I am trying to reimplement ACGAN code from ACGAN_Chromos/acgan128.py at master · jvirico/ACGAN_Chromos · GitHub for X-ray images.
I have modified the Custom dataset class according to my own need. Here is modified code:
import argparse
import os
import numpy as np
import math
import matplotlib.pyplot as plt
import cv2
import glob
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torchvision.datasets.folder import pil_loader
from torchvision.datasets.utils import list_dir, list_files
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--n_classes", type=int, default=10, help="number of classes for dataset")
parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
opt = parser.parse_args()
## Hyperparameter customization
# (overrides command line arguments,
# will be removed at the end)
#####################################
opt.n_epochs = 1
opt.batch_size = 64
# Adam Optimizer
opt.lr = 0.0002
opt.b1 = 0.5
opt.b2 = 0.999
#
opt.n_cpu = 8
#
opt.latent_dim = 100
opt.n_classes = 2
opt.img_size = 128
opt.channels = 3
opt.sample_interval = 400
# Results
save_ckp_every = 10 #epochs
results_folder = 'results/model'+str(opt.img_size)+'_ep'+ str(opt.n_epochs)+'_bs'+ str(opt.batch_size)
ckp_folder = results_folder + '/checkpoints'
#####################################
## Dataset manipulation
#####################################
transform0 = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
## Data Transformation to apply
transform = transform0
#####################################
os.makedirs(ckp_folder, exist_ok=True)
os.makedirs(results_folder + "/images", exist_ok=True)
os.makedirs(results_folder + "/plots", exist_ok=True)
print(opt)
class CustomDataset(Dataset):
def __init__(self):
self.imgs_path = "Un-norm/"
file_list = glob.glob(self.imgs_path + "*")
print(file_list)
self.data = []
for class_path in file_list:
class_name = class_path.split("/")[-1]
for img_path in glob.glob(class_path + "/*.jpg"):
self.data.append([img_path, class_name])
print(self.data)
self.class_map = {"NORMAL" : 0, "PNEUMONIA": 1}
self.img_dim = (128, 128)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
img_path, class_name = self.data[idx]
img = cv2.imread(img_path)
img = cv2.resize(img, self.img_dim)
img = (img-127.5) / 127.5
class_id = self.class_map[class_name]
img_tensor = torch.from_numpy(img)
img_tensor = img_tensor.permute(2, 0, 1)
class_id = torch.tensor([class_id])
return img_tensor, class_id
train_set = CustomDataset()
dataloader = DataLoader(train_set, batch_size=opt.batch_size, shuffle=True, drop_last=False, num_workers=2, pin_memory=True)
cuda = True if torch.cuda.is_available() else False
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.label_emb = nn.Embedding(opt.n_classes, opt.latent_dim)
self.init_size = opt.img_size // 4 # Initial size before upsampling
self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))
self.conv_blocks = nn.Sequential(
nn.BatchNorm2d(128),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 128, 3, stride=1, padding=1),
nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 128, 3, stride=1, padding=1),
#nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, opt.channels, 3, stride=1, padding=1),
nn.Tanh(),
)
def forward(self, noise, labels):
gen_input = torch.mul(self.label_emb(labels), noise)
out = self.l1(gen_input)
out = out.view(out.shape[0], 128, self.init_size, self.init_size)
img = self.conv_blocks(out)
return img
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
def discriminator_block(in_filters, out_filters, bn=True):
"""Returns layers of each discriminator block"""
block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
if bn:
block.append(nn.BatchNorm2d(out_filters, 0.8))
return block
self.conv_blocks = nn.Sequential(
*discriminator_block(opt.channels, 16, bn=False),
*discriminator_block(16, 32),
*discriminator_block(32, 64),
*discriminator_block(64, 128),
)
# The height and width of downsampled image
ds_size = opt.img_size // 2 ** 4
# Output layers
self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())
self.aux_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, opt.n_classes), nn.Softmax())
def forward(self, img):
out = self.conv_blocks(img)
out = out.view(out.shape[0], -1)
validity = self.adv_layer(out)
label = self.aux_layer(out)
return validity, label
# Loss functions
adversarial_loss = torch.nn.BCELoss()
auxiliary_loss = torch.nn.CrossEntropyLoss()
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
auxiliary_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor
def sample_image(n_row, batches_done):
"""Saves a grid of generated images ranging from 0 to n_classes"""
# Sample noise
z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))))
# Get labels ranging from 0 to n_classes for n rows
labels = np.array([num for _ in range(n_row) for num in range(n_row)])
labels = Variable(LongTensor(labels))
gen_imgs = generator(z, labels)
save_image(gen_imgs.data, results_folder + "/images/%d.png" % batches_done, nrow=n_row, normalize=True)
# ----------
# Training
# ----------
losses = []
accuracies = []
iteration_checkpoints = []
for epoch in range(opt.n_epochs):
for i, (imgs, labels) in enumerate(dataloader):
batch_size = imgs.shape[0]
# Adversarial ground truths
valid = Variable(FloatTensor(batch_size, 1).fill_(1.0), requires_grad=False)
fake = Variable(FloatTensor(batch_size, 1).fill_(0.0), requires_grad=False)
# Configure input
real_imgs = Variable(imgs.type(FloatTensor))
labels = Variable(labels.type(LongTensor))
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Sample noise and labels as generator input
z = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim))))
gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size)))
# Generate a batch of images
gen_imgs = generator(z, gen_labels)
# Loss measures generator's ability to fool the discriminator
validity, pred_label = discriminator(gen_imgs)
g_loss = 0.5 * (adversarial_loss(validity, valid) + auxiliary_loss(pred_label, gen_labels))
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Loss for real images
real_pred, real_aux = discriminator(real_imgs)
d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2
# Loss for fake images
fake_pred, fake_aux = discriminator(gen_imgs.detach())
d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, gen_labels)) / 2
# Total discriminator loss
d_loss = (d_real_loss + d_fake_loss) / 2
# Calculate discriminator accuracy
pred = np.concatenate([real_aux.data.cpu().numpy(), fake_aux.data.cpu().numpy()], axis=0)
gt = np.concatenate([labels.data.cpu().numpy(), gen_labels.data.cpu().numpy()], axis=0)
d_acc = np.mean(np.argmax(pred, axis=1) == gt)
d_loss.backward()
optimizer_D.step()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]"
% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), 100 * d_acc, g_loss.item())
)
batches_done = epoch * len(dataloader) + i
if batches_done % opt.sample_interval == 0:
sample_image(n_row=10, batches_done=batches_done)
# Saving losses and accuracy
losses.append((d_loss, g_loss))
accuracies.append(100.0 * d_acc)
iteration_checkpoints.append(epoch + 1)
# save models every 10 epochs
if (epoch + 1 ) % opt.n_epochs == 0 or ((epoch+1) < opt.n_epochs and (epoch+1) % save_ckp_every == 0):
torch.save(generator.state_dict(), ckp_folder+'/G_{0}.pt'.format(epoch+1))
torch.save(discriminator.state_dict(), ckp_folder+'/D_{0}.pt'.format(epoch+1))
losses = np.array(losses)
# Plot training losses for Discriminator and Generator
plt.figure(figsize=(15, 5))
plt.plot(iteration_checkpoints, losses.T[0], label="Discriminator loss")
plt.plot(iteration_checkpoints, losses.T[1], label="Generator loss")
plt.xticks(iteration_checkpoints, rotation=90)
plt.title("Training Loss")
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.legend()
plt.savefig(results_folder + "/plots/Losses.png")
accuracies = np.array(accuracies)
# Plot Discriminator accuracy
plt.figure(figsize=(15, 5))
plt.plot(iteration_checkpoints, accuracies, label="Discriminator accuracy")
plt.xticks(iteration_checkpoints, rotation=90)
plt.yticks(range(0, 100, 5))
plt.title("Discriminator Accuracy")
plt.xlabel("Iteration")
plt.ylabel("Accuracy (%)")
plt.legend()
plt.savefig(results_folder + "/plots/Accuracy.png")
plt.show()
I am facing this error using Linux on GPU clusters.
The error log is:
Loading opencv3-py37-cuda10.1-gcc/3.4.11
Loading requirement: openblas/dynamic/0.2.20 hdf5_18/1.8.20
cuda10.1/toolkit/10.1.243 gcc5/5.5.0 python37
ml-pythondeps-py37-cuda10.1-gcc/4.1.2
Loading tensorflow2-py37-cuda10.1-gcc/2.2.0
Loading requirement: cudnn7.6-cuda10.1/7.6.5.32 keras-py37-cuda10.1-gcc/2.3.1
protobuf3-gcc/3.8.0 nccl2-cuda10.1-gcc/2.7.8
Currently Loaded Modulefiles:
1) gcc/8.2.0 9) ml-pythondeps-py37-cuda10.1-gcc/4.1.2
2) slurm/18.08.9 10) opencv3-py37-cuda10.1-gcc/3.4.11
3) shared 11) cudnn7.6-cuda10.1/7.6.5.32
4) openblas/dynamic/0.2.20 12) keras-py37-cuda10.1-gcc/2.3.1
5) hdf5_18/1.8.20 13) protobuf3-gcc/3.8.0
6) cuda10.1/toolkit/10.1.243 14) nccl2-cuda10.1-gcc/2.7.8
7) gcc5/5.5.0 15) tensorflow2-py37-cuda10.1-gcc/2.2.0
8) python37
/home/r00206978/.local/lib/python3.7/site-packages/torch/nn/modules/container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
input = module(input)
Traceback (most recent call last):
File "acgan_xraypt.py", line 281, in <module>
d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2
File "/home/r00206978/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/r00206978/.local/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 1165, in forward
label_smoothing=self.label_smoothing)
File "/home/r00206978/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 2996, in cross_entropy
return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
RuntimeError: 0D or 1D target tensor expected, multi-target not supported
Can anyone please solve this error and help me where the code modification is required?
My dataset has two classes (N has 1340 images and P has 2680 images) with 128x128 X-ray images. Total images are 4020.
Thanks a million.