mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""Classes for image discrimination"""
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import functools
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import torch.nn as nn
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from imaginairy.modules.sgm.autoencoding.lpips.util import ActNorm
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def weights_init(m):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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nn.init.normal_(m.weight.data, 0.0, 0.02)
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elif classname.find("BatchNorm") != -1:
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nn.init.normal_(m.weight.data, 1.0, 0.02)
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nn.init.constant_(m.bias.data, 0)
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class NLayerDiscriminator(nn.Module):
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"""Defines a PatchGAN discriminator as in Pix2Pix
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--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
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"""
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def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
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"""Construct a PatchGAN discriminator
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Parameters:
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input_nc (int) -- the number of channels in input images
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ndf (int) -- the number of filters in the last conv layer
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n_layers (int) -- the number of conv layers in the discriminator
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norm_layer -- normalization layer
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"""
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super().__init__()
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norm_layer = nn.BatchNorm2d if not use_actnorm else ActNorm
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if (
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type(norm_layer) == functools.partial
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): # no need to use bias as BatchNorm2d has affine parameters
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use_bias = norm_layer.func != nn.BatchNorm2d
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else:
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use_bias = norm_layer != nn.BatchNorm2d
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kw = 4
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padw = 1
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sequence = [
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nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
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nn.LeakyReLU(0.2, True),
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]
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nf_mult = 1
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nf_mult_prev = 1
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for n in range(1, n_layers): # gradually increase the number of filters
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nf_mult_prev = nf_mult
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nf_mult = min(2**n, 8)
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sequence += [
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nn.Conv2d(
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ndf * nf_mult_prev,
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ndf * nf_mult,
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kernel_size=kw,
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stride=2,
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padding=padw,
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bias=use_bias,
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),
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norm_layer(ndf * nf_mult),
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nn.LeakyReLU(0.2, True),
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]
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nf_mult_prev = nf_mult
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nf_mult = min(2**n_layers, 8)
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sequence += [
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nn.Conv2d(
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ndf * nf_mult_prev,
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ndf * nf_mult,
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kernel_size=kw,
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stride=1,
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padding=padw,
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bias=use_bias,
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),
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norm_layer(ndf * nf_mult),
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nn.LeakyReLU(0.2, True),
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]
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sequence += [
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nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
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] # output 1 channel prediction map
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self.main = nn.Sequential(*sequence)
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def forward(self, input_tensor):
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"""Standard forward."""
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return self.main(input_tensor)
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