def test_empty_calibrator_gen(self):
model_path = resource_loader.get_path_to_datafile(
'test_data/mobilenet_like_model.bin')
float_model = open(model_path, 'rb').read()
quantizer = _calibrator.Calibrator(float_model)
def empty_input_gen():
for i in ():
yield i
with self.assertRaises(RuntimeError):
quantizer.calibrate_and_quantize(empty_input_gen, constants.FLOAT,
constants.FLOAT, False)
def test_invalid_type_calibrator_gen(self):
model_path = resource_loader.get_path_to_datafile(
'test_data/mobilenet_like_model.bin')
float_model = open(model_path, 'rb').read()
quantizer = _calibrator.Calibrator(float_model)
# Input generator with incorrect shape.
def input_gen():
for _ in range(10):
yield np.ones(shape=(1, 5, 5, 3), dtype=np.int32)
with self.assertRaises(ValueError):
quantizer.calibrate_and_quantize(input_gen)
def test_calibration_with_quantization(self):
model_path = resource_loader.get_path_to_datafile(
'test_data/mobilenet_like_model.bin')
float_model = open(model_path, 'rb').read()
quantizer = _calibrator.Calibrator(float_model)
# Input generator for the model.
def input_gen():
for _ in range(10):
yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
quantized_model = quantizer.calibrate_and_quantize(input_gen)
self.assertIsNotNone(quantized_model)
def test_calibration_with_string_input(self):
model_path = resource_loader.get_path_to_datafile(
'test_data/string_input_flex_model.bin')
with open(model_path, 'rb') as fp:
model_with_string_input = fp.read()
quantizer = _calibrator.Calibrator(model_with_string_input)
# Input generator for the model.
def input_gen():
for i in range(10):
yield [np.array(u'Test' + str(i))]
quantized_model = quantizer.calibrate_and_quantize_single(
input_gen, dtypes.float32, dtypes.float32, True, 'Identity')
self.assertIsNotNone(quantized_model)
def test_invalid_shape_calibrator_gen(self, enable_mlir):
model_path = resource_loader.get_path_to_datafile(
'test_data/mobilenet_like_model.bin')
float_model = open(model_path, 'rb').read()
quantizer = _calibrator.Calibrator(float_model)
# Input generator with incorrect shape.
def input_gen():
for _ in range(10):
yield [np.ones(shape=(1, 2, 2, 3), dtype=np.float32)]
with self.assertRaisesRegex(ValueError, 'Size mismatch'):
quantizer.calibrate_and_quantize(input_gen, constants.FLOAT,
constants.FLOAT, False, enable_mlir)
def test_calibration_with_quantization_multiple_inputs(self):
# Load multi add model from test data.
# This model has 4 inputs of size (1, 8, 8, 3).
model_path = resource_loader.get_path_to_datafile(
'../../testdata/multi_add.bin')
float_model = open(model_path, 'rb').read()
quantizer = _calibrator.Calibrator(float_model)
# Input generator for the model.
def input_gen():
for _ in range(10):
yield [np.ones(shape=(1, 8, 8, 3), dtype=np.float32) for _ in range(4)]
quantized_model = quantizer.calibrate_and_quantize(input_gen,
constants.FLOAT,
constants.FLOAT, False)
self.assertIsNotNone(quantized_model)
def run(args):
"""Calibrate and quantize model.
"""
import numpy as np
import tensorflow as tf
from tensorflow.lite.python.optimize import calibrator
image_file_list = None
with open(args.image_file_list_path) as f:
image_file_list = [l.split()[0] for l in f.readlines()]
model_preprocessor_fn = preprocessors()[args.model_preprocessor]
def _input_gen():
"""Setup a graph to yield one batch of images.
Returns:
[image] generator: A batch of N images, each image shape being exactly [1, model_input_size, model_input_size, num_channels].
"""
import cv2
i = 0
for image_file in image_file_list:
log.info('Image %d of %d: %s' % (
i,
len(image_file_list) - 1,
image_file,
))
cv2_image = cv2.imread(
os.path.join(args.dataset_split_path, image_file))
preprocessed_image = model_preprocessor_fn(cv2_image)
image = np.array(preprocessed_image)
yield [[image]]
i += 1
float32_tflite_model = open(args.input_tflite_model_path, 'rb').read()
quantizer = calibrator.Calibrator(float32_tflite_model)
tflite_quantized_model = quantizer.calibrate_and_quantize(
_input_gen,
allow_float=False,
input_type=tf.int8,
output_type=tf.int8,
)
with open(args.output_tflite_model_path, 'wb') as outfile:
outfile.write(tflite_quantized_model)
def test_invalid_model_buffer(self):
float_model = b'\0' * 100
with self.assertRaisesWithRegexpMatch(ValueError,
'Failed to parse the model'):
_calibrator.Calibrator(float_model)
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format. Either a TFLite Flatbuffer or a
Graphviz graph depending on value in `output_format`.
Raises:
ValueError:
Input shape is not specified.
None value for dimension in input_tensor.
"""
# Checks dimensions in input tensor.
if self._has_valid_tensors():
for tensor in self._input_tensors:
shape = tensor.get_shape()
if not shape:
raise ValueError("Provide an input shape for input array "
"'{0}'.".format(_tensor_name(tensor)))
# Note that shape_list might be empty for scalar shapes.
shape_list = shape.as_list()
if None in shape_list[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(_tensor_name(tensor),
shape_list))
elif shape_list and shape_list[0] is None:
self._set_batch_size(batch_size=1)
# Get quantization stats. Ensures there is one stat per name if the stats
# are specified.
if self.quantized_input_stats:
quantized_stats = []
invalid_stats = []
for name in self.get_input_arrays():
if name in self.quantized_input_stats:
quantized_stats.append(self.quantized_input_stats[name])
else:
invalid_stats.append(name)
if invalid_stats:
raise ValueError(
"Quantization input stats are not available for input "
"tensors '{0}'.".format(",".join(invalid_stats)))
else:
quantized_stats = None
if self.representative_dataset:
if not isinstance(self.representative_dataset,
RepresentativeDataset):
raise TypeError(
"representative_dataset must be an instance of "
"RepresentativeDataset")
if self.representative_dataset.input_gen is None:
raise ValueError(
"Provide an input generator for representative_dataset")
# TODO(shashishekhar): For now use optimizations order is ignored.
# Both size and latency optimizations decide whether to apply post
# training optimizations.
post_training_optimize = bool(
len(
set(self.optimizations) & set([
Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE
])))
# Do weights only quantization if there is no dataset for calibration.
weights_only_quantize_flag = (post_training_optimize and
(self.representative_dataset is None))
converter_kwargs = {
"inference_type": self.inference_type,
"inference_input_type": self.inference_input_type,
"input_format": constants.TENSORFLOW_GRAPHDEF,
"output_format": self.output_format,
"quantized_input_stats": quantized_stats,
"default_ranges_stats": self.default_ranges_stats,
"drop_control_dependency": self.drop_control_dependency,
"reorder_across_fake_quant": self.reorder_across_fake_quant,
"change_concat_input_ranges": self.change_concat_input_ranges,
"allow_custom_ops": self.allow_custom_ops,
"post_training_quantize": weights_only_quantize_flag,
"target_ops": self.target_ops,
"dump_graphviz_dir": self.dump_graphviz_dir,
"dump_graphviz_video": self.dump_graphviz_video
}
optimized_graph = None
if self.inference_type == constants.QUANTIZED_UINT8:
optimized_graph = self._graph_def
else:
try:
optimized_graph = _run_graph_optimizations(
self._graph_def, self._input_tensors, self._output_tensors)
except Exception:
optimized_graph = self._graph_def
# Converts model.
if self._has_valid_tensors():
result = _toco_convert_impl(input_data=optimized_graph,
input_tensors=self._input_tensors,
output_tensors=self._output_tensors,
**converter_kwargs)
else:
result = _toco_convert_graph_def(
input_data=optimized_graph,
input_arrays_with_shape=self._input_arrays_with_shape,
output_arrays=self._output_arrays,
**converter_kwargs)
if self.representative_dataset and post_training_optimize:
calibrate_quantize = _calibrator.Calibrator(result)
result = calibrate_quantize.calibrate_and_quantize(
self.representative_dataset.input_gen)
return result
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format.
Raises:
ValueError:
Input shape is not specified.
None value for dimension in input_tensor.
"""
graph_def = _convert_to_constants.convert_variables_to_constants_v2(
self._func)
input_tensors = [
tensor for tensor in self._func.inputs
if tensor.dtype != _dtypes.resource
]
output_tensors = self._func.outputs
# Run a Grappler pass.
graph_def = _run_graph_optimizations(graph_def, input_tensors,
output_tensors, self._func.graph)
# Checks dimensions in input tensor.
for tensor in input_tensors:
# Note that shape_list might be empty for scalar shapes.
shape_list = tensor.get_shape().as_list()
if None in shape_list[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(_tensor_name(tensor),
shape_list))
elif shape_list and shape_list[0] is None:
self._set_batch_size(batch_size=1)
if self.representative_dataset:
if not isinstance(self.representative_dataset,
RepresentativeDataset):
raise TypeError(
"representative_dataset must be an instance of "
"RepresentativeDataset")
if self.representative_dataset.input_gen is None:
raise ValueError(
"Provide an input generator for representative_dataset")
# TODO(shashishekhar): For now use optimizations order is ignored.
# Both size and latency optimizations decide whether to apply post
# training optimizations.
post_training_optimize = bool(
len(
set(self.optimizations)
& set([
Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE
])))
# Do weights only quantization if there is no dataset for calibration.
weights_only_quantize_flag = (post_training_optimize and
(self.representative_dataset is None))
converter_kwargs = {
"input_format": constants.TENSORFLOW_GRAPHDEF,
"allow_custom_ops": self.allow_custom_ops,
"post_training_quantize": weights_only_quantize_flag,
"target_ops": self.target_ops,
}
# Converts model.
result = _toco_convert_impl(input_data=graph_def,
input_tensors=input_tensors,
output_tensors=output_tensors,
**converter_kwargs)
if self.representative_dataset and post_training_optimize:
calibrate_quantize = _calibrator.Calibrator(result)
result = calibrate_quantize.calibrate_and_quantize(
self.representative_dataset.input_gen)
return result
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format. Either a TFLite Flatbuffer or a
Graphviz graph depending on value in `output_format`.
Raises:
ValueError:
Input shape is not specified.
None value for dimension in input_tensor.
"""
# Checks dimensions in input tensor.
if self._has_valid_tensors():
for tensor in self._input_tensors:
shape = tensor.shape
if not shape:
raise ValueError("Provide an input shape for input array "
"'{0}'.".format(_get_tensor_name(tensor)))
# Note that shape_list might be empty for scalar shapes.
shape_list = shape.as_list()
if None in shape_list[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(
_get_tensor_name(tensor), shape_list))
elif shape_list and shape_list[0] is None:
self._set_batch_size(batch_size=1)
# Get quantization stats. Ensures there is one stat per name if the stats
# are specified.
if self.quantized_input_stats:
quantized_stats = []
invalid_stats = []
for name in self.get_input_arrays():
if name in self.quantized_input_stats:
quantized_stats.append(self.quantized_input_stats[name])
else:
invalid_stats.append(name)
if invalid_stats:
raise ValueError("Quantization input stats are not available for input "
"tensors '{0}'.".format(",".join(invalid_stats)))
else:
quantized_stats = None
if self.representative_dataset:
if not isinstance(self.representative_dataset, RepresentativeDataset):
raise TypeError(
"representative_dataset must be an instance of "
"RepresentativeDataset")
if self.representative_dataset.input_gen is None:
raise ValueError(
"Provide an input generator for representative_dataset")
post_training_optimize = bool(
len(set(self.optimizations) & set([Optimize.OPTIMIZE_FOR_LATENCY,
Optimize.OPTIMIZE_FOR_SIZE])))
# Do weights only quantization if there is no dataset for calibration.
weights_only_quantize_flag = (
post_training_optimize and (self.representative_dataset is None))
toco_inference_input_type = self.inference_input_type
inference_input_type = self.inference_input_type
inference_output_type = self.inference_output_type
if post_training_optimize:
# Post training optimizations require that TOCO outputs a float model.
if self.inference_type != constants.FLOAT:
raise ValueError(
"`optimizations` require that `inference_type` is set to float.")
toco_inference_input_type = constants.FLOAT
# Set up default values.
if inference_input_type is None:
inference_input_type = constants.FLOAT
if inference_output_type is None:
inference_output_type = constants.FLOAT
if weights_only_quantize_flag:
# Currently, weight only quantization requires float inputs and outputs.
if (inference_input_type != constants.FLOAT or
inference_output_type != constants.FLOAT):
raise ValueError(
"Provide an inference_input_type and inference_output_type of type "
"tf.float32.")
if not post_training_optimize and self.inference_output_type is not None:
raise ValueError(
"inference_output_type is currently not supported if optimizations "
"are not enabled.")
converter_kwargs = {
"inference_type": self.inference_type,
"inference_input_type": toco_inference_input_type,
"input_format": constants.TENSORFLOW_GRAPHDEF,
"output_format": self.output_format,
"quantized_input_stats": quantized_stats,
"default_ranges_stats": self.default_ranges_stats,
"drop_control_dependency": self.drop_control_dependency,
"reorder_across_fake_quant": self.reorder_across_fake_quant,
"change_concat_input_ranges": self.change_concat_input_ranges,
"allow_custom_ops": self.allow_custom_ops,
"post_training_quantize": weights_only_quantize_flag,
"target_ops": self.target_ops,
"dump_graphviz_dir": self.dump_graphviz_dir,
"dump_graphviz_video": self.dump_graphviz_video
}
optimized_graph = None
if self.inference_type == constants.QUANTIZED_UINT8:
optimized_graph = self._graph_def
else:
try:
is_only_flex_enabled = set([OpsSet.SELECT_TF_OPS]) == self.target_ops
config = _get_grappler_config(
enable_layout_optimizer=is_only_flex_enabled)
optimized_graph = _run_graph_optimizations(
self._graph_def, self._input_tensors, self._output_tensors, config)
except Exception:
optimized_graph = self._graph_def
# Converts model.
if self._has_valid_tensors():
result = _toco_convert_impl(
input_data=optimized_graph,
input_tensors=self._input_tensors,
output_tensors=self._output_tensors,
**converter_kwargs)
else:
result = _toco_convert_graph_def(
input_data=optimized_graph,
input_arrays_with_shape=self._input_arrays_with_shape,
output_arrays=self._output_arrays,
**converter_kwargs)
if self.representative_dataset and post_training_optimize:
calibrate_quantize = _calibrator.Calibrator(result)
result = calibrate_quantize.calibrate_and_quantize(
self.representative_dataset.input_gen, inference_input_type,
inference_output_type)
return result
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format.
Raises:
ValueError:
Multiple concrete functions are specified.
Input shape is not specified.
Invalid quantization parameters.
"""
# TODO(b/130297984): Add support for converting multiple function.
if len(self._funcs) != 1:
raise ValueError("This converter can only convert a single "
"ConcreteFunction. Converting multiple functions is "
"under development.")
frozen_func = _convert_to_constants.convert_variables_to_constants_v2(
self._funcs[0])
input_tensors = [
tensor for tensor in frozen_func.inputs
if tensor.dtype != _dtypes.resource
]
output_tensors = frozen_func.outputs
# Run a Grappler pass.
is_only_flex_enabled = set(
[OpsSet.SELECT_TF_OPS]) == self.target_spec.supported_ops
config = _get_grappler_config(enable_layout_optimizer=is_only_flex_enabled)
graph_def = _run_graph_optimizations(
frozen_func.graph.as_graph_def(),
input_tensors,
output_tensors,
config,
graph=frozen_func.graph)
# Checks dimensions in input tensor.
for tensor in input_tensors:
# Note that shape_list might be empty for scalar shapes.
shape_list = tensor.shape.as_list()
if None in shape_list[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(_get_tensor_name(tensor), shape_list))
elif shape_list and shape_list[0] is None:
# Set the batch size to 1 if undefined.
shape = tensor.shape.as_list()
shape[0] = 1
tensor.set_shape(shape)
if self.representative_dataset:
if not isinstance(self.representative_dataset, RepresentativeDataset):
raise TypeError("`representative_dataset` must be an instance of "
"`RepresentativeDataset`")
if self.representative_dataset.input_gen is None:
raise ValueError(
"Provide an input generator for `representative_dataset`")
# TODO(shashishekhar): For now use optimizations order is ignored.
# Both size and latency optimizations decide whether to apply post
# training optimizations.
post_training_optimize = bool(
len(
set(self.optimizations)
& set([Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE])))
# Do weights only quantization if there is no dataset for calibration.
weights_only_quantize_flag = (
post_training_optimize and (self.representative_dataset is None))
converter_kwargs = {
"input_format": constants.TENSORFLOW_GRAPHDEF,
"allow_custom_ops": self.allow_custom_ops,
"post_training_quantize": weights_only_quantize_flag,
"target_ops": self.target_spec.supported_ops,
}
# Converts model.
result = _toco_convert_impl(
input_data=graph_def,
input_tensors=input_tensors,
output_tensors=output_tensors,
**converter_kwargs)
if self.representative_dataset and post_training_optimize:
calibrate_quantize = _calibrator.Calibrator(result)
result = calibrate_quantize.calibrate_and_quantize(
self.representative_dataset.input_gen)
return result
def test_invalid_model_buffer(self, enable_mlir):
float_model = b'\0' * 100
with self.assertRaisesRegex(ValueError, 'Failed to parse the model'):
_calibrator.Calibrator(float_model)
def _calibrate_quantize_model(self, result, inference_input_type,
inference_output_type):
calibrate_quantize = _calibrator.Calibrator(result)
return calibrate_quantize.calibrate_and_quantize(
self.representative_dataset.input_gen, inference_input_type,
inference_output_type)
