Files
Reinforced-Learning-Godot/rl/Lib/site-packages/onnxruntime/quantization/operators/gavgpool.py
2024-10-30 22:14:35 +01:00

63 lines
2.3 KiB
Python

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 input to this node is not quantized then keep this node.
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]]
# Create an entry for output quantized value.
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])
# Just use input scale and zp if parameters for output is not specified.
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]