138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
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import jittor as jt
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from jittor import init
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from jittor import nn
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from jittor.dataset.mnist import MNIST
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import jittor.transform as transform
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import argparse
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import os
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import numpy as np
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import math
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import time
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import cv2
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jt.flags.use_cuda = 1
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os.makedirs('images', exist_ok=True)
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parser = argparse.ArgumentParser()
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parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
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parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
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parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
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parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
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parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
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parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
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parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
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parser.add_argument('--img_size', type=int, default=28, help='size of each image dimension')
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parser.add_argument('--channels', type=int, default=1, help='number of image channels')
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parser.add_argument('--sample_interval', type=int, default=400, help='interval betwen image samples')
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opt = parser.parse_args()
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print(opt)
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img_shape = (opt.channels, opt.img_size, opt.img_size)
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def save_image(img, path, nrow=10):
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N,C,W,H = img.shape
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img2=img.reshape([-1,W*nrow*nrow,H])
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img=img2[:,:W*nrow,:]
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for i in range(1,nrow):
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img=np.concatenate([img,img2[:,W*nrow*i:W*nrow*(i+1),:]],axis=2)
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img=(img+1.0)/2.0*255
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img=img.transpose((1,2,0))
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cv2.imwrite(path,img)
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class Generator(nn.Module):
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def __init__(self):
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super(Generator, self).__init__()
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def block(in_feat, out_feat, normalize=True):
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layers = [nn.Linear(in_feat, out_feat)]
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if normalize:
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layers.append(nn.BatchNorm1d(out_feat, 0.8))
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layers.append(nn.LeakyReLU(scale=0.2))
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return layers
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self.model = nn.Sequential(*block(opt.latent_dim, 128, normalize=False), *block(128, 256), *block(256, 512), *block(512, 1024), nn.Linear(1024, int(np.prod(img_shape))), nn.Tanh())
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def execute(self, z):
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img = self.model(z)
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img = img.view((img.shape[0], *img_shape))
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return img
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class Discriminator(nn.Module):
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def __init__(self):
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super(Discriminator, self).__init__()
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self.model = nn.Sequential(nn.Linear(int(np.prod(img_shape)), 512), nn.LeakyReLU(scale=0.2), nn.Linear(512, 256), nn.LeakyReLU(scale=0.2), nn.Linear(256, 1), nn.Sigmoid())
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def execute(self, img):
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img_flat = img.view((img.shape[0], (- 1)))
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validity = self.model(img_flat)
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return validity
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adversarial_loss = nn.BCELoss()
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# Initialize generator and discriminator
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generator = Generator()
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discriminator = Discriminator()
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# Configure data loader
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transform = transform.Compose([
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transform.Resize(size=opt.img_size),
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transform.Gray(),
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transform.ImageNormalize(mean=[0.5], std=[0.5]),
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])
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dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)
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# Optimizers
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optimizer_G = jt.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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optimizer_D = jt.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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warmup_times = -1
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run_times = 3000
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total_time = 0.
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cnt = 0
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# ----------
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# Training
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# ----------
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for epoch in range(opt.n_epochs):
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for (i, (real_imgs, _)) in enumerate(dataloader):
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valid = jt.ones([real_imgs.shape[0], 1]).stop_grad()
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fake = jt.zeros([real_imgs.shape[0], 1]).stop_grad()
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# -----------------
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# Train Generator
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# -----------------
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z = jt.array(np.random.normal(0, 1, (real_imgs.shape[0], opt.latent_dim)).astype(np.float32))
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gen_imgs = generator(z)
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g_loss = adversarial_loss(discriminator(gen_imgs), valid)
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optimizer_G.step(g_loss)
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# ---------------------
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# Train Discriminator
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# ---------------------
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real_loss = adversarial_loss(discriminator(real_imgs), valid)
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fake_loss = adversarial_loss(discriminator(gen_imgs), fake)
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d_loss = ((real_loss + fake_loss) / 2)
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optimizer_D.step(d_loss)
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if warmup_times==-1:
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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])))
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batches_done = ((epoch * len(dataloader)) + i)
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if ((batches_done % opt.sample_interval) == 0):
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save_image(gen_imgs.data[:25], ('images/%d.png' % batches_done), nrow=5)
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else:
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jt.sync_all()
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cnt += 1
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print(cnt)
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if cnt == warmup_times:
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jt.sync_all(True)
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sta = time.time()
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if cnt > warmup_times + run_times:
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jt.sync_all(True)
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total_time = time.time() - sta
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print(f"run {run_times} iters cost {total_time} seconds, and avg {total_time / run_times} one iter.")
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exit(0)
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