1b781f694884d17874706dd1a51a36e08240c8c1,tests/depthwise_conv2d.py,,,#,33

Before Change

        pytorch_output = output.data.numpy()
        keras_output = k_model.predict(input_np)

        error = np.max(pytorch_output - keras_output)
        print(error)
        if max_error < error:
            max_error = error

After Change

            max_error = error

        // This isn"t a depthwise convolution, so shouldn"t be detected as such
        non_dw_model = TestConv2dNonDepthwise(inp)
        output = non_dw_model(input_var)
        k_model = pytorch_to_keras(non_dw_model, input_var, (inp, inp, inp,), verbose=True)
        check_error(output, k_model, input_np)

    print("Max error: {0}".format(max_error))

Instances