Initial commit

datasets/DownsampledImageNet.py

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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 __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('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True , None) 
+  valid = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False, None) 
+
+  print ( len(train) )
+  print ( len(valid) )
+  image, label = train[111]
+  trainX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True , None, 200)
+  validX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False , None, 200)
+  print ( len(trainX) )
+  print ( len(validX) )
+  #import pdb; pdb.set_trace()

datasets/LandmarkDataset.py

@@ -0,0 +1,191 @@
+# 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

datasets/SearchDatasetWrap.py

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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

datasets/__init__.py

@@ -0,0 +1,5 @@
+##################################################
+# 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

datasets/get_dataset_with_transform.py

@@ -0,0 +1,227 @@
+##################################################
+# 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.
+    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.:
+      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()

datasets/landmark_utils/__init__.py

@@ -0,0 +1 @@
+from .point_meta import PointMeta2V, apply_affine2point, apply_boundary

datasets/landmark_utils/point_meta.py

@@ -0,0 +1,116 @@
+# 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

datasets/test_utils.py

@@ -0,0 +1,20 @@
+##################################################
+# 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')