193 lines
7.5 KiB
Python
193 lines
7.5 KiB
Python
import jittor as jt
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from jittor import init
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from jittor import nn
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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 scipy
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import itertools
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import mnistm
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from jittor.dataset.mnist import MNIST
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import jittor.transform as transform
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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=32, 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=32, help="size of each image dimension")
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parser.add_argument("--channels", type=int, default=3, 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 weights_init_normal(m):
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classname = m.__class__.__name__
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if (classname.find('Linear') != (- 1)):
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init.gauss_(m.weight, mean=0.0, std=0.02)
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elif (classname.find('BatchNorm') != (- 1)):
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init.gauss_(m.weight, mean=1.0, std=0.02)
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init.constant_(m.bias, value=0.0)
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class CoupledGenerators(nn.Module):
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def __init__(self):
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super(CoupledGenerators, self).__init__()
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self.init_size = (opt.img_size // 4)
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self.fc = nn.Sequential(nn.Linear(opt.latent_dim, (128 * (self.init_size ** 2))))
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self.shared_conv = nn.Sequential(nn.BatchNorm(128), nn.Upsample(scale_factor=2), nn.Conv(128, 128, 3, stride=1, padding=1), nn.BatchNorm(128, eps=0.8), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2))
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self.G1 = nn.Sequential(nn.Conv(128, 64, 3, stride=1, padding=1), nn.BatchNorm(64, eps=0.8), nn.LeakyReLU(0.2), nn.Conv(64, opt.channels, 3, stride=1, padding=1), nn.Tanh())
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self.G2 = nn.Sequential(nn.Conv(128, 64, 3, stride=1, padding=1), nn.BatchNorm(64, eps=0.8), nn.LeakyReLU(0.2), nn.Conv(64, opt.channels, 3, stride=1, padding=1), nn.Tanh())
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for m in self.modules():
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weights_init_normal(m)
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def execute(self, noise):
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out = self.fc(noise)
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out = out.view((out.shape[0], 128, self.init_size, self.init_size))
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img_emb = self.shared_conv(out)
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img1 = self.G1(img_emb)
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img2 = self.G2(img_emb)
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return (img1, img2)
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class CoupledDiscriminators(nn.Module):
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def __init__(self):
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super(CoupledDiscriminators, self).__init__()
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def discriminator_block(in_filters, out_filters, bn=True):
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block = [nn.Conv(in_filters, out_filters, 3, stride=2, padding=1)]
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if bn:
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block.append(nn.BatchNorm(out_filters, eps=0.8))
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block.extend([nn.LeakyReLU(0.2), nn.Dropout(p=0.25)])
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return block
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self.shared_conv = nn.Sequential(*discriminator_block(opt.channels, 16, bn=False), *discriminator_block(16, 32), *discriminator_block(32, 64), *discriminator_block(64, 128))
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ds_size = (opt.img_size // (2 ** 4))
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self.D1 = nn.Linear((128 * (ds_size ** 2)), 1)
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self.D2 = nn.Linear((128 * (ds_size ** 2)), 1)
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for m in self.modules():
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weights_init_normal(m)
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def execute(self, img1, img2):
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out = self.shared_conv(img1)
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out = out.view((out.shape[0], (- 1)))
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validity1 = self.D1(out)
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out = self.shared_conv(img2)
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out = out.view((out.shape[0], (- 1)))
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validity2 = self.D2(out)
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return (validity1, validity2)
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import cv2
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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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# Loss function
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adversarial_loss = nn.MSELoss()
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# Initialize models
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coupled_generators = CoupledGenerators()
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coupled_discriminators = CoupledDiscriminators()
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print(coupled_generators)
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print(coupled_discriminators)
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transform = transform.Compose([
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transform.Resize(opt.img_size),
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transform.ImageNormalize(mean=[0.5], std=[0.5]),
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])
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dataloader1 = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)
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dataloader2 = mnistm.MNISTM(mnist_root = "../../data/mnistm", 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(coupled_generators.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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optimizer_D = nn.Adam(coupled_discriminators.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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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, ((imgs1, _), (imgs2, _)) in enumerate(zip(dataloader1, dataloader2)):
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jt.sync_all(True)
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batch_size = imgs1.shape[0]
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# Adversarial ground truths
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valid = jt.ones([batch_size, 1]).float32().stop_grad()
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fake = jt.zeros([batch_size, 1]).float32().stop_grad()
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# ------------------
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# Train Generators
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# ------------------
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# Sample noise as generator input
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z = jt.array(np.random.normal(0, 1, (batch_size, opt.latent_dim))).float32()
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# Generate a batch of images
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gen_imgs1, gen_imgs2 = coupled_generators(z)
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# Determine validity of generated images
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validity1, validity2 = coupled_discriminators(gen_imgs1, gen_imgs2)
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g_loss = (adversarial_loss(validity1, valid) + adversarial_loss(validity2, valid)) / 2
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optimizer_G.step(g_loss)
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# ----------------------
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# Train Discriminators
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# ----------------------
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# Determine validity of real and generated images
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validity1_real, validity2_real = coupled_discriminators(imgs1, imgs2)
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validity1_fake, validity2_fake = coupled_discriminators(gen_imgs1.stop_grad(), gen_imgs2.stop_grad())
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d_loss = (
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adversarial_loss(validity1_real, valid)
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+ adversarial_loss(validity1_fake, fake)
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+ adversarial_loss(validity2_real, valid)
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+ adversarial_loss(validity2_fake, fake)
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) / 4
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optimizer_D.step(d_loss)
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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]"
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% (epoch, opt.n_epochs, i, len(dataloader1), d_loss.data[0], g_loss.data[0])
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)
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batches_done = epoch * len(dataloader1) + i
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if batches_done % opt.sample_interval == 0:
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gen_imgs = np.concatenate([gen_imgs1.numpy(), gen_imgs2.numpy()], 0)
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save_image(gen_imgs, "images/%d.png" % batches_done, nrow=8)
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