PyTorch: torchvision.datasets.ImageFolder と DataLoader を使用したテスト 質問する

コード：

import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data as data
import torchvision
from torchvision import transforms

EPOCHS = 2
BATCH_SIZE = 10
LEARNING_RATE = 0.003
TRAIN_DATA_PATH = "./train_cl/"
TEST_DATA_PATH = "./test_named_cl/"
TRANSFORM_IMG = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(256),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225] )
    ])

train_data = torchvision.datasets.ImageFolder(root=TRAIN_DATA_PATH, transform=TRANSFORM_IMG)
train_data_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True,  num_workers=4)
test_data = torchvision.datasets.ImageFolder(root=TEST_DATA_PATH, transform=TRANSFORM_IMG)
test_data_loader  = data.DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4) 

class CNN(nn.Module):
    # omitted...

if __name__ == '__main__':

    print("Number of train samples: ", len(train_data))
    print("Number of test samples: ", len(test_data))
    print("Detected Classes are: ", train_data.class_to_idx) # classes are detected by folder structure

    model = CNN()    
    optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
    loss_func = nn.CrossEntropyLoss()    

    # Training and Testing
    for epoch in range(EPOCHS):        
        for step, (x, y) in enumerate(train_data_loader):
            b_x = Variable(x)   # batch x (image)
            b_y = Variable(y)   # batch y (target)
            output = model(b_x)[0]          
            loss = loss_func(output, b_y)   
            optimizer.zero_grad()           
            loss.backward()                 
            optimizer.step()

            # Test -> this is where I have no clue
            if step % 50 == 0:
                test_x = Variable(test_data_loader)
                test_output, last_layer = model(test_x)
                pred_y = torch.max(test_output, 1)[1].data.squeeze()
                accuracy = sum(pred_y == test_y) / float(test_y.size(0))
                print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)