220 lines
7.0 KiB
Python
220 lines
7.0 KiB
Python
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 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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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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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=62, 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=1, help="number of image channels")
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parser.add_argument("--sample_interval", type=int, default=500, help="number of image channels")
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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("Conv") != -1:
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init.gauss_(m.weight, 0.0, 0.02)
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init.gauss_(m.bias, 0.0, 0.02)
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elif classname.find("BatchNorm") != -1:
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init.gauss_(m.weight, 1.0, 0.02)
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init.constant_(m.bias, 0.0)
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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.init_size = (opt.img_size // 4)
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self.l1 = nn.Sequential(
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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), 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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nn.Tanh()
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)
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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.l1(noise)
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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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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.down = nn.Sequential(
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nn.Conv(opt.channels, 64, 3, 2, 1),
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nn.Relu()
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)
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self.down_size = (opt.img_size // 2)
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down_dim = (64 * ((opt.img_size // 2) ** 2))
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self.fc = nn.Sequential(
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nn.Linear(down_dim, 32),
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nn.BatchNorm1d(32, 0.8),
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nn.Relu(),
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nn.Linear(32, down_dim),
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nn.BatchNorm1d(down_dim),
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nn.Relu()
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)
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self.up = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv(64, opt.channels, 3, 1, 1)
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)
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for m in self.modules():
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weights_init_normal(m)
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def execute(self, img):
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out = self.down(img)
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out = self.fc(jt.reshape(out, [out.shape[0], (- 1)]))
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out = self.up(jt.reshape(out, [out.shape[0], 64, self.down_size, self.down_size]))
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return out
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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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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=False)
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# Optimizers
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optimizer_G = nn.Adam(
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generator.parameters(),
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lr=opt.lr,
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betas=(opt.b1, opt.b2)
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)
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optimizer_D = nn.Adam(
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discriminator.parameters(),
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lr=opt.lr,
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betas=(opt.b1, opt.b2)
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)
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# ----------
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# Training
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# ----------
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# BEGAN hyper parameters
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gamma = 0.75
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lambda_k = 0.001
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k = 0.0
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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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# Configure input
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real_imgs = jt.array(imgs)
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# -----------------
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# Train Generator
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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, (imgs.shape[0], opt.latent_dim))).float32()
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# Generate a batch of images
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gen_imgs = generator(z)
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# Loss measures generator's ability to fool the discriminator
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g_loss = jt.mean(jt.abs(discriminator(gen_imgs) - gen_imgs))
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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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# Measure discriminator's ability to classify real from generated samples
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d_real = discriminator(real_imgs)
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d_fake = discriminator(gen_imgs.stop_grad())
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d_loss_real = jt.mean(jt.abs(d_real - real_imgs))
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d_loss_fake = jt.mean(jt.abs(d_fake - gen_imgs.stop_grad()))
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d_loss = d_loss_real - k * d_loss_fake
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optimizer_D.step(d_loss)
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# ----------------
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# Update weights
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# ----------------
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diff = jt.mean(gamma * d_loss_real - d_loss_fake)
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# Update weight term for fake samples
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k = k + lambda_k * diff.data[0]
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k = min(max(k, 0), 1) # Constraint to interval [0, 1]
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# Update convergence metric
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M = (d_loss_real + jt.abs(diff)).data[0]
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# --------------
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# Log Progress
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# --------------
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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] -- M: %f, k: %f"
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% (epoch, opt.n_epochs, i, len(train_loader), d_loss.data[0], g_loss.data[0], M, k)
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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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save_image(gen_imgs.numpy()[:25], "images/%d.png" % batches_done, nrow=5) |