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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
class Conv2d(nn.Module):
def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, D=1, activation=nn.ReLU()):
super(Conv2d, self).__init__()
if activation:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=padding, dilation=D),
activation
)
else:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=padding, dilation=D)
)
def forward(self, x):
x = self.conv(x)
return x
def init_He(module):
for m in module.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def pad_divide_by(in_list, d, in_size):
out_list = []
h, w = in_size
if h % d > 0:
new_h = h + d - h % d
else:
new_h = h
if w % d > 0:
new_w = w + d - w % d
else:
new_w = w
lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)
lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)
pad_array = (int(lw), int(uw), int(lh), int(uh))
for inp in in_list:
out_list.append(F.pad(inp, pad_array))
return out_list, pad_array |