178 lines
6.4 KiB
Python
178 lines
6.4 KiB
Python
import argparse
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import os
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import numpy as np
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import time
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import cv2
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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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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=10, help="dimensionality of the latent code")
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parser.add_argument("--img_size", type=int, default=32, 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=3000, help="interval between image sampling")
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opt = parser.parse_args()
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print(opt)
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def reparameterization(mu, logvar):
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std = jt.exp(logvar / 2)
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sampled_z = jt.array(np.random.normal(0, 1, (mu.shape[0], opt.latent_dim))).float32()
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z = sampled_z * std + mu
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return z
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img_shape = (opt.channels, opt.img_size, opt.img_size)
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class Encoder(nn.Module):
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def __init__(self):
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super(Encoder, self).__init__()
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self.model = nn.Sequential(nn.Linear(int(np.prod(img_shape)), 512), nn.Leaky_relu(0.2), nn.Linear(512, 512), nn.BatchNorm1d(512), nn.Leaky_relu(0.2))
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self.mu = nn.Linear(512, opt.latent_dim)
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self.logvar = nn.Linear(512, opt.latent_dim)
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def execute(self, img):
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img_flat = jt.reshape(img, [img.shape[0], (- 1)])
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x = self.model(img_flat)
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mu = self.mu(x)
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logvar = self.logvar(x)
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z = reparameterization(mu, logvar)
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return z
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class Decoder(nn.Module):
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def __init__(self):
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super(Decoder, self).__init__()
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self.model = nn.Sequential(nn.Linear(opt.latent_dim, 512), nn.Leaky_relu(0.2), nn.Linear(512, 512), nn.BatchNorm1d(512), nn.Leaky_relu(0.2), nn.Linear(512, int(np.prod(img_shape))), nn.Tanh())
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def execute(self, z):
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img_flat = self.model(z)
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img = jt.reshape(img_flat, [img_flat.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(opt.latent_dim, 512), nn.Leaky_relu(0.2), nn.Linear(512, 256), nn.Leaky_relu(0.2), nn.Linear(256, 1), nn.Sigmoid())
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def execute(self, z):
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validity = self.model(z)
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return validity
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# Use binary cross-entropy loss
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adversarial_loss = nn.BCELoss()
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pixelwise_loss = nn.L1Loss()
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# Initialize generator and discriminator
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encoder = Encoder()
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decoder = Decoder()
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discriminator = Discriminator()
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# Configure data loader
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from jittor.dataset.mnist import MNIST
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import jittor.transform as transform
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transform = transform.Compose([
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transform.Resize(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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train_loader = 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 = nn.Adam(
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encoder.parameters() + decoder.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)
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)
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optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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def save_image(img, path, nrow=10, padding=5):
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N,C,W,H = img.shape
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if (N%nrow!=0):
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print("N%nrow!=0")
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return
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ncol=int(N/nrow)
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img_all = []
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for i in range(ncol):
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img_ = []
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for j in range(nrow):
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img_.append(img[i*nrow+j])
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img_.append(np.zeros((C,W,padding)))
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img_all.append(np.concatenate(img_, 2))
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img_all.append(np.zeros((C,padding,img_all[0].shape[2])))
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img = np.concatenate(img_all, 1)
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img = np.concatenate([np.zeros((C,padding,img.shape[2])), img], 1)
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img = np.concatenate([np.zeros((C,img.shape[1],padding)), img], 2)
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min_=img.min()
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max_=img.max()
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img=(img-min_)/(max_-min_)*255
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img=img.transpose((1,2,0))
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if C==3:
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img = img[:,:,::-1]
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cv2.imwrite(path,img)
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def sample_image(n_row, batches_done):
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"""Saves a grid of generated digits"""
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# Sample noise
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z = jt.array(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))).float32().stop_grad()
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gen_imgs = decoder(z)
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save_image(gen_imgs.numpy(), "images/%d.png" % batches_done, nrow=n_row)
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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, (imgs, _) in enumerate(train_loader):
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sta = time.time()
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# Adversarial ground truths
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valid = jt.ones([imgs.shape[0], 1]).stop_grad()
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fake = jt.zeros([imgs.shape[0], 1]).stop_grad()
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# Configure input
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real_imgs = jt.array(imgs).stop_grad()
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# -----------------
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# Train Generator
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# -----------------
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encoded_imgs = encoder(real_imgs)
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decoded_imgs = decoder(encoded_imgs)
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# Loss measures generator's ability to fool the discriminator
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g_loss = (0.001 * adversarial_loss(discriminator(encoded_imgs), valid) + 0.999 * pixelwise_loss(
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decoded_imgs, real_imgs
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))
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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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# Sample noise as discriminator ground truth
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z = jt.array(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))).float32().stop_grad()
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# Measure discriminator's ability to classify real from generated samples
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real_loss = adversarial_loss(discriminator(z), valid).float32()
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fake_loss = adversarial_loss(discriminator(encoded_imgs.detach()), fake).float32()
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d_loss = 0.5 * (real_loss + fake_loss)
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optimizer_D.step(d_loss)
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jt.sync_all()
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if i % 50 == 0:
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print(
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"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f] [Time: %f]"
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% (epoch, opt.n_epochs, i, len(train_loader), d_loss.data[0], g_loss.data[0], time.time() - sta)
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)
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batches_done = epoch * len(train_loader) + i
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if batches_done % opt.sample_interval == 0:
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sample_image(n_row=10, batches_done=batches_done) |