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Updated train.py to use variable image sizes #21

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bf69e8e
Updated train.py to use variable image sizes
DylanMMyers Apr 20, 2024
718bdaa
Update train.py
DylanMMyers Apr 28, 2024
391f650
Update cond_DCGAN_network.py to work with variable image sizes
DylanMMyers Apr 28, 2024
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182 changes: 71 additions & 111 deletions synthesis_pipeline/condGAN/cond_DCGAN_network.py
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Original file line number Diff line number Diff line change
@@ -1,157 +1,117 @@
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import math

# ** GENERATOR MODEL (takes noise; returns tensor) **
class Generator(nn.Module):
def __init__(self,
n_categories,
noise_size,
channels=3,
feature_map_size=64,
embedding_size=50):
embedding_size=50,
img_size=64): # Assuming default power of 2 as 64x64 image size

super(Generator, self).__init__()

ngf = feature_map_size
power = int(math.log2(img_size))

self.label_embedding = nn.Embedding(n_categories, embedding_size)

self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d(
in_channels=embedding_size + noise_size,
out_channels=ngf * 8,
kernel_size=4,
stride=1,
padding=0,
bias=False
),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (gen_dimesions*8) x 4 x 4
nn.ConvTranspose2d(
in_channels=ngf * 8,
out_channels=ngf * 4,
kernel_size=4,
stride=2,
padding=1, bias=False
),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (gen_dimesions*4) x 8 x 8
nn.ConvTranspose2d(
in_channels=ngf * 4,
out_channels=ngf * 2,
kernel_size=4,
stride=2,
padding=1, bias=False
),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (gen_dimesions*2) x 16 x 16
nn.ConvTranspose2d(
in_channels=ngf * 2,
out_channels=ngf,
kernel_size=4,
stride= 2,
padding=1,
bias=False
),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (gen_dimesions) x 32 x 32
nn.ConvTranspose2d(
in_channels=ngf,
out_channels=channels,
layers = []
prev_channels = noise_size + embedding_size # Initial input channels
out_channels = (ngf * 8)

for i in range(power-1):
layers.append(nn.ConvTranspose2d(
in_channels=prev_channels,
out_channels=out_channels, # out_channels is still itself
kernel_size=4,
stride=2,
padding=1,
bias=False
),
nn.Tanh()
# state size. (nc) x 64 x 64
)
))
layers.append(nn.BatchNorm2d(out_channels))
layers.append(nn.ReLU(True))
prev_channels = out_channels # Update input channels for the next layer
out_channels //= 2

# Output layer
layers.append(nn.ConvTranspose2d(
in_channels=prev_channels,
out_channels=channels,
kernel_size=4,
stride=2,
padding=1,
bias=False
))
layers.append(nn.Tanh())

self.main = nn.Sequential(*layers)

def forward(self, noise, label):
label_embedding = self.label_embedding(label).view(label.size(0), -1, 1, 1)
combined_input = torch.cat((noise, label_embedding), 1)
output = self.main(combined_input)
return output

# Discriminator currently has same isues*
# Kernel size 4x4 can't be larger than input 1x1

# ** DISCRIMINATOR MODEL (takes tensor; returns probability) **
class Discriminator(nn.Module):
def __init__(self,
n_categories,
channels=3,
feature_map_size=64,
embedding_size=50):
embedding_size=50,
img_size=64):
super(Discriminator, self).__init__()

ndf = feature_map_size
power = int(math.log2(img_size))

self.label_embedding = nn.Embedding(n_categories, embedding_size)

self.main = nn.Sequential(
# input is (nc) x 64 x 64
nn.Conv2d(
in_channels=channels + embedding_size,
out_channels=ndf,
kernel_size=4,
stride=2,
padding=1,
bias=False
),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndvf) x 32 x 32
nn.Conv2d(
in_channels=ndf,
out_channels=ndf * 2,
layers = []
prev_channels = channels + embedding_size # Initial input channels
for i in range(power-1):
out_channels = (ndf * 8) // (2 ** i)
layers.append(nn.Conv2d(
in_channels=prev_channels,
out_channels=out_channels,
kernel_size=4,
stride=2,
padding=1,
bias=False
),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 16 x 16
nn.Conv2d(
in_channels=ndf * 2,
out_channels=ndf * 4,
kernel_size=4,
stride=2,
padding=1,
bias=False
),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 8 x 8
nn.Conv2d(
in_channels=ndf * 4,
out_channels=ndf * 8,
kernel_size=4,
stride=2,
padding=1,
bias=False
),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 4
nn.Conv2d(
in_channels=ndf * 8,
out_channels=1,
kernel_size=4,
stride=1,
padding=0,
bias=False
),
nn.Sigmoid()
))
layers.append(nn.LeakyReLU(0.2, inplace=True))
prev_channels = out_channels # Update input channels for the next layer

# Output layer
self.output_conv = nn.Conv2d(
in_channels=prev_channels,
out_channels=1,
kernel_size=2,
stride=2,
padding=0,
)

self.sigmoid = nn.Sigmoid()

self.main = nn.Sequential(*layers)

def forward(self, input, label):
label_embedding = self.label_embedding(label).view(label.size(0), -1, 1, 1)
expanded_labels = label_embedding.expand(label.size(0), label_embedding.size(1), input.size(2), input.size(3))
combined_input = torch.cat((input, expanded_labels), 1)
return self.main(combined_input)

label_embedding = label_embedding.expand(-1, -1, input.size(2), input.size(3)) # Expand along height and width dimensions
combined_input = torch.cat((input, label_embedding), 1)
# print('before main', combined_input.shape)
x = self.main(combined_input)
# print('after main', x.shape)
x = self.output_conv(x)
x = x.flatten()
# print('after output conv', x.shape)
x = self.sigmoid(x)
# print('after sigmoid', x.shape)
return x
9 changes: 6 additions & 3 deletions synthesis_pipeline/condGAN/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@
batch_size = args.batch_size

dataset = MultiClassDataset(data_path=args.data,
category_max=20,
category_max=batch_size,
transform=transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
Expand All @@ -67,8 +67,11 @@
beta1 = 0.5

# initialize networks
netG = Generator(n_categories=dataset.num_labels, noise_size=args.noise_size).to(device)
netD = Discriminator(n_categories=dataset.num_labels).to(device)
netG = Generator(n_categories=dataset.num_labels, noise_size=args.noise_size, img_size=args.image_size).to(device)
netD = Discriminator(n_categories=dataset.num_labels, img_size=args.image_size).to(device)

print(netG)
print(netD)

# randomly initialize network weights
netG.apply(torch_utils.weights_init)
Expand Down