Spaces:
Runtime error
Runtime error
File size: 1,620 Bytes
9d72f44 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
nn.BatchNorm2d(cout)
)
self.act = nn.ReLU()
self.residual = residual
def forward(self, x):
out = self.conv_block(x)
if self.residual:
out += x
return self.act(out)
class nonorm_Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
)
self.act = nn.LeakyReLU(0.01, inplace=True)
def forward(self, x):
out = self.conv_block(x)
return self.act(out)
class Conv2dTranspose(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding),
nn.BatchNorm2d(cout)
)
self.act = nn.ReLU()
def forward(self, x):
out = self.conv_block(x)
return self.act(out)
|