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80 lines
2.9 KiB
80 lines
2.9 KiB
5 years ago
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""" Conv2d w/ Same Padding
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Hacked together by Ross Wightman
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Union, List, Tuple, Optional, Callable
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import math
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from .conv_helpers import get_padding
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def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
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return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
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def _calc_same_pad(i: int, k: int, s: int, d: int):
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return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
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def conv2d_same(
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x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1),
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padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1):
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ih, iw = x.size()[-2:]
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kh, kw = weight.size()[-2:]
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pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
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pad_w = _calc_same_pad(iw, kw, stride[1], dilation[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])
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return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
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class Conv2dSame(nn.Conv2d):
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""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
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"""
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def __init__(self, in_channels, out_channels, kernel_size, stride=1,
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padding=0, dilation=1, groups=1, bias=True):
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super(Conv2dSame, self).__init__(
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in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
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def forward(self, x):
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return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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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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def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
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padding = kwargs.pop('padding', '')
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kwargs.setdefault('bias', False)
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padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
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if is_dynamic:
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return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
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else:
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return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
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