用deepfashion数据集的所有标准信息能提供示例代码吗进行图片分类并画出轮廓吗 用torch

当然可以。以下是使用DeepFashion数据集进行图像分类任务并画出轮廓的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from torchvision import datasets, transforms, models
import os

# Set device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Set data directories
data_dir = 'path/to/deepfashion/data'
train_dir = os.path.join(data_dir, 'train')
test_dir = os.path.join(data_dir, 'test')

# Set classes
classes = ['Tops', 'Dresses', 'Skirts', 'Trousers', 'Outerwear', 'Knitwear']

# Set image size
img_size = 224

# Define transforms
train_transforms = transforms.Compose([
    transforms.RandomResizedCrop(img_size),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

test_transforms = transforms.Compose([
    transforms.Resize(img_size),
    transforms.CenterCrop(img_size),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Load datasets
train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
test_data = datasets.ImageFolder(test_dir, transform=test_transforms)

# Create dataloaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=False)

# Define model
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(classes))

# Set optimizer and criterion
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Move model to device
model = model.to(device)

# Train the model
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    model.train()
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)
    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc.double() / len(train_loader.dataset)

    # Evaluate the model
    val_loss = 0.0
    val_acc = 0.0
    model.eval()
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        loss = criterion(outputs, labels)
        val_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        val_acc += torch.sum(preds == labels.data)
    val_loss = val_loss / len(test_loader.dataset)
    val_acc = val_acc.double() / len(test_loader.dataset)

    print("Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}".format(epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))

# Plot some images with their predicted labels
model.eval()
dataiter = iter(test_loader)
images, labels = dataiter.next()
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, preds = torch.max(outputs, 1)

fig = plt.figure(figsize=(20, 20))
for idx in np.arange(32):
    ax = fig.add_subplot(8, 8, idx+1, xticks=[], yticks=[])
    plt.imshow(np.transpose(images[idx].cpu().numpy(), (1, 2, 0)))
    ax.set_title("{} ({})".format(classes[preds[idx]], classes[labels[idx]]), color=("green" if preds[idx]==labels[idx] else "red"))
plt.show()

# Visualize the feature maps of a sample image
model.eval()
dataiter = iter(test_loader)
images, labels = dataiter.next()
images = images.to(device)
labels = labels.to(device)
outputs = model(images)

# Get the feature maps of the first convolutional layer
conv1 = nn.Sequential(*list(model.children())[0][:4])
output1 = conv1(images)

# Plot the original image and the feature maps
fig = plt.figure(figsize=(30, 10))
ax = fig.add_subplot(1, 4, 1, xticks=[], yticks=[])
plt.imshow(np.transpose(images[0].cpu().numpy(), (1, 2, 0)))
ax.set_title("Original Image")
for i in range(3):
    ax = fig.add_subplot(1, 4, i+2, xticks=[], yticks=[])
    plt.imshow(output1[0,i,:,:].detach().cpu().numpy(), cmap='gray')
    ax.set_title("Feature Map {}".format(i+1))
plt.show()

在上面的代码中，我们使用了PyTorch的torchvision模块来加载DeepFashion数据集，并进行数据增强。然后，我们定义了一个预训练的ResNet18模型，并使用Adam优化器来训练模型。最后，我们使用matplotlib.pyplot模块来画出一些样本图片及其预测标签，并画出一个样本图片的第一层卷积层的特征图。