def _test_model(self,
model,
num_samples=1,
mode='random',
delta=1e-2,
model_dir=None,
transpose_keras_result=True,
one_dim_seq_flags=None,
model_precision=_MLMODEL_FULL_PRECISION):
# Get the model path
use_tmp_folder = False
if model_dir is None:
use_tmp_folder = True
model_dir = tempfile.mkdtemp()
_ = os.path.join(model_dir, 'keras.mlmodel')
# Get converted coreml model and sample input
(input_names, output_names, _, coreml_input
) = self.keras_tester._get_coreml_model_params_and_test_input(
model, mode, one_dim_seq_flags)
from test_keras2_numeric import _get_coreml_model
coreml_model = _get_coreml_model(model,
input_names,
output_names,
model_precision=model_precision)
# Now we quantize the model and dequantize it. We then use this model
# as our full precision model since quantizing this model again will
# result in 0 quantization error.
coreml_spec = coreml_model.get_spec()
quantization_utils._quantize_spec_weights(spec=coreml_spec,
nbits=self.qbits,
quantization_mode=self.qmode,
lut_function=self.custom_lut)
# De-quantize model
quantization_utils._dequantize_nn_spec(spec=coreml_spec.neuralNetwork)
full_precision_model_spec = coreml_spec
# Quantize model from another copy
quantized_model_spec = quantization_utils._quantize_spec_weights(
spec=coreml_model.get_spec(),
nbits=self.qbits,
quantization_mode=self.qmode,
lut_function=self.custom_lut)
full_precision_model = coremltools.models.MLModel(
full_precision_model_spec)
quantized_model = coremltools.models.MLModel(quantized_model_spec)
self._run_quantized_test(coreml_input, full_precision_model,
quantized_model, delta)
# Clean up after ourselves
if use_tmp_folder and os.path.exists(model_dir):
shutil.rmtree(model_dir)
def test_batched_matmul_1bit_weight_quantized(self):
self.initialize()
self.builder.add_batched_mat_mul(
name="batched_matmul",
input_names=["data"],
output_name="output",
weight_matrix_rows=self.Cin,
weight_matrix_columns=self.Cout,
W=self.W,
)
_quantize_spec_weights(self.builder.spec, 1,
_QUANTIZATION_MODE_LINEAR_QUANTIZATION)
self.compare()
