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Add functional test of ONNX PTQ of segmentation models (#1160)
### Changes Add graph test for unet, icnet. ### Reason for changes Extend support of ONNX PTQ models ### Related tickets 83446 ### Tests As stated in the title
1 parent da72001 commit 3412fa2

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-1234
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‎.gitattributes

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*.png filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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* text=auto eol=lf
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*.{tfrecord,h5} binary

‎nncf/experimental/onnx/algorithms/quantization/default_quantization.py

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"""
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from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ConvolutionMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import LinearMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import SigmoidMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import GlobalAveragePoolMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import AddLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import MulLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ConcatLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import BatchNormMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ResizeMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXConvolutionMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXLinearMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXSigmoidMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXHardSigmoidMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXAveragePoolMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXGlobalAveragePoolMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXAddLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXMulLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXConcatLayerMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXBatchNormMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXResizeMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXSoftmaxMetatype
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from nncf.common.graph.operator_metatypes import UnknownMetatype
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DEFAULT_ONNX_QUANT_TRAIT_TO_OP_DICT = {
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QuantizationTrait.INPUTS_QUANTIZABLE: [
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ConvolutionMetatype,
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LinearMetatype,
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GlobalAveragePoolMetatype,
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AddLayerMetatype,
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MulLayerMetatype,
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BatchNormMetatype,
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ResizeMetatype,
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ONNXConvolutionMetatype,
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ONNXLinearMetatype,
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ONNXAveragePoolMetatype,
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ONNXGlobalAveragePoolMetatype,
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ONNXAddLayerMetatype,
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ONNXMulLayerMetatype,
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ONNXBatchNormMetatype,
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ONNXHardSigmoidMetatype,
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ONNXResizeMetatype,
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],
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QuantizationTrait.NON_QUANTIZABLE: [SigmoidMetatype,
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QuantizationTrait.NON_QUANTIZABLE: [ONNXSigmoidMetatype,
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ONNXSoftmaxMetatype,
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UnknownMetatype],
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QuantizationTrait.CONCAT: [ConcatLayerMetatype],
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QuantizationTrait.CONCAT: [ONNXConcatLayerMetatype],
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QuantizationTrait.OUTPUT_QUANTIZATION_AS_WEIGHTS: []
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}

‎nncf/experimental/onnx/graph/metatypes/onnx_ops.py

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@@ -30,114 +30,134 @@ def get_all_aliases(cls) -> List[str]:
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@ONNX_OPERATION_METATYPES.register()
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class ConvolutionMetatype(ONNXOpMetatype):
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class ONNXConvolutionMetatype(ONNXOpMetatype):
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name = 'ConvOp'
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op_names = ['Conv']
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hw_config_names = [HWConfigOpName.CONVOLUTION]
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@ONNX_OPERATION_METATYPES.register()
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class LinearMetatype(ONNXOpMetatype):
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class ONNXLinearMetatype(ONNXOpMetatype):
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name = 'LinearOp'
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op_names = ['Gemm']
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hw_config_names = [HWConfigOpName.MATMUL]
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@ONNX_OPERATION_METATYPES.register()
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class ReluMetatype(ONNXOpMetatype):
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class ONNXReluMetatype(ONNXOpMetatype):
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name = 'ReluOp'
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op_names = ['Relu', 'Clip']
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@ONNX_OPERATION_METATYPES.register()
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class SigmoidMetatype(ONNXOpMetatype):
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class ONNXSigmoidMetatype(ONNXOpMetatype):
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name = 'SigmoidOp'
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op_names = ['Sigmoid']
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@ONNX_OPERATION_METATYPES.register()
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class GlobalAveragePoolMetatype(ONNXOpMetatype):
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class ONNXHardSigmoidMetatype(ONNXOpMetatype):
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name = 'HardSigmoidOp'
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op_names = ['HardSigmoid']
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@ONNX_OPERATION_METATYPES.register()
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class ONNXGlobalAveragePoolMetatype(ONNXOpMetatype):
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name = 'GlobalAveragePoolOp'
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op_names = ['GlobalAveragePool']
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hw_config_names = [HWConfigOpName.AVGPOOL]
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@ONNX_OPERATION_METATYPES.register()
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class MaxPoolMetatype(ONNXOpMetatype):
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class ONNXAveragePoolMetatype(ONNXOpMetatype):
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name = 'AveragePoolOp'
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op_names = ['AveragePool']
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hw_config_names = [HWConfigOpName.AVGPOOL]
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@ONNX_OPERATION_METATYPES.register()
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class ONNXMaxPoolMetatype(ONNXOpMetatype):
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name = 'MaxPoolOp'
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op_names = ['MaxPool']
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hw_config_names = [HWConfigOpName.MAXPOOL]
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@ONNX_OPERATION_METATYPES.register()
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class ConstantMetatype(ONNXOpMetatype):
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class ONNXConstantMetatype(ONNXOpMetatype):
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name = 'ConstantOp'
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op_names = ['Constant']
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@ONNX_OPERATION_METATYPES.register()
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class AddLayerMetatype(ONNXOpMetatype):
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class ONNXAddLayerMetatype(ONNXOpMetatype):
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name = 'AddOp'
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op_names = ['Add']
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hw_config_names = [HWConfigOpName.ADD]
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@ONNX_OPERATION_METATYPES.register()
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class MulLayerMetatype(ONNXOpMetatype):
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class ONNXMulLayerMetatype(ONNXOpMetatype):
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name = 'MulOp'
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op_names = ['Mul']
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hw_config_names = [HWConfigOpName.MULTIPLY]
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@ONNX_OPERATION_METATYPES.register()
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class SumMetatype(ONNXOpMetatype):
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class ONNXSumMetatype(ONNXOpMetatype):
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name = 'SumOp'
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op_names = ['Sum']
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hw_config_names = [HWConfigOpName.REDUCESUM]
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@ONNX_OPERATION_METATYPES.register()
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class ConcatLayerMetatype(ONNXOpMetatype):
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class ONNXConcatLayerMetatype(ONNXOpMetatype):
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name = 'ConcatOp'
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op_names = ['Concat']
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hw_config_names = [HWConfigOpName.CONCAT]
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@ONNX_OPERATION_METATYPES.register()
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class BatchNormMetatype(ONNXOpMetatype):
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class ONNXBatchNormMetatype(ONNXOpMetatype):
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name = 'BatchNormalizationOp'
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op_names = ['BatchNormalization']
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@ONNX_OPERATION_METATYPES.register()
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class ResizeMetatype(ONNXOpMetatype):
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class ONNXResizeMetatype(ONNXOpMetatype):
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name = 'ResizeOp'
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op_names = ['Resize']
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hw_config_names = [HWConfigOpName.INTERPOLATE]
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@ONNX_OPERATION_METATYPES.register()
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class ReshapeMetatype(ONNXOpMetatype):
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class ONNXReshapeMetatype(ONNXOpMetatype):
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name = 'ReshapeOp'
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op_names = ['Reshape']
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hw_config_names = [HWConfigOpName.RESHAPE]
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@ONNX_OPERATION_METATYPES.register()
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class TransposeMetatype(ONNXOpMetatype):
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class ONNXTransposeMetatype(ONNXOpMetatype):
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name = 'TransposeOp'
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op_names = ['Transpose']
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hw_config_names = [HWConfigOpName.TRANSPOSE]
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@ONNX_OPERATION_METATYPES.register()
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class FlattenMetatype(ONNXOpMetatype):
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class ONNXFlattenMetatype(ONNXOpMetatype):
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name = 'FlattenOp'
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op_names = ['Flatten']
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hw_config_names = [HWConfigOpName.FLATTEN]
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GENERAL_WEIGHT_LAYER_METATYPES = [ConvolutionMetatype,
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LinearMetatype]
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@ONNX_OPERATION_METATYPES.register()
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class ONNXSoftmaxMetatype(ONNXOpMetatype):
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name = 'SoftmaxOp'
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op_names = ['Softmax']
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GENERAL_WEIGHT_LAYER_METATYPES = [ONNXConvolutionMetatype,
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ONNXLinearMetatype]
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def get_operator_metatypes() -> List[Type[OperatorMetatype]]:

‎nncf/experimental/onnx/graph/nncf_graph_builder.py

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from nncf.experimental.onnx.graph.onnx_graph import ONNXGraph
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNX_OPERATION_METATYPES
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ConstantMetatype
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from nncf.experimental.onnx.graph.metatypes.onnx_ops import ONNXConstantMetatype
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class GraphConverter:
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node_name = node.name
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node_type = node.op_type
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metatype = ONNX_OPERATION_METATYPES.get_operator_metatype_by_op_name(node_type)
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if metatype == ConstantMetatype: # We don't need to quantize Constants
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if metatype == ONNXConstantMetatype: # We don't need to quantize Constants
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continue
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nncf_graph.add_nncf_node(node_name=node_name,
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node_type=node_type,

‎nncf/experimental/onnx/hardware/fused_patterns.py

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from nncf.experimental.onnx.hardware.pattern_operations import ARITHMETIC_OPERATIONS
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from nncf.experimental.onnx.hardware.pattern_operations import MATMUL_OPERATIONS
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from nncf.experimental.onnx.hardware.patterns import create_h_sigmoid_act
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from nncf.experimental.onnx.hardware.patterns import create_swish_activation
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from nncf.experimental.onnx.hardware.patterns import create_input_preprocessing_pattern
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atomic_activations = GraphPattern()
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atomic_activations.add_node(**ATOMIC_ACTIVATIONS_OPERATIONS)
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h_sigmoid = create_h_sigmoid_act()
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activations = atomic_activations | h_sigmoid
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swish = create_swish_activation()
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activations = atomic_activations | swish
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hw_fused_patterns.register(activations, 'ACTIVATIONS', match=False)
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arithmetic_ops = GraphPattern()

‎nncf/experimental/onnx/hardware/pattern_operations.py

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from nncf.common.graph.patterns import merge_two_types_of_operations
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LINEAR_OPERATIONS = {'type': ['Conv',
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'ConvTranspose'
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'ConvTranspose',
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'Gemm'
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],
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'label': 'LINEAR'}
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‎nncf/experimental/onnx/hardware/patterns.py

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from nncf.common.graph.patterns import GraphPattern
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def create_h_sigmoid_act() -> GraphPattern:
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def create_swish_activation() -> GraphPattern:
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pattern = GraphPattern()
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input_pattern_node = pattern.add_node(label='*INPUT_NODE*', type=GraphPattern.NON_PATTERN_NODE_TYPE)
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sigmoid_node = pattern.add_node(label='SIGMOID', type='Sigmoid')
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mul_node = pattern.add_node(label='MUL', type='Mul')
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input_pattern_node_1 = pattern.add_node(label='*INPUT_NODE*', type=GraphPattern.NON_PATTERN_NODE_TYPE)
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sigmoid_node_1 = pattern.add_node(label='SIGMOID', type='Sigmoid')
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mul_node_1 = pattern.add_node(label='MUL', type='Mul')
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pattern.add_edge(input_pattern_node_1, sigmoid_node_1)
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pattern.add_edge(input_pattern_node_1, mul_node_1)
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pattern.add_edge(sigmoid_node_1, mul_node_1)
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input_pattern_node_2 = pattern.add_node(label='*INPUT_NODE*', type=GraphPattern.NON_PATTERN_NODE_TYPE)
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sigmoid_node_2 = pattern.add_node(label='HARDSIGMOID', type='HardSigmoid')
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mul_node_2 = pattern.add_node(label='MUL', type='Mul')
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pattern.add_edge(input_pattern_node_2, sigmoid_node_2)
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pattern.add_edge(input_pattern_node_2, mul_node_2)
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pattern.add_edge(sigmoid_node_2, mul_node_2)
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pattern.add_edge(input_pattern_node, sigmoid_node)
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pattern.add_edge(input_pattern_node, mul_node)
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pattern.add_edge(sigmoid_node, mul_node)
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return pattern
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‎tests/onnx/data/models/icnet_camvid.onnx

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‎tests/onnx/data/models/tiny-yolov2.onnx

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‎tests/onnx/data/models/unet_camvid.onnx

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‎tests/onnx/data/models/yolov2-coco-9.onnx

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