Updates
This commit is contained in:
D-X-Y
8
.github/workflows/basic_test.yml
vendored
8
.github/workflows/basic_test.yml
vendored
@@ -48,5 +48,13 @@ jobs:
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ls
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python --version
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python -m pytest ./tests/test_basic_space.py -s
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shell: bash
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- name: Test Synthetic Data
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run: |
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python -m pip install pytest numpy
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python -m pip install parameterized
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python -m pip install torch
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python --version
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python -m pytest ./tests/test_synthetic.py -s
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shell: bash
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Submodule .latent-data/NATS-Bench updated: 3a8794322f...33bfb2eb13
@@ -5,118 +5,133 @@ import os, sys, hashlib, torch
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import numpy as np
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from PIL import Image
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import torch.utils.data as data
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if sys.version_info[0] == 2:
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import cPickle as pickle
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import cPickle as pickle
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else:
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import pickle
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import pickle
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def calculate_md5(fpath, chunk_size=1024 * 1024):
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md5 = hashlib.md5()
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with open(fpath, 'rb') as f:
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for chunk in iter(lambda: f.read(chunk_size), b''):
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md5.update(chunk)
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return md5.hexdigest()
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md5 = hashlib.md5()
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with open(fpath, "rb") as f:
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for chunk in iter(lambda: f.read(chunk_size), b""):
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md5.update(chunk)
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return md5.hexdigest()
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def check_md5(fpath, md5, **kwargs):
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return md5 == calculate_md5(fpath, **kwargs)
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return md5 == calculate_md5(fpath, **kwargs)
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def check_integrity(fpath, md5=None):
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if not os.path.isfile(fpath): return False
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if md5 is None: return True
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else : return check_md5(fpath, md5)
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if not os.path.isfile(fpath):
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return False
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if md5 is None:
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return True
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else:
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return check_md5(fpath, md5)
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class ImageNet16(data.Dataset):
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# http://image-net.org/download-images
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# A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
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# https://arxiv.org/pdf/1707.08819.pdf
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# http://image-net.org/download-images
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# A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
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# https://arxiv.org/pdf/1707.08819.pdf
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train_list = [
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['train_data_batch_1', '27846dcaa50de8e21a7d1a35f30f0e91'],
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['train_data_batch_2', 'c7254a054e0e795c69120a5727050e3f'],
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['train_data_batch_3', '4333d3df2e5ffb114b05d2ffc19b1e87'],
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['train_data_batch_4', '1620cdf193304f4a92677b695d70d10f'],
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['train_data_batch_5', '348b3c2fdbb3940c4e9e834affd3b18d'],
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['train_data_batch_6', '6e765307c242a1b3d7d5ef9139b48945'],
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['train_data_batch_7', '564926d8cbf8fc4818ba23d2faac7564'],
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['train_data_batch_8', 'f4755871f718ccb653440b9dd0ebac66'],
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['train_data_batch_9', 'bb6dd660c38c58552125b1a92f86b5d4'],
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['train_data_batch_10','8f03f34ac4b42271a294f91bf480f29b'],
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train_list = [
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["train_data_batch_1", "27846dcaa50de8e21a7d1a35f30f0e91"],
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["train_data_batch_2", "c7254a054e0e795c69120a5727050e3f"],
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["train_data_batch_3", "4333d3df2e5ffb114b05d2ffc19b1e87"],
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["train_data_batch_4", "1620cdf193304f4a92677b695d70d10f"],
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["train_data_batch_5", "348b3c2fdbb3940c4e9e834affd3b18d"],
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["train_data_batch_6", "6e765307c242a1b3d7d5ef9139b48945"],
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["train_data_batch_7", "564926d8cbf8fc4818ba23d2faac7564"],
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["train_data_batch_8", "f4755871f718ccb653440b9dd0ebac66"],
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["train_data_batch_9", "bb6dd660c38c58552125b1a92f86b5d4"],
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["train_data_batch_10", "8f03f34ac4b42271a294f91bf480f29b"],
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]
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valid_list = [
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['val_data', '3410e3017fdaefba8d5073aaa65e4bd6'],
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valid_list = [
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["val_data", "3410e3017fdaefba8d5073aaa65e4bd6"],
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]
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def __init__(self, root, train, transform, use_num_of_class_only=None):
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self.root = root
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self.transform = transform
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self.train = train # training set or valid set
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if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.')
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def __init__(self, root, train, transform, use_num_of_class_only=None):
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self.root = root
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self.transform = transform
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self.train = train # training set or valid set
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if not self._check_integrity():
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raise RuntimeError("Dataset not found or corrupted.")
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if self.train: downloaded_list = self.train_list
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else : downloaded_list = self.valid_list
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self.data = []
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self.targets = []
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# now load the picked numpy arrays
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for i, (file_name, checksum) in enumerate(downloaded_list):
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file_path = os.path.join(self.root, file_name)
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#print ('Load {:}/{:02d}-th : {:}'.format(i, len(downloaded_list), file_path))
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with open(file_path, 'rb') as f:
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if sys.version_info[0] == 2:
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entry = pickle.load(f)
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if self.train:
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downloaded_list = self.train_list
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else:
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entry = pickle.load(f, encoding='latin1')
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self.data.append(entry['data'])
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self.targets.extend(entry['labels'])
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self.data = np.vstack(self.data).reshape(-1, 3, 16, 16)
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self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
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if use_num_of_class_only is not None:
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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)
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new_data, new_targets = [], []
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for I, L in zip(self.data, self.targets):
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if 1 <= L <= use_num_of_class_only:
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new_data.append( I )
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new_targets.append( L )
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self.data = new_data
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self.targets = new_targets
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# self.mean.append(entry['mean'])
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#self.mean = np.vstack(self.mean).reshape(-1, 3, 16, 16)
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#self.mean = np.mean(np.mean(np.mean(self.mean, axis=0), axis=1), axis=1)
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#print ('Mean : {:}'.format(self.mean))
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#temp = self.data - np.reshape(self.mean, (1, 1, 1, 3))
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#std_data = np.std(temp, axis=0)
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#std_data = np.mean(np.mean(std_data, axis=0), axis=0)
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#print ('Std : {:}'.format(std_data))
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downloaded_list = self.valid_list
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self.data = []
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self.targets = []
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def __repr__(self):
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return ('{name}({num} images, {classes} classes)'.format(name=self.__class__.__name__, num=len(self.data), classes=len(set(self.targets))))
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# now load the picked numpy arrays
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for i, (file_name, checksum) in enumerate(downloaded_list):
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file_path = os.path.join(self.root, file_name)
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# print ('Load {:}/{:02d}-th : {:}'.format(i, len(downloaded_list), file_path))
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with open(file_path, "rb") as f:
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if sys.version_info[0] == 2:
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entry = pickle.load(f)
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else:
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entry = pickle.load(f, encoding="latin1")
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self.data.append(entry["data"])
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self.targets.extend(entry["labels"])
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self.data = np.vstack(self.data).reshape(-1, 3, 16, 16)
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self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
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if use_num_of_class_only is not None:
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assert (
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isinstance(use_num_of_class_only, int)
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and use_num_of_class_only > 0
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and use_num_of_class_only < 1000
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), "invalid use_num_of_class_only : {:}".format(use_num_of_class_only)
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new_data, new_targets = [], []
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for I, L in zip(self.data, self.targets):
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if 1 <= L <= use_num_of_class_only:
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new_data.append(I)
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new_targets.append(L)
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self.data = new_data
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self.targets = new_targets
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# self.mean.append(entry['mean'])
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# self.mean = np.vstack(self.mean).reshape(-1, 3, 16, 16)
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# self.mean = np.mean(np.mean(np.mean(self.mean, axis=0), axis=1), axis=1)
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# print ('Mean : {:}'.format(self.mean))
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# temp = self.data - np.reshape(self.mean, (1, 1, 1, 3))
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# std_data = np.std(temp, axis=0)
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# std_data = np.mean(np.mean(std_data, axis=0), axis=0)
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# print ('Std : {:}'.format(std_data))
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def __repr__(self):
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return "{name}({num} images, {classes} classes)".format(
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name=self.__class__.__name__,
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num=len(self.data),
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classes=len(set(self.targets)),
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)
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def __getitem__(self, index):
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img, target = self.data[index], self.targets[index] - 1
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def __getitem__(self, index):
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img, target = self.data[index], self.targets[index] - 1
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img = Image.fromarray(img)
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img = Image.fromarray(img)
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if self.transform is not None:
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img = self.transform(img)
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if self.transform is not None:
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img = self.transform(img)
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return img, target
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return img, target
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def __len__(self):
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return len(self.data)
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def __len__(self):
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return len(self.data)
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def _check_integrity(self):
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root = self.root
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for fentry in self.train_list + self.valid_list:
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filename, md5 = fentry[0], fentry[1]
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fpath = os.path.join(root, filename)
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if not check_integrity(fpath, md5):
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return False
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return True
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def _check_integrity(self):
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root = self.root
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for fentry in (self.train_list + self.valid_list):
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filename, md5 = fentry[0], fentry[1]
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fpath = os.path.join(root, filename)
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if not check_integrity(fpath, md5):
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return False
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return True
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"""
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if __name__ == '__main__':
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@@ -20,172 +20,282 @@ import torch.utils.data as data
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class LandmarkDataset(data.Dataset):
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def __init__(
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self,
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transform,
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sigma,
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downsample,
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heatmap_type,
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shape,
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use_gray,
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mean_file,
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data_indicator,
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cache_images=None,
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):
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def __init__(self, transform, sigma, downsample, heatmap_type, shape, use_gray, mean_file, data_indicator, cache_images=None):
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self.transform = transform
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self.sigma = sigma
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self.downsample = downsample
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self.heatmap_type = heatmap_type
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self.dataset_name = data_indicator
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self.shape = shape # [H,W]
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self.use_gray = use_gray
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assert transform is not None, 'transform : {:}'.format(transform)
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self.mean_file = mean_file
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if mean_file is None:
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self.mean_data = None
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warnings.warn('LandmarkDataset initialized with mean_data = None')
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else:
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assert osp.isfile(mean_file), '{:} is not a file.'.format(mean_file)
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self.mean_data = torch.load(mean_file)
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self.reset()
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self.cutout = None
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self.cache_images = cache_images
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print ('The general dataset initialization done : {:}'.format(self))
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warnings.simplefilter( 'once' )
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def __repr__(self):
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return ('{name}(point-num={NUM_PTS}, shape={shape}, sigma={sigma}, heatmap_type={heatmap_type}, length={length}, cutout={cutout}, dataset={dataset_name}, mean={mean_file})'.format(name=self.__class__.__name__, **self.__dict__))
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def set_cutout(self, length):
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if length is not None and length >= 1:
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self.cutout = CutOut( int(length) )
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else: self.cutout = None
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def reset(self, num_pts=-1, boxid='default', only_pts=False):
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self.NUM_PTS = num_pts
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if only_pts: return
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self.length = 0
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self.datas = []
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self.labels = []
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self.NormDistances = []
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self.BOXID = boxid
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if self.mean_data is None:
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self.mean_face = None
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else:
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self.mean_face = torch.Tensor(self.mean_data[boxid].copy().T)
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assert (self.mean_face >= -1).all() and (self.mean_face <= 1).all(), 'mean-{:}-face : {:}'.format(boxid, self.mean_face)
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#assert self.dataset_name is not None, 'The dataset name is None'
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def __len__(self):
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assert len(self.datas) == self.length, 'The length is not correct : {}'.format(self.length)
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return self.length
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def append(self, data, label, distance):
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assert osp.isfile(data), 'The image path is not a file : {:}'.format(data)
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self.datas.append( data ) ; self.labels.append( label )
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self.NormDistances.append( distance )
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self.length = self.length + 1
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def load_list(self, file_lists, num_pts, boxindicator, normalizeL, reset):
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if reset: self.reset(num_pts, boxindicator)
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else : assert self.NUM_PTS == num_pts and self.BOXID == boxindicator, 'The number of point is inconsistance : {:} vs {:}'.format(self.NUM_PTS, num_pts)
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if isinstance(file_lists, str): file_lists = [file_lists]
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samples = []
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for idx, file_path in enumerate(file_lists):
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print (':::: load list {:}/{:} : {:}'.format(idx, len(file_lists), file_path))
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xdata = torch.load(file_path)
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if isinstance(xdata, list) : data = xdata # image or video dataset list
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elif isinstance(xdata, dict): data = xdata['datas'] # multi-view dataset list
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else: raise ValueError('Invalid Type Error : {:}'.format( type(xdata) ))
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samples = samples + data
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# samples is a dict, where the key is the image-path and the value is the annotation
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# each annotation is a dict, contains 'points' (3,num_pts), and various box
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print ('GeneralDataset-V2 : {:} samples'.format(len(samples)))
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#for index, annotation in enumerate(samples):
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for index in tqdm( range( len(samples) ) ):
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annotation = samples[index]
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image_path = annotation['current_frame']
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points, box = annotation['points'], annotation['box-{:}'.format(boxindicator)]
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label = PointMeta2V(self.NUM_PTS, points, box, image_path, self.dataset_name)
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if normalizeL is None: normDistance = None
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else : normDistance = annotation['normalizeL-{:}'.format(normalizeL)]
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self.append(image_path, label, normDistance)
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assert len(self.datas) == self.length, 'The length and the data is not right {} vs {}'.format(self.length, len(self.datas))
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assert len(self.labels) == self.length, 'The length and the labels is not right {} vs {}'.format(self.length, len(self.labels))
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assert len(self.NormDistances) == self.length, 'The length and the NormDistances is not right {} vs {}'.format(self.length, len(self.NormDistance))
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print ('Load data done for LandmarkDataset, which has {:} images.'.format(self.length))
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def __getitem__(self, index):
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assert index >= 0 and index < self.length, 'Invalid index : {:}'.format(index)
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if self.cache_images is not None and self.datas[index] in self.cache_images:
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image = self.cache_images[ self.datas[index] ].clone()
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else:
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image = pil_loader(self.datas[index], self.use_gray)
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target = self.labels[index].copy()
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return self._process_(image, target, index)
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def _process_(self, image, target, index):
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# transform the image and points
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image, target, theta = self.transform(image, target)
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(C, H, W), (height, width) = image.size(), self.shape
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# obtain the visiable indicator vector
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if target.is_none(): nopoints = True
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else : nopoints = False
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if index == -1: __path = None
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else : __path = self.datas[index]
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if isinstance(theta, list) or isinstance(theta, tuple):
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affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta = [], [], [], [], [], []
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for _theta in theta:
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_affineImage, _heatmaps, _mask, _norm_trans_points, _theta, _transpose_theta \
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= self.__process_affine(image, target, _theta, nopoints, 'P[{:}]@{:}'.format(index, __path))
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affineImage.append(_affineImage)
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heatmaps.append(_heatmaps)
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mask.append(_mask)
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norm_trans_points.append(_norm_trans_points)
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THETA.append(_theta)
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transpose_theta.append(_transpose_theta)
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affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta = \
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torch.stack(affineImage), torch.stack(heatmaps), torch.stack(mask), torch.stack(norm_trans_points), torch.stack(THETA), torch.stack(transpose_theta)
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else:
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affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta = self.__process_affine(image, target, theta, nopoints, 'S[{:}]@{:}'.format(index, __path))
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torch_index = torch.IntTensor([index])
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torch_nopoints = torch.ByteTensor( [ nopoints ] )
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torch_shape = torch.IntTensor([H,W])
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return affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta, torch_index, torch_nopoints, torch_shape
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|
||||
def __process_affine(self, image, target, theta, nopoints, aux_info=None):
|
||||
image, target, theta = image.clone(), target.copy(), theta.clone()
|
||||
(C, H, W), (height, width) = image.size(), self.shape
|
||||
if nopoints: # do not have label
|
||||
norm_trans_points = torch.zeros((3, self.NUM_PTS))
|
||||
heatmaps = torch.zeros((self.NUM_PTS+1, height//self.downsample, width//self.downsample))
|
||||
mask = torch.ones((self.NUM_PTS+1, 1, 1), dtype=torch.uint8)
|
||||
transpose_theta = identity2affine(False)
|
||||
else:
|
||||
norm_trans_points = apply_affine2point(target.get_points(), theta, (H,W))
|
||||
norm_trans_points = apply_boundary(norm_trans_points)
|
||||
real_trans_points = norm_trans_points.clone()
|
||||
real_trans_points[:2, :] = denormalize_points(self.shape, real_trans_points[:2,:])
|
||||
heatmaps, mask = generate_label_map(real_trans_points.numpy(), height//self.downsample, width//self.downsample, self.sigma, self.downsample, nopoints, self.heatmap_type) # H*W*C
|
||||
heatmaps = torch.from_numpy(heatmaps.transpose((2, 0, 1))).type(torch.FloatTensor)
|
||||
mask = torch.from_numpy(mask.transpose((2, 0, 1))).type(torch.ByteTensor)
|
||||
if self.mean_face is None:
|
||||
#warnings.warn('In LandmarkDataset use identity2affine for transpose_theta because self.mean_face is None.')
|
||||
transpose_theta = identity2affine(False)
|
||||
else:
|
||||
if torch.sum(norm_trans_points[2,:] == 1) < 3:
|
||||
warnings.warn('In LandmarkDataset after transformation, no visiable point, using identity instead. Aux: {:}'.format(aux_info))
|
||||
transpose_theta = identity2affine(False)
|
||||
self.transform = transform
|
||||
self.sigma = sigma
|
||||
self.downsample = downsample
|
||||
self.heatmap_type = heatmap_type
|
||||
self.dataset_name = data_indicator
|
||||
self.shape = shape # [H,W]
|
||||
self.use_gray = use_gray
|
||||
assert transform is not None, "transform : {:}".format(transform)
|
||||
self.mean_file = mean_file
|
||||
if mean_file is None:
|
||||
self.mean_data = None
|
||||
warnings.warn("LandmarkDataset initialized with mean_data = None")
|
||||
else:
|
||||
transpose_theta = solve2theta(norm_trans_points, self.mean_face.clone())
|
||||
assert osp.isfile(mean_file), "{:} is not a file.".format(mean_file)
|
||||
self.mean_data = torch.load(mean_file)
|
||||
self.reset()
|
||||
self.cutout = None
|
||||
self.cache_images = cache_images
|
||||
print("The general dataset initialization done : {:}".format(self))
|
||||
warnings.simplefilter("once")
|
||||
|
||||
affineImage = affine2image(image, theta, self.shape)
|
||||
if self.cutout is not None: affineImage = self.cutout( affineImage )
|
||||
def __repr__(self):
|
||||
return "{name}(point-num={NUM_PTS}, shape={shape}, sigma={sigma}, heatmap_type={heatmap_type}, length={length}, cutout={cutout}, dataset={dataset_name}, mean={mean_file})".format(
|
||||
name=self.__class__.__name__, **self.__dict__
|
||||
)
|
||||
|
||||
return affineImage, heatmaps, mask, norm_trans_points, theta, transpose_theta
|
||||
def set_cutout(self, length):
|
||||
if length is not None and length >= 1:
|
||||
self.cutout = CutOut(int(length))
|
||||
else:
|
||||
self.cutout = None
|
||||
|
||||
def reset(self, num_pts=-1, boxid="default", only_pts=False):
|
||||
self.NUM_PTS = num_pts
|
||||
if only_pts:
|
||||
return
|
||||
self.length = 0
|
||||
self.datas = []
|
||||
self.labels = []
|
||||
self.NormDistances = []
|
||||
self.BOXID = boxid
|
||||
if self.mean_data is None:
|
||||
self.mean_face = None
|
||||
else:
|
||||
self.mean_face = torch.Tensor(self.mean_data[boxid].copy().T)
|
||||
assert (self.mean_face >= -1).all() and (
|
||||
self.mean_face <= 1
|
||||
).all(), "mean-{:}-face : {:}".format(boxid, self.mean_face)
|
||||
# assert self.dataset_name is not None, 'The dataset name is None'
|
||||
|
||||
def __len__(self):
|
||||
assert len(self.datas) == self.length, "The length is not correct : {}".format(
|
||||
self.length
|
||||
)
|
||||
return self.length
|
||||
|
||||
def append(self, data, label, distance):
|
||||
assert osp.isfile(data), "The image path is not a file : {:}".format(data)
|
||||
self.datas.append(data)
|
||||
self.labels.append(label)
|
||||
self.NormDistances.append(distance)
|
||||
self.length = self.length + 1
|
||||
|
||||
def load_list(self, file_lists, num_pts, boxindicator, normalizeL, reset):
|
||||
if reset:
|
||||
self.reset(num_pts, boxindicator)
|
||||
else:
|
||||
assert (
|
||||
self.NUM_PTS == num_pts and self.BOXID == boxindicator
|
||||
), "The number of point is inconsistance : {:} vs {:}".format(
|
||||
self.NUM_PTS, num_pts
|
||||
)
|
||||
if isinstance(file_lists, str):
|
||||
file_lists = [file_lists]
|
||||
samples = []
|
||||
for idx, file_path in enumerate(file_lists):
|
||||
print(
|
||||
":::: load list {:}/{:} : {:}".format(idx, len(file_lists), file_path)
|
||||
)
|
||||
xdata = torch.load(file_path)
|
||||
if isinstance(xdata, list):
|
||||
data = xdata # image or video dataset list
|
||||
elif isinstance(xdata, dict):
|
||||
data = xdata["datas"] # multi-view dataset list
|
||||
else:
|
||||
raise ValueError("Invalid Type Error : {:}".format(type(xdata)))
|
||||
samples = samples + data
|
||||
# samples is a dict, where the key is the image-path and the value is the annotation
|
||||
# each annotation is a dict, contains 'points' (3,num_pts), and various box
|
||||
print("GeneralDataset-V2 : {:} samples".format(len(samples)))
|
||||
|
||||
# for index, annotation in enumerate(samples):
|
||||
for index in tqdm(range(len(samples))):
|
||||
annotation = samples[index]
|
||||
image_path = annotation["current_frame"]
|
||||
points, box = (
|
||||
annotation["points"],
|
||||
annotation["box-{:}".format(boxindicator)],
|
||||
)
|
||||
label = PointMeta2V(
|
||||
self.NUM_PTS, points, box, image_path, self.dataset_name
|
||||
)
|
||||
if normalizeL is None:
|
||||
normDistance = None
|
||||
else:
|
||||
normDistance = annotation["normalizeL-{:}".format(normalizeL)]
|
||||
self.append(image_path, label, normDistance)
|
||||
|
||||
assert (
|
||||
len(self.datas) == self.length
|
||||
), "The length and the data is not right {} vs {}".format(
|
||||
self.length, len(self.datas)
|
||||
)
|
||||
assert (
|
||||
len(self.labels) == self.length
|
||||
), "The length and the labels is not right {} vs {}".format(
|
||||
self.length, len(self.labels)
|
||||
)
|
||||
assert (
|
||||
len(self.NormDistances) == self.length
|
||||
), "The length and the NormDistances is not right {} vs {}".format(
|
||||
self.length, len(self.NormDistance)
|
||||
)
|
||||
print(
|
||||
"Load data done for LandmarkDataset, which has {:} images.".format(
|
||||
self.length
|
||||
)
|
||||
)
|
||||
|
||||
def __getitem__(self, index):
|
||||
assert index >= 0 and index < self.length, "Invalid index : {:}".format(index)
|
||||
if self.cache_images is not None and self.datas[index] in self.cache_images:
|
||||
image = self.cache_images[self.datas[index]].clone()
|
||||
else:
|
||||
image = pil_loader(self.datas[index], self.use_gray)
|
||||
target = self.labels[index].copy()
|
||||
return self._process_(image, target, index)
|
||||
|
||||
def _process_(self, image, target, index):
|
||||
|
||||
# transform the image and points
|
||||
image, target, theta = self.transform(image, target)
|
||||
(C, H, W), (height, width) = image.size(), self.shape
|
||||
|
||||
# obtain the visiable indicator vector
|
||||
if target.is_none():
|
||||
nopoints = True
|
||||
else:
|
||||
nopoints = False
|
||||
if index == -1:
|
||||
__path = None
|
||||
else:
|
||||
__path = self.datas[index]
|
||||
if isinstance(theta, list) or isinstance(theta, tuple):
|
||||
affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta = (
|
||||
[],
|
||||
[],
|
||||
[],
|
||||
[],
|
||||
[],
|
||||
[],
|
||||
)
|
||||
for _theta in theta:
|
||||
(
|
||||
_affineImage,
|
||||
_heatmaps,
|
||||
_mask,
|
||||
_norm_trans_points,
|
||||
_theta,
|
||||
_transpose_theta,
|
||||
) = self.__process_affine(
|
||||
image, target, _theta, nopoints, "P[{:}]@{:}".format(index, __path)
|
||||
)
|
||||
affineImage.append(_affineImage)
|
||||
heatmaps.append(_heatmaps)
|
||||
mask.append(_mask)
|
||||
norm_trans_points.append(_norm_trans_points)
|
||||
THETA.append(_theta)
|
||||
transpose_theta.append(_transpose_theta)
|
||||
affineImage, heatmaps, mask, norm_trans_points, THETA, transpose_theta = (
|
||||
torch.stack(affineImage),
|
||||
torch.stack(heatmaps),
|
||||
torch.stack(mask),
|
||||
torch.stack(norm_trans_points),
|
||||
torch.stack(THETA),
|
||||
torch.stack(transpose_theta),
|
||||
)
|
||||
else:
|
||||
(
|
||||
affineImage,
|
||||
heatmaps,
|
||||
mask,
|
||||
norm_trans_points,
|
||||
THETA,
|
||||
transpose_theta,
|
||||
) = self.__process_affine(
|
||||
image, target, theta, nopoints, "S[{:}]@{:}".format(index, __path)
|
||||
)
|
||||
|
||||
torch_index = torch.IntTensor([index])
|
||||
torch_nopoints = torch.ByteTensor([nopoints])
|
||||
torch_shape = torch.IntTensor([H, W])
|
||||
|
||||
return (
|
||||
affineImage,
|
||||
heatmaps,
|
||||
mask,
|
||||
norm_trans_points,
|
||||
THETA,
|
||||
transpose_theta,
|
||||
torch_index,
|
||||
torch_nopoints,
|
||||
torch_shape,
|
||||
)
|
||||
|
||||
def __process_affine(self, image, target, theta, nopoints, aux_info=None):
|
||||
image, target, theta = image.clone(), target.copy(), theta.clone()
|
||||
(C, H, W), (height, width) = image.size(), self.shape
|
||||
if nopoints: # do not have label
|
||||
norm_trans_points = torch.zeros((3, self.NUM_PTS))
|
||||
heatmaps = torch.zeros(
|
||||
(self.NUM_PTS + 1, height // self.downsample, width // self.downsample)
|
||||
)
|
||||
mask = torch.ones((self.NUM_PTS + 1, 1, 1), dtype=torch.uint8)
|
||||
transpose_theta = identity2affine(False)
|
||||
else:
|
||||
norm_trans_points = apply_affine2point(target.get_points(), theta, (H, W))
|
||||
norm_trans_points = apply_boundary(norm_trans_points)
|
||||
real_trans_points = norm_trans_points.clone()
|
||||
real_trans_points[:2, :] = denormalize_points(
|
||||
self.shape, real_trans_points[:2, :]
|
||||
)
|
||||
heatmaps, mask = generate_label_map(
|
||||
real_trans_points.numpy(),
|
||||
height // self.downsample,
|
||||
width // self.downsample,
|
||||
self.sigma,
|
||||
self.downsample,
|
||||
nopoints,
|
||||
self.heatmap_type,
|
||||
) # H*W*C
|
||||
heatmaps = torch.from_numpy(heatmaps.transpose((2, 0, 1))).type(
|
||||
torch.FloatTensor
|
||||
)
|
||||
mask = torch.from_numpy(mask.transpose((2, 0, 1))).type(torch.ByteTensor)
|
||||
if self.mean_face is None:
|
||||
# warnings.warn('In LandmarkDataset use identity2affine for transpose_theta because self.mean_face is None.')
|
||||
transpose_theta = identity2affine(False)
|
||||
else:
|
||||
if torch.sum(norm_trans_points[2, :] == 1) < 3:
|
||||
warnings.warn(
|
||||
"In LandmarkDataset after transformation, no visiable point, using identity instead. Aux: {:}".format(
|
||||
aux_info
|
||||
)
|
||||
)
|
||||
transpose_theta = identity2affine(False)
|
||||
else:
|
||||
transpose_theta = solve2theta(
|
||||
norm_trans_points, self.mean_face.clone()
|
||||
)
|
||||
|
||||
affineImage = affine2image(image, theta, self.shape)
|
||||
if self.cutout is not None:
|
||||
affineImage = self.cutout(affineImage)
|
||||
|
||||
return affineImage, heatmaps, mask, norm_trans_points, theta, transpose_theta
|
||||
|
||||
@@ -6,41 +6,49 @@ import torch.utils.data as data
|
||||
|
||||
|
||||
class SearchDataset(data.Dataset):
|
||||
def __init__(self, name, data, train_split, valid_split, check=True):
|
||||
self.datasetname = name
|
||||
if isinstance(data, (list, tuple)): # new type of SearchDataset
|
||||
assert len(data) == 2, "invalid length: {:}".format(len(data))
|
||||
self.train_data = data[0]
|
||||
self.valid_data = data[1]
|
||||
self.train_split = train_split.copy()
|
||||
self.valid_split = valid_split.copy()
|
||||
self.mode_str = "V2" # new mode
|
||||
else:
|
||||
self.mode_str = "V1" # old mode
|
||||
self.data = data
|
||||
self.train_split = train_split.copy()
|
||||
self.valid_split = valid_split.copy()
|
||||
if check:
|
||||
intersection = set(train_split).intersection(set(valid_split))
|
||||
assert (
|
||||
len(intersection) == 0
|
||||
), "the splitted train and validation sets should have no intersection"
|
||||
self.length = len(self.train_split)
|
||||
|
||||
def __init__(self, name, data, train_split, valid_split, check=True):
|
||||
self.datasetname = name
|
||||
if isinstance(data, (list, tuple)): # new type of SearchDataset
|
||||
assert len(data) == 2, 'invalid length: {:}'.format( len(data) )
|
||||
self.train_data = data[0]
|
||||
self.valid_data = data[1]
|
||||
self.train_split = train_split.copy()
|
||||
self.valid_split = valid_split.copy()
|
||||
self.mode_str = 'V2' # new mode
|
||||
else:
|
||||
self.mode_str = 'V1' # old mode
|
||||
self.data = data
|
||||
self.train_split = train_split.copy()
|
||||
self.valid_split = valid_split.copy()
|
||||
if check:
|
||||
intersection = set(train_split).intersection(set(valid_split))
|
||||
assert len(intersection) == 0, 'the splitted train and validation sets should have no intersection'
|
||||
self.length = len(self.train_split)
|
||||
def __repr__(self):
|
||||
return "{name}(name={datasetname}, train={tr_L}, valid={val_L}, version={ver})".format(
|
||||
name=self.__class__.__name__,
|
||||
datasetname=self.datasetname,
|
||||
tr_L=len(self.train_split),
|
||||
val_L=len(self.valid_split),
|
||||
ver=self.mode_str,
|
||||
)
|
||||
|
||||
def __repr__(self):
|
||||
return ('{name}(name={datasetname}, train={tr_L}, valid={val_L}, version={ver})'.format(name=self.__class__.__name__, datasetname=self.datasetname, tr_L=len(self.train_split), val_L=len(self.valid_split), ver=self.mode_str))
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
def __getitem__(self, index):
|
||||
assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
|
||||
train_index = self.train_split[index]
|
||||
valid_index = random.choice( self.valid_split )
|
||||
if self.mode_str == 'V1':
|
||||
train_image, train_label = self.data[train_index]
|
||||
valid_image, valid_label = self.data[valid_index]
|
||||
elif self.mode_str == 'V2':
|
||||
train_image, train_label = self.train_data[train_index]
|
||||
valid_image, valid_label = self.valid_data[valid_index]
|
||||
else: raise ValueError('invalid mode : {:}'.format(self.mode_str))
|
||||
return train_image, train_label, valid_image, valid_label
|
||||
def __getitem__(self, index):
|
||||
assert index >= 0 and index < self.length, "invalid index = {:}".format(index)
|
||||
train_index = self.train_split[index]
|
||||
valid_index = random.choice(self.valid_split)
|
||||
if self.mode_str == "V1":
|
||||
train_image, train_label = self.data[train_index]
|
||||
valid_image, valid_label = self.data[valid_index]
|
||||
elif self.mode_str == "V2":
|
||||
train_image, train_label = self.train_data[train_index]
|
||||
valid_image, valid_label = self.valid_data[valid_index]
|
||||
else:
|
||||
raise ValueError("invalid mode : {:}".format(self.mode_str))
|
||||
return train_image, train_label, valid_image, valid_label
|
||||
|
||||
@@ -4,4 +4,5 @@
|
||||
from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
|
||||
from .SearchDatasetWrap import SearchDataset
|
||||
|
||||
from .synthetic_adaptive_environment import QuadraticFunction
|
||||
from .synthetic_adaptive_environment import SynAdaptiveEnv
|
||||
|
||||
@@ -14,214 +14,349 @@ from .SearchDatasetWrap import SearchDataset
|
||||
from config_utils import load_config
|
||||
|
||||
|
||||
Dataset2Class = {'cifar10' : 10,
|
||||
'cifar100': 100,
|
||||
'imagenet-1k-s':1000,
|
||||
'imagenet-1k' : 1000,
|
||||
'ImageNet16' : 1000,
|
||||
'ImageNet16-150': 150,
|
||||
'ImageNet16-120': 120,
|
||||
'ImageNet16-200': 200}
|
||||
Dataset2Class = {
|
||||
"cifar10": 10,
|
||||
"cifar100": 100,
|
||||
"imagenet-1k-s": 1000,
|
||||
"imagenet-1k": 1000,
|
||||
"ImageNet16": 1000,
|
||||
"ImageNet16-150": 150,
|
||||
"ImageNet16-120": 120,
|
||||
"ImageNet16-200": 200,
|
||||
}
|
||||
|
||||
|
||||
class CUTOUT(object):
|
||||
def __init__(self, length):
|
||||
self.length = length
|
||||
|
||||
def __init__(self, length):
|
||||
self.length = length
|
||||
def __repr__(self):
|
||||
return "{name}(length={length})".format(
|
||||
name=self.__class__.__name__, **self.__dict__
|
||||
)
|
||||
|
||||
def __repr__(self):
|
||||
return ('{name}(length={length})'.format(name=self.__class__.__name__, **self.__dict__))
|
||||
def __call__(self, img):
|
||||
h, w = img.size(1), img.size(2)
|
||||
mask = np.ones((h, w), np.float32)
|
||||
y = np.random.randint(h)
|
||||
x = np.random.randint(w)
|
||||
|
||||
def __call__(self, img):
|
||||
h, w = img.size(1), img.size(2)
|
||||
mask = np.ones((h, w), np.float32)
|
||||
y = np.random.randint(h)
|
||||
x = np.random.randint(w)
|
||||
y1 = np.clip(y - self.length // 2, 0, h)
|
||||
y2 = np.clip(y + self.length // 2, 0, h)
|
||||
x1 = np.clip(x - self.length // 2, 0, w)
|
||||
x2 = np.clip(x + self.length // 2, 0, w)
|
||||
|
||||
y1 = np.clip(y - self.length // 2, 0, h)
|
||||
y2 = np.clip(y + self.length // 2, 0, h)
|
||||
x1 = np.clip(x - self.length // 2, 0, w)
|
||||
x2 = np.clip(x + self.length // 2, 0, w)
|
||||
|
||||
mask[y1: y2, x1: x2] = 0.
|
||||
mask = torch.from_numpy(mask)
|
||||
mask = mask.expand_as(img)
|
||||
img *= mask
|
||||
return img
|
||||
mask[y1:y2, x1:x2] = 0.0
|
||||
mask = torch.from_numpy(mask)
|
||||
mask = mask.expand_as(img)
|
||||
img *= mask
|
||||
return img
|
||||
|
||||
|
||||
imagenet_pca = {
|
||||
'eigval': np.asarray([0.2175, 0.0188, 0.0045]),
|
||||
'eigvec': np.asarray([
|
||||
[-0.5675, 0.7192, 0.4009],
|
||||
[-0.5808, -0.0045, -0.8140],
|
||||
[-0.5836, -0.6948, 0.4203],
|
||||
])
|
||||
"eigval": np.asarray([0.2175, 0.0188, 0.0045]),
|
||||
"eigvec": np.asarray(
|
||||
[
|
||||
[-0.5675, 0.7192, 0.4009],
|
||||
[-0.5808, -0.0045, -0.8140],
|
||||
[-0.5836, -0.6948, 0.4203],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
class Lighting(object):
|
||||
def __init__(self, alphastd,
|
||||
eigval=imagenet_pca['eigval'],
|
||||
eigvec=imagenet_pca['eigvec']):
|
||||
self.alphastd = alphastd
|
||||
assert eigval.shape == (3,)
|
||||
assert eigvec.shape == (3, 3)
|
||||
self.eigval = eigval
|
||||
self.eigvec = eigvec
|
||||
def __init__(
|
||||
self, alphastd, eigval=imagenet_pca["eigval"], eigvec=imagenet_pca["eigvec"]
|
||||
):
|
||||
self.alphastd = alphastd
|
||||
assert eigval.shape == (3,)
|
||||
assert eigvec.shape == (3, 3)
|
||||
self.eigval = eigval
|
||||
self.eigvec = eigvec
|
||||
|
||||
def __call__(self, img):
|
||||
if self.alphastd == 0.:
|
||||
return img
|
||||
rnd = np.random.randn(3) * self.alphastd
|
||||
rnd = rnd.astype('float32')
|
||||
v = rnd
|
||||
old_dtype = np.asarray(img).dtype
|
||||
v = v * self.eigval
|
||||
v = v.reshape((3, 1))
|
||||
inc = np.dot(self.eigvec, v).reshape((3,))
|
||||
img = np.add(img, inc)
|
||||
if old_dtype == np.uint8:
|
||||
img = np.clip(img, 0, 255)
|
||||
img = Image.fromarray(img.astype(old_dtype), 'RGB')
|
||||
return img
|
||||
def __call__(self, img):
|
||||
if self.alphastd == 0.0:
|
||||
return img
|
||||
rnd = np.random.randn(3) * self.alphastd
|
||||
rnd = rnd.astype("float32")
|
||||
v = rnd
|
||||
old_dtype = np.asarray(img).dtype
|
||||
v = v * self.eigval
|
||||
v = v.reshape((3, 1))
|
||||
inc = np.dot(self.eigvec, v).reshape((3,))
|
||||
img = np.add(img, inc)
|
||||
if old_dtype == np.uint8:
|
||||
img = np.clip(img, 0, 255)
|
||||
img = Image.fromarray(img.astype(old_dtype), "RGB")
|
||||
return img
|
||||
|
||||
def __repr__(self):
|
||||
return self.__class__.__name__ + '()'
|
||||
def __repr__(self):
|
||||
return self.__class__.__name__ + "()"
|
||||
|
||||
|
||||
def get_datasets(name, root, cutout):
|
||||
|
||||
if name == 'cifar10':
|
||||
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
|
||||
std = [x / 255 for x in [63.0, 62.1, 66.7]]
|
||||
elif name == 'cifar100':
|
||||
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
|
||||
std = [x / 255 for x in [68.2, 65.4, 70.4]]
|
||||
elif name.startswith('imagenet-1k'):
|
||||
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
|
||||
elif name.startswith('ImageNet16'):
|
||||
mean = [x / 255 for x in [122.68, 116.66, 104.01]]
|
||||
std = [x / 255 for x in [63.22, 61.26 , 65.09]]
|
||||
else:
|
||||
raise TypeError("Unknow dataset : {:}".format(name))
|
||||
if name == "cifar10":
|
||||
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
|
||||
std = [x / 255 for x in [63.0, 62.1, 66.7]]
|
||||
elif name == "cifar100":
|
||||
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
|
||||
std = [x / 255 for x in [68.2, 65.4, 70.4]]
|
||||
elif name.startswith("imagenet-1k"):
|
||||
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
|
||||
elif name.startswith("ImageNet16"):
|
||||
mean = [x / 255 for x in [122.68, 116.66, 104.01]]
|
||||
std = [x / 255 for x in [63.22, 61.26, 65.09]]
|
||||
else:
|
||||
raise TypeError("Unknow dataset : {:}".format(name))
|
||||
|
||||
# Data Argumentation
|
||||
if name == 'cifar10' or name == 'cifar100':
|
||||
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||||
if cutout > 0 : lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
|
||||
xshape = (1, 3, 32, 32)
|
||||
elif name.startswith('ImageNet16'):
|
||||
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(16, padding=2), transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||||
if cutout > 0 : lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
|
||||
xshape = (1, 3, 16, 16)
|
||||
elif name == 'tiered':
|
||||
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||||
if cutout > 0 : lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
|
||||
xshape = (1, 3, 32, 32)
|
||||
elif name.startswith('imagenet-1k'):
|
||||
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
if name == 'imagenet-1k':
|
||||
xlists = [transforms.RandomResizedCrop(224)]
|
||||
xlists.append(
|
||||
transforms.ColorJitter(
|
||||
brightness=0.4,
|
||||
contrast=0.4,
|
||||
saturation=0.4,
|
||||
hue=0.2))
|
||||
xlists.append( Lighting(0.1))
|
||||
elif name == 'imagenet-1k-s':
|
||||
xlists = [transforms.RandomResizedCrop(224, scale=(0.2, 1.0))]
|
||||
else: raise ValueError('invalid name : {:}'.format(name))
|
||||
xlists.append( transforms.RandomHorizontalFlip(p=0.5) )
|
||||
xlists.append( transforms.ToTensor() )
|
||||
xlists.append( normalize )
|
||||
train_transform = transforms.Compose(xlists)
|
||||
test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
|
||||
xshape = (1, 3, 224, 224)
|
||||
else:
|
||||
raise TypeError("Unknow dataset : {:}".format(name))
|
||||
# Data Argumentation
|
||||
if name == "cifar10" or name == "cifar100":
|
||||
lists = [
|
||||
transforms.RandomHorizontalFlip(),
|
||||
transforms.RandomCrop(32, padding=4),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean, std),
|
||||
]
|
||||
if cutout > 0:
|
||||
lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose(
|
||||
[transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||||
)
|
||||
xshape = (1, 3, 32, 32)
|
||||
elif name.startswith("ImageNet16"):
|
||||
lists = [
|
||||
transforms.RandomHorizontalFlip(),
|
||||
transforms.RandomCrop(16, padding=2),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean, std),
|
||||
]
|
||||
if cutout > 0:
|
||||
lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose(
|
||||
[transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||||
)
|
||||
xshape = (1, 3, 16, 16)
|
||||
elif name == "tiered":
|
||||
lists = [
|
||||
transforms.RandomHorizontalFlip(),
|
||||
transforms.RandomCrop(80, padding=4),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean, std),
|
||||
]
|
||||
if cutout > 0:
|
||||
lists += [CUTOUT(cutout)]
|
||||
train_transform = transforms.Compose(lists)
|
||||
test_transform = transforms.Compose(
|
||||
[
|
||||
transforms.CenterCrop(80),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean, std),
|
||||
]
|
||||
)
|
||||
xshape = (1, 3, 32, 32)
|
||||
elif name.startswith("imagenet-1k"):
|
||||
normalize = transforms.Normalize(
|
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
)
|
||||
if name == "imagenet-1k":
|
||||
xlists = [transforms.RandomResizedCrop(224)]
|
||||
xlists.append(
|
||||
transforms.ColorJitter(
|
||||
brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2
|
||||
)
|
||||
)
|
||||
xlists.append(Lighting(0.1))
|
||||
elif name == "imagenet-1k-s":
|
||||
xlists = [transforms.RandomResizedCrop(224, scale=(0.2, 1.0))]
|
||||
else:
|
||||
raise ValueError("invalid name : {:}".format(name))
|
||||
xlists.append(transforms.RandomHorizontalFlip(p=0.5))
|
||||
xlists.append(transforms.ToTensor())
|
||||
xlists.append(normalize)
|
||||
train_transform = transforms.Compose(xlists)
|
||||
test_transform = transforms.Compose(
|
||||
[
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
normalize,
|
||||
]
|
||||
)
|
||||
xshape = (1, 3, 224, 224)
|
||||
else:
|
||||
raise TypeError("Unknow dataset : {:}".format(name))
|
||||
|
||||
if name == 'cifar10':
|
||||
train_data = dset.CIFAR10 (root, train=True , transform=train_transform, download=True)
|
||||
test_data = dset.CIFAR10 (root, train=False, transform=test_transform , download=True)
|
||||
assert len(train_data) == 50000 and len(test_data) == 10000
|
||||
elif name == 'cifar100':
|
||||
train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
|
||||
test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
|
||||
assert len(train_data) == 50000 and len(test_data) == 10000
|
||||
elif name.startswith('imagenet-1k'):
|
||||
train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
|
||||
test_data = dset.ImageFolder(osp.join(root, 'val'), test_transform)
|
||||
assert len(train_data) == 1281167 and len(test_data) == 50000, 'invalid number of images : {:} & {:} vs {:} & {:}'.format(len(train_data), len(test_data), 1281167, 50000)
|
||||
elif name == 'ImageNet16':
|
||||
train_data = ImageNet16(root, True , train_transform)
|
||||
test_data = ImageNet16(root, False, test_transform)
|
||||
assert len(train_data) == 1281167 and len(test_data) == 50000
|
||||
elif name == 'ImageNet16-120':
|
||||
train_data = ImageNet16(root, True , train_transform, 120)
|
||||
test_data = ImageNet16(root, False, test_transform , 120)
|
||||
assert len(train_data) == 151700 and len(test_data) == 6000
|
||||
elif name == 'ImageNet16-150':
|
||||
train_data = ImageNet16(root, True , train_transform, 150)
|
||||
test_data = ImageNet16(root, False, test_transform , 150)
|
||||
assert len(train_data) == 190272 and len(test_data) == 7500
|
||||
elif name == 'ImageNet16-200':
|
||||
train_data = ImageNet16(root, True , train_transform, 200)
|
||||
test_data = ImageNet16(root, False, test_transform , 200)
|
||||
assert len(train_data) == 254775 and len(test_data) == 10000
|
||||
else: raise TypeError("Unknow dataset : {:}".format(name))
|
||||
if name == "cifar10":
|
||||
train_data = dset.CIFAR10(
|
||||
root, train=True, transform=train_transform, download=True
|
||||
)
|
||||
test_data = dset.CIFAR10(
|
||||
root, train=False, transform=test_transform, download=True
|
||||
)
|
||||
assert len(train_data) == 50000 and len(test_data) == 10000
|
||||
elif name == "cifar100":
|
||||
train_data = dset.CIFAR100(
|
||||
root, train=True, transform=train_transform, download=True
|
||||
)
|
||||
test_data = dset.CIFAR100(
|
||||
root, train=False, transform=test_transform, download=True
|
||||
)
|
||||
assert len(train_data) == 50000 and len(test_data) == 10000
|
||||
elif name.startswith("imagenet-1k"):
|
||||
train_data = dset.ImageFolder(osp.join(root, "train"), train_transform)
|
||||
test_data = dset.ImageFolder(osp.join(root, "val"), test_transform)
|
||||
assert (
|
||||
len(train_data) == 1281167 and len(test_data) == 50000
|
||||
), "invalid number of images : {:} & {:} vs {:} & {:}".format(
|
||||
len(train_data), len(test_data), 1281167, 50000
|
||||
)
|
||||
elif name == "ImageNet16":
|
||||
train_data = ImageNet16(root, True, train_transform)
|
||||
test_data = ImageNet16(root, False, test_transform)
|
||||
assert len(train_data) == 1281167 and len(test_data) == 50000
|
||||
elif name == "ImageNet16-120":
|
||||
train_data = ImageNet16(root, True, train_transform, 120)
|
||||
test_data = ImageNet16(root, False, test_transform, 120)
|
||||
assert len(train_data) == 151700 and len(test_data) == 6000
|
||||
elif name == "ImageNet16-150":
|
||||
train_data = ImageNet16(root, True, train_transform, 150)
|
||||
test_data = ImageNet16(root, False, test_transform, 150)
|
||||
assert len(train_data) == 190272 and len(test_data) == 7500
|
||||
elif name == "ImageNet16-200":
|
||||
train_data = ImageNet16(root, True, train_transform, 200)
|
||||
test_data = ImageNet16(root, False, test_transform, 200)
|
||||
assert len(train_data) == 254775 and len(test_data) == 10000
|
||||
else:
|
||||
raise TypeError("Unknow dataset : {:}".format(name))
|
||||
|
||||
class_num = Dataset2Class[name]
|
||||
return train_data, test_data, xshape, class_num
|
||||
class_num = Dataset2Class[name]
|
||||
return train_data, test_data, xshape, class_num
|
||||
|
||||
|
||||
def get_nas_search_loaders(train_data, valid_data, dataset, config_root, batch_size, workers):
|
||||
if isinstance(batch_size, (list,tuple)):
|
||||
batch, test_batch = batch_size
|
||||
else:
|
||||
batch, test_batch = batch_size, batch_size
|
||||
if dataset == 'cifar10':
|
||||
#split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
|
||||
cifar_split = load_config('{:}/cifar-split.txt'.format(config_root), None, None)
|
||||
train_split, valid_split = cifar_split.train, cifar_split.valid # search over the proposed training and validation set
|
||||
#logger.log('Load split file from {:}'.format(split_Fpath)) # they are two disjoint groups in the original CIFAR-10 training set
|
||||
# To split data
|
||||
xvalid_data = deepcopy(train_data)
|
||||
if hasattr(xvalid_data, 'transforms'): # to avoid a print issue
|
||||
xvalid_data.transforms = valid_data.transform
|
||||
xvalid_data.transform = deepcopy( valid_data.transform )
|
||||
search_data = SearchDataset(dataset, train_data, train_split, valid_split)
|
||||
# data loader
|
||||
search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
train_loader = torch.utils.data.DataLoader(train_data , batch_size=batch, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split), num_workers=workers, pin_memory=True)
|
||||
valid_loader = torch.utils.data.DataLoader(xvalid_data, batch_size=test_batch, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=workers, pin_memory=True)
|
||||
elif dataset == 'cifar100':
|
||||
cifar100_test_split = load_config('{:}/cifar100-test-split.txt'.format(config_root), None, None)
|
||||
search_train_data = train_data
|
||||
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
|
||||
search_data = SearchDataset(dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), cifar100_test_split.xvalid)
|
||||
search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
train_loader = torch.utils.data.DataLoader(train_data , batch_size=batch, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=test_batch, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_test_split.xvalid), num_workers=workers, pin_memory=True)
|
||||
elif dataset == 'ImageNet16-120':
|
||||
imagenet_test_split = load_config('{:}/imagenet-16-120-test-split.txt'.format(config_root), None, None)
|
||||
search_train_data = train_data
|
||||
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
|
||||
search_data = SearchDataset(dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), imagenet_test_split.xvalid)
|
||||
search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
train_loader = torch.utils.data.DataLoader(train_data , batch_size=batch, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=test_batch, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_test_split.xvalid), num_workers=workers, pin_memory=True)
|
||||
else:
|
||||
raise ValueError('invalid dataset : {:}'.format(dataset))
|
||||
return search_loader, train_loader, valid_loader
|
||||
def get_nas_search_loaders(
|
||||
train_data, valid_data, dataset, config_root, batch_size, workers
|
||||
):
|
||||
if isinstance(batch_size, (list, tuple)):
|
||||
batch, test_batch = batch_size
|
||||
else:
|
||||
batch, test_batch = batch_size, batch_size
|
||||
if dataset == "cifar10":
|
||||
# split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
|
||||
cifar_split = load_config("{:}/cifar-split.txt".format(config_root), None, None)
|
||||
train_split, valid_split = (
|
||||
cifar_split.train,
|
||||
cifar_split.valid,
|
||||
) # search over the proposed training and validation set
|
||||
# logger.log('Load split file from {:}'.format(split_Fpath)) # they are two disjoint groups in the original CIFAR-10 training set
|
||||
# To split data
|
||||
xvalid_data = deepcopy(train_data)
|
||||
if hasattr(xvalid_data, "transforms"): # to avoid a print issue
|
||||
xvalid_data.transforms = valid_data.transform
|
||||
xvalid_data.transform = deepcopy(valid_data.transform)
|
||||
search_data = SearchDataset(dataset, train_data, train_split, valid_split)
|
||||
# data loader
|
||||
search_loader = torch.utils.data.DataLoader(
|
||||
search_data,
|
||||
batch_size=batch,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=batch,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
xvalid_data,
|
||||
batch_size=test_batch,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
elif dataset == "cifar100":
|
||||
cifar100_test_split = load_config(
|
||||
"{:}/cifar100-test-split.txt".format(config_root), None, None
|
||||
)
|
||||
search_train_data = train_data
|
||||
search_valid_data = deepcopy(valid_data)
|
||||
search_valid_data.transform = train_data.transform
|
||||
search_data = SearchDataset(
|
||||
dataset,
|
||||
[search_train_data, search_valid_data],
|
||||
list(range(len(search_train_data))),
|
||||
cifar100_test_split.xvalid,
|
||||
)
|
||||
search_loader = torch.utils.data.DataLoader(
|
||||
search_data,
|
||||
batch_size=batch,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=batch,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
valid_data,
|
||||
batch_size=test_batch,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(
|
||||
cifar100_test_split.xvalid
|
||||
),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
elif dataset == "ImageNet16-120":
|
||||
imagenet_test_split = load_config(
|
||||
"{:}/imagenet-16-120-test-split.txt".format(config_root), None, None
|
||||
)
|
||||
search_train_data = train_data
|
||||
search_valid_data = deepcopy(valid_data)
|
||||
search_valid_data.transform = train_data.transform
|
||||
search_data = SearchDataset(
|
||||
dataset,
|
||||
[search_train_data, search_valid_data],
|
||||
list(range(len(search_train_data))),
|
||||
imagenet_test_split.xvalid,
|
||||
)
|
||||
search_loader = torch.utils.data.DataLoader(
|
||||
search_data,
|
||||
batch_size=batch,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=batch,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
valid_data,
|
||||
batch_size=test_batch,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(
|
||||
imagenet_test_split.xvalid
|
||||
),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
else:
|
||||
raise ValueError("invalid dataset : {:}".format(dataset))
|
||||
return search_loader, train_loader, valid_loader
|
||||
|
||||
#if __name__ == '__main__':
|
||||
|
||||
# if __name__ == '__main__':
|
||||
# train_data, test_data, xshape, class_num = dataset = get_datasets('cifar10', '/data02/dongxuanyi/.torch/cifar.python/', -1)
|
||||
# import pdb; pdb.set_trace()
|
||||
|
||||
@@ -9,108 +9,211 @@ from xvision import normalize_points
|
||||
from xvision import denormalize_points
|
||||
|
||||
|
||||
class PointMeta():
|
||||
# points : 3 x num_pts (x, y, oculusion)
|
||||
# image_size: original [width, height]
|
||||
def __init__(self, num_point, points, box, image_path, dataset_name):
|
||||
class PointMeta:
|
||||
# points : 3 x num_pts (x, y, oculusion)
|
||||
# image_size: original [width, height]
|
||||
def __init__(self, num_point, points, box, image_path, dataset_name):
|
||||
|
||||
self.num_point = num_point
|
||||
if box is not None:
|
||||
assert (isinstance(box, tuple) or isinstance(box, list)) and len(box) == 4
|
||||
self.box = torch.Tensor(box)
|
||||
else: self.box = None
|
||||
if points is None:
|
||||
self.points = points
|
||||
else:
|
||||
assert len(points.shape) == 2 and points.shape[0] == 3 and points.shape[1] == self.num_point, 'The shape of point is not right : {}'.format( points )
|
||||
self.points = torch.Tensor(points.copy())
|
||||
self.image_path = image_path
|
||||
self.datasets = dataset_name
|
||||
self.num_point = num_point
|
||||
if box is not None:
|
||||
assert (isinstance(box, tuple) or isinstance(box, list)) and len(box) == 4
|
||||
self.box = torch.Tensor(box)
|
||||
else:
|
||||
self.box = None
|
||||
if points is None:
|
||||
self.points = points
|
||||
else:
|
||||
assert (
|
||||
len(points.shape) == 2
|
||||
and points.shape[0] == 3
|
||||
and points.shape[1] == self.num_point
|
||||
), "The shape of point is not right : {}".format(points)
|
||||
self.points = torch.Tensor(points.copy())
|
||||
self.image_path = image_path
|
||||
self.datasets = dataset_name
|
||||
|
||||
def __repr__(self):
|
||||
if self.box is None: boxstr = 'None'
|
||||
else : boxstr = 'box=[{:.1f}, {:.1f}, {:.1f}, {:.1f}]'.format(*self.box.tolist())
|
||||
return ('{name}(points={num_point}, '.format(name=self.__class__.__name__, **self.__dict__) + boxstr + ')')
|
||||
def __repr__(self):
|
||||
if self.box is None:
|
||||
boxstr = "None"
|
||||
else:
|
||||
boxstr = "box=[{:.1f}, {:.1f}, {:.1f}, {:.1f}]".format(*self.box.tolist())
|
||||
return (
|
||||
"{name}(points={num_point}, ".format(
|
||||
name=self.__class__.__name__, **self.__dict__
|
||||
)
|
||||
+ boxstr
|
||||
+ ")"
|
||||
)
|
||||
|
||||
def get_box(self, return_diagonal=False):
|
||||
if self.box is None: return None
|
||||
if not return_diagonal:
|
||||
return self.box.clone()
|
||||
else:
|
||||
W = (self.box[2]-self.box[0]).item()
|
||||
H = (self.box[3]-self.box[1]).item()
|
||||
return math.sqrt(H*H+W*W)
|
||||
def get_box(self, return_diagonal=False):
|
||||
if self.box is None:
|
||||
return None
|
||||
if not return_diagonal:
|
||||
return self.box.clone()
|
||||
else:
|
||||
W = (self.box[2] - self.box[0]).item()
|
||||
H = (self.box[3] - self.box[1]).item()
|
||||
return math.sqrt(H * H + W * W)
|
||||
|
||||
def get_points(self, ignore_indicator=False):
|
||||
if ignore_indicator: last = 2
|
||||
else : last = 3
|
||||
if self.points is not None: return self.points.clone()[:last, :]
|
||||
else : return torch.zeros((last, self.num_point))
|
||||
def get_points(self, ignore_indicator=False):
|
||||
if ignore_indicator:
|
||||
last = 2
|
||||
else:
|
||||
last = 3
|
||||
if self.points is not None:
|
||||
return self.points.clone()[:last, :]
|
||||
else:
|
||||
return torch.zeros((last, self.num_point))
|
||||
|
||||
def is_none(self):
|
||||
#assert self.box is not None, 'The box should not be None'
|
||||
return self.points is None
|
||||
#if self.box is None: return True
|
||||
#else : return self.points is None
|
||||
def is_none(self):
|
||||
# assert self.box is not None, 'The box should not be None'
|
||||
return self.points is None
|
||||
# if self.box is None: return True
|
||||
# else : return self.points is None
|
||||
|
||||
def copy(self):
|
||||
return copy.deepcopy(self)
|
||||
def copy(self):
|
||||
return copy.deepcopy(self)
|
||||
|
||||
def visiable_pts_num(self):
|
||||
with torch.no_grad():
|
||||
ans = self.points[2,:] > 0
|
||||
ans = torch.sum(ans)
|
||||
ans = ans.item()
|
||||
return ans
|
||||
def visiable_pts_num(self):
|
||||
with torch.no_grad():
|
||||
ans = self.points[2, :] > 0
|
||||
ans = torch.sum(ans)
|
||||
ans = ans.item()
|
||||
return ans
|
||||
|
||||
def special_fun(self, indicator):
|
||||
if indicator == '68to49': # For 300W or 300VW, convert the default 68 points to 49 points.
|
||||
assert self.num_point == 68, 'num-point must be 68 vs. {:}'.format(self.num_point)
|
||||
self.num_point = 49
|
||||
out = torch.ones((68), dtype=torch.uint8)
|
||||
out[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,60,64]] = 0
|
||||
if self.points is not None: self.points = self.points.clone()[:, out]
|
||||
else:
|
||||
raise ValueError('Invalid indicator : {:}'.format( indicator ))
|
||||
|
||||
def apply_horizontal_flip(self):
|
||||
#self.points[0, :] = width - self.points[0, :] - 1
|
||||
# Mugsy spefic or Synthetic
|
||||
if self.datasets.startswith('HandsyROT'):
|
||||
ori = np.array(list(range(0, 42)))
|
||||
pos = np.array(list(range(21,42)) + list(range(0,21)))
|
||||
self.points[:, pos] = self.points[:, ori]
|
||||
elif self.datasets.startswith('face68'):
|
||||
ori = np.array(list(range(0, 68)))
|
||||
pos = np.array([17,16,15,14,13,12,11,10, 9, 8,7,6,5,4,3,2,1, 27,26,25,24,23,22,21,20,19,18, 28,29,30,31, 36,35,34,33,32, 46,45,44,43,48,47, 40,39,38,37,42,41, 55,54,53,52,51,50,49,60,59,58,57,56,65,64,63,62,61,68,67,66])-1
|
||||
self.points[:, ori] = self.points[:, pos]
|
||||
else:
|
||||
raise ValueError('Does not support {:}'.format(self.datasets))
|
||||
def special_fun(self, indicator):
|
||||
if (
|
||||
indicator == "68to49"
|
||||
): # For 300W or 300VW, convert the default 68 points to 49 points.
|
||||
assert self.num_point == 68, "num-point must be 68 vs. {:}".format(
|
||||
self.num_point
|
||||
)
|
||||
self.num_point = 49
|
||||
out = torch.ones((68), dtype=torch.uint8)
|
||||
out[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 60, 64]] = 0
|
||||
if self.points is not None:
|
||||
self.points = self.points.clone()[:, out]
|
||||
else:
|
||||
raise ValueError("Invalid indicator : {:}".format(indicator))
|
||||
|
||||
def apply_horizontal_flip(self):
|
||||
# self.points[0, :] = width - self.points[0, :] - 1
|
||||
# Mugsy spefic or Synthetic
|
||||
if self.datasets.startswith("HandsyROT"):
|
||||
ori = np.array(list(range(0, 42)))
|
||||
pos = np.array(list(range(21, 42)) + list(range(0, 21)))
|
||||
self.points[:, pos] = self.points[:, ori]
|
||||
elif self.datasets.startswith("face68"):
|
||||
ori = np.array(list(range(0, 68)))
|
||||
pos = (
|
||||
np.array(
|
||||
[
|
||||
17,
|
||||
16,
|
||||
15,
|
||||
14,
|
||||
13,
|
||||
12,
|
||||
11,
|
||||
10,
|
||||
9,
|
||||
8,
|
||||
7,
|
||||
6,
|
||||
5,
|
||||
4,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
27,
|
||||
26,
|
||||
25,
|
||||
24,
|
||||
23,
|
||||
22,
|
||||
21,
|
||||
20,
|
||||
19,
|
||||
18,
|
||||
28,
|
||||
29,
|
||||
30,
|
||||
31,
|
||||
36,
|
||||
35,
|
||||
34,
|
||||
33,
|
||||
32,
|
||||
46,
|
||||
45,
|
||||
44,
|
||||
43,
|
||||
48,
|
||||
47,
|
||||
40,
|
||||
39,
|
||||
38,
|
||||
37,
|
||||
42,
|
||||
41,
|
||||
55,
|
||||
54,
|
||||
53,
|
||||
52,
|
||||
51,
|
||||
50,
|
||||
49,
|
||||
60,
|
||||
59,
|
||||
58,
|
||||
57,
|
||||
56,
|
||||
65,
|
||||
64,
|
||||
63,
|
||||
62,
|
||||
61,
|
||||
68,
|
||||
67,
|
||||
66,
|
||||
]
|
||||
)
|
||||
- 1
|
||||
)
|
||||
self.points[:, ori] = self.points[:, pos]
|
||||
else:
|
||||
raise ValueError("Does not support {:}".format(self.datasets))
|
||||
|
||||
|
||||
# shape = (H,W)
|
||||
def apply_affine2point(points, theta, shape):
|
||||
assert points.size(0) == 3, 'invalid points shape : {:}'.format(points.size())
|
||||
with torch.no_grad():
|
||||
ok_points = points[2,:] == 1
|
||||
assert torch.sum(ok_points).item() > 0, 'there is no visiable point'
|
||||
points[:2,:] = normalize_points(shape, points[:2,:])
|
||||
assert points.size(0) == 3, "invalid points shape : {:}".format(points.size())
|
||||
with torch.no_grad():
|
||||
ok_points = points[2, :] == 1
|
||||
assert torch.sum(ok_points).item() > 0, "there is no visiable point"
|
||||
points[:2, :] = normalize_points(shape, points[:2, :])
|
||||
|
||||
norm_trans_points = ok_points.unsqueeze(0).repeat(3, 1).float()
|
||||
norm_trans_points = ok_points.unsqueeze(0).repeat(3, 1).float()
|
||||
|
||||
trans_points, ___ = torch.gesv(points[:, ok_points], theta)
|
||||
trans_points, ___ = torch.gesv(points[:, ok_points], theta)
|
||||
|
||||
norm_trans_points[:, ok_points] = trans_points
|
||||
|
||||
return norm_trans_points
|
||||
norm_trans_points[:, ok_points] = trans_points
|
||||
|
||||
return norm_trans_points
|
||||
|
||||
|
||||
def apply_boundary(norm_trans_points):
|
||||
with torch.no_grad():
|
||||
norm_trans_points = norm_trans_points.clone()
|
||||
oks = torch.stack((norm_trans_points[0]>-1, norm_trans_points[0]<1, norm_trans_points[1]>-1, norm_trans_points[1]<1, norm_trans_points[2]>0))
|
||||
oks = torch.sum(oks, dim=0) == 5
|
||||
norm_trans_points[2, :] = oks
|
||||
return norm_trans_points
|
||||
with torch.no_grad():
|
||||
norm_trans_points = norm_trans_points.clone()
|
||||
oks = torch.stack(
|
||||
(
|
||||
norm_trans_points[0] > -1,
|
||||
norm_trans_points[0] < 1,
|
||||
norm_trans_points[1] > -1,
|
||||
norm_trans_points[1] < 1,
|
||||
norm_trans_points[2] > 0,
|
||||
)
|
||||
)
|
||||
oks = torch.sum(oks, dim=0) == 5
|
||||
norm_trans_points[2, :] = oks
|
||||
return norm_trans_points
|
||||
|
||||
@@ -1,39 +1,123 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
|
||||
#####################################################
|
||||
import math
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
import torch
|
||||
import torch.utils.data as data
|
||||
|
||||
|
||||
class QuadraticFunction:
|
||||
"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
|
||||
|
||||
def __init__(self, list_of_points=None):
|
||||
self._params = dict(a=None, b=None, c=None)
|
||||
if list_of_points is not None:
|
||||
self.fit(list_of_points)
|
||||
|
||||
def set(self, a, b, c):
|
||||
self._params["a"] = a
|
||||
self._params["b"] = b
|
||||
self._params["c"] = c
|
||||
|
||||
def check_valid(self):
|
||||
for key, value in self._params.items():
|
||||
if value is None:
|
||||
raise ValueError("The {:} is None".format(key))
|
||||
|
||||
def __getitem__(self, x):
|
||||
self.check_valid()
|
||||
return self._params["a"] * x * x + self._params["b"] * x + self._params["c"]
|
||||
|
||||
def fit(
|
||||
self,
|
||||
list_of_points,
|
||||
transf=lambda x: x,
|
||||
max_iter=900,
|
||||
lr_max=1.0,
|
||||
verbose=False,
|
||||
):
|
||||
with torch.no_grad():
|
||||
data = torch.Tensor(list_of_points).type(torch.float32)
|
||||
assert data.ndim == 2 and data.size(1) == 2, "Invalid shape : {:}".format(
|
||||
data.shape
|
||||
)
|
||||
x, y = data[:, 0], data[:, 1]
|
||||
weights = torch.nn.Parameter(torch.Tensor(3))
|
||||
torch.nn.init.normal_(weights, mean=0.0, std=1.0)
|
||||
optimizer = torch.optim.Adam([weights], lr=lr_max, amsgrad=True)
|
||||
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(max_iter*0.25), int(max_iter*0.5), int(max_iter*0.75)], gamma=0.1)
|
||||
if verbose:
|
||||
print("The optimizer: {:}".format(optimizer))
|
||||
|
||||
best_loss = None
|
||||
for _iter in range(max_iter):
|
||||
y_hat = transf(weights[0] * x * x + weights[1] * x + weights[2])
|
||||
loss = torch.mean(torch.abs(y - y_hat))
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
if verbose:
|
||||
print(
|
||||
"In QuadraticFunction's fit, loss at the {:02d}/{:02d}-th iter is {:}".format(
|
||||
_iter, max_iter, loss.item()
|
||||
)
|
||||
)
|
||||
# Update the params
|
||||
if best_loss is None or best_loss > loss.item():
|
||||
best_loss = loss.item()
|
||||
self._params["a"] = weights[0].item()
|
||||
self._params["b"] = weights[1].item()
|
||||
self._params["c"] = weights[2].item()
|
||||
|
||||
def __repr__(self):
|
||||
return "{name}(y = {a} * x^2 + {b} * x + {c})".format(
|
||||
name=self.__class__.__name__,
|
||||
a=self._params["a"],
|
||||
b=self._params["b"],
|
||||
c=self._params["c"],
|
||||
)
|
||||
|
||||
|
||||
class SynAdaptiveEnv(data.Dataset):
|
||||
"""The synethtic dataset for adaptive environment."""
|
||||
"""The synethtic dataset for adaptive environment.
|
||||
|
||||
- x in [0, 1]
|
||||
- y = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
|
||||
- where
|
||||
- the amplitude scale is a quadratic function of x
|
||||
- the period-phase-shift is another quadratic function of x
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_num_phase: int = 100,
|
||||
interval: float = 0.1,
|
||||
max_scale: float = 4,
|
||||
offset_scale: float = 1.5,
|
||||
num: int = 100,
|
||||
num_sin_phase: int = 4,
|
||||
min_amplitude: float = 1,
|
||||
max_amplitude: float = 4,
|
||||
phase_shift: float = 0,
|
||||
mode: Optional[str] = None,
|
||||
):
|
||||
self._amplitude_scale = QuadraticFunction(
|
||||
[(0, min_amplitude), (0.5, max_amplitude), (0, min_amplitude)]
|
||||
)
|
||||
|
||||
self._max_num_phase = max_num_phase
|
||||
self._interval = interval
|
||||
self._num_sin_phase = num_sin_phase
|
||||
self._interval = 1.0 / (float(num) - 1)
|
||||
self._total_num = num
|
||||
|
||||
self._period_phase_shift = QuadraticFunction()
|
||||
|
||||
fitting_data = []
|
||||
temp_max_scalar = 2 ** num_sin_phase
|
||||
for i in range(num_sin_phase):
|
||||
value = (2 ** i) / temp_max_scalar
|
||||
fitting_data.append((value, math.sin(value)))
|
||||
self._period_phase_shift.fit(fitting_data, transf=lambda x: torch.sin(x))
|
||||
|
||||
self._times = np.arange(0, np.pi * self._max_num_phase, self._interval)
|
||||
xmin, xmax = self._times.min(), self._times.max()
|
||||
self._inputs = []
|
||||
self._total_num = len(self._times)
|
||||
for i in range(self._total_num):
|
||||
scale = (i + 1.0) / self._total_num * max_scale
|
||||
sin_scale = (i + 1.0) / self._total_num * 0.7
|
||||
sin_scale = -4 * (sin_scale - 0.5) ** 2 + 1
|
||||
# scale = -(self._times[i] - (xmin - xmax) / 2) + max_scale
|
||||
self._inputs.append(
|
||||
np.sin(self._times[i] * sin_scale) * (offset_scale - scale)
|
||||
)
|
||||
self._inputs = np.array(self._inputs)
|
||||
# Training Set 60%
|
||||
num_of_train = int(self._total_num * 0.6)
|
||||
# Validation Set 20%
|
||||
@@ -70,10 +154,11 @@ class SynAdaptiveEnv(data.Dataset):
|
||||
def __getitem__(self, index):
|
||||
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
|
||||
index = self._indexes[index]
|
||||
value = float(self._inputs[index])
|
||||
if self._transform is not None:
|
||||
value = self._transform(value)
|
||||
return index, float(self._times[index]), value
|
||||
position = self._interval * index
|
||||
value = self._amplitude_scale[position] * math.sin(
|
||||
self._period_phase_shift[position]
|
||||
)
|
||||
return index, position, value
|
||||
|
||||
def __len__(self):
|
||||
return len(self._indexes)
|
||||
|
||||
@@ -5,16 +5,20 @@ import os
|
||||
|
||||
|
||||
def test_imagenet_data(imagenet):
|
||||
total_length = len(imagenet)
|
||||
assert total_length == 1281166 or total_length == 50000, 'The length of ImageNet is wrong : {}'.format(total_length)
|
||||
map_id = {}
|
||||
for index in range(total_length):
|
||||
path, target = imagenet.imgs[index]
|
||||
folder, image_name = os.path.split(path)
|
||||
_, folder = os.path.split(folder)
|
||||
if folder not in map_id:
|
||||
map_id[folder] = target
|
||||
else:
|
||||
assert map_id[folder] == target, 'Class : {} is not {}'.format(folder, target)
|
||||
assert image_name.find(folder) == 0, '{} is wrong.'.format(path)
|
||||
print ('Check ImageNet Dataset OK')
|
||||
total_length = len(imagenet)
|
||||
assert (
|
||||
total_length == 1281166 or total_length == 50000
|
||||
), "The length of ImageNet is wrong : {}".format(total_length)
|
||||
map_id = {}
|
||||
for index in range(total_length):
|
||||
path, target = imagenet.imgs[index]
|
||||
folder, image_name = os.path.split(path)
|
||||
_, folder = os.path.split(folder)
|
||||
if folder not in map_id:
|
||||
map_id[folder] = target
|
||||
else:
|
||||
assert map_id[folder] == target, "Class : {} is not {}".format(
|
||||
folder, target
|
||||
)
|
||||
assert image_name.find(folder) == 0, "{} is wrong.".format(path)
|
||||
print("Check ImageNet Dataset OK")
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -13,9 +13,33 @@ print("library path: {:}".format(lib_dir))
|
||||
if str(lib_dir) not in sys.path:
|
||||
sys.path.insert(0, str(lib_dir))
|
||||
|
||||
from datasets import QuadraticFunction
|
||||
from datasets import SynAdaptiveEnv
|
||||
|
||||
|
||||
class TestQuadraticFunction(unittest.TestCase):
|
||||
"""Test the quadratic function."""
|
||||
|
||||
def test_simple(self):
|
||||
function = QuadraticFunction([[0, 1], [0.5, 4], [1, 1]])
|
||||
print(function)
|
||||
for x in (0, 0.5, 1):
|
||||
print("f({:})={:}".format(x, function[x]))
|
||||
thresh = 0.2
|
||||
self.assertTrue(abs(function[0] - 1) < thresh)
|
||||
self.assertTrue(abs(function[0.5] - 4) < thresh)
|
||||
self.assertTrue(abs(function[1] - 1) < thresh)
|
||||
|
||||
def test_none(self):
|
||||
function = QuadraticFunction()
|
||||
function.fit([[0, 1], [0.5, 4], [1, 1]], max_iter=3000, verbose=True)
|
||||
print(function)
|
||||
thresh = 0.2
|
||||
self.assertTrue(abs(function[0] - 1) < thresh)
|
||||
self.assertTrue(abs(function[0.5] - 4) < thresh)
|
||||
self.assertTrue(abs(function[1] - 1) < thresh)
|
||||
|
||||
|
||||
class TestSynAdaptiveEnv(unittest.TestCase):
|
||||
"""Test the synethtic adaptive environment."""
|
||||
|
||||
|
||||
Reference in New Issue
Block a user