File size: 2,850 Bytes
ba1bf39 |
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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import functools
import torch.nn as nn
from ..util import ActNorm
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator as in Pix2Pix
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if not use_actnorm:
norm_layer = nn.BatchNorm2d
else:
norm_layer = ActNorm
if (
type(norm_layer) == functools.partial
): # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func != nn.BatchNorm2d
else:
use_bias = norm_layer != nn.BatchNorm2d
kw = 4
padw = 1
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True),
]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
sequence += [
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=2,
padding=padw,
bias=use_bias,
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
nf_mult_prev = nf_mult
nf_mult = min(2**n_layers, 8)
sequence += [
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=1,
padding=padw,
bias=use_bias,
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
sequence += [
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.main(input)
|