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fix PR comments and modify opset names enum (change to str enum and r…
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…emoved OPSET prefix)
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@ofirgo
ofirgo committed Jan 5, 2025
1 parent 151522d commit 37c09bb
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4 changes: 3 additions & 1 deletion model_compression_toolkit/core/common/quantization/quantization_config.py
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Expand Up @@ -20,6 +20,8 @@

from model_compression_toolkit import DefaultDict
from model_compression_toolkit.constants import MIN_THRESHOLD
from model_compression_toolkit.target_platform_capabilities.target_platform.targetplatform2framework.attach2fw import \
CustomOpsetLayers


class QuantizationErrorMethod(Enum):
Expand Down Expand Up @@ -88,7 +90,7 @@ class QuantizationConfig:
concat_threshold_update: bool = False
activation_bias_correction: bool = False
activation_bias_correction_threshold: float = 0.0
custom_tpc_opset_to_layer: Optional[Dict[str, Tuple[List[Any], Optional[Dict[str, DefaultDict]]]]] = None
custom_tpc_opset_to_layer: Optional[Dict[str, CustomOpsetLayers]] = None


# Default quantization configuration the library use.
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114 changes: 57 additions & 57 deletions model_compression_toolkit/target_platform_capabilities/schema/v1.py
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Expand Up @@ -24,63 +24,63 @@


class OperatorSetNames(str, Enum):
OPSET_CONV = "Conv"
OPSET_DEPTHWISE_CONV = "DepthwiseConv2D"
OPSET_CONV_TRANSPOSE = "ConvTranspose"
OPSET_FULLY_CONNECTED = "FullyConnected"
OPSET_CONCATENATE = "Concatenate"
OPSET_STACK = "Stack"
OPSET_UNSTACK = "Unstack"
OPSET_GATHER = "Gather"
OPSET_EXPAND = "Expend"
OPSET_BATCH_NORM = "BatchNorm"
OPSET_L2NORM = "L2Norm"
OPSET_RELU = "ReLU"
OPSET_RELU6 = "ReLU6"
OPSET_LEAKY_RELU = "LEAKYReLU"
OPSET_ELU = "Elu"
OPSET_HARD_TANH = "HardTanh"
OPSET_ADD = "Add"
OPSET_SUB = "Sub"
OPSET_MUL = "Mul"
OPSET_DIV = "Div"
OPSET_MIN = "Min"
OPSET_MAX = "Max"
OPSET_PRELU = "PReLU"
OPSET_ADD_BIAS = "AddBias"
OPSET_SWISH = "Swish"
OPSET_SIGMOID = "Sigmoid"
OPSET_SOFTMAX = "Softmax"
OPSET_LOG_SOFTMAX = "LogSoftmax"
OPSET_TANH = "Tanh"
OPSET_GELU = "Gelu"
OPSET_HARDSIGMOID = "HardSigmoid"
OPSET_HARDSWISH = "HardSwish"
OPSET_FLATTEN = "Flatten"
OPSET_GET_ITEM = "GetItem"
OPSET_RESHAPE = "Reshape"
OPSET_UNSQUEEZE = "Unsqueeze"
OPSET_SQUEEZE = "Squeeze"
OPSET_PERMUTE = "Permute"
OPSET_TRANSPOSE = "Transpose"
OPSET_DROPOUT = "Dropout"
OPSET_SPLIT_CHUNK = "SplitChunk"
OPSET_MAXPOOL = "MaxPool"
OPSET_AVGPOOL = "AvgPool"
OPSET_SIZE = "Size"
OPSET_SHAPE = "Shape"
OPSET_EQUAL = "Equal"
OPSET_ARGMAX = "ArgMax"
OPSET_TOPK = "TopK"
OPSET_FAKE_QUANT = "FakeQuant"
OPSET_COMBINED_NON_MAX_SUPPRESSION = "CombinedNonMaxSuppression"
OPSET_ZERO_PADDING2d = "ZeroPadding2D"
OPSET_CAST = "Cast"
OPSET_RESIZE = "Resize"
OPSET_PAD = "Pad"
OPSET_FOLD = "Fold"
OPSET_STRIDED_SLICE = "StridedSlice"
OPSET_SSD_POST_PROCESS = "SSDPostProcess"
CONV = "Conv"
DEPTHWISE_CONV = "DepthwiseConv2D"
CONV_TRANSPOSE = "ConvTranspose"
FULLY_CONNECTED = "FullyConnected"
CONCATENATE = "Concatenate"
STACK = "Stack"
UNSTACK = "Unstack"
GATHER = "Gather"
EXPAND = "Expend"
BATCH_NORM = "BatchNorm"
L2NORM = "L2Norm"
RELU = "ReLU"
RELU6 = "ReLU6"
LEAKY_RELU = "LEAKYReLU"
ELU = "Elu"
HARD_TANH = "HardTanh"
ADD = "Add"
SUB = "Sub"
MUL = "Mul"
DIV = "Div"
MIN = "Min"
MAX = "Max"
PRELU = "PReLU"
ADD_BIAS = "AddBias"
SWISH = "Swish"
SIGMOID = "Sigmoid"
SOFTMAX = "Softmax"
LOG_SOFTMAX = "LogSoftmax"
TANH = "Tanh"
GELU = "Gelu"
HARDSIGMOID = "HardSigmoid"
HARDSWISH = "HardSwish"
FLATTEN = "Flatten"
GET_ITEM = "GetItem"
RESHAPE = "Reshape"
UNSQUEEZE = "Unsqueeze"
SQUEEZE = "Squeeze"
PERMUTE = "Permute"
TRANSPOSE = "Transpose"
DROPOUT = "Dropout"
SPLIT_CHUNK = "SplitChunk"
MAXPOOL = "MaxPool"
AVGPOOL = "AvgPool"
SIZE = "Size"
SHAPE = "Shape"
EQUAL = "Equal"
ARGMAX = "ArgMax"
TOPK = "TopK"
FAKE_QUANT = "FakeQuant"
COMBINED_NON_MAX_SUPPRESSION = "CombinedNonMaxSuppression"
ZERO_PADDING2D = "ZeroPadding2D"
CAST = "Cast"
RESIZE = "Resize"
PAD = "Pad"
FOLD = "Fold"
STRIDED_SLICE = "StridedSlice"
SSD_POST_PROCESS = "SSDPostProcess"

@classmethod
def get_values(cls):
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18 changes: 16 additions & 2 deletions ...oolkit/target_platform_capabilities/target_platform/targetplatform2framework/attach2fw.py
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@@ -1,4 +1,4 @@
from typing import Dict, Tuple, List, Any, Optional
from typing import Dict, NamedTuple, List, Any, Optional

from model_compression_toolkit import DefaultDict
from model_compression_toolkit.logger import Logger
Expand All @@ -8,6 +8,20 @@
OperationsSetToLayers


class CustomOpsetLayers(NamedTuple):
"""
This struct is used to define a set of operators to map to a custom operator set defined in the TPC.
Args:
operators: a list of framework operators to map to a certain custom opset name.
attr_mapping: a mapping between an opset name to a mapping between its attributes' general names and names in
the framework.
"""

operators: List
attr_mapping: Optional[Dict[str, DefaultDict]]


class AttachTpcToFramework:

def __init__(self):
Expand All @@ -19,7 +33,7 @@ def __init__(self):
self._opset2attr_mapping = None # Mapping of operation sets to their corresponding framework-specific layers

def attach(self, tpc_model: TargetPlatformModel,
custom_opset2layer: Optional[Dict[str, Tuple[List[Any], Optional[Dict[str, DefaultDict]]]]] = None
custom_opset2layer: Optional[Dict[str, CustomOpsetLayers]] = None
) -> TargetPlatformCapabilities:
"""
Attaching a TargetPlatformModel which includes a platform capabilities description to specific
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128 changes: 64 additions & 64 deletions ...kit/target_platform_capabilities/target_platform/targetplatform2framework/attach2keras.py
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Expand Up @@ -44,85 +44,85 @@ def __init__(self):
super().__init__()

self._opset2layer = {
OperatorSetNames.OPSET_CONV: [Conv2D, tf.nn.conv2d],
OperatorSetNames.OPSET_DEPTHWISE_CONV: [DepthwiseConv2D, tf.nn.depthwise_conv2d],
OperatorSetNames.OPSET_CONV_TRANSPOSE: [Conv2DTranspose, tf.nn.conv2d_transpose],
OperatorSetNames.OPSET_FULLY_CONNECTED: [Dense],
OperatorSetNames.OPSET_CONCATENATE: [tf.concat, Concatenate],
OperatorSetNames.OPSET_STACK: [tf.stack],
OperatorSetNames.OPSET_UNSTACK: [tf.unstack],
OperatorSetNames.OPSET_GATHER: [tf.gather, tf.compat.v1.gather],
OperatorSetNames.OPSET_EXPAND: [],
OperatorSetNames.OPSET_BATCH_NORM: [BatchNormalization, tf.nn.batch_normalization],
OperatorSetNames.OPSET_RELU: [tf.nn.relu, ReLU, LayerFilterParams(Activation, activation="relu")],
OperatorSetNames.OPSET_RELU6: [tf.nn.relu6],
OperatorSetNames.OPSET_LEAKY_RELU: [tf.nn.leaky_relu, LeakyReLU, LayerFilterParams(Activation, activation="leaky_relu")],
OperatorSetNames.OPSET_HARD_TANH: [LayerFilterParams(Activation, activation="hard_tanh")],
OperatorSetNames.OPSET_ADD: [tf.add, Add],
OperatorSetNames.OPSET_SUB: [tf.subtract, Subtract],
OperatorSetNames.OPSET_MUL: [tf.math.multiply, Multiply],
OperatorSetNames.OPSET_DIV: [tf.math.divide, tf.math.truediv],
OperatorSetNames.OPSET_MIN: [tf.math.minimum, Minimum],
OperatorSetNames.OPSET_MAX: [tf.math.maximum, Maximum],
OperatorSetNames.OPSET_PRELU: [PReLU],
OperatorSetNames.OPSET_SWISH: [tf.nn.swish, LayerFilterParams(Activation, activation="swish")],
OperatorSetNames.OPSET_HARDSWISH: [LayerFilterParams(Activation, activation="hard_swish")],
OperatorSetNames.OPSET_SIGMOID: [tf.nn.sigmoid, LayerFilterParams(Activation, activation="sigmoid")],
OperatorSetNames.OPSET_TANH: [tf.nn.tanh, LayerFilterParams(Activation, activation="tanh")],
OperatorSetNames.OPSET_GELU: [tf.nn.gelu, LayerFilterParams(Activation, activation="gelu")],
OperatorSetNames.OPSET_HARDSIGMOID: [tf.keras.activations.hard_sigmoid,
LayerFilterParams(Activation, activation="hard_sigmoid")],
OperatorSetNames.OPSET_FLATTEN: [Flatten],
OperatorSetNames.OPSET_GET_ITEM: [tf.__operators__.getitem],
OperatorSetNames.OPSET_RESHAPE: [Reshape, tf.reshape],
OperatorSetNames.OPSET_PERMUTE: [Permute],
OperatorSetNames.OPSET_TRANSPOSE: [tf.transpose],
OperatorSetNames.OPSET_UNSQUEEZE: [tf.expand_dims],
OperatorSetNames.OPSET_SQUEEZE: [tf.squeeze],
OperatorSetNames.OPSET_DROPOUT: [Dropout],
OperatorSetNames.OPSET_SPLIT_CHUNK: [tf.split],
OperatorSetNames.OPSET_MAXPOOL: [MaxPooling2D, tf.nn.avg_pool2d],
OperatorSetNames.OPSET_AVGPOOL: [AveragePooling2D],
OperatorSetNames.OPSET_SIZE: [tf.size],
OperatorSetNames.OPSET_RESIZE: [tf.image.resize],
OperatorSetNames.OPSET_PAD: [tf.pad, Cropping2D],
OperatorSetNames.OPSET_FOLD: [tf.space_to_batch_nd],
OperatorSetNames.OPSET_SHAPE: [tf.shape, tf.compat.v1.shape],
OperatorSetNames.OPSET_EQUAL: [tf.math.equal],
OperatorSetNames.OPSET_ARGMAX: [tf.math.argmax],
OperatorSetNames.OPSET_TOPK: [tf.nn.top_k],
OperatorSetNames.OPSET_FAKE_QUANT: [tf.quantization.fake_quant_with_min_max_vars],
OperatorSetNames.OPSET_COMBINED_NON_MAX_SUPPRESSION: [tf.image.combined_non_max_suppression],
OperatorSetNames.OPSET_ZERO_PADDING2d: [ZeroPadding2D],
OperatorSetNames.OPSET_CAST: [tf.cast],
OperatorSetNames.OPSET_STRIDED_SLICE: [tf.strided_slice],
OperatorSetNames.OPSET_ELU: [tf.nn.elu, LayerFilterParams(Activation, activation="elu")],
OperatorSetNames.OPSET_SOFTMAX: [tf.nn.softmax, Softmax,
LayerFilterParams(Activation, activation="softmax")],
OperatorSetNames.OPSET_LOG_SOFTMAX: [tf.nn.log_softmax],
OperatorSetNames.OPSET_ADD_BIAS: [tf.nn.bias_add],
OperatorSetNames.OPSET_L2NORM: [tf.math.l2_normalize],
OperatorSetNames.CONV: [Conv2D, tf.nn.conv2d],
OperatorSetNames.DEPTHWISE_CONV: [DepthwiseConv2D, tf.nn.depthwise_conv2d],
OperatorSetNames.CONV_TRANSPOSE: [Conv2DTranspose, tf.nn.conv2d_transpose],
OperatorSetNames.FULLY_CONNECTED: [Dense],
OperatorSetNames.CONCATENATE: [tf.concat, Concatenate],
OperatorSetNames.STACK: [tf.stack],
OperatorSetNames.UNSTACK: [tf.unstack],
OperatorSetNames.GATHER: [tf.gather, tf.compat.v1.gather],
OperatorSetNames.EXPAND: [],
OperatorSetNames.BATCH_NORM: [BatchNormalization, tf.nn.batch_normalization],
OperatorSetNames.RELU: [tf.nn.relu, ReLU, LayerFilterParams(Activation, activation="relu")],
OperatorSetNames.RELU6: [tf.nn.relu6],
OperatorSetNames.LEAKY_RELU: [tf.nn.leaky_relu, LeakyReLU, LayerFilterParams(Activation, activation="leaky_relu")],
OperatorSetNames.HARD_TANH: [LayerFilterParams(Activation, activation="hard_tanh")],
OperatorSetNames.ADD: [tf.add, Add],
OperatorSetNames.SUB: [tf.subtract, Subtract],
OperatorSetNames.MUL: [tf.math.multiply, Multiply],
OperatorSetNames.DIV: [tf.math.divide, tf.math.truediv],
OperatorSetNames.MIN: [tf.math.minimum, Minimum],
OperatorSetNames.MAX: [tf.math.maximum, Maximum],
OperatorSetNames.PRELU: [PReLU],
OperatorSetNames.SWISH: [tf.nn.swish, LayerFilterParams(Activation, activation="swish")],
OperatorSetNames.HARDSWISH: [LayerFilterParams(Activation, activation="hard_swish")],
OperatorSetNames.SIGMOID: [tf.nn.sigmoid, LayerFilterParams(Activation, activation="sigmoid")],
OperatorSetNames.TANH: [tf.nn.tanh, LayerFilterParams(Activation, activation="tanh")],
OperatorSetNames.GELU: [tf.nn.gelu, LayerFilterParams(Activation, activation="gelu")],
OperatorSetNames.HARDSIGMOID: [tf.keras.activations.hard_sigmoid,
LayerFilterParams(Activation, activation="hard_sigmoid")],
OperatorSetNames.FLATTEN: [Flatten],
OperatorSetNames.GET_ITEM: [tf.__operators__.getitem],
OperatorSetNames.RESHAPE: [Reshape, tf.reshape],
OperatorSetNames.PERMUTE: [Permute],
OperatorSetNames.TRANSPOSE: [tf.transpose],
OperatorSetNames.UNSQUEEZE: [tf.expand_dims],
OperatorSetNames.SQUEEZE: [tf.squeeze],
OperatorSetNames.DROPOUT: [Dropout],
OperatorSetNames.SPLIT_CHUNK: [tf.split],
OperatorSetNames.MAXPOOL: [MaxPooling2D, tf.nn.avg_pool2d],
OperatorSetNames.AVGPOOL: [AveragePooling2D],
OperatorSetNames.SIZE: [tf.size],
OperatorSetNames.RESIZE: [tf.image.resize],
OperatorSetNames.PAD: [tf.pad, Cropping2D],
OperatorSetNames.FOLD: [tf.space_to_batch_nd],
OperatorSetNames.SHAPE: [tf.shape, tf.compat.v1.shape],
OperatorSetNames.EQUAL: [tf.math.equal],
OperatorSetNames.ARGMAX: [tf.math.argmax],
OperatorSetNames.TOPK: [tf.nn.top_k],
OperatorSetNames.FAKE_QUANT: [tf.quantization.fake_quant_with_min_max_vars],
OperatorSetNames.COMBINED_NON_MAX_SUPPRESSION: [tf.image.combined_non_max_suppression],
OperatorSetNames.ZERO_PADDING2D: [ZeroPadding2D],
OperatorSetNames.CAST: [tf.cast],
OperatorSetNames.STRIDED_SLICE: [tf.strided_slice],
OperatorSetNames.ELU: [tf.nn.elu, LayerFilterParams(Activation, activation="elu")],
OperatorSetNames.SOFTMAX: [tf.nn.softmax, Softmax,
LayerFilterParams(Activation, activation="softmax")],
OperatorSetNames.LOG_SOFTMAX: [tf.nn.log_softmax],
OperatorSetNames.ADD_BIAS: [tf.nn.bias_add],
OperatorSetNames.L2NORM: [tf.math.l2_normalize],
}

if FOUND_SONY_CUSTOM_LAYERS:
self._opset2layer[OperatorSetNames.OPSET_SSD_POST_PROCESS] = [SSDPostProcess]
self._opset2layer[OperatorSetNames.SSD_POST_PROCESS] = [SSDPostProcess]
else:
# If Custom layers is not installed then we don't want the user to fail, but just ignore custom layers
# in the initialized framework TPC
self._opset2layer[OperatorSetNames.OPSET_SSD_POST_PROCESS] = []
self._opset2layer[OperatorSetNames.SSD_POST_PROCESS] = []

self._opset2attr_mapping = {
OperatorSetNames.OPSET_CONV: {
OperatorSetNames.CONV: {
KERNEL_ATTR: DefaultDict(default_value=KERAS_KERNEL),
BIAS_ATTR: DefaultDict(default_value=BIAS)},
OperatorSetNames.OPSET_CONV_TRANSPOSE: {
OperatorSetNames.CONV_TRANSPOSE: {
KERNEL_ATTR: DefaultDict(default_value=KERAS_KERNEL),
BIAS_ATTR: DefaultDict(default_value=BIAS)},
OperatorSetNames.OPSET_DEPTHWISE_CONV: {
OperatorSetNames.DEPTHWISE_CONV: {
KERNEL_ATTR: DefaultDict({
DepthwiseConv2D: KERAS_DEPTHWISE_KERNEL,
tf.nn.depthwise_conv2d: KERAS_DEPTHWISE_KERNEL}, default_value=KERAS_KERNEL),
BIAS_ATTR: DefaultDict(default_value=BIAS)},
OperatorSetNames.OPSET_FULLY_CONNECTED: {
OperatorSetNames.FULLY_CONNECTED: {
KERNEL_ATTR: DefaultDict(default_value=KERAS_KERNEL),
BIAS_ATTR: DefaultDict(default_value=BIAS)}}
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