Question

我只想在训练时做一些随机增强。

我将扩充作为图形的一部分进行了组合-由于同一图形也用于测试，因此我认为这是一种错误-而且我不希望扩充测试图像。

x = tf.placeholder(tf.float32, shape=[None, _IMAGE_SIZE * _IMAGE_SIZE * _IMAGE_CHANNELS], name='Input')
y = tf.placeholder(tf.float32, shape=[None, _NUM_CLASSES], name='Output')

#reshape the input so we can apply conv2d########
x_image = tf.reshape(x, [-1,32,32,3])


x_image = tf.map_fn(lambda frame: tf.random_crop(frame, [_IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS]), x_image)
x_image = tf.map_fn(lambda frame: tf.image.random_flip_left_right(frame), x_image)
x_image = tf.map_fn(lambda frame: tf.image.random_brightness(frame, max_delta=63), x_image)
x_image = tf.map_fn(lambda frame: tf.image.random_contrast(frame, lower=0.2, upper=1.8), x_image)
x_image = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame), x_image)

我希望上述增强功能只能在测试时应用-如何完成？

Answer 1

解决方案很简单

def pre_process_image(image, training):
if training:
    Do things
else:
    Do some other things
return image


def pre_process(images, training):
    images = tf.map_fn(lambda image: pre_process_image(image, training), images)
    return images

然后根据需要在模型内部调用pre_process

if is_training == True:
    with tf.variable_scope('augment', reuse=False):
        with tf.device('/cpu:0'):
            x_image = tf.reshape(x, [-1, _IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS], name='images')
            x_image = pre_process(x_image, is_training)
else:

    with tf.variable_scope('augment', reuse=True):
        with tf.device('/cpu:0'):
            x_image = tf.reshape(x, [-1, _IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS], name='images')
            x_image = pre_process(x_image, is_training)

仅使用张量流进行训练时的数据扩充

1 个答案: