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Running on Zero
| """ Padding Helpers | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| import math | |
| from typing import List, Tuple | |
| import torch | |
| import torch.nn.functional as F | |
| # Calculate symmetric padding for a convolution | |
| def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int: | |
| padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 | |
| return padding | |
| # Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution | |
| def get_same_padding(x: int, kernel_size: int, stride: int, dilation: int): | |
| if isinstance(x, torch.Tensor): | |
| return torch.clamp(((x / stride).ceil() - 1) * stride + (kernel_size - 1) * dilation + 1 - x, min=0) | |
| else: | |
| return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0) | |
| # Can SAME padding for given args be done statically? | |
| def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_): | |
| return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0 | |
| def pad_same_arg( | |
| input_size: List[int], | |
| kernel_size: List[int], | |
| stride: List[int], | |
| dilation: List[int] = (1, 1), | |
| ) -> List[int]: | |
| ih, iw = input_size | |
| kh, kw = kernel_size | |
| pad_h = get_same_padding(ih, kh, stride[0], dilation[0]) | |
| pad_w = get_same_padding(iw, kw, stride[1], dilation[1]) | |
| return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2] | |
| # Dynamically pad input x with 'SAME' padding for conv with specified args | |
| def pad_same( | |
| x, | |
| kernel_size: List[int], | |
| stride: List[int], | |
| dilation: List[int] = (1, 1), | |
| value: float = 0, | |
| ): | |
| ih, iw = x.size()[-2:] | |
| pad_h = get_same_padding(ih, kernel_size[0], stride[0], dilation[0]) | |
| pad_w = get_same_padding(iw, kernel_size[1], stride[1], dilation[1]) | |
| x = F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2), value=value) | |
| return x | |
| def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]: | |
| dynamic = False | |
| if isinstance(padding, str): | |
| # for any string padding, the padding will be calculated for you, one of three ways | |
| padding = padding.lower() | |
| if padding == 'same': | |
| # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact | |
| if is_static_pad(kernel_size, **kwargs): | |
| # static case, no extra overhead | |
| padding = get_padding(kernel_size, **kwargs) | |
| else: | |
| # dynamic 'SAME' padding, has runtime/GPU memory overhead | |
| padding = 0 | |
| dynamic = True | |
| elif padding == 'valid': | |
| # 'VALID' padding, same as padding=0 | |
| padding = 0 | |
| else: | |
| # Default to PyTorch style 'same'-ish symmetric padding | |
| padding = get_padding(kernel_size, **kwargs) | |
| return padding, dynamic | |