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""" Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
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, k: int, s: int, d: int):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 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
# 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
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
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