deal with comments

x2paddle/op_mapper/tf2paddle/tf_op_mapper.py

@@ -1622,18 +1622,27 @@ class TFOpMapper():
         #zero-pad
         constant_values = 0
         pad_name = gen_name("space_to_batch", "pad")
-        paddings = [0, 0] + paddings + [0, 0]
+        paddings = [0, 0, 0, 0] + paddings
         self.paddle_graph.add_layer(
-            kernel="paddle.nn.functional.pad",
+            kernel="paddle.transpose",
             inputs={"x": input_name},
+            outputs=[input_name + "_transpose"],
+            perm=[0, 3, 1, 2])
+        self.paddle_graph.add_layer(
+            kernel="paddle.nn.functional.pad",
+            inputs={"x": input_name + "_transpose"},
             outputs=[pad_name],
             pad=paddings,
-            value=constant_values,
-            data_format=string('NHWC'))
+            value=constant_values)
+        self.paddle_graph.add_layer(
+            kernel="paddle.transpose",
+            inputs={"x": pad_name},
+            outputs=[pad_name + "_transpose"],
+            perm=[0, 2, 3, 1])
         #reshape
         n, h, w, c = x.out_shapes[0]
-        h = h + paddings[2] + paddings[3]
-        w = w + paddings[4] + paddings[5]
+        h = h + paddings[4] + paddings[5]
+        w = w + paddings[6] + paddings[7]
         shape = [
             n, h // block_shape[0], block_shape[0], w // block_shape[1],
             block_shape[1], c
@@ -1641,7 +1650,7 @@ class TFOpMapper():
         reshape_name = gen_name("space_to_batch", "reshape")
         self.paddle_graph.add_layer(
             kernel="paddle.reshape",
-            inputs={"x": pad_name},
+            inputs={"x": pad_name + "_transpose"},
             outputs=[reshape_name],
             shape=shape)
         #transpose