You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
47 lines
1.8 KiB
47 lines
1.8 KiB
""" Padding Helpers
|
|
|
|
Hacked together by Ross Wightman
|
|
"""
|
|
import math
|
|
from typing import List
|
|
from .helpers import ntuple
|
|
|
|
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:
|
|
if isinstance(kernel_size, (list, tuple)):
|
|
stride = ntuple(len(kernel_size))(stride)
|
|
dilation = ntuple(len(kernel_size))(dilation)
|
|
return [get_padding(k, s, d) for k, s, d in zip(kernel_size, stride, dilation)]
|
|
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), shift: int = 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:
|
|
if shift == 0:
|
|
pl = [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2] # ul
|
|
elif shift == 1:
|
|
pl = [pad_w - pad_w // 2, pad_w // 2, pad_h - pad_h // 2, pad_h // 2] # lr
|
|
elif shift == 2:
|
|
pl = [pad_w - pad_w // 2, pad_w // 2, pad_h // 2, pad_h - pad_h // 2] # ur
|
|
else:
|
|
pl = [pad_w // 2, pad_w - pad_w // 2, pad_h - pad_h // 2, pad_h // 2] # ll
|
|
x = F.pad(x, pl)
|
|
return x
|