149 lines
5.2 KiB
Python
149 lines
5.2 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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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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else:
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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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def check_md5(fpath, md5, **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):
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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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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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]
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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:
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downloaded_list = self.train_list
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else:
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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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# 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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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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return img, target
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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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"""
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if __name__ == '__main__':
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train = ImageNet16('~/.torch/cifar.python/ImageNet16', True , None)
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valid = ImageNet16('~/.torch/cifar.python/ImageNet16', False, None)
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print ( len(train) )
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print ( len(valid) )
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image, label = train[111]
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trainX = ImageNet16('~/.torch/cifar.python/ImageNet16', True , None, 200)
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validX = ImageNet16('~/.torch/cifar.python/ImageNet16', False , None, 200)
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print ( len(trainX) )
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print ( len(validX) )
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"""
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