isnan(results))]
+    v = np.sum(results)
+    t = sum(result_t)
+    result_list.clear()
+    result_t.clear()
+    return v, t
+
+
+
+@measure('pearson', bn=True)
+def compute_pearson(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    try:
+        pearson, t = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        pearson, t = np.nan, None
+    return pearson, t

correlation/foresight/pruners/measures/plain.py

@@ -0,0 +1,44 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import torch.nn.functional as F
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+@measure('plain', bn=True, mode='param')
+def compute_plain_per_weight(net, inputs, targets, mode, loss_fn, split_data=1):
+
+    net.zero_grad()
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+
+        outputs = net.forward(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+
+    # select the gradients that we want to use for search/prune
+    def plain(layer):
+        if layer.weight.grad is not None:
+            return layer.weight.grad * layer.weight
+        else:
+            return torch.zeros_like(layer.weight)
+
+    grads_abs = get_layer_metric_array(net, plain, mode)
+    return grads_abs

correlation/foresight/pruners/measures/snip.py

@@ -0,0 +1,69 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import copy
+import types
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+def snip_forward_conv2d(self, x):
+        return F.conv2d(x, self.weight * self.weight_mask, self.bias,
+                        self.stride, self.padding, self.dilation, self.groups)
+
+def snip_forward_linear(self, x):
+        return F.linear(x, self.weight * self.weight_mask, self.bias)
+
+@measure('snip', bn=True, mode='param')
+def compute_snip_per_weight(net, inputs, targets, mode, loss_fn, split_data=1):
+    for layer in net.modules():
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            layer.weight_mask = nn.Parameter(torch.ones_like(layer.weight))
+            layer.weight.requires_grad = False
+
+        # Override the forward methods:
+        if isinstance(layer, nn.Conv2d):
+            layer.forward = types.MethodType(snip_forward_conv2d, layer)
+
+        if isinstance(layer, nn.Linear):
+            layer.forward = types.MethodType(snip_forward_linear, layer)
+
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+    
+        outputs = net.forward(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+
+    # select the gradients that we want to use for search/prune
+    def snip(layer):
+        if layer.weight_mask.grad is not None:
+            return torch.abs(layer.weight_mask.grad)
+        else:
+            return torch.zeros_like(layer.weight)
+    
+    grads_abs = get_layer_metric_array(net, snip, mode)
+
+    return grads_abs

correlation/foresight/pruners/measures/synflow.py

@@ -0,0 +1,69 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+@measure('synflow', bn=False, mode='param')
+@measure('synflow_bn', bn=True, mode='param')
+def compute_synflow_per_weight(net, inputs, targets, mode, split_data=1, loss_fn=None):
+
+    device = inputs.device
+
+    #convert params to their abs. Keep sign for converting it back.
+    @torch.no_grad()
+    def linearize(net):
+        signs = {}
+        for name, param in net.state_dict().items():
+            signs[name] = torch.sign(param)
+            param.abs_()
+        return signs
+
+    #convert to orig values
+    @torch.no_grad()
+    def nonlinearize(net, signs):
+        for name, param in net.state_dict().items():
+            if 'weight_mask' not in name:
+                param.mul_(signs[name])
+
+    # keep signs of all params
+    signs = linearize(net)
+    
+    # Compute gradients with input of 1s 
+    net.zero_grad()
+    net.double()
+    input_dim = list(inputs[0,:].shape)
+    inputs = torch.ones([1] + input_dim).double().to(device)
+    output = net.forward(inputs)
+    torch.sum(output).backward() 
+
+    # select the gradients that we want to use for search/prune
+    def synflow(layer):
+        if layer.weight.grad is not None:
+            return torch.abs(layer.weight * layer.weight.grad)
+        else:
+            return torch.zeros_like(layer.weight)
+
+    grads_abs = get_layer_metric_array(net, synflow, mode)
+
+    # apply signs of all params
+    nonlinearize(net, signs)
+
+    return grads_abs
+
+

correlation/foresight/pruners/measures/var.py

@@ -0,0 +1,55 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    def forward_hook(module, data_input, data_output):
+        var = torch.var(data_input[0])
+        result_list.append(var)
+    net.classifier.register_forward_hook(forward_hook)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        y = net(x[st:en])
+    v = result_list[0].item()
+    result_list.clear()
+    return v
+
+
+
+@measure('var', bn=True)
+def compute_var(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    # print('var:', feature.shape)
+    try:
+        var= get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        var= np.nan
+    # print(jc)
+    # print(f'var time: {t} s')
+    return var

correlation/foresight/pruners/measures/zen.py

@@ -0,0 +1,110 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+from torch import nn
+import numpy as np
+
+from . import measure
+
+
+def network_weight_gaussian_init(net: nn.Module):
+    with torch.no_grad():
+        for n, m in net.named_modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.normal_(m.weight)
+                if hasattr(m, 'bias') and m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                try:
+                    nn.init.ones_(m.weight)
+                    nn.init.zeros_(m.bias)
+                except:
+                    pass
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight)
+                if hasattr(m, 'bias') and m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            else:
+                continue
+
+    return net
+
+
+def get_zen(gpu, model, mixup_gamma=1e-2, resolution=32, batch_size=64, repeat=32,
+                      fp16=False):
+    info = {}
+    nas_score_list = []
+    if gpu is not None:
+        device = torch.device(gpu)
+    else:
+        device = torch.device('cpu')
+
+    if fp16:
+        dtype = torch.half
+    else:
+        dtype = torch.float32
+
+    with torch.no_grad():
+        for repeat_count in range(repeat):
+            network_weight_gaussian_init(model)
+            input = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype)
+            input2 = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype)
+            mixup_input = input + mixup_gamma * input2
+            output = model.forward_pre_GAP(input)
+            mixup_output = model.forward_pre_GAP(mixup_input)
+
+            nas_score = torch.sum(torch.abs(output - mixup_output), dim=[1, 2, 3])
+            nas_score = torch.mean(nas_score)
+
+            # compute BN scaling
+            log_bn_scaling_factor = 0.0
+            for m in model.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    try:
+                        bn_scaling_factor = torch.sqrt(torch.mean(m.running_var))
+                        log_bn_scaling_factor += torch.log(bn_scaling_factor)
+                    except:
+                        pass
+                pass
+            pass
+            nas_score = torch.log(nas_score) + log_bn_scaling_factor
+            nas_score_list.append(float(nas_score))
+
+    std_nas_score = np.std(nas_score_list)
+    avg_precision = 1.96 * std_nas_score / np.sqrt(len(nas_score_list))
+    avg_nas_score = np.mean(nas_score_list)
+
+    info = float(avg_nas_score)
+    return info
+
+
+
+
+
+@measure('zen', bn=True)
+def compute_zen(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+
+    try:
+        zen = get_zen(device,net)
+    except Exception as e:
+        print(e)
+        zen= np.nan
+
+    return zen

correlation/foresight/pruners/measures/zico.py

@@ -0,0 +1,106 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+import time
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+
+from . import measure
+from torch import nn
+
+from ...dataset import get_cifar_dataloaders
+
+
+def getgrad(model: torch.nn.Module, grad_dict: dict, step_iter=0):
+    if step_iter == 0:
+        for name, mod in model.named_modules():
+            if isinstance(mod, nn.Conv2d) or isinstance(mod, nn.Linear):
+                # print(mod.weight.grad.data.size())
+                # print(mod.weight.data.size())
+                try:
+                    grad_dict[name] = [mod.weight.grad.data.cpu().reshape(-1).numpy()]
+                except:
+                    continue
+    else:
+        for name, mod in model.named_modules():
+            if isinstance(mod, nn.Conv2d) or isinstance(mod, nn.Linear):
+                try:
+                    grad_dict[name].append(mod.weight.grad.data.cpu().reshape(-1).numpy())
+                except:
+                    continue
+    return grad_dict
+
+
+def caculate_zico(grad_dict):
+    allgrad_array = None
+    for i, modname in enumerate(grad_dict.keys()):
+        grad_dict[modname] = np.array(grad_dict[modname])
+    nsr_mean_sum = 0
+    nsr_mean_sum_abs = 0
+    nsr_mean_avg = 0
+    nsr_mean_avg_abs = 0
+    for j, modname in enumerate(grad_dict.keys()):
+        nsr_std = np.std(grad_dict[modname], axis=0)
+        # print(grad_dict[modname].shape)
+        # print(grad_dict[modname].shape, nsr_std.shape)
+        nonzero_idx = np.nonzero(nsr_std)[0]
+        nsr_mean_abs = np.mean(np.abs(grad_dict[modname]), axis=0)
+        tmpsum = np.sum(nsr_mean_abs[nonzero_idx] / nsr_std[nonzero_idx])
+        if tmpsum == 0:
+            pass
+        else:
+            nsr_mean_sum_abs += np.log(tmpsum)
+            nsr_mean_avg_abs += np.log(np.mean(nsr_mean_abs[nonzero_idx] / nsr_std[nonzero_idx]))
+    return nsr_mean_sum_abs
+
+
+def getzico(network, inputs, targets, loss_fn, split_data=2):
+    grad_dict = {}
+    network.train()
+    device = inputs.device
+    network.to(device)
+    N = inputs.shape[0]
+    split_data = 2
+
+    for sp in range(split_data):
+        st = sp * N // split_data
+        en = (sp + 1) * N // split_data
+        outputs = network.forward(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+        grad_dict = getgrad(network, grad_dict, sp)
+    # print(grad_dict)
+    res = caculate_zico(grad_dict)
+    return res
+
+
+
+
+
+@measure('zico', bn=True)
+def compute_zico(net, inputs, targets, split_data=2, loss_fn=None):
+
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    # print('var:', feature.shape)
+    try:
+        zico = getzico(net, inputs, targets, loss_fn, split_data=split_data)
+    except Exception as e:
+        print(e)
+        zico= np.nan
+    # print(jc)
+    # print(f'var time: {t} s')
+    return zico

correlation/foresight/pruners/p_utils.py

@@ -0,0 +1,83 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..models import *
+
+def get_some_data(train_dataloader, num_batches, device):
+    traindata = []
+    dataloader_iter = iter(train_dataloader)
+    for _ in range(num_batches):
+        traindata.append(next(dataloader_iter))
+    inputs  = torch.cat([a for a,_ in traindata])
+    targets = torch.cat([b for _,b in traindata])
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    return inputs, targets
+
+def get_some_data_grasp(train_dataloader, num_classes, samples_per_class, device):
+    datas = [[] for _ in range(num_classes)]
+    labels = [[] for _ in range(num_classes)]
+    mark = dict()
+    dataloader_iter = iter(train_dataloader)
+    while True:
+        inputs, targets = next(dataloader_iter)
+        for idx in range(inputs.shape[0]):
+            x, y = inputs[idx:idx+1], targets[idx:idx+1]
+            category = y.item()
+            if len(datas[category]) == samples_per_class:
+                mark[category] = True
+                continue
+            datas[category].append(x)
+            labels[category].append(y)
+        if len(mark) == num_classes:
+            break
+
+    x = torch.cat([torch.cat(_, 0) for _ in datas]).to(device) 
+    y = torch.cat([torch.cat(_) for _ in labels]).view(-1).to(device)
+    return x, y
+
+def get_layer_metric_array(net, metric, mode): 
+    metric_array = []
+
+    for layer in net.modules():
+        if mode=='channel' and hasattr(layer,'dont_ch_prune'):
+            continue
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            metric_array.append(metric(layer))
+    
+    return metric_array
+
+def reshape_elements(elements, shapes, device):
+    def broadcast_val(elements, shapes):
+        ret_grads = []
+        for e,sh in zip(elements, shapes):
+            ret_grads.append(torch.stack([torch.Tensor(sh).fill_(v) for v in e], dim=0).to(device))
+        return ret_grads
+    if type(elements[0]) == list:
+        outer = []
+        for e,sh in zip(elements, shapes):
+            outer.append(broadcast_val(e,sh))
+        return outer
+    else:
+        return broadcast_val(elements, shapes)
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+

correlation/foresight/pruners/predictive.py

@@ -0,0 +1,116 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .p_utils import *
+from . import measures
+
+import types
+import copy
+
+
+def no_op(self,x):
+    return x
+
+def copynet(self, bn):
+    net = copy.deepcopy(self)
+    if bn==False:
+        for l in net.modules():
+            if isinstance(l,nn.BatchNorm2d) or isinstance(l,nn.BatchNorm1d) :
+                l.forward = types.MethodType(no_op, l)
+    return net
+
+def find_measures_arrays(net_orig, trainloader, dataload_info, device, measure_names=None, loss_fn=F.cross_entropy):
+    if measure_names is None:
+        measure_names = measures.available_measures
+
+    dataload, num_imgs_or_batches, num_classes = dataload_info
+
+    if not hasattr(net_orig,'get_prunable_copy'):
+        net_orig.get_prunable_copy = types.MethodType(copynet, net_orig)
+
+    #move to cpu to free up mem
+    torch.cuda.empty_cache()
+    net_orig = net_orig.cpu() 
+    torch.cuda.empty_cache()
+
+    #given 1 minibatch of data
+    if dataload == 'random':
+        inputs, targets = get_some_data(trainloader, num_batches=num_imgs_or_batches, device=device)
+    elif dataload == 'grasp':
+        inputs, targets = get_some_data_grasp(trainloader, num_classes, samples_per_class=num_imgs_or_batches, device=device)
+    else:
+        raise NotImplementedError(f'dataload {dataload} is not supported')
+
+    done, ds = False, 1
+    measure_values = {}
+
+    while not done:
+        try:
+            for measure_name in measure_names:
+                if measure_name not in measure_values:
+                    val = measures.calc_measure(measure_name, net_orig, device, inputs, targets, loss_fn=loss_fn, split_data=ds)
+                    measure_values[measure_name] = val
+
+            done = True
+        except RuntimeError as e:
+            if 'out of memory' in str(e):
+                done=False
+                if ds == inputs.shape[0]//2:
+                    raise ValueError(f'Can\'t split data anymore, but still unable to run. Something is wrong') 
+                ds += 1
+                while inputs.shape[0] % ds != 0:
+                    ds += 1
+                torch.cuda.empty_cache()
+                print(f'Caught CUDA OOM, retrying with data split into {ds} parts')
+            else:
+                raise e
+
+    net_orig = net_orig.to(device).train()
+    return measure_values
+
+def find_measures(net_orig,                  # neural network
+                  dataloader,                # a data loader (typically for training data)
+                  dataload_info,             # a tuple with (dataload_type = {random, grasp}, number_of_batches_for_random_or_images_per_class_for_grasp, number of classes)
+                  device,                    # GPU/CPU device used
+                  loss_fn=F.cross_entropy,   # loss function to use within the zero-cost metrics
+                  measure_names=None,        # an array of measure names to compute, if left blank, all measures are computed by default
+                  measures_arr=None):        # [not used] if the measures are already computed but need to be summarized, pass them here
+    
+    #Given a neural net
+    #and some information about the input data (dataloader)
+    #and loss function (loss_fn)
+    #this function returns an array of zero-cost proxy metrics.
+
+    def sum_arr(arr):
+        sum = 0.
+        for i in range(len(arr)):
+            sum += torch.sum(arr[i])
+        return sum.item()
+
+    if measures_arr is None:
+        measures_arr = find_measures_arrays(net_orig, dataloader, dataload_info, device, loss_fn=loss_fn, measure_names=measure_names)
+
+    measures = {}
+    for k,v in measures_arr.items():
+        if k in ['jacob_cov', 'var', 'cor', 'norm', 'meco', 'zico', 'ntk', 'gradsign', 'zen']:
+            measures[k] = v
+        else:
+            measures[k] = sum_arr(v)
+
+    return measures

correlation/foresight/version.py

@@ -0,0 +1,51 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+version = '1.0.0'
+repo = 'unknown'
+commit = 'unknown'
+has_repo = False
+
+try:
+    import git
+    from pathlib import Path
+
+    try:
+        r = git.Repo(Path(__file__).parents[1])
+        has_repo = True
+
+        if not r.remotes:
+            repo = 'local'
+        else:
+            repo = r.remotes.origin.url
+
+        commit = r.head.commit.hexsha
+        if r.is_dirty():
+            commit += ' (dirty)'
+    except git.InvalidGitRepositoryError:
+        raise ImportError()
+except ImportError:
+    pass
+
+try:
+    from . import _dist_info as info
+    assert not has_repo, '_dist_info should not exist when repo is in place'
+    assert version == info.version
+    repo = info.repo
+    commit = info.commit
+except (ImportError, SystemError):
+    pass
+
+__all__ = ['version', 'repo', 'commit', 'has_repo']

correlation/foresight/weight_initializers.py

@@ -0,0 +1,84 @@
+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import torch.nn as nn
+
+def init_net(net, w_type, b_type):
+    if w_type == 'none':
+        pass
+    elif w_type == 'xavier':
+        net.apply(init_weights_vs)
+    elif w_type == 'kaiming':
+        net.apply(init_weights_he)
+    elif w_type == 'zero':
+        net.apply(init_weights_zero)
+    elif w_type == 'one':
+        net.apply(init_weights_one)
+    else:
+        raise NotImplementedError(f'init_type={w_type} is not supported.')
+
+    if b_type == 'none':
+        pass
+    elif b_type == 'xavier':
+        net.apply(init_bias_vs)
+    elif b_type == 'kaiming':
+        net.apply(init_bias_he)
+    elif b_type == 'zero':
+        net.apply(init_bias_zero)
+    elif b_type == 'one':
+        net.apply(init_bias_one)
+    else:
+        raise NotImplementedError(f'init_type={b_type} is not supported.')
+
+
+
+
+def init_weights_vs(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        nn.init.xavier_normal_(m.weight)
+
+def init_bias_vs(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        if m.bias is not None:
+            nn.init.xavier_normal_(m.bias)
+
+def init_weights_he(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        nn.init.kaiming_normal_(m.weight)
+
+def init_bias_he(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        if m.bias is not None:
+            nn.init.kaiming_normal_(m.bias)
+
+def init_weights_zero(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        m.weight.data.fill_(.0)
+
+def init_weights_one(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        m.weight.data.fill_(1.)
+
+def init_bias_zero(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        if m.bias is not None:
+            m.bias.data.fill_(.0)
+
+
+def init_bias_one(m):
+    if type(m) == nn.Linear or type(m) == nn.Conv2d:
+        if m.bias is not None:
+            m.bias.data.fill_(1.)
+    

correlation/models/CifarDenseNet.py

@@ -0,0 +1,117 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+
+
+class Bottleneck(nn.Module):
+    def __init__(self, nChannels, growthRate):
+        super(Bottleneck, self).__init__()
+        interChannels = 4 * growthRate
+        self.bn1 = nn.BatchNorm2d(nChannels)
+        self.conv1 = nn.Conv2d(nChannels, interChannels, kernel_size=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(interChannels)
+        self.conv2 = nn.Conv2d(
+            interChannels, growthRate, kernel_size=3, padding=1, bias=False
+        )
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = self.conv2(F.relu(self.bn2(out)))
+        out = torch.cat((x, out), 1)
+        return out
+
+
+class SingleLayer(nn.Module):
+    def __init__(self, nChannels, growthRate):
+        super(SingleLayer, self).__init__()
+        self.bn1 = nn.BatchNorm2d(nChannels)
+        self.conv1 = nn.Conv2d(
+            nChannels, growthRate, kernel_size=3, padding=1, bias=False
+        )
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = torch.cat((x, out), 1)
+        return out
+
+
+class Transition(nn.Module):
+    def __init__(self, nChannels, nOutChannels):
+        super(Transition, self).__init__()
+        self.bn1 = nn.BatchNorm2d(nChannels)
+        self.conv1 = nn.Conv2d(nChannels, nOutChannels, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = F.avg_pool2d(out, 2)
+        return out
+
+
+class DenseNet(nn.Module):
+    def __init__(self, growthRate, depth, reduction, nClasses, bottleneck):
+        super(DenseNet, self).__init__()
+
+        if bottleneck:
+            nDenseBlocks = int((depth - 4) / 6)
+        else:
+            nDenseBlocks = int((depth - 4) / 3)
+
+        self.message = "CifarDenseNet : block : {:}, depth : {:}, reduction : {:}, growth-rate = {:}, class = {:}".format(
+            "bottleneck" if bottleneck else "basic",
+            depth,
+            reduction,
+            growthRate,
+            nClasses,
+        )
+
+        nChannels = 2 * growthRate
+        self.conv1 = nn.Conv2d(3, nChannels, kernel_size=3, padding=1, bias=False)
+
+        self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+        nChannels += nDenseBlocks * growthRate
+        nOutChannels = int(math.floor(nChannels * reduction))
+        self.trans1 = Transition(nChannels, nOutChannels)
+
+        nChannels = nOutChannels
+        self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+        nChannels += nDenseBlocks * growthRate
+        nOutChannels = int(math.floor(nChannels * reduction))
+        self.trans2 = Transition(nChannels, nOutChannels)
+
+        nChannels = nOutChannels
+        self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+        nChannels += nDenseBlocks * growthRate
+
+        self.act = nn.Sequential(
+            nn.BatchNorm2d(nChannels), nn.ReLU(inplace=True), nn.AvgPool2d(8)
+        )
+        self.fc = nn.Linear(nChannels, nClasses)
+
+        self.apply(initialize_resnet)
+
+    def get_message(self):
+        return self.message
+
+    def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck):
+        layers = []
+        for i in range(int(nDenseBlocks)):
+            if bottleneck:
+                layers.append(Bottleneck(nChannels, growthRate))
+            else:
+                layers.append(SingleLayer(nChannels, growthRate))
+            nChannels += growthRate
+        return nn.Sequential(*layers)
+
+    def forward(self, inputs):
+        out = self.conv1(inputs)
+        out = self.trans1(self.dense1(out))
+        out = self.trans2(self.dense2(out))
+        out = self.dense3(out)
+        features = self.act(out)
+        features = features.view(features.size(0), -1)
+        out = self.fc(features)
+        return features, out

correlation/models/CifarResNet.py

@@ -0,0 +1,180 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+from .SharedUtils import additive_func
+
+
+class Downsample(nn.Module):
+    def __init__(self, nIn, nOut, stride):
+        super(Downsample, self).__init__()
+        assert stride == 2 and nOut == 2 * nIn, "stride:{} IO:{},{}".format(
+            stride, nIn, nOut
+        )
+        self.in_dim = nIn
+        self.out_dim = nOut
+        self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=1, padding=0, bias=False)
+
+    def forward(self, x):
+        x = self.avg(x)
+        out = self.conv(x)
+        return out
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, nIn, nOut, kernel, stride, padding, bias, relu):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(
+            nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, bias=bias
+        )
+        self.bn = nn.BatchNorm2d(nOut)
+        if relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+        self.out_dim = nOut
+        self.num_conv = 1
+
+    def forward(self, x):
+        conv = self.conv(x)
+        bn = self.bn(conv)
+        if self.relu:
+            return self.relu(bn)
+        else:
+            return bn
+
+
+class ResNetBasicblock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, True)
+        self.conv_b = ConvBNReLU(planes, planes, 3, 1, 1, False, False)
+        if stride == 2:
+            self.downsample = Downsample(inplanes, planes, stride)
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, False)
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.num_conv = 2
+
+    def forward(self, inputs):
+
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return F.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(inplanes, planes, 1, 1, 0, False, True)
+        self.conv_3x3 = ConvBNReLU(planes, planes, 3, stride, 1, False, True)
+        self.conv_1x4 = ConvBNReLU(
+            planes, planes * self.expansion, 1, 1, 0, False, False
+        )
+        if stride == 2:
+            self.downsample = Downsample(inplanes, planes * self.expansion, stride)
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes, planes * self.expansion, 1, 1, 0, False, False
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+        self.num_conv = 3
+
+    def forward(self, inputs):
+
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, bottleneck)
+        return F.relu(out, inplace=True)
+
+
+class CifarResNet(nn.Module):
+    def __init__(self, block_name, depth, num_classes, zero_init_residual):
+        super(CifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = "CifarResNet : Block : {:}, Depth : {:}, Layers for each block : {:}".format(
+            block_name, depth, layer_blocks
+        )
+        self.num_classes = num_classes
+        self.channels = [16]
+        self.layers = nn.ModuleList([ConvBNReLU(3, 16, 3, 1, 1, False, True)])
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        assert (
+            sum(x.num_conv for x in self.layers) + 1 == depth
+        ), "invalid depth check {:} vs {:}".format(
+            sum(x.num_conv for x in self.layers) + 1, depth
+        )
+
+        self.apply(initialize_resnet)
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, ResNetBasicblock):
+                    nn.init.constant_(m.conv_b.bn.weight, 0)
+                elif isinstance(m, ResNetBottleneck):
+                    nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/CifarWideResNet.py

@@ -0,0 +1,115 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+
+
+class WideBasicblock(nn.Module):
+    def __init__(self, inplanes, planes, stride, dropout=False):
+        super(WideBasicblock, self).__init__()
+
+        self.bn_a = nn.BatchNorm2d(inplanes)
+        self.conv_a = nn.Conv2d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+        )
+
+        self.bn_b = nn.BatchNorm2d(planes)
+        if dropout:
+            self.dropout = nn.Dropout2d(p=0.5, inplace=True)
+        else:
+            self.dropout = None
+        self.conv_b = nn.Conv2d(
+            planes, planes, kernel_size=3, stride=1, padding=1, bias=False
+        )
+
+        if inplanes != planes:
+            self.downsample = nn.Conv2d(
+                inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+
+        basicblock = self.bn_a(x)
+        basicblock = F.relu(basicblock)
+        basicblock = self.conv_a(basicblock)
+
+        basicblock = self.bn_b(basicblock)
+        basicblock = F.relu(basicblock)
+        if self.dropout is not None:
+            basicblock = self.dropout(basicblock)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return x + basicblock
+
+
+class CifarWideResNet(nn.Module):
+    """
+    ResNet optimized for the Cifar dataset, as specified in
+    https://arxiv.org/abs/1512.03385.pdf
+    """
+
+    def __init__(self, depth, widen_factor, num_classes, dropout):
+        super(CifarWideResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        assert (depth - 4) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+        layer_blocks = (depth - 4) // 6
+        print(
+            "CifarPreResNet : Depth : {} , Layers for each block : {}".format(
+                depth, layer_blocks
+            )
+        )
+
+        self.num_classes = num_classes
+        self.dropout = dropout
+        self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
+
+        self.message = "Wide ResNet : depth={:}, widen_factor={:}, class={:}".format(
+            depth, widen_factor, num_classes
+        )
+        self.inplanes = 16
+        self.stage_1 = self._make_layer(
+            WideBasicblock, 16 * widen_factor, layer_blocks, 1
+        )
+        self.stage_2 = self._make_layer(
+            WideBasicblock, 32 * widen_factor, layer_blocks, 2
+        )
+        self.stage_3 = self._make_layer(
+            WideBasicblock, 64 * widen_factor, layer_blocks, 2
+        )
+        self.lastact = nn.Sequential(
+            nn.BatchNorm2d(64 * widen_factor), nn.ReLU(inplace=True)
+        )
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(64 * widen_factor, num_classes)
+
+        self.apply(initialize_resnet)
+
+    def get_message(self):
+        return self.message
+
+    def _make_layer(self, block, planes, blocks, stride):
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, self.dropout))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, 1, self.dropout))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv_3x3(x)
+        x = self.stage_1(x)
+        x = self.stage_2(x)
+        x = self.stage_3(x)
+        x = self.lastact(x)
+        x = self.avgpool(x)
+        features = x.view(x.size(0), -1)
+        outs = self.classifier(features)
+        return features, outs

correlation/models/ImageNet_MobileNetV2.py

@@ -0,0 +1,117 @@
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
+from torch import nn
+from .initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        super(ConvBNReLU, self).__init__()
+        padding = (kernel_size - 1) // 2
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size,
+            stride,
+            padding,
+            groups=groups,
+            bias=False,
+        )
+        self.bn = nn.BatchNorm2d(out_planes)
+        self.relu = nn.ReLU6(inplace=True)
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.bn(out)
+        out = self.relu(out)
+        return out
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            # pw
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend(
+            [
+                # dw
+                ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+                # pw-linear
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(oup),
+            ]
+        )
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(
+        self, num_classes, width_mult, input_channel, last_channel, block_name, dropout
+    ):
+        super(MobileNetV2, self).__init__()
+        if block_name == "InvertedResidual":
+            block = InvertedResidual
+        else:
+            raise ValueError("invalid block name : {:}".format(block_name))
+        inverted_residual_setting = [
+            # t, c,  n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            [6, 160, 3, 2],
+            [6, 320, 1, 1],
+        ]
+
+        # building first layer
+        input_channel = int(input_channel * width_mult)
+        self.last_channel = int(last_channel * max(1.0, width_mult))
+        features = [ConvBNReLU(3, input_channel, stride=2)]
+        # building inverted residual blocks
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = int(c * width_mult)
+            for i in range(n):
+                stride = s if i == 0 else 1
+                features.append(
+                    block(input_channel, output_channel, stride, expand_ratio=t)
+                )
+                input_channel = output_channel
+        # building last several layers
+        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
+        # make it nn.Sequential
+        self.features = nn.Sequential(*features)
+
+        # building classifier
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(self.last_channel, num_classes),
+        )
+        self.message = "MobileNetV2 : width_mult={:}, in-C={:}, last-C={:}, block={:}, dropout={:}".format(
+            width_mult, input_channel, last_channel, block_name, dropout
+        )
+
+        # weight initialization
+        self.apply(initialize_resnet)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        features = self.features(inputs)
+        vectors = features.mean([2, 3])
+        predicts = self.classifier(vectors)
+        return features, predicts

correlation/models/ImageNet_ResNet.py

@@ -0,0 +1,217 @@
+# Deep Residual Learning for Image Recognition, CVPR 2016
+import torch.nn as nn
+from .initialization import initialize_resnet
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1):
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        groups=groups,
+        bias=False,
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(
+        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
+    ):
+        super(BasicBlock, self).__init__()
+        if groups != 1 or base_width != 64:
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(
+        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
+    ):
+        super(Bottleneck, self).__init__()
+        width = int(planes * (base_width / 64.0)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = conv3x3(width, width, stride, groups)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(
+        self,
+        block_name,
+        layers,
+        deep_stem,
+        num_classes,
+        zero_init_residual,
+        groups,
+        width_per_group,
+    ):
+        super(ResNet, self).__init__()
+
+        # planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
+        if block_name == "BasicBlock":
+            block = BasicBlock
+        elif block_name == "Bottleneck":
+            block = Bottleneck
+        else:
+            raise ValueError("invalid block-name : {:}".format(block_name))
+
+        if not deep_stem:
+            self.conv = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
+                nn.BatchNorm2d(64),
+                nn.ReLU(inplace=True),
+            )
+        else:
+            self.conv = nn.Sequential(
+                nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(32),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
+                nn.BatchNorm2d(32),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
+                nn.BatchNorm2d(64),
+                nn.ReLU(inplace=True),
+            )
+        self.inplanes = 64
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(
+            block, 64, layers[0], stride=1, groups=groups, base_width=width_per_group
+        )
+        self.layer2 = self._make_layer(
+            block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group
+        )
+        self.layer3 = self._make_layer(
+            block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group
+        )
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        self.message = (
+            "block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}".format(
+                block, layers, deep_stem, num_classes
+            )
+        )
+
+        self.apply(initialize_resnet)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride, groups, base_width):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            if stride == 2:
+                downsample = nn.Sequential(
+                    nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                    conv1x1(self.inplanes, planes * block.expansion, 1),
+                    nn.BatchNorm2d(planes * block.expansion),
+                )
+            elif stride == 1:
+                downsample = nn.Sequential(
+                    conv1x1(self.inplanes, planes * block.expansion, stride),
+                    nn.BatchNorm2d(planes * block.expansion),
+                )
+            else:
+                raise ValueError("invalid stride [{:}] for downsample".format(stride))
+
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, stride, downsample, groups, base_width)
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, 1, None, groups, base_width))
+
+        return nn.Sequential(*layers)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.fc(features)
+
+        return features, logits

correlation/models/SharedUtils.py

@@ -0,0 +1,37 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch
+import torch.nn as nn
+
+
+def additive_func(A, B):
+    assert A.dim() == B.dim() and A.size(0) == B.size(0), "{:} vs {:}".format(
+        A.size(), B.size()
+    )
+    C = min(A.size(1), B.size(1))
+    if A.size(1) == B.size(1):
+        return A + B
+    elif A.size(1) < B.size(1):
+        out = B.clone()
+        out[:, :C] += A
+        return out
+    else:
+        out = A.clone()
+        out[:, :C] += B
+        return out
+
+
+def change_key(key, value):
+    def func(m):
+        if hasattr(m, key):
+            setattr(m, key, value)
+
+    return func
+
+
+def parse_channel_info(xstring):
+    blocks = xstring.split(" ")
+    blocks = [x.split("-") for x in blocks]
+    blocks = [[int(_) for _ in x] for x in blocks]
+    return blocks

correlation/models/__init__.py

@@ -0,0 +1,329 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from os import path as osp
+from typing import List, Text
+import torch
+
+__all__ = [
+    "change_key",
+    "get_cell_based_tiny_net",
+    "get_search_spaces",
+    "get_cifar_models",
+    "get_imagenet_models",
+    "obtain_model",
+    "obtain_search_model",
+    "load_net_from_checkpoint",
+    "CellStructure",
+    "CellArchitectures",
+]
+
+# useful modules
+from xautodl.config_utils import dict2config
+from .SharedUtils import change_key
+from .cell_searchs import CellStructure, CellArchitectures
+
+
+# Cell-based NAS Models
+def get_cell_based_tiny_net(config):
+    if isinstance(config, dict):
+        config = dict2config(config, None)  # to support the argument being a dict
+        # print(config)
+    super_type = getattr(config, "super_type", "basic")
+    # print(super_type)
+    group_names = ["DARTS-V1", "DARTS-V2", "GDAS", "SETN", "ENAS", "RANDOM", "generic"]
+    if super_type == "basic" and config.name in group_names:
+        from .cell_searchs import nas201_super_nets as nas_super_nets
+
+        try:
+            return nas_super_nets[config.name](
+                config.C,
+                config.N,
+                config.max_nodes,
+                config.num_classes,
+                config.space,
+                config.affine,
+                config.track_running_stats,
+            )
+        except:
+            return nas_super_nets[config.name](
+                config.C, config.N, config.max_nodes, config.num_classes, config.space
+            )
+    elif super_type == "search-shape":
+        from .shape_searchs import GenericNAS301Model
+
+        genotype = CellStructure.str2structure(config.genotype)
+        return GenericNAS301Model(
+            config.candidate_Cs,
+            config.max_num_Cs,
+            genotype,
+            config.num_classes,
+            config.affine,
+            config.track_running_stats,
+        )
+    elif super_type == "nasnet-super":
+        from .cell_searchs import nasnet_super_nets as nas_super_nets
+
+        return nas_super_nets[config.name](
+            config.C,
+            config.N,
+            config.steps,
+            config.multiplier,
+            config.stem_multiplier,
+            config.num_classes,
+            config.space,
+            config.affine,
+            config.track_running_stats,
+        )
+    elif config.name == "infer.tiny":
+        from .cell_infers import TinyNetwork
+
+        if hasattr(config, "genotype"):
+            genotype = config.genotype
+        elif hasattr(config, "arch_str"):
+            genotype = CellStructure.str2structure(config.arch_str)
+        else:
+            raise ValueError(
+                "Can not find genotype from this config : {:}".format(config)
+            )
+        return TinyNetwork(config.C, config.N, genotype, config.num_classes)
+    # sss 网络用到的
+    elif config.name == "infer.shape.tiny":
+        from .shape_infers import DynamicShapeTinyNet
+
+        if isinstance(config.channels, str):
+            channels = tuple([int(x) for x in config.channels.split(":")])
+        else:
+            channels = config.channels
+        genotype = CellStructure.str2structure(config.genotype)
+        return DynamicShapeTinyNet(channels, genotype, config.num_classes)
+    elif config.name == "infer.nasnet-cifar":
+        from .cell_infers import NASNetonCIFAR
+
+        raise NotImplementedError
+    else:
+        raise ValueError("invalid network name : {:}".format(config.name))
+
+
+# obtain the search space, i.e., a dict mapping the operation name into a python-function for this op
+def get_search_spaces(xtype, name) -> List[Text]:
+    if xtype == "cell" or xtype == "tss":  # The topology search space.
+        from .cell_operations import SearchSpaceNames
+
+        assert name in SearchSpaceNames, "invalid name [{:}] in {:}".format(
+            name, SearchSpaceNames.keys()
+        )
+        return SearchSpaceNames[name]
+    elif xtype == "sss":  # The size search space.
+        if name in ["nats-bench", "nats-bench-size"]:
+            return {"candidates": [8, 16, 24, 32, 40, 48, 56, 64], "numbers": 5}
+        else:
+            raise ValueError("Invalid name : {:}".format(name))
+    else:
+        raise ValueError("invalid search-space type is {:}".format(xtype))
+
+
+def get_cifar_models(config, extra_path=None):
+    super_type = getattr(config, "super_type", "basic")
+    if super_type == "basic":
+        from .CifarResNet import CifarResNet
+        from .CifarDenseNet import DenseNet
+        from .CifarWideResNet import CifarWideResNet
+
+        if config.arch == "resnet":
+            return CifarResNet(
+                config.module, config.depth, config.class_num, config.zero_init_residual
+            )
+        elif config.arch == "densenet":
+            return DenseNet(
+                config.growthRate,
+                config.depth,
+                config.reduction,
+                config.class_num,
+                config.bottleneck,
+            )
+        elif config.arch == "wideresnet":
+            return CifarWideResNet(
+                config.depth, config.wide_factor, config.class_num, config.dropout
+            )
+        else:
+            raise ValueError("invalid module type : {:}".format(config.arch))
+    elif super_type.startswith("infer"):
+        from .shape_infers import InferWidthCifarResNet
+        from .shape_infers import InferDepthCifarResNet
+        from .shape_infers import InferCifarResNet
+        from .cell_infers import NASNetonCIFAR
+
+        assert len(super_type.split("-")) == 2, "invalid super_type : {:}".format(
+            super_type
+        )
+        infer_mode = super_type.split("-")[1]
+        if infer_mode == "width":
+            return InferWidthCifarResNet(
+                config.module,
+                config.depth,
+                config.xchannels,
+                config.class_num,
+                config.zero_init_residual,
+            )
+        elif infer_mode == "depth":
+            return InferDepthCifarResNet(
+                config.module,
+                config.depth,
+                config.xblocks,
+                config.class_num,
+                config.zero_init_residual,
+            )
+        elif infer_mode == "shape":
+            return InferCifarResNet(
+                config.module,
+                config.depth,
+                config.xblocks,
+                config.xchannels,
+                config.class_num,
+                config.zero_init_residual,
+            )
+        elif infer_mode == "nasnet.cifar":
+            genotype = config.genotype
+            if extra_path is not None:  # reload genotype by extra_path
+                if not osp.isfile(extra_path):
+                    raise ValueError("invalid extra_path : {:}".format(extra_path))
+                xdata = torch.load(extra_path)
+                current_epoch = xdata["epoch"]
+                genotype = xdata["genotypes"][current_epoch - 1]
+            C = config.C if hasattr(config, "C") else config.ichannel
+            N = config.N if hasattr(config, "N") else config.layers
+            return NASNetonCIFAR(
+                C, N, config.stem_multi, config.class_num, genotype, config.auxiliary
+            )
+        else:
+            raise ValueError("invalid infer-mode : {:}".format(infer_mode))
+    else:
+        raise ValueError("invalid super-type : {:}".format(super_type))
+
+
+def get_imagenet_models(config):
+    super_type = getattr(config, "super_type", "basic")
+    if super_type == "basic":
+        from .ImageNet_ResNet import ResNet
+        from .ImageNet_MobileNetV2 import MobileNetV2
+
+        if config.arch == "resnet":
+            return ResNet(
+                config.block_name,
+                config.layers,
+                config.deep_stem,
+                config.class_num,
+                config.zero_init_residual,
+                config.groups,
+                config.width_per_group,
+            )
+        elif config.arch == "mobilenet_v2":
+            return MobileNetV2(
+                config.class_num,
+                config.width_multi,
+                config.input_channel,
+                config.last_channel,
+                "InvertedResidual",
+                config.dropout,
+            )
+        else:
+            raise ValueError("invalid arch : {:}".format(config.arch))
+    elif super_type.startswith("infer"):  # NAS searched architecture
+        assert len(super_type.split("-")) == 2, "invalid super_type : {:}".format(
+            super_type
+        )
+        infer_mode = super_type.split("-")[1]
+        if infer_mode == "shape":
+            from .shape_infers import InferImagenetResNet
+            from .shape_infers import InferMobileNetV2
+
+            if config.arch == "resnet":
+                return InferImagenetResNet(
+                    config.block_name,
+                    config.layers,
+                    config.xblocks,
+                    config.xchannels,
+                    config.deep_stem,
+                    config.class_num,
+                    config.zero_init_residual,
+                )
+            elif config.arch == "MobileNetV2":
+                return InferMobileNetV2(
+                    config.class_num, config.xchannels, config.xblocks, config.dropout
+                )
+            else:
+                raise ValueError("invalid arch-mode : {:}".format(config.arch))
+        else:
+            raise ValueError("invalid infer-mode : {:}".format(infer_mode))
+    else:
+        raise ValueError("invalid super-type : {:}".format(super_type))
+
+
+# Try to obtain the network by config.
+def obtain_model(config, extra_path=None):
+    if config.dataset == "cifar":
+        return get_cifar_models(config, extra_path)
+    elif config.dataset == "imagenet":
+        return get_imagenet_models(config)
+    else:
+        raise ValueError("invalid dataset in the model config : {:}".format(config))
+
+
+def obtain_search_model(config):
+    if config.dataset == "cifar":
+        if config.arch == "resnet":
+            from .shape_searchs import SearchWidthCifarResNet
+            from .shape_searchs import SearchDepthCifarResNet
+            from .shape_searchs import SearchShapeCifarResNet
+
+            if config.search_mode == "width":
+                return SearchWidthCifarResNet(
+                    config.module, config.depth, config.class_num
+                )
+            elif config.search_mode == "depth":
+                return SearchDepthCifarResNet(
+                    config.module, config.depth, config.class_num
+                )
+            elif config.search_mode == "shape":
+                return SearchShapeCifarResNet(
+                    config.module, config.depth, config.class_num
+                )
+            else:
+                raise ValueError("invalid search mode : {:}".format(config.search_mode))
+        elif config.arch == "simres":
+            from .shape_searchs import SearchWidthSimResNet
+
+            if config.search_mode == "width":
+                return SearchWidthSimResNet(config.depth, config.class_num)
+            else:
+                raise ValueError("invalid search mode : {:}".format(config.search_mode))
+        else:
+            raise ValueError(
+                "invalid arch : {:} for dataset [{:}]".format(
+                    config.arch, config.dataset
+                )
+            )
+    elif config.dataset == "imagenet":
+        from .shape_searchs import SearchShapeImagenetResNet
+
+        assert config.search_mode == "shape", "invalid search-mode : {:}".format(
+            config.search_mode
+        )
+        if config.arch == "resnet":
+            return SearchShapeImagenetResNet(
+                config.block_name, config.layers, config.deep_stem, config.class_num
+            )
+        else:
+            raise ValueError("invalid model config : {:}".format(config))
+    else:
+        raise ValueError("invalid dataset in the model config : {:}".format(config))
+
+
+def load_net_from_checkpoint(checkpoint):
+    assert osp.isfile(checkpoint), "checkpoint {:} does not exist".format(checkpoint)
+    checkpoint = torch.load(checkpoint)
+    model_config = dict2config(checkpoint["model-config"], None)
+    model = obtain_model(model_config)
+    model.load_state_dict(checkpoint["base-model"])
+    return model

correlation/models/cell_infers/__init__.py

@@ -0,0 +1,5 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from .tiny_network import TinyNetwork
+from .nasnet_cifar import NASNetonCIFAR

correlation/models/cell_infers/cells.py

@@ -0,0 +1,155 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+
+import torch
+import torch.nn as nn
+from copy import deepcopy
+
+from xautodl.models.cell_operations import OPS
+
+
+# Cell for NAS-Bench-201
+class InferCell(nn.Module):
+    def __init__(
+        self, genotype, C_in, C_out, stride, affine=True, track_running_stats=True
+    ):
+        super(InferCell, self).__init__()
+
+        self.layers = nn.ModuleList()
+        self.node_IN = []
+        self.node_IX = []
+        self.genotype = deepcopy(genotype)
+        for i in range(1, len(genotype)):
+            node_info = genotype[i - 1]
+            cur_index = []
+            cur_innod = []
+            for (op_name, op_in) in node_info:
+                if op_in == 0:
+                    layer = OPS[op_name](
+                        C_in, C_out, stride, affine, track_running_stats
+                    )
+                else:
+                    layer = OPS[op_name](C_out, C_out, 1, affine, track_running_stats)
+                cur_index.append(len(self.layers))
+                cur_innod.append(op_in)
+                self.layers.append(layer)
+            self.node_IX.append(cur_index)
+            self.node_IN.append(cur_innod)
+        self.nodes = len(genotype)
+        self.in_dim = C_in
+        self.out_dim = C_out
+
+    def extra_repr(self):
+        string = "info :: nodes={nodes}, inC={in_dim}, outC={out_dim}".format(
+            **self.__dict__
+        )
+        laystr = []
+        for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)):
+            y = [
+                "I{:}-L{:}".format(_ii, _il)
+                for _il, _ii in zip(node_layers, node_innods)
+            ]
+            x = "{:}<-({:})".format(i + 1, ",".join(y))
+            laystr.append(x)
+        return (
+            string
+            + ", [{:}]".format(" | ".join(laystr))
+            + ", {:}".format(self.genotype.tostr())
+        )
+
+    def forward(self, inputs):
+        nodes = [inputs]
+        for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)):
+            node_feature = sum(
+                self.layers[_il](nodes[_ii])
+                for _il, _ii in zip(node_layers, node_innods)
+            )
+            nodes.append(node_feature)
+        return nodes[-1]
+
+
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+class NASNetInferCell(nn.Module):
+    def __init__(
+        self,
+        genotype,
+        C_prev_prev,
+        C_prev,
+        C,
+        reduction,
+        reduction_prev,
+        affine,
+        track_running_stats,
+    ):
+        super(NASNetInferCell, self).__init__()
+        self.reduction = reduction
+        if reduction_prev:
+            self.preprocess0 = OPS["skip_connect"](
+                C_prev_prev, C, 2, affine, track_running_stats
+            )
+        else:
+            self.preprocess0 = OPS["nor_conv_1x1"](
+                C_prev_prev, C, 1, affine, track_running_stats
+            )
+        self.preprocess1 = OPS["nor_conv_1x1"](
+            C_prev, C, 1, affine, track_running_stats
+        )
+
+        if not reduction:
+            nodes, concats = genotype["normal"], genotype["normal_concat"]
+        else:
+            nodes, concats = genotype["reduce"], genotype["reduce_concat"]
+        self._multiplier = len(concats)
+        self._concats = concats
+        self._steps = len(nodes)
+        self._nodes = nodes
+        self.edges = nn.ModuleDict()
+        for i, node in enumerate(nodes):
+            for in_node in node:
+                name, j = in_node[0], in_node[1]
+                stride = 2 if reduction and j < 2 else 1
+                node_str = "{:}<-{:}".format(i + 2, j)
+                self.edges[node_str] = OPS[name](
+                    C, C, stride, affine, track_running_stats
+                )
+
+    # [TODO] to support drop_prob in this function..
+    def forward(self, s0, s1, unused_drop_prob):
+        s0 = self.preprocess0(s0)
+        s1 = self.preprocess1(s1)
+
+        states = [s0, s1]
+        for i, node in enumerate(self._nodes):
+            clist = []
+            for in_node in node:
+                name, j = in_node[0], in_node[1]
+                node_str = "{:}<-{:}".format(i + 2, j)
+                op = self.edges[node_str]
+                clist.append(op(states[j]))
+            states.append(sum(clist))
+        return torch.cat([states[x] for x in self._concats], dim=1)
+
+
+class AuxiliaryHeadCIFAR(nn.Module):
+    def __init__(self, C, num_classes):
+        """assuming input size 8x8"""
+        super(AuxiliaryHeadCIFAR, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(
+                5, stride=3, padding=0, count_include_pad=False
+            ),  # image size = 2 x 2
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 768, 2, bias=False),
+            nn.BatchNorm2d(768),
+            nn.ReLU(inplace=True),
+        )
+        self.classifier = nn.Linear(768, num_classes)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x

correlation/models/cell_infers/nasnet_cifar.py

@@ -0,0 +1,118 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+
+from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR
+
+
+# The macro structure is based on NASNet
+class NASNetonCIFAR(nn.Module):
+    def __init__(
+        self,
+        C,
+        N,
+        stem_multiplier,
+        num_classes,
+        genotype,
+        auxiliary,
+        affine=True,
+        track_running_stats=True,
+    ):
+        super(NASNetonCIFAR, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+        self.auxiliary_index = None
+        self.auxiliary_head = None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            cell = InferCell(
+                genotype,
+                C_prev_prev,
+                C_prev,
+                C_curr,
+                reduction,
+                reduction_prev,
+                affine,
+                track_running_stats,
+            )
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = (
+                C_prev,
+                cell._multiplier * C_curr,
+                reduction,
+            )
+            if reduction and C_curr == C * 4 and auxiliary:
+                self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
+                self.auxiliary_index = index
+        self._Layer = len(self.cells)
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.drop_path_prob = -1
+
+    def update_drop_path(self, drop_path_prob):
+        self.drop_path_prob = drop_path_prob
+
+    def auxiliary_param(self):
+        if self.auxiliary_head is None:
+            return []
+        else:
+            return list(self.auxiliary_head.parameters())
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def forward(self, inputs):
+        stem_feature, logits_aux = self.stem(inputs), None
+        cell_results = [stem_feature, stem_feature]
+        for i, cell in enumerate(self.cells):
+            cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
+            cell_results.append(cell_feature)
+            if (
+                self.auxiliary_index is not None
+                and i == self.auxiliary_index
+                and self.training
+            ):
+                logits_aux = self.auxiliary_head(cell_results[-1])
+        out = self.lastact(cell_results[-1])
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+        if logits_aux is None:
+            return out, logits
+        else:
+            return out, [logits, logits_aux]

correlation/models/cell_infers/tiny_network.py

@@ -0,0 +1,63 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+from ..cell_operations import ResNetBasicblock
+from .cells import InferCell
+
+
+# The macro structure for architectures in NAS-Bench-201
+class TinyNetwork(nn.Module):
+    def __init__(self, C, N, genotype, num_classes):
+        super(TinyNetwork, self).__init__()
+        self._C = C
+        self._layerN = N
+
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev = C
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2, True)
+            else:
+                cell = InferCell(genotype, C_prev, C_curr, 1)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self._Layer = len(self.cells)
+
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def forward(self, inputs):
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_operations.py

@@ -0,0 +1,553 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+import torch.nn as nn
+
+__all__ = ["OPS", "RAW_OP_CLASSES", "ResNetBasicblock", "SearchSpaceNames"]
+
+OPS = {
+    "none": lambda C_in, C_out, stride, affine, track_running_stats: Zero(
+        C_in, C_out, stride
+    ),
+    "avg_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING(
+        C_in, C_out, stride, "avg", affine, track_running_stats
+    ),
+    "max_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING(
+        C_in, C_out, stride, "max", affine, track_running_stats
+    ),
+    "nor_conv_7x7": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
+        C_in,
+        C_out,
+        (7, 7),
+        (stride, stride),
+        (3, 3),
+        (1, 1),
+        affine,
+        track_running_stats,
+    ),
+    "nor_conv_3x3": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
+        C_in,
+        C_out,
+        (3, 3),
+        (stride, stride),
+        (1, 1),
+        (1, 1),
+        affine,
+        track_running_stats,
+    ),
+    "nor_conv_1x1": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
+        C_in,
+        C_out,
+        (1, 1),
+        (stride, stride),
+        (0, 0),
+        (1, 1),
+        affine,
+        track_running_stats,
+    ),
+    "dua_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(
+        C_in,
+        C_out,
+        (3, 3),
+        (stride, stride),
+        (1, 1),
+        (1, 1),
+        affine,
+        track_running_stats,
+    ),
+    "dua_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(
+        C_in,
+        C_out,
+        (5, 5),
+        (stride, stride),
+        (2, 2),
+        (1, 1),
+        affine,
+        track_running_stats,
+    ),
+    "dil_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: SepConv(
+        C_in,
+        C_out,
+        (3, 3),
+        (stride, stride),
+        (2, 2),
+        (2, 2),
+        affine,
+        track_running_stats,
+    ),
+    "dil_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: SepConv(
+        C_in,
+        C_out,
+        (5, 5),
+        (stride, stride),
+        (4, 4),
+        (2, 2),
+        affine,
+        track_running_stats,
+    ),
+    "skip_connect": lambda C_in, C_out, stride, affine, track_running_stats: Identity()
+    if stride == 1 and C_in == C_out
+    else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
+}
+
+CONNECT_NAS_BENCHMARK = ["none", "skip_connect", "nor_conv_3x3"]
+NAS_BENCH_201 = ["none", "skip_connect", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3"]
+DARTS_SPACE = [
+    "none",
+    "skip_connect",
+    "dua_sepc_3x3",
+    "dua_sepc_5x5",
+    "dil_sepc_3x3",
+    "dil_sepc_5x5",
+    "avg_pool_3x3",
+    "max_pool_3x3",
+]
+
+SearchSpaceNames = {
+    "connect-nas": CONNECT_NAS_BENCHMARK,
+    "nats-bench": NAS_BENCH_201,
+    "nas-bench-201": NAS_BENCH_201,
+    "darts": DARTS_SPACE,
+}
+
+
+class ReLUConvBN(nn.Module):
+    def __init__(
+        self,
+        C_in,
+        C_out,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        affine,
+        track_running_stats=True,
+    ):
+        super(ReLUConvBN, self).__init__()
+        self.op = nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in,
+                C_out,
+                kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                bias=not affine,
+            ),
+            nn.BatchNorm2d(
+                C_out, affine=affine, track_running_stats=track_running_stats
+            ),
+        )
+
+    def forward(self, x):
+        return self.op(x)
+
+
+class SepConv(nn.Module):
+    def __init__(
+        self,
+        C_in,
+        C_out,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        affine,
+        track_running_stats=True,
+    ):
+        super(SepConv, self).__init__()
+        self.op = nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in,
+                C_in,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=C_in,
+                bias=False,
+            ),
+            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=not affine),
+            nn.BatchNorm2d(
+                C_out, affine=affine, track_running_stats=track_running_stats
+            ),
+        )
+
+    def forward(self, x):
+        return self.op(x)
+
+
+class DualSepConv(nn.Module):
+    def __init__(
+        self,
+        C_in,
+        C_out,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        affine,
+        track_running_stats=True,
+    ):
+        super(DualSepConv, self).__init__()
+        self.op_a = SepConv(
+            C_in,
+            C_in,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            affine,
+            track_running_stats,
+        )
+        self.op_b = SepConv(
+            C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats
+        )
+
+    def forward(self, x):
+        x = self.op_a(x)
+        x = self.op_b(x)
+        return x
+
+
+class ResNetBasicblock(nn.Module):
+    def __init__(self, inplanes, planes, stride, affine=True, track_running_stats=True):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ReLUConvBN(
+            inplanes, planes, 3, stride, 1, 1, affine, track_running_stats
+        )
+        self.conv_b = ReLUConvBN(
+            planes, planes, 3, 1, 1, 1, affine, track_running_stats
+        )
+        if stride == 2:
+            self.downsample = nn.Sequential(
+                nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                nn.Conv2d(
+                    inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False
+                ),
+            )
+        elif inplanes != planes:
+            self.downsample = ReLUConvBN(
+                inplanes, planes, 1, 1, 0, 1, affine, track_running_stats
+            )
+        else:
+            self.downsample = None
+        self.in_dim = inplanes
+        self.out_dim = planes
+        self.stride = stride
+        self.num_conv = 2
+
+    def extra_repr(self):
+        string = "{name}(inC={in_dim}, outC={out_dim}, stride={stride})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+        return string
+
+    def forward(self, inputs):
+
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        return residual + basicblock
+
+
+class POOLING(nn.Module):
+    def __init__(
+        self, C_in, C_out, stride, mode, affine=True, track_running_stats=True
+    ):
+        super(POOLING, self).__init__()
+        if C_in == C_out:
+            self.preprocess = None
+        else:
+            self.preprocess = ReLUConvBN(
+                C_in, C_out, 1, 1, 0, 1, affine, track_running_stats
+            )
+        if mode == "avg":
+            self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
+        elif mode == "max":
+            self.op = nn.MaxPool2d(3, stride=stride, padding=1)
+        else:
+            raise ValueError("Invalid mode={:} in POOLING".format(mode))
+
+    def forward(self, inputs):
+        if self.preprocess:
+            x = self.preprocess(inputs)
+        else:
+            x = inputs
+        return self.op(x)
+
+
+class Identity(nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+
+class Zero(nn.Module):
+    def __init__(self, C_in, C_out, stride):
+        super(Zero, self).__init__()
+        self.C_in = C_in
+        self.C_out = C_out
+        self.stride = stride
+        self.is_zero = True
+
+    def forward(self, x):
+        if self.C_in == self.C_out:
+            if self.stride == 1:
+                return x.mul(0.0)
+            else:
+                return x[:, :, :: self.stride, :: self.stride].mul(0.0)
+        else:
+            shape = list(x.shape)
+            shape[1] = self.C_out
+            zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
+            return zeros
+
+    def extra_repr(self):
+        return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
+
+
+class FactorizedReduce(nn.Module):
+    def __init__(self, C_in, C_out, stride, affine, track_running_stats):
+        super(FactorizedReduce, self).__init__()
+        self.stride = stride
+        self.C_in = C_in
+        self.C_out = C_out
+        self.relu = nn.ReLU(inplace=False)
+        if stride == 2:
+            # assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
+            C_outs = [C_out // 2, C_out - C_out // 2]
+            self.convs = nn.ModuleList()
+            for i in range(2):
+                self.convs.append(
+                    nn.Conv2d(
+                        C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine
+                    )
+                )
+            self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
+        elif stride == 1:
+            self.conv = nn.Conv2d(
+                C_in, C_out, 1, stride=stride, padding=0, bias=not affine
+            )
+        else:
+            raise ValueError("Invalid stride : {:}".format(stride))
+        self.bn = nn.BatchNorm2d(
+            C_out, affine=affine, track_running_stats=track_running_stats
+        )
+
+    def forward(self, x):
+        if self.stride == 2:
+            x = self.relu(x)
+            y = self.pad(x)
+            out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
+        else:
+            out = self.conv(x)
+        out = self.bn(out)
+        return out
+
+    def extra_repr(self):
+        return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
+
+
+# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
+class PartAwareOp(nn.Module):
+    def __init__(self, C_in, C_out, stride, part=4):
+        super().__init__()
+        self.part = 4
+        self.hidden = C_in // 3
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.local_conv_list = nn.ModuleList()
+        for i in range(self.part):
+            self.local_conv_list.append(
+                nn.Sequential(
+                    nn.ReLU(),
+                    nn.Conv2d(C_in, self.hidden, 1),
+                    nn.BatchNorm2d(self.hidden, affine=True),
+                )
+            )
+        self.W_K = nn.Linear(self.hidden, self.hidden)
+        self.W_Q = nn.Linear(self.hidden, self.hidden)
+
+        if stride == 2:
+            self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
+        elif stride == 1:
+            self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
+        else:
+            raise ValueError("Invalid Stride : {:}".format(stride))
+
+    def forward(self, x):
+        batch, C, H, W = x.size()
+        assert H >= self.part, "input size too small : {:} vs {:}".format(
+            x.shape, self.part
+        )
+        IHs = [0]
+        for i in range(self.part):
+            IHs.append(min(H, int((i + 1) * (float(H) / self.part))))
+        local_feat_list = []
+        for i in range(self.part):
+            feature = x[:, :, IHs[i] : IHs[i + 1], :]
+            xfeax = self.avg_pool(feature)
+            xfea = self.local_conv_list[i](xfeax)
+            local_feat_list.append(xfea)
+        part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
+        part_feature = part_feature.transpose(1, 2).contiguous()
+        part_K = self.W_K(part_feature)
+        part_Q = self.W_Q(part_feature).transpose(1, 2).contiguous()
+        weight_att = torch.bmm(part_K, part_Q)
+        attention = torch.softmax(weight_att, dim=2)
+        aggreateF = torch.bmm(attention, part_feature).transpose(1, 2).contiguous()
+        features = []
+        for i in range(self.part):
+            feature = aggreateF[:, :, i : i + 1].expand(
+                batch, self.hidden, IHs[i + 1] - IHs[i]
+            )
+            feature = feature.view(batch, self.hidden, IHs[i + 1] - IHs[i], 1)
+            features.append(feature)
+        features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
+        final_fea = torch.cat((x, features), dim=1)
+        outputs = self.last(final_fea)
+        return outputs
+
+
+def drop_path(x, drop_prob):
+    if drop_prob > 0.0:
+        keep_prob = 1.0 - drop_prob
+        mask = x.new_zeros(x.size(0), 1, 1, 1)
+        mask = mask.bernoulli_(keep_prob)
+        x = torch.div(x, keep_prob)
+        x.mul_(mask)
+    return x
+
+
+# Searching for A Robust Neural Architecture in Four GPU Hours
+class GDAS_Reduction_Cell(nn.Module):
+    def __init__(
+        self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats
+    ):
+        super(GDAS_Reduction_Cell, self).__init__()
+        if reduction_prev:
+            self.preprocess0 = FactorizedReduce(
+                C_prev_prev, C, 2, affine, track_running_stats
+            )
+        else:
+            self.preprocess0 = ReLUConvBN(
+                C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats
+            )
+        self.preprocess1 = ReLUConvBN(
+            C_prev, C, 1, 1, 0, 1, affine, track_running_stats
+        )
+
+        self.reduction = True
+        self.ops1 = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.ReLU(inplace=False),
+                    nn.Conv2d(
+                        C,
+                        C,
+                        (1, 3),
+                        stride=(1, 2),
+                        padding=(0, 1),
+                        groups=8,
+                        bias=not affine,
+                    ),
+                    nn.Conv2d(
+                        C,
+                        C,
+                        (3, 1),
+                        stride=(2, 1),
+                        padding=(1, 0),
+                        groups=8,
+                        bias=not affine,
+                    ),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                    nn.ReLU(inplace=False),
+                    nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                ),
+                nn.Sequential(
+                    nn.ReLU(inplace=False),
+                    nn.Conv2d(
+                        C,
+                        C,
+                        (1, 3),
+                        stride=(1, 2),
+                        padding=(0, 1),
+                        groups=8,
+                        bias=not affine,
+                    ),
+                    nn.Conv2d(
+                        C,
+                        C,
+                        (3, 1),
+                        stride=(2, 1),
+                        padding=(1, 0),
+                        groups=8,
+                        bias=not affine,
+                    ),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                    nn.ReLU(inplace=False),
+                    nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                ),
+            ]
+        )
+
+        self.ops2 = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.MaxPool2d(3, stride=2, padding=1),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                ),
+                nn.Sequential(
+                    nn.MaxPool2d(3, stride=2, padding=1),
+                    nn.BatchNorm2d(
+                        C, affine=affine, track_running_stats=track_running_stats
+                    ),
+                ),
+            ]
+        )
+
+    @property
+    def multiplier(self):
+        return 4
+
+    def forward(self, s0, s1, drop_prob=-1):
+        s0 = self.preprocess0(s0)
+        s1 = self.preprocess1(s1)
+
+        X0 = self.ops1[0](s0)
+        X1 = self.ops1[1](s1)
+        if self.training and drop_prob > 0.0:
+            X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
+
+        # X2 = self.ops2[0] (X0+X1)
+        X2 = self.ops2[0](s0)
+        X3 = self.ops2[1](s1)
+        if self.training and drop_prob > 0.0:
+            X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
+        return torch.cat([X0, X1, X2, X3], dim=1)
+
+
+# To manage the useful classes in this file.
+RAW_OP_CLASSES = {"gdas_reduction": GDAS_Reduction_Cell}

correlation/models/cell_searchs/__init__.py

@@ -0,0 +1,33 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+# The macro structure is defined in NAS-Bench-201
+from .search_model_darts import TinyNetworkDarts
+from .search_model_gdas import TinyNetworkGDAS
+from .search_model_setn import TinyNetworkSETN
+from .search_model_enas import TinyNetworkENAS
+from .search_model_random import TinyNetworkRANDOM
+from .generic_model import GenericNAS201Model
+from .genotypes import Structure as CellStructure, architectures as CellArchitectures
+
+# NASNet-based macro structure
+from .search_model_gdas_nasnet import NASNetworkGDAS
+from .search_model_gdas_frc_nasnet import NASNetworkGDAS_FRC
+from .search_model_darts_nasnet import NASNetworkDARTS
+
+
+nas201_super_nets = {
+    "DARTS-V1": TinyNetworkDarts,
+    "DARTS-V2": TinyNetworkDarts,
+    "GDAS": TinyNetworkGDAS,
+    "SETN": TinyNetworkSETN,
+    "ENAS": TinyNetworkENAS,
+    "RANDOM": TinyNetworkRANDOM,
+    "generic": GenericNAS201Model,
+}
+
+nasnet_super_nets = {
+    "GDAS": NASNetworkGDAS,
+    "GDAS_FRC": NASNetworkGDAS_FRC,
+    "DARTS": NASNetworkDARTS,
+}

correlation/models/cell_searchs/_test_module.py

@@ -0,0 +1,14 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+from search_model_enas_utils import Controller
+
+
+def main():
+    controller = Controller(6, 4)
+    predictions = controller()
+
+
+if __name__ == "__main__":
+    main()

correlation/models/cell_searchs/generic_model.py

@@ -0,0 +1,366 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
+#####################################################
+import torch, random
+import torch.nn as nn
+from copy import deepcopy
+from typing import Text
+from torch.distributions.categorical import Categorical
+
+from ..cell_operations import ResNetBasicblock, drop_path
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+
+
+class Controller(nn.Module):
+    # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
+    def __init__(
+        self,
+        edge2index,
+        op_names,
+        max_nodes,
+        lstm_size=32,
+        lstm_num_layers=2,
+        tanh_constant=2.5,
+        temperature=5.0,
+    ):
+        super(Controller, self).__init__()
+        # assign the attributes
+        self.max_nodes = max_nodes
+        self.num_edge = len(edge2index)
+        self.edge2index = edge2index
+        self.num_ops = len(op_names)
+        self.op_names = op_names
+        self.lstm_size = lstm_size
+        self.lstm_N = lstm_num_layers
+        self.tanh_constant = tanh_constant
+        self.temperature = temperature
+        # create parameters
+        self.register_parameter(
+            "input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size))
+        )
+        self.w_lstm = nn.LSTM(
+            input_size=self.lstm_size,
+            hidden_size=self.lstm_size,
+            num_layers=self.lstm_N,
+        )
+        self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
+        self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
+
+        nn.init.uniform_(self.input_vars, -0.1, 0.1)
+        nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
+        nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
+        nn.init.uniform_(self.w_embd.weight, -0.1, 0.1)
+        nn.init.uniform_(self.w_pred.weight, -0.1, 0.1)
+
+    def convert_structure(self, _arch):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                op_index = _arch[self.edge2index[node_str]]
+                op_name = self.op_names[op_index]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def forward(self):
+
+        inputs, h0 = self.input_vars, None
+        log_probs, entropys, sampled_arch = [], [], []
+        for iedge in range(self.num_edge):
+            outputs, h0 = self.w_lstm(inputs, h0)
+
+            logits = self.w_pred(outputs)
+            logits = logits / self.temperature
+            logits = self.tanh_constant * torch.tanh(logits)
+            # distribution
+            op_distribution = Categorical(logits=logits)
+            op_index = op_distribution.sample()
+            sampled_arch.append(op_index.item())
+
+            op_log_prob = op_distribution.log_prob(op_index)
+            log_probs.append(op_log_prob.view(-1))
+            op_entropy = op_distribution.entropy()
+            entropys.append(op_entropy.view(-1))
+
+            # obtain the input embedding for the next step
+            inputs = self.w_embd(op_index)
+        return (
+            torch.sum(torch.cat(log_probs)),
+            torch.sum(torch.cat(entropys)),
+            self.convert_structure(sampled_arch),
+        )
+
+
+class GenericNAS201Model(nn.Module):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(GenericNAS201Model, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._max_nodes = max_nodes
+        self._stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+        C_prev, num_edge, edge2index = C, None, None
+        self._cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self._cells.append(cell)
+            C_prev = cell.out_dim
+        self._op_names = deepcopy(search_space)
+        self._Layer = len(self._cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(
+            nn.BatchNorm2d(
+                C_prev, affine=affine, track_running_stats=track_running_stats
+            ),
+            nn.ReLU(inplace=True),
+        )
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self._num_edge = num_edge
+        # algorithm related
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self._mode = None
+        self.dynamic_cell = None
+        self._tau = None
+        self._algo = None
+        self._drop_path = None
+        self.verbose = False
+
+    def set_algo(self, algo: Text):
+        # used for searching
+        assert self._algo is None, "This functioin can only be called once."
+        self._algo = algo
+        if algo == "enas":
+            self.controller = Controller(
+                self.edge2index, self._op_names, self._max_nodes
+            )
+        else:
+            self.arch_parameters = nn.Parameter(
+                1e-3 * torch.randn(self._num_edge, len(self._op_names))
+            )
+            if algo == "gdas":
+                self._tau = 10
+
+    def set_cal_mode(self, mode, dynamic_cell=None):
+        assert mode in ["gdas", "enas", "urs", "joint", "select", "dynamic"]
+        self._mode = mode
+        if mode == "dynamic":
+            self.dynamic_cell = deepcopy(dynamic_cell)
+        else:
+            self.dynamic_cell = None
+
+    def set_drop_path(self, progress, drop_path_rate):
+        if drop_path_rate is None:
+            self._drop_path = None
+        elif progress is None:
+            self._drop_path = drop_path_rate
+        else:
+            self._drop_path = progress * drop_path_rate
+
+    @property
+    def mode(self):
+        return self._mode
+
+    @property
+    def drop_path(self):
+        return self._drop_path
+
+    @property
+    def weights(self):
+        xlist = list(self._stem.parameters())
+        xlist += list(self._cells.parameters())
+        xlist += list(self.lastact.parameters())
+        xlist += list(self.global_pooling.parameters())
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def set_tau(self, tau):
+        self._tau = tau
+
+    @property
+    def tau(self):
+        return self._tau
+
+    @property
+    def alphas(self):
+        if self._algo == "enas":
+            return list(self.controller.parameters())
+        else:
+            return [self.arch_parameters]
+
+    @property
+    def message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self._cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self._cells), cell.extra_repr()
+            )
+        return string
+
+    def show_alphas(self):
+        with torch.no_grad():
+            if self._algo == "enas":
+                return "w_pred :\n{:}".format(self.controller.w_pred.weight)
+            else:
+                return "arch-parameters :\n{:}".format(
+                    nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
+                )
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    @property
+    def genotype(self):
+        genotypes = []
+        for i in range(1, self._max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                with torch.no_grad():
+                    weights = self.arch_parameters[self.edge2index[node_str]]
+                    op_name = self._op_names[weights.argmax().item()]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def dync_genotype(self, use_random=False):
+        genotypes = []
+        with torch.no_grad():
+            alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
+        for i in range(1, self._max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                if use_random:
+                    op_name = random.choice(self._op_names)
+                else:
+                    weights = alphas_cpu[self.edge2index[node_str]]
+                    op_index = torch.multinomial(weights, 1).item()
+                    op_name = self._op_names[op_index]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def get_log_prob(self, arch):
+        with torch.no_grad():
+            logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
+        select_logits = []
+        for i, node_info in enumerate(arch.nodes):
+            for op, xin in node_info:
+                node_str = "{:}<-{:}".format(i + 1, xin)
+                op_index = self._op_names.index(op)
+                select_logits.append(logits[self.edge2index[node_str], op_index])
+        return sum(select_logits).item()
+
+    def return_topK(self, K, use_random=False):
+        archs = Structure.gen_all(self._op_names, self._max_nodes, False)
+        pairs = [(self.get_log_prob(arch), arch) for arch in archs]
+        if K < 0 or K >= len(archs):
+            K = len(archs)
+        if use_random:
+            return random.sample(archs, K)
+        else:
+            sorted_pairs = sorted(pairs, key=lambda x: -x[0])
+            return_pairs = [sorted_pairs[_][1] for _ in range(K)]
+            return return_pairs
+
+    def normalize_archp(self):
+        if self.mode == "gdas":
+            while True:
+                gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
+                logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
+                probs = nn.functional.softmax(logits, dim=1)
+                index = probs.max(-1, keepdim=True)[1]
+                one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+                hardwts = one_h - probs.detach() + probs
+                if (
+                    (torch.isinf(gumbels).any())
+                    or (torch.isinf(probs).any())
+                    or (torch.isnan(probs).any())
+                ):
+                    continue
+                else:
+                    break
+            with torch.no_grad():
+                hardwts_cpu = hardwts.detach().cpu()
+            return hardwts, hardwts_cpu, index, "GUMBEL"
+        else:
+            alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
+            index = alphas.max(-1, keepdim=True)[1]
+            with torch.no_grad():
+                alphas_cpu = alphas.detach().cpu()
+            return alphas, alphas_cpu, index, "SOFTMAX"
+
+    def forward(self, inputs):
+        alphas, alphas_cpu, index, verbose_str = self.normalize_archp()
+        feature = self._stem(inputs)
+        for i, cell in enumerate(self._cells):
+            if isinstance(cell, SearchCell):
+                if self.mode == "urs":
+                    feature = cell.forward_urs(feature)
+                    if self.verbose:
+                        verbose_str += "-forward_urs"
+                elif self.mode == "select":
+                    feature = cell.forward_select(feature, alphas_cpu)
+                    if self.verbose:
+                        verbose_str += "-forward_select"
+                elif self.mode == "joint":
+                    feature = cell.forward_joint(feature, alphas)
+                    if self.verbose:
+                        verbose_str += "-forward_joint"
+                elif self.mode == "dynamic":
+                    feature = cell.forward_dynamic(feature, self.dynamic_cell)
+                    if self.verbose:
+                        verbose_str += "-forward_dynamic"
+                elif self.mode == "gdas":
+                    feature = cell.forward_gdas(feature, alphas, index)
+                    if self.verbose:
+                        verbose_str += "-forward_gdas"
+                elif self.mode == "gdas_v1":
+                    feature = cell.forward_gdas_v1(feature, alphas, index)
+                    if self.verbose:
+                        verbose_str += "-forward_gdas_v1"
+                else:
+                    raise ValueError("invalid mode={:}".format(self.mode))
+            else:
+                feature = cell(feature)
+            if self.drop_path is not None:
+                feature = drop_path(feature, self.drop_path)
+        if self.verbose and random.random() < 0.001:
+            print(verbose_str)
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+        return out, logits

correlation/models/cell_searchs/genotypes.py

@@ -0,0 +1,274 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from copy import deepcopy
+
+
+def get_combination(space, num):
+    combs = []
+    for i in range(num):
+        if i == 0:
+            for func in space:
+                combs.append([(func, i)])
+        else:
+            new_combs = []
+            for string in combs:
+                for func in space:
+                    xstring = string + [(func, i)]
+                    new_combs.append(xstring)
+            combs = new_combs
+    return combs
+
+
+class Structure:
+    def __init__(self, genotype):
+        assert isinstance(genotype, list) or isinstance(
+            genotype, tuple
+        ), "invalid class of genotype : {:}".format(type(genotype))
+        self.node_num = len(genotype) + 1
+        self.nodes = []
+        self.node_N = []
+        for idx, node_info in enumerate(genotype):
+            assert isinstance(node_info, list) or isinstance(
+                node_info, tuple
+            ), "invalid class of node_info : {:}".format(type(node_info))
+            assert len(node_info) >= 1, "invalid length : {:}".format(len(node_info))
+            for node_in in node_info:
+                assert isinstance(node_in, list) or isinstance(
+                    node_in, tuple
+                ), "invalid class of in-node : {:}".format(type(node_in))
+                assert (
+                    len(node_in) == 2 and node_in[1] <= idx
+                ), "invalid in-node : {:}".format(node_in)
+            self.node_N.append(len(node_info))
+            self.nodes.append(tuple(deepcopy(node_info)))
+
+    def tolist(self, remove_str):
+        # convert this class to the list, if remove_str is 'none', then remove the 'none' operation.
+        # note that we re-order the input node in this function
+        # return the-genotype-list and success [if unsuccess, it is not a connectivity]
+        genotypes = []
+        for node_info in self.nodes:
+            node_info = list(node_info)
+            node_info = sorted(node_info, key=lambda x: (x[1], x[0]))
+            node_info = tuple(filter(lambda x: x[0] != remove_str, node_info))
+            if len(node_info) == 0:
+                return None, False
+            genotypes.append(node_info)
+        return genotypes, True
+
+    def node(self, index):
+        assert index > 0 and index <= len(self), "invalid index={:} < {:}".format(
+            index, len(self)
+        )
+        return self.nodes[index]
+
+    def tostr(self):
+        strings = []
+        for node_info in self.nodes:
+            string = "|".join([x[0] + "~{:}".format(x[1]) for x in node_info])
+            string = "|{:}|".format(string)
+            strings.append(string)
+        return "+".join(strings)
+
+    def check_valid(self):
+        nodes = {0: True}
+        for i, node_info in enumerate(self.nodes):
+            sums = []
+            for op, xin in node_info:
+                if op == "none" or nodes[xin] is False:
+                    x = False
+                else:
+                    x = True
+                sums.append(x)
+            nodes[i + 1] = sum(sums) > 0
+        return nodes[len(self.nodes)]
+
+    def to_unique_str(self, consider_zero=False):
+        # this is used to identify the isomorphic cell, which rerquires the prior knowledge of operation
+        # two operations are special, i.e., none and skip_connect
+        nodes = {0: "0"}
+        for i_node, node_info in enumerate(self.nodes):
+            cur_node = []
+            for op, xin in node_info:
+                if consider_zero is None:
+                    x = "(" + nodes[xin] + ")" + "@{:}".format(op)
+                elif consider_zero:
+                    if op == "none" or nodes[xin] == "#":
+                        x = "#"  # zero
+                    elif op == "skip_connect":
+                        x = nodes[xin]
+                    else:
+                        x = "(" + nodes[xin] + ")" + "@{:}".format(op)
+                else:
+                    if op == "skip_connect":
+                        x = nodes[xin]
+                    else:
+                        x = "(" + nodes[xin] + ")" + "@{:}".format(op)
+                cur_node.append(x)
+            nodes[i_node + 1] = "+".join(sorted(cur_node))
+        return nodes[len(self.nodes)]
+
+    def check_valid_op(self, op_names):
+        for node_info in self.nodes:
+            for inode_edge in node_info:
+                # assert inode_edge[0] in op_names, 'invalid op-name : {:}'.format(inode_edge[0])
+                if inode_edge[0] not in op_names:
+                    return False
+        return True
+
+    def __repr__(self):
+        return "{name}({node_num} nodes with {node_info})".format(
+            name=self.__class__.__name__, node_info=self.tostr(), **self.__dict__
+        )
+
+    def __len__(self):
+        return len(self.nodes) + 1
+
+    def __getitem__(self, index):
+        return self.nodes[index]
+
+    @staticmethod
+    def str2structure(xstr):
+        if isinstance(xstr, Structure):
+            return xstr
+        assert isinstance(xstr, str), "must take string (not {:}) as input".format(
+            type(xstr)
+        )
+        nodestrs = xstr.split("+")
+        genotypes = []
+        for i, node_str in enumerate(nodestrs):
+            inputs = list(filter(lambda x: x != "", node_str.split("|")))
+            for xinput in inputs:
+                assert len(xinput.split("~")) == 2, "invalid input length : {:}".format(
+                    xinput
+                )
+            inputs = (xi.split("~") for xi in inputs)
+            input_infos = tuple((op, int(IDX)) for (op, IDX) in inputs)
+            genotypes.append(input_infos)
+        return Structure(genotypes)
+
+    @staticmethod
+    def str2fullstructure(xstr, default_name="none"):
+        assert isinstance(xstr, str), "must take string (not {:}) as input".format(
+            type(xstr)
+        )
+        nodestrs = xstr.split("+")
+        genotypes = []
+        for i, node_str in enumerate(nodestrs):
+            inputs = list(filter(lambda x: x != "", node_str.split("|")))
+            for xinput in inputs:
+                assert len(xinput.split("~")) == 2, "invalid input length : {:}".format(
+                    xinput
+                )
+            inputs = (xi.split("~") for xi in inputs)
+            input_infos = list((op, int(IDX)) for (op, IDX) in inputs)
+            all_in_nodes = list(x[1] for x in input_infos)
+            for j in range(i):
+                if j not in all_in_nodes:
+                    input_infos.append((default_name, j))
+            node_info = sorted(input_infos, key=lambda x: (x[1], x[0]))
+            genotypes.append(tuple(node_info))
+        return Structure(genotypes)
+
+    @staticmethod
+    def gen_all(search_space, num, return_ori):
+        assert isinstance(search_space, list) or isinstance(
+            search_space, tuple
+        ), "invalid class of search-space : {:}".format(type(search_space))
+        assert (
+            num >= 2
+        ), "There should be at least two nodes in a neural cell instead of {:}".format(
+            num
+        )
+        all_archs = get_combination(search_space, 1)
+        for i, arch in enumerate(all_archs):
+            all_archs[i] = [tuple(arch)]
+
+        for inode in range(2, num):
+            cur_nodes = get_combination(search_space, inode)
+            new_all_archs = []
+            for previous_arch in all_archs:
+                for cur_node in cur_nodes:
+                    new_all_archs.append(previous_arch + [tuple(cur_node)])
+            all_archs = new_all_archs
+        if return_ori:
+            return all_archs
+        else:
+            return [Structure(x) for x in all_archs]
+
+
+ResNet_CODE = Structure(
+    [
+        (("nor_conv_3x3", 0),),  # node-1
+        (("nor_conv_3x3", 1),),  # node-2
+        (("skip_connect", 0), ("skip_connect", 2)),
+    ]  # node-3
+)
+
+AllConv3x3_CODE = Structure(
+    [
+        (("nor_conv_3x3", 0),),  # node-1
+        (("nor_conv_3x3", 0), ("nor_conv_3x3", 1)),  # node-2
+        (("nor_conv_3x3", 0), ("nor_conv_3x3", 1), ("nor_conv_3x3", 2)),
+    ]  # node-3
+)
+
+AllFull_CODE = Structure(
+    [
+        (
+            ("skip_connect", 0),
+            ("nor_conv_1x1", 0),
+            ("nor_conv_3x3", 0),
+            ("avg_pool_3x3", 0),
+        ),  # node-1
+        (
+            ("skip_connect", 0),
+            ("nor_conv_1x1", 0),
+            ("nor_conv_3x3", 0),
+            ("avg_pool_3x3", 0),
+            ("skip_connect", 1),
+            ("nor_conv_1x1", 1),
+            ("nor_conv_3x3", 1),
+            ("avg_pool_3x3", 1),
+        ),  # node-2
+        (
+            ("skip_connect", 0),
+            ("nor_conv_1x1", 0),
+            ("nor_conv_3x3", 0),
+            ("avg_pool_3x3", 0),
+            ("skip_connect", 1),
+            ("nor_conv_1x1", 1),
+            ("nor_conv_3x3", 1),
+            ("avg_pool_3x3", 1),
+            ("skip_connect", 2),
+            ("nor_conv_1x1", 2),
+            ("nor_conv_3x3", 2),
+            ("avg_pool_3x3", 2),
+        ),
+    ]  # node-3
+)
+
+AllConv1x1_CODE = Structure(
+    [
+        (("nor_conv_1x1", 0),),  # node-1
+        (("nor_conv_1x1", 0), ("nor_conv_1x1", 1)),  # node-2
+        (("nor_conv_1x1", 0), ("nor_conv_1x1", 1), ("nor_conv_1x1", 2)),
+    ]  # node-3
+)
+
+AllIdentity_CODE = Structure(
+    [
+        (("skip_connect", 0),),  # node-1
+        (("skip_connect", 0), ("skip_connect", 1)),  # node-2
+        (("skip_connect", 0), ("skip_connect", 1), ("skip_connect", 2)),
+    ]  # node-3
+)
+
+architectures = {
+    "resnet": ResNet_CODE,
+    "all_c3x3": AllConv3x3_CODE,
+    "all_c1x1": AllConv1x1_CODE,
+    "all_idnt": AllIdentity_CODE,
+    "all_full": AllFull_CODE,
+}

correlation/models/cell_searchs/search_cells.py

@@ -0,0 +1,267 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, random, torch
+import warnings
+import torch.nn as nn
+import torch.nn.functional as F
+from copy import deepcopy
+from ..cell_operations import OPS
+
+
+# This module is used for NAS-Bench-201, represents a small search space with a complete DAG
+class NAS201SearchCell(nn.Module):
+    def __init__(
+        self,
+        C_in,
+        C_out,
+        stride,
+        max_nodes,
+        op_names,
+        affine=False,
+        track_running_stats=True,
+    ):
+        super(NAS201SearchCell, self).__init__()
+
+        self.op_names = deepcopy(op_names)
+        self.edges = nn.ModuleDict()
+        self.max_nodes = max_nodes
+        self.in_dim = C_in
+        self.out_dim = C_out
+        for i in range(1, max_nodes):
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                if j == 0:
+                    xlists = [
+                        OPS[op_name](C_in, C_out, stride, affine, track_running_stats)
+                        for op_name in op_names
+                    ]
+                else:
+                    xlists = [
+                        OPS[op_name](C_in, C_out, 1, affine, track_running_stats)
+                        for op_name in op_names
+                    ]
+                self.edges[node_str] = nn.ModuleList(xlists)
+        self.edge_keys = sorted(list(self.edges.keys()))
+        self.edge2index = {key: i for i, key in enumerate(self.edge_keys)}
+        self.num_edges = len(self.edges)
+
+    def extra_repr(self):
+        string = "info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}".format(
+            **self.__dict__
+        )
+        return string
+
+    def forward(self, inputs, weightss):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = weightss[self.edge2index[node_str]]
+                inter_nodes.append(
+                    sum(
+                        layer(nodes[j]) * w
+                        for layer, w in zip(self.edges[node_str], weights)
+                    )
+                )
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # GDAS
+    def forward_gdas(self, inputs, hardwts, index):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = hardwts[self.edge2index[node_str]]
+                argmaxs = index[self.edge2index[node_str]].item()
+                weigsum = sum(
+                    weights[_ie] * edge(nodes[j]) if _ie == argmaxs else weights[_ie]
+                    for _ie, edge in enumerate(self.edges[node_str])
+                )
+                inter_nodes.append(weigsum)
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # GDAS Variant: https://github.com/D-X-Y/AutoDL-Projects/issues/119
+    def forward_gdas_v1(self, inputs, hardwts, index):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = hardwts[self.edge2index[node_str]]
+                argmaxs = index[self.edge2index[node_str]].item()
+                weigsum = weights[argmaxs] * self.edges[node_str](nodes[j])
+                inter_nodes.append(weigsum)
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # joint
+    def forward_joint(self, inputs, weightss):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = weightss[self.edge2index[node_str]]
+                # aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
+                aggregation = sum(
+                    layer(nodes[j]) * w
+                    for layer, w in zip(self.edges[node_str], weights)
+                )
+                inter_nodes.append(aggregation)
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # uniform random sampling per iteration, SETN
+    def forward_urs(self, inputs):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            while True:  # to avoid select zero for all ops
+                sops, has_non_zero = [], False
+                for j in range(i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    candidates = self.edges[node_str]
+                    select_op = random.choice(candidates)
+                    sops.append(select_op)
+                    if not hasattr(select_op, "is_zero") or select_op.is_zero is False:
+                        has_non_zero = True
+                if has_non_zero:
+                    break
+            inter_nodes = []
+            for j, select_op in enumerate(sops):
+                inter_nodes.append(select_op(nodes[j]))
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # select the argmax
+    def forward_select(self, inputs, weightss):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = weightss[self.edge2index[node_str]]
+                inter_nodes.append(
+                    self.edges[node_str][weights.argmax().item()](nodes[j])
+                )
+                # inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+    # forward with a specific structure
+    def forward_dynamic(self, inputs, structure):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            cur_op_node = structure.nodes[i - 1]
+            inter_nodes = []
+            for op_name, j in cur_op_node:
+                node_str = "{:}<-{:}".format(i, j)
+                op_index = self.op_names.index(op_name)
+                inter_nodes.append(self.edges[node_str][op_index](nodes[j]))
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
+
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+
+
+class MixedOp(nn.Module):
+    def __init__(self, space, C, stride, affine, track_running_stats):
+        super(MixedOp, self).__init__()
+        self._ops = nn.ModuleList()
+        for primitive in space:
+            op = OPS[primitive](C, C, stride, affine, track_running_stats)
+            self._ops.append(op)
+
+    def forward_gdas(self, x, weights, index):
+        return self._ops[index](x) * weights[index]
+
+    def forward_darts(self, x, weights):
+        return sum(w * op(x) for w, op in zip(weights, self._ops))
+
+
+class NASNetSearchCell(nn.Module):
+    def __init__(
+        self,
+        space,
+        steps,
+        multiplier,
+        C_prev_prev,
+        C_prev,
+        C,
+        reduction,
+        reduction_prev,
+        affine,
+        track_running_stats,
+    ):
+        super(NASNetSearchCell, self).__init__()
+        self.reduction = reduction
+        self.op_names = deepcopy(space)
+        if reduction_prev:
+            self.preprocess0 = OPS["skip_connect"](
+                C_prev_prev, C, 2, affine, track_running_stats
+            )
+        else:
+            self.preprocess0 = OPS["nor_conv_1x1"](
+                C_prev_prev, C, 1, affine, track_running_stats
+            )
+        self.preprocess1 = OPS["nor_conv_1x1"](
+            C_prev, C, 1, affine, track_running_stats
+        )
+        self._steps = steps
+        self._multiplier = multiplier
+
+        self._ops = nn.ModuleList()
+        self.edges = nn.ModuleDict()
+        for i in range(self._steps):
+            for j in range(2 + i):
+                node_str = "{:}<-{:}".format(
+                    i, j
+                )  # indicate the edge from node-(j) to node-(i+2)
+                stride = 2 if reduction and j < 2 else 1
+                op = MixedOp(space, C, stride, affine, track_running_stats)
+                self.edges[node_str] = op
+        self.edge_keys = sorted(list(self.edges.keys()))
+        self.edge2index = {key: i for i, key in enumerate(self.edge_keys)}
+        self.num_edges = len(self.edges)
+
+    @property
+    def multiplier(self):
+        return self._multiplier
+
+    def forward_gdas(self, s0, s1, weightss, indexs):
+        s0 = self.preprocess0(s0)
+        s1 = self.preprocess1(s1)
+
+        states = [s0, s1]
+        for i in range(self._steps):
+            clist = []
+            for j, h in enumerate(states):
+                node_str = "{:}<-{:}".format(i, j)
+                op = self.edges[node_str]
+                weights = weightss[self.edge2index[node_str]]
+                index = indexs[self.edge2index[node_str]].item()
+                clist.append(op.forward_gdas(h, weights, index))
+            states.append(sum(clist))
+
+        return torch.cat(states[-self._multiplier :], dim=1)
+
+    def forward_darts(self, s0, s1, weightss):
+        s0 = self.preprocess0(s0)
+        s1 = self.preprocess1(s1)
+
+        states = [s0, s1]
+        for i in range(self._steps):
+            clist = []
+            for j, h in enumerate(states):
+                node_str = "{:}<-{:}".format(i, j)
+                op = self.edges[node_str]
+                weights = weightss[self.edge2index[node_str]]
+                clist.append(op.forward_darts(h, weights))
+            states.append(sum(clist))
+
+        return torch.cat(states[-self._multiplier :], dim=1)

correlation/models/cell_searchs/search_model_darts.py

@@ -0,0 +1,122 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+########################################################
+# DARTS: Differentiable Architecture Search, ICLR 2019 #
+########################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+
+
+class TinyNetworkDarts(nn.Module):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(TinyNetworkDarts, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.max_nodes = max_nodes
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev, num_edge, edge2index = C, None, None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def get_alphas(self):
+        return [self.arch_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            return "arch-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
+            )
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                with torch.no_grad():
+                    weights = self.arch_parameters[self.edge2index[node_str]]
+                    op_name = self.op_names[weights.argmax().item()]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def forward(self, inputs):
+        alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
+
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if isinstance(cell, SearchCell):
+                feature = cell(feature, alphas)
+            else:
+                feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_darts_nasnet.py

@@ -0,0 +1,178 @@
+####################
+# DARTS, ICLR 2019 #
+####################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import List, Text, Dict
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkDARTS(nn.Module):
+    def __init__(
+        self,
+        C: int,
+        N: int,
+        steps: int,
+        multiplier: int,
+        stem_multiplier: int,
+        num_classes: int,
+        search_space: List[Text],
+        affine: bool,
+        track_running_stats: bool,
+    ):
+        super(NASNetworkDARTS, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._steps = steps
+        self._multiplier = multiplier
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        num_edge, edge2index = None, None
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            cell = SearchCell(
+                search_space,
+                steps,
+                multiplier,
+                C_prev_prev,
+                C_prev,
+                C_curr,
+                reduction,
+                reduction_prev,
+                affine,
+                track_running_stats,
+            )
+            if num_edge is None:
+                num_edge, edge2index = cell.num_edges, cell.edge2index
+            else:
+                assert (
+                    num_edge == cell.num_edges and edge2index == cell.edge2index
+                ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_normal_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.arch_reduce_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+
+    def get_weights(self) -> List[torch.nn.Parameter]:
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def get_alphas(self) -> List[torch.nn.Parameter]:
+        return [self.arch_normal_parameters, self.arch_reduce_parameters]
+
+    def show_alphas(self) -> Text:
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
+            )
+            B = "arch-reduce-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
+            )
+        return "{:}\n{:}".format(A, B)
+
+    def get_message(self) -> Text:
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self) -> Text:
+        return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self) -> Dict[Text, List]:
+        def _parse(weights):
+            gene = []
+            for i in range(self._steps):
+                edges = []
+                for j in range(2 + i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    ws = weights[self.edge2index[node_str]]
+                    for k, op_name in enumerate(self.op_names):
+                        if op_name == "none":
+                            continue
+                        edges.append((op_name, j, ws[k]))
+                # (TODO) xuanyidong:
+                # Here the selected two edges might come from the same input node.
+                # And this case could be a problem that two edges will collapse into a single one
+                # due to our assumption -- at most one edge from an input node during evaluation.
+                edges = sorted(edges, key=lambda x: -x[-1])
+                selected_edges = edges[:2]
+                gene.append(tuple(selected_edges))
+            return gene
+
+        with torch.no_grad():
+            gene_normal = _parse(
+                torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy()
+            )
+            gene_reduce = _parse(
+                torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+            "reduce": gene_reduce,
+            "reduce_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+
+        normal_w = nn.functional.softmax(self.arch_normal_parameters, dim=1)
+        reduce_w = nn.functional.softmax(self.arch_reduce_parameters, dim=1)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if cell.reduction:
+                ww = reduce_w
+            else:
+                ww = normal_w
+            s0, s1 = s1, cell.forward_darts(s0, s1, ww)
+        out = self.lastact(s1)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_enas.py

@@ -0,0 +1,114 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##########################################################################
+# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
+##########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+from .search_model_enas_utils import Controller
+
+
+class TinyNetworkENAS(nn.Module):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(TinyNetworkENAS, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.max_nodes = max_nodes
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev, num_edge, edge2index = C, None, None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        # to maintain the sampled architecture
+        self.sampled_arch = None
+
+    def update_arch(self, _arch):
+        if _arch is None:
+            self.sampled_arch = None
+        elif isinstance(_arch, Structure):
+            self.sampled_arch = _arch
+        elif isinstance(_arch, (list, tuple)):
+            genotypes = []
+            for i in range(1, self.max_nodes):
+                xlist = []
+                for j in range(i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    op_index = _arch[self.edge2index[node_str]]
+                    op_name = self.op_names[op_index]
+                    xlist.append((op_name, j))
+                genotypes.append(tuple(xlist))
+            self.sampled_arch = Structure(genotypes)
+        else:
+            raise ValueError("invalid type of input architecture : {:}".format(_arch))
+        return self.sampled_arch
+
+    def create_controller(self):
+        return Controller(len(self.edge2index), len(self.op_names))
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def forward(self, inputs):
+
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if isinstance(cell, SearchCell):
+                feature = cell.forward_dynamic(feature, self.sampled_arch)
+            else:
+                feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_enas_utils.py

@@ -0,0 +1,74 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##########################################################################
+# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
+##########################################################################
+import torch
+import torch.nn as nn
+from torch.distributions.categorical import Categorical
+
+
+class Controller(nn.Module):
+    # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
+    def __init__(
+        self,
+        num_edge,
+        num_ops,
+        lstm_size=32,
+        lstm_num_layers=2,
+        tanh_constant=2.5,
+        temperature=5.0,
+    ):
+        super(Controller, self).__init__()
+        # assign the attributes
+        self.num_edge = num_edge
+        self.num_ops = num_ops
+        self.lstm_size = lstm_size
+        self.lstm_N = lstm_num_layers
+        self.tanh_constant = tanh_constant
+        self.temperature = temperature
+        # create parameters
+        self.register_parameter(
+            "input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size))
+        )
+        self.w_lstm = nn.LSTM(
+            input_size=self.lstm_size,
+            hidden_size=self.lstm_size,
+            num_layers=self.lstm_N,
+        )
+        self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
+        self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
+
+        nn.init.uniform_(self.input_vars, -0.1, 0.1)
+        nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
+        nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
+        nn.init.uniform_(self.w_embd.weight, -0.1, 0.1)
+        nn.init.uniform_(self.w_pred.weight, -0.1, 0.1)
+
+    def forward(self):
+
+        inputs, h0 = self.input_vars, None
+        log_probs, entropys, sampled_arch = [], [], []
+        for iedge in range(self.num_edge):
+            outputs, h0 = self.w_lstm(inputs, h0)
+
+            logits = self.w_pred(outputs)
+            logits = logits / self.temperature
+            logits = self.tanh_constant * torch.tanh(logits)
+            # distribution
+            op_distribution = Categorical(logits=logits)
+            op_index = op_distribution.sample()
+            sampled_arch.append(op_index.item())
+
+            op_log_prob = op_distribution.log_prob(op_index)
+            log_probs.append(op_log_prob.view(-1))
+            op_entropy = op_distribution.entropy()
+            entropys.append(op_entropy.view(-1))
+
+            # obtain the input embedding for the next step
+            inputs = self.w_embd(op_index)
+        return (
+            torch.sum(torch.cat(log_probs)),
+            torch.sum(torch.cat(entropys)),
+            sampled_arch,
+        )

correlation/models/cell_searchs/search_model_gdas.py

@@ -0,0 +1,142 @@
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+
+
+class TinyNetworkGDAS(nn.Module):
+
+    # def __init__(self, C, N, max_nodes, num_classes, search_space, affine=False, track_running_stats=True):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(TinyNetworkGDAS, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.max_nodes = max_nodes
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev, num_edge, edge2index = C, None, None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.tau = 10
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def set_tau(self, tau):
+        self.tau = tau
+
+    def get_tau(self):
+        return self.tau
+
+    def get_alphas(self):
+        return [self.arch_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            return "arch-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
+            )
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                with torch.no_grad():
+                    weights = self.arch_parameters[self.edge2index[node_str]]
+                    op_name = self.op_names[weights.argmax().item()]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def forward(self, inputs):
+        while True:
+            gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
+            logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
+            probs = nn.functional.softmax(logits, dim=1)
+            index = probs.max(-1, keepdim=True)[1]
+            one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+            hardwts = one_h - probs.detach() + probs
+            if (
+                (torch.isinf(gumbels).any())
+                or (torch.isinf(probs).any())
+                or (torch.isnan(probs).any())
+            ):
+                continue
+            else:
+                break
+
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if isinstance(cell, SearchCell):
+                feature = cell.forward_gdas(feature, hardwts, index)
+            else:
+                feature = cell(feature)
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_gdas_frc_nasnet.py

@@ -0,0 +1,200 @@
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+
+from .search_cells import NASNetSearchCell as SearchCell
+from ..cell_operations import RAW_OP_CLASSES
+
+
+# The macro structure is based on NASNet
+class NASNetworkGDAS_FRC(nn.Module):
+    def __init__(
+        self,
+        C,
+        N,
+        steps,
+        multiplier,
+        stem_multiplier,
+        num_classes,
+        search_space,
+        affine,
+        track_running_stats,
+    ):
+        super(NASNetworkGDAS_FRC, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._steps = steps
+        self._multiplier = multiplier
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        num_edge, edge2index = None, None
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = RAW_OP_CLASSES["gdas_reduction"](
+                    C_prev_prev,
+                    C_prev,
+                    C_curr,
+                    reduction_prev,
+                    affine,
+                    track_running_stats,
+                )
+            else:
+                cell = SearchCell(
+                    search_space,
+                    steps,
+                    multiplier,
+                    C_prev_prev,
+                    C_prev,
+                    C_curr,
+                    reduction,
+                    reduction_prev,
+                    affine,
+                    track_running_stats,
+                )
+            if num_edge is None:
+                num_edge, edge2index = cell.num_edges, cell.edge2index
+            else:
+                assert (
+                    reduction
+                    or num_edge == cell.num_edges
+                    and edge2index == cell.edge2index
+                ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = (
+                C_prev,
+                cell.multiplier * C_curr,
+                reduction,
+            )
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.tau = 10
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def set_tau(self, tau):
+        self.tau = tau
+
+    def get_tau(self):
+        return self.tau
+
+    def get_alphas(self):
+        return [self.arch_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
+            )
+        return "{:}".format(A)
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        def _parse(weights):
+            gene = []
+            for i in range(self._steps):
+                edges = []
+                for j in range(2 + i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    ws = weights[self.edge2index[node_str]]
+                    for k, op_name in enumerate(self.op_names):
+                        if op_name == "none":
+                            continue
+                        edges.append((op_name, j, ws[k]))
+                edges = sorted(edges, key=lambda x: -x[-1])
+                selected_edges = edges[:2]
+                gene.append(tuple(selected_edges))
+            return gene
+
+        with torch.no_grad():
+            gene_normal = _parse(
+                torch.softmax(self.arch_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+        def get_gumbel_prob(xins):
+            while True:
+                gumbels = -torch.empty_like(xins).exponential_().log()
+                logits = (xins.log_softmax(dim=1) + gumbels) / self.tau
+                probs = nn.functional.softmax(logits, dim=1)
+                index = probs.max(-1, keepdim=True)[1]
+                one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+                hardwts = one_h - probs.detach() + probs
+                if (
+                    (torch.isinf(gumbels).any())
+                    or (torch.isinf(probs).any())
+                    or (torch.isnan(probs).any())
+                ):
+                    continue
+                else:
+                    break
+            return hardwts, index
+
+        hardwts, index = get_gumbel_prob(self.arch_parameters)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if cell.reduction:
+                s0, s1 = s1, cell(s0, s1)
+            else:
+                s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+        out = self.lastact(s1)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_gdas_nasnet.py

@@ -0,0 +1,197 @@
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkGDAS(nn.Module):
+    def __init__(
+        self,
+        C,
+        N,
+        steps,
+        multiplier,
+        stem_multiplier,
+        num_classes,
+        search_space,
+        affine,
+        track_running_stats,
+    ):
+        super(NASNetworkGDAS, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._steps = steps
+        self._multiplier = multiplier
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        num_edge, edge2index = None, None
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            cell = SearchCell(
+                search_space,
+                steps,
+                multiplier,
+                C_prev_prev,
+                C_prev,
+                C_curr,
+                reduction,
+                reduction_prev,
+                affine,
+                track_running_stats,
+            )
+            if num_edge is None:
+                num_edge, edge2index = cell.num_edges, cell.edge2index
+            else:
+                assert (
+                    num_edge == cell.num_edges and edge2index == cell.edge2index
+                ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_normal_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.arch_reduce_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.tau = 10
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def set_tau(self, tau):
+        self.tau = tau
+
+    def get_tau(self):
+        return self.tau
+
+    def get_alphas(self):
+        return [self.arch_normal_parameters, self.arch_reduce_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
+            )
+            B = "arch-reduce-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
+            )
+        return "{:}\n{:}".format(A, B)
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        def _parse(weights):
+            gene = []
+            for i in range(self._steps):
+                edges = []
+                for j in range(2 + i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    ws = weights[self.edge2index[node_str]]
+                    for k, op_name in enumerate(self.op_names):
+                        if op_name == "none":
+                            continue
+                        edges.append((op_name, j, ws[k]))
+                edges = sorted(edges, key=lambda x: -x[-1])
+                selected_edges = edges[:2]
+                gene.append(tuple(selected_edges))
+            return gene
+
+        with torch.no_grad():
+            gene_normal = _parse(
+                torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy()
+            )
+            gene_reduce = _parse(
+                torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+            "reduce": gene_reduce,
+            "reduce_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+        def get_gumbel_prob(xins):
+            while True:
+                gumbels = -torch.empty_like(xins).exponential_().log()
+                logits = (xins.log_softmax(dim=1) + gumbels) / self.tau
+                probs = nn.functional.softmax(logits, dim=1)
+                index = probs.max(-1, keepdim=True)[1]
+                one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+                hardwts = one_h - probs.detach() + probs
+                if (
+                    (torch.isinf(gumbels).any())
+                    or (torch.isinf(probs).any())
+                    or (torch.isnan(probs).any())
+                ):
+                    continue
+                else:
+                    break
+            return hardwts, index
+
+        normal_hardwts, normal_index = get_gumbel_prob(self.arch_normal_parameters)
+        reduce_hardwts, reduce_index = get_gumbel_prob(self.arch_reduce_parameters)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if cell.reduction:
+                hardwts, index = reduce_hardwts, reduce_index
+            else:
+                hardwts, index = normal_hardwts, normal_index
+            s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+        out = self.lastact(s1)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_random.py

@@ -0,0 +1,102 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##############################################################################
+# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 #
+##############################################################################
+import torch, random
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+
+
+class TinyNetworkRANDOM(nn.Module):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(TinyNetworkRANDOM, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.max_nodes = max_nodes
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev, num_edge, edge2index = C, None, None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_cache = None
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def random_genotype(self, set_cache):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                op_name = random.choice(self.op_names)
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        arch = Structure(genotypes)
+        if set_cache:
+            self.arch_cache = arch
+        return arch
+
+    def forward(self, inputs):
+
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if isinstance(cell, SearchCell):
+                feature = cell.forward_dynamic(feature, self.arch_cache)
+            else:
+                feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+        return out, logits

correlation/models/cell_searchs/search_model_setn.py

@@ -0,0 +1,178 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+######################################################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
+######################################################################################
+import torch, random
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells import NAS201SearchCell as SearchCell
+from .genotypes import Structure
+
+
+class TinyNetworkSETN(nn.Module):
+    def __init__(
+        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
+    ):
+        super(TinyNetworkSETN, self).__init__()
+        self._C = C
+        self._layerN = N
+        self.max_nodes = max_nodes
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev, num_edge, edge2index = C, None, None
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = SearchCell(
+                    C_prev,
+                    C_curr,
+                    1,
+                    max_nodes,
+                    search_space,
+                    affine,
+                    track_running_stats,
+                )
+                if num_edge is None:
+                    num_edge, edge2index = cell.num_edges, cell.edge2index
+                else:
+                    assert (
+                        num_edge == cell.num_edges and edge2index == cell.edge2index
+                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev = cell.out_dim
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.mode = "urs"
+        self.dynamic_cell = None
+
+    def set_cal_mode(self, mode, dynamic_cell=None):
+        assert mode in ["urs", "joint", "select", "dynamic"]
+        self.mode = mode
+        if mode == "dynamic":
+            self.dynamic_cell = deepcopy(dynamic_cell)
+        else:
+            self.dynamic_cell = None
+
+    def get_cal_mode(self):
+        return self.mode
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def get_alphas(self):
+        return [self.arch_parameters]
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                with torch.no_grad():
+                    weights = self.arch_parameters[self.edge2index[node_str]]
+                    op_name = self.op_names[weights.argmax().item()]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def dync_genotype(self, use_random=False):
+        genotypes = []
+        with torch.no_grad():
+            alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                if use_random:
+                    op_name = random.choice(self.op_names)
+                else:
+                    weights = alphas_cpu[self.edge2index[node_str]]
+                    op_index = torch.multinomial(weights, 1).item()
+                    op_name = self.op_names[op_index]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def get_log_prob(self, arch):
+        with torch.no_grad():
+            logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
+        select_logits = []
+        for i, node_info in enumerate(arch.nodes):
+            for op, xin in node_info:
+                node_str = "{:}<-{:}".format(i + 1, xin)
+                op_index = self.op_names.index(op)
+                select_logits.append(logits[self.edge2index[node_str], op_index])
+        return sum(select_logits).item()
+
+    def return_topK(self, K):
+        archs = Structure.gen_all(self.op_names, self.max_nodes, False)
+        pairs = [(self.get_log_prob(arch), arch) for arch in archs]
+        if K < 0 or K >= len(archs):
+            K = len(archs)
+        sorted_pairs = sorted(pairs, key=lambda x: -x[0])
+        return_pairs = [sorted_pairs[_][1] for _ in range(K)]
+        return return_pairs
+
+    def forward(self, inputs):
+        alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
+        with torch.no_grad():
+            alphas_cpu = alphas.detach().cpu()
+
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if isinstance(cell, SearchCell):
+                if self.mode == "urs":
+                    feature = cell.forward_urs(feature)
+                elif self.mode == "select":
+                    feature = cell.forward_select(feature, alphas_cpu)
+                elif self.mode == "joint":
+                    feature = cell.forward_joint(feature, alphas)
+                elif self.mode == "dynamic":
+                    feature = cell.forward_dynamic(feature, self.dynamic_cell)
+                else:
+                    raise ValueError("invalid mode={:}".format(self.mode))
+            else:
+                feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/cell_searchs/search_model_setn_nasnet.py

@@ -0,0 +1,205 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+######################################################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
+######################################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import List, Text, Dict
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkSETN(nn.Module):
+    def __init__(
+        self,
+        C: int,
+        N: int,
+        steps: int,
+        multiplier: int,
+        stem_multiplier: int,
+        num_classes: int,
+        search_space: List[Text],
+        affine: bool,
+        track_running_stats: bool,
+    ):
+        super(NASNetworkSETN, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._steps = steps
+        self._multiplier = multiplier
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        num_edge, edge2index = None, None
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            cell = SearchCell(
+                search_space,
+                steps,
+                multiplier,
+                C_prev_prev,
+                C_prev,
+                C_curr,
+                reduction,
+                reduction_prev,
+                affine,
+                track_running_stats,
+            )
+            if num_edge is None:
+                num_edge, edge2index = cell.num_edges, cell.edge2index
+            else:
+                assert (
+                    num_edge == cell.num_edges and edge2index == cell.edge2index
+                ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_normal_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.arch_reduce_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.mode = "urs"
+        self.dynamic_cell = None
+
+    def set_cal_mode(self, mode, dynamic_cell=None):
+        assert mode in ["urs", "joint", "select", "dynamic"]
+        self.mode = mode
+        if mode == "dynamic":
+            self.dynamic_cell = deepcopy(dynamic_cell)
+        else:
+            self.dynamic_cell = None
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def get_alphas(self):
+        return [self.arch_normal_parameters, self.arch_reduce_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
+            )
+            B = "arch-reduce-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
+            )
+        return "{:}\n{:}".format(A, B)
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def dync_genotype(self, use_random=False):
+        genotypes = []
+        with torch.no_grad():
+            alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                if use_random:
+                    op_name = random.choice(self.op_names)
+                else:
+                    weights = alphas_cpu[self.edge2index[node_str]]
+                    op_index = torch.multinomial(weights, 1).item()
+                    op_name = self.op_names[op_index]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    def genotype(self):
+        def _parse(weights):
+            gene = []
+            for i in range(self._steps):
+                edges = []
+                for j in range(2 + i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    ws = weights[self.edge2index[node_str]]
+                    for k, op_name in enumerate(self.op_names):
+                        if op_name == "none":
+                            continue
+                        edges.append((op_name, j, ws[k]))
+                edges = sorted(edges, key=lambda x: -x[-1])
+                selected_edges = edges[:2]
+                gene.append(tuple(selected_edges))
+            return gene
+
+        with torch.no_grad():
+            gene_normal = _parse(
+                torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy()
+            )
+            gene_reduce = _parse(
+                torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+            "reduce": gene_reduce,
+            "reduce_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+        normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
+        reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            # [TODO]
+            raise NotImplementedError
+            if cell.reduction:
+                hardwts, index = reduce_hardwts, reduce_index
+            else:
+                hardwts, index = normal_hardwts, normal_index
+            s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+        out = self.lastact(s1)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits

correlation/models/clone_weights.py

@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+
+
+def copy_conv(module, init):
+    assert isinstance(module, nn.Conv2d), "invalid module : {:}".format(module)
+    assert isinstance(init, nn.Conv2d), "invalid module : {:}".format(init)
+    new_i, new_o = module.in_channels, module.out_channels
+    module.weight.copy_(init.weight.detach()[:new_o, :new_i])
+    if module.bias is not None:
+        module.bias.copy_(init.bias.detach()[:new_o])
+
+
+def copy_bn(module, init):
+    assert isinstance(module, nn.BatchNorm2d), "invalid module : {:}".format(module)
+    assert isinstance(init, nn.BatchNorm2d), "invalid module : {:}".format(init)
+    num_features = module.num_features
+    if module.weight is not None:
+        module.weight.copy_(init.weight.detach()[:num_features])
+    if module.bias is not None:
+        module.bias.copy_(init.bias.detach()[:num_features])
+    if module.running_mean is not None:
+        module.running_mean.copy_(init.running_mean.detach()[:num_features])
+    if module.running_var is not None:
+        module.running_var.copy_(init.running_var.detach()[:num_features])
+
+
+def copy_fc(module, init):
+    assert isinstance(module, nn.Linear), "invalid module : {:}".format(module)
+    assert isinstance(init, nn.Linear), "invalid module : {:}".format(init)
+    new_i, new_o = module.in_features, module.out_features
+    module.weight.copy_(init.weight.detach()[:new_o, :new_i])
+    if module.bias is not None:
+        module.bias.copy_(init.bias.detach()[:new_o])
+
+
+def copy_base(module, init):
+    assert type(module).__name__ in [
+        "ConvBNReLU",
+        "Downsample",
+    ], "invalid module : {:}".format(module)
+    assert type(init).__name__ in [
+        "ConvBNReLU",
+        "Downsample",
+    ], "invalid module : {:}".format(init)
+    if module.conv is not None:
+        copy_conv(module.conv, init.conv)
+    if module.bn is not None:
+        copy_bn(module.bn, init.bn)
+
+
+def copy_basic(module, init):
+    copy_base(module.conv_a, init.conv_a)
+    copy_base(module.conv_b, init.conv_b)
+    if module.downsample is not None:
+        if init.downsample is not None:
+            copy_base(module.downsample, init.downsample)
+        # else:
+        # import pdb; pdb.set_trace()
+
+
+def init_from_model(network, init_model):
+    with torch.no_grad():
+        copy_fc(network.classifier, init_model.classifier)
+        for base, target in zip(init_model.layers, network.layers):
+            assert (
+                type(base).__name__ == type(target).__name__
+            ), "invalid type : {:} vs {:}".format(base, target)
+            if type(base).__name__ == "ConvBNReLU":
+                copy_base(target, base)
+            elif type(base).__name__ == "ResNetBasicblock":
+                copy_basic(target, base)
+            else:
+                raise ValueError("unknown type name : {:}".format(type(base).__name__))

correlation/models/initialization.py

@@ -0,0 +1,16 @@
+import torch
+import torch.nn as nn
+
+
+def initialize_resnet(m):
+    if isinstance(m, nn.Conv2d):
+        nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+        if m.bias is not None:
+            nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.BatchNorm2d):
+        nn.init.constant_(m.weight, 1)
+        if m.bias is not None:
+            nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.Linear):
+        nn.init.normal_(m.weight, 0, 0.01)
+        nn.init.constant_(m.bias, 0)

correlation/models/shape_infers/InferCifarResNet.py

@@ -0,0 +1,287 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(nOut)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+
+    def forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.bn:
+            out = self.bn(conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    num_conv = 2
+    expansion = 1
+
+    def __init__(self, iCs, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs)
+
+        self.conv_a = ConvBNReLU(
+            iCs[0],
+            iCs[1],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[2]
+        elif iCs[0] != iCs[2]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        # self.out_dim  = max(residual_in, iCs[2])
+        self.out_dim = iCs[2]
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + basicblock
+        return F.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, iCs, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs)
+        self.conv_1x1 = ConvBNReLU(
+            iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            iCs[1],
+            iCs[2],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        elif iCs[0] != iCs[3]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        else:
+            self.downsample = None
+        # self.out_dim = max(residual_in, iCs[3])
+        self.out_dim = iCs[3]
+
+    def forward(self, inputs):
+
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + bottleneck
+        return F.relu(out, inplace=True)
+
+
+class InferCifarResNet(nn.Module):
+    def __init__(
+        self, block_name, depth, xblocks, xchannels, num_classes, zero_init_residual
+    ):
+        super(InferCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+        assert len(xblocks) == 3, "invalid xblocks : {:}".format(xblocks)
+
+        self.message = (
+            "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.xchannels = xchannels
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    xchannels[0],
+                    xchannels[1],
+                    3,
+                    1,
+                    1,
+                    False,
+                    has_avg=False,
+                    has_bn=True,
+                    has_relu=True,
+                )
+            ]
+        )
+        last_channel_idx = 1
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                num_conv = block.num_conv
+                iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1]
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iCs, stride)
+                last_channel_idx += num_conv
+                self.xchannels[last_channel_idx] = module.out_dim
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iCs,
+                    module.out_dim,
+                    stride,
+                )
+                if iL + 1 == xblocks[stage]:  # reach the maximum depth
+                    out_channel = module.out_dim
+                    for iiL in range(iL + 1, layer_blocks):
+                        last_channel_idx += num_conv
+                    self.xchannels[last_channel_idx] = module.out_dim
+                    break
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+
+        self.apply(initialize_resnet)
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, ResNetBasicblock):
+                    nn.init.constant_(m.conv_b.bn.weight, 0)
+                elif isinstance(m, ResNetBottleneck):
+                    nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_infers/InferCifarResNet_depth.py

@@ -0,0 +1,263 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(nOut)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+
+    def forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.bn:
+            out = self.bn(conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    num_conv = 2
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+
+        self.conv_a = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + basicblock
+        return F.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(
+            inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            planes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            planes,
+            planes * self.expansion,
+            1,
+            1,
+            0,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=False,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=False,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+
+    def forward(self, inputs):
+
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + bottleneck
+        return F.relu(out, inplace=True)
+
+
+class InferDepthCifarResNet(nn.Module):
+    def __init__(self, block_name, depth, xblocks, num_classes, zero_init_residual):
+        super(InferDepthCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+        assert len(xblocks) == 3, "invalid xblocks : {:}".format(xblocks)
+
+        self.message = (
+            "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
+                )
+            ]
+        )
+        self.channels = [16]
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    planes,
+                    module.out_dim,
+                    stride,
+                )
+                if iL + 1 == xblocks[stage]:  # reach the maximum depth
+                    break
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(self.channels[-1], num_classes)
+
+        self.apply(initialize_resnet)
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, ResNetBasicblock):
+                    nn.init.constant_(m.conv_b.bn.weight, 0)
+                elif isinstance(m, ResNetBottleneck):
+                    nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_infers/InferCifarResNet_width.py

@@ -0,0 +1,277 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(nOut)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+
+    def forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.bn:
+            out = self.bn(conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    num_conv = 2
+    expansion = 1
+
+    def __init__(self, iCs, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs)
+
+        self.conv_a = ConvBNReLU(
+            iCs[0],
+            iCs[1],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[2]
+        elif iCs[0] != iCs[2]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        # self.out_dim  = max(residual_in, iCs[2])
+        self.out_dim = iCs[2]
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + basicblock
+        return F.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, iCs, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs)
+        self.conv_1x1 = ConvBNReLU(
+            iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            iCs[1],
+            iCs[2],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        elif iCs[0] != iCs[3]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=False,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        else:
+            self.downsample = None
+        # self.out_dim = max(residual_in, iCs[3])
+        self.out_dim = iCs[3]
+
+    def forward(self, inputs):
+
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + bottleneck
+        return F.relu(out, inplace=True)
+
+
+class InferWidthCifarResNet(nn.Module):
+    def __init__(self, block_name, depth, xchannels, num_classes, zero_init_residual):
+        super(InferWidthCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = (
+            "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.xchannels = xchannels
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    xchannels[0],
+                    xchannels[1],
+                    3,
+                    1,
+                    1,
+                    False,
+                    has_avg=False,
+                    has_bn=True,
+                    has_relu=True,
+                )
+            ]
+        )
+        last_channel_idx = 1
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                num_conv = block.num_conv
+                iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1]
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iCs, stride)
+                last_channel_idx += num_conv
+                self.xchannels[last_channel_idx] = module.out_dim
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iCs,
+                    module.out_dim,
+                    stride,
+                )
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+
+        self.apply(initialize_resnet)
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, ResNetBasicblock):
+                    nn.init.constant_(m.conv_b.bn.weight, 0)
+                elif isinstance(m, ResNetBottleneck):
+                    nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_infers/InferImagenetResNet.py

@@ -0,0 +1,324 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+
+    num_conv = 1
+
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(nOut)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+
+    def forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.bn:
+            out = self.bn(conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    num_conv = 2
+    expansion = 1
+
+    def __init__(self, iCs, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs)
+
+        self.conv_a = ConvBNReLU(
+            iCs[0],
+            iCs[1],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=True,
+                has_relu=False,
+            )
+            residual_in = iCs[2]
+        elif iCs[0] != iCs[2]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[2],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        # self.out_dim  = max(residual_in, iCs[2])
+        self.out_dim = iCs[2]
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + basicblock
+        return F.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, iCs, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        assert isinstance(iCs, tuple) or isinstance(
+            iCs, list
+        ), "invalid type of iCs : {:}".format(iCs)
+        assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs)
+        self.conv_1x1 = ConvBNReLU(
+            iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            iCs[1],
+            iCs[2],
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        residual_in = iCs[0]
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=True,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        elif iCs[0] != iCs[3]:
+            self.downsample = ConvBNReLU(
+                iCs[0],
+                iCs[3],
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+            residual_in = iCs[3]
+        else:
+            self.downsample = None
+        # self.out_dim = max(residual_in, iCs[3])
+        self.out_dim = iCs[3]
+
+    def forward(self, inputs):
+
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = residual + bottleneck
+        return F.relu(out, inplace=True)
+
+
+class InferImagenetResNet(nn.Module):
+    def __init__(
+        self,
+        block_name,
+        layers,
+        xblocks,
+        xchannels,
+        deep_stem,
+        num_classes,
+        zero_init_residual,
+    ):
+        super(InferImagenetResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "BasicBlock":
+            block = ResNetBasicblock
+        elif block_name == "Bottleneck":
+            block = ResNetBottleneck
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+        assert len(xblocks) == len(
+            layers
+        ), "invalid layers : {:} vs xblocks : {:}".format(layers, xblocks)
+
+        self.message = "InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}".format(
+            sum(layers) * block.num_conv, sum(xblocks) * block.num_conv, xblocks
+        )
+        self.num_classes = num_classes
+        self.xchannels = xchannels
+        if not deep_stem:
+            self.layers = nn.ModuleList(
+                [
+                    ConvBNReLU(
+                        xchannels[0],
+                        xchannels[1],
+                        7,
+                        2,
+                        3,
+                        False,
+                        has_avg=False,
+                        has_bn=True,
+                        has_relu=True,
+                    )
+                ]
+            )
+            last_channel_idx = 1
+        else:
+            self.layers = nn.ModuleList(
+                [
+                    ConvBNReLU(
+                        xchannels[0],
+                        xchannels[1],
+                        3,
+                        2,
+                        1,
+                        False,
+                        has_avg=False,
+                        has_bn=True,
+                        has_relu=True,
+                    ),
+                    ConvBNReLU(
+                        xchannels[1],
+                        xchannels[2],
+                        3,
+                        1,
+                        1,
+                        False,
+                        has_avg=False,
+                        has_bn=True,
+                        has_relu=True,
+                    ),
+                ]
+            )
+            last_channel_idx = 2
+        self.layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
+        for stage, layer_blocks in enumerate(layers):
+            for iL in range(layer_blocks):
+                num_conv = block.num_conv
+                iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1]
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iCs, stride)
+                last_channel_idx += num_conv
+                self.xchannels[last_channel_idx] = module.out_dim
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iCs,
+                    module.out_dim,
+                    stride,
+                )
+                if iL + 1 == xblocks[stage]:  # reach the maximum depth
+                    out_channel = module.out_dim
+                    for iiL in range(iL + 1, layer_blocks):
+                        last_channel_idx += num_conv
+                    self.xchannels[last_channel_idx] = module.out_dim
+                    break
+        assert last_channel_idx + 1 == len(self.xchannels), "{:} vs {:}".format(
+            last_channel_idx, len(self.xchannels)
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+
+        self.apply(initialize_resnet)
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, ResNetBasicblock):
+                    nn.init.constant_(m.conv_b.bn.weight, 0)
+                elif isinstance(m, ResNetBottleneck):
+                    nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_infers/InferMobileNetV2.py

@@ -0,0 +1,176 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
+#####################################################
+from torch import nn
+
+from ..initialization import initialize_resnet
+from ..SharedUtils import parse_channel_info
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(
+        self,
+        in_planes,
+        out_planes,
+        kernel_size,
+        stride,
+        groups,
+        has_bn=True,
+        has_relu=True,
+    ):
+        super(ConvBNReLU, self).__init__()
+        padding = (kernel_size - 1) // 2
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size,
+            stride,
+            padding,
+            groups=groups,
+            bias=False,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(out_planes)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU6(inplace=True)
+        else:
+            self.relu = None
+
+    def forward(self, x):
+        out = self.conv(x)
+        if self.bn:
+            out = self.bn(out)
+        if self.relu:
+            out = self.relu(out)
+        return out
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, channels, stride, expand_ratio, additive):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2], "invalid stride : {:}".format(stride)
+        assert len(channels) in [2, 3], "invalid channels : {:}".format(channels)
+
+        if len(channels) == 2:
+            layers = []
+        else:
+            layers = [ConvBNReLU(channels[0], channels[1], 1, 1, 1)]
+        layers.extend(
+            [
+                # dw
+                ConvBNReLU(channels[-2], channels[-2], 3, stride, channels[-2]),
+                # pw-linear
+                ConvBNReLU(channels[-2], channels[-1], 1, 1, 1, True, False),
+            ]
+        )
+        self.conv = nn.Sequential(*layers)
+        self.additive = additive
+        if self.additive and channels[0] != channels[-1]:
+            self.shortcut = ConvBNReLU(channels[0], channels[-1], 1, 1, 1, True, False)
+        else:
+            self.shortcut = None
+        self.out_dim = channels[-1]
+
+    def forward(self, x):
+        out = self.conv(x)
+        # if self.additive: return additive_func(out, x)
+        if self.shortcut:
+            return out + self.shortcut(x)
+        else:
+            return out
+
+
+class InferMobileNetV2(nn.Module):
+    def __init__(self, num_classes, xchannels, xblocks, dropout):
+        super(InferMobileNetV2, self).__init__()
+        block = InvertedResidual
+        inverted_residual_setting = [
+            # t, c,  n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            [6, 160, 3, 2],
+            [6, 320, 1, 1],
+        ]
+        assert len(inverted_residual_setting) == len(
+            xblocks
+        ), "invalid number of layers : {:} vs {:}".format(
+            len(inverted_residual_setting), len(xblocks)
+        )
+        for block_num, ir_setting in zip(xblocks, inverted_residual_setting):
+            assert block_num <= ir_setting[2], "{:} vs {:}".format(
+                block_num, ir_setting
+            )
+        xchannels = parse_channel_info(xchannels)
+        # for i, chs in enumerate(xchannels):
+        #  if i > 0: assert chs[0] == xchannels[i-1][-1], 'Layer[{:}] is invalid {:} vs {:}'.format(i, xchannels[i-1], chs)
+        self.xchannels = xchannels
+        self.message = "InferMobileNetV2 : xblocks={:}".format(xblocks)
+        # building first layer
+        features = [ConvBNReLU(xchannels[0][0], xchannels[0][1], 3, 2, 1)]
+        last_channel_idx = 1
+
+        # building inverted residual blocks
+        for stage, (t, c, n, s) in enumerate(inverted_residual_setting):
+            for i in range(n):
+                stride = s if i == 0 else 1
+                additv = True if i > 0 else False
+                module = block(self.xchannels[last_channel_idx], stride, t, additv)
+                features.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, Cs={:}, stride={:}, expand={:}, original-C={:}".format(
+                    stage,
+                    i,
+                    n,
+                    len(features),
+                    self.xchannels[last_channel_idx],
+                    stride,
+                    t,
+                    c,
+                )
+                last_channel_idx += 1
+                if i + 1 == xblocks[stage]:
+                    out_channel = module.out_dim
+                    for iiL in range(i + 1, n):
+                        last_channel_idx += 1
+                    self.xchannels[last_channel_idx][0] = module.out_dim
+                    break
+        # building last several layers
+        features.append(
+            ConvBNReLU(
+                self.xchannels[last_channel_idx][0],
+                self.xchannels[last_channel_idx][1],
+                1,
+                1,
+                1,
+            )
+        )
+        assert last_channel_idx + 2 == len(self.xchannels), "{:} vs {:}".format(
+            last_channel_idx, len(self.xchannels)
+        )
+        # make it nn.Sequential
+        self.features = nn.Sequential(*features)
+
+        # building classifier
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(self.xchannels[last_channel_idx][1], num_classes),
+        )
+
+        # weight initialization
+        self.apply(initialize_resnet)
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        features = self.features(inputs)
+        vectors = features.mean([2, 3])
+        predicts = self.classifier(vectors)
+        return features, predicts

correlation/models/shape_infers/InferTinyCellNet.py

@@ -0,0 +1,74 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from typing import List, Text, Any
+import torch.nn as nn
+
+from ..cell_operations import ResNetBasicblock
+from ..cell_infers.cells import InferCell
+
+
+class DynamicShapeTinyNet(nn.Module):
+    def __init__(self, channels: List[int], genotype: Any, num_classes: int):
+        super(DynamicShapeTinyNet, self).__init__()
+        self._channels = channels
+        if len(channels) % 3 != 2:
+            raise ValueError("invalid number of layers : {:}".format(len(channels)))
+        self._num_stage = N = len(channels) // 3
+
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(channels[0]),
+        )
+
+        # layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        c_prev = channels[0]
+        self.cells = nn.ModuleList()
+        for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)):
+            if reduction:
+                cell = ResNetBasicblock(c_prev, c_curr, 2, True)
+            else:
+                cell = InferCell(genotype, c_prev, c_curr, 1)
+            self.cells.append(cell)
+            c_prev = cell.out_dim
+        self._num_layer = len(self.cells)
+
+        self.lastact = nn.Sequential(nn.BatchNorm2d(c_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(c_prev, num_classes)
+
+    def get_message(self) -> Text:
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_channels}, N={_num_stage}, L={_num_layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def forward(self, inputs):
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return logits
+
+    def forward_pre_GAP(self, inputs):
+        feature = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            feature = cell(feature)
+
+        out = self.lastact(feature)
+        return out
+

correlation/models/shape_infers/__init__.py

@@ -0,0 +1,9 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from .InferCifarResNet_width import InferWidthCifarResNet
+from .InferImagenetResNet import InferImagenetResNet
+from .InferCifarResNet_depth import InferDepthCifarResNet
+from .InferCifarResNet import InferCifarResNet
+from .InferMobileNetV2 import InferMobileNetV2
+from .InferTinyCellNet import DynamicShapeTinyNet

correlation/models/shape_infers/shared_utils.py

@@ -0,0 +1,5 @@
+def parse_channel_info(xstring):
+    blocks = xstring.split(" ")
+    blocks = [x.split("-") for x in blocks]
+    blocks = [[int(_) for _ in x] for x in blocks]
+    return blocks

correlation/models/shape_searchs/SearchCifarResNet.py

@@ -0,0 +1,760 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils import additive_func
+from .SoftSelect import select2withP, ChannelWiseInter
+from .SoftSelect import linear_forward
+from .SoftSelect import get_width_choices
+
+
+def get_depth_choices(nDepth, return_num):
+    if nDepth == 2:
+        choices = (1, 2)
+    elif nDepth == 3:
+        choices = (1, 2, 3)
+    elif nDepth > 3:
+        choices = list(range(1, nDepth + 1, 2))
+        if choices[-1] < nDepth:
+            choices.append(nDepth)
+    else:
+        raise ValueError("invalid nDepth : {:}".format(nDepth))
+    if return_num:
+        return len(choices)
+    else:
+        return choices
+
+
+def conv_forward(inputs, conv, choices):
+    iC = conv.in_channels
+    fill_size = list(inputs.size())
+    fill_size[1] = iC - fill_size[1]
+    filled = torch.zeros(fill_size, device=inputs.device)
+    xinputs = torch.cat((inputs, filled), dim=1)
+    outputs = conv(xinputs)
+    selecteds = [outputs[:, :oC] for oC in choices]
+    return selecteds
+
+
+class ConvBNReLU(nn.Module):
+    num_conv = 1
+
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        self.InShape = None
+        self.OutShape = None
+        self.choices = get_width_choices(nOut)
+        self.register_buffer("choices_tensor", torch.Tensor(self.choices))
+
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        # if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+        # else       : self.bn  = None
+        self.has_bn = has_bn
+        self.BNs = nn.ModuleList()
+        for i, _out in enumerate(self.choices):
+            self.BNs.append(nn.BatchNorm2d(_out))
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+        self.in_dim = nIn
+        self.out_dim = nOut
+        self.search_mode = "basic"
+
+    def get_flops(self, channels, check_range=True, divide=1):
+        iC, oC = channels
+        if check_range:
+            assert (
+                iC <= self.conv.in_channels and oC <= self.conv.out_channels
+            ), "{:} vs {:}  |  {:} vs {:}".format(
+                iC, self.conv.in_channels, oC, self.conv.out_channels
+            )
+        assert (
+            isinstance(self.InShape, tuple) and len(self.InShape) == 2
+        ), "invalid in-shape : {:}".format(self.InShape)
+        assert (
+            isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
+        ), "invalid out-shape : {:}".format(self.OutShape)
+        # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+        conv_per_position_flops = (
+            self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
+        )
+        all_positions = self.OutShape[0] * self.OutShape[1]
+        flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+        if self.conv.bias is not None:
+            flops += all_positions / divide
+        return flops
+
+    def get_range(self):
+        return [self.choices]
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, index, prob = tuple_inputs
+        index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+        probability = torch.squeeze(probability)
+        assert len(index) == 2, "invalid length : {:}".format(index)
+        # compute expected flop
+        # coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+        expected_outC = (self.choices_tensor * probability).sum()
+        expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        # convolutional layer
+        out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+        out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+        # merge
+        out_channel = max([x.size(1) for x in out_bns])
+        outA = ChannelWiseInter(out_bns[0], out_channel)
+        outB = ChannelWiseInter(out_bns[1], out_channel)
+        out = outA * prob[0] + outB * prob[1]
+        # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        return out, expected_outC, expected_flop
+
+    def basic_forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.has_bn:
+            out = self.BNs[-1](conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+            self.OutShape = (out.size(-2), out.size(-1))
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    expansion = 1
+    num_conv = 2
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return self.conv_a.get_range() + self.conv_b.get_range()
+
+    def get_flops(self, channels):
+        assert len(channels) == 3, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_C = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_C = (
+                channels[0]
+                * channels[-1]
+                * self.conv_b.OutShape[0]
+                * self.conv_b.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+        out_a, expected_inC_a, expected_flop_a = self.conv_a(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_b, expected_inC_b, expected_flop_b = self.conv_b(
+            (out_a, expected_inC_a, probability[1], indexes[1], probs[1])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[1], indexes[1], probs[1])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_b)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_b,
+            sum([expected_flop_a, expected_flop_b, expected_flop_c]),
+        )
+
+    def basic_forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return nn.functional.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(
+            inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            planes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            planes,
+            planes * self.expansion,
+            1,
+            1,
+            0,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=False,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return (
+            self.conv_1x1.get_range()
+            + self.conv_3x3.get_range()
+            + self.conv_1x4.get_range()
+        )
+
+    def get_flops(self, channels):
+        assert len(channels) == 4, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+        flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_D = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_D = (
+                channels[0]
+                * channels[-1]
+                * self.conv_1x4.OutShape[0]
+                * self.conv_1x4.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C + flop_D
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def basic_forward(self, inputs):
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, bottleneck)
+        return nn.functional.relu(out, inplace=True)
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+        out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3(
+            (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1])
+        )
+        out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4(
+            (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[2], indexes[2], probs[2])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_1x4)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_1x4,
+            sum(
+                [
+                    expected_flop_1x1,
+                    expected_flop_3x3,
+                    expected_flop_1x4,
+                    expected_flop_c,
+                ]
+            ),
+        )
+
+
+class SearchShapeCifarResNet(nn.Module):
+    def __init__(self, block_name, depth, num_classes):
+        super(SearchShapeCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = (
+            "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.channels = [16]
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
+                )
+            ]
+        )
+        self.InShape = None
+        self.depth_info = OrderedDict()
+        self.depth_at_i = OrderedDict()
+        for stage in range(3):
+            cur_block_choices = get_depth_choices(layer_blocks, False)
+            assert (
+                cur_block_choices[-1] == layer_blocks
+            ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks)
+            self.message += (
+                "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format(
+                    stage, cur_block_choices, layer_blocks
+                )
+            )
+            block_choices, xstart = [], len(self.layers)
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+                # added for depth
+                layer_index = len(self.layers) - 1
+                if iL + 1 in cur_block_choices:
+                    block_choices.append(layer_index)
+                if iL + 1 == layer_blocks:
+                    self.depth_info[layer_index] = {
+                        "choices": block_choices,
+                        "stage": stage,
+                        "xstart": xstart,
+                    }
+        self.depth_info_list = []
+        for xend, info in self.depth_info.items():
+            self.depth_info_list.append((xend, info))
+            xstart, xstage = info["xstart"], info["stage"]
+            for ilayer in range(xstart, xend + 1):
+                idx = bisect_right(info["choices"], ilayer - 1)
+                self.depth_at_i[ilayer] = (xstage, idx)
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        self.InShape = None
+        self.tau = -1
+        self.search_mode = "basic"
+        # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+        # parameters for width
+        self.Ranges = []
+        self.layer2indexRange = []
+        for i, layer in enumerate(self.layers):
+            start_index = len(self.Ranges)
+            self.Ranges += layer.get_range()
+            self.layer2indexRange.append((start_index, len(self.Ranges)))
+        assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format(
+            len(self.Ranges) + 1, depth
+        )
+
+        self.register_parameter(
+            "width_attentions",
+            nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))),
+        )
+        self.register_parameter(
+            "depth_attentions",
+            nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))),
+        )
+        nn.init.normal_(self.width_attentions, 0, 0.01)
+        nn.init.normal_(self.depth_attentions, 0, 0.01)
+        self.apply(initialize_resnet)
+
+    def arch_parameters(self, LR=None):
+        if LR is None:
+            return [self.width_attentions, self.depth_attentions]
+        else:
+            return [
+                {"params": self.width_attentions, "lr": LR},
+                {"params": self.depth_attentions, "lr": LR},
+            ]
+
+    def base_parameters(self):
+        return (
+            list(self.layers.parameters())
+            + list(self.avgpool.parameters())
+            + list(self.classifier.parameters())
+        )
+
+    def get_flop(self, mode, config_dict, extra_info):
+        if config_dict is not None:
+            config_dict = config_dict.copy()
+        # select channels
+        channels = [3]
+        for i, weight in enumerate(self.width_attentions):
+            if mode == "genotype":
+                with torch.no_grad():
+                    probe = nn.functional.softmax(weight, dim=0)
+                    C = self.Ranges[i][torch.argmax(probe).item()]
+            elif mode == "max":
+                C = self.Ranges[i][-1]
+            elif mode == "fix":
+                C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+            elif mode == "random":
+                assert isinstance(extra_info, float), "invalid extra_info : {:}".format(
+                    extra_info
+                )
+                with torch.no_grad():
+                    prob = nn.functional.softmax(weight, dim=0)
+                    approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+                    for j in range(prob.size(0)):
+                        prob[j] = 1 / (
+                            abs(j - (approximate_C - self.Ranges[i][j])) + 0.2
+                        )
+                    C = self.Ranges[i][torch.multinomial(prob, 1, False).item()]
+            else:
+                raise ValueError("invalid mode : {:}".format(mode))
+            channels.append(C)
+        # select depth
+        if mode == "genotype":
+            with torch.no_grad():
+                depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+                choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+        elif mode == "max" or mode == "fix":
+            choices = [depth_probs.size(1) - 1 for _ in range(depth_probs.size(0))]
+        elif mode == "random":
+            with torch.no_grad():
+                depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+                choices = torch.multinomial(depth_probs, 1, False).cpu().tolist()
+        else:
+            raise ValueError("invalid mode : {:}".format(mode))
+        selected_layers = []
+        for choice, xvalue in zip(choices, self.depth_info_list):
+            xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1
+            selected_layers.append(xtemp)
+        flop = 0
+        for i, layer in enumerate(self.layers):
+            s, e = self.layer2indexRange[i]
+            xchl = tuple(channels[s : e + 1])
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                if xatti <= choices[xstagei]:  # leave this depth
+                    flop += layer.get_flops(xchl)
+                else:
+                    flop += 0  # do not use this layer
+            else:
+                flop += layer.get_flops(xchl)
+        # the last fc layer
+        flop += channels[-1] * self.classifier.out_features
+        if config_dict is None:
+            return flop / 1e6
+        else:
+            config_dict["xchannels"] = channels
+            config_dict["xblocks"] = selected_layers
+            config_dict["super_type"] = "infer-shape"
+            config_dict["estimated_FLOP"] = flop / 1e6
+            return flop / 1e6, config_dict
+
+    def get_arch_info(self):
+        string = (
+            "for depth and width, there are {:} + {:} attention probabilities.".format(
+                len(self.depth_attentions), len(self.width_attentions)
+            )
+        )
+        string += "\n{:}".format(self.depth_info)
+        discrepancy = []
+        with torch.no_grad():
+            for i, att in enumerate(self.depth_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.depth_attentions), " ".join(prob)
+                )
+                logt = ["{:.4f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:17s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || discrepancy={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+            string += "\n-----------------------------------------------"
+            for i, att in enumerate(self.width_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.width_attentions), " ".join(prob)
+                )
+                logt = ["{:.3f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:52s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || dis={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+        return string, discrepancy
+
+    def set_tau(self, tau_max, tau_min, epoch_ratio):
+        assert (
+            epoch_ratio >= 0 and epoch_ratio <= 1
+        ), "invalid epoch-ratio : {:}".format(epoch_ratio)
+        tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+        self.tau = tau
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, inputs):
+        flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1)
+        flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        flop_depth_probs = torch.flip(
+            torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1]
+        )
+        selected_widths, selected_width_probs = select2withP(
+            self.width_attentions, self.tau
+        )
+        selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+        with torch.no_grad():
+            selected_widths = selected_widths.cpu()
+
+        x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+        feature_maps = []
+        for i, layer in enumerate(self.layers):
+            selected_w_index = selected_widths[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            selected_w_probs = selected_width_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            layer_prob = flop_width_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            x, expected_inC, expected_flop = layer(
+                (x, expected_inC, layer_prob, selected_w_index, selected_w_probs)
+            )
+            feature_maps.append(x)
+            last_channel_idx += layer.num_conv
+            if i in self.depth_info:  # aggregate the information
+                choices = self.depth_info[i]["choices"]
+                xstagei = self.depth_info[i]["stage"]
+                # print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist()))
+                # for A, W in zip(choices, selected_depth_probs[xstagei]):
+                #  print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W))
+                possible_tensors = []
+                max_C = max(feature_maps[A].size(1) for A in choices)
+                for tempi, A in enumerate(choices):
+                    xtensor = ChannelWiseInter(feature_maps[A], max_C)
+                    # drop_ratio = 1-(tempi+1.0)/len(choices)
+                    # xtensor = drop_path(xtensor, drop_ratio)
+                    possible_tensors.append(xtensor)
+                weighted_sum = sum(
+                    xtensor * W
+                    for xtensor, W in zip(
+                        possible_tensors, selected_depth_probs[xstagei]
+                    )
+                )
+                x = weighted_sum
+
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop
+            else:
+                x_expected_flop = expected_flop
+            flops.append(x_expected_flop)
+        flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6))
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = linear_forward(features, self.classifier)
+        return logits, torch.stack([sum(flops)])
+
+    def basic_forward(self, inputs):
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_searchs/SearchCifarResNet_depth.py

@@ -0,0 +1,515 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils import additive_func
+from .SoftSelect import select2withP, ChannelWiseInter
+from .SoftSelect import linear_forward
+from .SoftSelect import get_width_choices
+
+
+def get_depth_choices(nDepth, return_num):
+    if nDepth == 2:
+        choices = (1, 2)
+    elif nDepth == 3:
+        choices = (1, 2, 3)
+    elif nDepth > 3:
+        choices = list(range(1, nDepth + 1, 2))
+        if choices[-1] < nDepth:
+            choices.append(nDepth)
+    else:
+        raise ValueError("invalid nDepth : {:}".format(nDepth))
+    if return_num:
+        return len(choices)
+    else:
+        return choices
+
+
+class ConvBNReLU(nn.Module):
+    num_conv = 1
+
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        self.InShape = None
+        self.OutShape = None
+        self.choices = get_width_choices(nOut)
+        self.register_buffer("choices_tensor", torch.Tensor(self.choices))
+
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        if has_bn:
+            self.bn = nn.BatchNorm2d(nOut)
+        else:
+            self.bn = None
+        if has_relu:
+            self.relu = nn.ReLU(inplace=False)
+        else:
+            self.relu = None
+        self.in_dim = nIn
+        self.out_dim = nOut
+
+    def get_flops(self, divide=1):
+        iC, oC = self.in_dim, self.out_dim
+        assert (
+            iC <= self.conv.in_channels and oC <= self.conv.out_channels
+        ), "{:} vs {:}  |  {:} vs {:}".format(
+            iC, self.conv.in_channels, oC, self.conv.out_channels
+        )
+        assert (
+            isinstance(self.InShape, tuple) and len(self.InShape) == 2
+        ), "invalid in-shape : {:}".format(self.InShape)
+        assert (
+            isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
+        ), "invalid out-shape : {:}".format(self.OutShape)
+        # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+        conv_per_position_flops = (
+            self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
+        )
+        all_positions = self.OutShape[0] * self.OutShape[1]
+        flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+        if self.conv.bias is not None:
+            flops += all_positions / divide
+        return flops
+
+    def forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.bn:
+            out = self.bn(conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+            self.OutShape = (out.size(-2), out.size(-1))
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    expansion = 1
+    num_conv = 2
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.search_mode = "basic"
+
+    def get_flops(self, divide=1):
+        flop_A = self.conv_a.get_flops(divide)
+        flop_B = self.conv_b.get_flops(divide)
+        if hasattr(self.downsample, "get_flops"):
+            flop_C = self.downsample.get_flops(divide)
+        else:
+            flop_C = 0
+        return flop_A + flop_B + flop_C
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return nn.functional.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(
+            inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            planes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            planes,
+            planes * self.expansion,
+            1,
+            1,
+            0,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=False,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return (
+            self.conv_1x1.get_range()
+            + self.conv_3x3.get_range()
+            + self.conv_1x4.get_range()
+        )
+
+    def get_flops(self, divide):
+        flop_A = self.conv_1x1.get_flops(divide)
+        flop_B = self.conv_3x3.get_flops(divide)
+        flop_C = self.conv_1x4.get_flops(divide)
+        if hasattr(self.downsample, "get_flops"):
+            flop_D = self.downsample.get_flops(divide)
+        else:
+            flop_D = 0
+        return flop_A + flop_B + flop_C + flop_D
+
+    def forward(self, inputs):
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, bottleneck)
+        return nn.functional.relu(out, inplace=True)
+
+
+class SearchDepthCifarResNet(nn.Module):
+    def __init__(self, block_name, depth, num_classes):
+        super(SearchDepthCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = (
+            "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.channels = [16]
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
+                )
+            ]
+        )
+        self.InShape = None
+        self.depth_info = OrderedDict()
+        self.depth_at_i = OrderedDict()
+        for stage in range(3):
+            cur_block_choices = get_depth_choices(layer_blocks, False)
+            assert (
+                cur_block_choices[-1] == layer_blocks
+            ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks)
+            self.message += (
+                "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format(
+                    stage, cur_block_choices, layer_blocks
+                )
+            )
+            block_choices, xstart = [], len(self.layers)
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+                # added for depth
+                layer_index = len(self.layers) - 1
+                if iL + 1 in cur_block_choices:
+                    block_choices.append(layer_index)
+                if iL + 1 == layer_blocks:
+                    self.depth_info[layer_index] = {
+                        "choices": block_choices,
+                        "stage": stage,
+                        "xstart": xstart,
+                    }
+        self.depth_info_list = []
+        for xend, info in self.depth_info.items():
+            self.depth_info_list.append((xend, info))
+            xstart, xstage = info["xstart"], info["stage"]
+            for ilayer in range(xstart, xend + 1):
+                idx = bisect_right(info["choices"], ilayer - 1)
+                self.depth_at_i[ilayer] = (xstage, idx)
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        self.InShape = None
+        self.tau = -1
+        self.search_mode = "basic"
+        # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+        self.register_parameter(
+            "depth_attentions",
+            nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))),
+        )
+        nn.init.normal_(self.depth_attentions, 0, 0.01)
+        self.apply(initialize_resnet)
+
+    def arch_parameters(self):
+        return [self.depth_attentions]
+
+    def base_parameters(self):
+        return (
+            list(self.layers.parameters())
+            + list(self.avgpool.parameters())
+            + list(self.classifier.parameters())
+        )
+
+    def get_flop(self, mode, config_dict, extra_info):
+        if config_dict is not None:
+            config_dict = config_dict.copy()
+        # select depth
+        if mode == "genotype":
+            with torch.no_grad():
+                depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+                choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+        elif mode == "max":
+            choices = [depth_probs.size(1) - 1 for _ in range(depth_probs.size(0))]
+        elif mode == "random":
+            with torch.no_grad():
+                depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+                choices = torch.multinomial(depth_probs, 1, False).cpu().tolist()
+        else:
+            raise ValueError("invalid mode : {:}".format(mode))
+        selected_layers = []
+        for choice, xvalue in zip(choices, self.depth_info_list):
+            xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1
+            selected_layers.append(xtemp)
+        flop = 0
+        for i, layer in enumerate(self.layers):
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                if xatti <= choices[xstagei]:  # leave this depth
+                    flop += layer.get_flops()
+                else:
+                    flop += 0  # do not use this layer
+            else:
+                flop += layer.get_flops()
+        # the last fc layer
+        flop += self.classifier.in_features * self.classifier.out_features
+        if config_dict is None:
+            return flop / 1e6
+        else:
+            config_dict["xblocks"] = selected_layers
+            config_dict["super_type"] = "infer-depth"
+            config_dict["estimated_FLOP"] = flop / 1e6
+            return flop / 1e6, config_dict
+
+    def get_arch_info(self):
+        string = "for depth, there are {:} attention probabilities.".format(
+            len(self.depth_attentions)
+        )
+        string += "\n{:}".format(self.depth_info)
+        discrepancy = []
+        with torch.no_grad():
+            for i, att in enumerate(self.depth_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.depth_attentions), " ".join(prob)
+                )
+                logt = ["{:.4f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:17s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || discrepancy={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+        return string, discrepancy
+
+    def set_tau(self, tau_max, tau_min, epoch_ratio):
+        assert (
+            epoch_ratio >= 0 and epoch_ratio <= 1
+        ), "invalid epoch-ratio : {:}".format(epoch_ratio)
+        tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+        self.tau = tau
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, inputs):
+        flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        flop_depth_probs = torch.flip(
+            torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1]
+        )
+        selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+
+        x, flops = inputs, []
+        feature_maps = []
+        for i, layer in enumerate(self.layers):
+            layer_i = layer(x)
+            feature_maps.append(layer_i)
+            if i in self.depth_info:  # aggregate the information
+                choices = self.depth_info[i]["choices"]
+                xstagei = self.depth_info[i]["stage"]
+                possible_tensors = []
+                for tempi, A in enumerate(choices):
+                    xtensor = feature_maps[A]
+                    possible_tensors.append(xtensor)
+                weighted_sum = sum(
+                    xtensor * W
+                    for xtensor, W in zip(
+                        possible_tensors, selected_depth_probs[xstagei]
+                    )
+                )
+                x = weighted_sum
+            else:
+                x = layer_i
+
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                # print ('layer-{:03d}, stage={:}, att={:}, prob={:}, flop={:}'.format(i, xstagei, xatti, flop_depth_probs[xstagei, xatti].item(), layer.get_flops(1e6)))
+                x_expected_flop = flop_depth_probs[xstagei, xatti] * layer.get_flops(
+                    1e6
+                )
+            else:
+                x_expected_flop = layer.get_flops(1e6)
+            flops.append(x_expected_flop)
+        flops.append(
+            (self.classifier.in_features * self.classifier.out_features * 1.0 / 1e6)
+        )
+
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = linear_forward(features, self.classifier)
+        return logits, torch.stack([sum(flops)])
+
+    def basic_forward(self, inputs):
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_searchs/SearchCifarResNet_width.py

@@ -0,0 +1,619 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils import additive_func
+from .SoftSelect import select2withP, ChannelWiseInter
+from .SoftSelect import linear_forward
+from .SoftSelect import get_width_choices as get_choices
+
+
+def conv_forward(inputs, conv, choices):
+    iC = conv.in_channels
+    fill_size = list(inputs.size())
+    fill_size[1] = iC - fill_size[1]
+    filled = torch.zeros(fill_size, device=inputs.device)
+    xinputs = torch.cat((inputs, filled), dim=1)
+    outputs = conv(xinputs)
+    selecteds = [outputs[:, :oC] for oC in choices]
+    return selecteds
+
+
+class ConvBNReLU(nn.Module):
+    num_conv = 1
+
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        self.InShape = None
+        self.OutShape = None
+        self.choices = get_choices(nOut)
+        self.register_buffer("choices_tensor", torch.Tensor(self.choices))
+
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        # if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+        # else       : self.bn  = None
+        self.has_bn = has_bn
+        self.BNs = nn.ModuleList()
+        for i, _out in enumerate(self.choices):
+            self.BNs.append(nn.BatchNorm2d(_out))
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+        self.in_dim = nIn
+        self.out_dim = nOut
+        self.search_mode = "basic"
+
+    def get_flops(self, channels, check_range=True, divide=1):
+        iC, oC = channels
+        if check_range:
+            assert (
+                iC <= self.conv.in_channels and oC <= self.conv.out_channels
+            ), "{:} vs {:}  |  {:} vs {:}".format(
+                iC, self.conv.in_channels, oC, self.conv.out_channels
+            )
+        assert (
+            isinstance(self.InShape, tuple) and len(self.InShape) == 2
+        ), "invalid in-shape : {:}".format(self.InShape)
+        assert (
+            isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
+        ), "invalid out-shape : {:}".format(self.OutShape)
+        # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+        conv_per_position_flops = (
+            self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
+        )
+        all_positions = self.OutShape[0] * self.OutShape[1]
+        flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+        if self.conv.bias is not None:
+            flops += all_positions / divide
+        return flops
+
+    def get_range(self):
+        return [self.choices]
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, index, prob = tuple_inputs
+        index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+        probability = torch.squeeze(probability)
+        assert len(index) == 2, "invalid length : {:}".format(index)
+        # compute expected flop
+        # coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+        expected_outC = (self.choices_tensor * probability).sum()
+        expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        # convolutional layer
+        out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+        out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+        # merge
+        out_channel = max([x.size(1) for x in out_bns])
+        outA = ChannelWiseInter(out_bns[0], out_channel)
+        outB = ChannelWiseInter(out_bns[1], out_channel)
+        out = outA * prob[0] + outB * prob[1]
+        # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        return out, expected_outC, expected_flop
+
+    def basic_forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.has_bn:
+            out = self.BNs[-1](conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+            self.OutShape = (out.size(-2), out.size(-1))
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    expansion = 1
+    num_conv = 2
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return self.conv_a.get_range() + self.conv_b.get_range()
+
+    def get_flops(self, channels):
+        assert len(channels) == 3, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_C = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_C = (
+                channels[0]
+                * channels[-1]
+                * self.conv_b.OutShape[0]
+                * self.conv_b.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+        out_a, expected_inC_a, expected_flop_a = self.conv_a(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_b, expected_inC_b, expected_flop_b = self.conv_b(
+            (out_a, expected_inC_a, probability[1], indexes[1], probs[1])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[1], indexes[1], probs[1])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_b)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_b,
+            sum([expected_flop_a, expected_flop_b, expected_flop_c]),
+        )
+
+    def basic_forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return nn.functional.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(
+            inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            planes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            planes,
+            planes * self.expansion,
+            1,
+            1,
+            0,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=False,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return (
+            self.conv_1x1.get_range()
+            + self.conv_3x3.get_range()
+            + self.conv_1x4.get_range()
+        )
+
+    def get_flops(self, channels):
+        assert len(channels) == 4, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+        flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_D = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_D = (
+                channels[0]
+                * channels[-1]
+                * self.conv_1x4.OutShape[0]
+                * self.conv_1x4.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C + flop_D
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def basic_forward(self, inputs):
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, bottleneck)
+        return nn.functional.relu(out, inplace=True)
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+        out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3(
+            (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1])
+        )
+        out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4(
+            (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[2], indexes[2], probs[2])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_1x4)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_1x4,
+            sum(
+                [
+                    expected_flop_1x1,
+                    expected_flop_3x3,
+                    expected_flop_1x4,
+                    expected_flop_c,
+                ]
+            ),
+        )
+
+
+class SearchWidthCifarResNet(nn.Module):
+    def __init__(self, block_name, depth, num_classes):
+        super(SearchWidthCifarResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "ResNetBasicblock":
+            block = ResNetBasicblock
+            assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
+            layer_blocks = (depth - 2) // 6
+        elif block_name == "ResNetBottleneck":
+            block = ResNetBottleneck
+            assert (depth - 2) % 9 == 0, "depth should be one of 164"
+            layer_blocks = (depth - 2) // 9
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = (
+            "SearchWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.channels = [16]
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
+                )
+            ]
+        )
+        self.InShape = None
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        self.InShape = None
+        self.tau = -1
+        self.search_mode = "basic"
+        # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+        # parameters for width
+        self.Ranges = []
+        self.layer2indexRange = []
+        for i, layer in enumerate(self.layers):
+            start_index = len(self.Ranges)
+            self.Ranges += layer.get_range()
+            self.layer2indexRange.append((start_index, len(self.Ranges)))
+        assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format(
+            len(self.Ranges) + 1, depth
+        )
+
+        self.register_parameter(
+            "width_attentions",
+            nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))),
+        )
+        nn.init.normal_(self.width_attentions, 0, 0.01)
+        self.apply(initialize_resnet)
+
+    def arch_parameters(self):
+        return [self.width_attentions]
+
+    def base_parameters(self):
+        return (
+            list(self.layers.parameters())
+            + list(self.avgpool.parameters())
+            + list(self.classifier.parameters())
+        )
+
+    def get_flop(self, mode, config_dict, extra_info):
+        if config_dict is not None:
+            config_dict = config_dict.copy()
+        # weights = [F.softmax(x, dim=0) for x in self.width_attentions]
+        channels = [3]
+        for i, weight in enumerate(self.width_attentions):
+            if mode == "genotype":
+                with torch.no_grad():
+                    probe = nn.functional.softmax(weight, dim=0)
+                    C = self.Ranges[i][torch.argmax(probe).item()]
+            elif mode == "max":
+                C = self.Ranges[i][-1]
+            elif mode == "fix":
+                C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+            elif mode == "random":
+                assert isinstance(extra_info, float), "invalid extra_info : {:}".format(
+                    extra_info
+                )
+                with torch.no_grad():
+                    prob = nn.functional.softmax(weight, dim=0)
+                    approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+                    for j in range(prob.size(0)):
+                        prob[j] = 1 / (
+                            abs(j - (approximate_C - self.Ranges[i][j])) + 0.2
+                        )
+                    C = self.Ranges[i][torch.multinomial(prob, 1, False).item()]
+            else:
+                raise ValueError("invalid mode : {:}".format(mode))
+            channels.append(C)
+        flop = 0
+        for i, layer in enumerate(self.layers):
+            s, e = self.layer2indexRange[i]
+            xchl = tuple(channels[s : e + 1])
+            flop += layer.get_flops(xchl)
+        # the last fc layer
+        flop += channels[-1] * self.classifier.out_features
+        if config_dict is None:
+            return flop / 1e6
+        else:
+            config_dict["xchannels"] = channels
+            config_dict["super_type"] = "infer-width"
+            config_dict["estimated_FLOP"] = flop / 1e6
+            return flop / 1e6, config_dict
+
+    def get_arch_info(self):
+        string = "for width, there are {:} attention probabilities.".format(
+            len(self.width_attentions)
+        )
+        discrepancy = []
+        with torch.no_grad():
+            for i, att in enumerate(self.width_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.width_attentions), " ".join(prob)
+                )
+                logt = ["{:.3f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:52s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || dis={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+        return string, discrepancy
+
+    def set_tau(self, tau_max, tau_min, epoch_ratio):
+        assert (
+            epoch_ratio >= 0 and epoch_ratio <= 1
+        ), "invalid epoch-ratio : {:}".format(epoch_ratio)
+        tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+        self.tau = tau
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, inputs):
+        flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
+        selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
+        with torch.no_grad():
+            selected_widths = selected_widths.cpu()
+
+        x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+        for i, layer in enumerate(self.layers):
+            selected_w_index = selected_widths[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            selected_w_probs = selected_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            layer_prob = flop_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            x, expected_inC, expected_flop = layer(
+                (x, expected_inC, layer_prob, selected_w_index, selected_w_probs)
+            )
+            last_channel_idx += layer.num_conv
+            flops.append(expected_flop)
+        flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6))
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = linear_forward(features, self.classifier)
+        return logits, torch.stack([sum(flops)])
+
+    def basic_forward(self, inputs):
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_searchs/SearchImagenetResNet.py

@@ -0,0 +1,766 @@
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils import additive_func
+from .SoftSelect import select2withP, ChannelWiseInter
+from .SoftSelect import linear_forward
+from .SoftSelect import get_width_choices
+
+
+def get_depth_choices(layers):
+    min_depth = min(layers)
+    info = {"num": min_depth}
+    for i, depth in enumerate(layers):
+        choices = []
+        for j in range(1, min_depth + 1):
+            choices.append(int(float(depth) * j / min_depth))
+        info[i] = choices
+    return info
+
+
+def conv_forward(inputs, conv, choices):
+    iC = conv.in_channels
+    fill_size = list(inputs.size())
+    fill_size[1] = iC - fill_size[1]
+    filled = torch.zeros(fill_size, device=inputs.device)
+    xinputs = torch.cat((inputs, filled), dim=1)
+    outputs = conv(xinputs)
+    selecteds = [outputs[:, :oC] for oC in choices]
+    return selecteds
+
+
+class ConvBNReLU(nn.Module):
+    num_conv = 1
+
+    def __init__(
+        self,
+        nIn,
+        nOut,
+        kernel,
+        stride,
+        padding,
+        bias,
+        has_avg,
+        has_bn,
+        has_relu,
+        last_max_pool=False,
+    ):
+        super(ConvBNReLU, self).__init__()
+        self.InShape = None
+        self.OutShape = None
+        self.choices = get_width_choices(nOut)
+        self.register_buffer("choices_tensor", torch.Tensor(self.choices))
+
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        # if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+        # else       : self.bn  = None
+        self.has_bn = has_bn
+        self.BNs = nn.ModuleList()
+        for i, _out in enumerate(self.choices):
+            self.BNs.append(nn.BatchNorm2d(_out))
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+
+        if last_max_pool:
+            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        else:
+            self.maxpool = None
+        self.in_dim = nIn
+        self.out_dim = nOut
+        self.search_mode = "basic"
+
+    def get_flops(self, channels, check_range=True, divide=1):
+        iC, oC = channels
+        if check_range:
+            assert (
+                iC <= self.conv.in_channels and oC <= self.conv.out_channels
+            ), "{:} vs {:}  |  {:} vs {:}".format(
+                iC, self.conv.in_channels, oC, self.conv.out_channels
+            )
+        assert (
+            isinstance(self.InShape, tuple) and len(self.InShape) == 2
+        ), "invalid in-shape : {:}".format(self.InShape)
+        assert (
+            isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
+        ), "invalid out-shape : {:}".format(self.OutShape)
+        # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+        conv_per_position_flops = (
+            self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
+        )
+        all_positions = self.OutShape[0] * self.OutShape[1]
+        flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+        if self.conv.bias is not None:
+            flops += all_positions / divide
+        return flops
+
+    def get_range(self):
+        return [self.choices]
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, index, prob = tuple_inputs
+        index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+        probability = torch.squeeze(probability)
+        assert len(index) == 2, "invalid length : {:}".format(index)
+        # compute expected flop
+        # coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+        expected_outC = (self.choices_tensor * probability).sum()
+        expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        # convolutional layer
+        out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+        out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+        # merge
+        out_channel = max([x.size(1) for x in out_bns])
+        outA = ChannelWiseInter(out_bns[0], out_channel)
+        outB = ChannelWiseInter(out_bns[1], out_channel)
+        out = outA * prob[0] + outB * prob[1]
+        # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+        if self.relu:
+            out = self.relu(out)
+        if self.maxpool:
+            out = self.maxpool(out)
+        return out, expected_outC, expected_flop
+
+    def basic_forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.has_bn:
+            out = self.BNs[-1](conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+            self.OutShape = (out.size(-2), out.size(-1))
+        if self.maxpool:
+            out = self.maxpool(out)
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    expansion = 1
+    num_conv = 2
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_a = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_b = ConvBNReLU(
+            planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=True,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return self.conv_a.get_range() + self.conv_b.get_range()
+
+    def get_flops(self, channels):
+        assert len(channels) == 3, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_C = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_C = (
+                channels[0]
+                * channels[-1]
+                * self.conv_b.OutShape[0]
+                * self.conv_b.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+        # import pdb; pdb.set_trace()
+        out_a, expected_inC_a, expected_flop_a = self.conv_a(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_b, expected_inC_b, expected_flop_b = self.conv_b(
+            (out_a, expected_inC_a, probability[1], indexes[1], probs[1])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[1], indexes[1], probs[1])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_b)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_b,
+            sum([expected_flop_a, expected_flop_b, expected_flop_c]),
+        )
+
+    def basic_forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return nn.functional.relu(out, inplace=True)
+
+
+class ResNetBottleneck(nn.Module):
+    expansion = 4
+    num_conv = 3
+
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBottleneck, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv_1x1 = ConvBNReLU(
+            inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
+        )
+        self.conv_3x3 = ConvBNReLU(
+            planes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        self.conv_1x4 = ConvBNReLU(
+            planes,
+            planes * self.expansion,
+            1,
+            1,
+            0,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=False,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=True,
+                has_relu=False,
+            )
+        elif inplanes != planes * self.expansion:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes * self.expansion,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes * self.expansion
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return (
+            self.conv_1x1.get_range()
+            + self.conv_3x3.get_range()
+            + self.conv_1x4.get_range()
+        )
+
+    def get_flops(self, channels):
+        assert len(channels) == 4, "invalid channels : {:}".format(channels)
+        flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+        flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+        flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_D = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_D = (
+                channels[0]
+                * channels[-1]
+                * self.conv_1x4.OutShape[0]
+                * self.conv_1x4.OutShape[1]
+            )
+        return flop_A + flop_B + flop_C + flop_D
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def basic_forward(self, inputs):
+        bottleneck = self.conv_1x1(inputs)
+        bottleneck = self.conv_3x3(bottleneck)
+        bottleneck = self.conv_1x4(bottleneck)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, bottleneck)
+        return nn.functional.relu(out, inplace=True)
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+        out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3(
+            (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1])
+        )
+        out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4(
+            (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[2], indexes[2], probs[2])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out_1x4)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_inC_1x4,
+            sum(
+                [
+                    expected_flop_1x1,
+                    expected_flop_3x3,
+                    expected_flop_1x4,
+                    expected_flop_c,
+                ]
+            ),
+        )
+
+
+class SearchShapeImagenetResNet(nn.Module):
+    def __init__(self, block_name, layers, deep_stem, num_classes):
+        super(SearchShapeImagenetResNet, self).__init__()
+
+        # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+        if block_name == "BasicBlock":
+            block = ResNetBasicblock
+        elif block_name == "Bottleneck":
+            block = ResNetBottleneck
+        else:
+            raise ValueError("invalid block : {:}".format(block_name))
+
+        self.message = (
+            "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format(
+                sum(layers) * block.num_conv, layers
+            )
+        )
+        self.num_classes = num_classes
+        if not deep_stem:
+            self.layers = nn.ModuleList(
+                [
+                    ConvBNReLU(
+                        3,
+                        64,
+                        7,
+                        2,
+                        3,
+                        False,
+                        has_avg=False,
+                        has_bn=True,
+                        has_relu=True,
+                        last_max_pool=True,
+                    )
+                ]
+            )
+            self.channels = [64]
+        else:
+            self.layers = nn.ModuleList(
+                [
+                    ConvBNReLU(
+                        3, 32, 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True
+                    ),
+                    ConvBNReLU(
+                        32,
+                        64,
+                        3,
+                        1,
+                        1,
+                        False,
+                        has_avg=False,
+                        has_bn=True,
+                        has_relu=True,
+                        last_max_pool=True,
+                    ),
+                ]
+            )
+            self.channels = [32, 64]
+
+        meta_depth_info = get_depth_choices(layers)
+        self.InShape = None
+        self.depth_info = OrderedDict()
+        self.depth_at_i = OrderedDict()
+        for stage, layer_blocks in enumerate(layers):
+            cur_block_choices = meta_depth_info[stage]
+            assert (
+                cur_block_choices[-1] == layer_blocks
+            ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks)
+            block_choices, xstart = [], len(self.layers)
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 64 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = block(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+                # added for depth
+                layer_index = len(self.layers) - 1
+                if iL + 1 in cur_block_choices:
+                    block_choices.append(layer_index)
+                if iL + 1 == layer_blocks:
+                    self.depth_info[layer_index] = {
+                        "choices": block_choices,
+                        "stage": stage,
+                        "xstart": xstart,
+                    }
+        self.depth_info_list = []
+        for xend, info in self.depth_info.items():
+            self.depth_info_list.append((xend, info))
+            xstart, xstage = info["xstart"], info["stage"]
+            for ilayer in range(xstart, xend + 1):
+                idx = bisect_right(info["choices"], ilayer - 1)
+                self.depth_at_i[ilayer] = (xstage, idx)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        self.InShape = None
+        self.tau = -1
+        self.search_mode = "basic"
+        # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+        # parameters for width
+        self.Ranges = []
+        self.layer2indexRange = []
+        for i, layer in enumerate(self.layers):
+            start_index = len(self.Ranges)
+            self.Ranges += layer.get_range()
+            self.layer2indexRange.append((start_index, len(self.Ranges)))
+
+        self.register_parameter(
+            "width_attentions",
+            nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))),
+        )
+        self.register_parameter(
+            "depth_attentions",
+            nn.Parameter(torch.Tensor(len(layers), meta_depth_info["num"])),
+        )
+        nn.init.normal_(self.width_attentions, 0, 0.01)
+        nn.init.normal_(self.depth_attentions, 0, 0.01)
+        self.apply(initialize_resnet)
+
+    def arch_parameters(self, LR=None):
+        if LR is None:
+            return [self.width_attentions, self.depth_attentions]
+        else:
+            return [
+                {"params": self.width_attentions, "lr": LR},
+                {"params": self.depth_attentions, "lr": LR},
+            ]
+
+    def base_parameters(self):
+        return (
+            list(self.layers.parameters())
+            + list(self.avgpool.parameters())
+            + list(self.classifier.parameters())
+        )
+
+    def get_flop(self, mode, config_dict, extra_info):
+        if config_dict is not None:
+            config_dict = config_dict.copy()
+        # select channels
+        channels = [3]
+        for i, weight in enumerate(self.width_attentions):
+            if mode == "genotype":
+                with torch.no_grad():
+                    probe = nn.functional.softmax(weight, dim=0)
+                    C = self.Ranges[i][torch.argmax(probe).item()]
+            else:
+                raise ValueError("invalid mode : {:}".format(mode))
+            channels.append(C)
+        # select depth
+        if mode == "genotype":
+            with torch.no_grad():
+                depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+                choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+        else:
+            raise ValueError("invalid mode : {:}".format(mode))
+        selected_layers = []
+        for choice, xvalue in zip(choices, self.depth_info_list):
+            xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1
+            selected_layers.append(xtemp)
+        flop = 0
+        for i, layer in enumerate(self.layers):
+            s, e = self.layer2indexRange[i]
+            xchl = tuple(channels[s : e + 1])
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                if xatti <= choices[xstagei]:  # leave this depth
+                    flop += layer.get_flops(xchl)
+                else:
+                    flop += 0  # do not use this layer
+            else:
+                flop += layer.get_flops(xchl)
+        # the last fc layer
+        flop += channels[-1] * self.classifier.out_features
+        if config_dict is None:
+            return flop / 1e6
+        else:
+            config_dict["xchannels"] = channels
+            config_dict["xblocks"] = selected_layers
+            config_dict["super_type"] = "infer-shape"
+            config_dict["estimated_FLOP"] = flop / 1e6
+            return flop / 1e6, config_dict
+
+    def get_arch_info(self):
+        string = (
+            "for depth and width, there are {:} + {:} attention probabilities.".format(
+                len(self.depth_attentions), len(self.width_attentions)
+            )
+        )
+        string += "\n{:}".format(self.depth_info)
+        discrepancy = []
+        with torch.no_grad():
+            for i, att in enumerate(self.depth_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.depth_attentions), " ".join(prob)
+                )
+                logt = ["{:.4f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:17s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || discrepancy={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+            string += "\n-----------------------------------------------"
+            for i, att in enumerate(self.width_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.width_attentions), " ".join(prob)
+                )
+                logt = ["{:.3f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:52s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || dis={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+        return string, discrepancy
+
+    def set_tau(self, tau_max, tau_min, epoch_ratio):
+        assert (
+            epoch_ratio >= 0 and epoch_ratio <= 1
+        ), "invalid epoch-ratio : {:}".format(epoch_ratio)
+        tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+        self.tau = tau
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, inputs):
+        flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1)
+        flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        flop_depth_probs = torch.flip(
+            torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1]
+        )
+        selected_widths, selected_width_probs = select2withP(
+            self.width_attentions, self.tau
+        )
+        selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+        with torch.no_grad():
+            selected_widths = selected_widths.cpu()
+
+        x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+        feature_maps = []
+        for i, layer in enumerate(self.layers):
+            selected_w_index = selected_widths[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            selected_w_probs = selected_width_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            layer_prob = flop_width_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            x, expected_inC, expected_flop = layer(
+                (x, expected_inC, layer_prob, selected_w_index, selected_w_probs)
+            )
+            feature_maps.append(x)
+            last_channel_idx += layer.num_conv
+            if i in self.depth_info:  # aggregate the information
+                choices = self.depth_info[i]["choices"]
+                xstagei = self.depth_info[i]["stage"]
+                # print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist()))
+                # for A, W in zip(choices, selected_depth_probs[xstagei]):
+                #  print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W))
+                possible_tensors = []
+                max_C = max(feature_maps[A].size(1) for A in choices)
+                for tempi, A in enumerate(choices):
+                    xtensor = ChannelWiseInter(feature_maps[A], max_C)
+                    possible_tensors.append(xtensor)
+                weighted_sum = sum(
+                    xtensor * W
+                    for xtensor, W in zip(
+                        possible_tensors, selected_depth_probs[xstagei]
+                    )
+                )
+                x = weighted_sum
+
+            if i in self.depth_at_i:
+                xstagei, xatti = self.depth_at_i[i]
+                x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop
+            else:
+                x_expected_flop = expected_flop
+            flops.append(x_expected_flop)
+        flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6))
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = linear_forward(features, self.classifier)
+        return logits, torch.stack([sum(flops)])
+
+    def basic_forward(self, inputs):
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_searchs/SearchSimResNet_width.py

@@ -0,0 +1,466 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils import additive_func
+from .SoftSelect import select2withP, ChannelWiseInter
+from .SoftSelect import linear_forward
+from .SoftSelect import get_width_choices as get_choices
+
+
+def conv_forward(inputs, conv, choices):
+    iC = conv.in_channels
+    fill_size = list(inputs.size())
+    fill_size[1] = iC - fill_size[1]
+    filled = torch.zeros(fill_size, device=inputs.device)
+    xinputs = torch.cat((inputs, filled), dim=1)
+    outputs = conv(xinputs)
+    selecteds = [outputs[:, :oC] for oC in choices]
+    return selecteds
+
+
+class ConvBNReLU(nn.Module):
+    num_conv = 1
+
+    def __init__(
+        self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
+    ):
+        super(ConvBNReLU, self).__init__()
+        self.InShape = None
+        self.OutShape = None
+        self.choices = get_choices(nOut)
+        self.register_buffer("choices_tensor", torch.Tensor(self.choices))
+
+        if has_avg:
+            self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+        else:
+            self.avg = None
+        self.conv = nn.Conv2d(
+            nIn,
+            nOut,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            dilation=1,
+            groups=1,
+            bias=bias,
+        )
+        # if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+        # else       : self.bn  = None
+        self.has_bn = has_bn
+        self.BNs = nn.ModuleList()
+        for i, _out in enumerate(self.choices):
+            self.BNs.append(nn.BatchNorm2d(_out))
+        if has_relu:
+            self.relu = nn.ReLU(inplace=True)
+        else:
+            self.relu = None
+        self.in_dim = nIn
+        self.out_dim = nOut
+        self.search_mode = "basic"
+
+    def get_flops(self, channels, check_range=True, divide=1):
+        iC, oC = channels
+        if check_range:
+            assert (
+                iC <= self.conv.in_channels and oC <= self.conv.out_channels
+            ), "{:} vs {:}  |  {:} vs {:}".format(
+                iC, self.conv.in_channels, oC, self.conv.out_channels
+            )
+        assert (
+            isinstance(self.InShape, tuple) and len(self.InShape) == 2
+        ), "invalid in-shape : {:}".format(self.InShape)
+        assert (
+            isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
+        ), "invalid out-shape : {:}".format(self.OutShape)
+        # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+        conv_per_position_flops = (
+            self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
+        )
+        all_positions = self.OutShape[0] * self.OutShape[1]
+        flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+        if self.conv.bias is not None:
+            flops += all_positions / divide
+        return flops
+
+    def get_range(self):
+        return [self.choices]
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, index, prob = tuple_inputs
+        index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+        probability = torch.squeeze(probability)
+        assert len(index) == 2, "invalid length : {:}".format(index)
+        # compute expected flop
+        # coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+        expected_outC = (self.choices_tensor * probability).sum()
+        expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        # convolutional layer
+        out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+        out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+        # merge
+        out_channel = max([x.size(1) for x in out_bns])
+        outA = ChannelWiseInter(out_bns[0], out_channel)
+        outB = ChannelWiseInter(out_bns[1], out_channel)
+        out = outA * prob[0] + outB * prob[1]
+        # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        return out, expected_outC, expected_flop
+
+    def basic_forward(self, inputs):
+        if self.avg:
+            out = self.avg(inputs)
+        else:
+            out = inputs
+        conv = self.conv(out)
+        if self.has_bn:
+            out = self.BNs[-1](conv)
+        else:
+            out = conv
+        if self.relu:
+            out = self.relu(out)
+        else:
+            out = out
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+            self.OutShape = (out.size(-2), out.size(-1))
+        return out
+
+
+class SimBlock(nn.Module):
+    expansion = 1
+    num_conv = 1
+
+    def __init__(self, inplanes, planes, stride):
+        super(SimBlock, self).__init__()
+        assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
+        self.conv = ConvBNReLU(
+            inplanes,
+            planes,
+            3,
+            stride,
+            1,
+            False,
+            has_avg=False,
+            has_bn=True,
+            has_relu=True,
+        )
+        if stride == 2:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=True,
+                has_bn=False,
+                has_relu=False,
+            )
+        elif inplanes != planes:
+            self.downsample = ConvBNReLU(
+                inplanes,
+                planes,
+                1,
+                1,
+                0,
+                False,
+                has_avg=False,
+                has_bn=True,
+                has_relu=False,
+            )
+        else:
+            self.downsample = None
+        self.out_dim = planes
+        self.search_mode = "basic"
+
+    def get_range(self):
+        return self.conv.get_range()
+
+    def get_flops(self, channels):
+        assert len(channels) == 2, "invalid channels : {:}".format(channels)
+        flop_A = self.conv.get_flops([channels[0], channels[1]])
+        if hasattr(self.downsample, "get_flops"):
+            flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+        else:
+            flop_C = 0
+        if (
+            channels[0] != channels[-1] and self.downsample is None
+        ):  # this short-cut will be added during the infer-train
+            flop_C = (
+                channels[0]
+                * channels[-1]
+                * self.conv.OutShape[0]
+                * self.conv.OutShape[1]
+            )
+        return flop_A + flop_C
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, tuple_inputs):
+        assert (
+            isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
+        ), "invalid type input : {:}".format(type(tuple_inputs))
+        inputs, expected_inC, probability, indexes, probs = tuple_inputs
+        assert (
+            indexes.size(0) == 1 and probs.size(0) == 1 and probability.size(0) == 1
+        ), "invalid size : {:}, {:}, {:}".format(
+            indexes.size(), probs.size(), probability.size()
+        )
+        out, expected_next_inC, expected_flop = self.conv(
+            (inputs, expected_inC, probability[0], indexes[0], probs[0])
+        )
+        if self.downsample is not None:
+            residual, _, expected_flop_c = self.downsample(
+                (inputs, expected_inC, probability[-1], indexes[-1], probs[-1])
+            )
+        else:
+            residual, expected_flop_c = inputs, 0
+        out = additive_func(residual, out)
+        return (
+            nn.functional.relu(out, inplace=True),
+            expected_next_inC,
+            sum([expected_flop, expected_flop_c]),
+        )
+
+    def basic_forward(self, inputs):
+        basicblock = self.conv(inputs)
+        if self.downsample is not None:
+            residual = self.downsample(inputs)
+        else:
+            residual = inputs
+        out = additive_func(residual, basicblock)
+        return nn.functional.relu(out, inplace=True)
+
+
+class SearchWidthSimResNet(nn.Module):
+    def __init__(self, depth, num_classes):
+        super(SearchWidthSimResNet, self).__init__()
+
+        assert (
+            depth - 2
+        ) % 3 == 0, "depth should be one of 5, 8, 11, 14, ... instead of {:}".format(
+            depth
+        )
+        layer_blocks = (depth - 2) // 3
+        self.message = (
+            "SearchWidthSimResNet : Depth : {:} , Layers for each block : {:}".format(
+                depth, layer_blocks
+            )
+        )
+        self.num_classes = num_classes
+        self.channels = [16]
+        self.layers = nn.ModuleList(
+            [
+                ConvBNReLU(
+                    3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
+                )
+            ]
+        )
+        self.InShape = None
+        for stage in range(3):
+            for iL in range(layer_blocks):
+                iC = self.channels[-1]
+                planes = 16 * (2 ** stage)
+                stride = 2 if stage > 0 and iL == 0 else 1
+                module = SimBlock(iC, planes, stride)
+                self.channels.append(module.out_dim)
+                self.layers.append(module)
+                self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
+                    stage,
+                    iL,
+                    layer_blocks,
+                    len(self.layers) - 1,
+                    iC,
+                    module.out_dim,
+                    stride,
+                )
+
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(module.out_dim, num_classes)
+        self.InShape = None
+        self.tau = -1
+        self.search_mode = "basic"
+        # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+        # parameters for width
+        self.Ranges = []
+        self.layer2indexRange = []
+        for i, layer in enumerate(self.layers):
+            start_index = len(self.Ranges)
+            self.Ranges += layer.get_range()
+            self.layer2indexRange.append((start_index, len(self.Ranges)))
+        assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format(
+            len(self.Ranges) + 1, depth
+        )
+
+        self.register_parameter(
+            "width_attentions",
+            nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))),
+        )
+        nn.init.normal_(self.width_attentions, 0, 0.01)
+        self.apply(initialize_resnet)
+
+    def arch_parameters(self):
+        return [self.width_attentions]
+
+    def base_parameters(self):
+        return (
+            list(self.layers.parameters())
+            + list(self.avgpool.parameters())
+            + list(self.classifier.parameters())
+        )
+
+    def get_flop(self, mode, config_dict, extra_info):
+        if config_dict is not None:
+            config_dict = config_dict.copy()
+        # weights = [F.softmax(x, dim=0) for x in self.width_attentions]
+        channels = [3]
+        for i, weight in enumerate(self.width_attentions):
+            if mode == "genotype":
+                with torch.no_grad():
+                    probe = nn.functional.softmax(weight, dim=0)
+                    C = self.Ranges[i][torch.argmax(probe).item()]
+            elif mode == "max":
+                C = self.Ranges[i][-1]
+            elif mode == "fix":
+                C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+            elif mode == "random":
+                assert isinstance(extra_info, float), "invalid extra_info : {:}".format(
+                    extra_info
+                )
+                with torch.no_grad():
+                    prob = nn.functional.softmax(weight, dim=0)
+                    approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
+                    for j in range(prob.size(0)):
+                        prob[j] = 1 / (
+                            abs(j - (approximate_C - self.Ranges[i][j])) + 0.2
+                        )
+                    C = self.Ranges[i][torch.multinomial(prob, 1, False).item()]
+            else:
+                raise ValueError("invalid mode : {:}".format(mode))
+            channels.append(C)
+        flop = 0
+        for i, layer in enumerate(self.layers):
+            s, e = self.layer2indexRange[i]
+            xchl = tuple(channels[s : e + 1])
+            flop += layer.get_flops(xchl)
+        # the last fc layer
+        flop += channels[-1] * self.classifier.out_features
+        if config_dict is None:
+            return flop / 1e6
+        else:
+            config_dict["xchannels"] = channels
+            config_dict["super_type"] = "infer-width"
+            config_dict["estimated_FLOP"] = flop / 1e6
+            return flop / 1e6, config_dict
+
+    def get_arch_info(self):
+        string = "for width, there are {:} attention probabilities.".format(
+            len(self.width_attentions)
+        )
+        discrepancy = []
+        with torch.no_grad():
+            for i, att in enumerate(self.width_attentions):
+                prob = nn.functional.softmax(att, dim=0)
+                prob = prob.cpu()
+                selc = prob.argmax().item()
+                prob = prob.tolist()
+                prob = ["{:.3f}".format(x) for x in prob]
+                xstring = "{:03d}/{:03d}-th : {:}".format(
+                    i, len(self.width_attentions), " ".join(prob)
+                )
+                logt = ["{:.3f}".format(x) for x in att.cpu().tolist()]
+                xstring += "  ||  {:52s}".format(" ".join(logt))
+                prob = sorted([float(x) for x in prob])
+                disc = prob[-1] - prob[-2]
+                xstring += "  || dis={:.2f} || select={:}/{:}".format(
+                    disc, selc, len(prob)
+                )
+                discrepancy.append(disc)
+                string += "\n{:}".format(xstring)
+        return string, discrepancy
+
+    def set_tau(self, tau_max, tau_min, epoch_ratio):
+        assert (
+            epoch_ratio >= 0 and epoch_ratio <= 1
+        ), "invalid epoch-ratio : {:}".format(epoch_ratio)
+        tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+        self.tau = tau
+
+    def get_message(self):
+        return self.message
+
+    def forward(self, inputs):
+        if self.search_mode == "basic":
+            return self.basic_forward(inputs)
+        elif self.search_mode == "search":
+            return self.search_forward(inputs)
+        else:
+            raise ValueError("invalid search_mode = {:}".format(self.search_mode))
+
+    def search_forward(self, inputs):
+        flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
+        selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
+        with torch.no_grad():
+            selected_widths = selected_widths.cpu()
+
+        x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+        for i, layer in enumerate(self.layers):
+            selected_w_index = selected_widths[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            selected_w_probs = selected_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            layer_prob = flop_probs[
+                last_channel_idx : last_channel_idx + layer.num_conv
+            ]
+            x, expected_inC, expected_flop = layer(
+                (x, expected_inC, layer_prob, selected_w_index, selected_w_probs)
+            )
+            last_channel_idx += layer.num_conv
+            flops.append(expected_flop)
+        flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6))
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = linear_forward(features, self.classifier)
+        return logits, torch.stack([sum(flops)])
+
+    def basic_forward(self, inputs):
+        if self.InShape is None:
+            self.InShape = (inputs.size(-2), inputs.size(-1))
+        x = inputs
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+        features = self.avgpool(x)
+        features = features.view(features.size(0), -1)
+        logits = self.classifier(features)
+        return features, logits

correlation/models/shape_searchs/SoftSelect.py

@@ -0,0 +1,128 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+
+
+def select2withP(logits, tau, just_prob=False, num=2, eps=1e-7):
+    if tau <= 0:
+        new_logits = logits
+        probs = nn.functional.softmax(new_logits, dim=1)
+    else:
+        while True:  # a trick to avoid the gumbels bug
+            gumbels = -torch.empty_like(logits).exponential_().log()
+            new_logits = (logits.log_softmax(dim=1) + gumbels) / tau
+            probs = nn.functional.softmax(new_logits, dim=1)
+            if (
+                (not torch.isinf(gumbels).any())
+                and (not torch.isinf(probs).any())
+                and (not torch.isnan(probs).any())
+            ):
+                break
+
+    if just_prob:
+        return probs
+
+    # with torch.no_grad(): # add eps for unexpected torch error
+    #  probs = nn.functional.softmax(new_logits, dim=1)
+    #  selected_index = torch.multinomial(probs + eps, 2, False)
+    with torch.no_grad():  # add eps for unexpected torch error
+        probs = probs.cpu()
+        selected_index = torch.multinomial(probs + eps, num, False).to(logits.device)
+    selected_logit = torch.gather(new_logits, 1, selected_index)
+    selcted_probs = nn.functional.softmax(selected_logit, dim=1)
+    return selected_index, selcted_probs
+
+
+def ChannelWiseInter(inputs, oC, mode="v2"):
+    if mode == "v1":
+        return ChannelWiseInterV1(inputs, oC)
+    elif mode == "v2":
+        return ChannelWiseInterV2(inputs, oC)
+    else:
+        raise ValueError("invalid mode : {:}".format(mode))
+
+
+def ChannelWiseInterV1(inputs, oC):
+    assert inputs.dim() == 4, "invalid dimension : {:}".format(inputs.size())
+
+    def start_index(a, b, c):
+        return int(math.floor(float(a * c) / b))
+
+    def end_index(a, b, c):
+        return int(math.ceil(float((a + 1) * c) / b))
+
+    batch, iC, H, W = inputs.size()
+    outputs = torch.zeros((batch, oC, H, W), dtype=inputs.dtype, device=inputs.device)
+    if iC == oC:
+        return inputs
+    for ot in range(oC):
+        istartT, iendT = start_index(ot, oC, iC), end_index(ot, oC, iC)
+        values = inputs[:, istartT:iendT].mean(dim=1)
+        outputs[:, ot, :, :] = values
+    return outputs
+
+
+def ChannelWiseInterV2(inputs, oC):
+    assert inputs.dim() == 4, "invalid dimension : {:}".format(inputs.size())
+    batch, C, H, W = inputs.size()
+    if C == oC:
+        return inputs
+    else:
+        return nn.functional.adaptive_avg_pool3d(inputs, (oC, H, W))
+    # inputs_5D = inputs.view(batch, 1, C, H, W)
+    # otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'area', None)
+    # otputs    = otputs_5D.view(batch, oC, H, W)
+    # otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'trilinear', False)
+    # return otputs
+
+
+def linear_forward(inputs, linear):
+    if linear is None:
+        return inputs
+    iC = inputs.size(1)
+    weight = linear.weight[:, :iC]
+    if linear.bias is None:
+        bias = None
+    else:
+        bias = linear.bias
+    return nn.functional.linear(inputs, weight, bias)
+
+
+def get_width_choices(nOut):
+    xsrange = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+    if nOut is None:
+        return len(xsrange)
+    else:
+        Xs = [int(nOut * i) for i in xsrange]
+        # xs = [ int(nOut * i // 10) for i in range(2, 11)]
+        # Xs = [x for i, x in enumerate(xs) if i+1 == len(xs) or xs[i+1] > x+1]
+        Xs = sorted(list(set(Xs)))
+        return tuple(Xs)
+
+
+def get_depth_choices(nDepth):
+    if nDepth is None:
+        return 3
+    else:
+        assert nDepth >= 3, "nDepth should be greater than 2 vs {:}".format(nDepth)
+        if nDepth == 1:
+            return (1, 1, 1)
+        elif nDepth == 2:
+            return (1, 1, 2)
+        elif nDepth >= 3:
+            return (nDepth // 3, nDepth * 2 // 3, nDepth)
+        else:
+            raise ValueError("invalid Depth : {:}".format(nDepth))
+
+
+def drop_path(x, drop_prob):
+    if drop_prob > 0.0:
+        keep_prob = 1.0 - drop_prob
+        mask = x.new_zeros(x.size(0), 1, 1, 1)
+        mask = mask.bernoulli_(keep_prob)
+        x = x * (mask / keep_prob)
+        # x.div_(keep_prob)
+        # x.mul_(mask)
+    return x

correlation/models/shape_searchs/__init__.py

@@ -0,0 +1,9 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from .SearchCifarResNet_width import SearchWidthCifarResNet
+from .SearchCifarResNet_depth import SearchDepthCifarResNet
+from .SearchCifarResNet import SearchShapeCifarResNet
+from .SearchSimResNet_width import SearchWidthSimResNet
+from .SearchImagenetResNet import SearchShapeImagenetResNet
+from .generic_size_tiny_cell_model import GenericNAS301Model

correlation/models/shape_searchs/generic_size_tiny_cell_model.py

@@ -0,0 +1,209 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+# Here, we utilized three techniques to search for the number of channels:
+# - channel-wise interpolation from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
+# - masking + Gumbel-Softmax (mask_gumbel) from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
+# - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
+from typing import List, Text, Any
+import random, torch
+import torch.nn as nn
+
+from ..cell_operations import ResNetBasicblock
+from ..cell_infers.cells import InferCell
+from .SoftSelect import select2withP, ChannelWiseInter
+
+
+class GenericNAS301Model(nn.Module):
+    def __init__(
+        self,
+        candidate_Cs: List[int],
+        max_num_Cs: int,
+        genotype: Any,
+        num_classes: int,
+        affine: bool,
+        track_running_stats: bool,
+    ):
+        super(GenericNAS301Model, self).__init__()
+        self._max_num_Cs = max_num_Cs
+        self._candidate_Cs = candidate_Cs
+        if max_num_Cs % 3 != 2:
+            raise ValueError("invalid number of layers : {:}".format(max_num_Cs))
+        self._num_stage = N = max_num_Cs // 3
+        self._max_C = max(candidate_Cs)
+
+        stem = nn.Sequential(
+            nn.Conv2d(3, self._max_C, kernel_size=3, padding=1, bias=not affine),
+            nn.BatchNorm2d(
+                self._max_C, affine=affine, track_running_stats=track_running_stats
+            ),
+        )
+
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        c_prev = self._max_C
+        self._cells = nn.ModuleList()
+        self._cells.append(stem)
+        for index, reduction in enumerate(layer_reductions):
+            if reduction:
+                cell = ResNetBasicblock(c_prev, self._max_C, 2, True)
+            else:
+                cell = InferCell(
+                    genotype, c_prev, self._max_C, 1, affine, track_running_stats
+                )
+            self._cells.append(cell)
+            c_prev = cell.out_dim
+        self._num_layer = len(self._cells)
+
+        self.lastact = nn.Sequential(
+            nn.BatchNorm2d(
+                c_prev, affine=affine, track_running_stats=track_running_stats
+            ),
+            nn.ReLU(inplace=True),
+        )
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(c_prev, num_classes)
+        # algorithm related
+        self.register_buffer("_tau", torch.zeros(1))
+        self._algo = None
+        self._warmup_ratio = None
+
+    def set_algo(self, algo: Text):
+        # used for searching
+        assert self._algo is None, "This functioin can only be called once."
+        assert algo in ["mask_gumbel", "mask_rl", "tas"], "invalid algo : {:}".format(
+            algo
+        )
+        self._algo = algo
+        self._arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(self._max_num_Cs, len(self._candidate_Cs))
+        )
+        # if algo == 'mask_gumbel' or algo == 'mask_rl':
+        self.register_buffer(
+            "_masks", torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs))
+        )
+        for i in range(len(self._candidate_Cs)):
+            self._masks.data[i, : self._candidate_Cs[i]] = 1
+
+    @property
+    def tau(self):
+        return self._tau
+
+    def set_tau(self, tau):
+        self._tau.data[:] = tau
+
+    @property
+    def warmup_ratio(self):
+        return self._warmup_ratio
+
+    def set_warmup_ratio(self, ratio: float):
+        self._warmup_ratio = ratio
+
+    @property
+    def weights(self):
+        xlist = list(self._cells.parameters())
+        xlist += list(self.lastact.parameters())
+        xlist += list(self.global_pooling.parameters())
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    @property
+    def alphas(self):
+        return [self._arch_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            return "arch-parameters :\n{:}".format(
+                nn.functional.softmax(self._arch_parameters, dim=-1).cpu()
+            )
+
+    @property
+    def random(self):
+        cs = []
+        for i in range(self._max_num_Cs):
+            index = random.randint(0, len(self._candidate_Cs) - 1)
+            cs.append(str(self._candidate_Cs[index]))
+        return ":".join(cs)
+
+    @property
+    def genotype(self):
+        cs = []
+        for i in range(self._max_num_Cs):
+            with torch.no_grad():
+                index = self._arch_parameters[i].argmax().item()
+                cs.append(str(self._candidate_Cs[index]))
+        return ":".join(cs)
+
+    def get_message(self) -> Text:
+        string = self.extra_repr()
+        for i, cell in enumerate(self._cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self._cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(candidates={_candidate_Cs}, num={_max_num_Cs}, N={_num_stage}, L={_num_layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def forward(self, inputs):
+        feature = inputs
+
+        log_probs = []
+        for i, cell in enumerate(self._cells):
+            feature = cell(feature)
+            # apply different searching algorithms
+            idx = max(0, i - 1)
+            if self._warmup_ratio is not None:
+                if random.random() < self._warmup_ratio:
+                    mask = self._masks[-1]
+                else:
+                    mask = self._masks[random.randint(0, len(self._masks) - 1)]
+                feature = feature * mask.view(1, -1, 1, 1)
+            elif self._algo == "mask_gumbel":
+                weights = nn.functional.gumbel_softmax(
+                    self._arch_parameters[idx : idx + 1], tau=self.tau, dim=-1
+                )
+                mask = torch.matmul(weights, self._masks).view(1, -1, 1, 1)
+                feature = feature * mask
+            elif self._algo == "tas":
+                selected_cs, selected_probs = select2withP(
+                    self._arch_parameters[idx : idx + 1], self.tau, num=2
+                )
+                with torch.no_grad():
+                    i1, i2 = selected_cs.cpu().view(-1).tolist()
+                c1, c2 = self._candidate_Cs[i1], self._candidate_Cs[i2]
+                out_channel = max(c1, c2)
+                out1 = ChannelWiseInter(feature[:, :c1], out_channel)
+                out2 = ChannelWiseInter(feature[:, :c2], out_channel)
+                out = out1 * selected_probs[0, 0] + out2 * selected_probs[0, 1]
+                if feature.shape[1] == out.shape[1]:
+                    feature = out
+                else:
+                    miss = torch.zeros(
+                        feature.shape[0],
+                        feature.shape[1] - out.shape[1],
+                        feature.shape[2],
+                        feature.shape[3],
+                        device=feature.device,
+                    )
+                    feature = torch.cat((out, miss), dim=1)
+            elif self._algo == "mask_rl":
+                prob = nn.functional.softmax(
+                    self._arch_parameters[idx : idx + 1], dim=-1
+                )
+                dist = torch.distributions.Categorical(prob)
+                action = dist.sample()
+                log_probs.append(dist.log_prob(action))
+                mask = self._masks[action.item()].view(1, -1, 1, 1)
+                feature = feature * mask
+            else:
+                raise ValueError("invalid algorithm : {:}".format(self._algo))
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits, log_probs