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| from .qdq_base_operator import QDQOperatorBase |
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| class QDQNormalization(QDQOperatorBase): |
| def __init__(self, onnx_quantizer, onnx_node): |
| super().__init__(onnx_quantizer, onnx_node) |
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| def quantize(self): |
| node = self.node |
| assert node.op_type in {"InstanceNormalization", "LayerNormalization", "BatchNormalization"} |
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| self.quantizer.quantize_activation_tensor(node.input[0]) |
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| 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 |
| ) |
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| 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]) |
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| |
| 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]) |
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| 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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