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"""Training GANs with DiffAugment.""" |
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import numpy as np |
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import torch |
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import torch.nn.functional as F |
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def DiffAugment(x: torch.Tensor, policy: str = '', channels_first: bool = True) -> torch.Tensor: |
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if policy: |
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if not channels_first: |
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x = x.permute(0, 3, 1, 2) |
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for p in policy.split(','): |
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for f in AUGMENT_FNS[p]: |
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x = f(x) |
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if not channels_first: |
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x = x.permute(0, 2, 3, 1) |
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x = x.contiguous() |
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return x |
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def rand_brightness(x: torch.Tensor) -> torch.Tensor: |
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x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5) |
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return x |
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def rand_saturation(x: torch.Tensor) -> torch.Tensor: |
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x_mean = x.mean(dim=1, keepdim=True) |
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean |
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return x |
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def rand_contrast(x: torch.Tensor) -> torch.Tensor: |
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x_mean = x.mean(dim=[1, 2, 3], keepdim=True) |
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean |
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return x |
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def rand_translation(x: torch.Tensor, ratio: float = 0.125) -> torch.Tensor: |
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shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) |
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translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device) |
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translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device) |
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grid_batch, grid_x, grid_y = torch.meshgrid( |
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torch.arange(x.size(0), dtype=torch.long, device=x.device), |
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torch.arange(x.size(2), dtype=torch.long, device=x.device), |
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torch.arange(x.size(3), dtype=torch.long, device=x.device), |
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) |
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grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1) |
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grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1) |
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x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0]) |
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x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) |
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return x |
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def rand_cutout(x: torch.Tensor, ratio: float = 0.2) -> torch.Tensor: |
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cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) |
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offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device) |
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offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device) |
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grid_batch, grid_x, grid_y = torch.meshgrid( |
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torch.arange(x.size(0), dtype=torch.long, device=x.device), |
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torch.arange(cutout_size[0], dtype=torch.long, device=x.device), |
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torch.arange(cutout_size[1], dtype=torch.long, device=x.device), |
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) |
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grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1) |
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grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1) |
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mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device) |
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mask[grid_batch, grid_x, grid_y] = 0 |
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x = x * mask.unsqueeze(1) |
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return x |
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def rand_resize(x: torch.Tensor, min_ratio: float = 0.8, max_ratio: float = 1.2) -> torch.Tensor: |
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resize_ratio = np.random.rand()*(max_ratio-min_ratio) + min_ratio |
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resized_img = F.interpolate(x, size=int(resize_ratio*x.shape[3]), mode='bilinear') |
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org_size = x.shape[3] |
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if int(resize_ratio*x.shape[3]) < x.shape[3]: |
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left_pad = (x.shape[3]-int(resize_ratio*x.shape[3]))/2. |
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left_pad = int(left_pad) |
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right_pad = x.shape[3] - left_pad - resized_img.shape[3] |
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x = F.pad(resized_img, (left_pad, right_pad, left_pad, right_pad), "constant", 0.) |
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else: |
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left = (int(resize_ratio*x.shape[3])-x.shape[3])/2. |
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left = int(left) |
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x = resized_img[:, :, left:(left+x.shape[3]), left:(left+x.shape[3])] |
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assert x.shape[2] == org_size |
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assert x.shape[3] == org_size |
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return x |
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AUGMENT_FNS = { |
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'color': [rand_brightness, rand_saturation, rand_contrast], |
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'translation': [rand_translation], |
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'resize': [rand_resize], |
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'cutout': [rand_cutout], |
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} |
