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| import math | |
| import torch | |
| import warnings | |
| import ml_collections | |
| import random | |
| import torch.nn.functional as F | |
| def DiffAugment(x, types=[], prob = 0.5, detach=True): | |
| """ | |
| x.shape = B, C, H, W | |
| """ | |
| if random.random() < prob: | |
| with torch.set_grad_enabled(not detach): | |
| x = random_hflip(x, prob=0.5) | |
| for p in types: | |
| for f in AUGMENT_FNS[p]: | |
| x = f(x) | |
| x = x.contiguous() | |
| return x | |
| def random_hflip(tensor, prob): | |
| if prob > random.random(): | |
| return tensor | |
| return torch.flip(tensor, dims=(3,)) | |
| def rand_brightness(x): | |
| x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5) | |
| return x | |
| def rand_saturation(x): | |
| x_mean = x.mean(dim=1, keepdim=True) | |
| x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean | |
| return x | |
| def rand_contrast(x): | |
| x_mean = x.mean(dim=[1, 2, 3], keepdim=True) | |
| x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean | |
| return x | |
| def rand_translation(x, ratio=0.125): | |
| shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) | |
| translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device) | |
| translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device) | |
| grid_batch, grid_x, grid_y = torch.meshgrid( | |
| torch.arange(x.size(0), dtype=torch.long, device=x.device), | |
| torch.arange(x.size(2), dtype=torch.long, device=x.device), | |
| torch.arange(x.size(3), dtype=torch.long, device=x.device), | |
| ) | |
| grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1) | |
| grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1) | |
| x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0]) | |
| x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) | |
| return x | |
| def rand_offset(x, ratio=1, ratio_h=1, ratio_v=1): | |
| w, h = x.size(2), x.size(3) | |
| imgs = [] | |
| for img in x.unbind(dim = 0): | |
| max_h = int(w * ratio * ratio_h) | |
| max_v = int(h * ratio * ratio_v) | |
| value_h = random.randint(0, max_h) * 2 - max_h | |
| value_v = random.randint(0, max_v) * 2 - max_v | |
| if abs(value_h) > 0: | |
| img = torch.roll(img, value_h, 2) | |
| if abs(value_v) > 0: | |
| img = torch.roll(img, value_v, 1) | |
| imgs.append(img) | |
| return torch.stack(imgs) | |
| def rand_offset_h(x, ratio=1): | |
| return rand_offset(x, ratio=1, ratio_h=ratio, ratio_v=0) | |
| def rand_offset_v(x, ratio=1): | |
| return rand_offset(x, ratio=1, ratio_h=0, ratio_v=ratio) | |
| def rand_cutout(x, ratio=0.5): | |
| cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) | |
| offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device) | |
| offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device) | |
| grid_batch, grid_x, grid_y = torch.meshgrid( | |
| torch.arange(x.size(0), dtype=torch.long, device=x.device), | |
| torch.arange(cutout_size[0], dtype=torch.long, device=x.device), | |
| torch.arange(cutout_size[1], dtype=torch.long, device=x.device), | |
| ) | |
| grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1) | |
| grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1) | |
| mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device) | |
| mask[grid_batch, grid_x, grid_y] = 0 | |
| x = x * mask.unsqueeze(1) | |
| return x | |
| AUGMENT_FNS = { | |
| 'color': [rand_brightness, rand_saturation, rand_contrast], | |
| 'offset': [rand_offset], | |
| 'offset_h': [rand_offset_h], | |
| 'offset_v': [rand_offset_v], | |
| 'translation': [rand_translation], | |
| 'cutout': [rand_cutout], | |
| } | |
| def _no_grad_trunc_normal_(tensor, mean, std, a, b): | |
| # Cut & paste from PyTorch official master until it's in a few official releases - RW | |
| # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf | |
| def norm_cdf(x): | |
| # Computes standard normal cumulative distribution function | |
| return (1. + math.erf(x / math.sqrt(2.))) / 2. | |
| if (mean < a - 2 * std) or (mean > b + 2 * std): | |
| warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " | |
| "The distribution of values may be incorrect.", | |
| stacklevel=2) | |
| with torch.no_grad(): | |
| # Values are generated by using a truncated uniform distribution and | |
| # then using the inverse CDF for the normal distribution. | |
| # Get upper and lower cdf values | |
| l = norm_cdf((a - mean) / std) | |
| u = norm_cdf((b - mean) / std) | |
| # Uniformly fill tensor with values from [l, u], then translate to | |
| # [2l-1, 2u-1]. | |
| tensor.uniform_(2 * l - 1, 2 * u - 1) | |
| # Use inverse cdf transform for normal distribution to get truncated | |
| # standard normal | |
| tensor.erfinv_() | |
| # Transform to proper mean, std | |
| tensor.mul_(std * math.sqrt(2.)) | |
| tensor.add_(mean) | |
| # Clamp to ensure it's in the proper range | |
| tensor.clamp_(min=a, max=b) | |
| return tensor | |
| def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): | |
| # type: (Tensor, float, float, float, float) -> Tensor | |
| return _no_grad_trunc_normal_(tensor, mean, std, a, b) | |
| def get_testing(): | |
| """Returns a minimal configuration for testing.""" | |
| config = ml_collections.ConfigDict() | |
| config.patches = ml_collections.ConfigDict({'size': (16, 16)}) | |
| config.hidden_size = 1 | |
| config.transformer = ml_collections.ConfigDict() | |
| config.transformer.mlp_dim = 1 | |
| config.transformer.num_heads = 1 | |
| config.transformer.num_layers = 1 | |
| config.transformer.attention_dropout_rate = 0.0 | |
| config.transformer.dropout_rate = 0.1 | |
| config.classifier = 'token' | |
| config.representation_size = None | |
| return config | |
| def get_b16_config(): | |
| """Returns the ViT-B/16 configuration.""" | |
| config = ml_collections.ConfigDict() | |
| config.patches = ml_collections.ConfigDict({'size': (16, 16)}) | |
| config.hidden_size = 768 | |
| config.transformer = ml_collections.ConfigDict() | |
| config.transformer.mlp_dim = 3072 | |
| config.transformer.num_heads = 12 | |
| config.transformer.num_layers = 12 | |
| config.transformer.attention_dropout_rate = 0.0 | |
| config.transformer.dropout_rate = 0.1 | |
| config.classifier = 'token' | |
| config.representation_size = None | |
| return config | |
| def get_r50_b16_config(): | |
| """Returns the Resnet50 + ViT-B/16 configuration.""" | |
| config = get_b16_config() | |
| del config.patches.size | |
| config.patches.grid = (14, 14) | |
| config.resnet = ml_collections.ConfigDict() | |
| config.resnet.num_layers = (3, 4, 9) | |
| config.resnet.width_factor = 1 | |
| return config | |
| def get_b32_config(): | |
| """Returns the ViT-B/32 configuration.""" | |
| config = get_b16_config() | |
| config.patches.size = (32, 32) | |
| return config | |
| def get_l16_config(): | |
| """Returns the ViT-L/16 configuration.""" | |
| config = ml_collections.ConfigDict() | |
| config.patches = ml_collections.ConfigDict({'size': (16, 16)}) | |
| config.hidden_size = 1024 | |
| config.transformer = ml_collections.ConfigDict() | |
| config.transformer.mlp_dim = 4096 | |
| config.transformer.num_heads = 16 | |
| config.transformer.num_layers = 24 | |
| config.transformer.attention_dropout_rate = 0.0 | |
| config.transformer.dropout_rate = 0.1 | |
| config.classifier = 'token' | |
| config.representation_size = None | |
| return config | |
| def get_l32_config(): | |
| """Returns the ViT-L/32 configuration.""" | |
| config = get_l16_config() | |
| config.patches.size = (32, 32) | |
| return config | |
| def get_h14_config(): | |
| """Returns the ViT-L/16 configuration.""" | |
| config = ml_collections.ConfigDict() | |
| config.patches = ml_collections.ConfigDict({'size': (14, 14)}) | |
| config.hidden_size = 1280 | |
| config.transformer = ml_collections.ConfigDict() | |
| config.transformer.mlp_dim = 5120 | |
| config.transformer.num_heads = 16 | |
| config.transformer.num_layers = 32 | |
| config.transformer.attention_dropout_rate = 0.0 | |
| config.transformer.dropout_rate = 0.1 | |
| config.classifier = 'token' | |
| config.representation_size = None | |
| return config | |