add pt2tf tool

pt2tf/onnx-tensorflow/onnx_tf/handlers/backend/shrink.py

@@ -0,0 +1,36 @@
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+
+
+@onnx_op("Shrink")
+class Shrink(BackendHandler):
+
+  @classmethod
+  def version_9(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    input_tensor = tensor_dict[node.inputs[0]]
+    input_shape = tf.shape(input_tensor, out_type=tf.int64)
+
+    # handle defaults for attributes
+    lambd = node.attrs["lambd"] if "lambd" in node.attrs else 0.5
+    bias = node.attrs["bias"] if "bias" in node.attrs else 0.0
+
+    # make tensors in the right shape
+    lambd_tensor = tf.fill(input_shape, tf.constant(lambd, input_tensor.dtype))
+    lambd_neg_tensor = tf.fill(input_shape,
+                               tf.constant(lambd * -1, input_tensor.dtype))
+    bias_tensor = tf.fill(input_shape, tf.constant(bias, input_tensor.dtype))
+    zeros_tensor = tf.zeros(input_shape, input_tensor.dtype)
+
+    # prepare return values and conditions
+    input_plus = tf.add(input_tensor, bias_tensor)
+    input_minus = tf.subtract(input_tensor, bias_tensor)
+    greater_cond = tf.greater(input_tensor, lambd_tensor)
+    less_cond = tf.less(input_tensor, lambd_neg_tensor)
+
+    return [
+        tf.where(less_cond, input_plus,
+                 tf.where(greater_cond, input_minus, zeros_tensor))
+    ]