def test_darknet_model(self):
# Create synthetic input data
input1 = np.arange(28 * 28, dtype=np.float).reshape(28, 28, 1) / 255
# Create an ELL predictor from the darknet model files
predictor = darknet_to_ell.predictor_from_darknet_model(
'unittest.cfg', 'unittest.weights')
# Feed the input through the model
result1 = predictor.Predict(input1.ravel())
# Verify its what we expect
expectedResult1 = [
0.09134083986282349, 0.09748589247465134, 0.09064911305904388,
0.13794259727001190, 0.16832095384597778, 0.08976214379072190,
0.06458559632301330, 0.07894224673509598, 0.12377665191888809,
0.05719388648867607
]
np.testing.assert_array_almost_equal(
result1, expectedResult1, 5,
'prediction of first input does not match expected results!')
# Feed the next input through the model
input2 = np.flipud(input1)
result2 = predictor.Predict(input2.ravel())
# Verify its what we expect
expectedResult2 = [
0.08052270114421844, 0.08739096671342850, 0.08180813491344452,
0.24630726873874664, 0.12944690883159637, 0.08548084646463394,
0.06091265007853508, 0.07173667103052139, 0.11159289628267288,
0.04480091854929924
]
np.testing.assert_array_almost_equal(
result2, expectedResult2, 5,
'prediction of second input does not match expected results!')
return
def get_ell_predictor(modelConfig):
"""Imports a model and returns an ELL.Predictor."""
predictor = None
if (modelConfig.import_from == ImportFrom.darknet):
# Import a pretrained Darknet model
predictor = darknet_to_ell.predictor_from_darknet_model(modelConfig.model_files[0], modelConfig.model_files[1])
else:
# Import a pretrained CNTK model
predictor = cntk_to_ell.predictor_from_cntk_model(modelConfig.model_files[0])
return predictor
def test_darknet_model(self):
# Create synthetic input data
input1 = np.arange(28 * 28, dtype=np.float).reshape(28, 28, 1)
# Create an ELL predictor from the darknet model files
predictor = darknet_to_ell.predictor_from_darknet_model(
'unittest.cfg', 'unittest.weights')
# Feed the input through the model
result1 = predictor.Predict(input1.ravel())
# Verify its what we expect
expectedResult1 = [0.09134083986282349, 0.09748589247465134, 0.09064911305904388, 0.13794259727001190,
0.16832095384597778, 0.08976214379072190, 0.06458559632301330, 0.07894224673509598,
0.12377665191888809, 0.05719388648867607]
np.testing.assert_array_almost_equal(
result1, expectedResult1, 5, 'prediction of first input does not match expected results!')
# Feed the next input through the model
input2 = np.flipud(input1)
result2 = predictor.Predict(input2.ravel())
# Verify its what we expect
expectedResult2 = [0.08052270114421844, 0.08739096671342850, 0.08180813491344452, 0.24630726873874664,
0.12944690883159637, 0.08548084646463394, 0.06091265007853508, 0.07173667103052139,
0.11159289628267288, 0.04480091854929924]
np.testing.assert_array_almost_equal(
result2, expectedResult2, 5, 'prediction of second input does not match expected results!')
# create a map and save to file
ell_map = ell.neural.utilities.ell_map_from_predictor(predictor)
ell_map.Save("darknet_test.map")
# create a map and save to file
ell_map = ell.neural.utilities.ell_map_from_predictor(
predictor, step_interval_msec=100, lag_threshold_msec=150, function_prefix="DarknetTest")
ell_map.Save("darknet_steppable_test.map")
# now compile it
compiler_options = ell.model.MapCompilerOptions()
compiler_options.useBlas = False
compiled = ell_map.Compile("host", "model", "test1", compilerOptions=compiler_options, dtype=np.float32)
# call the compiled model
compiledResults = np.array(compiled.Compute(input2.ravel(), dtype=np.float32))
np.testing.assert_array_almost_equal(
compiledResults, expectedResult2, 5,
"Compiled prediction does not match expected results to 5 decimal places!")
return
def run(self):
predictor = darknet_to_ell.predictor_from_darknet_model(
self.config_file, self.weights_file)
weights_directory, weights_filename = os.path.split(self.weights_file)
if self.output_directory:
output_directory = self.output_directory
else:
output_directory = weights_directory
filename_base = os.path.splitext(weights_filename)[0]
model_file_name = filename_base + '.ell'
model_file_path = os.path.join(output_directory, model_file_name)
ell_map = ell_utilities.ell_map_from_float_predictor(predictor)
print("Saving model file: '" + model_file_name + "'")
ell_map.Save(model_file_path)
def run(self):
predictor = darknet_to_ell.predictor_from_darknet_model(
self.config_file, self.weights_file)
weights_directory, weights_filename = os.path.split(self.weights_file)
if self.output_directory:
output_directory = self.output_directory
else:
output_directory = weights_directory
filename_base = os.path.splitext(weights_filename)[0]
model_file_name = filename_base + '.ell'
model_file_path = os.path.join(output_directory, model_file_name)
ell_map = ell.neural.utilities.ell_map_from_float_predictor(
predictor, self.step_interval, self.lag_threshold)
self.logger.info("Saving model file: '" + model_file_name + "'")
ell_map.Save(model_file_path)
def get_ell_predictor(helper):
"""Imports a model and returns an ELL.Predictor."""
return darknet_to_ell.predictor_from_darknet_model(helper.model_files[0],
helper.model_files[1])
def get_ell_predictor(helper):
"""Imports a model and returns an ELL.Predictor."""
return darknet_to_ell.predictor_from_darknet_model(helper.model_files[0], helper.model_files[1])
