363 lines
12 KiB
Python
363 lines
12 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, torch
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import os.path as osp
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import numpy as np
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import torchvision.datasets as dset
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import torchvision.transforms as transforms
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from copy import deepcopy
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from PIL import Image
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from .DownsampledImageNet import ImageNet16
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from .SearchDatasetWrap import SearchDataset
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from config_utils import load_config
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Dataset2Class = {
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"cifar10": 10,
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"cifar100": 100,
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"imagenet-1k-s": 1000,
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"imagenet-1k": 1000,
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"ImageNet16": 1000,
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"ImageNet16-150": 150,
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"ImageNet16-120": 120,
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"ImageNet16-200": 200,
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}
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class CUTOUT(object):
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def __init__(self, length):
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self.length = length
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def __repr__(self):
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return "{name}(length={length})".format(
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name=self.__class__.__name__, **self.__dict__
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)
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def __call__(self, img):
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h, w = img.size(1), img.size(2)
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mask = np.ones((h, w), np.float32)
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y = np.random.randint(h)
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x = np.random.randint(w)
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y1 = np.clip(y - self.length // 2, 0, h)
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y2 = np.clip(y + self.length // 2, 0, h)
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x1 = np.clip(x - self.length // 2, 0, w)
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x2 = np.clip(x + self.length // 2, 0, w)
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mask[y1:y2, x1:x2] = 0.0
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mask = torch.from_numpy(mask)
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mask = mask.expand_as(img)
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img *= mask
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return img
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imagenet_pca = {
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"eigval": np.asarray([0.2175, 0.0188, 0.0045]),
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"eigvec": np.asarray(
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[
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[-0.5675, 0.7192, 0.4009],
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[-0.5808, -0.0045, -0.8140],
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[-0.5836, -0.6948, 0.4203],
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]
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),
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}
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class Lighting(object):
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def __init__(
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self, alphastd, eigval=imagenet_pca["eigval"], eigvec=imagenet_pca["eigvec"]
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):
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self.alphastd = alphastd
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assert eigval.shape == (3,)
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assert eigvec.shape == (3, 3)
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self.eigval = eigval
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self.eigvec = eigvec
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def __call__(self, img):
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if self.alphastd == 0.0:
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return img
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rnd = np.random.randn(3) * self.alphastd
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rnd = rnd.astype("float32")
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v = rnd
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old_dtype = np.asarray(img).dtype
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v = v * self.eigval
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v = v.reshape((3, 1))
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inc = np.dot(self.eigvec, v).reshape((3,))
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img = np.add(img, inc)
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if old_dtype == np.uint8:
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img = np.clip(img, 0, 255)
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img = Image.fromarray(img.astype(old_dtype), "RGB")
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return img
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def __repr__(self):
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return self.__class__.__name__ + "()"
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def get_datasets(name, root, cutout):
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if name == "cifar10":
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mean = [x / 255 for x in [125.3, 123.0, 113.9]]
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std = [x / 255 for x in [63.0, 62.1, 66.7]]
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elif name == "cifar100":
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mean = [x / 255 for x in [129.3, 124.1, 112.4]]
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std = [x / 255 for x in [68.2, 65.4, 70.4]]
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elif name.startswith("imagenet-1k"):
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mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
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elif name.startswith("ImageNet16"):
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mean = [x / 255 for x in [122.68, 116.66, 104.01]]
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std = [x / 255 for x in [63.22, 61.26, 65.09]]
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else:
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raise TypeError("Unknow dataset : {:}".format(name))
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# Data Argumentation
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if name == "cifar10" or name == "cifar100":
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lists = [
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(32, padding=4),
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transforms.ToTensor(),
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transforms.Normalize(mean, std),
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]
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if cutout > 0:
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lists += [CUTOUT(cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize(mean, std)]
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)
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xshape = (1, 3, 32, 32)
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elif name.startswith("ImageNet16"):
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lists = [
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(16, padding=2),
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transforms.ToTensor(),
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transforms.Normalize(mean, std),
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]
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if cutout > 0:
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lists += [CUTOUT(cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize(mean, std)]
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)
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xshape = (1, 3, 16, 16)
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elif name == "tiered":
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lists = [
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(80, padding=4),
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transforms.ToTensor(),
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transforms.Normalize(mean, std),
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]
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if cutout > 0:
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lists += [CUTOUT(cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose(
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[
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transforms.CenterCrop(80),
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transforms.ToTensor(),
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transforms.Normalize(mean, std),
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]
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)
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xshape = (1, 3, 32, 32)
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elif name.startswith("imagenet-1k"):
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normalize = transforms.Normalize(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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if name == "imagenet-1k":
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xlists = [transforms.RandomResizedCrop(224)]
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xlists.append(
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transforms.ColorJitter(
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brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2
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)
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)
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xlists.append(Lighting(0.1))
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elif name == "imagenet-1k-s":
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xlists = [transforms.RandomResizedCrop(224, scale=(0.2, 1.0))]
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else:
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raise ValueError("invalid name : {:}".format(name))
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xlists.append(transforms.RandomHorizontalFlip(p=0.5))
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xlists.append(transforms.ToTensor())
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xlists.append(normalize)
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train_transform = transforms.Compose(xlists)
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test_transform = transforms.Compose(
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[
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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normalize,
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]
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)
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xshape = (1, 3, 224, 224)
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else:
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raise TypeError("Unknow dataset : {:}".format(name))
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if name == "cifar10":
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train_data = dset.CIFAR10(
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root, train=True, transform=train_transform, download=True
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)
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test_data = dset.CIFAR10(
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root, train=False, transform=test_transform, download=True
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)
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assert len(train_data) == 50000 and len(test_data) == 10000
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elif name == "cifar100":
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train_data = dset.CIFAR100(
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root, train=True, transform=train_transform, download=True
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)
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test_data = dset.CIFAR100(
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root, train=False, transform=test_transform, download=True
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)
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assert len(train_data) == 50000 and len(test_data) == 10000
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elif name.startswith("imagenet-1k"):
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train_data = dset.ImageFolder(osp.join(root, "train"), train_transform)
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test_data = dset.ImageFolder(osp.join(root, "val"), test_transform)
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assert (
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len(train_data) == 1281167 and len(test_data) == 50000
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), "invalid number of images : {:} & {:} vs {:} & {:}".format(
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len(train_data), len(test_data), 1281167, 50000
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)
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elif name == "ImageNet16":
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train_data = ImageNet16(root, True, train_transform)
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test_data = ImageNet16(root, False, test_transform)
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assert len(train_data) == 1281167 and len(test_data) == 50000
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elif name == "ImageNet16-120":
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train_data = ImageNet16(root, True, train_transform, 120)
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test_data = ImageNet16(root, False, test_transform, 120)
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assert len(train_data) == 151700 and len(test_data) == 6000
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elif name == "ImageNet16-150":
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train_data = ImageNet16(root, True, train_transform, 150)
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test_data = ImageNet16(root, False, test_transform, 150)
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assert len(train_data) == 190272 and len(test_data) == 7500
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elif name == "ImageNet16-200":
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train_data = ImageNet16(root, True, train_transform, 200)
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test_data = ImageNet16(root, False, test_transform, 200)
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assert len(train_data) == 254775 and len(test_data) == 10000
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else:
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raise TypeError("Unknow dataset : {:}".format(name))
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class_num = Dataset2Class[name]
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return train_data, test_data, xshape, class_num
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def get_nas_search_loaders(
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train_data, valid_data, dataset, config_root, batch_size, workers
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):
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if isinstance(batch_size, (list, tuple)):
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batch, test_batch = batch_size
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else:
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batch, test_batch = batch_size, batch_size
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if dataset == "cifar10":
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# split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config("{:}/cifar-split.txt".format(config_root), None, None)
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train_split, valid_split = (
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cifar_split.train,
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cifar_split.valid,
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) # search over the proposed training and validation set
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# logger.log('Load split file from {:}'.format(split_Fpath)) # they are two disjoint groups in the original CIFAR-10 training set
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# To split data
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xvalid_data = deepcopy(train_data)
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if hasattr(xvalid_data, "transforms"): # to avoid a print issue
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xvalid_data.transforms = valid_data.transform
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xvalid_data.transform = deepcopy(valid_data.transform)
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search_data = SearchDataset(dataset, train_data, train_split, valid_split)
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# data loader
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search_loader = torch.utils.data.DataLoader(
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search_data,
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batch_size=batch,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=batch,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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num_workers=workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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xvalid_data,
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batch_size=test_batch,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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num_workers=workers,
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pin_memory=True,
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)
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elif dataset == "cifar100":
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cifar100_test_split = load_config(
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"{:}/cifar100-test-split.txt".format(config_root), None, None
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)
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search_train_data = train_data
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search_valid_data = deepcopy(valid_data)
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search_valid_data.transform = train_data.transform
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search_data = SearchDataset(
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dataset,
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[search_train_data, search_valid_data],
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list(range(len(search_train_data))),
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cifar100_test_split.xvalid,
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)
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search_loader = torch.utils.data.DataLoader(
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search_data,
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batch_size=batch,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=batch,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=test_batch,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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cifar100_test_split.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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)
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elif dataset == "ImageNet16-120":
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imagenet_test_split = load_config(
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"{:}/imagenet-16-120-test-split.txt".format(config_root), None, None
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)
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search_train_data = train_data
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search_valid_data = deepcopy(valid_data)
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search_valid_data.transform = train_data.transform
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search_data = SearchDataset(
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dataset,
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[search_train_data, search_valid_data],
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list(range(len(search_train_data))),
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imagenet_test_split.xvalid,
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)
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search_loader = torch.utils.data.DataLoader(
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search_data,
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batch_size=batch,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=batch,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=test_batch,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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imagenet_test_split.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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)
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else:
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raise ValueError("invalid dataset : {:}".format(dataset))
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return search_loader, train_loader, valid_loader
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# if __name__ == '__main__':
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# train_data, test_data, xshape, class_num = dataset = get_datasets('cifar10', '/data02/dongxuanyi/.torch/cifar.python/', -1)
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# import pdb; pdb.set_trace()
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