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- # -------------------------------------------------------------------------
- # Copyright (c) Microsoft Corporation. All rights reserved.
- # Licensed under the MIT License.
- # --------------------------------------------------------------------------
- from .qdq_base_operator import QDQOperatorBase
- class QDQNormalization(QDQOperatorBase):
- def __init__(self, onnx_quantizer, onnx_node):
- super().__init__(onnx_quantizer, onnx_node)
- def quantize(self):
- node = self.node
- assert node.op_type in {"InstanceNormalization", "LayerNormalization", "BatchNormalization"}
- # Input
- self.quantizer.quantize_activation_tensor(node.input[0])
- # Scale
- scale_is_initializer = self.quantizer.is_input_a_initializer(node.input[1])
- scale_is_per_channel, scale_channel_axis = self.quantizer.is_tensor_per_channel(
- node.input[1], default_axis=1, op_type=node.op_type
- )
- if scale_is_per_channel:
- self.quantizer.quantize_weight_tensor_per_channel(node.input[1], axis=scale_channel_axis)
- elif scale_is_initializer:
- self.quantizer.quantize_weight_tensor(node.input[1])
- else:
- self.quantizer.quantize_activation_tensor(node.input[1])
- # Bias
- if len(node.input) > 2 and node.input[2]:
- self.quantizer.quantize_bias_tensor(node.name, node.input[2], node.input[0], node.input[1])
- # Output
- if not self.disable_qdq_for_node_output:
- for output_name in node.output:
- self.quantizer.quantize_activation_tensor(output_name)
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