重命名 pt2tf 为 pt2pb

2020-10-14 08:55:07 +08:00
parent 324ab60a5d
commit 90ae190559
407 changed files with 0 additions and 0 deletions
@@ -0,0 +1,70 @@
+import tensorflow as tf
+
+from onnx_tf.common import exception
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+from onnx_tf.handlers.handler import partial_support
+from onnx_tf.handlers.handler import ps_description
+
+
+@onnx_op("Clip")
+@partial_support(True)
+@ps_description("Clip input in uint64 is not supported in Tensorflow.")
+class Clip(BackendHandler):
+
+  @classmethod
+  def args_check(cls, node, **kwargs):
+    x = kwargs["tensor_dict"][node.inputs[0]]
+    # uint64 cannot upcast to any tensorflow supported datatype
+    # for tf.clip_by_value that didn't lose precision
+    if x.dtype == tf.uint64:
+      exception.OP_UNSUPPORTED_EXCEPT(
+          "Clip input, min and max in " + str(x.dtype) + " datatype",
+          "Tensorflow")
+
+  @classmethod
+  def _common(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    x = tensor_dict[node.inputs[0]]
+    x_dtype = x.dtype
+
+    if cls.SINCE_VERSION < 11:
+      # min/max were required and passed as attributes
+      clip_value_min = node.attrs.get("min", tf.reduce_min(x))
+      clip_value_max = node.attrs.get("max", tf.reduce_max(x))
+    else:
+      # min/max are optional and passed as inputs
+      clip_value_min = tensor_dict[node.inputs[1]] if len(
+          node.inputs) > 1 and node.inputs[1] != "" else x_dtype.min
+      clip_value_max = tensor_dict[node.inputs[2]] if len(
+          node.inputs) > 2 and node.inputs[2] != "" else x_dtype.max
+
+    # tf.clip_by_value doesn't support uint8, uint16, uint32, int8 and int16
+    # dtype for x, therefore need to upcast it to tf.int32 or tf.int64
+    if x_dtype in [tf.uint8, tf.uint16, tf.uint32, tf.int8, tf.int16]:
+      cast_to = tf.int64 if x_dtype == tf.uint32 else tf.int32
+      x = tf.cast(x, cast_to)
+      clip_value_min = tf.cast(clip_value_min, cast_to)
+      clip_value_max = tf.cast(clip_value_max, cast_to)
+      y = tf.clip_by_value(x, clip_value_min, clip_value_max)
+      y = tf.cast(y, x_dtype)
+    else:
+      y = tf.clip_by_value(x, clip_value_min, clip_value_max)
+
+    return [y]
+
+  @classmethod
+  def version_1(cls, node, **kwargs):
+    return cls._common(node, **kwargs)
+
+  @classmethod
+  def version_6(cls, node, **kwargs):
+    return cls._common(node, **kwargs)
+
+  @classmethod
+  def version_11(cls, node, **kwargs):
+    return cls._common(node, **kwargs)
+
+  @classmethod
+  def version_12(cls, node, **kwargs):
+    return cls._common(node, **kwargs)