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discriminator.py
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discriminator.py
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import torch
import torch.nn as nn
import numpy as np
import math
import warnings
from model_blocks import *
class DiscriminatorCNN(nn.Module):
"""
D(x | theta)
"""
def __init__(self, input_shape, first_hidden_dim=256):
super().__init__()
self.input_shape = input_shape
def discriminator_block(in_filters, out_filters, bn=True):
block = [nn.Conv2d(in_filters, out_filters, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
if bn:
block.append(nn.BatchNorm2d(out_filters, 0.8))
return block
# self.model = nn.Sequential(
# *discriminator_block(self.input_shape[0], 16, bn=False),
# *discriminator_block(16, 32),
# *discriminator_block(32, 64),
# *discriminator_block(64, 128),
# )
self.model = nn.Sequential(
*discriminator_block(self.input_shape[0], first_hidden_dim, bn=False),
*discriminator_block(first_hidden_dim, first_hidden_dim * 2),
*discriminator_block(first_hidden_dim * 2, first_hidden_dim * 4),
*discriminator_block(first_hidden_dim * 4, first_hidden_dim * 8) )
# The height and width of downsampled image
ds_size = self.input_shape[1] // 2 ** 4
self.adv_layer = nn.Sequential(nn.Linear(first_hidden_dim * 8 * ds_size ** 2, 256),
nn.Linear(256, 1),
nn.Sigmoid())
self.name = "DiscriminatorCNN"
def forward(self, img):
out = self.model(img)
out = out.reshape(out.shape[0], -1)
validity = self.adv_layer(out)
return validity
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
class DiscriminatorTransformer(nn.Module):
def __init__(self, input_shape):
super().__init__()
self.input_shape = input_shape
self.patch_size = 3
self.latent_dim = 64
self.patch_embed = nn.Conv2d(self.input_shape[0], self.latent_dim, kernel_size=self.patch_size, stride=self.patch_size, padding=0)
num_patches = (self.input_shape[1] // self.patch_size)**2
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.latent_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, self.latent_dim))
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=self.latent_dim, nhead=4, dim_feedforward=self.latent_dim*4) for i in range(2)])
self.norm = nn.LayerNorm(self.latent_dim)
# self.head = nn.Linear(self.latent_dim, 1)
self.head = nn.Sequential(nn.Linear(self.latent_dim, 1), nn.Sigmoid())
self.apply(self._init_weights)
self.name = "DiscriminatorTransformer"
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, img):
img = self.patch_embed(img)
img = img.flatten(2)
img = img.permute(0,2,1)
B = img.shape[0]
cls_tokens = self.cls_token.expand(B, 1, -1)
img = torch.cat((cls_tokens, img), dim=1)
img += self.pos_embed
for blk in self.blocks:
img = blk(img)
img = self.norm(img)[:,0]
validity = self.head(img)
return validity
class DiscriminatorAutoGAN(nn.Module):
def __init__(self, channels, in_channels=3, d_spectral_norm=False, activation=nn.ReLU()):
super(DiscriminatorAutoGAN, self).__init__()
self.channels = channels
self.activation = activation
self.block1 = OptimizedDisBlock(d_spectral_norm, in_channels, self.channels)
self.block2 = DisBlock(
d_spectral_norm, self.channels, self.channels, activation=activation, downsample=True
)
self.block3 = DisBlock(
d_spectral_norm, self.channels, self.channels, activation=activation, downsample=False
)
self.block4 = DisBlock(
d_spectral_norm, self.channels, self.channels, activation=activation, downsample=False
)
self.l5 = nn.Linear(self.channels, 1, bias=False)
if d_spectral_norm:
self.l5 = nn.utils.spectral_norm(self.l5)
self.name = "DiscriminatorAutoGAN"
def forward(self, x):
h = x
layers = [self.block1, self.block2, self.block3]
model = nn.Sequential(*layers)
h = model(h)
h = self.block4(h)
h = self.activation(h)
# Global average pooling
h = h.sum(2).sum(2)
output = self.l5(h)
return output