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D-X-Y
2021-05-18 14:08:00 +00:00
parent 98fadf8086
commit 94a149b33f
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148
xautodl/datasets/DownsampledImageNet.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, hashlib, torch
import numpy as np
from PIL import Image
import torch.utils.data as data
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
def calculate_md5(fpath, chunk_size=1024 * 1024):
md5 = hashlib.md5()
with open(fpath, "rb") as f:
for chunk in iter(lambda: f.read(chunk_size), b""):
md5.update(chunk)
return md5.hexdigest()
def check_md5(fpath, md5, **kwargs):
return md5 == calculate_md5(fpath, **kwargs)
def check_integrity(fpath, md5=None):
if not os.path.isfile(fpath):
return False
if md5 is None:
return True
else:
return check_md5(fpath, md5)
class ImageNet16(data.Dataset):
# http://image-net.org/download-images
# A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
# https://arxiv.org/pdf/1707.08819.pdf
train_list = [
["train_data_batch_1", "27846dcaa50de8e21a7d1a35f30f0e91"],
["train_data_batch_2", "c7254a054e0e795c69120a5727050e3f"],
["train_data_batch_3", "4333d3df2e5ffb114b05d2ffc19b1e87"],
["train_data_batch_4", "1620cdf193304f4a92677b695d70d10f"],
["train_data_batch_5", "348b3c2fdbb3940c4e9e834affd3b18d"],
["train_data_batch_6", "6e765307c242a1b3d7d5ef9139b48945"],
["train_data_batch_7", "564926d8cbf8fc4818ba23d2faac7564"],
["train_data_batch_8", "f4755871f718ccb653440b9dd0ebac66"],
["train_data_batch_9", "bb6dd660c38c58552125b1a92f86b5d4"],
["train_data_batch_10", "8f03f34ac4b42271a294f91bf480f29b"],
]
valid_list = [
["val_data", "3410e3017fdaefba8d5073aaa65e4bd6"],
]
def __init__(self, root, train, transform, use_num_of_class_only=None):
self.root = root
self.transform = transform
self.train = train # training set or valid set
if not self._check_integrity():
raise RuntimeError("Dataset not found or corrupted.")
if self.train:
downloaded_list = self.train_list
else:
downloaded_list = self.valid_list
self.data = []
self.targets = []
# now load the picked numpy arrays
for i, (file_name, checksum) in enumerate(downloaded_list):
file_path = os.path.join(self.root, file_name)
# print ('Load {:}/{:02d}-th : {:}'.format(i, len(downloaded_list), file_path))
with open(file_path, "rb") as f:
if sys.version_info[0] == 2:
entry = pickle.load(f)
else:
entry = pickle.load(f, encoding="latin1")
self.data.append(entry["data"])
self.targets.extend(entry["labels"])
self.data = np.vstack(self.data).reshape(-1, 3, 16, 16)
self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
if use_num_of_class_only is not None:
assert (
isinstance(use_num_of_class_only, int)
and use_num_of_class_only > 0
and use_num_of_class_only < 1000
), "invalid use_num_of_class_only : {:}".format(use_num_of_class_only)
new_data, new_targets = [], []
for I, L in zip(self.data, self.targets):
if 1 <= L <= use_num_of_class_only:
new_data.append(I)
new_targets.append(L)
self.data = new_data
self.targets = new_targets
# self.mean.append(entry['mean'])
# self.mean = np.vstack(self.mean).reshape(-1, 3, 16, 16)
# self.mean = np.mean(np.mean(np.mean(self.mean, axis=0), axis=1), axis=1)
# print ('Mean : {:}'.format(self.mean))
# temp = self.data - np.reshape(self.mean, (1, 1, 1, 3))
# std_data = np.std(temp, axis=0)
# std_data = np.mean(np.mean(std_data, axis=0), axis=0)
# print ('Std : {:}'.format(std_data))
def __repr__(self):
return "{name}({num} images, {classes} classes)".format(
name=self.__class__.__name__,
num=len(self.data),
classes=len(set(self.targets)),
)
def __getitem__(self, index):
img, target = self.data[index], self.targets[index] - 1
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
return img, target
def __len__(self):
return len(self.data)
def _check_integrity(self):
root = self.root
for fentry in self.train_list + self.valid_list:
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(root, filename)
if not check_integrity(fpath, md5):
return False
return True
"""
if __name__ == '__main__':
train = ImageNet16('~/.torch/cifar.python/ImageNet16', True , None)
valid = ImageNet16('~/.torch/cifar.python/ImageNet16', False, None)
print ( len(train) )
print ( len(valid) )
image, label = train[111]
trainX = ImageNet16('~/.torch/cifar.python/ImageNet16', True , None, 200)
validX = ImageNet16('~/.torch/cifar.python/ImageNet16', False , None, 200)
print ( len(trainX) )
print ( len(validX) )
"""

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xautodl/datasets/LandmarkDataset.py Normal file
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from os import path as osp
from copy import deepcopy as copy
from tqdm import tqdm
import warnings, time, random, numpy as np
from pts_utils import generate_label_map
from xvision import denormalize_points
from xvision import identity2affine, solve2theta, affine2image
from .dataset_utils import pil_loader
from .landmark_utils import PointMeta2V
from .augmentation_utils import CutOut
import torch
import torch.utils.data as data
class LandmarkDataset(data.Dataset):
def __init__(
self,
transform,
sigma,
downsample,
heatmap_type,
shape,
use_gray,
mean_file,
data_indicator,
cache_images=None,
):
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:
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")
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__
)
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

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xautodl/datasets/SearchDatasetWrap.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch, copy, random
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 __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 __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

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xautodl/datasets/__init__.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
from .SearchDatasetWrap import SearchDataset

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xautodl/datasets/get_dataset_with_transform.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, torch
import os.path as osp
import numpy as np
import torchvision.datasets as dset
import torchvision.transforms as transforms
from copy import deepcopy
from PIL import Image
from .DownsampledImageNet import ImageNet16
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,
}
class CUTOUT(object):
def __init__(self, length):
self.length = length
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)
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] = 0.0
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],
]
),
}
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 __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 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))
# 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))
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
# 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()

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xautodl/datasets/landmark_utils/__init__.py Normal file
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from .point_meta import PointMeta2V, apply_affine2point, apply_boundary

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xautodl/datasets/landmark_utils/point_meta.py Normal file
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import copy, math, torch, numpy as np
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):
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 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 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 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))
# 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, :])
norm_trans_points = ok_points.unsqueeze(0).repeat(3, 1).float()
trans_points, ___ = torch.gesv(points[:, ok_points], theta)
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

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xautodl/datasets/math_adv_funcs.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import copy
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
from .math_base_funcs import FitFunc
from .math_base_funcs import QuadraticFunc
from .math_base_funcs import QuarticFunc
class ConstantFunc(FitFunc):
"""The constant function: f(x) = c."""
def __init__(self, constant=None):
param = dict()
param[0] = constant
super(ConstantFunc, self).__init__(0, None, param)
def __call__(self, x):
self.check_valid()
return self._params[0]
def fit(self, **kwargs):
raise NotImplementedError
def _getitem(self, x, weights):
raise NotImplementedError
def __repr__(self):
return "{name}({a})".format(name=self.__class__.__name__, a=self._params[0])
class ComposedSinFunc(FitFunc):
"""The composed sin function that outputs:
f(x) = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
- the amplitude scale is a quadratic function of x
- the period-phase-shift is another quadratic function of x
"""
def __init__(self, **kwargs):
super(ComposedSinFunc, self).__init__(0, None)
self.fit(**kwargs)
def __call__(self, x):
self.check_valid()
scale = self._params["amplitude_scale"](x)
period_phase = self._params["period_phase_shift"](x)
return scale * math.sin(period_phase)
def fit(self, **kwargs):
num_sin_phase = kwargs.get("num_sin_phase", 7)
sin_speed_use_power = kwargs.get("sin_speed_use_power", True)
min_amplitude = kwargs.get("min_amplitude", 1)
max_amplitude = kwargs.get("max_amplitude", 4)
phase_shift = kwargs.get("phase_shift", 0.0)
# create parameters
if kwargs.get("amplitude_scale", None) is None:
amplitude_scale = QuadraticFunc(
[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
)
else:
amplitude_scale = kwargs.get("amplitude_scale")
if kwargs.get("period_phase_shift", None) is None:
fitting_data = []
if sin_speed_use_power:
temp_max_scalar = 2 ** (num_sin_phase - 1)
else:
temp_max_scalar = num_sin_phase - 1
for i in range(num_sin_phase):
if sin_speed_use_power:
value = (2 ** i) / temp_max_scalar
next_value = (2 ** (i + 1)) / temp_max_scalar
else:
value = i / temp_max_scalar
next_value = (i + 1) / temp_max_scalar
for _phase in (0, 0.25, 0.5, 0.75):
inter_value = value + (next_value - value) * _phase
fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
period_phase_shift = QuarticFunc(fitting_data)
else:
period_phase_shift = kwargs.get("period_phase_shift")
self.set(
dict(amplitude_scale=amplitude_scale, period_phase_shift=period_phase_shift)
)
def _getitem(self, x, weights):
raise NotImplementedError
def __repr__(self):
return "{name}({amplitude_scale} * sin({period_phase_shift}))".format(
name=self.__class__.__name__,
amplitude_scale=self._params["amplitude_scale"],
period_phase_shift=self._params["period_phase_shift"],
)

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xautodl/datasets/math_base_funcs.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import copy
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
class FitFunc(abc.ABC):
"""The fit function that outputs f(x) = a * x^2 + b * x + c."""
def __init__(self, freedom: int, list_of_points=None, params=None):
self._params = dict()
for i in range(freedom):
self._params[i] = None
self._freedom = freedom
if list_of_points is not None and params is not None:
raise ValueError("list_of_points and params can not be set simultaneously")
if list_of_points is not None:
self.fit(list_of_points=list_of_points)
if params is not None:
self.set(params)
def set(self, params):
self._params = copy.deepcopy(params)
def check_valid(self):
for key, value in self._params.items():
if value is None:
raise ValueError("The {:} is None".format(key))
@abc.abstractmethod
def __call__(self, x):
raise NotImplementedError
def noise_call(self, x, std=0.1):
clean_y = self.__call__(x)
if isinstance(clean_y, np.ndarray):
noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
else:
raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
return noise_y
@abc.abstractmethod
def _getitem(self, x):
raise NotImplementedError
def fit(self, **kwargs):
list_of_points = kwargs["list_of_points"]
max_iter, lr_max, verbose = (
kwargs.get("max_iter", 900),
kwargs.get("lr_max", 1.0),
kwargs.get("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(self._freedom))
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 = self._getitem(x, weights)
loss = torch.mean(torch.abs(y - y_hat))
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
if verbose:
print(
"In the 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()
for i in range(self._freedom):
self._params[i] = weights[i].item()
def __repr__(self):
return "{name}(freedom={freedom})".format(
name=self.__class__.__name__, freedom=freedom
)
class LinearFunc(FitFunc):
"""The linear function that outputs f(x) = a * x + b."""
def __init__(self, list_of_points=None, params=None):
super(LinearFunc, self).__init__(2, list_of_points, params)
def __call__(self, x):
self.check_valid()
return self._params[0] * x + self._params[1]
def _getitem(self, x, weights):
return weights[0] * x + weights[1]
def __repr__(self):
return "{name}({a} * x + {b})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
)
class QuadraticFunc(FitFunc):
"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
def __init__(self, list_of_points=None, params=None):
super(QuadraticFunc, self).__init__(3, list_of_points, params)
def __call__(self, x):
self.check_valid()
return self._params[0] * x * x + self._params[1] * x + self._params[2]
def _getitem(self, x, weights):
return weights[0] * x * x + weights[1] * x + weights[2]
def __repr__(self):
return "{name}({a} * x^2 + {b} * x + {c})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
)
class CubicFunc(FitFunc):
"""The cubic function that outputs f(x) = a * x^3 + b * x^2 + c * x + d."""
def __init__(self, list_of_points=None):
super(CubicFunc, self).__init__(4, list_of_points)
def __call__(self, x):
self.check_valid()
return (
self._params[0] * x ** 3
+ self._params[1] * x ** 2
+ self._params[2] * x
+ self._params[3]
)
def _getitem(self, x, weights):
return weights[0] * x ** 3 + weights[1] * x ** 2 + weights[2] * x + weights[3]
def __repr__(self):
return "{name}({a} * x^3 + {b} * x^2 + {c} * x + {d})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
)
class QuarticFunc(FitFunc):
"""The quartic function that outputs f(x) = a * x^4 + b * x^3 + c * x^2 + d * x + e."""
def __init__(self, list_of_points=None):
super(QuarticFunc, self).__init__(5, list_of_points)
def __call__(self, x):
self.check_valid()
return (
self._params[0] * x ** 4
+ self._params[1] * x ** 3
+ self._params[2] * x ** 2
+ self._params[3] * x
+ self._params[4]
)
def _getitem(self, x, weights):
return (
weights[0] * x ** 4
+ weights[1] * x ** 3
+ weights[2] * x ** 2
+ weights[3] * x
+ weights[4]
)
def __repr__(self):
return "{name}({a} * x^4 + {b} * x^3 + {c} * x^2 + {d} * x + {e})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
e=self._params[3],
)

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xautodl/datasets/math_core.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
#####################################################
from .math_base_funcs import LinearFunc, QuadraticFunc, CubicFunc, QuarticFunc
from .math_dynamic_funcs import DynamicLinearFunc
from .math_dynamic_funcs import DynamicQuadraticFunc
from .math_adv_funcs import ConstantFunc
from .math_adv_funcs import ComposedSinFunc

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xautodl/datasets/math_dynamic_funcs.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import copy
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
from .math_base_funcs import FitFunc
class DynamicFunc(FitFunc):
"""The dynamic quadratic function, where each param is a function."""
def __init__(self, freedom: int, params=None):
super(DynamicFunc, self).__init__(freedom, None, params)
self._timestamp = None
def __call__(self, x, timestamp=None):
raise NotImplementedError
def _getitem(self, x, weights):
raise NotImplementedError
def set_timestamp(self, timestamp):
self._timestamp = timestamp
def noise_call(self, x, timestamp=None, std=0.1):
clean_y = self.__call__(x, timestamp)
if isinstance(clean_y, np.ndarray):
noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
else:
raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
return noise_y
class DynamicLinearFunc(DynamicFunc):
"""The dynamic linear function that outputs f(x) = a * x + b.
The a and b is a function of timestamp.
"""
def __init__(self, params=None):
super(DynamicLinearFunc, self).__init__(3, params)
def __call__(self, x, timestamp=None):
self.check_valid()
if timestamp is None:
timestamp = self._timestamp
a = self._params[0](timestamp)
b = self._params[1](timestamp)
convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
a, b = convert_fn(a), convert_fn(b)
return a * x + b
def __repr__(self):
return "{name}({a} * x + {b}, timestamp={timestamp})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
timestamp=self._timestamp,
)
class DynamicQuadraticFunc(DynamicFunc):
"""The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
The a, b, and c is a function of timestamp.
"""
def __init__(self, params=None):
super(DynamicQuadraticFunc, self).__init__(3, params)
def __call__(self, x, timestamp=None):
self.check_valid()
if timestamp is None:
timestamp = self._timestamp
a = self._params[0](timestamp)
b = self._params[1](timestamp)
c = self._params[2](timestamp)
convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
a, b, c = convert_fn(a), convert_fn(b), convert_fn(c)
return a * x * x + b * x + c
def __repr__(self):
return "{name}({a} * x^2 + {b} * x + {c}, timestamp={timestamp})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
timestamp=self._timestamp,
)

58
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
#####################################################
from .synthetic_utils import TimeStamp
from .synthetic_env import EnvSampler
from .synthetic_env import SyntheticDEnv
from .math_core import LinearFunc
from .math_core import DynamicLinearFunc
from .math_core import DynamicQuadraticFunc
from .math_core import ConstantFunc, ComposedSinFunc
__all__ = ["TimeStamp", "SyntheticDEnv", "get_synthetic_env"]
def get_synthetic_env(total_timestamp=1000, num_per_task=1000, mode=None, version="v1"):
if version == "v1":
mean_generator = ConstantFunc(0)
std_generator = ConstantFunc(1)
elif version == "v2":
mean_generator = ComposedSinFunc()
std_generator = ComposedSinFunc(min_amplitude=0.5, max_amplitude=1.5)
else:
raise ValueError("Unknown version: {:}".format(version))
dynamic_env = SyntheticDEnv(
[mean_generator],
[[std_generator]],
num_per_task=num_per_task,
timestamp_config=dict(
min_timestamp=-0.5, max_timestamp=1.5, num=total_timestamp, mode=mode
),
)
if version == "v1":
function = DynamicLinearFunc()
function_param = dict()
function_param[0] = ComposedSinFunc(
amplitude_scale=ConstantFunc(3.0),
num_sin_phase=9,
sin_speed_use_power=False,
)
function_param[1] = ConstantFunc(constant=0.9)
elif version == "v2":
function = DynamicQuadraticFunc()
function_param = dict()
function_param[0] = ComposedSinFunc(
num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0
)
function_param[1] = ConstantFunc(constant=0.9)
function_param[2] = ComposedSinFunc(
num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9
)
else:
raise ValueError("Unknown version: {:}".format(version))
function.set(function_param)
# dynamic_env.set_oracle_map(copy.deepcopy(function))
dynamic_env.set_oracle_map(function)
return dynamic_env

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import math
import random
import numpy as np
from typing import List, Optional, Dict
import torch
import torch.utils.data as data
from .synthetic_utils import TimeStamp
def is_list_tuple(x):
return isinstance(x, (tuple, list))
def zip_sequence(sequence):
def _combine(*alist):
if is_list_tuple(alist[0]):
return [_combine(*xlist) for xlist in zip(*alist)]
else:
return torch.cat(alist, dim=0)
def unsqueeze(a):
if is_list_tuple(a):
return [unsqueeze(x) for x in a]
else:
return a.unsqueeze(dim=0)
with torch.no_grad():
sequence = [unsqueeze(a) for a in sequence]
return _combine(*sequence)
class SyntheticDEnv(data.Dataset):
"""The synethtic dynamic environment."""
def __init__(
self,
mean_functors: List[data.Dataset],
cov_functors: List[List[data.Dataset]],
num_per_task: int = 5000,
timestamp_config: Optional[Dict] = None,
mode: Optional[str] = None,
timestamp_noise_scale: float = 0.3,
):
self._ndim = len(mean_functors)
assert self._ndim == len(
cov_functors
), "length does not match {:} vs. {:}".format(self._ndim, len(cov_functors))
for cov_functor in cov_functors:
assert self._ndim == len(
cov_functor
), "length does not match {:} vs. {:}".format(self._ndim, len(cov_functor))
self._num_per_task = num_per_task
if timestamp_config is None:
timestamp_config = dict(mode=mode)
elif "mode" not in timestamp_config:
timestamp_config["mode"] = mode
self._timestamp_generator = TimeStamp(**timestamp_config)
self._timestamp_noise_scale = timestamp_noise_scale
self._mean_functors = mean_functors
self._cov_functors = cov_functors
self._oracle_map = None
self._seq_length = None
@property
def min_timestamp(self):
return self._timestamp_generator.min_timestamp
@property
def max_timestamp(self):
return self._timestamp_generator.max_timestamp
@property
def timestamp_interval(self):
return self._timestamp_generator.interval
def random_timestamp(self):
return (
random.random() * (self.max_timestamp - self.min_timestamp)
+ self.min_timestamp
)
def reset_max_seq_length(self, seq_length):
self._seq_length = seq_length
def get_timestamp(self, index):
if index is None:
timestamps = []
for index in range(len(self._timestamp_generator)):
timestamps.append(self._timestamp_generator[index][1])
return tuple(timestamps)
else:
index, timestamp = self._timestamp_generator[index]
return timestamp
def set_oracle_map(self, functor):
self._oracle_map = functor
def __iter__(self):
self._iter_num = 0
return self
def __next__(self):
if self._iter_num >= len(self):
raise StopIteration
self._iter_num += 1
return self.__getitem__(self._iter_num - 1)
def __getitem__(self, index):
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
index, timestamp = self._timestamp_generator[index]
if self._seq_length is None:
return self.__call__(timestamp)
else:
noise = (
random.random() * self.timestamp_interval * self._timestamp_noise_scale
)
timestamps = [
timestamp + i * self.timestamp_interval + noise
for i in range(self._seq_length)
]
xdata = [self.__call__(timestamp) for timestamp in timestamps]
return zip_sequence(xdata)
def __call__(self, timestamp):
mean_list = [functor(timestamp) for functor in self._mean_functors]
cov_matrix = [
[abs(cov_gen(timestamp)) for cov_gen in cov_functor]
for cov_functor in self._cov_functors
]
dataset = np.random.multivariate_normal(
mean_list, cov_matrix, size=self._num_per_task
)
if self._oracle_map is None:
return torch.Tensor([timestamp]), torch.Tensor(dataset)
else:
targets = self._oracle_map.noise_call(dataset, timestamp)
return torch.Tensor([timestamp]), (
torch.Tensor(dataset),
torch.Tensor(targets),
)
def __len__(self):
return len(self._timestamp_generator)
def __repr__(self):
return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task}, range=[{xrange_min:.5f}~{xrange_max:.5f}], mode={mode})".format(
name=self.__class__.__name__,
cur_num=len(self),
total=len(self._timestamp_generator),
ndim=self._ndim,
num_per_task=self._num_per_task,
xrange_min=self.min_timestamp,
xrange_max=self.max_timestamp,
mode=self._timestamp_generator.mode,
)
class EnvSampler:
def __init__(self, env, batch, enlarge):
indexes = list(range(len(env)))
self._indexes = indexes * enlarge
self._batch = batch
self._iterations = len(self._indexes) // self._batch
def __iter__(self):
random.shuffle(self._indexes)
for it in range(self._iterations):
indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
yield indexes
def __len__(self):
return self._iterations

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import copy
from .math_dynamic_funcs import DynamicLinearFunc, DynamicQuadraticFunc
from .math_adv_funcs import ConstantFunc, ComposedSinFunc
from .synthetic_env import SyntheticDEnv
def create_example(timestamp_config=None, num_per_task=5000, indicator="v1"):
if indicator == "v1":
return create_example_v1(timestamp_config, num_per_task)
elif indicator == "v2":
return create_example_v2(timestamp_config, num_per_task)
else:
raise ValueError("Unkonwn indicator: {:}".format(indicator))
def create_example_v1(
timestamp_config=None,
num_per_task=5000,
):
mean_generator = ComposedSinFunc()
std_generator = ComposedSinFunc(min_amplitude=0.5, max_amplitude=0.5)
dynamic_env = SyntheticDEnv(
[mean_generator],
[[std_generator]],
num_per_task=num_per_task,
timestamp_config=timestamp_config,
)
function = DynamicQuadraticFunc()
function_param = dict()
function_param[0] = ComposedSinFunc(
num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0
)
function_param[1] = ConstantFunc(constant=0.9)
function_param[2] = ComposedSinFunc(
num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9
)
function.set(function_param)
dynamic_env.set_oracle_map(copy.deepcopy(function))
return dynamic_env, function
def create_example_v2(
timestamp_config=None,
num_per_task=5000,
):
mean_generator = ConstantFunc(0)
std_generator = ConstantFunc(1)
dynamic_env = SyntheticDEnv(
[mean_generator],
[[std_generator]],
num_per_task=num_per_task,
timestamp_config=timestamp_config,
)
function = DynamicLinearFunc()
function_param = dict()
function_param[0] = ComposedSinFunc(
amplitude_scale=ConstantFunc(1.0), period_phase_shift=ConstantFunc(1.0)
)
function_param[1] = ConstantFunc(constant=0.9)
function.set(function_param)
dynamic_env.set_oracle_map(copy.deepcopy(function))
return dynamic_env, function

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
class UnifiedSplit:
"""A class to unify the split strategy."""
def __init__(self, total_num, mode):
# Training Set 60%
num_of_train = int(total_num * 0.6)
# Validation Set 20%
num_of_valid = int(total_num * 0.2)
# Test Set 20%
num_of_set = total_num - num_of_train - num_of_valid
all_indexes = list(range(total_num))
if mode is None:
self._indexes = all_indexes
elif mode.lower() in ("train", "training"):
self._indexes = all_indexes[:num_of_train]
elif mode.lower() in ("valid", "validation"):
self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
elif mode.lower() in ("test", "testing"):
self._indexes = all_indexes[num_of_train + num_of_valid :]
else:
raise ValueError("Unkonwn mode of {:}".format(mode))
self._all_indexes = all_indexes
self._mode = mode
@property
def mode(self):
return self._mode
class TimeStamp(UnifiedSplit, data.Dataset):
"""The timestamp dataset."""
def __init__(
self,
min_timestamp: float = 0.0,
max_timestamp: float = 1.0,
num: int = 100,
mode: Optional[str] = None,
):
self._min_timestamp = min_timestamp
self._max_timestamp = max_timestamp
self._interval = (max_timestamp - min_timestamp) / (float(num) - 1)
self._total_num = num
UnifiedSplit.__init__(self, self._total_num, mode)
@property
def min_timestamp(self):
return self._min_timestamp + self._interval * min(self._indexes)
@property
def max_timestamp(self):
return self._min_timestamp + self._interval * max(self._indexes)
@property
def interval(self):
return self._interval
def __iter__(self):
self._iter_num = 0
return self
def __next__(self):
if self._iter_num >= len(self):
raise StopIteration
self._iter_num += 1
return self.__getitem__(self._iter_num - 1)
def __getitem__(self, index):
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
index = self._indexes[index]
timestamp = self._min_timestamp + self._interval * index
return index, timestamp
def __len__(self):
return len(self._indexes)
def __repr__(self):
return "{name}({cur_num:}/{total} elements)".format(
name=self.__class__.__name__,
cur_num=len(self),
total=self._total_num,
)

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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
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")