def kornia_list(MAGN: int = 4):
"""
Returns standard list of kornia transforms, each with magnitude `MAGN`.
Args:
MAGN (int): Magnitude of each transform in the returned list.
"""
transform_list = [
# SPATIAL
K.RandomHorizontalFlip(p=1),
K.RandomRotation(degrees=90., p=1),
K.RandomAffine(degrees=MAGN * 5.,
shear=MAGN / 5,
translate=MAGN / 20,
p=1),
K.RandomPerspective(distortion_scale=MAGN / 25, p=1),
# PIXEL-LEVEL
K.ColorJitter(brightness=MAGN / 30, p=1), # brightness
K.ColorJitter(saturation=MAGN / 30, p=1), # saturation
K.ColorJitter(contrast=MAGN / 30, p=1), # contrast
K.ColorJitter(hue=MAGN / 30, p=1), # hue
K.ColorJitter(p=0), # identity
K.RandomMotionBlur(kernel_size=2 * (MAGN // 3) + 1,
angle=MAGN,
direction=1.,
p=1),
K.RandomErasing(scale=(MAGN / 100, MAGN / 50),
ratio=(MAGN / 20, MAGN),
p=1),
]
return transform_list
def test_random_erasing(self, device, dtype):
fill_value = 0.5
input = torch.randn(3, 3, 100, 100, device=device, dtype=dtype)
aug = K.AugmentationSequential(
K.RandomErasing(p=1., value=fill_value),
data_keys=["input", "mask"],
)
reproducibility_test((input, input), aug)
out = aug(input, input)
assert torch.all(out[1][out[0] == fill_value] == 0.)
def __init__(self,
brightness=(0.75, 1.25),
contrast=(0.75, 1.25),
saturation=(0., 2.),
translate=(0.125, 0.125),
normalized=True,
mean=0.5,
std=0.5,
device=None):
if normalized:
if isinstance(mean,
(tuple, list)) and isinstance(std, (tuple, list)):
if not device:
raise Exception(
'Please specify a torch.device() object when using mean and std for each channels'
)
mean = torch.Tensor(mean).to(device)
std = torch.Tensor(std).to(device)
self.normalize = aug.Normalize(mean, std)
self.denormalize = aug.Denormalize(mean, std)
else:
self.normalize, self.denormalize = None, None
color_jitter = aug.ColorJitter(
brightness=brightness,
contrast=contrast,
saturation=saturation,
p=1.) # rand_brightness, rand_contrast, rand_saturation
affine = aug.RandomAffine(degrees=0,
translate=translate,
padding_mode=SamplePadding.BORDER,
p=1.) # rand_translate
cutout = aug.RandomErasing(value=0.5, p=1.) # rand_cutout
self.augmentations = {
'color': color_jitter,
'translation': affine,
'cutout': cutout
}
def __init__(self, cut_size, cutn, cut_pow=1.): super().__init__() self.cut_size = cut_size self.cutn = cutn self.cut_pow = cut_pow self.augs = nn.Sequential( # K.RandomHorizontalFlip(p=0.5), # K.RandomVerticalFlip(p=0.5), # K.RandomSolarize(0.01, 0.01, p=0.7), # K.RandomSharpness(0.3, p=0.4), # K.RandomResizedCrop( # size=(self.cut_size, self.cut_size), # scale=(0.1, 1), ratio=(0.75, 1.333), # cropping_mode="resample", p=0.5 # ), # K.RandomCrop( # size=(self.cut_size, self.cut_size), p=0.5 # ), K.RandomAffine( degrees=15, translate=0.1, p=0.7, padding_mode="border" ), K.RandomPerspective(0.7, p=0.7), K.ColorJitter(hue=0.1, saturation=0.1, p=0.7), K.RandomErasing( (.1, .4), (.3, 1/.3), same_on_batch=True, p=0.7 ), ) self.noise_fac = 0.1 self.av_pool = nn.AdaptiveAvgPool2d( (self.cut_size, self.cut_size) ) self.max_pool = nn.AdaptiveMaxPool2d( (self.cut_size, self.cut_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)
g_optim = th.optim.Adam(
generator.parameters(), lr=args.lr * g_reg_ratio, betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio),
)
d_optim = th.optim.Adam(
discriminator.parameters(), lr=args.lr * d_reg_ratio, betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio),
def __init__(self, probability: float = 0.5):
self._probability = probability
self._operation = aug.RandomErasing(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))
def random_erase(data, opt):
rec_er = K.RandomErasing(0.5, (.02, .4), (.3, 1 / .3))
out = rec_er(data.view([-1] + list(data.shape[-3:])))
return out.view(data.shape)
def main(argv=None):
# CLI
parser = argparse.ArgumentParser()
parser.add_argument("name", help="Name of the experiment")
parser.add_argument(
"-a",
"--augment",
action="store_true",
help="If True, we apply augmentations",
)
parser.add_argument("-b",
"--batch-size",
type=int,
default=16,
help="Batch size")
parser.add_argument(
"--b1",
type=float,
default=0.5,
help="Adam optimizer hyperparamter",
)
parser.add_argument(
"--b2",
type=float,
default=0.999,
help="Adam optimizer hyperparamter",
)
parser.add_argument(
"-d",
"--device",
type=str,
default="cpu",
choices=["cpu", "cuda"],
help="Device to use",
)
parser.add_argument(
"--eval-frequency",
type=int,
default=400,
help="Generate generator images every `eval_frequency` epochs",
)
parser.add_argument(
"--latent-dim",
type=int,
default=100,
help="Dimensionality of the random noise",
)
parser.add_argument("--lr",
type=float,
default=0.0002,
help="Learning rate")
parser.add_argument(
"--ndf",
type=int,
default=32,
help="Number of discriminator feature maps (after first convolution)",
)
parser.add_argument(
"--ngf",
type=int,
default=32,
help=
"Number of generator feature maps (before last transposed convolution)",
)
parser.add_argument(
"-n",
"--n-epochs",
type=int,
default=200,
help="Number of training epochs",
)
parser.add_argument(
"--mosaic-size",
type=int,
default=10,
help="Size of the side of the rectangular mosaic",
)
parser.add_argument(
"-p",
"--prob",
type=float,
default=0.9,
help="Probability of applying an augmentation",
)
args = parser.parse_args(argv)
args_d = vars(args)
print(args)
img_size = 128
# Additional parameters
device = torch.device(args.device)
mosaic_kwargs = {"nrow": args.mosaic_size, "normalize": True}
n_mosaic_cells = args.mosaic_size * args.mosaic_size
sample_showcase_ix = (
0 # this one will be used to demonstrate the augmentations
)
augment_module = torch.nn.Sequential(
K.RandomAffine(degrees=0, translate=(1 / 8, 1 / 8), p=args.prob),
K.RandomErasing((0.0, 0.5), p=args.prob),
)
# Loss function
adversarial_loss = torch.nn.BCELoss()
# Initialize generator and discriminator
generator = Generator(latent_dim=args.latent_dim, ngf=args.ngf)
discriminator = Discriminator(
ndf=args.ndf, augment_module=augment_module if args.augment else None)
generator.to(device)
discriminator.to(device)
# Initialize weights
generator.apply(init_weights_)
discriminator.apply(init_weights_)
# Configure data loader
data_path = pathlib.Path("data")
tform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
dataset = DatasetImages(
data_path,
transform=tform,
)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
# Output path and metadata
output_path = pathlib.Path("outputs") / args.name
output_path.mkdir(exist_ok=True, parents=True)
# Add other parameters (not included in CLI)
args_d["time"] = datetime.now()
args_d["kornia"] = str(augment_module)
# Prepare tensorboard writer
writer = SummaryWriter(output_path)
# Log hyperparameters as text
writer.add_text(
"hyperparameter",
pprint.pformat(args_d).replace(
"\n", " \n"), # markdown needs 2 spaces before newline
0,
)
# Log true data
writer.add_image(
"true_data",
make_grid(torch.stack([dataset[i] for i in range(n_mosaic_cells)]),
**mosaic_kwargs),
0,
)
# Log augmented data
batch_showcase = dataset[sample_showcase_ix][None, ...].repeat(
n_mosaic_cells, 1, 1, 1)
batch_showcase_aug = discriminator.augment_module(batch_showcase)
writer.add_image("augmentations",
make_grid(batch_showcase_aug, **mosaic_kwargs), 0)
# Prepate evaluation noise
z_eval = torch.randn(n_mosaic_cells, args.latent_dim).to(device)
for epoch in tqdm(range(args.n_epochs)):
for i, imgs in enumerate(dataloader):
n_samples, *_ = imgs.shape
batches_done = epoch * len(dataloader) + i
# Adversarial ground truths
valid = 0.9 * torch.ones(
n_samples, 1, device=device, dtype=torch.float32)
fake = torch.zeros(n_samples,
1,
device=device,
dtype=torch.float32)
# D preparation
optimizer_D.zero_grad()
# D loss on reals
real_imgs = imgs.to(device)
d_x = discriminator(real_imgs)
real_loss = adversarial_loss(d_x, valid)
real_loss.backward()
# D loss on fakes
z = torch.randn(n_samples, args.latent_dim).to(device)
gen_imgs = generator(z)
d_g_z1 = discriminator(gen_imgs.detach())
fake_loss = adversarial_loss(d_g_z1, fake)
fake_loss.backward()
optimizer_D.step() # we called backward twice, the result is a sum
# G preparation
optimizer_G.zero_grad()
# G loss
d_g_z2 = discriminator(gen_imgs)
g_loss = adversarial_loss(d_g_z2, valid)
g_loss.backward()
optimizer_G.step()
# Logging
if batches_done % 50 == 0:
writer.add_scalar("d_x", d_x.mean().item(), batches_done)
writer.add_scalar("d_g_z1", d_g_z1.mean().item(), batches_done)
writer.add_scalar("d_g_z2", d_g_z2.mean().item(), batches_done)
writer.add_scalar("D_loss", (real_loss + fake_loss).item(),
batches_done)
writer.add_scalar("G_loss", g_loss.item(), batches_done)
if epoch % args.eval_frequency == 0 and i == 0:
generator.eval()
discriminator.eval()
# Generate fake images
gen_imgs_eval = generator(z_eval)
# Generate nice mosaic
writer.add_image(
"fake",
make_grid(gen_imgs_eval.data, **mosaic_kwargs),
batches_done,
)
# Save checkpoint (and potentially overwrite an existing one)
torch.save(generator, output_path / "model.pt")
# Make sure generator and discriminator in the training mode
generator.train()
discriminator.train()
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),
Ka.RandomErasing(scale=(1 / 100, 1 / 50),
ratio=(1 / 20, 1),
p=0.25),
),
),
ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
),
"collate":
kornia_collate,
"per_batch_transform_on_device":
ApplyToKeys(
DataKeys.INPUT,
Ka.RandomHorizontalFlip(p=0.25),
),
}
# construct datamodule
def __init__(
self,
image_size,
latent_dim=512,
style_depth=8,
network_capacity=16,
transparent=False,
fp16=False,
cl_reg=False,
augment_fn=None,
steps=1,
lr=1e-4,
fq_layers=[],
fq_dict_size=256,
attn_layers=[],
):
super().__init__()
self.lr = lr
self.steps = steps
self.ema_updater = EMA(0.995)
self.S = StyleVectorizer(latent_dim, style_depth)
self.G = Generator(image_size,
latent_dim,
network_capacity,
transparent=transparent,
attn_layers=attn_layers)
self.D = Discriminator(
image_size,
network_capacity,
fq_layers=fq_layers,
fq_dict_size=fq_dict_size,
attn_layers=attn_layers,
transparent=transparent,
)
self.SE = StyleVectorizer(latent_dim, style_depth)
self.GE = Generator(image_size,
latent_dim,
network_capacity,
transparent=transparent,
attn_layers=attn_layers)
set_requires_grad(self.SE, False)
set_requires_grad(self.GE, False)
generator_params = list(self.G.parameters()) + list(
self.S.parameters())
self.G_opt = DiffGrad(generator_params, lr=self.lr, betas=(0.5, 0.9))
self.D_opt = DiffGrad(self.D.parameters(),
lr=self.lr,
betas=(0.5, 0.9))
self._init_weights()
self.reset_parameter_averaging()
self.cuda()
if fp16:
(self.S, self.G, self.D, self.SE,
self.GE), (self.G_opt, self.D_opt) = amp.initialize(
[self.S, self.G, self.D, self.SE, self.GE],
[self.G_opt, self.D_opt],
opt_level="O2")
# experimental contrastive loss discriminator regularization
if augment_fn is not None:
self.augment_fn = augment_fn
else:
self.augment_fn = nn.Sequential(
nn.ReflectionPad2d(int((sqrt(2) - 1) * image_size / 4)),
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.7),
augs.RandomGrayscale(p=0.2),
augs.RandomHorizontalFlip(),
RandomApply(augs.RandomAffine(degrees=0,
translate=(0.25, 0.25),
shear=(15, 15)),
p=0.3),
RandomApply(nn.Sequential(
augs.RandomRotation(180),
augs.CenterCrop(size=(image_size, image_size))),
p=0.2),
augs.RandomResizedCrop(size=(image_size, image_size)),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
RandomApply(augs.RandomErasing(), p=0.1),
)
self.D_cl = (ContrastiveLearner(self.D,
image_size,
augment_fn=self.augment_fn,
fp16=fp16,
hidden_layer="flatten")
if cl_reg else None)
def get_augmenter(augmenter_type: str,
image_size: ImageSizeType,
dataset_mean: DatasetStatType,
dataset_std: DatasetStatType,
padding: PaddingInputType = 1. / 8.,
pad_if_needed: bool = False,
subset_size: int = 2) -> Union[Module, Callable]:
"""
Args:
augmenter_type: augmenter type
image_size: (height, width) image size
dataset_mean: dataset mean value in CHW
dataset_std: dataset standard deviation in CHW
padding: percent of image size to pad on each border of the image. If a sequence of length 4 is provided,
it is used to pad left, top, right, bottom borders respectively. If a sequence of length 2 is provided, it is
used to pad left/right, top/bottom borders, respectively.
pad_if_needed: bool flag for RandomCrop "pad_if_needed" option
subset_size: number of augmentations used in subset
Returns: nn.Module for Kornia augmentation or Callable for torchvision transform
"""
if not isinstance(padding, tuple):
assert isinstance(padding, float)
padding = (padding, padding, padding, padding)
assert len(padding) == 2 or len(padding) == 4
if len(padding) == 2:
# padding of length 2 is used to pad left/right, top/bottom borders, respectively
# padding of length 4 is used to pad left, top, right, bottom borders respectively
padding = (padding[0], padding[1], padding[0], padding[1])
# image_size is of shape (h,w); padding values is [left, top, right, bottom] borders
padding = (int(image_size[1] * padding[0]), int(
image_size[0] * padding[1]), int(image_size[1] * padding[2]),
int(image_size[0] * padding[3]))
augmenter_type = augmenter_type.strip().lower()
if augmenter_type == "simple":
return nn.Sequential(
K.RandomCrop(size=image_size,
padding=padding,
pad_if_needed=pad_if_needed,
padding_mode='reflect'),
K.RandomHorizontalFlip(p=0.5),
K.Normalize(mean=torch.tensor(dataset_mean, dtype=torch.float32),
std=torch.tensor(dataset_std, dtype=torch.float32)),
)
elif augmenter_type == "fixed":
return nn.Sequential(
K.RandomHorizontalFlip(p=0.5),
# K.RandomVerticalFlip(p=0.2),
K.RandomResizedCrop(size=image_size,
scale=(0.8, 1.0),
ratio=(1., 1.)),
RandomAugmentation(p=0.5,
augmentation=F.GaussianBlur2d(
kernel_size=(3, 3),
sigma=(1.5, 1.5),
border_type='constant')),
K.ColorJitter(contrast=(0.75, 1.5)),
# additive Gaussian noise
K.RandomErasing(p=0.1),
# Multiply
K.RandomAffine(degrees=(-25., 25.),
translate=(0.2, 0.2),
scale=(0.8, 1.2),
shear=(-8., 8.)),
K.Normalize(mean=torch.tensor(dataset_mean, dtype=torch.float32),
std=torch.tensor(dataset_std, dtype=torch.float32)),
)
elif augmenter_type in ["validation", "test"]:
return nn.Sequential(
K.Normalize(mean=torch.tensor(dataset_mean, dtype=torch.float32),
std=torch.tensor(dataset_std, dtype=torch.float32)), )
elif augmenter_type == "randaugment":
return nn.Sequential(
K.RandomCrop(size=image_size,
padding=padding,
pad_if_needed=pad_if_needed,
padding_mode='reflect'),
K.RandomHorizontalFlip(p=0.5),
RandAugmentNS(n=subset_size, m=10),
K.Normalize(mean=torch.tensor(dataset_mean, dtype=torch.float32),
std=torch.tensor(dataset_std, dtype=torch.float32)),
)
else:
raise NotImplementedError(
f"\"{augmenter_type}\" is not a supported augmenter type")
