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import functools
import torch.nn as nn
from ..modules.normalize import ActNorm
from ..modules.conv import CausalConv3d
from einops import rearrange
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)
def weights_init_conv(m):
if hasattr(m, 'conv'):
m = m.conv
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)
class NLayerDiscriminator3D(nn.Module):
"""Defines a 3D PatchGAN discriminator as in Pix2Pix but for 3D inputs."""
def __init__(self, input_nc=1, ndf=64, n_layers=3, use_actnorm=False):
"""
Construct a 3D PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input volumes
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
use_actnorm (bool) -- flag to use actnorm instead of batchnorm
"""
super(NLayerDiscriminator3D, self).__init__()
if not use_actnorm:
norm_layer = nn.BatchNorm3d
else:
raise NotImplementedError("Not implemented.")
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func != nn.BatchNorm3d
else:
use_bias = norm_layer != nn.BatchNorm3d
kw = 3
padw = 1
sequence = [nn.Conv3d(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.Conv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=(kw, kw, kw), stride=(2 if n==1 else 1,2,2), 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.Conv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=(kw, kw, kw), stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [nn.Conv3d(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)
# class NLayerDiscriminator3D(nn.Module):
# """Defines a 3D PatchGAN discriminator as in Pix2Pix but for 3D inputs."""
# def __init__(self, input_nc=1, ndf=64, n_layers=3, use_actnorm=False):
# """
# Construct a 3D PatchGAN discriminator
# Parameters:
# input_nc (int) -- the number of channels in input volumes
# ndf (int) -- the number of filters in the last conv layer
# n_layers (int) -- the number of conv layers in the discriminator
# use_actnorm (bool) -- flag to use actnorm instead of batchnorm
# """
# super(NLayerDiscriminator3D, self).__init__()
# if not use_actnorm:
# norm_layer = nn.BatchNorm3d
# else:
# raise NotImplementedError("Not implemented.")
# if type(norm_layer) == functools.partial:
# use_bias = norm_layer.func != nn.BatchNorm3d
# else:
# use_bias = norm_layer != nn.BatchNorm3d
# kw = 4
# padw = 1
# sequence = [CausalConv3d(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 += [
# CausalConv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=(kw, kw, kw), stride=(2 if n==1 else 1,2,2), 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 += [
# CausalConv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=(kw, kw, kw), stride=1, padding=padw, bias=use_bias),
# norm_layer(ndf * nf_mult),
# nn.LeakyReLU(0.2, True)
# ]
# sequence += [CausalConv3d(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) |