Added support for DynamicPartition

tests/test_backend.py

@@ -3258,6 +3258,30 @@ def func(x):
             #self._run_test_case([_OUTPUT, _OUTPUT1], {_INPUT: x_val})
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @check_opset_min_version(9, "Compress")
+    def test_dynamic_partition_both_vector(self):
+        data_val = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float32)
+        part_val = np.array([0, 0, 1, 1, 0, 2, 1, 0], dtype=np.int32)
+        def func(data, partitions):
+            p1, p2, p3 = tf.dynamic_partition(data, partitions, num_partitions=3)
+            p1_ = tf.identity(p1, name=_TFOUTPUT)
+            p2_ = tf.identity(p2, name=_TFOUTPUT1)
+            p3_ = tf.identity(p3, name=_TFOUTPUT2)
+            return p1_, p2_, p3_
+        self._run_test_case(func, [_OUTPUT, _OUTPUT1, _OUTPUT2], {_INPUT: data_val, _INPUT1: part_val})
+
+    @check_opset_min_version(9, "Compress")
+    def test_dynamic_partition_data_tensor(self):
+        data_val = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], dtype=np.float32)
+        part_val = np.array([0, 2, 1, 0, 1], dtype=np.int32)
+        def func(data, partitions):
+            p1, p2, p3 = tf.dynamic_partition(data, partitions, num_partitions=3)
+            p1_ = tf.identity(p1, name=_TFOUTPUT)
+            p2_ = tf.identity(p2, name=_TFOUTPUT1)
+            p3_ = tf.identity(p3, name=_TFOUTPUT2)
+            return p1_, p2_, p3_
+        self._run_test_case(func, [_OUTPUT, _OUTPUT1, _OUTPUT2], {_INPUT: data_val, _INPUT1: part_val})
+
     @check_opset_min_version(10, "Conv2DBackpropInput")
     def test_Conv2DBackpropInput_const(self):
         input_sizes_val_ = np.array([1, 10, 10, 3], dtype=np.int32)

tf2onnx/onnx_opset/tensor.py

@@ -1790,6 +1790,34 @@ def version_11(cls, ctx, node, **kwargs):
             # FIXME: the indices in onnx are not the same as in tensorflow.
 
 
+@tf_op("DynamicPartition")
+class DynamicPartition:
+    @classmethod
+    def version_9(cls, ctx, node, **kwargs):
+        # For desired behavior, see diagram: https://www.tensorflow.org/api_docs/python/tf/raw_ops/DynamicPartition
+        data_inp = node.input[0]
+        partition_inp = node.input[1]
+        partition_shape = ctx.get_shape(partition_inp)
+        num_partitions = node.get_attr_value('num_partitions')
+        utils.make_sure(partition_shape is not None, "DynamicPartition requires known rank")
+        utils.make_sure(len(partition_shape) == 1, "DynamicPartition only implemented for partitions of rank 1")
+        # Put partitions into OneHot format
+        range_val = np.arange(num_partitions, dtype=np.int32).reshape([num_partitions, 1])
+        range_const = ctx.make_const(utils.make_name('range_const'), range_val)
+        equal_node = ctx.make_node("Equal", [partition_inp, range_const.output[0]])
+        # Cast bool to int since ORT doesn't implement Split on bool.
+        equal_int32 = ctx.make_node("Cast", [equal_node.output[0]], attr={"to": TensorProto.INT32})
+        split_node = ctx.make_node("Split", [equal_int32.output[0]], output_count=num_partitions, attr={'axis': 0})
+        for i in range(num_partitions):
+            cond_bools = ctx.make_node("Cast", [split_node.output[i]], attr={"to": TensorProto.BOOL})
+            squeeze_node = ctx.make_node("Squeeze", [cond_bools.output[0]], attr={'axes': [0]})
+            compress_node = ctx.make_node("Compress", [data_inp, squeeze_node.output[0]], attr={'axis': 0})
+            ctx.replace_all_inputs(node.output[i], compress_node.output[0])
+            ctx.copy_dtype(node.output[i], compress_node.output[0])
+            ctx.copy_shape(node.output[i], compress_node.output[0])
+        ctx.remove_node(node.name)
+
+
 @tf_op("MatrixDiagPart")
 class MatrixDiagPart:
     @classmethod