Initialisation du repository de Beta

venv/lib/python3.12/site-packages/onnxruntime/quantization/operators/gavgpool.py

@@ -0,0 +1,62 @@
+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]