add pt2tf tool

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

@@ -0,0 +1,41 @@
+import copy
+import numpy as np
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+from onnx_tf.handlers.handler import tf_func
+
+
+@onnx_op("EyeLike")
+class EyeLike(BackendHandler):
+
+  @classmethod
+  def version_9(cls, node, **kwargs):
+
+    attrs = copy.deepcopy(node.attrs)
+    inp = kwargs["tensor_dict"][node.inputs[0]]
+    dtype = attrs.pop("dtype", inp.dtype)
+    offset = attrs.pop("k", 0)
+    shape = inp.shape.as_list()
+
+    # calculate upper and lower bound of max eye shape
+    max_eye_shape_ub = shape[1] if offset > 0 else shape[0]
+    max_eye_shape_lb = shape[0] if offset > 0 else shape[1]
+    offset = max_eye_shape_ub * np.sign(offset) if abs(
+        offset) > max_eye_shape_ub else offset
+    abs_offset = abs(offset)
+    eye_shape = min(max_eye_shape_ub - abs_offset, max_eye_shape_lb)
+    tensor = tf.eye(eye_shape, num_columns=eye_shape, dtype=dtype)
+    if offset > 0:
+      tb_paddings = [0, shape[0] - eye_shape]
+      lr_paddings = [offset, shape[1] - offset - eye_shape]
+    else:
+      tb_paddings = [abs_offset, shape[0] - abs_offset - eye_shape]
+      lr_paddings = [0, shape[1] - eye_shape]
+    paddings = tf.constant([tb_paddings, lr_paddings], dtype=tf.int32)
+    attrs["paddings"] = paddings
+    return [
+        cls.make_tensor_from_onnx_node(
+            node, tf_func=tf.pad, inputs=[tensor], attrs=attrs, **kwargs)
+    ]