support vm for space_to_depth

mindspore/ops/_op_impl/tbe/__init__.py

@@ -164,6 +164,8 @@ from .avg_pool_grad import _avg_pool_grad_tbe
 from .ones_like import _ones_like_tbe
 from .batch_to_space import _batch_to_space_tbe
 from .space_to_batch import _space_to_batch_tbe
+from .depth_to_space import _depth_to_space_tbe
+from .space_to_depth import _space_to_depth_tbe
 from .floor import _floor_tbe
 from .log1p import _log1p_tbe
 from .resize_bilinear import _resize_bilinear_tbe

mindspore/ops/_op_impl/tbe/depth_to_space.py

@@ -0,0 +1,46 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""DepthToSpace op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+depth_to_space_op_info = TBERegOp("DepthToSpace") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("depth_to_space.so") \
+    .compute_cost(10) \
+    .kernel_name("depth_to_space") \
+    .partial_flag(True) \
+    .attr("block_size", "required", "int", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
+    .dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
+    .dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
+    .dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
+    .dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
+    .dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
+    .get_op_info()
+
+
+@op_info_register(depth_to_space_op_info)
+def _depth_to_space_tbe():
+    """DepthToSpace TBE register"""
+    return

mindspore/ops/_op_impl/tbe/space_to_depth.py

@@ -0,0 +1,46 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""SpaceToDepth op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+space_to_depth_op_info = TBERegOp("SpaceToDepth") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("space_to_depth.so") \
+    .compute_cost(10) \
+    .kernel_name("space_to_depth") \
+    .partial_flag(True) \
+    .attr("block_size", "required", "int", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
+    .dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
+    .dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
+    .dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
+    .dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
+    .dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
+    .get_op_info()
+
+
+@op_info_register(space_to_depth_op_info)
+def _space_to_depth_tbe():
+    """SpaceToDepth TBE register"""
+    return

mindspore/ops/operations/array_ops.py

@@ -2127,7 +2127,6 @@ class SpaceToDepth(PrimitiveWithInfer):
         validator.check_value_type('block_size', block_size, [int], self.name)
         validator.check('block_size', block_size, '', 2, Rel.GE)
         self.block_size = block_size
-        self.add_prim_attr("data_format", "NCHW")
 
     def infer_shape(self, x_shape):
         validator.check('x dimension', len(x_shape), '', 4, Rel.EQ)
@@ -2185,7 +2184,6 @@ class DepthToSpace(PrimitiveWithInfer):
         validator.check_value_type('block_size', block_size, [int], self.name)
         validator.check('block_size', block_size, '', 2, Rel.GE, self.name)
         self.block_size = block_size
-        self.add_prim_attr("data_format", "NCHW")
 
     def infer_shape(self, x_shape):
         validator.check('x dimension', len(x_shape), '', 4, Rel.EQ)

tests/ut/python/ops/test_array_ops.py

@@ -243,6 +243,25 @@ class UnpackNet(Cell):
 
     def construct(self, x):
         return self.unpack(x)
+class SpaceToDepthNet(Cell):
+    def __init__(self):
+        super(SpaceToDepthNet, self).__init__()
+        block_size = 2
+        self.space_to_depth = P.SpaceToDepth(block_size)
+
+    def construct(self, x):
+        return self.space_to_depth(x)
+
+
+class DepthToSpaceNet(Cell):
+    def __init__(self):
+        super(DepthToSpaceNet, self).__init__()
+        block_size = 2
+        self.depth_to_space = P.DepthToSpace(block_size)
+
+    def construct(self, x):
+        return self.depth_to_space(x)
+
 
 test_case_array_ops = [
     ('CustNet1', {
@@ -272,6 +291,12 @@ test_case_array_ops = [
     ('UnpackNet', {
         'block': UnpackNet(),
         'desc_inputs': [Tensor(np.array([[1, 2], [3, 4]]).astype(np.float16))]}),
+    ('SpaceToDepthNet', {
+        'block': SpaceToDepthNet(),
+        'desc_inputs': [Tensor(np.random.rand(1,3,2,2).astype(np.float16))]}),
+    ('DepthToSpaceNet', {
+        'block': DepthToSpaceNet(),
+        'desc_inputs': [Tensor(np.random.rand(1,12,1,1).astype(np.float16))]}),
 ]
 
 test_case_lists = [test_case_array_ops]