156 lines
6.0 KiB
Python
156 lines
6.0 KiB
Python
|
|
import jittor as jt
|
|
from jittor import init
|
|
from jittor import nn
|
|
from jittor.dataset.mnist import MNIST
|
|
import jittor.transform as transform
|
|
import argparse
|
|
import os
|
|
import numpy as np
|
|
import math
|
|
import cv2
|
|
import time
|
|
|
|
jt.flags.use_cuda = 1
|
|
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('--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=1000, help='number of image channels')
|
|
opt = parser.parse_args()
|
|
print(opt)
|
|
|
|
def save_image(img, path, nrow=10):
|
|
N,C,W,H = img.shape
|
|
img2=img.reshape([-1,W*nrow*nrow,H])
|
|
img=img2[:,:W*nrow,:]
|
|
for i in range(1,nrow):
|
|
img=np.concatenate([img,img2[:,W*nrow*i:W*nrow*(i+1),:]],axis=2)
|
|
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):
|
|
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)
|
|
|
|
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)
|
|
return img
|
|
|
|
class Discriminator(nn.Module):
|
|
|
|
def __init__(self):
|
|
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)
|
|
|
|
def execute(self, img):
|
|
out = self.model(img)
|
|
out = out.view((out.shape[0], (- 1)))
|
|
validity = self.adv_layer(out)
|
|
return validity
|
|
|
|
|
|
adversarial_loss = nn.MSELoss()
|
|
|
|
# Initialize generator and discriminator
|
|
generator = Generator()
|
|
discriminator = Discriminator()
|
|
|
|
# Configure data loader
|
|
transform = transform.Compose([
|
|
transform.Resize(size=opt.img_size),
|
|
transform.Gray(),
|
|
transform.ImageNormalize(mean=[0.5], std=[0.5]),
|
|
])
|
|
dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)
|
|
|
|
# Optimizers
|
|
optimizer_G = jt.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
optimizer_D = jt.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
|
|
warmup_times = -1
|
|
run_times = 3000
|
|
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()
|
|
|
|
# -----------------
|
|
# Train Generator
|
|
# -----------------
|
|
|
|
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(discriminator(gen_imgs), valid)
|
|
optimizer_G.step(g_loss)
|
|
|
|
# ---------------------
|
|
# Train Discriminator
|
|
# ---------------------
|
|
|
|
real_loss = adversarial_loss(discriminator(real_imgs), valid)
|
|
fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
|
|
d_loss = (0.5 * (real_loss + fake_loss))
|
|
optimizer_D.step(d_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), d_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) |