PyTorch模型精度

问题描述

我下面有一个模型，该模型可以训练对图片中的多个对象进行计数的图像。我想计算模型的准确性。此刻的代码计算出训练损失，错误以及测试损失和错误。为了找到最终精度，我需要多少％？非常感谢

# main training loop
global_step = 0
best_test_error = 10000
for epoch in range(15):
    print("Epoch %d" % epoch)
    model.train()
    for images,paths in tqdm(loader_train):
        images = images.to(device)
        targets = torch.tensor([metadata['count'][os.path.split(path)[-1]] for path in paths]) # B
        targets = targets.float().to(device)

        # forward pass:
        output = model(images) # B x 1 x 9 x 9 (analogous to a heatmap)
        preds = output.sum(dim=[1,2,3]) # predicted cell counts (vector of length B)
        
        # backward pass:
        loss = torch.mean((preds - targets)**2)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # logging:
        count_error = torch.abs(preds - targets).mean()
        writer.add_scalar('train_loss',loss.item(),global_step=global_step)
        writer.add_scalar('train_count_error',count_error.item(),global_step=global_step)

        print("Step %d,loss=%f,count error=%f" % (global_step,count_error.item()))

        global_step += 1
    
    mean_test_error = 0
    model.eval()
    for images,paths in tqdm(loader_test):
        images = images.to(device)
        targets = torch.tensor([metadata['count'][os.path.split(path)[-1]] for path in paths]) # B
        targets = targets.float().to(device)

        # forward pass:
        output = model(images) # B x 1 x 9 x 9 (analogous to a heatmap)
        preds = output.sum(dim=[1,3]) # predicted cell counts (vector of length B)

        # logging:
        loss = torch.mean((preds - targets)**2)
        count_error = torch.abs(preds - targets).mean()
        mean_test_error += count_error
        writer.add_scalar('test_loss',global_step=global_step)
        writer.add_scalar('test_count_error',global_step=global_step)
        
        global_step += 1
    
    mean_test_error = mean_test_error / len(loader_test)
    print("Test count error: %f" % mean_test_error)
    if mean_test_error < best_test_error:
        best_test_error = mean_test_error
        torch.save({'state_dict':model.state_dict(),'optimizer_state_dict':optimizer.state_dict(),'globalStep':global_step,'train_paths':dataset_train.files,'test_paths':dataset_test.files},checkpoint_path)

解决方法

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