def cityscapes_train(resize_height, resize_width, crop_height, crop_width,
batch_size, num_workers):
"""A loader that loads images and ground truth for segmentation from the
cityscapes training set.
"""
labels = labels_cityscape_seg.getlabels()
num_classes = len(labels_cityscape_seg.gettrainid2label())
transforms = [
tf.RandomHorizontalFlip(),
tf.CreateScaledImage(),
tf.Resize((resize_height, resize_width)),
tf.RandomRescale(1.5),
tf.RandomCrop((crop_height, crop_width)),
tf.ConvertSegmentation(),
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_train_seg'),
tf.AddKeyValue('purposes', ('segmentation', 'domain')),
tf.AddKeyValue('num_classes', num_classes)
]
dataset_name = 'cityscapes'
dataset = StandardDataset(dataset=dataset_name,
trainvaltest_split='train',
video_mode='mono',
stereo_mode='mono',
labels_mode='fromid',
disable_const_items=True,
labels=labels,
keys_to_load=('color', 'segmentation'),
data_transforms=transforms,
video_frames=(0, ))
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 train set for segmentation training",
flush=True)
return loader
def cityscapes_validation(resize_height, resize_width, batch_size,
num_workers):
"""A loader that loads images and ground truth for segmentation from the
cityscapes validation set
"""
labels = labels_cityscape_seg.getlabels()
num_classes = len(labels_cityscape_seg.gettrainid2label())
transforms = [
tf.CreateScaledImage(True),
tf.Resize((resize_height, resize_width), image_types=('color', )),
tf.ConvertSegmentation(),
tf.CreateColoraug(),
tf.ToTensor(),
tf.NormalizeZeroMean(),
tf.AddKeyValue('domain', 'cityscapes_val_seg'),
tf.AddKeyValue('purposes', ('segmentation', )),
tf.AddKeyValue('num_classes', num_classes)
]
dataset = StandardDataset(dataset='cityscapes',
trainvaltest_split='validation',
video_mode='mono',
stereo_mode='mono',
labels_mode='fromid',
labels=labels,
keys_to_load=['color', 'segmentation'],
data_transforms=transforms,
disable_const_items=True)
loader = DataLoader(dataset,
batch_size,
False,
num_workers=num_workers,
pin_memory=True,
drop_last=False)
print(
f" - Can use {len(dataset)} images from the cityscapes validation set for segmentation validation",
flush=True)
return loader
def kitti_odom09_validation(img_height, img_width, batch_size, num_workers):
"""A loader that loads images and depth ground truth for
depth validation from the kitti validation set.
"""
transforms = [
tf.CreateScaledImage(True),
tf.Resize(
(img_height, img_width),
image_types=('color', )
),
tf.CreateColoraug(),
tf.ToTensor(),
tf.NormalizeZeroMean(),
tf.AddKeyValue('domain', 'kitti_odom09_val_pose'),
tf.AddKeyValue('purposes', ('depth', )),
]
dataset = StandardDataset(
dataset='kitti',
split='odom09_split',
trainvaltest_split='test',
video_mode='video',
stereo_mode='mono',
keys_to_load=('color', 'poses'),
keys_to_video=('color', ),
data_transforms=transforms,
video_frames=(0, -1, 1),
disable_const_items=True
)
loader = DataLoader(
dataset, batch_size, False,
num_workers=num_workers, pin_memory=True, drop_last=False
)
print(f" - Can use {len(dataset)} images from the kitti (odom09 split) validation set for pose validation",
flush=True)
return loader
def motsynth_validation(img_height, img_width, batch_size, num_workers):
"""A loader that loads images and depth ground truth for
depth validation from the kitti validation set.
"""
transforms = [
tf.CreateScaledImage(True),
tf.Resize((img_height, img_width), image_types=('color', )),
tf.ConvertDepth(),
tf.CreateColoraug(),
tf.ToTensor(),
tf.NormalizeZeroMean(),
tf.AddKeyValue('domain', 'kitti_zhou_val_depth'),
tf.AddKeyValue('validation_mask', 'validation_mask_kitti_zhou'),
tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'),
tf.AddKeyValue('purposes', ('depth', )),
]
dataset = StandardDataset(dataset='kek',
trainvaltest_split='validation',
video_mode='mono',
stereo_mode='mono',
keys_to_load=('color', 'depth'),
data_transforms=transforms,
video_frames=(0, ),
simple_mode=True,
labels_mode='fromid',
seq_to_load=['001'])
loader = DataLoader(dataset,
batch_size,
False,
num_workers=num_workers,
pin_memory=True,
drop_last=False)
print(
f" - Can use {len(dataset)} images from the motsynth validation set for depth validation",
flush=True)
return loader
def kitti_2015_train(img_height, img_width, batch_size, num_workers):
"""A loader that loads images and depth ground truth for
depth evaluation from the kitti_2015 training set (but for evaluation).
"""
transforms = [
tf.CreateScaledImage(True),
tf.Resize((img_height, img_width), image_types=('color', )),
tf.ConvertDepth(),
tf.CreateColoraug(),
tf.ToTensor(),
tf.NormalizeZeroMean(),
tf.AddKeyValue('domain', 'kitti_2015_train_depth'),
tf.AddKeyValue('validation_mask', 'validation_mask_kitti_kitti'),
tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'),
tf.AddKeyValue('purposes', ('depth', )),
]
dataset = StandardDataset(dataset='kitti_2015',
trainvaltest_split='train',
video_mode='mono',
stereo_mode='mono',
keys_to_load=('color', 'depth'),
data_transforms=transforms,
video_frames=(0, ),
disable_const_items=True)
loader = DataLoader(dataset,
batch_size,
False,
num_workers=num_workers,
pin_memory=True,
drop_last=False)
print(
f" - Can use {len(dataset)} images from the kitti_2015 test set for depth evaluation",
flush=True)
return loader
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
