Develop op MaxPoolWithArgMax

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -148,8 +148,6 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
 }
 
 std::map<std::string, FAttrsPass> TbeAdapter::build_json_attr_pass_map_ = {
-  {"MaxPoolWithArgmax", TbeAdapter::MaxPoolWithArgmaxAttrJsonPass},
-  {"MaxPoolGradWithArgmax", TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass},
   {"Conv2D", TbeAdapter::Conv2DAttrJsonPass},
   {"Conv2DBackpropFilter", TbeAdapter::Conv2DBackpropFilterAttrJsonPass},
   {"Conv2DBackpropInput", TbeAdapter::Conv2DBackpropInputAttrJsonPass},
@@ -170,48 +168,6 @@ bool TbeAdapter::RunAttrPass(const mindspore::AnfNodePtr &anf_node,
   return false;
 }
 
-void TbeAdapter::MaxPoolWithArgmaxAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MS_EXCEPTION_IF_NULL(anf_node);
-  MS_EXCEPTION_IF_NULL(attrs_json);
-  auto attr_num = op_info_attrs.size();
-  auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
-  MS_EXCEPTION_IF_NULL(primitive);
-  for (size_t i = 0; i < attr_num; i++) {
-    nlohmann::json attr_obj;
-    MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
-    std::string attr_name = op_info_attrs[i]->name();
-    if (primitive->GetAttr(attr_name) != nullptr) {
-      auto value = primitive->GetAttr(attr_name);
-      if (attr_name == "pad_mode") {
-        std::string attr_value = GetValue<std::string>(value);
-        (void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
-        attr_obj["value"] = attr_value;
-      } else {
-        std::vector<int> attr_value;
-        int data = GetValue<int>(value);
-        attr_value.push_back(1);
-        attr_value.push_back(data);
-        attr_value.push_back(data);
-        attr_value.push_back(1);
-        attr_obj["value"] = attr_value;
-      }
-      attr_obj["valid"] = true;
-    } else {
-      attr_obj["valid"] = false;
-    }
-    attr_obj["name"] = attr_name;
-    attrs_json->push_back(attr_obj);
-  }
-}
-
-void TbeAdapter::MaxPoolGradWithArgmaxAttrJsonPass(
-  const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
-  nlohmann::json *attrs_json) {
-  MaxPoolWithArgmaxAttrJsonPass(anf_node, op_info_attrs, attrs_json);
-}
-
 void TbeAdapter::Conv2DAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
                                     const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
                                     nlohmann::json *attrs_json) {

mindspore/ccsrc/transform/convert.cc

@@ -161,6 +161,7 @@ const char kNameTopK[] = "TopK";
 const char kNameSoftmaxGrad[] = "SoftmaxGrad";
 const char kNameMaxPool[] = "MaxPool";
 const char kNameAvgPool[] = "AvgPool";
+const char kNameMaxPoolWithArgmax[] = "MaxPoolWithArgmax";
 const char kNameBatchNorm[] = "BatchNorm";
 const char kNameBatchNormGrad[] = "BatchNormGrad";
 const char kNameROIAlign[] = "ROIAlign";
@@ -198,6 +199,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameApplyMomentum), ADPT_DESC(ApplyMomentum)},
     {string(kNameMaxPool), ADPT_DESC(MaxPool)},
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
+    {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
     {string(kNameTopK), ADPT_DESC(TopKV2)},
     {string(kNamePack), ADPT_DESC(Pack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},

mindspore/ccsrc/transform/op_declare.cc

@@ -734,14 +734,22 @@ ATTR_MAP(AvgPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<
 OUTPUT_MAP(AvgPoolGrad) = {{0, OUTPUT_DESC(out_grad)}};
 
 // MaxPoolWithArgmax
+INPUT_MAP(MaxPoolWithArgmax) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                               {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+                               {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}};
+
+// MaxPoolGradWithArgmax
 INPUT_MAP(MaxPoolGradWithArgmax) = {
   {1, INPUT_DESC(x)},
-  {2, INPUT_DESC(argmax)},
+  {2, INPUT_DESC(grad)},
-  {3, INPUT_DESC(grad)},
+  {3, INPUT_DESC(argmax)},
 };
-ATTR_MAP(MaxPoolGradWithArgmax) = {{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
+ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                   {"window", ATTR_DESC(ksize, "window", AnyTraits<std::vector<int64_t>>())},
+                                   {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                   {"stride", ATTR_DESC(strides, "stride", AnyTraits<std::vector<int64_t>>())}};
+                                   {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};

mindspore/ccsrc/transform/op_declare.h

@@ -88,8 +88,10 @@ DECLARE_OP_ADAPTER(FusedBatchNormGrad)
 DECLARE_OP_USE_OUTPUT(FusedBatchNormGrad)
 DECLARE_OP_ADAPTER(BiasAddGrad)
 DECLARE_OP_USE_OUTPUT(BiasAddGrad)
+DECLARE_OP_ADAPTER(MaxPoolWithArgmax)
+DECLARE_OP_USE_OUTPUT(MaxPoolWithArgmax)
 DECLARE_OP_ADAPTER(MaxPoolGradWithArgmax)
-DECLARE_OP_USE_ENUM(MaxPoolGradWithArgmax)
+DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax)
 DECLARE_OP_ADAPTER(Conv2D)
 DECLARE_OP_USE_ENUM(Conv2D)
 DECLARE_OP_USE_OUTPUT(Conv2D)

mindspore/model_zoo/resnet.py

@@ -168,7 +168,7 @@ class ResNet(nn.Cell):
         self.conv1 = _conv7x7(3, 64, stride=2)
         self.bn1 = _bn(64)
         self.relu = P.ReLU()
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode='same', window=3, stride=2)
+        self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 
         self.layer1 = self._make_layer(block,
                                        layer_nums[0],

mindspore/nn/layer/pooling.py

@@ -13,36 +13,52 @@
 # limitations under the License.
 # ============================================================================
 """pooling"""
-
 from mindspore.ops import operations as P
 from mindspore._checkparam import ParamValidator as validator
 from mindspore._checkparam import Rel
+from ... import context
 from ..cell import Cell
 
 
 class _PoolNd(Cell):
     """N-D  AvgPool"""
 
-    def __init__(self,
+    def __init__(self, kernel_size, stride, pad_mode):
-                 kernel_size,
+        name = self.__class__.__name__
-                 stride,
-                 pad_mode,
-                 padding=0,
-                 pool=None):
         super(_PoolNd, self).__init__()
-        self.kernel_size = kernel_size
+        validator.check_type('kernel_size', kernel_size, [int, tuple])
-        self.stride = stride
+        validator.check_type('stride', stride, [int, tuple])
-        self.pad_mode = pad_mode
+        self.pad_mode = validator.check_string('pad_mode', pad_mode.upper(), ['VALID', 'SAME'])
-        self.padding = validator.check_integer('padding', padding, 0, Rel.GE)
-        self.pool = pool
-        if self.pool is None:
-            raise NotImplementedError
 
-    def construct(self, x):
+        if isinstance(kernel_size, int):
-        return self.pool(x)
+            validator.check_integer("kernel_size", kernel_size, 1, Rel.GE)
+        else:
+            if (len(kernel_size) != 2 or
+                    (not isinstance(kernel_size[0], int)) or
+                    (not isinstance(kernel_size[1], int)) or
+                    kernel_size[0] <= 0 or
+                    kernel_size[1] <= 0):
+                raise ValueError(f'The kernel_size passed to cell {name} should be an positive int number or'
+                                 f'a tuple of two positive int numbers, but got {kernel_size}')
+        self.kernel_size = kernel_size
+
+        if isinstance(stride, int):
+            validator.check_integer("stride", stride, 1, Rel.GE)
+        else:
+            if (len(stride) != 2 or
+                    (not isinstance(stride[0], int)) or
+                    (not isinstance(stride[1], int)) or
+                    stride[0] <= 0 or
+                    stride[1] <= 0):
+                raise ValueError(f'The stride passed to cell {name} should be an positive int number or'
+                                 f'a tuple of two positive int numbers, but got {stride}')
+        self.stride = stride
+
+    def construct(self, *inputs):
+        pass
 
     def extend_repr(self):
-        return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__)
+        return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__)
 
 
 class MaxPool2d(_PoolNd):
@@ -63,19 +79,23 @@ class MaxPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over. Default 1.
+        kernel_size (Union[int, tuple[int]]): The size of kernel used to take the max value,
-        stride (int): Stride size of the window. Default: 1.
+            is an int number that represents height and width are both kernel_size,
-        pad_mode (str): Select the mode of the pad. The optional values are
+            or a tuple of two int numbers that represent height and width respectively.
-            "same" and "valid". Default: "valid".
+            Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
 
             - same: Adopts the way of completion. Output height and width will be the same as
               the input. Total number of padding will be calculated for horizontal and vertical
-              direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
+              direction and evenly distributed to top and bottom, left and right if possible.
-              last extra padding will be done from the bottom and the right side.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
 
-            - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
-              without padding. Extra pixels will be discarded.
+              will be return without padding. Extra pixels will be discarded.
-        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -103,31 +123,22 @@ class MaxPool2d(_PoolNd):
           [[7. 8.]
            [8. 8.]]]]
     """
-    def __init__(self,
-                 kernel_size=1,
-                 stride=1,
-                 pad_mode="VALID",
-                 padding=0):
-        max_pool = P.MaxPool(ksize=kernel_size,
-                             strides=stride,
-                             padding=pad_mode)
-        self.is_autodiff_backend = False
-        if self.is_autodiff_backend:
 
-            # At present, pad mode of max pool is not unified, so it is a temporarily avoided
+    def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
-            pad_mode = validator.check_string('pad_mode', pad_mode.lower(), ['valid', 'same'])
+        super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode)
-
+        self.max_pool = P.MaxPool(ksize=self.kernel_size,
-            max_pool = P.MaxPoolWithArgmax(window=kernel_size,
+                                  strides=self.stride,
-                                           stride=stride,
+                                  padding=self.pad_mode)
-                                           pad_mode=pad_mode,
+        self.max_pool_with_arg_max = P.MaxPoolWithArgmax(ksize=self.kernel_size,
-                                           pad=padding)
+                                                         strides=self.stride,
-        super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, padding, max_pool)
+                                                         padding=self.pad_mode)
+        self.is_tbe = context.get_context("device_target") == "Ascend"
 
     def construct(self, x):
-        if self.is_autodiff_backend:
+        if self.is_tbe and self.training:
-            out = self.pool(x)[0]
+            out = self.max_pool_with_arg_max(x)[0]
         else:
-            out = self.pool(x)
+            out = self.max_pool(x)
         return out
 
 
@@ -149,19 +160,24 @@ class AvgPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over. Default: 1.
+        kernel_size (Union[int, tuple[int]]): The size of kernel used to take the average value,
-        stride (int): Stride size of the window. Default: 1.
+            is an int number that represents height and width are both kernel_size,
-        pad_mode (str): Select the mode of the pad. The optional values are
+            or a tuple of two int numbers that represent height and width respectively.
-            "same", "valid". Default: "valid".
+            Default: 1.
+        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
 
             - same: Adopts the way of completion. Output height and width will be the same as
               the input. Total number of padding will be calculated for horizontal and vertical
-              direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
+              direction and evenly distributed to top and bottom, left and right if possible.
-              last extra padding will be done from the bottom and the right side.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
-            - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
-              without padding. Extra pixels will be discarded.
-        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -170,7 +186,7 @@ class AvgPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = AvgPool2d(kernel_size=3, stride=1)
+        >>> pool = AvgPool2d(kernel_size=3, strides=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
@@ -189,12 +205,15 @@ class AvgPool2d(_PoolNd):
           [[4.2222223 4.5555553]
            [3.2222223 4.5555553]]]]
     """
+
     def __init__(self,
                  kernel_size=1,
                  stride=1,
-                 pad_mode="VALID",
+                 pad_mode="valid"):
-                 padding=0):
+        super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode)
-        avg_pool = P.AvgPool(ksize=kernel_size,
+        self.avg_pool = P.AvgPool(ksize=self.kernel_size,
-                             strides=stride,
+                                  strides=self.stride,
-                             padding=pad_mode)
+                                  padding=self.pad_mode)
-        super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode, padding, avg_pool)
+
+    def construct(self, x):
+        return self.avg_pool(x)

mindspore/ops/_grad/grad_nn_ops.py

@@ -76,14 +76,9 @@ def get_bprop_depthwise_conv2d_native(self):
 def get_bprop_max_pool_with_argmax(self):
     """Grad definition for `MaxPoolWithArgmax` operation."""
     maxpool_grad = G.MaxPoolGradWithArgmax(
-        pad_mode=self.pad_mode,
+        ksize=self.ksize,
-        window=self.window,
+        strides=self.strides,
-        pad=self.pad,
+        padding=self.padding,)
-        stride=self.stride,
-        data_mode=self.data_mode,
-        ceil_mode=self.ceil_mode,
-        alpha=self.alpha,
-        beta=self.beta)
 
     def bprop(x, out, dout):
         dx = maxpool_grad(x, dout[0], out[1])

mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py

@@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register
     "partial_flag": true,
     "attr": [
         {
-            "name": "window",
+            "name": "ksize",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "stride",
+            "name": "strides",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "pad_mode",
+            "name": "padding",
             "param_type": "required",
             "type": "str",
             "value": "all"

mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py

@@ -28,19 +28,19 @@ from mindspore.ops.op_info_register import op_info_register
     "partial_flag": true,
     "attr": [
         {
-            "name": "window",
+            "name": "ksize",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "stride",
+            "name": "strides",
             "param_type": "required",
             "type": "listInt",
             "value": "all"
         },
         {
-            "name": "pad_mode",
+            "name": "padding",
             "param_type": "required",
             "type": "str",
             "value": "all"

mindspore/ops/operations/_grad_ops.py

@@ -15,7 +15,6 @@
 
 """Operators for gradients."""
 
-import math
 from ..._c_expression import signature_rw as sig_rw
 from ..._c_expression import signature_kind as sig_kind
 from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
@@ -340,59 +339,60 @@ class _PoolGrad(PrimitiveWithInfer):
     """Gradients of the max/avg pool operation."""
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize, strides, padding="VALID"):
         self.init_prim_io_names(inputs=['x_origin', 'out_origin', 'grad'], outputs=['output'])
-        self.ksize = ksize
-        self.strides = strides
-        self.padding = padding
 
-        self.ksize = validator.check_type('ksize', self.ksize, [int, tuple])
+        validator.check_type('ksize', ksize, [int, tuple])
-        self.strides = validator.check_type('strides', self.strides, [int, tuple])
+        validator.check_type('strides', strides, [int, tuple])
-
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'])
-        validator.check_type('padding', self.padding, [str])
-        self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME'])
         self.add_prim_attr("padding", self.padding)
+        self.is_maxpoolgradwithargmax = (self.name == "MaxPoolGradWithArgmax")
+        if not self.is_maxpoolgradwithargmax:
             self.add_prim_attr('data_format', "NCHW")
 
-        if isinstance(self.ksize, int):
+        if isinstance(ksize, int):
-            self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE)
+            validator.check_integer("ksize", ksize, 1, Rel.GE)
-            self.pool_w = self.pool_h
+            if self.is_maxpoolgradwithargmax:
-            self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize))
+                self.ksize = (1, ksize, ksize, 1)
-        elif isinstance(self.ksize, tuple):
-            if (len(self.ksize) != 2 and len(self.ksize) != 4):
-                raise ValueError('Attr \'ksize\' of \'Pool\' Op passed ' +
-                                 str(self.ksize)+', should be a int or a tuple of length 2 or 4.')
-            for ksize_val in self.ksize:
-                if (not isinstance(ksize_val, int)) or (ksize_val <= 0):
-                    raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' +
-                                     str(self.ksize)+', should be int and greater than 0.')
-            self.pool_h = self.ksize[-2]
-            self.pool_w = self.ksize[-1]
-            self.add_prim_attr("ksize", (1, 1, self.ksize[-2], self.ksize[-1]))
-
-        if isinstance(self.strides, int):
-            self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE)
-            self.stride_w = self.stride_h
-            self.add_prim_attr("strides", (1, 1, self.strides, self.strides))
-        elif isinstance(self.strides, tuple):
-            if (len(self.strides) != 2 and len(self.strides) != 4):
-                raise ValueError('Attr \'strides\' of \'MaxPool\' Op passed ' +
-                                 str(self.strides)+', should be a int or a tuple of length 2 or 4.')
-            for stride_val in self.strides:
-                if (not isinstance(stride_val, int)) or (stride_val <= 0):
-                    raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' +
-                                     str(self.strides)+', should be int and greater than 0.')
-            self.stride_h = self.strides[-2]
-            self.stride_w = self.strides[-1]
-            self.add_prim_attr("strides", (1, 1, self.strides[-2], self.strides[-1]))
-
-        if self.padding == "VALID":
-            self.pad = 0
-        elif self.padding == "SAME":
-            self.pad = math.floor((self.pool_h - 1) / 2)
             else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
+                self.ksize = (1, 1, ksize, ksize)
-                             ' but got {}.'.format(self.padding))
+        else:
+            ksize_error = ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number"
+                                     f"or a tuple of two or four positive int numbers, but got {ksize}")
+            if len(ksize) != 2 and len(ksize) != 4:
+                raise ksize_error
+            for ksize_val in ksize:
+                if not isinstance(ksize_val, int) or (ksize_val <= 0):
+                    raise ksize_error
+            if len(ksize) == 2 and self.is_maxpoolgradwithargmax:
+                self.ksize = (1, ksize[0], ksize[1], 1)
+            elif len(ksize) == 2 and not self.is_maxpoolgradwithargmax:
+                self.ksize = (1, 1, ksize[0], ksize[1])
+            else:
+                self.ksize = ksize
+        self.add_prim_attr("ksize", self.ksize)
+
+        if isinstance(strides, int):
+            validator.check_integer("strides", strides, 1, Rel.GE)
+            if self.is_maxpoolgradwithargmax:
+                self.strides = (1, strides, strides, 1)
+            else:
+                self.strides = (1, 1, strides, strides)
+        else:
+            strides_error = ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number"
+                                       f"or a tuple of two or four positive int numbers, but got {strides}")
+            if len(strides) != 2 and len(strides) != 4:
+                raise strides_error
+            for strides_val in strides:
+                if not isinstance(strides_val, int) or (strides_val <= 0):
+                    raise strides_error
+            if len(strides) == 2 and self.is_maxpoolgradwithargmax:
+                self.strides = (1, strides[0], strides[1], 1)
+            elif len(strides) == 2 and not self.is_maxpoolgradwithargmax:
+                self.strides = (1, 1, strides[0], strides[1])
+            else:
+                self.strides = strides
+        self.add_prim_attr("strides", self.strides)
 
 
 class AvgPoolGrad(_PoolGrad):
@@ -451,28 +451,13 @@ class MaximumGrad(Primitive):
         raise NotImplementedError
 
 
-class MaxPoolGradWithArgmax(PrimitiveWithInfer):
+class MaxPoolGradWithArgmax(_PoolGrad):
     """Computes the gradients of MaxPoolWithArgmax."""
 
     @prim_attr_register
-    def __init__(self,
+    def __init__(self, ksize=1, strides=1, padding="VALID",):
-                 pad_mode="valid",
-                 window=0,
-                 pad=0,
-                 stride=1,
-                 data_mode=1,
-                 ceil_mode=0,
-                 alpha=1.0,
-                 beta=0.0):
         self.init_prim_io_names(inputs=['x', 'grad', 'argmax'], outputs=['output'])
-
+        super(MaxPoolGradWithArgmax, self).__init__(ksize, strides, padding)
-        self.window = window
-        self.pool_h = self.pool_w = window
-        self.pad = pad
-        self.pad_mode = pad_mode
-        self.stride = stride
-        self.data_mode = data_mode
-        self.ceil_mode = ceil_mode
 
     def infer_shape(self, x_shape, grad_shape, argmax_shape):
         if not grad_shape:

mindspore/ops/operations/nn_ops.py

@@ -682,186 +682,83 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
         return x_dtype
 
 
-class MaxPoolWithArgmax(PrimitiveWithInfer):
-    r"""
-    Performs max pooling on the input Tensor and return both max values and indices.
-
-    Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs
-    regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size
-    :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows.
-
-    .. math::
-        \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1}
-        \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
-
-    Args:
-        pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
-        window (Union[int, tuple[int]]): The size of window, which is the kernel size, two `int` for width
-            and height. Default: 1.
-        pad (Union[int, tuple[int]]): If `pad_mode` is `pad`, the pad value to fill, two `int` for width
-            and height. Default: 0.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
-           width and height. Default: 1.
-
-    Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
-
-    Outputs:
-        Tuple of 2 Tensor, the maxpool result and where max values from.
-
-        - **output** (Tensor) -  Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
-        - **mask** (Tensor) -  Max values' index represented by the mask.
-    """
-
-    @prim_attr_register
-    def __init__(self,
-                 pad_mode="valid",
-                 window=1,
-                 pad=0,
-                 stride=1,
-                 data_mode=1,
-                 ceil_mode=0,
-                 alpha=1.0,
-                 beta=0.0):
-        self.init_prim_io_names(inputs=['x'], outputs=['output', 'argmax'])
-        self.window = validator.check_type('window', window, [int, tuple])
-        if isinstance(window, int) and window <= 0:
-            raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed '
-                             + str(self.window)+', should be a int or tuple and greater than 0.')
-        if isinstance(window, tuple) and (len(window) != 2 or
-                                          (not isinstance(window[0], int)) or
-                                          (not isinstance(window[1], int)) or
-                                          window[0] <= 0 or window[1] <= 0):
-            raise ValueError('Attr \'window\' of \'MaxPoolWithArgmax\' Op passed '
-                             + str(self.window)+', should be a int or tuple and greater than 0.')
-        self.pool_h = self.pool_w = window
-        self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'])
-        if self.pad_mode == "valid":
-            self.pad = 0
-        elif self.pad_mode == "same":
-            self.pad = math.floor((self.window - 1) / 2)
-        elif self.pad_mode == "pad":
-            self.pad = validator.check_integer('pad', pad, 0, Rel.GE)
-
-        self.data_mode = validator.check_integer('data_mode', data_mode, 1, Rel.EQ)
-        self.ceil_mode = validator.check_integer('ceil_mode', ceil_mode, 0, Rel.EQ)
-        self.stride = validator.check_integer('stride', stride, 1, Rel.GE)
-        self.alpha = validator.check_type('alpha', alpha, [int, float])
-        self.beta = validator.check_type('beta', beta, [int, float])
-        self.is_tbe = not context.get_context("enable_ge") and context.get_context("device_target") == "Ascend"
-
-    def infer_shape(self, x_shape):
-        validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ)
-        pad = self.pad
-        h_input = x_shape[2]
-        w_input = x_shape[3]
-        h_out = (h_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1
-        h_out = math.floor(h_out)
-        w_out = (w_input + 2 * pad - (self.window - 1) - 1) / self.stride + 1
-        w_out = math.floor(w_out)
-        out_shape = [x_shape[0], x_shape[1], h_out, w_out]
-        for shape_value in out_shape:
-            if shape_value <= 0:
-                raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.")
-        k_size_vec = [1, self.window, self.window, 1]
-        argmax_shape = []
-        if self.is_tbe:
-            for i in range(4):
-                if i == 2:
-                    dim = k_size_vec[i - 1] * k_size_vec[i]
-                    argmax_shape.append(dim)
-                elif i == 3:
-                    dim = math.ceil(out_shape[i - 1] * out_shape[i] / 16) + 1
-                    argmax_shape.append(dim)
-                else:
-                    argmax_shape.append(x_shape[i])
-        else:
-            argmax_shape = out_shape
-        return out_shape, argmax_shape
-
-    def infer_dtype(self, x_dtype):
-        out_dtype = x_dtype
-        validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32))
-        argmax_dtype = mstype.int32
-        return out_dtype, argmax_dtype
-
-
 class _Pool(PrimitiveWithInfer):
     r"""
     Performs max/avg pooling operation.
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple
+        ksize (Union[int, tuple[int]]): The size of the kernel, that should be a tuple
-           of two `int` for width and height. Default: 1.
+           of two `int` for height and width. Default: 1.
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
+        strides (Union[int, tuple[int]]): The stride of the window, that should be
-           width and height. Default: 1.
+            a tuple of two `int` for height and width. Default: 1.
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize=1, strides=1, padding="valid"):
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-        validator.check_type('padding', padding, [str])
-        self.ksize = ksize
-        self.strides = strides
-        self.padding = padding.upper()
-        self.ksize = validator.check_type('ksize', self.ksize, [int, tuple])
-        self.strides = validator.check_type('strides', self.strides, [int, tuple])
-        self.padding = validator.check_string('padding', self.padding, ['VALID', 'SAME'])
         self.init_prim_io_names(inputs=['x'], outputs=['output'])
+        validator.check_type('ksize', ksize, [int, tuple])
+        validator.check_type('strides', strides, [int, tuple])
+        self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'])
         self.add_prim_attr("padding", self.padding)
+        self.is_maxpoolwithargmax = (self.name == "MaxPoolWithArgmax")
+        if not self.is_maxpoolwithargmax:
             self.add_prim_attr('data_format', "NCHW")
 
-        if isinstance(self.ksize, int):
+        if isinstance(ksize, int):
-            self.pool_h = validator.check_integer("ksize", self.ksize, 1, Rel.GE)
+            validator.check_integer("ksize", ksize, 1, Rel.GE)
-            self.pool_w = self.pool_h
+            self.ksize = (1, 1, ksize, ksize)
-            self.add_prim_attr("ksize", (1, 1, self.ksize, self.ksize))
-        elif isinstance(self.ksize, tuple):
-            if (len(self.ksize) != 2 or (not isinstance(self.ksize[0], int)) or (not isinstance(self.ksize[1], int))
-                    or self.ksize[0] <= 0 or self.ksize[1] <= 0):
-                raise ValueError('Each value of attr \'ksize\' of \'MaxPool\' Op passed ' +
-                                 str(self.ksize) + ', should be a int or a tuple of length 2 and greater than 0.')
-            self.pool_h = self.ksize[0]
-            self.pool_w = self.ksize[1]
-            self.add_prim_attr("ksize", (1, 1, self.ksize[0], self.ksize[1]))
-
-        if isinstance(self.strides, int):
-            self.stride_h = validator.check_integer("strides", self.strides, 1, Rel.GE)
-            self.stride_w = self.stride_h
-            self.add_prim_attr("strides", (1, 1, self.strides, self.strides))
-        elif isinstance(self.strides, tuple):
-            if (len(self.strides) != 2 or (not isinstance(self.strides[0], int)) or
-                    (not isinstance(self.strides[1], int)) or self.strides[0] <= 0 or self.strides[1] <= 0):
-                raise ValueError('Each value of attr \'strides\' of \'MaxPool\' Op passed ' +
-                                 str(self.strides) + ', should be a int or a tuple of length 2 and greater than 0.')
-            self.stride_h = self.strides[0]
-            self.stride_w = self.strides[1]
-            self.add_prim_attr("strides", (1, 1, self.strides[0], self.strides[1]))
-
-        if self.padding == "VALID":
-            self.pad = 0
-        elif self.padding == "SAME":
-            self.pad = math.floor((self.pool_h - 1) / 2)
         else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
+            if (len(ksize) != 2 or
-                             ' but got {}.'.format(self.padding))
+                    (not isinstance(ksize[0], int)) or
-        self.add_prim_attr('pad', self.pad)
+                    (not isinstance(ksize[1], int)) or
+                    ksize[0] <= 0 or
+                    ksize[1] <= 0):
+                raise ValueError(f"The 'ksize' passed to operator {self.name} should be an positive int number or"
+                                 f"a tuple of two positive int numbers, but got {ksize}")
+            self.ksize = (1, 1, ksize[0], ksize[1])
+        if self.is_maxpoolwithargmax:
+            self.ksize = (1, self.ksize[-2], self.ksize[-1], 1)
+        self.add_prim_attr("ksize", self.ksize)
+
+        if isinstance(strides, int):
+            validator.check_integer("strides", strides, 1, Rel.GE)
+            self.strides = (1, 1, strides, strides)
+        else:
+            if (len(strides) != 2 or
+                    (not isinstance(strides[0], int)) or
+                    (not isinstance(strides[1], int)) or
+                    strides[0] <= 0 or
+                    strides[1] <= 0):
+                raise ValueError(f"The 'strides' passed to operator {self.name} should be an positive int number or"
+                                 f"a tuple of two positive int numbers, but got {strides}")
+            self.strides = (1, 1, strides[0], strides[1])
+        if self.is_maxpoolwithargmax:
+            self.strides = (1, self.strides[-2], self.strides[-1], 1)
+        self.add_prim_attr("strides", self.strides)
 
     def infer_shape(self, x_shape):
         validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ)
-        h_input = x_shape[2]
+        batch, channel, input_h, input_w = x_shape
-        w_input = x_shape[3]
+        if self.is_maxpoolwithargmax:
-        if self.padding == "VALID":
+            _, kernel_h, kernel_w, _ = self.ksize
-            h_out = math.ceil((h_input - (self.pool_h - 1)) / self.stride_h)
+            _, stride_h, stride_w, _ = self.strides
-            w_out = math.ceil((w_input - (self.pool_w - 1)) / self.stride_w)
-        elif self.padding == "SAME":
-            h_out = math.ceil(h_input / self.stride_h)
-            w_out = math.ceil(w_input / self.stride_w)
         else:
-            raise ValueError('The padding should be str and must be SAME or VALID,'
+            _, _, kernel_h, kernel_w = self.ksize
-                             ' but got {}.'.format(self.padding))
+            _, _, stride_h, stride_w = self.strides
+
+        if self.padding == "VALID":
+            out_h = math.ceil((input_h - (kernel_h - 1)) / stride_h)
+            out_w = math.ceil((input_w - (kernel_w - 1)) / stride_w)
+        elif self.padding == "SAME":
+            out_h = math.ceil(input_h / stride_h)
+            out_w = math.ceil(input_w / stride_w)
+        else:
+            raise ValueError(f"The padding of operator {self.name} should be a str and must be 'SAME' or 'VALID', "
+                             f"but got {self.padding}.")
+        out_shape = [batch, channel, out_h, out_w]
 
-        out_shape = [x_shape[0], x_shape[1], h_out, w_out]
         for shape_value in out_shape:
             if shape_value <= 0:
                 raise ValueError("The kernel size is not valid please check it if is larger than data's shape size.")
@@ -887,11 +784,22 @@ class MaxPool(_Pool):
         \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a max over, that should be a tuple
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
-           of two `int` for width and height. Default: 1.
+            is an int number that represents height and width are both ksize, or a tuple
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
+            of two int numbers that represent height and width respectively. Default: 1.
-           width and height. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -901,10 +809,83 @@ class MaxPool(_Pool):
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize=1, strides=1, padding="valid"):
         super(MaxPool, self).__init__(ksize, strides, padding)
 
 
+class MaxPoolWithArgmax(_Pool):
+    r"""
+    Performs max pooling on the input Tensor and return both max values and indices.
+
+    Typically the input is of shape :math:`(N_{in}, C_{in}, H_{in}, W_{in})`, MaxPool outputs
+    regional maximum in the :math:`(H_{in}, W_{in})`-dimension. Given kernel size
+    :math:`ks = (h_{ker}, w_{ker})` and stride :math:`s = (s_0, s_1)`, the operation is as follows.
+
+    .. math::
+        \text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1}
+        \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
+
+    Args:
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value and arg value,
+            is an int number that represents height and width are both ksize, or a tuple of
+            two int numbers that represent height and width respectively. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
+
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tuple of 2 Tensor, the maxpool result and where max values from.
+
+        - **output** (Tensor) -  Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+        - **mask** (Tensor) -  Max values' index represented by the mask.
+    """
+
+    def __init__(self, ksize=1, strides=1, padding="valid"):
+        super(MaxPoolWithArgmax, self).__init__(ksize, strides, padding)
+        self.is_tbe = context.get_context("device_target") == "Ascend"
+
+    def infer_shape(self, x_shape):
+        out_shape = _Pool.infer_shape(self, x_shape)
+        _, _, out_h, out_w = out_shape
+        _, kernel_h, kernel_w, _ = self.ksize
+
+        argmax_shape = []
+        if self.is_tbe:
+            for i in range(4):
+                if i == 2:
+                    dim = kernel_h * kernel_w
+                    argmax_shape.append(dim)
+                elif i == 3:
+                    dim = math.ceil(out_h * out_w / 16) + 1
+                    argmax_shape.append(dim)
+                else:
+                    argmax_shape.append(x_shape[i])
+        else:
+            argmax_shape = out_shape
+
+        return out_shape, argmax_shape
+
+    def infer_dtype(self, x_dtype):
+        out_dtype = x_dtype
+        validator.check_typename("x_type", x_dtype, (mstype.float16, mstype.float32))
+        argmax_dtype = mstype.uint16
+        return out_dtype, argmax_dtype
+
+
 class AvgPool(_Pool):
     r"""
     Average pooling operation.
@@ -919,11 +900,22 @@ class AvgPool(_Pool):
         \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)
 
     Args:
-        ksize (Union[int, tuple[int]]): The size of the window to take a average over, that should be a tuple
+        ksize (Union[int, tuple[int]]): The size of kernel used to take the average value,
-           of two `int` for width and height. Default: 1.
+            is an int number that represents height and width are both ksize, or a tuple
-        stride (Union[int, tuple[int]]): The stride of the window, that should be a tuple of two `int` for
+            of two int numbers that represent height and width respectively. Default: 1.
-           width and height. Default: 1.
+        strides (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
-        padding (str): The optional values for pad mode "SAME", "VALID". Default: "VALID".
+            the height and width of movement are both strides, or a tuple of two int numbers that
+            represent height and width of movement respectively. Default: 1.
+        padding (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
+            Default: "valid".
+
+            - same: Adopts the way of completion. Output height and width will be the same as
+              the input. Total number of padding will be calculated for horizontal and vertical
+              direction and evenly distributed to top and bottom, left and right if possible.
+              Otherwise, the last extra padding will be done from the bottom and the right side.
+
+            - valid: Adopts the way of discarding. The possibly largest height and width of output
+              will be return without padding. Extra pixels will be discarded.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -933,7 +925,7 @@ class AvgPool(_Pool):
     """
 
     @prim_attr_register
-    def __init__(self, ksize=1, strides=1, padding="VALID"):
+    def __init__(self, ksize=1, strides=1, padding="valid"):
         if context.get_context("device_target") == "GPU":
             self.target = "GPU"
         else:

tests/perf_test/resnet_example.py

@@ -103,7 +103,7 @@ class ResNet50(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='valid')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid')
 
         self.layer1 = self.MakeLayer(
             block, 3, in_channels=64, out_channels=256, stride=1)

tests/st/networks/test_cpu_lenet.py

@@ -21,6 +21,7 @@ import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore import Tensor
 
+
 class LeNet(nn.Cell):
     def __init__(self):
         super(LeNet, self).__init__()
@@ -50,8 +51,10 @@ class LeNet(nn.Cell):
         output = self.fc3(output)
         return output
 
+
 context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
 
+
 def train(net, data, label):
     learning_rate = 0.01
     momentum = 0.9
@@ -67,6 +70,7 @@ def train(net, data, label):
     print("+++++++++++++++++++++++++++")
     assert res
 
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard

tests/st/networks/test_gpu_alexnet.py

@@ -38,7 +38,7 @@ class AlexNet(nn.Cell):
         self.conv4 = nn.Conv2d(384, 384, 3, stride=1, pad_mode="same")
         self.conv5 = nn.Conv2d(384, 256, 3, stride=1, pad_mode="same")
         self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2,pad_mode="valid",padding=0)
+        self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="valid")
         self.flatten = nn.Flatten()
         self.fc1 = nn.Dense(6*6*256, 4096)
         self.fc2 = nn.Dense(4096, 4096)

tests/st/ops/davinci/test_maxpool_with_argmax.py

@@ -20,24 +20,27 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
 
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
+        self.maxpool = P.MaxPoolWithArgmax(padding="same",
-                             window=3,
+                                           ksize=3,
-                             stride=2)
+                                           strides=2)
         self.x = Parameter(initializer(
             'normal', [1, 64, 112, 112]), name='w')
         self.add = P.TensorAdd()
 
-
     @ms_function
     def construct(self):
         output = self.maxpool(self.x)
         return output[0]
 
+
 def test_net():
     x = np.random.randn(1, 64, 112, 112).astype(np.float32)
     maxpool = Net()

tests/st/ops/davinci/test_maxpool_with_argmax_grad.py

@@ -37,9 +37,9 @@ class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
 
-        self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
+        self.maxpool = P.MaxPoolWithArgmax(padding="same",
-                                                 window=3,
+                                           ksize=3,
-                                                 stride=2)
+                                           strides=2)
 
     @ms_function
     def construct(self, x):

tests/st/tbe_networks/resnet.py

@@ -267,7 +267,7 @@ class ResNet(nn.Cell):
 
         self.bn1 = bn_with_initialize(64)
         self.relu = P.ReLU()
-        self.maxpool = P.MaxPoolWithArgmax(window=3, stride=2, pad_mode="same")
+        self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME")
 
         self.layer1 = MakeLayer0(block, layer_num[0], in_channels=64, out_channels=256, stride=1)
         self.layer2 = MakeLayer1(block, layer_num[1], in_channels=256, out_channels=512, stride=2)

tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py

@@ -21,7 +21,7 @@ addn = P.AddN()
 add = P.TensorAdd()
 sub = P.Sub()
 mul = P.Mul()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 four2five = Primitive('Four2Five')
 five2four = Primitive('Five2Four')

tests/ut/cpp/python_input/gtest_input/pre_activate/insert_trans_op_test.py

@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 transdata = Primitive("TransData")
 

tests/ut/cpp/python_input/gtest_input/pre_activate/mixed_precision_test.py

@@ -21,7 +21,7 @@ addn = P.AddN()
 add = P.TensorAdd()
 sub = P.Sub()
 mul = P.Mul()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 cast = Primitive('Cast')
 

tests/ut/cpp/python_input/gtest_input/pre_activate/transdata_split_test.py

@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 four2five = Primitive('Four2Five')
 five2four = Primitive('Five2Four')

tests/ut/cpp/python_input/gtest_input/pre_activate/transpose_transdata_fusion_test.py

@@ -17,7 +17,7 @@ from mindspore.ops import Primitive
 
 tuple_getitem = Primitive('tuple_getitem')
 add = P.TensorAdd()
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 make_tuple = Primitive('make_tuple')
 transdata = Primitive("TransData")
 Transpose = P.Transpose()

tests/ut/cpp/python_input/gtest_input/session/session_test.py

@@ -22,7 +22,7 @@ add = P.TensorAdd()
 reshape = P.Reshape()
 cast = P.Cast()
 tuple_getitem = Primitive('tuple_getitem')
-max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
 
 def test_addn_cast(x, y, z):
     sum = addn((x, y))

tests/ut/python/communication/test_data_parallel_resnet.py

@@ -107,7 +107,7 @@ class ResNet18(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 2, in_channels=64, out_channels=256, stride=1)
@@ -176,7 +176,7 @@ class ResNet9(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='same')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 1, in_channels=64, out_channels=256, stride=1)

tests/ut/python/exec/resnet_example.py

@@ -189,7 +189,7 @@ class ResNet50(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, weight_init=weight_conv)
         self.bn1 = bn_with_initialize(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
 
         self.layer1 = MakeLayer3(
             block, in_channels=64, out_channels=256, stride=1)

tests/ut/python/exec/test_pooling.py

@@ -23,12 +23,10 @@ class MaxNet(nn.Cell):
     """MaxNet definition"""
     def __init__(self,
                  kernel_size,
-                 stride=None,
+                 stride=None):
-                 padding=0):
         super(MaxNet, self).__init__()
         self.maxpool = nn.MaxPool2d(kernel_size,
-                                    stride,
+                                    stride)
-                                    padding=padding)
 
     def construct(self, input_x):
         return self.maxpool(input_x)

tests/ut/python/model/res18_example.py

@@ -106,7 +106,7 @@ class ResNet18(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, pad_mode='pad')
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
 
         self.layer1 = self.MakeLayer(
             block, 2, in_channels=64, out_channels=256, stride=1)
@@ -175,7 +175,7 @@ class ResNet9(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
 
         self.layer1 = self.MakeLayer(
             block, 1, in_channels=64, out_channels=256, stride=1)

tests/ut/python/nn/test_cell.py

@@ -87,7 +87,7 @@ class ConvNet(nn.Cell):
         self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode="pad", padding=3)
         self.bn1 = nn.BatchNorm2d(ConvNet.output_ch)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="pad", padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(
             int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)),

tests/ut/python/nn/test_pooling.py

@@ -46,8 +46,7 @@ class MaxNet(nn.Cell):
                  padding=0):
         super(MaxNet, self).__init__()
         self.maxpool = nn.MaxPool2d(kernel_size,
-                                    stride,
+                                    stride)
-                                    padding=padding)
 
     def construct(self, x):
         return self.maxpool(x)

tests/ut/python/ops/test_nn_ops.py

@@ -108,6 +108,7 @@ class ResidualBlock(nn.Cell):
 
 class VirtualLossGrad(PrimitiveWithInfer):
     """ VirtualLossGrad definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLossGrad"""
@@ -124,6 +125,7 @@ class VirtualLossGrad(PrimitiveWithInfer):
 
 class VirtualLoss(PrimitiveWithInfer):
     """ VirtualLoss definition """
+
     @prim_attr_register
     def __init__(self):
         """init VirtualLoss"""
@@ -138,6 +140,7 @@ class VirtualLoss(PrimitiveWithInfer):
             # pylint: disable=unused-argument
             dx = loss_grad(x, out, dout)
             return (dx,)
+
         return bprop
 
     def infer_shape(self, x_shape):
@@ -149,6 +152,7 @@ class VirtualLoss(PrimitiveWithInfer):
 
 class VirtualNetWithLoss(nn.Cell):
     """ VirtualNetWithLoss definition """
+
     def __init__(self, network):
         super(VirtualNetWithLoss, self).__init__()
         self.loss = VirtualLoss()
@@ -161,6 +165,7 @@ class VirtualNetWithLoss(nn.Cell):
 
 class SoftMaxGrad(nn.Cell):
     """ SoftMaxGrad definition """
+
     def __init__(self, network):
         super(SoftMaxGrad, self).__init__()
         self.network = network
@@ -171,6 +176,7 @@ class SoftMaxGrad(nn.Cell):
 
 class DropoutGrad(nn.Cell):
     """ DropoutGrad definition """
+
     def __init__(self, network):
         super(DropoutGrad, self).__init__()
         self.network = network
@@ -181,6 +187,7 @@ class DropoutGrad(nn.Cell):
 
 class ScalarSummaryNet(nn.Cell):
     """ ScalarSummaryNet definition """
+
     def __init__(self):
         super(ScalarSummaryNet, self).__init__()
         self.summary = P.ScalarSummary()
@@ -193,6 +200,7 @@ class ScalarSummaryNet(nn.Cell):
 
 class FusedBatchNormGrad(nn.Cell):
     """ FusedBatchNormGrad definition """
+
     def __init__(self, network):
         super(FusedBatchNormGrad, self).__init__()
         self.grad = C.GradOperation(name="get_all", get_all=True, sens_param=True)
@@ -204,6 +212,7 @@ class FusedBatchNormGrad(nn.Cell):
 
 class NetWithLoss(nn.Cell):
     """ NetWithLoss definition """
+
     def __init__(self, network):
         super(NetWithLoss, self).__init__()
         self.loss = P.SmoothL1Loss()
@@ -216,6 +225,7 @@ class NetWithLoss(nn.Cell):
 
 class Grad(nn.Cell):
     """ GradWrap definition """
+
     def __init__(self, network):
         super(Grad, self).__init__()
         self.network = network
@@ -227,6 +237,7 @@ class Grad(nn.Cell):
 
 class BatchnormNet(nn.Cell):
     """ BatchnormNet definition """
+
     def __init__(self):
         super(BatchnormNet, self).__init__()
         self.conv1 = nn.Conv2d(3, 4, kernel_size=8, stride=2, pad_mode="pad", padding=3)
@@ -247,6 +258,7 @@ class BatchnormNet(nn.Cell):
 
 class NetWithLossClass(nn.Cell):
     """ NetWithLossClass definition """
+
     def __init__(self, network):
         super(NetWithLossClass, self).__init__(auto_prefix=False)
         self.loss = nn.SoftmaxCrossEntropyWithLogits()
@@ -259,12 +271,13 @@ class NetWithLossClass(nn.Cell):
 
 class BlockNet(nn.Cell):
     """ BlockNet definition """
+
     def __init__(self):
         super(BlockNet, self).__init__()
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode="pad", padding=3)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
         self.block_down_sample = ResidualBlock(
             64, 256, stride=1, down_sample=True
         )
@@ -281,6 +294,7 @@ class BlockNet(nn.Cell):
 
 class Conv2dWithBiasNet(nn.Cell):
     """ Conv2dWithBiasNet definition """
+
     def __init__(self):
         super(Conv2dWithBiasNet, self).__init__()
         self.conv = nn.Conv2d(3, 10, 1, bias_init='zeros')
@@ -292,6 +306,7 @@ class Conv2dWithBiasNet(nn.Cell):
 
 class Conv2dNativeNet(nn.Cell):
     """ Conv2dNativeNet definition """
+
     def __init__(self):
         super(Conv2dNativeNet, self).__init__()
         self.conv = P.DepthwiseConv2dNative(channel_multiplier=3, kernel_size=(3, 3))
@@ -309,6 +324,7 @@ class Conv2dNativeNet(nn.Cell):
 
 class MakeRefKeyNet(nn.Cell):
     """ MakeRefKeyNet definition """
+
     def __init__(self):
         super(MakeRefKeyNet, self).__init__()
         self.y = Parameter(Tensor([1.0], mindspore.float32), name="y")
@@ -321,6 +337,7 @@ class MakeRefKeyNet(nn.Cell):
 
 class StateNet(nn.Cell):
     """ StateTestTensor definition """
+
     def __init__(self):
         super(StateNet, self).__init__()
         weight = Tensor(np.ones([2, 1, 2, 2], np.float32))
@@ -347,6 +364,24 @@ class ComparisonNet(nn.Cell):
         return ret
 
 
+def test_max_pool_with_arg_max():
+    class NetMaxPoolWithArgMax(nn.Cell):
+        def __init__(self):
+            """ ComparisonNet definition """
+            super(NetMaxPoolWithArgMax, self).__init__()
+            self.max_pool_with_arg_max = P.MaxPoolWithArgmax(padding="valid", ksize=2, strides=1)
+
+        def construct(self, x):
+            ret = self.max_pool_with_arg_max(x)
+            return ret
+
+    x = Tensor(np.ones([1, 1, 3, 3], np.float32))
+    net = NetMaxPoolWithArgMax()
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    ret = net(x)
+    print(ret)
+
+
 test_cases = [
     ('SoftMaxGrad', {
         'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
@@ -407,7 +442,6 @@ test_cases = [
     }),
 ]
 
-
 test_cases_for_verify_exception = [
     ('Conv2d_ValueError_1', {
         'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': ValueError}),
@@ -418,39 +452,19 @@ test_cases_for_verify_exception = [
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_1', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode='sane'), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_2', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(data_mode=2), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_3', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(ceil_mode=2), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(ksize=-2), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('MaxPoolWithArgmax_ValueError_4', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad=-1), {'exception': ValueError}),
+        'block': (lambda _: P.MaxPoolWithArgmax(strides=-1), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_5', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(pad_mode="pad", pad='1'), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_6', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(window='1'), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_7', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(window=-2), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_8', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(stride=-1), {'exception': ValueError}),
-        'desc_inputs': [0],
-    }),
-    ('MaxPoolWithArgmax_ValueError_9', {
-        'block': (lambda _ : P.MaxPoolWithArgmax(alpha='1'), {'exception': ValueError}),
         'desc_inputs': [0],
     }),
     ('FusedBatchNorm_ValueError_1', {
@@ -502,19 +516,19 @@ test_cases_for_verify_exception = [
         'desc_inputs': [0],
     }),
     ('MaxPool2d_ValueError_1', {
-        'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid", padding=0), {'exception': ValueError}),
+        'block': (nn.MaxPool2d(kernel_size=120, stride=1, pad_mode="valid"), {'exception': ValueError}),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
     }),
     ('MaxPool2d_ValueError_2', {
         'block': (
-            lambda _ : nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid", padding=0),
+            lambda _: nn.MaxPool2d(kernel_size=120, stride=True, pad_mode="valid"),
             {'exception': ValueError},
         ),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
     }),
     ('MaxPool2d_ValueError_3', {
         'block': (
-            lambda _ : nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid", padding=0),
+            lambda _: nn.MaxPool2d(kernel_size=3, stride=True, pad_mode="valid"),
             {'exception': ValueError},
         ),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
@@ -532,4 +546,3 @@ def test_compile():
 @mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
 def test_check_exception():
     return test_cases_for_verify_exception
-

tests/ut/python/ops/test_ops.py

@@ -571,7 +571,7 @@ test_case_nn_ops = [
         'desc_bprop': [[3, 4, 6, 6]],
         'skip': ['backward']}),
     ('MaxPoolWithArgmax', {
-        'block': P.MaxPoolWithArgmax(window=2, stride=2),
+        'block': P.MaxPoolWithArgmax(ksize=2, strides=2),
         'desc_inputs': [[128, 32, 32, 64]],
         'desc_bprop': [[128, 32, 8, 16], [128, 32, 8, 16]]}),
     ('SoftmaxCrossEntropyWithLogits', {

tests/ut/python/ops/test_ops_check.py

@@ -160,16 +160,16 @@ test_case_check_ops = [
         'block': nn.Dense(1, 6, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))),
         'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
     ('MaxPool2d_1', {
-        'block': nn.MaxPool2d(5, pad_mode='same', padding=0),
+        'block': nn.MaxPool2d(5, pad_mode='same'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('MaxPool2d_2', {
-        'block': nn.MaxPool2d(5, pad_mode='valid', padding=0),
+        'block': nn.MaxPool2d(5, pad_mode='valid'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('AvgPool2d_1', {
-        'block': nn.AvgPool2d(5, pad_mode='same', padding=0),
+        'block': nn.AvgPool2d(5, pad_mode='same'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('AvgPool2d_2', {
-        'block': nn.AvgPool2d(5, pad_mode='valid', padding=0),
+        'block': nn.AvgPool2d(5, pad_mode='valid'),
         'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
     ('Conv2D_1', {
         'block': P.Conv2D(1, 6, pad_mode='same', pad=0),

tests/ut/python/pynative_mode/ge/ops/test_pooling.py

@@ -42,12 +42,10 @@ def test_maxpool2d():
     """ test_maxpool2d """
     kernel_size = 3
     stride = 3
-    padding = 0
 
-    max_pool = nn.MaxPool2d(kernel_size, stride, padding=padding)
+    max_pool = nn.MaxPool2d(kernel_size, stride)
     assert max_pool.kernel_size == 3
     assert max_pool.stride == 3
-    assert max_pool.padding == 0
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6]).astype(np.float32))
     output = max_pool(input_data)
     output_np = output.asnumpy()

tests/ut/python/pynative_mode/nn/test_cell.py

@@ -89,7 +89,7 @@ class ConvNet(nn.Cell):
         self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode='pad', padding=3)
         self.bn1 = nn.BatchNorm2d(ConvNet.output_ch)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='pad', padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(
             int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)),

tests/ut/python/pynative_mode/nn/test_pooling.py

@@ -49,23 +49,14 @@ def test_maxpool2d():
     """ test_maxpool2d """
     kernel_size = 3
     stride = 3
-    padding = 2
 
-    max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME', padding=padding)
+    max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME')
     assert max_pool.kernel_size == 3
     assert max_pool.stride == 3
-    assert max_pool.padding == 2
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 6, 6])*0.1)
     output = max_pool(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
 
-def test_maxpool2d_error_padding():
-    """ test_maxpool2d_error_padding """
-    kernel_size = 3.5
-    stride = 3
-    padding = 1
-    with pytest.raises(ValueError):
-        nn.MaxPool2d(kernel_size, stride, padding=padding)
 

tests/ut/python/pynative_mode/vm/test_vm.py

@@ -23,7 +23,7 @@ def test_avg_pooling():
                              [-9., -1., 3., 4.],
                              [1., -1., -3., -6.],
                              [-2., -1., -2., -15.]]]]).astype(np.float32)
-    out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    out = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     expect_out = [[[[-4.25, 0.0, 4.25],
                     [-2.5, -0.5, -0.5],
                     [-0.75, -1.75, -6.5]]]]
@@ -37,9 +37,9 @@ def test_avg_pool_grad():
                              [5, 6, 7, 8],
                              [9, 10, 11, 12],
                              [13, 14, 15, 16]]]]).astype(np.float32)
-    dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    dout = vm.avg_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     print("vm.avg_pooling dout: ", dout)
-    out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1, 0)
+    out = vm.avg_pool_grad(dout, input_data.shape, 2, 2, 1)
     print("vm.avg_pool_grad: ", out)
     assert True
 
@@ -202,7 +202,7 @@ def test_max_pooling():
         [-9., -1., 3., 4.],
         [1., -1., -3., -6.],
         [-2., -1., -2., -15.]]]]).astype(np.float32)
-    out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1, pad=0)
+    out = vm.max_pooling(input_data, pool_h=2, pool_w=2, stride=1)
     expect_out = [[[[-1., 3., 9.],
                     [1., 3., 4.],
                     [1., -1., -2.]]]]

tests/ut/python/utils/test_serialize.py

@@ -44,7 +44,7 @@ class Net(nn.Cell):
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(int(224*224*64/16), num_classes)
 

tests/vm_impl/nn_ops_vm_impl.py

@@ -19,66 +19,82 @@ from mindspore.ops.operations import _grad_ops as G
 from mindspore.common.tensor import Tensor
 from mindspore.ops.vm_impl_registry import vm_impl_registry as vm_impl_getters
 from .vm_interface import vm
+
+
 # pylint: disable=unused-argument
 
 
 @vm_impl_getters.register(P.ScalarSummary)
 def vm_impl_scalar_summary(self):
     """Generate vm_impl function for ScalarSummary"""
+
     def vm_impl(string_in, scalar):
         """Implement by vm mode."""
         return scalar
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.ReLU)
 def vm_impl_relu(self):
     """Generate vm_impl function for ReLU"""
+
     def vm_impl(x):
         x = x.asnumpy()
         output = Tensor(vm.relu(x))
         return output
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.Flatten)
 def vm_impl_flatten(self):
     """Generate vm_impl function for Flatten"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.flatten_batch(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Softmax)
 def vm_impl_softmax(self):
     """Generate vm_impl function for Softmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.softmax(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.LogSoftmax)
 def vm_impl_log_softmax(self):
     """Generate vm_impl function for LogSoftmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.logsoftmax(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Tanh)
 def vm_impl_tanh(self):
     """Generate vm_impl function for Tanh"""
+
     def vm_impl(x):
         x = x.asnumpy()
         return Tensor(vm.tanh(x))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.FusedBatchNorm)
 def vm_impl_fused_batch_norm(self):
     """Generate vm_impl function for FusedBatchNorm"""
+
     def vm_impl(x, scale, b, mean, variance):
         # pylint: disable=unused-argument
         x = x.asnumpy()
@@ -92,12 +108,14 @@ def vm_impl_fused_batch_norm(self):
                                                                       momentum=self.momentum)
         return Tensor(out), Tensor(x_mean), Tensor(x_var), \
                Tensor(running_mean), Tensor(running_var)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.BatchNorm)
 def vm_impl_batch_norm(self):
     """Generate vm_impl function for BatchNorm"""
+
     def vm_impl(x, scale, b, mean, variance):
         # pylint: disable=unused-argument
         x = x.asnumpy()
@@ -110,83 +128,106 @@ def vm_impl_batch_norm(self):
                                                                       eps=self.epsilon)
         return Tensor(out), Tensor(x_mean), Tensor(x_var), \
                Tensor(running_mean), Tensor(running_var)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Conv2D)
 def vm_impl_conv2d(self):
     """Generate vm_impl function for Conv2D"""
+
     def vm_impl(x, w):
         x = x.asnumpy()
         weight = w.asnumpy()
         bias = None
         out = vm.conv2d(x, weight, bias, self.stride, self.pad, self.dilation)
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.MaxPoolGradWithArgmax)
 def vm_impl_max_pool_grad_with_argmax(self):
     """Generate vm_impl function for MaxPoolGradWithArgmax"""
-    def vm_impl(x, argmax, dout):
+
+    def vm_impl(x, dout, argmax):
+        print("buxue")
+        print(argmax)
         x = x.asnumpy()
         dout = dout.asnumpy()
         arg_max = argmax.asnumpy()
-        dx = vm.max_pool_grad_with_argmax(x, arg_max, dout, self.pool_h, self.pool_w, self.stride, self.pad)
+        dx = vm.max_pool_grad_with_argmax(x, dout, arg_max,
+                                          self.ksize[1], self.ksize[2], self.strides[1])
         return Tensor(dx)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.MaxPoolWithArgmax)
 def vm_impl_max_pool_with_argmax(self):
     """Generate vm_impl function for MaxPoolWithArgmax"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out, out_argmax = vm.max_pool_with_argmax(x, self.pool_h, self.pool_w, self.stride, self.pad)
+        out, out_argmax = vm.max_pool_with_argmax(x, self.ksize[1], self.ksize[2], self.strides[1])
         return Tensor(out), Tensor(out_argmax)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.MaxPool)
 def vm_impl_max_pool(self):
     """Generate vm_impl function for MaxPool"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out = vm.max_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.max_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.MaxPoolGrad)
 def vm_impl_max_pool_grad(self):
     """Generate vm_impl function for MaxPoolGrad"""
+
     def vm_impl(x, out, dout):
         x = x.asnumpy()
         dout = dout.asnumpy()
-        out = vm.max_pool_grad(x, dout, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.max_pool_grad(x, dout, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.AvgPool)
-def vm_impl_max_pool(self):
+def vm_impl_avg_pool(self):
     """Generate vm_impl function for AvgPool"""
+
     def vm_impl(x):
         x = x.asnumpy()
-        out = vm.avg_pooling(x, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.avg_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.AvgPoolGrad)
 def vm_impl_avg_pool_grad(self):
     """Generate vm_impl function for AvgPoolGrad"""
+
     def vm_impl(dout, origin_shape):
         dout = dout.asnumpy()
-        out = vm.avg_pool_grad(dout, origin_shape, self.pool_h, self.pool_w, self.stride_h, self.pad)
+        out = vm.avg_pool_grad(dout, origin_shape, self.ksize[-2], self.ksize[-1], self.strides[-2])
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.FusedBatchNormGrad)
 def vm_impl_fused_batch_norm_grad(self):
     """Generate vm_impl function for FusedBatchNormGrad"""
+
     def vm_impl(dy, x, scale, save_mean, save_inv_variance):
         dy = dy.asnumpy()
         x = x.asnumpy()
@@ -195,11 +236,14 @@ def vm_impl_fused_batch_norm_grad(self):
         save_inv_variance = save_inv_variance.asnumpy()
         dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
         return (Tensor(dx), Tensor(dscale), Tensor(dshift))
+
     return vm_impl
 
+
 @vm_impl_getters.register(G.BatchNormGrad)
 def vm_impl_fused_batch_norm_grad(self):
     """Generate vm_impl function for BatchNormGrad"""
+
     def vm_impl(dy, x, scale, save_mean, save_inv_variance):
         dy = dy.asnumpy()
         x = x.asnumpy()
@@ -208,87 +252,103 @@ def vm_impl_fused_batch_norm_grad(self):
         save_inv_variance = save_inv_variance.asnumpy()
         dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
         return (Tensor(dx), Tensor(dscale), Tensor(dshift))
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.ReluGrad)
 def vm_impl_relu_grad(self):
     """Generate vm_impl function for ReluGrad"""
+
     def vm_impl(y_backprop, x):
         x = x.asnumpy()
         y_backprop = y_backprop.asnumpy()
         y_backprop = vm.relu_grad(x.copy()) * y_backprop
         return Tensor(y_backprop)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.Conv2DBackpropInput)
 def vm_impl_conv2d_backprop_input(self):
     """Generate vm_impl function for Conv2DBackpropInput"""
+
     def vm_impl(dout, w, x_size):
         dout = dout.asnumpy()
         w = w.asnumpy()
         dx = vm.conv2d_backprop_input(dout, x_size, w, self.stride, self.pad)
         return Tensor(dx)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.Conv2DBackpropFilter)
 def vm_impl_conv2d_backprop_filter(self):
     """Generate vm_impl function for Conv2DBackpropFilter"""
+
     def vm_impl(dout, x, w_size):
         x = x.asnumpy()
         dout = dout.asnumpy()
         dw = vm.conv2d_backprop_filter(dout, x, w_size, self.stride, self.pad)
         return Tensor(dw)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.FlattenGrad)
 def vm_impl_flatten_grad(self):
     """Generate vm_impl function for FlattenGrad"""
+
     def vm_impl(dout, x):
         dout = dout.asnumpy()
         dout = vm.flatten_grad(dout, x)
         return Tensor(dout)
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.BiasAdd)
 def vm_impl_bias_add(self):
     """Generate vm_impl function for BiasAdd"""
+
     def vm_impl(wx, bias):
         wx = wx.asnumpy()
         bias = bias.asnumpy()
         out = wx + bias
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.BiasAddGrad)
 def vm_impl_bias_add_grad(self):
     """Generate vm_impl function for BiasAddGrad"""
+
     def vm_impl(dout):
         dout = dout.asnumpy()
         shape = np.shape(dout)
         return Tensor(np.add.reduce(dout, axis=tuple(range(len(shape) - 1))))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.SoftmaxCrossEntropyWithLogits)
 def vm_impl_softmax_cross_entropy_with_logits(self):
     """Generate vm_impl function for SoftmaxCrossEntropyWithLogits"""
+
     def vm_impl(logits, labels):
         logits = logits.asnumpy()
         labels = labels.asnumpy()
         loss, dx = vm.softmax_cross_entropy_with_logits(logits, labels)
         return (Tensor(np.array(loss)), Tensor(dx))
+
     return vm_impl
 
 
 @vm_impl_getters.register(P.SparseSoftmaxCrossEntropyWithLogits)
 def vm_impl_sparse_softmax_cross_entropy_with_logits(self):
     """Generate vm_impl function for SparseSoftmaxCrossEntropyWithLogits"""
+
     def vm_impl(logits, labels):
         logits = logits.asnumpy()
         labels = labels.asnumpy()
@@ -301,11 +361,14 @@ def vm_impl_sparse_softmax_cross_entropy_with_logits(self):
         if self.is_grad:
             return (Tensor(dx),)
         return (Tensor(np.array(loss)),)
+
     return vm_impl
 
+
 @vm_impl_getters.register(P.ApplyMomentum)
 def vm_impl_momentum(self):
     """Generate vm_impl function for Momentum"""
+
     def vm_impl(variable,
                 accumulation,
                 learning_rate,
@@ -327,19 +390,24 @@ def vm_impl_momentum(self):
 
     return vm_impl
 
+
 @vm_impl_getters.register(P.ResizeBilinear)
 def vm_impl_resize_bilinear(self):
     """Generate vm_impl function for ResizeBilinear"""
+
     def vm_impl(x):
         out = vm.ResizeBilinear(x)
         return Tensor(out)
+
     return vm_impl
 
 
 @vm_impl_getters.register(G.ResizeBilinearGrad)
 def vm_impl_resize_bilinear_grad(self):
     """Generate vm_impl function for ResizeBilinearGrad"""
+
     def vm_impl(dout, original_image):
         out = vm.ResizeBilinearGrad(dout, original_image)
         return Tensor(out)
+
     return vm_impl

tests/vm_impl/vm_me.py

@@ -19,7 +19,7 @@ from mindspore._checkparam import Rel
 from mindspore._checkparam import ParamValidator as validator
 
 
-def avg_pooling(x, pool_h, pool_w, stride, pad):
+def avg_pooling(x, pool_h, pool_w, stride):
     """
     Applies average pooling over an input array.
 
@@ -28,26 +28,25 @@ def avg_pooling(x, pool_h, pool_w, stride, pad):
         pool_h (int): Height of the pooling window.
         pool_w (int): Width of the pooling window.
         stride (int): The stride of the sliding window.
-        pad (int): Padding to be added on height and width.
 
     Returns:
         numpy.ndarray, an output array after applying average pooling on input array.
     """
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
+    out_h = (height - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
+    out_w = (width - pool_w)//stride + 1
 
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
 
     out = np.mean(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
 
     return out
 
 
-def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad):
+def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride):
     """
     Gets grad of average pooling.
 
@@ -57,7 +56,6 @@ def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride, pad):
         pool_h (int): Height of the pooling window.
         pool_w (int): Width of the pooling window.
         stride (int): The stride of the sliding window.
-        pad (int): Padding to be added on height and width.
 
     Returns:
         numpy.ndarray, grad of avgerage pooling.
@@ -324,38 +322,38 @@ def matmul(x, w, b=None):
     return y
 
 
-def max_pooling(x, pool_h, pool_w, stride, pad):
+def max_pooling(x, pool_h, pool_w, stride):
     """Max pooling."""
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
+    out_h = (height - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
+    out_w = (width - pool_w)//stride + 1
 
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
 
     out = np.max(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
 
     return out
 
 
-def max_pool_grad(x, dout, pool_h, pool_w, stride, pad):
+def max_pool_grad(x, dout, pool_h, pool_w, stride):
     """Grad of max pooling."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
     dmax = np.zeros((dout.size, pool_size))
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
     arg_max = np.argmax(col, axis=1)
     dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
     dmax = dmax.reshape(dout.shape + (pool_size,))
     dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1)
-    dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad)
+    dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
     return dx
 
 
-def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad):
+def max_pool_grad_with_argmax(x, dout, arg_max, pool_h, pool_w, stride):
     """Grad of max pooling with argmax."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
@@ -363,22 +361,22 @@ def max_pool_grad_with_argmax(x, arg_max, dout, pool_h, pool_w, stride, pad):
     dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
     dmax = dmax.reshape(dout.shape + (pool_size,))
     dcol = dmax.reshape(dmax.shape[0]*dmax.shape[1]*dmax.shape[2], -1)
-    dx = col2im(dcol, x.shape, pool_h, pool_w, stride, pad)
+    dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
     return dx
 
 
-def max_pool_with_argmax(x, pool_h, pool_w, stride, pad):
+def max_pool_with_argmax(x, pool_h, pool_w, stride):
     """Max pooling with argmax."""
     validator.check_integer("stride", stride, 0, Rel.GT)
     num, channel, height, width = x.shape
-    out_h = (height + 2*pad - pool_h)//stride + 1
+    out_h = (height - pool_h)//stride + 1
-    out_w = (width + 2*pad - pool_w)//stride + 1
+    out_w = (width - pool_w)//stride + 1
-    col = im2col(x, pool_h, pool_w, stride, pad)
+    col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h*pool_w)
     out = np.max(col, axis=1)
     out_argmax = np.argmax(col, axis=1)
-    out = out.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
-    out_argmax = out_argmax.reshape(num, out_h, out_w, channel).transpose(0, 3, 1, 2)
+    out_argmax = out_argmax.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
     return out, out_argmax