def test_quantization_debugger_layer_metrics(self):
options = debugger.QuantizationDebugOptions(
layer_debug_metrics={
'l1_norm': lambda diffs: np.mean(np.abs(diffs))
})
quant_debugger = debugger.QuantizationDebugger(
quant_debug_model_content=QuantizationDebuggerTest.debug_model,
debug_dataset=_calibration_gen,
debug_options=options)
quant_debugger.run()
expected_metrics = {
'num_elements': 9,
'stddev': 0.03850026,
'mean_error': 0.01673192,
'max_abs_error': 0.10039272,
'mean_square_error': 0.0027558778,
'l1_norm': 0.023704167,
}
self.assertLen(quant_debugger.layer_statistics, 1)
actual_metrics = next(iter(quant_debugger.layer_statistics.values()))
self.assertCountEqual(expected_metrics.keys(), actual_metrics.keys())
for key, value in expected_metrics.items():
self.assertAlmostEqual(value, actual_metrics[key], places=5)
def test_quantization_debugger_layer_metrics(self, quantized_io):
if quantized_io:
debug_model = QuantizationDebuggerTest.debug_model_int8
else:
debug_model = QuantizationDebuggerTest.debug_model_float
options = debugger.QuantizationDebugOptions(
layer_debug_metrics={
'l1_norm': lambda diffs: np.mean(np.abs(diffs))
})
quant_debugger = debugger.QuantizationDebugger(
quant_debug_model_content=debug_model,
debug_dataset=_calibration_gen,
debug_options=options)
quant_debugger.run()
expected_metrics = {
'num_elements': 9,
'stddev': 0.03850026,
'mean_error': 0.01673192,
'max_abs_error': 0.10039272,
'mean_squared_error': 0.0027558778,
'l1_norm': 0.023704167,
}
self.assertLen(quant_debugger.layer_statistics, 1)
actual_metrics = next(iter(quant_debugger.layer_statistics.values()))
self.assertCountEqual(expected_metrics.keys(), actual_metrics.keys())
for key, value in expected_metrics.items():
self.assertAlmostEqual(value, actual_metrics[key], places=5)
buffer = io.StringIO()
quant_debugger.layer_statistics_dump(buffer)
reader = csv.DictReader(buffer.getvalue().split())
actual_values = next(iter(reader))
expected_values = expected_metrics.copy()
expected_values.update({
'op_name': 'CONV_2D',
'tensor_idx': 7 if quantized_io else 8,
'scales': [0.15686275],
'zero_points': [-128],
'tensor_name': r'Identity[1-9]?$'
})
for key, value in expected_values.items():
if isinstance(value, str):
self.assertIsNotNone(
re.match(value, actual_values[key]),
'String is different from expected string. Please fix test code if'
" it's being affected by graph manipulation changes.")
elif isinstance(value, list):
self.assertAlmostEqual(value[0],
float(actual_values[key][1:-1]),
places=5)
else:
self.assertAlmostEqual(value,
float(actual_values[key]),
places=5)
def test_quantization_debugger_model_metrics(self):
options = debugger.QuantizationDebugOptions(
model_debug_metrics={
'stdev': lambda x, y: np.std(x[0] - y[0])
})
quant_debugger = debugger.QuantizationDebugger(
quant_debug_model_content=QuantizationDebuggerTest.debug_model,
float_model_content=QuantizationDebuggerTest.float_model,
debug_dataset=_calibration_gen,
debug_options=options)
quant_debugger.run()
expected_metrics = {'stdev': 0.050998904}
actual_metrics = quant_debugger.model_statistics
self.assertCountEqual(expected_metrics.keys(), actual_metrics.keys())
for key, value in expected_metrics.items():
self.assertAlmostEqual(value, actual_metrics[key], places=5)