I am beginner in PyTorch, i am studying ACGAN with PyTorch(https://github.com/eriklindernoren/PyTorch-GAN), i got some problems when i use my data in my computer, i got the error message as below:
Namespace(b1=0.5, b2=0.999, batch_size=64, channels=1, img_size=32, latent_dim=100, lr=0.0002, n_classes=10, n_cpu=8, n_epochs=200, sample_interval=400)
A 64 torch.Size([64, 8192])
B 64 torch.Size([64, 128, 8, 8])
C 64 torch.Size([64, 128, 2, 2])
D 64 torch.Size([64, 512])
Warning (from warnings module):
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\container.py", line 92
input = module(input)
UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
AA
C 64 torch.Size([64, 128, 3, 2])
D 64 torch.Size([64, 768])
Traceback (most recent call last):
File "D:\P1070571\Desktop\ACGAN_TEST\acgan_number.py", line 232, in <module>
real_pred, real_aux = discriminator(real_imgs)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "D:\P1070571\Desktop\ACGAN_TEST\acgan_number.py", line 111, in forward
validity = self.adv_layer(out)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\container.py", line 92, in forward
input = module(input)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\modules\linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "C:\Users\P1070571\AppData\Local\Programs\Python\Python35\lib\site-packages\torch\nn\functional.py", line 1370, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [64 x 768], m2: [512 x 1] at C:\w\1\s\windows\pytorch\aten\src\TH/generic/THTensorMath.cpp:197
Here are my code:
import argparse
import os
import numpy as np
import math
import torchvision.datasets
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
os.makedirs("images", exist_ok=True)
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()
print(opt)
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, 64, 3, stride=1, padding=1),
nn.BatchNorm2d(64, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 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)
print('A', len(out), out.size())
out = out.view(out.shape[0], 128, self.init_size, self.init_size)
print('B', len(out), out.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)
print('C',len(out), out.size())
out = out.view(out.shape[0], -1)
#out = out.view( -1,out.shape[0])
print('D', len(out), out.size())
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)
# Load my data
dataloader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(
r'D:\Desktop\ACGAN_TEST\number',
transform=transforms.Compose([
transforms.Resize(opt.img_size),
transforms.ToTensor(),
transforms.ToPILImage(),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]),
),
batch_size=opt.batch_size,
shuffle=True,
)
# 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 digits 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, "images/%d.png" % batches_done, nrow=n_row, normalize=True)
# ----------
# Training
# ----------
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()
print('AA')
# Loss for real images
real_pred, real_aux = discriminator(real_imgs)
print('BB')
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)
This is my data path
D:\Desktop\ACGAN_TEST\number
D:\Desktop\ACGAN_TEST\number\0
D:\Desktop\ACGAN_TEST\number\0\0-0.jpg
D:\Desktop\ACGAN_TEST\number\0\0-1.jpg
D:\Desktop\ACGAN_TEST\number\1\
D:\Desktop\ACGAN_TEST\number\1\1-0.jpg
D:\Desktop\ACGAN_TEST\number\1\1-1.jpg
…
D:\Desktop\ACGAN_TEST\number\9
…
Any hints on how to solve the problems?
Than you!
