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)