193 lines
7.8 KiB
Python
193 lines
7.8 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, sys
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import numpy as np
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import math
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import cv2
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import time
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import random
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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('--n_classes', type=int, default=10, help='number of classes for dataset')
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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=400, help='interval between image sampling')
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opt = parser.parse_args()
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print(opt)
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def weights_init_normal(m):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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jt.init.gauss_(m.weight, 0.0, 0.02)
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elif classname.find("BatchNorm") != -1:
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jt.init.gauss_(m.weight, 1.0, 0.02)
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jt.init.constant_(m.bias, 0.0)
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def save_image(img, path):
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img2=img.reshape([-1,3200,32])
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img=img2[:,:320,:]
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for i in range(1,10):
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img=np.concatenate([img,img2[:,320*i:320*(i+1),:]],axis=2)
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print(img.shape)
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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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self.label_emb = nn.Embedding(opt.n_classes, opt.latent_dim)
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self.init_size = (opt.img_size // 4)
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self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, (128 * (self.init_size ** 2))))
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self.conv_blocks = nn.Sequential(
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nn.BatchNorm(128),
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nn.Upsample(scale_factor=2),
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nn.Conv(128, 128, 3, stride=1, padding=1),
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nn.BatchNorm(128, 0.8),
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nn.Leaky_relu(0.2),
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nn.Upsample(scale_factor=2),
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nn.Conv(128, 64, 3, stride=1, padding=1),
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nn.BatchNorm(64, 0.8),
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nn.Leaky_relu(0.2),
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nn.Conv(64, opt.channels, 3, stride=1, padding=1)
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)
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self.tanh = nn.Tanh()
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for m in self.conv_blocks:
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weights_init_normal(m)
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def execute(self, noise, labels):
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ebd = self.label_emb(labels)
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gen_input = ebd*noise
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out = self.l1(gen_input)
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out = jt.reshape(out, [out.shape[0], 128, self.init_size, self.init_size])
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img = self.conv_blocks(out)
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img = self.tanh(img)
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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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def discriminator_block(in_filters, out_filters, bn=True):
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'Returns layers of each discriminator block'
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block = [nn.Conv(in_filters, out_filters, 3, 2, 1), nn.Leaky_relu(0.2), nn.Dropout(0.25)]
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print("Conv shape",block[0].weight.shape)
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if bn:
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block.append(nn.BatchNorm(out_filters, 0.8))
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for m in block:
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weights_init_normal(m)
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return block
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self.conv_blocks = 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.adv_layer = nn.Sequential(nn.Linear((128 * (ds_size ** 2)), 1), nn.Sigmoid())
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self.aux_layer = nn.Sequential(nn.Linear((128 * (ds_size ** 2)), opt.n_classes), nn.Softmax(dim=1))
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def execute(self, img):
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out = self.conv_blocks(img)
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out = jt.reshape(out, [out.shape[0], (- 1)])
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validity = self.adv_layer(out)
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label = self.aux_layer(out)
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return (validity, label)
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adversarial_loss = nn.BCELoss()
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auxiliary_loss = nn.CrossEntropyLoss()
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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(), opt.lr, betas=(opt.b1, opt.b2))
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optimizer_D = jt.optim.Adam(discriminator.parameters(), opt.lr, betas=(opt.b1, opt.b2))
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def sample_image(n_row, batches_done):
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'Saves a grid of generated digits ranging from 0 to n_classes'
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z = jt.array(np.random.normal(0, 1, ((n_row ** 2), opt.latent_dim)).astype(np.float32))
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labels = np.array([num for _ in range(n_row) for num in range(n_row)]).astype(np.float32)
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labels = jt.array(labels)
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gen_imgs = generator(z, labels)
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gen_imgs = gen_imgs.tanh()
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save_image(gen_imgs.numpy(), ('images/%d.png' % batches_done))
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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, labels)) in enumerate(dataloader):
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batch_size = real_imgs.shape[0]
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valid = jt.ones([batch_size, 1]).stop_grad()
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fake = jt.zeros([batch_size, 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, (batch_size, opt.latent_dim)).astype(np.float32))
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gen_labels = jt.array(np.random.randint(0, opt.n_classes, batch_size).astype(np.float32))
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gen_imgs = generator(z, gen_labels)
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(validity, pred_label) = discriminator(gen_imgs)
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g_loss = (0.5 * (adversarial_loss(validity, valid) + auxiliary_loss(pred_label, gen_labels)))
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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_pred, real_aux) = discriminator(real_imgs)
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d_real_loss = ((adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2)
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(fake_pred, fake_aux) = discriminator(gen_imgs.detach())
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d_fake_loss = ((adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, gen_labels)) / 2)
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d_loss = ((d_real_loss + d_fake_loss) / 2)
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optimizer_D.step(d_loss)
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if warmup_times==-1:
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pred = np.concatenate([real_aux.numpy(), fake_aux.numpy()], axis=0)
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gt = np.concatenate([labels.numpy(), gen_labels.numpy()], axis=0)
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d_acc = np.mean((np.argmax(pred, axis=1) == gt))
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print(('[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]' % (epoch, opt.n_epochs, i, len(dataloader), d_loss.mean().data, (100 * d_acc), g_loss.mean().data)))
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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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sample_image(n_row=10, batches_done=batches_done)
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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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