# Copyright (c) 2022, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
import numpy as _np
from coremltools.converters.mil import Operation as _Operation
from coremltools.models._deprecation import deprecated as _deprecated
from coremltools.optimize.coreml import (
OpLinearQuantizerConfig as _OpLinearQuantizerConfig,
OpMagnitudePrunerConfig as _OpMagnitudePrunerConfig,
OpPalettizerConfig as _OpPalettizerConfig,
OpThresholdPrunerConfig as _OpThresholdPrunerConfig,
OptimizationConfig as _OptimizationConfig,
)
from coremltools.optimize.coreml import (
linear_quantize_weights as _linear_quantize_weights,
decompress_weights as _decompress_weights,
palettize_weights as _palettize_weights,
prune_weights as _prune_weights,
)
_DEFAULT_MIN_WEIGHT_SIZE_TO_COMPRESS = 2048
def _default_op_selector(const_op):
if not isinstance(const_op, _Operation) or const_op.op_type != "const":
raise ValueError("Input of the op_selector must be type of const Operation, got {}.".format(type(const_op)))
return const_op.val.val.size > _DEFAULT_MIN_WEIGHT_SIZE_TO_COMPRESS
[docs]@_deprecated(
suffix="Please use coremltools.optimize.coreml.affine_quantize_weights",
version="7.0",
obj_prefix="coremltools.compression_utils.",
)
def affine_quantize_weights(mlmodel, mode="linear_symmetric", op_selector=None, dtype=_np.int8):
"""
``coremltools.compression_utils.affine_quantize_weights`` is deprecated and will be removed in the future.
Please use :py:class:`coremltools.optimize.coreml.linear_quantize_weights`.
"""
if op_selector is None:
op_selector = _default_op_selector
op_config = _OpLinearQuantizerConfig(mode=mode, dtype=dtype, weight_threshold=None)
config = _OptimizationConfig(global_config=op_config, is_deprecated=True, op_selector=op_selector)
return _linear_quantize_weights(mlmodel, config)
[docs]@_deprecated(
suffix="Please use coremltools.optimize.coreml.palettize_weights",
version="7.0",
obj_prefix="coremltools.compression_utils.",
)
def palettize_weights(mlmodel, nbits=None, mode="kmeans", op_selector=None, lut_function=None):
"""
``coremltools.compression_utils.palettize_weights`` is deprecated and will be removed in the future.
Please use :py:class:`coremltools.optimize.coreml.palettize_weights`.
"""
if op_selector is None:
op_selector = _default_op_selector
op_config = _OpPalettizerConfig(nbits=nbits, mode=mode, lut_function=lut_function, weight_threshold=None)
config = _OptimizationConfig(global_config=op_config, is_deprecated=True, op_selector=op_selector)
return _palettize_weights(mlmodel, config)
[docs]@_deprecated(
suffix="Please use coremltools.optimize.coreml.sparsify_weights",
version="7.0",
obj_prefix="coremltools.compression_utils.",
)
def sparsify_weights(
mlmodel, mode="threshold_based", threshold=1e-12, target_percentile=1.0, op_selector=None
):
"""
``coremltools.compression_utils.sparsify_weights`` is deprecated and will be removed in the future.
Please use :py:class:`coremltools.optimize.coreml.prune_weights`.
"""
if op_selector is None:
op_selector = _default_op_selector
if mode.upper() == "THRESHOLD_BASED":
op_config = _OpThresholdPrunerConfig(
threshold=threshold,
minimum_sparsity_percentile=0.0,
weight_threshold=None,
)
elif mode.upper() == "PERCENTILE_BASED":
op_config = _OpMagnitudePrunerConfig(
target_sparsity=target_percentile,
weight_threshold=None,
)
else:
raise ValueError(
'Only modes "THRESHOLD_BASED" and "PERCENTILE_BASED" are supported for weight sparsification.'
f' Got mode: "{mode}".'
)
config = _OptimizationConfig(global_config=op_config, is_deprecated=True, op_selector=op_selector)
return _prune_weights(mlmodel, config)
@_deprecated(
suffix="Please use coremltools.optimize.coreml.decompress_weights",
version="7.0",
obj_prefix="coremltools.compression_utils.",
)
def decompress_weights(mlmodel):
"""
``coremltools.compression_utils.decompress_weights`` is deprecated and will be removed in the future.
Please use :py:class:`coremltools.optimize.coreml.decompress_weights`.
"""
return _decompress_weights(mlmodel)