if i % 100 == 0: image = images_pred.numpy()[0]#.transpose((1, 2, 0)) imageio.imsave(os.path.join(args.output_dir, 'deform_%05d.png'%i), (255*image).astype(np.uint8)) writer.append_data((255*image).astype(np.uint8)) # save optimized mesh model(1)[0].save_obj(os.path.join(args.output_dir, 'plane.obj'), save_texture=False) print(f"Cost {time.time() - sta} secs.")

这段代码是一个Python代码片段，通过使用PyTorch和OpenDR库，对一个平面进行了形变优化，并将优化过程中的每一步结果以图片和视频的形式保存下来，并将最终的形变结果保存为OBJ格式。具体来说：

- 第1行代码使用了Python中的取模运算符%，判断i是否为100的倍数，符合条件则进行下一步操作。
- 第2行代码将images_pred转换成numpy数组，并取第0个元素（假设该数组是包含一个元素的数组），然后使用imageio库将该图像以png格式保存到指定的文件夹中，并将它转换为0到255之间的整数值。
- 第3行代码使用了imageio库的append_data方法，将第2步中保存的图像添加到一个视频帧序列中。
- 第6行代码使用了OpenDR库中的save_obj方法，将最终的形变结果以OBJ格式保存到指定的文件夹中。
- 最后一行代码输出该代码块的运行时间。

综上所述，该代码块实现了对平面进行形变优化，并将优化过程中的每一步结果保存下来，最终将形变结果保存为OBJ格式。

相关问题

解释这段代码import time sta = time.time() loop = tqdm.tqdm(list(range(0, 1000))) writer = imageio.get_writer(os.path.join(args.output_dir, 'deform.gif'), mode='I') for i in loop: images_gt = jt.array(images) mesh, laplacian_loss, flatten_loss = model(args.batch_size) images_pred = renderer.render_mesh(mesh, mode='silhouettes') # optimize mesh with silhouette reprojection error and # geometry constraints loss = neg_iou_loss(images_pred, images_gt[:, 3]) + \ 0.03 * laplacian_loss + \ 0.0003 * flatten_loss loop.set_description('Loss: %.4f'%(loss.item())) optimizer.step(loss) if i % 100 == 0: image = images_pred.numpy()[0]#.transpose((1, 2, 0)) imageio.imsave(os.path.join(args.output_dir, 'deform_%05d.png'%i), (255*image).astype(np.uint8)) writer.append_data((255*image).astype(np.uint8)) # save optimized mesh model(1)[0].save_obj(os.path.join(args.output_dir, 'plane.obj'), save_texture=False) print(f"Cost {time.time() - sta} secs.")

这段代码主要做了以下几件事情：

1. 导入time模块，并记录当前的时间。

2. 初始化一个进度条对象，用于显示迭代的进度。

3. 初始化一个imageio的写入器，用于将渲染出来的图像帧写入到GIF文件中。

4. 循环执行1000次，对每个循环执行以下操作：

a. 将原始图像转换为Jittor的张量，并将其存储在images_gt中。

b. 调用模型，得到优化后的mesh、laplacian_loss和flatten_loss。其中，mesh表示优化后的三维模型网格，laplacian_loss和flatten_loss是用来约束优化过程的两个损失函数。

c. 使用renderer对象将优化后的mesh渲染为二值图像，并将其存储在images_pred中。

d. 计算损失函数loss，并更新进度条的描述信息。

e. 使用优化器optimizer对模型参数进行一次更新，以最小化损失函数loss。

f. 如果循环次数是100的倍数，则将当前渲染出来的图像帧保存为PNG格式，并添加到GIF动画中。

5. 循环结束后，将优化后的mesh保存为OBJ格式，并输出总共耗时多少秒。

因此，这段代码的主要作用是使用优化算法对三维模型进行形状优化，同时将优化过程的结果渲染为动态的GIF图像并保存。

给我一份完整的去掉注意力机制的correction_gan的代码

以下是去掉了注意力机制的 `correction_gan` 代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import os
import argparse
import glob
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np

class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 3, kernel_size=2, stride=2),
            nn.Sigmoid(),
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.encoder1 = self.conv_block(3, 64)
        self.encoder2 = self.conv_block(64, 128)
        self.encoder3 = self.conv_block(128, 256)
        self.encoder4 = self.conv_block(256, 512)
        self.bottleneck = self.conv_block(512, 1024)
        self.upconv4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.decoder4 = self.conv_block(1024, 512)
        self.upconv3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.decoder3 = self.conv_block(512, 256)
        self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.decoder2 = self.conv_block(256, 128)
        self.upconv1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.decoder1 = self.conv_block(128, 64)
        self.final_conv = nn.Conv2d(64, 3, kernel_size=1, stride=1, padding=0)
        self.sigmoid = nn.Sigmoid()

    def conv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        e1 = self.encoder1(x)
        e2 = self.encoder2(F.max_pool2d(e1, 2))
        e3 = self.encoder3(F.max_pool2d(e2, 2))
        e4 = self.encoder4(F.max_pool2d(e3, 2))
        b = self.bottleneck(F.max_pool2d(e4, 2))
        d4 = self.upconv4(b)
        d4 = torch.cat((e4, d4), dim=1)
        d4 = self.decoder4(d4)
        d3 = self.upconv3(d4)
        d3 = torch.cat((e3, d3), dim=1)
        d3 = self.decoder3(d3)
        d2 = self.upconv2(d3)
        d2 = torch.cat((e2, d2), dim=1)
        d2 = self.decoder2(d2)
        d1 = self.upconv1(d2)
        d1 = torch.cat((e1, d1), dim=1)
        d1 = self.decoder1(d1)
        out = self.final_conv(d1)
        out = self.sigmoid(out)
        return out

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 1, kernel_size=16),
        )

    def forward(self, x):
        return self.main(x).view(-1)

def compute_iou(outputs, targets, threshold=0.5):
    outputs = (outputs > threshold).float()
    targets = (targets > threshold).float()
    intersection = (outputs * targets).sum(dim=(1, 2, 3))
    union = outputs.sum(dim=(1, 2, 3)) + targets.sum(dim=(1, 2, 3)) - intersection
    iou = (intersection + 1e-6) / (union + 1e-6)
    return iou.mean().item()

from skimage.metrics import peak_signal_noise_ratio as psnr_metric
from skimage.metrics import structural_similarity as ssim_metric

def compute_psnr(outputs, targets):
    outputs = outputs.cpu().detach().numpy()
    targets = targets.cpu().detach().numpy()
    psnr = 0
    for i in range(outputs.shape[0]):
        psnr += psnr_metric(targets[i], outputs[i], data_range=1.0)
    return psnr / outputs.shape[0]

def compute_ssim(outputs, targets):
    outputs = outputs.cpu().detach().numpy()
    targets = targets.cpu().detach().numpy()
    ssim = 0
    for i in range(outputs.shape[0]):
        output_img = outputs[i].transpose(1, 2, 0)
        target_img = targets[i].transpose(1, 2, 0)
        H, W, _ = output_img.shape
        min_dim = min(H, W)
        win_size = min(7, min_dim if min_dim % 2 == 1 else min_dim - 1)
        win_size = max(win_size, 3)
        ssim += ssim_metric(target_img, output_img, data_range=1.0, channel_axis=-1, win_size=win_size)
    return ssim / outputs.shape[0]

def wasserstein_loss(pred, target):
    return torch.mean(pred * target)

from torch.autograd import grad

def compute_gradient_penalty(discriminator, real_samples, fake_samples, device):
    alpha = torch.rand(real_samples.size(0), 1, 1, 1, device=device)
    interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
    d_interpolates = discriminator(interpolates)
    fake = torch.ones(real_samples.size(0), device=device)
    gradients = grad(outputs=d_interpolates, inputs=interpolates, grad_outputs=fake, create_graph=True, retain_graph=True, only_inputs=True)[0]
    gradients = gradients.view(gradients.size(0), -1)
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
    return gradient_penalty

def train_correction_model(generator, discriminator, dataloader, optimizer_G, optimizer_D, device, lambda_gp, lambda_pixel, n_critic):
    generator.train()
    discriminator.train()
    running_g_loss = 0.0
    running_d_loss = 0.0
    running_iou = 0.0
    running_psnr = 0.0
    running_ssim = 0.0
    for batch_idx, (inputs, targets) in enumerate(tqdm(dataloader, desc="Training")):
        inputs = inputs.to(device)
        targets = targets.to(device)

        # Train Discriminator
        optimizer_D.zero_grad()
        corrected_images = generator(inputs)
        real_validity = discriminator(targets)
        fake_validity = discriminator(corrected_images.detach())
        gp = compute_gradient_penalty(discriminator, targets.data, corrected_images.data, device)
        d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + lambda_gp * gp
        d_loss.backward()
        optimizer_D.step()

        # Train Generator
        if batch_idx % n_critic == 0:
            optimizer_G.zero_grad()
            corrected_images = generator(inputs)
            fake_validity = discriminator(corrected_images)
            g_adv_loss = -torch.mean(fake_validity)
            pixelwise_loss = nn.L1Loss()
            g_pixel_loss = pixelwise_loss(corrected_images, targets)
            g_loss = g_adv_loss + lambda_pixel * g_pixel_loss
            g_loss.backward()
            optimizer_G.step()
        else:
            g_loss = torch.tensor(0.0)

        running_g_loss += g_loss.item()
        running_d_loss += d_loss.item()
        iou = compute_iou(corrected_images, targets)
        psnr = compute_psnr(corrected_images, targets)
        ssim = compute_ssim(corrected_images, targets)
        running_iou += iou
        running_psnr += psnr
        running_ssim += ssim

    epoch_g_loss = running_g_loss / len(dataloader)
    epoch_d_loss = running_d_loss / len(dataloader)
    epoch_iou = running_iou / len(dataloader)
    epoch_psnr = running_psnr / len(dataloader)
    epoch_ssim = running_ssim / len(dataloader)
    return epoch_g_loss, epoch_d_loss, epoch_iou, epoch_psnr, epoch_ssim

def validate_correction_model(generator, discriminator, dataloader, device, lambda_gp):
    generator.eval()
    discriminator.eval()
    running_g_loss = 0.0
    running_d_loss = 0.0
    running_iou = 0.0
    running_psnr = 0.0
    running_ssim = 0.0
    with torch.no_grad():
        for inputs, targets in tqdm(dataloader, desc="Validation"):
            inputs = inputs.to(device)
            targets = targets.to(device)
            corrected_images = generator(inputs)
            real_validity = discriminator(targets)
            fake_validity = discriminator(corrected_images)
            d_loss = -torch.mean(real_validity) + torch.mean(fake_validity)
            g_adv_loss = -torch.mean(fake_validity)
            g_loss = g_adv_loss
            running_g_loss += g_loss.item()
            running_d_loss += d_loss.item()
            iou = compute_iou(corrected_images, targets)
            psnr = compute_psnr(corrected_images, targets)
            ssim = compute_ssim(corrected_images, targets)
            running_iou += iou
            running_psnr += psnr
            running_ssim += ssim

    epoch_g_loss = running_g_loss / len(dataloader)
    epoch_d_loss = running_d_loss / len(dataloader)
    epoch_iou = running_iou / len(dataloader)
    epoch_psnr = running_psnr / len(dataloader)
    epoch_ssim = running_ssim / len(dataloader)
    return epoch_g_loss, epoch_d_loss, epoch_iou, epoch_psnr, epoch_ssim

def visualize_results(generator, dataloader, device, num_images=10, save_path='./results'):
    generator.eval()
    inputs, targets = next(iter(dataloader))
    inputs = inputs.to(device)
    targets = targets.to(device)
    with torch.no_grad():
        corrected_images = generator(inputs)
    inputs = inputs.cpu().numpy()
    targets = targets.cpu().numpy()
    corrected_images = corrected_images.cpu().numpy()
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    plt.figure(figsize=(20, 10))
    for i in range(num_images):
        plt.subplot(3, num_images, i + 1)
        plt.imshow(targets[i].transpose(1, 2, 0))
        plt.title("Original")
        plt.axis('off')
        plt.subplot(3, num_images, i + 1 + num_images)
        plt.imshow(inputs[i].transpose(1, 2, 0))
        plt.title("Simulated Colorblind")
        plt.axis('off')
        plt.subplot(3, num_images, i + 1 + 2 * num_images)
        plt.imshow(corrected_images[i].transpose(1, 2, 0))
        plt.title("Corrected")
        plt.axis('off')
    plt.tight_layout()
    plt.savefig(f'{save_path}_visualization.png')
    plt.show()

def plot_and_save_metrics(train_metrics, val_metrics, epoch, path='./metrics_plots'):
    if not os.path.exists(path):
        os.makedirs(path)
    epochs = np.arange(1, epoch + 1)
    train_g_losses, train_d_losses, train_ious, train_psnrs, train_ssims = zip(*train_metrics)
    val_g_losses, val_d_losses, val_ious, val_psnrs, val_ssims = zip(*val_metrics)
    
    plt.figure()
    plt.plot(epochs, train_g_losses, label='Training Generator Loss')
    plt.plot(epochs, val_g_losses, label='Validation Generator Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.title('Generator Loss over Epochs')
    plt.legend()
    plt.savefig(f'{path}/generator_loss.png')
    plt.close()
    
    plt.figure()
    plt.plot(epochs, train_d_losses, label='Training Discriminator Loss')
    plt.plot(epochs, val_d_losses, label='Validation Discriminator Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.title('Discriminator Loss over Epochs')
    plt.legend()
    plt.savefig(f'{path}/discriminator_loss.png')
    plt.close()
    
    plt.figure()
    plt.plot(epochs, train_ious, label='Training IoU')
    plt.plot(epochs, val_ious, label='Validation IoU')
    plt.xlabel('Epoch')
    plt.ylabel('IoU')
    plt.title('IoU over Epochs')
    plt.legend()
    plt.savefig(f'{path}/iou.png')
    plt.close()
    
    plt.figure()
    plt.plot(epochs, train_psnrs, label='Training PSNR')
    plt.plot(epochs, val_psnrs, label='Validation PSNR')
    plt.xlabel('Epoch')
    plt.ylabel('PSNR')
    plt.title('PSNR over Epochs')
    plt.legend()
    plt.savefig(f'{path}/psnr.png')
    plt.