anijain2305 commented on a change in pull request #6774:
URL: https://github.com/apache/incubator-tvm/pull/6774#discussion_r513037381
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File path: tests/python/frontend/tflite/test_forward.py
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@@ -2870,37 +2884,28 @@ def test_forward_tanh():
def _test_relu(data, quantized=False):
""" One iteration of ReLU """
- if quantized:
- 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
- )
- else:
- with tf.Graph().as_default():
- in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype="float32",
name="in_0")
+
+ if quantized:
+ 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(
+ data, "inq_0:0", [inq_data], [out], quantized=True,
input_range=input_range
+ )
+ else:
Review comment:
Yes, generally, we want to move towards keras quantization as TF gets
more mature. What issues do you see?
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