def cityscapes_sequence_train(resize_height, resize_width, crop_height, crop_width, batch_size, num_workers): """A loader that loads images for adaptation from the cityscapes_sequence training set. This loader returns sequences from the left camera, as well as from the right camera. """ transforms_common = [ tf.RandomHorizontalFlip(), tf.CreateScaledImage(), tf.Resize((resize_height * 568 // 512, resize_width * 1092 // 1024), image_types=('color', )), # crop away the sides and bottom parts of the image tf.SidesCrop((resize_height * 320 // 512, resize_width * 1024 // 1024), (resize_height * 32 // 512, resize_width * 33 // 1024)), tf.CreateColoraug(new_element=True), tf.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, gamma=0.0), tf.RemoveOriginals(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'cityscapes_sequence_adaptation'), tf.AddKeyValue('purposes', ('adaptation', )), ] dataset_name = 'cityscapes_sequence' cfg_common = { 'dataset': dataset_name, 'trainvaltest_split': 'train', 'video_mode': 'mono', 'stereo_mode': 'mono', } cfg_left = {'keys_to_load': ('color', ), 'keys_to_video': ('color', )} cfg_right = { 'keys_to_load': ('color_right', ), 'keys_to_video': ('color_right', ) } dataset_left = StandardDataset(data_transforms=transforms_common, **cfg_left, **cfg_common) dataset_right = StandardDataset(data_transforms=[tf.ExchangeStereo()] + transforms_common, **cfg_right, **cfg_common) dataset = ConcatDataset((dataset_left, dataset_right)) loader = DataLoader(dataset, batch_size, True, num_workers=num_workers, pin_memory=True, drop_last=True) print( f" - Can use {len(dataset)} images from the cityscapes_sequence train set for adaptation", flush=True) return loader
def kitti_zhou_train(resize_height, resize_width, crop_height, crop_width, batch_size, num_workers): """A loader that loads image sequences for depth training from the kitti training set. This loader returns sequences from the left camera, as well as from the right camera. """ transforms_common = [ tf.RandomHorizontalFlip(), tf.CreateScaledImage(), tf.Resize((resize_height, resize_width), image_types=('color', 'depth', 'camera_intrinsics', 'K')), tf.ConvertDepth(), tf.CreateColoraug(new_element=True), tf.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, gamma=0.0, fraction=0.5), tf.RemoveOriginals(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_zhou_train_depth'), tf.AddKeyValue('purposes', ('depth', 'domain')), ] dataset_name = 'kitti' cfg_common = { 'dataset': dataset_name, 'trainvaltest_split': 'train', 'video_mode': 'video', 'stereo_mode': 'mono', 'split': 'zhou_split', 'video_frames': (0, -1, 1), 'disable_const_items': False } cfg_left = {'keys_to_load': ('color', ), 'keys_to_video': ('color', )} cfg_right = { 'keys_to_load': ('color_right', ), 'keys_to_video': ('color_right', ) } dataset_left = StandardDataset(data_transforms=transforms_common, **cfg_left, **cfg_common) dataset_right = StandardDataset(data_transforms=[tf.ExchangeStereo()] + transforms_common, **cfg_right, **cfg_common) dataset = ConcatDataset((dataset_left, dataset_right)) loader = DataLoader(dataset, batch_size, True, num_workers=num_workers, pin_memory=True, drop_last=True) print( f" - Can use {len(dataset)} images from the kitti (zhou_split) train split for depth training", flush=True) return loader