Thank you so much @ptrblck  who I learned pytorch with your comments here.
Actually my model in keras (for stereoscopic video processing) is like bellow:
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some code
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combined = concatenate([left.output, right.output])
combined = Conv3D(128, (3, 3, 3), activation='relu', strides=1, kernel_initializer='he_uniform' , padding='same')(combined)

combined = BatchNormalization()(combined)

combined = Conv3D(256, (3, 3, 3), activation='relu', strides=1 , kernel_initializer='he_uniform' , padding='same')(combined)

combined = BatchNormalization()(combined)

combined = MaxPooling3D(pool_size=(2, 2, 2))(combined)

combined = Flatten()(combined)

# apply a FC layer and then a regression prediction on the

# combined outputs

z = Dense(128, activation="relu")(combined)

z = BatchNormalization()(z)

z = Dropout(0.5)(z)

z = Dense(32, activation="relu")(z)

z = Dense(1, activation="linear")(z)

model = Model(inputs=[left.input, right.input], outputs=z)

return model

What I did not change in pytorch is the “kernel_initializer=‘he_uniform’”
I dont know how to set it in pytorch.
Do you think it can be the source of my big loss?
Regards