ANSHUMAN87 commented on a change in pull request #6774:
URL: https://github.com/apache/incubator-tvm/pull/6774#discussion_r513179685
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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:
The issue here was the test case was not behaving as expected. When we
need a quantized graph with quantized inference, then we should provide quant
input(Uint8 or int8, based on settings chosen). But here it was feeding float
input to the graph, so the failure. Also i think current change is to keep a
uniform steps as followed similar to case of other operators. Any specific
reason to move towards Keras quantization than TF quant ?
Please let me know, if still need to keep Keras quant. Thanks!
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