c94623ad0dac5fa5ce7a9a3c4ecb794351ebc610,tests/python/frontend/tflite/test_forward.py,,_test_relu,#Any#Any#,2870

Before Change

        if package_version.parse(tf.VERSION) < package_version.parse("2.1.0"):
            pytest.skip("Testcase requires tflite version >= 2.1.0")
        data_in = tf.keras.layers.Input(shape=data.shape[1:])
        relu = tf.keras.layers.ReLU()(data_in)
        keras_model = tf.keras.models.Model(inputs=data_in, outputs=relu)
        input_name = data_in.name.split(":")[0]

        // To create quantized values with dynamic range of activations, needs representative dataset
        def representative_data_gen():
            for i in range(1):
                yield [data]

        tflite_model_quant = _quantize_keras_model(keras_model, representative_data_gen)

        tflite_output = run_tflite_graph(tflite_model_quant, data)
        tvm_output = run_tvm_graph(tflite_model_quant, data, input_name)
        tvm.testing.assert_allclose(
            np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-5
        )

After Change

            inq_data = tf.quantization.fake_quant_with_min_max_args(
                in_data, min=-10, max=10, name="inq_0"
            )
            input_range = {"inq_0": (-10, 10)}
            out = nn_ops.relu(inq_data)
            out = tf.quantization.fake_quant_with_min_max_args(out, min=0, max=6, name="out")
            compare_tflite_with_tvm(

Instances