zhutian
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import tensorflow as tf
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from onnx_tf.handlers.backend_handler import BackendHandler
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from onnx_tf.handlers.handler import onnx_op
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@onnx_op("DequantizeLinear")
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class DequantizeLinear(BackendHandler):
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@classmethod
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def args_check(cls, node, **kwargs):
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tensor_dict = kwargs["tensor_dict"]
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if len(node.inputs) == 3:
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x = tensor_dict[node.inputs[0]]
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x_scale = tensor_dict[node.inputs[1]]
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x_zero_point = tensor_dict[node.inputs[2]]
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if x_scale.shape != x_zero_point.shape:
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raise ValueError("DequantizeLinear x_scale(shape=" +
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str(x_scale.shape) + ") and x_zero_point(shape=" +
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str(x_zero_point.shape) +
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") must be in the same shape")
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if x_zero_point.dtype != x.dtype:
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raise ValueError("DequantizeLinear x_zero_point(" +
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str(x_zero_point.dtype) + ") and x(" + str(x.dtype) +
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") must be in the same dtype")
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@classmethod
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def version_10(cls, node, **kwargs):
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tensor_dict = kwargs["tensor_dict"]
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x = tensor_dict[node.inputs[0]]
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x = tf.cast(x, tf.float32)
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x_scale = tensor_dict[node.inputs[1]]
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if len(node.inputs) == 3 and x.dtype != tf.int32:
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x_zero_point = tensor_dict[node.inputs[2]]
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x_zero_point = tf.cast(x_zero_point, tf.float32)
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x = tf.subtract(x, x_zero_point)
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y = tf.multiply(x, x_scale)
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return [y]
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