dock AcoshGrad for GE and AvgPool AvgPoolGrad for Vm

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -38,6 +38,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"reduce_mean", "reduce_mean_d"},
   {"reduce_max", "reduce_max_d"},
   {"reduce_min", "reduce_min_d"},
+  {"avg_pool_grad", "avg_pool_grad_d"},
   {"conv2d_backprop_filter", "conv2d_backprop_filter_d"},
   {"conv2d_backprop_input", "conv2d_backprop_input_d"},
   {"depthwise_conv2d_native", "depthwise_conv2d"},

mindspore/ccsrc/operator/ops.cc

@@ -170,6 +170,7 @@ const PrimitivePtr kPrimPooling = std::make_shared<Primitive>("Pooling");
 const PrimitivePtr kPrimPoolingGrad = std::make_shared<Primitive>("PoolingGrad");
 const PrimitivePtr kPrimMaxPool = std::make_shared<Primitive>("MaxPool");
 const PrimitivePtr kPrimMaxPoolGrad = std::make_shared<Primitive>("MaxPoolGrad");
+const PrimitivePtr kPrimAvgPoolGrad = std::make_shared<Primitive>("AvgPoolGrad");
 const PrimitivePtr kPrimFusedBatchNorm = std::make_shared<Primitive>("FusedBatchNorm");
 const PrimitivePtr kPrimConv2D = std::make_shared<Primitive>("Conv2D");
 const PrimitivePtr kPrimFusedBatchNormGrad = std::make_shared<Primitive>("FusedBatchNormGrad");

mindspore/ccsrc/operator/ops.h

@@ -178,6 +178,7 @@ extern const PrimitivePtr kPrimFusedBatchNorm;
 extern const PrimitivePtr kPrimConv2D;
 extern const PrimitivePtr kPrimMaxPool;
 extern const PrimitivePtr kPrimMaxPoolGrad;
+extern const PrimitivePtr kPrimAvgPoolGrad;
 extern const PrimitivePtr kPrimFusedBatchNormGrad;
 extern const PrimitivePtr kPrimReluGrad;
 extern const PrimitivePtr kPrimConv2DBackpropInput;

mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc

@@ -25,6 +25,7 @@ namespace mindspore {
 namespace opt {
 ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() {
   Register(prim::kPrimCast->name(), {1});
+  Register(prim::kPrimAvgPoolGrad->name(), {0});
   Register(prim::kPrimConv2DBackpropInput->name(), {2});
   Register(prim::kPrimConv2DBackpropFilter->name(), {2});
   Register(prim::kPrimDepthwiseConv2dNativeBackpropFilter->name(), {1});

mindspore/ccsrc/transform/convert.cc

@@ -178,6 +178,7 @@ const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy";
 const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad";
 const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad";
 const char kNameAcosh[] = "Acosh";
+const char kNameAcoshGrad[] = "AcoshGrad";
 const char kNameFloorMod[] = "FloorMod";
 const char kNameSpaceToDepth[] = "SpaceToDepth";
 const char kNameDepthToSpace[] = "DepthToSpace";
@@ -375,6 +376,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)},
     {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)},
     {string(kNameAcosh), ADPT_DESC(Acosh)},
+    {string(kNameAcoshGrad), ADPT_DESC(AcoshGrad)},
     {string(kNameFloorMod), ADPT_DESC(FloorMod)},
     {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)},
     {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)},

mindspore/ccsrc/transform/op_declare.cc

@@ -357,6 +357,11 @@ INPUT_MAP(Acosh) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Acosh) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Acosh) = {{0, OUTPUT_DESC(y)}};
 
+// AcoshGrad
+INPUT_MAP(AcoshGrad) = {{1, INPUT_DESC(y)}, {2, INPUT_DESC(dy)}};
+ATTR_MAP(AcoshGrad) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(AcoshGrad) = {{0, OUTPUT_DESC(z)}};
+
 // Floor
 INPUT_MAP(Floor) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Floor) = EMPTY_ATTR_MAP;

mindspore/ccsrc/transform/op_declare.h

@@ -327,13 +327,15 @@ DECLARE_OP_ADAPTER(Const)
 DECLARE_OP_USE_OUTPUT(Const)
 DECLARE_OP_ADAPTER(Cos)
 DECLARE_OP_USE_OUTPUT(Cos)
+
 DECLARE_OP_ADAPTER(Acos)
 DECLARE_OP_USE_OUTPUT(Acos)
-
 DECLARE_OP_ADAPTER(AcosGrad)
 DECLARE_OP_USE_OUTPUT(AcosGrad)
 DECLARE_OP_ADAPTER(Acosh)
 DECLARE_OP_USE_OUTPUT(Acosh)
+DECLARE_OP_ADAPTER(AcoshGrad)
+DECLARE_OP_USE_OUTPUT(AcoshGrad)
 
 DECLARE_OP_ADAPTER(Floor)
 DECLARE_OP_USE_OUTPUT(Floor)

mindspore/nn/layer/basic.py

@@ -21,6 +21,7 @@ from mindspore._checkparam import check_int_positive, check_bool
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops.functional import identity
+from mindspore.ops.operations import _inner_ops as inner
 from mindspore.common.parameter import Parameter
 from mindspore._extends import cell_attr_register
 from mindspore.common.api import ms_function
@@ -480,7 +481,7 @@ class Unfold(Cell):
     """
     def __init__(self, ksizes, strides, rates, padding="valid"):
         super(Unfold, self).__init__()
-        self.extract_image_patches = P.ExtractImagePatches(ksizes, strides, rates, padding)
+        self.extract_image_patches = inner.ExtractImagePatches(ksizes, strides, rates, padding)
         self.transpose = P.Transpose()
         self.format_NHWC = (0, 2, 3, 1)
         self.format_NCHW = (0, 3, 1, 2)

mindspore/ops/_grad/grad_nn_ops.py

@@ -18,6 +18,7 @@ from mindspore.common import dtype as mstype
 from .. import functional as F
 from .. import operations as P
 from ..operations import _grad_ops as G
+from ..operations import _inner_ops as inner
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
 from .grad_base import bprop_getters
 
@@ -29,6 +30,7 @@ def get_bprop_bias_add(self):
 
     def bprop(x, w, out, dout):
         return dout, bias_grad(dout)
+
     return bprop
 
 
@@ -49,18 +51,19 @@ def get_bprop_conv2d(self):
         dx = input_grad(dout, w, get_shape(x))
         dw = filter_grad(dout, x, get_shape(w))
         return dx, dw
+
     return bprop
 
 
-@bprop_getters.register(P.ExtractImagePatches)
+@bprop_getters.register(inner.ExtractImagePatches)
 def get_bprop_extract_image_patches(self):
     """Grad definition for `ExtractImagePatches` operation."""
     get_shape = P.Shape()
     reshape = P.Reshape()
-    extract_image_patches = P.ExtractImagePatches(ksizes=self.ksizes,
-                                                  strides=self.strides,
-                                                  rates=self.rates,
-                                                  padding=self.padding)
+    extract_image_patches = inner.ExtractImagePatches(ksizes=self.ksizes,
+                                                      strides=self.strides,
+                                                      rates=self.rates,
+                                                      padding=self.padding)
     concat = P.Concat(axis=-1)
     expand_dims = P.ExpandDims()
     scatter_nd = P.ScatterNd()
@@ -104,6 +107,7 @@ def get_bprop_extract_image_patches(self):
         dx = transpose(dx, (2, 0, 1, 3))
 
         return (dx,)
+
     return bprop
 
 
@@ -124,6 +128,7 @@ def get_bprop_depthwise_conv2d_native(self):
         dx = input_grad(get_shape(x), w, dout)
         dw = filter_grad(x, get_shape(w), dout)
         return dx, dw
+
     return bprop
 
 
@@ -133,11 +138,12 @@ def get_bprop_max_pool_with_argmax(self):
     maxpool_grad = G.MaxPoolGradWithArgmax(
         ksize=self.ksize,
         strides=self.strides,
-        padding=self.padding,)
+        padding=self.padding)
 
     def bprop(x, out, dout):
         dx = maxpool_grad(x, dout[0], out[1])
         return (dx,)
+
     return bprop
 
 
@@ -152,6 +158,7 @@ def get_bprop_max_pool_grad(self):
     def bprop(x, out, dout):
         dx = maxpool_grad(x, out, dout)
         return (dx,)
+
     return bprop
 
 
@@ -192,6 +199,7 @@ def get_bprop_dropout_gen_mask(self):
 
     def bprop(shape, keep_prob, out, dout):
         return (zeros_like(shape), zeros_like(keep_prob))
+
     return bprop
 
 
@@ -202,6 +210,7 @@ def get_bprop_dropout_do_mask(self):
 
     def bprop(x, y, keep_prob, out, dout):
         return (do_mask(dout, y, keep_prob), zeros_like(y), zeros_like(keep_prob))
+
     return bprop
 
 
@@ -213,6 +222,7 @@ def get_bprop_relu(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, out)
         return (dx,)
+
     return bprop
 
 
@@ -224,6 +234,7 @@ def get_bprop_relu6(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, x)
         return (dx,)
+
     return bprop
 
 
@@ -236,6 +247,7 @@ def get_bprop_relu_v2(self):
         mask = out[1]
         dx = input_grad(dout[0], mask)
         return (dx,)
+
     return bprop
 
 
@@ -247,6 +259,7 @@ def get_bprop_hswish(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, x)
         return (dx,)
+
     return bprop
 
 
@@ -258,6 +271,7 @@ def get_bprop_hsigmoid(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, x)
         return (dx,)
+
     return bprop
 
 
@@ -269,6 +283,7 @@ def get_bprop_elu(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, x)
         return (dx,)
+
     return bprop
 
 
@@ -280,6 +295,7 @@ def get_bprop_sigmoid(self):
     def bprop(x, out, dout):
         dx = input_grad(out, dout)
         return (dx,)
+
     return bprop
 
 
@@ -294,6 +310,7 @@ def get_bprop_softmax(self):
     def bprop(x, out, dout):
         dx = mul(sub(dout, sum_func(mul(dout, out), axis)), out)
         return (dx,)
+
     return bprop
 
 
@@ -305,6 +322,7 @@ def get_bprop_log_softmax(self):
     def bprop(x, out, dout):
         dx = logsoftmax_grad(out, dout)
         return (dx,)
+
     return bprop
 
 
@@ -316,6 +334,7 @@ def get_bprop_tanh(self):
     def bprop(x, out, dout):
         dx = logsoftmax_grad(out, dout)
         return (dx,)
+
     return bprop
 
 
@@ -327,6 +346,7 @@ def get_bprop_gelu(self):
     def bprop(x, out, dout):
         dx = input_grad(dout, x, out)
         return (dx,)
+
     return bprop
 
 
@@ -343,6 +363,7 @@ def get_bprop_fused_batch_norm(self):
         dscale = out[1]
         dbias = out[2]
         return dx, dscale, dbias, zeros_like(mean), zeros_like(variance)
+
     return bprop
 
 
@@ -366,6 +387,7 @@ def get_bprop_batch_norm(self):
         dscale = out[1]
         dbias = out[2]
         return dx, dscale, dbias, zeros_like(mean), zeros_like(variance)
+
     return bprop
 
 
@@ -377,6 +399,7 @@ def get_bprop_layer_norm(self):
     def bprop(x, gamma, beta, out, dout):
         dx, d_gamma, d_beta = layer_norm_grad(x, dout[0], out[2], out[1], gamma)
         return dx, d_gamma, d_beta
+
     return bprop
 
 
@@ -388,6 +411,7 @@ def get_bprop_l2normalize(self):
     def bprop(x, out, dout):
         dx = input_grad(x, out, dout)
         return (dx,)
+
     return bprop
 
 
@@ -400,6 +424,7 @@ def get_bprop_softmax_cross_entropy_with_logits(self):
         grad = out[1]
         grad = grad * expand(dout[0], -1)
         return grad, zeros_like(labels)
+
     return bprop
 
 
@@ -417,6 +442,7 @@ def get_bprop_sparse_softmax_cross_entropy_with_logits(self):
             grad = F.depend(grad, out)
             grad = grad * dout
         return grad, zeros_like(labels)
+
     return bprop
 
 
@@ -428,6 +454,7 @@ def get_bprop_resize_bilinear(self):
     def bprop(x, out, dout):
         dx = resize_grad(dout, x)
         return (dx,)
+
     return bprop
 
 
@@ -437,6 +464,7 @@ def get_bprop_onehot(self):
 
     def bprop(indices, depth, on_value, off_value, out, dout):
         return zeros_like(indices), zeros_like(depth), zeros_like(on_value), zeros_like(off_value)
+
     return bprop
 
 
@@ -453,6 +481,7 @@ def get_bprop_top_kv2(self):
         updates = dout[0]
         shapes = shape_op(input_x)
         return scatter(indices, updates, shapes), zeros_like(k)
+
     return bprop
 
 
@@ -518,6 +547,7 @@ def get_bprop_lstm(self):
         dx, dhx, dcx = lstm_grad_data(y, dy, dhy, dcy, w, hx, cx, reserve, state)
         dw = lstm_grad_weight(F.depend(x, dx), hx, y, reserve, state)
         return dx, dhx, dcx, dw
+
     return bprop
 
 
@@ -529,6 +559,7 @@ def get_bprop_sigmoid_crossentropy_with_logits(self):
     def bprop(x, y, out, dout):
         dx = op(x, y, dout)
         return (dx, zeros_like(y))
+
     return bprop
 
 
@@ -545,6 +576,7 @@ def get_bprop_pad(self):
         shp = shape_op(x)
         dx = P.Slice()(dout, begin, shp)
         return (dx,)
+
     return bprop
 
 
@@ -556,6 +588,7 @@ def get_bprop_mirror_pad(self):
     def bprop(x, paddings, out, dout):
         dx = mirror_pad_grad(dout, paddings, x)
         return (dx, zeros_like(paddings))
+
     return bprop
 
 

mindspore/ops/_op_impl/tbe/__init__.py

@@ -151,3 +151,5 @@ from .greater_equal import _greater_equal_tbe
 from .not_equal import _not_equal_tbe
 from .floor_mod import _floor_mod_tbe
 from .scatter_nd_update import _scatter_nd_update_tbe
+from .avg_pool import _avg_pool_tbe
+from .avg_pool_grad import _avg_pool_grad_tbe

mindspore/ops/_op_impl/tbe/avg_pool.py

@@ -0,0 +1,39 @@
+# 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.
+# ============================================================================
+
+"""AvgPool op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+avg_pool_op_info = TBERegOp("AvgPool") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("avg_pool.so") \
+    .compute_cost(10) \
+    .kernel_name("avg_pool") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
+
+
+@op_info_register(avg_pool_op_info)
+def _avg_pool_tbe():
+    """AvgPool TBE register"""
+    return

mindspore/ops/_op_impl/tbe/avg_pool_grad.py

@@ -0,0 +1,42 @@
+# 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.
+# ============================================================================
+
+"""AvgPoolGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+avg_pool_grad_op_info = TBERegOp("AvgPoolGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("avg_pool_grad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("avg_pool_grad_d") \
+    .partial_flag(True) \
+    .attr("x_origin", "required", "listInt", "all") \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .input(0, "input_grad", False, "required", "all") \
+    .input(1, "mean_matrix", False, "optional", "all") \
+    .input(2, "kernel_matrix", False, "optional", "all") \
+    .output(0, "out_grad", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_C1HWNCoC0, DataType.F16_5HD) \
+    .get_op_info()
+
+
+@op_info_register(avg_pool_grad_op_info)
+def _avg_pool_grad_tbe():
+    """AvgPoolGrad TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -57,7 +57,7 @@ from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      Gelu, Elu,
                      GetNext, L2Normalize, LayerNorm, L2Loss,
                      LogSoftmax,
-                     MaxPool, ExtractImagePatches,
+                     MaxPool,
                      AvgPool, Conv2DBackpropInput, ConfusionMulGrad,
                      MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, ReLUV2, HSwish, HSigmoid,
                      ResizeBilinear, Sigmoid,
@@ -89,7 +89,6 @@ __all__ = [
     'Sqrt',
     'Square',
     'Conv2D',
-    'ExtractImagePatches',
     'Flatten',
     'MaxPoolWithArgmax',
     'FusedBatchNorm',

mindspore/ops/operations/_grad_ops.py

@@ -59,6 +59,23 @@ class ACosGrad(PrimitiveWithInfer):
         return x
 
 
+class AcoshGrad(PrimitiveWithInfer):
+    """Performs grad of Acosh operation."""
+
+    @prim_attr_register
+    def __init__(self):
+        """init AcoshGrad"""
+
+    def infer_shape(self, x, dout):
+        validator.check("x shape", x, "dout shape", dout, Rel.EQ, self.name)
+        return x
+
+    def infer_dtype(self, x, dout):
+        args = {"x": x, "dout": dout}
+        validator.check_tensor_type_same(args, mstype.number_type, self.name)
+        return x
+
+
 class BatchNormGrad(PrimitiveWithInfer):
     """Performs grad of BatchNorm operation."""
 

mindspore/ops/operations/_inner_ops.py

@@ -0,0 +1,98 @@
+# 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.
+# ============================================================================
+
+"""Inner operators."""
+
+from ..._checkparam import Validator as validator
+from ...common import dtype as mstype
+from ..primitive import  PrimitiveWithInfer, prim_attr_register
+
+
+class ExtractImagePatches(PrimitiveWithInfer):
+    """
+    Extract patches from images.
+    The input tensor must be a 4-D tensor and the data format is NHWC.
+
+    Args:
+        ksizes (Union[tuple[int], list[int]]): The size of sliding window, should be a tuple or list of int,
+            and the format is [1, ksize_row, ksize_col, 1].
+        strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
+            should be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
+        rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dim
+            pixel positions, should be a tuple or list of int, and the format is [1, rate_row, rate_col, 1].
+        padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
+            not case sensitive. Default: "valid".
+
+            - same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
+
+            - valid: Means that the patch area taken must be completely contained in the original image.
+
+    Inputs:
+        - **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_row, in_col, in_depth] and
+          data type is int8, float16, uint8.
+
+    Outputs:
+        Tensor, a 4-D tensor whose data type is same as 'input_x',
+        and the shape is [out_batch, out_row, out_col, out_depth], the out_batch is same as the in_batch.
+    """
+
+    @prim_attr_register
+    def __init__(self, ksizes, strides, rates, padding="valid"):
+        """init"""
+        def _check_tuple_or_list(arg_name, arg_val, prim_name):
+            validator.check_value_type(f"{arg_name}s", ksizes, [tuple, list], self.name)
+            if len(arg_val) != 4 or arg_val[0] != 1 or arg_val[3] != 1:
+                raise ValueError(f"For \'{prim_name}\' the format of {arg_name}s should be [1, {arg_name}_row, "
+                                 f"{arg_name}_col, 1], but got {arg_val}.")
+            if not isinstance(arg_val[1], int) or not isinstance(arg_val[2], int) or arg_val[1] < 1 or arg_val[2] < 1:
+                raise ValueError(f"For '{prim_name}' the {arg_name}_row and {arg_name}_col in {arg_name}s should be an "
+                                 f"positive integer number, but got {arg_name}_row is {arg_val[1]}, {arg_name}_col "
+                                 f"is {arg_val[2]}")
+
+        _check_tuple_or_list("ksize", ksizes, self.name)
+        _check_tuple_or_list("stride", strides, self.name)
+        _check_tuple_or_list("rate", rates, self.name)
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'], self.name)
+        self.add_prim_attr("padding", self.padding)
+
+    def infer_shape(self, input_x):
+        """infer shape"""
+        in_batch, in_row, in_col, in_depth = input_x
+        _, ksize_row, ksize_col, _ = self.ksizes
+        _, stride_row, stride_col, _ = self.strides
+        _, rate_row, rate_col, _ = self.rates
+        if len(input_x) != 4:
+            raise ValueError("The `input_x` should be a 4-D tensor, "
+                             f"but got a {len(input_x)}-D tensor whose shape is {input_x}")
+
+        out_batch = in_batch
+        out_depth = ksize_row * ksize_col * in_depth
+
+        if self.padding == "VALID":
+            out_row = \
+                (in_row - (ksize_row + (ksize_row - 1) * (rate_row - 1))) // stride_row + 1
+            out_col = \
+                (in_col - (ksize_col + (ksize_col - 1) * (rate_col - 1))) // stride_col + 1
+        else:
+            out_row = (in_row - 1) // stride_row + 1
+            out_col = (in_col - 1) // stride_col + 1
+
+        out_shape = [out_batch, out_row, out_col, out_depth]
+        return out_shape
+
+    def infer_dtype(self, input_x):
+        """infer dtype"""
+        validator.check_tensor_type_same({"input_x": input_x}, (mstype.int8, mstype.float16, mstype.float32), self.name)
+        return input_x

mindspore/ops/operations/nn_ops.py

@@ -2654,82 +2654,6 @@ class ApplyFtrl(PrimitiveWithInfer):
         return var_type
 
 
-class ExtractImagePatches(PrimitiveWithInfer):
-    """
-    Extract patches from images.
-    The input tensor must be a 4-D tensor and the data format is NHWC.
-
-    Args:
-        ksizes (Union[tuple[int], list[int]]): The size of sliding window, should be a tuple or list of int,
-            and the format is [1, ksize_row, ksize_col, 1].
-        strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
-            should be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
-        rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dim
-            pixel positions, should be a tuple or list of int, and the format is [1, rate_row, rate_col, 1].
-        padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
-            not case sensitive. Default: "valid".
-
-            - same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
-
-            - valid: Means that the patch area taken must be completely contained in the original image.
-
-    Inputs:
-        - **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_row, in_col, in_depth] and
-          data type is int8, float16, uint8.
-
-    Outputs:
-        Tensor, a 4-D tensor whose data type is same as 'input_x',
-        and the shape is [out_batch, out_row, out_col, out_depth], the out_batch is same as the in_batch.
-    """
-
-    @prim_attr_register
-    def __init__(self, ksizes, strides, rates, padding="valid"):
-        """init"""
-        def _check_tuple_or_list(arg_name, arg_val, prim_name):
-            validator.check_value_type(f"{arg_name}s", ksizes, [tuple, list], self.name)
-            if len(arg_val) != 4 or arg_val[0] != 1 or arg_val[3] != 1:
-                raise ValueError(f"For \'{prim_name}\' the format of {arg_name}s should be [1, {arg_name}_row, "
-                                 f"{arg_name}_col, 1], but got {arg_val}.")
-            if not isinstance(arg_val[1], int) or not isinstance(arg_val[2], int) or arg_val[1] < 1 or arg_val[2] < 1:
-                raise ValueError(f"For '{prim_name}' the {arg_name}_row and {arg_name}_col in {arg_name}s should be an "
-                                 f"positive integer number, but got {arg_name}_row is {arg_val[1]}, {arg_name}_col "
-                                 f"is {arg_val[2]}")
-
-        _check_tuple_or_list("ksize", ksizes, self.name)
-        _check_tuple_or_list("stride", strides, self.name)
-        _check_tuple_or_list("rate", rates, self.name)
-        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'], self.name)
-        self.add_prim_attr("padding", self.padding)
-
-    def infer_shape(self, input_x):
-        in_batch, in_row, in_col, in_depth = input_x
-        _, ksize_row, ksize_col, _ = self.ksizes
-        _, stride_row, stride_col, _ = self.strides
-        _, rate_row, rate_col, _ = self.rates
-        if len(input_x) != 4:
-            raise ValueError("The `input_x` should be a 4-D tensor, "
-                             f"but got a {len(input_x)}-D tensor whose shape is {input_x}")
-
-        out_batch = in_batch
-        out_depth = ksize_row * ksize_col * in_depth
-
-        if self.padding == "VALID":
-            out_row = \
-                (in_row - (ksize_row + (ksize_row - 1) * (rate_row - 1))) // stride_row + 1
-            out_col = \
-                (in_col - (ksize_col + (ksize_col - 1) * (rate_col - 1))) // stride_col + 1
-        else:
-            out_row = (in_row - 1) // stride_row + 1
-            out_col = (in_col - 1) // stride_col + 1
-
-        out_shape = [out_batch, out_row, out_col, out_depth]
-        return out_shape
-
-    def infer_dtype(self, input_x):
-        validator.check_tensor_type_same({"input_x": input_x}, (mstype.int8, mstype.float16, mstype.float32), self.name)
-        return input_x
-
-
 class ConfusionMulGrad(PrimitiveWithInfer):
     """
     `output0` is the result of which input0 dot multily input1.

tests/ut/python/ops/test_ops.py

@@ -265,8 +265,8 @@ test_case_math_ops = [
         'desc_bprop': [[2, 3]]}),
     ('Acosh', {
         'block': P.Acosh(),
-        'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16))],
-        'skip': ['backward']}),
+        'desc_inputs': [[3, 4, 5]],
+        'desc_bprop': [[3, 4, 5]]}),
     ('Sin', {
         'block': P.Sin(),
         'desc_inputs': [[2, 3]],