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