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Add test for the qnn_add operator #4282

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Nov 12, 2019
Merged

Add test for the qnn_add operator #4282

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977b3ff
Add test for the qnn_add operator
inadob Oct 29, 2019
636e640
Separate qnn uint8 test function from the fp32 elemwise tests
inadob Nov 11, 2019
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87 changes: 65 additions & 22 deletions tests/python/frontend/tflite/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,7 +122,7 @@ def run_tflite_graph(tflite_model_buf, input_data):


def compare_tflite_with_tvm(in_data, in_name, input_tensors,
output_tensors, init_global_variables=False, out_names=None):
output_tensors, init_global_variables=False, out_names=None, quantized=False):
"""Generic function to generate and compare TFLite and TVM output"""
in_data = convert_to_list(in_data)
in_name = convert_to_list(in_name)
Expand All @@ -137,6 +137,17 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
# convert to tflite model
converter = interpreter_wrapper.TFLiteConverter.from_session(
sess, input_tensors, output_tensors)

if quantized:
converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
input_arrays = converter.get_input_arrays()
input_stats = {}
# hardcode the mean_values and std_dev_values (m,s) to be the same for all inputs
# s = 255/(fmax-fmin); m = -fmin*s (the zero point)
for i in input_arrays:
input_stats[i] = (128., 1.275)
converter.quantized_input_stats = input_stats

tflite_model_buffer = converter.convert()
tflite_output = run_tflite_graph(tflite_model_buffer, in_data)

Expand All @@ -148,8 +159,13 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,

tvm_output = run_tvm_graph(tflite_model_buffer, in_data, in_node, target=device,
num_output=len(out_names), out_names=out_names)
for i in range(len(tflite_output)):
tvm.testing.assert_allclose(tflite_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
if quantized:
for i in range(len(tflite_output)):
# allow absolute tolerance of 1 in the quantized results
tvm.testing.assert_allclose(tflite_output[i], tvm_output[i], atol=1, rtol=1e-5)
else:
for i in range(len(tflite_output)):
tvm.testing.assert_allclose(tflite_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)


def with_fused_activation_function(input_tensor, fn_name):
Expand Down Expand Up @@ -545,34 +561,55 @@ def test_forward_concatenation():
# Element-wise
# ---

def _test_elemwise(math_op, data, fused_activation_function=None):
def _test_elemwise(math_op, data, fused_activation_function=None, quantized=False):
""" One iteration of elemwise """

assert len(data) == 2

# Test with two tensors
with tf.Graph().as_default():
in_data = [array_ops.placeholder(shape=data[0].shape, dtype=data[0].dtype, name='in_0'),
array_ops.placeholder(shape=data[1].shape, dtype=data[1].dtype, name='in_1')]
out = math_op(in_data[0], in_data[1])
out = with_fused_activation_function(out, fused_activation_function)
compare_tflite_with_tvm(data, ['in_0:0', 'in_1:0'], in_data, [out])
in_data = [array_ops.placeholder(shape=data[0].shape, dtype='float32', name='in_0'),
array_ops.placeholder(shape=data[1].shape, dtype='float32', name='in_1')]

if quantized:
# fake_quant will keep the tensors in float32 until the conversion in the session
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_0"),
tf.quantization.fake_quant_with_min_max_args(in_data[1], min=-100, max=100, name="inq_1")]
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out, fused_activation_function)
out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
compare_tflite_with_tvm(data, ['inq_0:0', 'inq_1:0'], inq_data, [out], quantized=True)

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else:
out = math_op(in_data[0], in_data[1])
out = with_fused_activation_function(out, fused_activation_function)
compare_tflite_with_tvm(data, ['in_0:0', 'in_1:0'], in_data, [out])

# Test with tensor and constant
with tf.Graph().as_default():
in_data = [array_ops.placeholder(shape=data[0].shape, dtype=data[0].dtype, name='in')]
out = math_op(in_data[0], ops.convert_to_tensor(data[1], dtype=data[1].dtype))
out = with_fused_activation_function(out, fused_activation_function)
compare_tflite_with_tvm([data[0]], ['in:0'], in_data, [out])
in_data = [array_ops.placeholder(shape=data[0].shape, dtype='float32', name='in_0')]

if quantized:
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_0")]
inq_const = tf.quantization.fake_quant_with_min_max_args(data[1], min=-100, max=100, name="const_tensor")
# the 2nd tensor is treated as constant and directly added as part of the operation
out = math_op(inq_data, ops.convert_to_tensor(inq_const, dtype='float32', name='inq_const'))
out = with_fused_activation_function(out, fused_activation_function)
out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
compare_tflite_with_tvm(data[0], ['inq_0:0'], inq_data, [out], quantized=True)

else:
out = math_op(in_data[0], ops.convert_to_tensor(data[1], dtype=data[1].dtype))
out = with_fused_activation_function(out, fused_activation_function)
compare_tflite_with_tvm(data[0], ['in_0:0'], in_data, [out])

#######################################################################
# Add
# ---

def _test_add(data, fused_activation_function=None):
def _test_add(data, fused_activation_function=None, quantized=False):
""" One iteration of add """
return _test_elemwise(math_ops.add, data, fused_activation_function)
return _test_elemwise(math_ops.add, data, fused_activation_function, quantized)

#######################################################################
# Subtract
Expand Down Expand Up @@ -624,17 +661,23 @@ def _test_greater(data):
""" One iteration of greater """
return _test_elemwise(math_ops.greater, data)

def _test_forward_elemwise(testop):
def _test_forward_elemwise(testop, quantized=False):
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""" Elewise"""
testop([np.arange(6.0, dtype=np.float32).reshape((2, 1, 1, 3)),
np.arange(1.0, 7.0, dtype=np.float32).reshape((2, 1, 1, 3))])
testop([np.arange(6.0, dtype=np.float32).reshape((2, 1, 3)),
np.arange(1.0, 7.0, dtype=np.float32).reshape((2, 1, 3))])
testop([np.arange(3.0, dtype=np.float32).reshape((1, 3)),
np.arange(1.0, 4.0, dtype=np.float32).reshape((1, 3))])
# test with two quantized tensors
if quantized:
testop([np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8)], quantized=True)
else:
testop([np.arange(6.0, dtype=np.float32).reshape((2, 1, 1, 3)),
np.arange(1.0, 7.0, dtype=np.float32).reshape((2, 1, 1, 3))])
testop([np.arange(6.0, dtype=np.float32).reshape((2, 1, 3)),
np.arange(1.0, 7.0, dtype=np.float32).reshape((2, 1, 3))])
testop([np.arange(3.0, dtype=np.float32).reshape((1, 3)),
np.arange(1.0, 4.0, dtype=np.float32).reshape((1, 3))])

def test_all_elemwise():
_test_forward_elemwise(_test_add)
_test_forward_elemwise(_test_add, quantized=True)
_test_forward_elemwise(partial(_test_add, fused_activation_function="RELU"))
_test_forward_elemwise(partial(_test_add, fused_activation_function="RELU6"))
_test_forward_elemwise(_test_sub)
Expand Down