Add ExtractImagePatches for new backend.

mindspore/ops/_op_impl/tbe/__init__.py

@@ -204,6 +204,8 @@ 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 .extract_image_patches import _extract_image_patches_tbe
+from .sort import _sort_tbe
 from .floor import _floor_tbe
 from .ceil import _ceil_tbe
 from .log1p import _log1p_tbe

mindspore/ops/_op_impl/tbe/extract_image_patches.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+
+"""ExtractImagePatches op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+extract_image_patches_op_info = TBERegOp("ExtractImagePatches") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("extract_image_patches.so") \
+    .compute_cost(10) \
+    .kernel_name("extract_image_patches") \
+    .partial_flag(True) \
+    .attr("ksizes", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("rates", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_NHWC) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_NHWC) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_NHWC) \
+    .get_op_info()
+
+
+@op_info_register(extract_image_patches_op_info)
+def _extract_image_patches_tbe():
+    """ExtractImagePatches TBE register"""
+    return

mindspore/ops/_op_impl/tbe/sort.py

@@ -0,0 +1,38 @@
+# 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.
+# ============================================================================
+
+"""Sort op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+sort_op_info = TBERegOp("Sort") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sort.so") \
+    .compute_cost(10) \
+    .kernel_name("sort") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "int", "all", "-1") \
+    .attr("descending", "optional", "bool", "all", "false") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y1", False, "required", "all") \
+    .output(1, "y2", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(sort_op_info)
+def _sort_tbe():
+    """Sort TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -29,7 +29,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
                         Shape, DynamicShape, Size, Slice, Split, TransShape, ParallelConcat, Padding,
                         ScatterNdAdd, ScatterNdSub, ScatterNonAliasingAdd, ReverseV2, Rint,
-                        Squeeze, StridedSlice, Tile, TensorScatterUpdate, EditDistance,
+                        Squeeze, StridedSlice, Tile, TensorScatterUpdate, EditDistance, Sort,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentProd,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
                         SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence, EmbeddingLookup,
@@ -93,6 +93,7 @@ from .sparse_ops import SparseToDense
 __all__ = [
     'Unique',
     'ReverseSequence',
+    'Sort',
     'EditDistance',
     'CropAndResize',
     'TensorAdd',

mindspore/ops/operations/array_ops.py

@@ -3670,6 +3670,44 @@ class TransShape(PrimitiveWithInfer):
                 'value': None}
 
 
+class Sort(PrimitiveWithInfer):
+    """
+    Sorts the elements of the input tensor along a given dimension in ascending order by value.
+
+    Args:
+        axis (int): The dimension to sort along. Default: -1.
+        descending (bool): Controls the sorting order. If descending is True then the elements
+            are sorted in descending order by value. Default: False.
+
+    Inputs:
+        - **x** (Tensor) - The input to sort, with float16 or float32 data type.
+
+    Outputs:
+        - **y1** (Tensor) - A tensor whose values are the sorted values, with the same shape and data type as input.
+        - **y2** (Tensor) - The indices of the elements in the original input tensor. Data type is int32.
+
+    Examples:
+        >>> x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mindspore.float16)
+        >>> sort = P.Sort()
+        >>> sort(x)
+        >>> ([[1.0, 2.0, 8.0], [3.0, 5.0, 9.0], [4.0, 6.0 ,7.0]],
+             [[2, 1, 0], [2, 0, 1], [0, 1, 2]])
+    """
+
+    @prim_attr_register
+    def __init__(self, axis=-1, descending=False):
+        """init Sort"""
+        self.axis = validator.check_value_type("axis", axis, [int], self.name)
+        self.descending = validator.check_value_type("descending", descending, [bool], self.name)
+
+    def infer_shape(self, x_shape):
+        return x_shape, x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({"x_dtype": x_dtype}, [mstype.float32, mstype.float16], self.name)
+        return x_dtype, mstype.tensor_type(mstype.int32)
+
+
 class EmbeddingLookup(PrimitiveWithInfer):
     """
     Returns a slice of input tensor based on the specified indices.