fixed SpaceToBatchND, Conv2DBackpropFilter

mindspore/ops/_op_impl/tbe/batch_to_space_nd.py

@@ -25,8 +25,8 @@ batch_to_space_nd_op_info = TBERegOp("BatchToSpaceND") \
     .partial_flag(True) \
     .attr("block_shape", "required", "listInt", "all") \
     .attr("crops", "required", "listListInt", "all") \
-    .input(0, "x", False, "required", "all") \
-    .output(0, "y", False, "required", "all") \
+    .input(0, "x", False, "required", "all", reshape_type="NH") \
+    .output(0, "y", False, "required", "all", reshape_type="NH") \
     .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py

@@ -27,6 +27,8 @@ conv2d_backprop_filter_op_info = TBERegOp("Conv2DBackpropFilter") \
     .attr("stride", "required", "listInt", "all") \
     .attr("pad_list", "required", "listInt", "all") \
     .attr("dilation", "required", "listInt", "all") \
+    .attr("groups", "optional", "int", "all") \
+    .attr("data_format", "optional", "str", "all") \
     .input(0, "out_backprop", False, "required", "all") \
     .input(1, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \

mindspore/ops/_op_impl/tbe/space_to_batch_nd.py

@@ -25,8 +25,8 @@ space_to_batch_nd_op_info = TBERegOp("SpaceToBatchND") \
     .partial_flag(True) \
     .attr("block_shape", "required", "listInt", "all") \
     .attr("paddings", "required", "listListInt", "all") \
-    .input(0, "x", False, "required", "all") \
-    .output(0, "y", False, "required", "all") \
+    .input(0, "x", False, "required", "all", reshape_type="NH") \
+    .output(0, "y", False, "required", "all", reshape_type="NH") \
     .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
     .get_op_info()

mindspore/ops/operations/_grad_ops.py

@@ -237,6 +237,7 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
             self.add_prim_attr('stride', self.stride)
         self.dilation = dilation
         self.group = group
+        self.add_prim_attr('groups', group)
         self.add_prim_attr('data_format', "NCHW")
 
     def __infer__(self, doutput, x, w_size):

mindspore/ops/operations/array_ops.py

@@ -2635,16 +2635,20 @@ class SpaceToBatchND(PrimitiveWithInfer):
 
     def infer_shape(self, x_shape):
         x_rank = len(x_shape)
+        validator.check_integer('x_shape rank', x_rank, 4, Rel.EQ, self.name)
         out_shape = copy.deepcopy(x_shape)
 
         block_shape_prod = 1
-        for i in range(x_rank - 2):
-            padded = out_shape[i + 2] + self.paddings[i][0] + \
+        offset = 2
+        if x_rank < 4:
+            offset = 1
+        for i in range(len(self.block_shape)):
+            padded = out_shape[i + offset] + self.paddings[i][0] + \
                      self.paddings[i][1]
             if padded % self.block_shape[i] != 0:
                 raise ValueError(f'For \'{self.name}\' padded[{i}] {padded} should be divisible by '
                                  f'block_shape[{i}] {self.block_shape[i]}')
-            out_shape[i + 2] = padded // self.block_shape[i]
+            out_shape[i + offset] = padded // self.block_shape[i]
             block_shape_prod = block_shape_prod * self.block_shape[i]
         out_shape[0] *= block_shape_prod
         return out_shape
@@ -2715,15 +2719,19 @@ class BatchToSpaceND(PrimitiveWithInfer):
 
     def infer_shape(self, x_shape):
         x_rank = len(x_shape)
+        validator.check_integer('x_shape rank', x_rank, 4, Rel.EQ, self.name)
         out_shape = copy.deepcopy(x_shape)
 
         block_shape_prod = 1
-        for i in range(x_rank - 2):
+        offset = 2
+        if x_rank < 4:
+            offset = 1
+        for i in range(len(self.block_shape)):
             block_shape_prod = block_shape_prod * self.block_shape[i]
-            x_block_prod = out_shape[i + 2] * self.block_shape[i]
+            x_block_prod = out_shape[i + offset] * self.block_shape[i]
             crops_sum = self.crops[i][0] + self.crops[i][1]
             validator.check("x block shape prod", x_block_prod, 'crops sum', crops_sum, Rel.GT, self.name)
-            out_shape[i + 2] = x_block_prod - crops_sum
+            out_shape[i + offset] = x_block_prod - crops_sum
 
         if out_shape[0] % block_shape_prod != 0:
             raise ValueError(f'For \'{self.name}\' input_x dimension 0 {out_shape[0]}  should be divisible by '