update

sota/cnn/init_projection.py

@@ -96,20 +96,6 @@ def project_op(model, input, target, args, cell_type, proj_queue=None, selected_
 
             model.candidate_flags[cell_type][selected_eid] = False
             # print(model.get_projected_weights())
-            if proj_crit == 'comb':
-                synflow = predictive.find_measures(model,
-                                                   proj_queue,
-                                                   ('random', 1, n_classes),
-                                                   torch.device("cuda"),
-                                                   measure_names=['synflow'])
-                var = predictive.find_measures(model,
-                                               proj_queue,
-                                               ('random', 1, n_classes),
-                                               torch.device("cuda"),
-                                               measure_names=['var'])
-                # print(synflow, var)
-                comb = np.log(synflow['synflow'] + 1) / (var['var'] + 0.1)
-                measures = {'comb': comb}
             else:
                 measures = predictive.find_measures(model,
                                                     proj_queue,

zero-cost-nas/foresight/pruners/measures/__init__.py

@@ -55,9 +55,6 @@ def load_all():
     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
 

zero-cost-nas/foresight/pruners/measures/cor.py

@@ -1,53 +0,0 @@
-# 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

zero-cost-nas/foresight/pruners/measures/norm.py

@@ -1,55 +0,0 @@
-# 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

zero-cost-nas/foresight/pruners/measures/param_count.py

@@ -1,16 +0,0 @@
-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

zero-cost-nas/foresight/pruners/measures/var.py

@@ -1,55 +0,0 @@
-# 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

zero-cost-nas/foresight/pruners/predictive.py

@@ -108,7 +108,7 @@ def find_measures(net_orig,                  # neural network
 
     measures = {}
     for k,v in measures_arr.items():
-        if k in ['jacob_cov', 'var', 'cor', 'norm', 'meco', 'zico']:
+        if k in ['jacob_cov', 'meco', 'zico']:
             measures[k] = v
         else:
             measures[k] = sum_arr(v)

zerocostnas/post_validate.py

@@ -223,20 +223,6 @@ def main():
                 else:
                     #score =  score_loop(network, None, train_queue, args.gpu, None, args.proj_crit)
                     network.requires_feature = False
-
-                    if args.proj_crit == 'comb':
-                        synflow = predictive.find_measures(network,
-                                                           train_queue,
-                                                           ('random', 1, n_classes),
-                                                           torch.device("cuda"),
-                                                           measure_names=['synflow'])
-                        var = predictive.find_measures(network,
-                                                       train_queue,
-                                                       ('random', 1, n_classes),
-                                                       torch.device("cuda"),
-                                                       measure_names=['var'])
-                        comb = np.log(synflow['synflow'] + 1) / (var['var'] + 0.1)
-                        measures = {'comb': comb}
                     else:
                         measures = predictive.find_measures(network,
                                                             train_queue,