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, 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 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 __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 __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,
batch_size=128,
):
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(),
augs.RandomVerticalFlip(),
augs.RandomSolarize(),
augs.RandomPosterize(),
augs.RandomSharpness(),
augs.RandomEqualize(),
augs.RandomRotation(degrees=8.0),
RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
augs.RandomResizedCrop((image_size, image_size), p=0.1),
)
self.b = batch_size
self.h = image_size
self.w = 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
random_data = (
(
torch.randn(1, 3, image_size, image_size),
torch.randn(1, 3, image_size, image_size),
torch.randn(1, 3, image_size, image_size),
),
torch.tensor([1]),
)
# send a mock image tensor to instantiate parameters
self.forward(random_data)
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_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")
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,
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)
