Trying to use SAM as a loss metric in KERAS

[NourO93]

I am training a CNN to perform single image super resolution, and I want to use SAM as a loss metric. I imported sewar and defined SAM function, and then added it to the loss metrics in the model arguments as so:

def sam(y_true,y_pred):
    print(tf.shape(y_true))
    print(tf.shape(y_pred))
    return sewar.full_ref.sam(y_true,y_pred)



opt=K.optimizers.Adam(learning_rate=0.0001) # Adam optimizer
model.compile(optimizer=opt,loss=perceptual_loss,metrics=[psnr,ssim,K.losses.mean_squared_error, sam])
history = model.fit_generator(generator=train_generator(),
                              steps_per_epoch=np.ceil(float(len(train_list)) / float(batch_size)),
                              epochs=40, #20
                              verbose=1,
                              validation_data=valid_generator(),
                              shuffle=True,
                              validation_steps=np.ceil(float(len(val_list)) / float(batch_size)))

However, I get the following error:

AssertionError: Supplied images have different sizes (None, 32, 32, 3) and (None, 32, 32, 3)

I am confused as to how to solve this, because it seems to me that the sizes are the same. Can someone help me with this?

[andrewekhalel]

It seems to me that the problem could be because you pass batch (multiple images) not a single image. Sewar metrics are implemented to support only images with 3 dims. so (32, 32, 3) is supported however (None, 32, 32, 3) has one extra dimension (won't work)