在运行facewap—GANFaceSwap_GAN_video_conversion模块时间，出现报错：

Question

在运行facewap—GAN的FaceSwap_GAN_video_conversion模块时间，出现报错：

Traceback (most recent call last):
  File "C:/Users/Lenovo/Desktop/faceswap-GAN-master/FaceSwap_GAN_video_conversion.py", line 13, in <module>
    model = FaceswapGANModel(**arch_config)
  File "C:\Users\Lenovo\Desktop\faceswap-GAN-master\networks\faceswap_gan_model.py", line 61, in __init__
    self.netGA = Model(x, self.decoder_A(self.encoder(x)))
  File "E:\AODnet-by-pytorch-master\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
    output = self.call(inputs, **kwargs)
  File "E:\AODnet-by-pytorch-master\lib\site-packages\keras\engine\network.py", line 583, in call
    output_tensors, _, _ = self.run_internal_graph(inputs, masks)
  File "E:\AODnet-by-pytorch-master\lib\site-packages\keras\engine\network.py", line 740, in run_internal_graph
    layer.call(computed_tensor, **kwargs))
  File "E:\AODnet-by-pytorch-master\lib\site-packages\keras\layers\convolutional.py", line 171, in call
    dilation_rate=self.dilation_rate)
  File "E:\AODnet-by-pytorch-master\lib\site-packages\keras\backend\tensorflow_backend.py", line 3717, in conv2d
    **kwargs)
  File "E:\AODnet-by-pytorch-master\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 898, in convolution
    name=name)
  File "E:\AODnet-by-pytorch-master\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 976, in convolution_internal
    strides = _get_sequence(strides, n, channel_index, "strides")
  File "E:\AODnet-by-pytorch-master\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 77, in _get_sequence
    name, n, n + 2, current_n))
ValueError: strides should be of length 1, 3 or 5 but was 2

进程已结束,退出代码1

相关代码

import keras.backend as K
# Input/Output resolution
RESOLUTION = 64 # 64x64, 128x128, 256x256
assert (RESOLUTION % 64) == 0, "RESOLUTION should be 64, 128, 256"
# Architecture configuration
arch_config = {}
#crd arch_config['IMAGE_SHAPE'] = (RESOLUTION, RESOLUTION, 3)
arch_config['IMAGE_SHAPE'] = (RESOLUTION, RESOLUTION, 3,1)
arch_config['use_self_attn'] = True
arch_config['norm'] = "instancenorm" # instancenorm, batchnorm, layernorm, groupnorm, none
arch_config['model_capacity'] = "standard" # standard, lite
from networks.faceswap_gan_model import FaceswapGANModel
model = FaceswapGANModel(**arch_config)

faceswap_gan_model.py部分：

class FaceswapGANModel():
    """
    faceswap-GAN v2.2 model
    
    Attributes:
        arch_config: A dictionary that contains architecture configurations (details are described in train notebook).
        nc_G_inp: int, number of generator input channels
        nc_D_inp: int, number of discriminator input channels
        lrG: float, learning rate of the generator
        lrD: float, learning rate of the discriminator
    """
    def __init__(self, **arch_config):
        self.nc_G_inp = 3
        self.nc_D_inp = 6 
        self.IMAGE_SHAPE = arch_config['IMAGE_SHAPE']
        self.lrD = 2e-4
        self.lrG = 1e-4
        self.use_self_attn = arch_config['use_self_attn']
        self.norm = arch_config['norm']
        self.model_capacity = arch_config['model_capacity']
        self.enc_nc_out = 256 if self.model_capacity == "lite" else 512
        
        # define networks
        self.encoder = self.build_encoder(nc_in=self.nc_G_inp, 
                                          input_size=self.IMAGE_SHAPE[0], 
                                          use_self_attn=self.use_self_attn,
                                          norm=self.norm,
                                          model_capacity=self.model_capacity
                                         )
        self.decoder_A = self.build_decoder(nc_in=self.enc_nc_out, 
                                            input_size=8, 
                                            output_size=self.IMAGE_SHAPE[0],
                                            use_self_attn=self.use_self_attn,
                                            norm=self.norm,
                                            model_capacity=self.model_capacity
                                           )
        self.decoder_B = self.build_decoder(nc_in=self.enc_nc_out, 
                                            input_size=8, 
                                            output_size=self.IMAGE_SHAPE[0],
                                            use_self_attn=self.use_self_attn,
                                            norm=self.norm,
                                            model_capacity=self.model_capacity
                                           )
        self.netDA = self.build_discriminator(nc_in=self.nc_D_inp, 
                                              input_size=self.IMAGE_SHAPE[0],
                                              use_self_attn=self.use_self_attn,
                                              norm=self.norm                                         
                                             )
        self.netDB = self.build_discriminator(nc_in=self.nc_D_inp, 
                                              input_size=self.IMAGE_SHAPE[0],
                                              use_self_attn=self.use_self_attn,
                                              norm=self.norm                                         
                                             )
        x = Input(shape=self.IMAGE_SHAPE) # dummy input tensor
        self.netGA = Model(x, self.decoder_A(self.encoder(x)))

尝试寻找报错部分的strides，但并没有找到，请问是什么原因导致的呢