def generate_kornia_transforms(image_size=224,
resize=256,
mean=[],
std=[],
include_jitter=False):
mean = torch.tensor(mean) if mean else torch.tensor([0.5, 0.5, 0.5])
std = torch.tensor(std) if std else torch.tensor([0.1, 0.1, 0.1])
if torch.cuda.is_available():
mean = mean.cuda()
std = std.cuda()
train_transforms = [G.Resize((resize, resize))]
if include_jitter:
train_transforms.append(
K.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1))
train_transforms.extend([
K.RandomHorizontalFlip(p=0.5),
K.RandomVerticalFlip(p=0.5),
K.RandomRotation(90),
K.RandomResizedCrop((image_size, image_size)),
K.Normalize(mean, std)
])
val_transforms = [
G.Resize((resize, resize)),
K.CenterCrop((image_size, image_size)),
K.Normalize(mean, std)
]
transforms = dict(train=nn.Sequential(*train_transforms),
val=nn.Sequential(*val_transforms))
if torch.cuda.is_available():
for k in transforms:
transforms[k] = transforms[k].cuda()
return transforms
def test_random_crops_and_flips(self, device, dtype):
width, height = 100, 100
crop_width, crop_height = 3, 3
input = torch.randn(3, 3, width, height, device=device, dtype=dtype)
bbox = torch.tensor([[[1.0, 1.0, 2.0, 2.0], [0.0, 0.0, 1.0, 2.0],
[0.0, 0.0, 2.0, 1.0]]],
device=device,
dtype=dtype).expand(3, -1, -1)
aug = K.AugmentationSequential(
K.RandomCrop((crop_width, crop_height),
padding=1,
cropping_mode='resample',
fill=0),
K.RandomHorizontalFlip(p=1.0),
data_keys=["input", "bbox_xyxy"],
)
reproducibility_test((input, bbox), aug)
_params = aug.forward_parameters(input.shape)
# specifying the crop locations allows us to compute by hand the expected outputs
crop_locations = torch.tensor(
[[1.0, 2.0], [1.0, 1.0], [2.0, 0.0]],
device=_params[0].data['src'].device,
dtype=_params[0].data['src'].dtype,
)
crops = crop_locations.expand(4, -1, -1).permute(1, 0, 2).clone()
crops[:, 1:3, 0] += crop_width - 1
crops[:, 2:4, 1] += crop_height - 1
_params[0].data['src'] = crops
# expected output bboxes after crop for specified crop locations and crop size (3,3)
expected_out_bbox = torch.tensor(
[
[[1.0, 0.0, 2.0, 1.0], [0.0, -1.0, 1.0, 1.0],
[0.0, -1.0, 2.0, 0.0]],
[[1.0, 1.0, 2.0, 2.0], [0.0, 0.0, 1.0, 2.0],
[0.0, 0.0, 2.0, 1.0]],
[[0.0, 2.0, 1.0, 3.0], [-1.0, 1.0, 0.0, 3.0],
[-1.0, 1.0, 1.0, 2.0]],
],
device=device,
dtype=dtype,
)
# horizontally flip boxes based on crop width
xmins = expected_out_bbox[..., 0].clone()
xmaxs = expected_out_bbox[..., 2].clone()
expected_out_bbox[..., 0] = crop_width - xmaxs
expected_out_bbox[..., 2] = crop_width - xmins
out = aug(input, bbox, params=_params)
assert out[1].shape == bbox.shape
assert_close(out[1], expected_out_bbox, atol=1e-4, rtol=1e-4)
out_inv = aug.inverse(*out)
assert out_inv[1].shape == bbox.shape
assert_close(out_inv[1], bbox, atol=1e-4, rtol=1e-4)
def __init__(
self,
net,
image_size,
hidden_layer=-2,
project_hidden=True,
project_dim=128,
augment_both=True,
use_nt_xent_loss=False,
augment_fn=None,
use_bilinear=False,
use_momentum=False,
momentum_value=0.999,
key_encoder=None,
temperature=0.1,
fp16=False,
):
super().__init__()
self.net = OutputHiddenLayer(net, layer=hidden_layer)
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size)),
)
self.augment = default(augment_fn, DEFAULT_AUG)
self.augment_both = augment_both
self.temperature = temperature
self.use_nt_xent_loss = use_nt_xent_loss
self.project_hidden = project_hidden
self.projection = None
self.project_dim = project_dim
self.use_bilinear = use_bilinear
self.bilinear_w = None
self.use_momentum = use_momentum
self.ema_updater = EMA(momentum_value)
self.key_encoder = key_encoder
# for accumulating queries and keys across calls
self.queries = None
self.keys = None
self.fp16 = fp16
# send a mock image tensor to instantiate parameters
init = torch.randn(1, 3, image_size, image_size, device="cuda")
if self.fp16:
init = init.half()
self.forward(init)
def __init__(self, opt):
super(PostTensorTransform, self).__init__()
self.random_crop = ProbTransform(A.RandomCrop(
(opt.input_height, opt.input_width), padding=opt.random_crop),
p=0.8)
self.random_rotation = ProbTransform(A.RandomRotation(
opt.random_rotation),
p=0.5)
if opt.dataset == "cifar10":
self.random_horizontal_flip = A.RandomHorizontalFlip(p=0.5)
def __init__(self, im_size=224, device=torch.device('cuda:0')):
super().__init__()
self.mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
self.std = torch.tensor([0.229, 0.224, 0.225]).to(device)
self.aug = torch.nn.Sequential(
kornia.geometry.transform.Resize(int(im_size * 1.2)),
Kaug.RandomCrop((im_size, im_size), padding=8),
Kaug.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
Kaug.RandomHorizontalFlip(),
Kaug.Normalize(mean=self.mean, std=self.std))
def __init__(self, cutn):
super().__init__()
self.cutn = cutn
self.augs = nn.Sequential(
K.RandomHorizontalFlip(p=0.5),
K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
#K.RandomSolarize(0.01, 0.01, p=0.7),
K.RandomSharpness(0.3, p=0.4),
K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'),
K.RandomPerspective(0.2, p=0.4), )
self.noise_fac = 0.1
def __init__(
self,
net,
image_size,
hidden_layer = -2,
projection_size = 256,
projection_hidden_size = 2048,
augment_fn = None,
augment_fn2 = None,
moving_average_decay = 0.99,
ppm_num_layers = 1,
ppm_gamma = 2,
distance_thres = 0.1, # the paper uses 0.7, but that leads to nearly all positive hits. need clarification on how the coordinates are normalized before distance calculation.
similarity_temperature = 0.3,
alpha = 1.
):
super().__init__()
# default SimCLR augmentation
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size)),
augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225]))
)
self.augment1 = default(augment_fn, DEFAULT_AUG)
self.augment2 = default(augment_fn2, self.augment1)
self.online_encoder = NetWrapper(net, projection_size, projection_hidden_size, layer=hidden_layer)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.distance_thres = distance_thres
self.similarity_temperature = similarity_temperature
self.alpha = alpha
self.propagate_pixels = PPM(
chan = projection_size,
num_layers = ppm_num_layers,
gamma = ppm_gamma
)
# get device of network and make wrapper same device
device = get_module_device(net)
self.to(device)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size, device=device))
def default_aug(image_size: Tuple[int, int] = (360, 360)) -> nn.Module:
return nn.Sequential(
aug.ColorJitter(contrast=0.1, brightness=0.1, saturation=0.1, p=0.8),
aug.RandomVerticalFlip(),
aug.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (0.5, 0.5)), p=0.1),
aug.RandomResizedCrop(size=image_size, scale=(0.5, 1)),
aug.Normalize(
mean=torch.tensor([0.485, 0.456, 0.406]),
std=torch.tensor([0.229, 0.224, 0.225]),
),
)
def default_augmentation(image_size: Tuple[int, int] = (224, 224)) -> nn.Module:
return nn.Sequential(
tf.Resize(size=image_size),
RandomApply(aug.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
aug.RandomGrayscale(p=0.2),
aug.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
aug.RandomResizedCrop(size=image_size),
aug.Normalize(
mean=torch.tensor([0.485, 0.456, 0.406]),
std=torch.tensor([0.229, 0.224, 0.225]),
),
)
def __init__(self, opt):
super().__init__()
self.wrapped_dataset = create_dataset(opt['dataset'])
self.cropped_img_size = opt['crop_size']
self.includes_labels = opt['includes_labels']
augmentations = [ \
RandomApply(augs.ColorJitter(0.4, 0.4, 0.4, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1)]
self.aug = nn.Sequential(*augmentations)
self.rrc = nn.Sequential(*[
augs.RandomHorizontalFlip(),
augs.RandomResizedCrop((self.cropped_img_size,
self.cropped_img_size))
])
def __init__(self, viz: bool = False):
super().__init__()
self.viz = viz
'''self.geometric = [
K.augmentation.RandomAffine(60., p=0.75),
]'''
self.augmentations = nn.Sequential(
augmentation.RandomRotation(degrees=30.),
augmentation.RandomPerspective(distortion_scale=0.4),
augmentation.RandomResizedCrop((224, 224)),
augmentation.RandomHorizontalFlip(p=0.5),
augmentation.RandomVerticalFlip(p=0.5),
# K.augmentation.GaussianBlur((3, 3), (0.1, 2.0), p=1.0),
# K.augmentation.ColorJitter(0.01, 0.01, 0.01, 0.01, p=0.25),
)
self.denorm = augmentation.Denormalize(Tensor(DATASET_IMAGE_MEAN), Tensor(DATASET_IMAGE_STD))
def __init__(self,
celeba_folder,
mask_folder,
mode="train",
use_transforms=False):
super().__init__()
self.images = []
self.labels = []
self.mode = mode
self.mask_folder = mask_folder
self.crop_aug = K.RandomCrop((218, 178), pad_if_needed=True)
self.flip_aug = K.RandomHorizontalFlip()
self.id_map = {}
with open(celeba_folder + "/identity_CelebA.txt") as id_f:
for line in id_f:
im, id = line.split()
self.id_map[im[:-4]] = int(id)
with open(celeba_folder + "/labels/list_attr_celeba.txt") as label_f:
id_count = label_f.readline().strip("\n")
self.attributes = np.array(label_f.readline().strip("\n").split())
for i, line in enumerate(label_f.read().split("\n")[:-1]):
image_data = line.split()
image = image_data[0]
if not os.path.exists(f"{mask_folder}"
f"/id-{self.id_map[image[:-4]]}"):
continue
image_labels = [int(label) for label in image_data[1:]]
image_n = int(image[:-4])
train_cond = mode == "train" and image_n < 162771
val_cond = mode == "val" and image_n >= 162771 and image_n < 182638
test_cond = mode == "test" and image_n >= 182638
full_cond = mode == "full"
if train_cond or val_cond or test_cond or full_cond:
self.images.append(celeba_folder + "/images/" + image)
self.labels.append(image_labels)
self.alt_background = TF.to_tensor(Image.open("unicorn.jpg"))
self.length = len(self.images)
def __init__(self, model, imageSize, embeddingLayer=-2, projectionDim=256, projectionHiddenDim=4096, emaDecay=0.99):
super(BYOL, self).__init__()
# Default SimCLR augmentations
self.augment = nn.Sequential(
RandomApply(augmentation.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augmentation.RandomGrayscale(p=0.2),
augmentation.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augmentation.RandomResizedCrop((imageSize, imageSize)),
color.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225]))
)
# Initialize models, predictors and EMA
self.onlineEncoder = ModelWrapper(model, projectionDim, projectionHiddenDim, embeddingLayer)
self.onlinePredictor = MLP(projectionDim, projectionDim, projectionHiddenDim)
self.targetEncoder = copy.deepcopy(self.onlineEncoder)
self.targetEMA = EMA(emaDecay)
def __init__(self, model_name, n_out):
super(AudioClassifier, self).__init__()
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=80, time_stripes_num=2,
freq_drop_width=16, freq_stripes_num=2)
self.net = timm.create_model(model_name, pretrained=True, in_chans=1)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout1 = nn.Dropout(0.3)
self.dropout2 = nn.Dropout(0.3)
n_features = self.net.classifier.in_features
self.net_classifier = nn.Linear(n_features, n_out)
self.init_weight()
# korrniaのrandom cropはh,wを想定しているため注意
self.transform = nn.Sequential(K.RandomHorizontalFlip(p=0.1),
# K.GaussianBlur(7, p=0.5),
# K.RandomCrop((round(IMAGE_HEIGHT*0.7), round(IMAGE_WIDTH*0.7)),p=0.3)
)
def __init__(self,
net,
image_size=32,
layer_name_list=[-2],
projection_size=256,
projection_hidden_size=4096,
augment_fn=None,
moving_average_decay=0.99,
device_='cuda',
number_of_classes=10,
mean_data=torch.tensor([0.485, 0.456, 0.406]),
std_data=torch.tensor([0.229, 0.224, 0.225])):
super().__init__()
# default SimCLR augmentation
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2), augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size)),
augs.Normalize(mean=mean_data, std=std_data))
self.augment = default(augment_fn, DEFAULT_AUG)
self.device = device_
self.online_encoder = NetWrapper(net,
projection_size,
projection_hidden_size,
layer_name_list=layer_name_list).to(
self.device)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size,
projection_hidden_size).to(self.device)
self.online_predictor1 = MLP(projection_size, projection_size,
512).to(self.device)
self.online_predictor2 = MLP(projection_size, projection_size,
512).to(self.device)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size).to(self.device))
def __init__(self, utes, mask, ct, length, opt):
super(TrainDataset, self).__init__()
self.utes = utes
self.label = ct
self.mask = mask
self.length = length
self.num_vols = utes.shape[0]
self.batch_size = opt.trainBatchSize
self.spatial = nn.Sequential(
ka.RandomAffine(45,
translate=(0.1, 0.1),
scale=(0.85, 1.15),
shear=(0.1, 0.1),
same_on_batch=True),
ka.RandomVerticalFlip(same_on_batch=True),
ka.RandomHorizontalFlip(same_on_batch=True))
self.dim = 2
self.counter = 0
def __init__(
self,
net,
image_size,
hidden_layer=-2,
projection_size=256,
projection_hidden_size=4096,
moving_average_decay=0.99,
use_momentum=True,
structural_mlp=False,
):
super().__init__()
self.online_encoder = NetWrapper(net,
projection_size,
projection_hidden_size,
layer=hidden_layer,
use_structural_mlp=structural_mlp)
augmentations = [ \
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size))]
self.aug = nn.Sequential(*augmentations)
self.use_momentum = use_momentum
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size,
projection_hidden_size)
# get device of network and make wrapper same device
device = get_module_device(net)
self.to(device)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size, device=device),
torch.randn(2, 3, image_size, image_size, device=device))
def __init__(self,
net,
image_size,
hidden_layer=-2,
projection_size=256,
projection_hidden_size=4096,
augment_fn=None,
augment_fn2=None,
moving_average_decay=0.99):
super().__init__()
# default SimCLR augmentation
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2), augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size)),
augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]),
std=torch.tensor([0.229, 0.224, 0.225])))
self.augment1 = default(augment_fn, DEFAULT_AUG)
self.augment2 = default(augment_fn2, self.augment1)
self.online_encoder = NetWrapper(net,
projection_size,
projection_hidden_size,
layer=hidden_layer)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size,
projection_hidden_size)
# get device of network and make wrapper same device
device = get_module_device(net)
self.to(device)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size, device=device))
def default_train_transforms():
image_size = ImageClassificationData.image_size
if _KORNIA_AVAILABLE and not os.getenv("FLASH_TESTING", "0") == "1":
# Better approach as all transforms are applied on tensor directly
return {
"post_tensor_transform":
nn.Sequential(K.RandomResizedCrop(image_size),
K.RandomHorizontalFlip()),
"per_batch_transform_on_device":
nn.Sequential(
K.Normalize(torch.tensor([0.485, 0.456, 0.406]),
torch.tensor([0.229, 0.224, 0.225])), )
}
else:
from torchvision import transforms as T # noqa F811
return {
"pre_tensor_transform":
nn.Sequential(T.RandomResizedCrop(image_size),
T.RandomHorizontalFlip()),
"post_tensor_transform":
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
}
def get_frame_aug(args):
train_transform = []
if 'cj' in args.frame_aug:
_cj = 0.1
train_transform += [
#K.RandomGrayscale(p=0.2),
K.ColorJitter(_cj, _cj, _cj, 0),
]
if 'flip' in args.frame_aug:
train_transform += [
K.RandomHorizontalFlip(same_on_batch=True),
]
train_transform.append(kornia.color.Normalize(mean=IMG_MEAN, std=IMG_STD))
train_transform = nn.Sequential(*train_transform)
print('Frame augs:', train_transform, args.frame_aug)
# HACK if you up value the args field, it is a pointer!!!
patch_size = np.array(args.patch_size)
def aug(x):
if 'grid' in args.frame_aug:
return patch_grid(x,
transform=train_transform,
shape=patch_size,
stride=args.pstride)
elif 'randpatch' in args.frame_aug:
return n_patches(x,
args.npatch,
transform=train_transform,
shape=patch_size,
scale=args.npatch_scale)
else:
return train_transform(x)
return aug
def __init__(self, net, image_size, hidden_layer = -2, projection_size = 256, projection_hidden_size = 4096, augment_fn = None, moving_average_decay = 0.99):
super().__init__()
# default SimCLR augmentation
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size)) )
self.augment = default(augment_fn, DEFAULT_AUG)
self.online_encoder = NetWrapper(net, projection_size, projection_hidden_size, layer=hidden_layer)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size, projection_hidden_size)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size))
def get_frame_transform(frame_transform_str, img_size, cuda=True):
tt = []
if 'gray' in frame_transform_str:
tt += [K.RandomGrayscale(p=1)]
if 'crop' in frame_transform_str:
tt += [
K.RandomResizedCrop(img_size, scale=(0.8, 0.95), ratio=(0.7, 1.3))
]
else:
tt += [kornia.geometry.transform.Resize((img_size, img_size))]
if 'cj' in frame_transform_str:
_cj = 0.1
tt += [ #K.RandomGrayscale(p=0.2),
K.ColorJitter(_cj, _cj, _cj, _cj)
]
if 'flip' in frame_transform_str:
tt += [K.RandomHorizontalFlip()]
return tt
def test_random_flips(self, device, dtype):
inp = torch.randn(1, 3, 510, 1020, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]], device=device, dtype=dtype)
expected_bbox_vertical_flip = torch.tensor(
[[[355, 499], [660, 499], [660, 259], [355, 259]]], device=device, dtype=dtype
)
expected_bbox_horizontal_flip = torch.tensor(
[[[664, 10], [359, 10], [359, 250], [664, 250]]], device=device, dtype=dtype
)
aug_ver = K.AugmentationSequential(
K.RandomVerticalFlip(p=1.0), data_keys=["input", "bbox"], return_transform=False, same_on_batch=False
)
aug_hor = K.AugmentationSequential(
K.RandomHorizontalFlip(p=1.0), data_keys=["input", "bbox"], return_transform=False, same_on_batch=False
)
out_ver = aug_ver(inp, bbox)
out_hor = aug_hor(inp, bbox)
assert_close(out_ver[1], expected_bbox_vertical_flip)
assert_close(out_hor[1], expected_bbox_horizontal_flip)
def __init__(self,
s_color=0.5,
p_color=0.8,
p_flip=0.5,
p_gray=0.2,
p_blur=0.5,
kernel_min=0.1,
kernel_max=2.) -> None:
super(KorniaAugmentationPipeline, self).__init__()
T_hflip = K.RandomHorizontalFlip(p=p_flip)
T_gray = K.RandomGrayscale(p=p_gray)
T_color = K.ColorJitter(p_color, 0.8 * s_color, 0.8 * s_color,
0.8 * s_color, 0.2 * s_color)
radius = kernel_max * 2 # https://dsp.stackexchange.com/questions/10057/gaussian-blur-standard-deviation-radius-and-kernel-size
kernel_size = int(radius * 2 + 1) # needs to be odd.
kernel_size = (kernel_size, kernel_size)
T_blur = K.GaussianBlur(kernel_size=kernel_size,
sigma=(kernel_min, kernel_max),
p=p_blur)
#T_blur = KorniaRandomGaussianBlur(kernel_size=kernel_size, sigma=(kernel_min, kernel_max), p=p_blur)
self.transform = nn.Sequential(T_hflip, T_color, T_gray, T_blur)
def __init__(self,
net,
layer_name_list=['avgpool'],
image_size=32,
hidden_layer=-2,
projection_size=256,
projection_hidden_size=4096,
augment_fn=None,
moving_average_decay=0.99,
device_='cuda',
number_of_classes=10,
mean_data=torch.tensor([0.485, 0.456, 0.406]),
std_data=torch.tensor([0.229, 0.224, 0.225])):
super().__init__()
DEFAULT_AUG = nn.Sequential(
augs.RandomHorizontalFlip(),
augs.RandomResizedCrop((image_size, image_size)),
augs.Normalize(mean=mean_data, std=std_data))
self.augment = default(augment_fn, DEFAULT_AUG)
self.device = device_
self.online_encoder = NetWrapper(net,
projection_size,
projection_hidden_size,
layer_name_list=layer_name_list).to(
self.device)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size,
projection_hidden_size).to(self.device)
# send a mock image tensor to instantiate singleton parameters
self.forward(torch.randn(2, 3, image_size, image_size).to(self.device))
def get_frame_transform(args, cuda=True):
imsz = args.img_size
norm_size = kornia.geometry.transform.Resize((imsz, imsz))
norm_imgs = kornia.color.Normalize(mean=IMG_MEAN, std=IMG_STD)
tt = []
fts = args.frame_transforms #.split(',')
if 'gray' in fts:
tt.append(K.RandomGrayscale(p=1))
if 'crop' in fts:
tt.append(
K.RandomResizedCrop(imsz, scale=(0.8, 0.95), ratio=(0.7, 1.3)))
else:
tt.append(norm_size)
if 'cj2' in fts:
_cj = 0.2
tt += [
K.RandomGrayscale(p=0.2),
K.ColorJitter(_cj, _cj, _cj, _cj),
]
elif 'cj' in fts:
_cj = 0.1
tt += [
# K.RandomGrayscale(p=0.2),
K.ColorJitter(_cj, _cj, _cj, 0),
]
if 'flip' in fts:
tt += [K.RandomHorizontalFlip()]
if args.npatch > 1 and args.frame_aug != '':
tt += [get_frame_aug(args)]
else:
tt += [norm_imgs]
print('Frame transforms:', tt, args.frame_transforms)
# frame_transform_train = MapTransform(transforms.Compose(tt))
frame_transform_train = transforms.Compose(tt)
plain = nn.Sequential(norm_size, norm_imgs)
def with_orig(x):
if cuda:
x = x.cuda()
if x.max() > 1 and x.min() >= 0:
x = x.float()
x -= x.min()
x /= x.max()
if x.shape[-1] == 3:
x = x.permute(0, 3, 1, 2)
patchify = (not args.visualize) or True
x = (frame_transform_train(x) if patchify else plain(x)).cpu(), \
plain(x[0:1]).cpu()
return x
return with_orig
validation.track_spectral_norm(mod)
g_ema = Generator(
args.size,
args.latent_size,
args.n_mlp,
channel_multiplier=args.channel_multiplier,
constant_input=args.constant_input,
).to(device)
g_ema.requires_grad_(False)
g_ema.eval()
accumulate(g_ema, generator, 0)
augment_fn = nn.Sequential(
nn.ReflectionPad2d(int((math.sqrt(2) - 1) * args.size / 4)), # zoom out
augs.RandomHorizontalFlip(),
RandomApply(augs.RandomAffine(degrees=0, translate=(0.25, 0.25), shear=(15, 15)), p=0.2),
RandomApply(augs.RandomRotation(180), p=0.2),
augs.RandomResizedCrop(size=(args.size, args.size), scale=(1, 1), ratio=(1, 1)),
RandomApply(augs.RandomResizedCrop(size=(args.size, args.size), scale=(0.5, 0.9)), p=0.1), # zoom in
RandomApply(augs.RandomErasing(), p=0.1),
)
contrast_learner = (
ContrastiveLearner(discriminator, args.size, augment_fn=augment_fn, hidden_layer=(-1, 0))
if args.contrastive > 0
else None
)
g_reg_ratio = args.g_reg_every / (args.g_reg_every + 1)
d_reg_ratio = args.d_reg_every / (args.d_reg_every + 1)
def __init__(self, probability: float = 0.1):
self._probability = probability
self._operation = aug.RandomHorizontalFlip(p=probability)
class TestVideoSequential:
@pytest.mark.parametrize('shape', [(3, 4), (2, 3, 4), (2, 3, 5, 6),
(2, 3, 4, 5, 6, 7)])
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_exception(self, shape, data_format, device, dtype):
aug_list = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1),
data_format=data_format,
same_on_frame=True)
with pytest.raises(AssertionError):
img = torch.randn(*shape, device=device, dtype=dtype)
aug_list(img)
@pytest.mark.parametrize(
'augmentation',
[
K.RandomAffine(360, p=1.0),
K.CenterCrop((3, 3), p=1.0),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomCrop((5, 5), p=1.0),
K.RandomErasing(p=1.0),
K.RandomGrayscale(p=1.0),
K.RandomHorizontalFlip(p=1.0),
K.RandomVerticalFlip(p=1.0),
K.RandomPerspective(p=1.0),
K.RandomResizedCrop((5, 5), p=1.0),
K.RandomRotation(360.0, p=1.0),
K.RandomSolarize(p=1.0),
K.RandomPosterize(p=1.0),
K.RandomSharpness(p=1.0),
K.RandomEqualize(p=1.0),
K.RandomMotionBlur(3, 35.0, 0.5, p=1.0),
K.Normalize(torch.tensor([0.5, 0.5, 0.5]),
torch.tensor([0.5, 0.5, 0.5]),
p=1.0),
K.Denormalize(torch.tensor([0.5, 0.5, 0.5]),
torch.tensor([0.5, 0.5, 0.5]),
p=1.0),
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_augmentation(self, augmentation, data_format, device, dtype):
input = torch.randint(255, (1, 3, 3, 5, 6), device=device,
dtype=dtype).repeat(2, 1, 1, 1, 1) / 255.0
torch.manual_seed(21)
aug_list = K.VideoSequential(augmentation,
data_format=data_format,
same_on_frame=True)
reproducibility_test(input, aug_list)
@pytest.mark.parametrize(
'augmentations',
[
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)
],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)
],
[K.RandomAffine(360, p=1.0),
kornia.color.BgrToRgb()],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0),
K.RandomAffine(360, p=0.0)
],
[K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0)],
[K.RandomAffine(360, p=0.0)],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0)
],
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
@pytest.mark.parametrize('random_apply',
[1, (1, 1), (1, ), 10, True, False])
def test_same_on_frame(self, augmentations, data_format, random_apply,
device, dtype):
aug_list = K.VideoSequential(*augmentations,
data_format=data_format,
same_on_frame=True,
random_apply=random_apply)
if data_format == 'BCTHW':
input = torch.randn(2, 3, 1, 5, 6, device=device,
dtype=dtype).repeat(1, 1, 4, 1, 1)
output = aug_list(input)
if aug_list.return_label:
output, _ = output
assert (output[:, :, 0] == output[:, :, 1]).all()
assert (output[:, :, 1] == output[:, :, 2]).all()
assert (output[:, :, 2] == output[:, :, 3]).all()
if data_format == 'BTCHW':
input = torch.randn(2, 1, 3, 5, 6, device=device,
dtype=dtype).repeat(1, 4, 1, 1, 1)
output = aug_list(input)
if aug_list.return_label:
output, _ = output
assert (output[:, 0] == output[:, 1]).all()
assert (output[:, 1] == output[:, 2]).all()
assert (output[:, 2] == output[:, 3]).all()
reproducibility_test(input, aug_list)
@pytest.mark.parametrize(
'augmentations',
[
[K.RandomAffine(360, p=1.0)],
[K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)],
[
K.RandomAffine(360, p=0.0),
K.ImageSequential(K.RandomAffine(360, p=0.0))
],
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_against_sequential(self, augmentations, data_format, device,
dtype):
aug_list_1 = K.VideoSequential(*augmentations,
data_format=data_format,
same_on_frame=False)
aug_list_2 = torch.nn.Sequential(*augmentations)
if data_format == 'BCTHW':
input = torch.randn(2, 3, 1, 5, 6, device=device,
dtype=dtype).repeat(1, 1, 4, 1, 1)
if data_format == 'BTCHW':
input = torch.randn(2, 1, 3, 5, 6, device=device,
dtype=dtype).repeat(1, 4, 1, 1, 1)
torch.manual_seed(0)
output_1 = aug_list_1(input)
torch.manual_seed(0)
if data_format == 'BCTHW':
input = input.transpose(1, 2)
output_2 = aug_list_2(input.reshape(-1, 3, 5, 6))
output_2 = output_2.view(2, 4, 3, 5, 6)
if data_format == 'BCTHW':
output_2 = output_2.transpose(1, 2)
assert (output_1 == output_2).all(), dict(aug_list_1._params)
@pytest.mark.jit
@pytest.mark.skip(reason="turn off due to Union Type")
def test_jit(self, device, dtype):
B, C, D, H, W = 2, 3, 5, 4, 4
img = torch.ones(B, C, D, H, W, device=device, dtype=dtype)
op = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1),
same_on_frame=True)
op_jit = torch.jit.script(op)
assert_close(op(img), op_jit(img))
mode="train",
download=True)
val_dataset = l2l.vision.datasets.MiniImagenet(root="data",
mode="validation",
download=True)
transform = {
"per_sample_transform":
nn.Sequential(
ApplyToKeys(
DataKeys.INPUT,
nn.Sequential(
torchvision.transforms.ToTensor(),
Kg.Resize((196, 196)),
# SPATIAL
Ka.RandomHorizontalFlip(p=0.25),
Ka.RandomRotation(degrees=90.0, p=0.25),
Ka.RandomAffine(degrees=1 * 5.0,
shear=1 / 5,
translate=1 / 20,
p=0.25),
Ka.RandomPerspective(distortion_scale=1 / 25, p=0.25),
# PIXEL-LEVEL
Ka.ColorJitter(brightness=1 / 30, p=0.25), # brightness
Ka.ColorJitter(saturation=1 / 30, p=0.25), # saturation
Ka.ColorJitter(contrast=1 / 30, p=0.25), # contrast
Ka.ColorJitter(hue=1 / 30, p=0.25), # hue
Ka.RandomMotionBlur(kernel_size=2 * (4 // 3) + 1,
angle=1,
direction=1.0,
p=0.25),
