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pretrain.py
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import jittor as jt
from jittor import nn, Module
from jittor.dataset.dataset import ImageFolder
import jittor.transform as transform
import jittor.init as init
from get_sketch import ResnetGenerator
import numpy as np
import cv2
import argparse
import os
import math
import time
jt.flags.use_cuda = 1
os.makedirs('images', exist_ok=True)
parser = argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=50, help='number of epochs of training')
parser.add_argument('--batch_size', type=int, default=16, help='size of the batches')
parser.add_argument('--lr', type=float, default=0.0001, 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('--img_size', type=int, default=112, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=3, help='number of image channels')
parser.add_argument('--sample_interval', type=int, default=1000, help='number of image channels')
opt = parser.parse_args()
print(opt)
transform = transform.Compose([
transform.Resize(size=[112,112]),
transform.ImageNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
train_dir = 'GANSketching-main\data\image'
dataloader = ImageFolder(train_dir).set_attrs(transform=transform, batch_size=16, shuffle=True)
# val_dir = 'GANSketching-main\data\image\cat'
# val_loader = ImageFolder(val_dir).set_attrs(transform=transform, batch_size=1, shuffle=True)
def save_image(img, path, nrow=10):
img=img[0,:,:,:]
img=(img+1.0)/2.0*255
img=img.transpose((1,2,0))
cv2.imwrite(path,img)
def weights_init_normal(m):
classname = m.__class__.__name__
if (classname.find('Conv') != (- 1)):
jt.init.gauss_(m.weight, 0.0, 0.02)
elif (classname.find('BatchNorm') != (- 1)):
jt.init.gauss_(m.weight, 1.0, 0.02)
jt.init.constant_(m.bias, 0.0)
class Generator(nn.Module):
def __init__(self, dim=3):
super(Generator, self).__init__()
self.init_size = (opt.img_size // 4)
self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, (128 * (self.init_size ** 2))))
self.conv_blocks = nn.Sequential(nn.Upsample(scale_factor=2), nn.Conv(128, 128, 3, stride=1, padding=1), nn.BatchNorm(128, eps=0.8), nn.LeakyReLU(scale=0.2), nn.Upsample(scale_factor=2), nn.Conv(128, 64, 3, stride=1, padding=1), nn.BatchNorm(64, eps=0.8), nn.LeakyReLU(scale=0.2), nn.Conv(64, opt.channels, 3, stride=1, padding=1), nn.Tanh())
for m in self.modules():
weights_init_normal(m)
# self.fc = nn.Linear(1024, 7*7*256)
# self.fc_bn = nn.BatchNorm(256)
# self.deconv1 = nn.ConvTranspose(256, 256, 3, 2, 1, 1)
# self.deconv1_bn = nn.BatchNorm(256)
# self.deconv2 = nn.ConvTranspose(256, 256, 3, 1, 1)
# self.deconv2_bn = nn.BatchNorm(256)
# self.deconv3 = nn.ConvTranspose(256, 256, 3, 2, 1, 1)
# self.deconv3_bn = nn.BatchNorm(256)
# self.deconv4 = nn.ConvTranspose(256, 256, 3, 1, 1)
# self.deconv4_bn = nn.BatchNorm(256)
# self.deconv5 = nn.ConvTranspose(256, 128, 3, 2, 1, 1)
# self.deconv5_bn = nn.BatchNorm(128)
# self.deconv6 = nn.ConvTranspose(128, 64, 3, 2, 1, 1)
# self.deconv6_bn = nn.BatchNorm(64)
# self.deconv7 = nn.ConvTranspose(64 , dim, 3, 1, 1)
# self.relu = nn.ReLU()
# self.tanh = nn.Tanh()
def execute(self, z):
out = self.l1(z)
out = out.view((out.shape[0], 128, self.init_size, self.init_size))
img = self.conv_blocks(out)
# x = self.fc(input).reshape((-1, 256, 7, 7))
# x = self.relu(self.fc_bn(x))
# x = self.relu(self.deconv1_bn(self.deconv1(x)))
# x = self.relu(self.deconv2_bn(self.deconv2(x)))
# x = self.relu(self.deconv3_bn(self.deconv3(x)))
# x = self.relu(self.deconv4_bn(self.deconv4(x)))
# x = self.relu(self.deconv5_bn(self.deconv5(x)))
# x = self.relu(self.deconv6_bn(self.deconv6(x)))
# x = self.tanh(self.deconv7(x))
return img
class Discriminator(nn.Module):
def __init__(self, dim=3):
super(Discriminator, self).__init__()
def discriminator_block(in_filters, out_filters, bn=True):
block = [nn.Conv(in_filters, out_filters, 3, stride=2, padding=1), nn.LeakyReLU(scale=0.2), nn.Dropout(p=0.25)]
if bn:
block.append(nn.BatchNorm(out_filters, eps=0.8))
return block
self.model = nn.Sequential(*discriminator_block(opt.channels, 16, bn=False), *discriminator_block(16, 32), *discriminator_block(32, 64), *discriminator_block(64, 128))
ds_size = (opt.img_size // (2 ** 4))
self.adv_layer = nn.Linear((128 * (ds_size ** 2)), 1)
for m in self.modules():
weights_init_normal(m)
# self.conv1 = nn.Conv(dim, 64, 5, 2, 2)
# self.conv2 = nn.Conv(64, 128, 5, 2, 2)
# self.conv2_bn = nn.BatchNorm(128)
# self.conv3 = nn.Conv(128, 256, 5, 2, 2)
# self.conv3_bn = nn.BatchNorm(256)
# self.conv4 = nn.Conv(256, 512, 5, 2, 2)
# self.conv4_bn = nn.BatchNorm(512)
# self.fc = nn.Linear(512*7*7, 1)
# self.leaky_relu = nn.Leaky_relu()
def execute(self, img):
out = self.model(img)
out = out.view((out.shape[0], (- 1)))
validity = self.adv_layer(out)
# x = self.leaky_relu(self.conv1(input), 0.2)
# x = self.leaky_relu(self.conv2_bn(self.conv2(x)), 0.2)
# x = self.leaky_relu(self.conv3_bn(self.conv3(x)), 0.2)
# x = self.leaky_relu(self.conv4_bn(self.conv4(x)), 0.2)
# x = x.reshape((x.shape[0], 512*7*7))
# x = self.fc(x)
return validity
adversarial_loss = nn.MSELoss()
# Initialize generator and discriminator
generator = Generator()
sketch_generator = ResnetGenerator(3, 1, n_blocks=9, use_dropout=False)
sketch_generator.load('GANSketching-main\pretrained\photosketch.pth')
discriminatorX = Discriminator()
discriminatorY = Discriminator()
# Optimizers
optimizer_G = jt.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_DX = jt.optim.Adam(discriminatorX.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_DY = jt.optim.Adam(discriminatorY.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
warmup_times = -1
run_times = 1000
total_time = 0.
cnt = 0
# ----------
# Training
# ----------
for epoch in range(opt.n_epochs):
for (i, (real_imgs, _)) in enumerate(dataloader):
valid = jt.ones([real_imgs.shape[0], 1]).stop_grad()
fake = jt.zeros([real_imgs.shape[0], 1]).stop_grad()
# valid_sketch = jt.ones([real_sketch.shape[0], 1]).stop_grad()
# fake_sketch = jt.zeros([real_sketch.shape[0], 1]).stop_grad()
# -----------------
# Generate
# -----------------
z = jt.array(np.random.normal(0, 1, (real_imgs.shape[0], opt.latent_dim)).astype(np.float32))
gen_imgs = generator(z)
g_loss = adversarial_loss(discriminatorX(gen_imgs), valid)
optimizer_G.step(g_loss)
# ---------------------
# Train Discriminator
# ---------------------
real_loss = adversarial_loss(discriminatorX(real_imgs), valid)
fake_loss = adversarial_loss(discriminatorX(gen_imgs.detach()), fake)
# real_sketch_loss = adversarial_loss(discriminatorY(real_sketch), valid_sketch)
# fake_sketch_loss = adversarial_loss(discriminatorY(sketch_generator(gen_imgs.detach())), fake)
dx_loss = (0.5 * (real_loss + fake_loss))
# dy_loss = (0.5 * (real_sketch_loss + fake_sketch_loss))
optimizer_DX.step(dx_loss)
# optimizer_DY.step(dy_loss)
# -----------------
# Train Generator
# -----------------
# g_loss = dy_loss + 0.7*dx_loss
# optimizer_G.step(g_loss)
if warmup_times==-1:
print(('[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]' % (epoch, opt.n_epochs, i, len(dataloader), dx_loss.numpy()[0], g_loss.numpy()[0])))
batches_done = ((epoch * len(dataloader)) + i)
if ((batches_done % opt.sample_interval) == 0):
save_image(gen_imgs.data[:25], ('images/%d.png' % batches_done), nrow=5)
else:
jt.sync_all()
cnt += 1
print(cnt)
if cnt == warmup_times:
jt.sync_all(True)
sta = time.time()
if cnt > warmup_times + run_times:
jt.sync_all(True)
total_time = time.time() - sta
print(f"run {run_times} iters cost {total_time} seconds, and avg {total_time / run_times} one iter.")
exit(0)
generator.save('cat.pth')