for img,label in train_loader

I'm sorry, as an AI language model, I cannot provide the code implementation for the given statement without any context or information about the code. Can you please provide more information or context about the code so that I can assist you better?

相关问题

在PaddlePaddle 2.0及以上版本中，对train_loader更改数据类型

在PaddlePaddle 2.0及以上版本中，你可以使用`paddle.io.Compose`，`paddle.vision.transforms`和`paddle.io.BatchSampler`等类来实现对`DataLoader`中输入数据类型的更改。

以下是一个示例代码，使用`Compose`和`paddle.vision.transforms`来对输入数据进行预处理：

import paddle
from paddle.vision.transforms import Compose, Resize, Normalize

train_dataset = paddle.vision.datasets.Cifar10(mode='train')
transform = Compose([
    Resize(size=(256, 256)),
    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset.transform = transform

batch_size = 32
batch_sampler = paddle.io.BatchSampler(
    train_dataset,
    batch_size=batch_size,
    shuffle=True
)

train_loader = paddle.io.DataLoader(
    train_dataset,
    batch_sampler=batch_sampler,
    num_workers=2,
    return_list=True
)

在上述代码中，我们首先创建一个`train_dataset`，然后使用`Compose`和`paddle.vision.transforms`来定义数据预处理的方法。接下来，我们创建一个`BatchSampler`对象并将其传递给`DataLoader`的`batch_sampler`参数。最后，我们创建一个`DataLoader`对象，使用`return_list=True`来确保数据以列表的形式返回，以便我们可以在自定义映射函数中对数据进行更改。

在自定义映射函数中，我们可以使用以下代码来将输入数据类型更改为float32：

def custom_mapper(sample):
    img, label = sample
    img = paddle.to_tensor(img, dtype='float32')
    return img, label

train_loader.set_batch_transformer(custom_mapper)

在上述代码中，我们使用`paddle.to_tensor`将输入图像的数据类型更改为float32，并将其作为一个元组返回。最后，我们使用`train_loader.set_batch_transformer(custom_mapper)`将`custom_mapper`设置为`train_loader`的批处理转换器函数。

epoch in range(epoch_num): for batch_id, (img, label, bbox, landmark) in enumerate(train_loader): img = img.to(device) label = label.to(device).long() bbox = bbox.to(device) landmark = landmark.to(device) class_out, bbox_out, landmark_out = model(img) cls_loss = class_loss(class_out, label) box_loss = bbox_loss(bbox_out, bbox, label) landmarks_loss = landmark_loss(landmark_out, landmark, label) total_loss = radio_cls_loss * cls_loss + radio_bbox_loss * box_loss + radio_landmark_loss * landmarks_loss optimizer.zero_grad() total_loss.backward() optimizer.step() if batch_id % 100 == 0: acc = accuracy(class_out, label) print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, ' 'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc)) scheduler.step()我想绘制cls_loss, box_loss, landmarks_loss, acc随自变量变化的图像，那自变量选什么比较合适，帮我修改成完整的代码

自变量应该选取 epoch，因为这是训练的迭代次数。下面是修改后的完整代码：

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime

# define your train_loader, model, optimizer, scheduler, etc.

# define the lists to store the training statistics
train_losses = []
train_cls_losses = []
train_box_losses = []
train_landmarks_losses = []
train_accs = []

epoch_num = 10
radio_cls_loss = 1.0
radio_bbox_loss = 1.0
radio_landmark_loss = 1.0

device = 'cuda' if torch.cuda.is_available() else 'cpu'

for epoch in range(epoch_num):
    for batch_id, (img, label, bbox, landmark) in enumerate(train_loader):
        img = img.to(device)
        label = label.to(device).long()
        bbox = bbox.to(device)
        landmark = landmark.to(device)
        class_out, bbox_out, landmark_out = model(img)
        cls_loss = class_loss(class_out, label)
        box_loss = bbox_loss(bbox_out, bbox, label)
        landmarks_loss = landmark_loss(landmark_out, landmark, label)
        total_loss = radio_cls_loss * cls_loss + radio_bbox_loss * box_loss + radio_landmark_loss * landmarks_loss
        optimizer.zero_grad()
        total_loss.backward()
        optimizer.step()
        if batch_id % 100 == 0:
            acc = accuracy(class_out, label)
            print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, '
                  'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc))
            # append the training statistics to the lists
            train_losses.append(total_loss.item())
            train_cls_losses.append(cls_loss.item())
            train_box_losses.append(box_loss.item())
            train_landmarks_losses.append(landmarks_loss.item())
            train_accs.append(acc.item())
    scheduler.step()

# plot the training statistics
x = np.arange(epoch_num)
plt.plot(x, train_losses, label='total_loss')
plt.plot(x, train_cls_losses, label='cls_loss')
plt.plot(x, train_box_losses, label='box_loss')
plt.plot(x, train_landmarks_losses, label='landmarks_loss')
plt.plot(x, train_accs, label='accuracy')
plt.legend()
plt.show()