| import onnx |
|
|
| from ..quant_utils import TENSOR_NAME_QUANT_SUFFIX, QuantizedValue, QuantizedValueType, attribute_to_kwarg, ms_domain |
| from .base_operator import QuantOperatorBase |
|
|
|
|
| class QGlobalAveragePool(QuantOperatorBase): |
| def __init__(self, onnx_quantizer, onnx_node): |
| super().__init__(onnx_quantizer, onnx_node) |
|
|
| def quantize(self): |
| node = self.node |
| assert node.op_type == "GlobalAveragePool" |
|
|
| |
| if node.input[0] not in self.quantizer.quantized_value_map: |
| return super().quantize() |
|
|
| quantized_input_value = self.quantizer.quantized_value_map[node.input[0]] |
|
|
| |
| quantized_input_value = self.quantizer.quantized_value_map[node.input[0]] |
| ( |
| data_found, |
| output_scale_name_from_parameter, |
| output_zp_name_from_parameter, |
| _, |
| _, |
| ) = self.quantizer._get_quantization_params(node.output[0]) |
| |
| output_scale_name = output_scale_name_from_parameter if data_found else quantized_input_value.scale_name |
| output_zp_name = output_zp_name_from_parameter if data_found else quantized_input_value.zp_name |
| quantized_output_value = QuantizedValue( |
| node.output[0], |
| node.output[0] + TENSOR_NAME_QUANT_SUFFIX, |
| output_scale_name, |
| output_zp_name, |
| QuantizedValueType.Input, |
| ) |
| self.quantizer.quantized_value_map[node.output[0]] = quantized_output_value |
|
|
| kwargs = {} |
| for attribute in node.attribute: |
| kwargs.update(attribute_to_kwarg(attribute)) |
| kwargs["domain"] = ms_domain |
| kwargs["channels_last"] = 0 |
| qnode_name = node.name + "_quant" if node.name else "" |
|
|
| qnode = onnx.helper.make_node( |
| "QLinear" + node.op_type, |
| [ |
| quantized_input_value.q_name, |
| quantized_input_value.scale_name, |
| quantized_input_value.zp_name, |
| output_scale_name, |
| output_zp_name, |
| ], |
| [quantized_output_value.q_name], |
| qnode_name, |
| **kwargs, |
| ) |
| self.quantizer.new_nodes += [qnode] |
|
|
