zhutian
37 lines
1.3 KiB
Python
37 lines
1.3 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("Shrink")
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class Shrink(BackendHandler):
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@classmethod
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def version_9(cls, node, **kwargs):
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tensor_dict = kwargs["tensor_dict"]
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input_tensor = tensor_dict[node.inputs[0]]
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input_shape = tf.shape(input_tensor, out_type=tf.int64)
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# handle defaults for attributes
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lambd = node.attrs["lambd"] if "lambd" in node.attrs else 0.5
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bias = node.attrs["bias"] if "bias" in node.attrs else 0.0
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# make tensors in the right shape
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lambd_tensor = tf.fill(input_shape, tf.constant(lambd, input_tensor.dtype))
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lambd_neg_tensor = tf.fill(input_shape,
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tf.constant(lambd * -1, input_tensor.dtype))
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bias_tensor = tf.fill(input_shape, tf.constant(bias, input_tensor.dtype))
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zeros_tensor = tf.zeros(input_shape, input_tensor.dtype)
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# prepare return values and conditions
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input_plus = tf.add(input_tensor, bias_tensor)
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input_minus = tf.subtract(input_tensor, bias_tensor)
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greater_cond = tf.greater(input_tensor, lambd_tensor)
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less_cond = tf.less(input_tensor, lambd_neg_tensor)
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return [
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tf.where(less_cond, input_plus,
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tf.where(greater_cond, input_minus, zeros_tensor))
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]
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