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)),
color.Normalize(mean=torch.tensor(
[0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225]))
)
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 = MultiLayerPerceptron(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 __init__(self,
s_color=0.1,
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, 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_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, opt):
super().__init__()
self.wrapped_dataset = create_dataset(opt['dataset'])
self.cropped_img_size = opt['crop_size']
self.key1 = opt_get(opt, ['key1'], 'hq')
self.key2 = opt_get(opt, ['key2'], 'lq')
for_sr = opt_get(
opt, ['for_sr'],
False) # When set, color alterations and blurs are disabled.
augmentations = [ \
augs.RandomHorizontalFlip(),
augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))]
if not for_sr:
augmentations.extend([
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1)
])
if opt['normalize']:
# The paper calls for normalization. Most datasets/models in this repo don't use this.
# Recommend setting true if you want to train exactly like the paper.
augmentations.append(
augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]),
std=torch.tensor([0.229, 0.224, 0.225])))
self.aug = nn.Sequential(*augmentations)
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().__init__()
self.wrapped_dataset = create_dataset(opt['dataset'])
augmentations = [
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 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 = RandomSharedRegionCrop(opt['latent_multiple'],
opt_get(opt, ['jitter_range'], 0))
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_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, 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,
encoder,
predictor,
image_size,
hidden_layer=-2,
projection_size=256,
projection_hidden_size=4096,
augment_fn=None,
augment_fn2=None,
moving_average_decay=0.99,
use_momentum=True):
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.online_encoder = encoder
self.use_momentum = use_momentum
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = predictor
# get device of network and make wrapper same device
# device = get_module_device(net)
device = torch.device(2)
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 __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 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 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
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 default(val, def_val):
return def_val if val is None else val
# augmentation utils
class RandomApply(nn.Module):
def __init__(self, fn, p):
super().__init__()
self.fn = fn
self.p = p
def forward(self, x):
if random.random() > self.p:
return x
return self.fn(x)
# default SimCLR augmentation
image_size = 256
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)))
#color.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225])))
if __name__ == '__main__':
meter = AverageMeter()
def __init__(self,
net,
image_size,
hidden_layer_pixel=-2,
hidden_layer_instance=-2,
projection_size=256,
projection_hidden_size=2048,
augment_fn=None,
augment_fn2=None,
prob_rand_hflip=0.25,
moving_average_decay=0.99,
ppm_num_layers=1,
ppm_gamma=2,
distance_thres=0.7,
similarity_temperature=0.3,
alpha=1.,
use_pixpro=True,
cutout_ratio_range=(0.6, 0.8),
cutout_interpolate_mode='nearest',
coord_cutout_interpolate_mode='bilinear'):
super().__init__()
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomSolarize(p=0.5),
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.prob_rand_hflip = prob_rand_hflip
self.online_encoder = NetWrapper(
net=net,
projection_size=projection_size,
projection_hidden_size=projection_hidden_size,
layer_pixel=hidden_layer_pixel,
layer_instance=hidden_layer_instance)
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.use_pixpro = use_pixpro
if use_pixpro:
self.propagate_pixels = PPM(chan=projection_size,
num_layers=ppm_num_layers,
gamma=ppm_gamma)
self.cutout_ratio_range = cutout_ratio_range
self.cutout_interpolate_mode = cutout_interpolate_mode
self.coord_cutout_interpolate_mode = coord_cutout_interpolate_mode
# instance level predictor
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 __init__(
self,
net,
image_size,
hidden_layer_pixel=-2,
hidden_layer_instance=-2,
instance_projection_size=256,
instance_projection_hidden_size=2048,
pix_projection_size=256,
pix_projection_hidden_size=2048,
augment_fn=None,
augment_fn2=None,
prob_rand_hflip=0.25,
moving_average_decay=0.99,
ppm_num_layers=1,
ppm_gamma=2,
distance_thres=0.7,
similarity_temperature=0.3,
cutout_ratio_range=(0.6, 0.8),
cutout_interpolate_mode='nearest',
coord_cutout_interpolate_mode='bilinear',
max_latent_dim=None # When set, this is the number of stochastically extracted pixels from the latent to extract. Must have an integer square root.
):
super().__init__()
DEFAULT_AUG = nn.Sequential(
RandomApply(augs.ColorJitter(0.6, 0.6, 0.6, 0.2), p=0.8),
augs.RandomGrayscale(p=0.2),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomSolarize(p=0.5),
# Normalize left out because it should be done at the model level.
)
self.augment1 = default(augment_fn, DEFAULT_AUG)
self.augment2 = default(augment_fn2, self.augment1)
self.prob_rand_hflip = prob_rand_hflip
self.online_encoder = NetWrapper(
net=net,
instance_projection_size=instance_projection_size,
instance_projection_hidden_size=instance_projection_hidden_size,
pix_projection_size=pix_projection_size,
pix_projection_hidden_size=pix_projection_hidden_size,
layer_pixel=hidden_layer_pixel,
layer_instance=hidden_layer_instance)
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.distance_thres = distance_thres
self.similarity_temperature = similarity_temperature
# This requirement is due to the way that these are processed, not a hard requirement.
assert math.sqrt(max_latent_dim) == int(math.sqrt(max_latent_dim))
self.max_latent_dim = max_latent_dim
self.propagate_pixels = PPM(chan=pix_projection_size,
num_layers=ppm_num_layers,
gamma=ppm_gamma)
self.cutout_ratio_range = cutout_ratio_range
self.cutout_interpolate_mode = cutout_interpolate_mode
self.coord_cutout_interpolate_mode = coord_cutout_interpolate_mode
# instance level predictor
self.online_predictor = MLP(instance_projection_size,
instance_projection_size,
instance_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 get_gpu_transforms(augs: DictConfig, mode: str = '2d') -> dict:
"""Makes GPU augmentations from the augs section of a configuration.
Parameters
----------
augs : DictConfig
Augmentation parameters
mode : str, optional
If '2d', stacks clip in channels. If 3d, returns 5-D tensor, by default '2d'
Returns
-------
xform : dict
keys: ['train', 'val', 'test']. Values: a nn.Sequential with Kornia augmentations.
Example: auged_images = xform['train'](images)
"""
# input is a tensor of shape N x C x F x H x W
train_transforms = [ToFloat()]
val_transforms = [ToFloat()]
kornia_transforms = []
if augs.LR > 0:
kornia_transforms.append(K.RandomHorizontalFlip(p=augs.LR,
same_on_batch=False,
return_transform=False))
if augs.UD > 0:
kornia_transforms.append(K.RandomVerticalFlip(p=augs.UD,
same_on_batch=False, return_transform=False))
if augs.degrees > 0:
kornia_transforms.append(K.RandomRotation(augs.degrees))
if augs.brightness > 0 or augs.contrast > 0 or augs.saturation > 0 or augs.hue > 0:
kornia_transforms.append(K.ColorJitter(brightness=augs.brightness,
contrast=augs.contrast,
saturation=augs.saturation,
hue=augs.hue,
p=augs.color_p,
same_on_batch=False,
return_transform=False))
if augs.grayscale > 0:
kornia_transforms.append(K.RandomGrayscale(p=augs.grayscale))
norm = NormalizeVideo(mean=augs.normalization.mean,
std=augs.normalization.std)
# kornia_transforms.append(norm)
kornia_transforms = VideoSequential(*kornia_transforms,
data_format='BCTHW',
same_on_frame=True)
train_transforms = [ToFloat(),
kornia_transforms,
norm]
val_transforms = [ToFloat(),
norm]
denormalize = []
if mode == '2d':
train_transforms.append(StackClipInChannels())
val_transforms.append(StackClipInChannels())
denormalize.append(UnstackClip())
denormalize.append(DenormalizeVideo(mean=augs.normalization.mean,
std=augs.normalization.std))
train_transforms = nn.Sequential(*train_transforms)
val_transforms = nn.Sequential(*val_transforms)
denormalize = nn.Sequential(*denormalize)
gpu_transforms = dict(train=train_transforms,
val=val_transforms,
test=val_transforms,
denormalize=denormalize)
log.info('GPU transforms: {}'.format(gpu_transforms))
return gpu_transforms
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)
