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import functools
import torch.nn as nn
from ..basic import ActNorm, CircularConv2d
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=1, output_nc=1, 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
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, output_nc, 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 LiDARNLayerDiscriminator(nn.Module):
"""Modified PatchGAN discriminator from Pix2Pix
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=1, output_nc=1, 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(LiDARNLayerDiscriminator, 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, 4)
sequence = [CircularConv2d(input_nc, ndf, kernel_size=kw, stride=(1, 2), padding=(1, 2, 1, 2)), 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 += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=(1, 2), bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
CircularConv2d(ndf * nf_mult, output_nc, kernel_size=kw, stride=1, padding=(1, 2, 1, 2))] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.main(input)
class LiDARNLayerDiscriminatorV2(nn.Module):
"""Modified PatchGAN discriminator from Pix2Pix (larger receptive field)
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=1, output_nc=1, 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(LiDARNLayerDiscriminatorV2, 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, 4)
sequence = [CircularConv2d(input_nc, ndf, kernel_size=kw, stride=(1, 2), padding=(1, 2, 1, 2)), nn.LeakyReLU(0.2, True),
CircularConv2d(ndf, ndf, kernel_size=kw, stride=(1, 2), padding=(1, 2, 1, 2)), 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 += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=(2, 2), bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
CircularConv2d(ndf * nf_mult, output_nc, kernel_size=kw, stride=1, padding=(1, 2, 1, 2))] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.main(input)
class LiDARNLayerDiscriminatorV3(nn.Module):
"""Modified PatchGAN discriminator from Pix2Pix (larger receptive field)
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=1, output_nc=1, 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(LiDARNLayerDiscriminatorV3, 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, 4)
sequence = [CircularConv2d(input_nc, ndf, kernel_size=(1, 4), stride=(1, 1), padding=(1, 2, 1, 2)), nn.LeakyReLU(0.2, True),
CircularConv2d(ndf, ndf, kernel_size=kw, stride=(2, 2), padding=(1, 2, 1, 2)), 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 += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=(2, 2), bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
CircularConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, bias=use_bias, padding=(1, 2, 1, 2)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
CircularConv2d(ndf * nf_mult, output_nc, kernel_size=kw, stride=1, padding=(1, 2, 1, 2))] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
import pdb; pdb.set_trace()
return self.main(input)
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