TensorFlow和Keras解决数据量过大内存溢出

将上万张图片的路径一次性读到内存中，自己实现一个分批读取函数，在该函数中根据自己的内存情况设置读取图片，只把这一批图片读入内存中，然后交给模型，模型再对这一批图片进行分批训练，因为内存一般大于等于显存，所以内存的批次大小和显存的批次大小通常不相同。

Tensorlow
在input.py里写get_batch函数。

def get_batch(X_train, y_train, img_w, img_h, color_type, batch_size, capacity):
    '''
    Args:
        X_train: train img path list
        y_train: train labels list
        img_w: image width
        img_h: image height
        batch_size: batch size
        capacity: the maximum elements in queue
    Returns:
        X_train_batch: 4D tensor [batch_size, width, height, chanel],\
                        dtype=tf.float32
        y_train_batch: 1D tensor [batch_size], dtype=int32
    '''
    X_train = tf.cast(X_train, tf.string)

    y_train = tf.cast(y_train, tf.int32)
    
    # make an input queue
    input_queue = tf.train.slice_input_producer([X_train, y_train])

    y_train = input_queue[1]
    X_train_contents = tf.read_file(input_queue[0])
    X_train = tf.image.decode_jpeg(X_train_contents, channels=color_type)

    X_train = tf.image.resize_images(X_train, [img_h, img_w], 
                                     tf.image.ResizeMethod.NEAREST_NEIGHBOR)

    X_train_batch, y_train_batch = tf.train.batch([X_train, y_train],
                                                  batch_size=batch_size,
                                                  num_threads=64,
                                                  capacity=capacity)
    y_train_batch = tf.one_hot(y_train_batch, 10)

    return X_train_batch, y_train_batch

在train.py文件中训练（下面不是纯TF代码，model.fit是Keras的拟合，用纯TF的替换就好了）。

X_train_batch, y_train_batch = inp.get_batch(X_train, y_train, 
                                             img_w, img_h, color_type, 
                                             train_batch_size, capacity)
X_valid_batch, y_valid_batch = inp.get_batch(X_valid, y_valid, 
                                             img_w, img_h, color_type, 
                                             valid_batch_size, capacity)
with tf.Session() as sess:

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    try:
        for step in np.arange(max_step):
            if coord.should_stop() :
                break
            X_train, y_train = sess.run([X_train_batch, 
                                             y_train_batch])
            X_valid, y_valid = sess.run([X_valid_batch,
                                             y_valid_batch])
              
            ckpt_path = 'log/weights-{val_loss:.4f}.hdf5'
            ckpt = tf.keras.callbacks.ModelCheckpoint(ckpt_path, 
                                                      monitor='val_loss', 
                                                      verbose=1, 
                                                      save_best_only=True, 
                                                      mode='min')
            model.fit(X_train, y_train, batch_size=64, 
                          epochs=50, verbose=1,
                          validation_data=(X_valid, y_valid),
                          callbacks=[ckpt])
            
            del X_train, y_train, X_valid, y_valid

    except tf.errors.OutOfRangeError:
        print('done!')
    finally:
        coord.request_stop()
    coord.join(threads)
    sess.close()

Keras
keras文档中对fit、predict、evaluate这些函数都有一个generator，这个generator就是解决分批问题的。

关键函数：fit_generator

# 读取图片函数
def get_im_cv2(paths, img_rows, img_cols, color_type=1, normalize=True):
    '''
    参数：
        paths：要读取的图片路径列表
        img_rows:图片行
        img_cols:图片列
        color_type:图片颜色通道
    返回: 
        imgs: 图片数组
    '''
    # Load as grayscale
    imgs = []
    for path in paths:
        if color_type == 1:
            img = cv2.imread(path, 0)
        elif color_type == 3:
            img = cv2.imread(path)
        # Reduce size
        resized = cv2.resize(img, (img_cols, img_rows))
        if normalize:
            resized = resized.astype('float32')
            resized /= 127.5
            resized -= 1. 
        
        imgs.append(resized)
        
    return np.array(imgs).reshape(len(paths), img_rows, img_cols, color_type)

获取批次函数，其实就是一个generator

def get_train_batch(X_train, y_train, batch_size, img_w, img_h, color_type, is_argumentation):
    '''
    参数：
        X_train：所有图片路径列表
        y_train: 所有图片对应的标签列表
        batch_size:批次
        img_w:图片宽
        img_h:图片高
        color_type:图片类型
        is_argumentation:是否需要数据增强
    返回: 
        一个generator，x: 获取的批次图片 y: 获取的图片对应的标签
    '''
    while 1:
        for i in range(0, len(X_train), batch_size):
            x = get_im_cv2(X_train[i:i+batch_size], img_w, img_h, color_type)
            y = y_train[i:i+batch_size]
            if is_argumentation:
                # 数据增强
                x, y = img_augmentation(x, y)
            # 最重要的就是这个yield，它代表返回，返回以后循环还是会继续，然后再返回。就比如有一个机器一直在作累加运算，但是会把每次累加中间结果告诉你一样，直到把所有数加完
            yield({'input': x}, {'output': y})

训练函数

result = model.fit_generator(generator=get_train_batch(X_train, y_train, train_batch_size, img_w, img_h, color_type, True), 
          steps_per_epoch=1351, 
          epochs=50, verbose=1,
          validation_data=get_train_batch(X_valid, y_valid, valid_batch_size,img_w, img_h, color_type, False),
          validation_steps=52,
          callbacks=[ckpt, early_stop],
          max_queue_size=capacity,
          workers=1)