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# Copyright 2020 Ross Wightman
# Conv2d w/ Same Padding
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
import math
from typing import List, Tuple
#from .padding import pad_same, get_padding_value
# Dynamically pad input x with 'SAME' padding for conv with specified args
def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1), value: float = 0):
ih, iw = x.size()[-2:]
pad_h, pad_w = get_same_padding(ih, k[0], s[0], d[0]), get_same_padding(iw, k[1], s[1], d[1])
if pad_h > 0 or pad_w > 0:
x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2], value=value)
return x
# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
def get_same_padding(x: int, k: int, s: int, d: int):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
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
def conv2d_same(
x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1):
x = pad_same(x, weight.shape[-2:], stride, dilation)
return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
class Conv2dSame(nn.Conv2d):
""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2dSame, self).__init__(
in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
def forward(self, x):
return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs.pop('padding', '')
kwargs.setdefault('bias', False)
padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
if is_dynamic:
return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
else:
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
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