mbaret commented on a change in pull request #8024:
URL: https://github.com/apache/tvm/pull/8024#discussion_r631831862
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File path: tests/python/frontend/tflite/test_forward.py
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@@ -3255,17 +3255,30 @@ def test_forward_log_softmax():
# ----
-def _test_tanh(data):
+def _test_tanh(data, quantized=False):
""" One iteration of TANH """
with tf.Graph().as_default():
- in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
- out = math_ops.tanh(in_data)
- compare_tflite_with_tvm(data, "Placeholder:0", [in_data], [out])
+ 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=-3, max=3, name="inq_0"
+ )
+ input_range = {"inq_0": (-3, 3)}
+ out = math_ops.tanh(inq_data)
+ out = tf.quantization.fake_quant_with_min_max_args(out, min=-1,
max=1, name="out")
+ compare_tflite_with_tvm(
+ data, "inq_0:0", [inq_data], [out], quantized=True,
input_range=input_range
+ )
+ else:
+ out = math_ops.tanh(in_data)
+ compare_tflite_with_tvm(data, "in_0:0", [in_data], [out])
def test_forward_tanh():
""" TANH """
- _test_tanh(np.arange(6.0, dtype=np.float32).reshape((1, 6)))
Review comment:
Here we've replaced deterministic test data (through np.arange) with
random data (through np.random.uniform). As we know the quantized TANH test
only passes when allowing a tolerance of 1, I'd prefer we stick to using
deterministic test data. This is in case there's some strange combination of
values that could lead to a difference of > 1 which may make the test flaky.
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