我目前正在使用simontomaskarlssons github存储库作为我的基线来研究CycleGAN和im。我的问题出在训练完成后，我想使用保存的模型来生成新样本。此处，已加载模型的模型架构与初始化的生成器不同。 saveModel函数的直接链接是here。
当我初始化将域A转换为B的生成器时，摘要如下（line in github）。这是预期的，因为我的输入图像为（140,140,1），而我的输出图像为（140,140,1）：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) (None, 140, 140, 1) 0
__________________________________________________________________________________________________
reflection_padding2d_1 (Reflect (None, 146, 146, 1) 0 input_5[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 140, 140, 32) 1600 reflection_padding2d_1[0][0]
__________________________________________________________________________________________________
instance_normalization_5 (Insta (None, 140, 140, 32) 64 conv2d_9[0][0]
__________________________________________________________________________________________________
...
__________________________________________________________________________________________________
activation_12 (Activation) (None, 140, 140, 32) 0 instance_normalization_23[0][0]
__________________________________________________________________________________________________
reflection_padding2d_16 (Reflec (None, 146, 146, 32) 0 activation_12[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 140, 140, 1) 1569 reflection_padding2d_16[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 140, 140, 1) 0 conv2d_26[0][0]
==================================================================================================
Total params: 2,258,177
Trainable params: 2,258,177
Non-trainable params: 0
完成训练后，我想加载保存的模型以生成新样本（从域A到域B的翻译）。在这种情况下，模型是否成功翻译图像都没有关系。我用以下代码加载模型：
# load json and create model
json_file = open('G_A2B_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json, custom_objects={'ReflectionPadding2D': ReflectionPadding2D, 'InstanceNormalization': InstanceNormalization})
或以下给出相同结果的结果。
loaded_model = load_model('G_A2B_model.h5', custom_objects={'ReflectionPadding2D': ReflectionPadding2D, 'InstanceNormalization': InstanceNormalization})
其中ReflectionPadding2D初始化为（请注意，我有一个单独的文件用于加载模型，然后用于训练CycleGAN：]：]
# reflection padding taken from # https://github.com/fastai/courses/blob/master/deeplearning2/neural-style.ipynb class ReflectionPadding2D(Layer): def __init__(self, padding=(1, 1), **kwargs): self.padding = tuple(padding) self.input_spec = [InputSpec(ndim=4)] super(ReflectionPadding2D, self).__init__(**kwargs) def compute_output_shape(self, s): return (s[0], s[1] + 2 * self.padding[0], s[2] + 2 * self.padding[1], s[3]) def call(self, x, mask=None): w_pad, h_pad = self.padding return tf.pad(x, [[0, 0], [h_pad, h_pad], [w_pad, w_pad], [0, 0]], 'REFLECT')现在加载了我的模型，我想将图像从域A转换为域B。在这里，我期望输出形状为（140,140,1），但令人惊讶的是（132,132,1）。我检查了G_A2B_model的体系结构摘要，该摘要清楚地表明输出的形状为（132,132,1）：
Model: "G_A2B_model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_5 (InputLayer) (None, 140, 140, 1) 0 __________________________________________________________________________________________________ reflection_padding2d_1 (Reflect (None, 142, 142, 1) 0 input_5[0][0] __________________________________________________________________________________________________ conv2d_9 (Conv2D) (None, 136, 136, 32) 1600 reflection_padding2d_1[0][0] __________________________________________________________________________________________________ instance_normalization_5 (Insta (None, 136, 136, 32) 64 conv2d_9[0][0] __________________________________________________________________________________________________ ... __________________________________________________________________________________________________ instance_normalization_23 (Inst (None, 136, 136, 32) 64 conv2d_transpose_2[0][0] __________________________________________________________________________________________________ activation_12 (Activation) (None, 136, 136, 32) 0 instance_normalization_23[0][0] __________________________________________________________________________________________________ reflection_padding2d_16 (Reflec (None, 138, 138, 32) 0 activation_12[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 132, 132, 1) 1569 reflection_padding2d_16[0][0] __________________________________________________________________________________________________ activation_13 (Activation) (None, 132, 132, 1) 0 conv2d_26[0][0] ================================================================================================== Total params: 2,258,177 Trainable params: 2,258,177 Non-trainable params: 0我不明白的是为什么输出形状为（132x132x1）。但是我可以看到，在reflectionPadding2D中出现了问题，其中初始化生成器的输出形状为（146,146,1），保存生成器的输出形状为（142,142,1）。但是我不知道为什么会这样吗？因为理论上它们应该大小相同。
我目前正在使用simontomaskarlssons github存储库作为我的基准来研究CycleGAN和im。我的问题是在训练完成后出现的，我想使用保存的模型来生成新的...
当您使用model.to_json保留体系结构时，将调用get_config方法，以便也保存图层属性。当您使用不带该方法的自定义类时，在调用model_from_json时将使用填充的默认值。