add pooling round_mode

x2paddle/decoder/caffe.proto

@@ -1378,6 +1378,11 @@ message PoolingParameter {
   // If global_pooling then it will pool over the size of the bottom by doing
   // kernel_h = bottom->height and kernel_w = bottom->width
   optional bool global_pooling = 12 [default = false];
+  enum RoundMode {
+    CEIL = 0;
+    FLOOR = 1;
+  }
+  optional RoundMode round_mode = 13 [default = CEIL];
 }
 
 message PowerParameter {

x2paddle/decoder/caffe_shape_inference.py

@@ -115,6 +115,12 @@ def shape_pooling(layer, input_shape):
         method = math.ceil
     else:
         method = math.floor
+    if not hasattr(params, 'ceil_mode'):
+        round_mode = getattr(params, 'round_mode', 0)
+        if round_mode == 1:
+            method = math.floor
+        else:
+            method = math.ceil
     return get_strided_kernel_output_shape(params, input_shape[0], method)
 
 
@@ -240,7 +246,9 @@ def shape_reshape(layer, input_shape):
     params = layer.reshape_param
     axis = params.axis if hasattr(params, 'axis') else 0
     num_axes = params.num_axes if hasattr(params, 'num_axes') else -1
+    is_unknow_batch = False
     if inshape[0] == -1:
+        is_unknow_batch = True
         inshape[0] = 1
     input_count = count(inshape)
 
@@ -310,6 +318,7 @@ def shape_reshape(layer, input_shape):
     output_count = count(output_shape)
     assert output_count == input_count, "[Reshape]output count[%d] must match input count[%d]" % (
         output_count, input_count)
+    if is_unknow_batch:
         output_shape[0] = -1
     return [output_shape]
 

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

@@ -358,7 +358,9 @@ class CaffeOpMapper(OpMapper):
         output_name = node.layer_name
         layer_outputs = [pool2d_name, output_name]
         params = node.layer.pooling_param
-        ceil_mode = getattr(params, "ceil_mod", True)
+        ceil_mode = getattr(params, "ceil_mode", True)
+        if not hasattr(params, 'ceil_mode'):
+            ceil_mode = True if getattr(params, "round_mode", 0) == 0 else False
         global_pool = getattr(params, "global_pooling", False)
         kernel_default = [1, 1]
         channel, kernel, stride, pad, dilation, group = _get_kernel_parameters(
@@ -635,7 +637,7 @@ class CaffeOpMapper(OpMapper):
                     "paddle.scale",
                     inputs={"x": input1_name},
                     outputs=[node.layer_name + '_mul1'],
-                    scale=coeff[2])
+                    scale=coeff[1])
                 inputs_dict = {}
                 inputs_dict['x'] = node.layer_name + '_mul0'
                 inputs_dict['y'] = node.layer_name + '_mul1'
@@ -972,12 +974,12 @@ class CaffeOpMapper(OpMapper):
         # operation = MEAN
         else: 
             layer_attrs = {
-                "dim": dim[axis:],
-                "keep_dim": False,
+                "axis": dim[axis:],
+                "keepdim": False,
             }
             self.paddle_graph.add_layer(
                 "paddle.mean",
-                inputs={"input": input.name},
+                inputs={"x": input.name},
                 outputs=[node.layer_name],
                 **layer_attrs)
         self.paddle_graph.add_layer(

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -395,6 +395,8 @@ class CaffeOpMapper(OpMapper):
     def Pooling(self, node):
         params = node.layer.pooling_param
         ceil_mode = getattr(params, 'ceil_mode', True)
+        if not hasattr(params, 'ceil_mode'):
+            ceil_mode = True if getattr(params, "round_mode", 0) == 0 else False
         global_pool = getattr(params, 'global_pooling', False)
         kernel_default = [1, 1]
         channel, kernel, stride, pad, dilation, group = _get_kernel_parameters(
@@ -679,7 +681,7 @@ class CaffeOpMapper(OpMapper):
                     "paddle.scale",
                     inputs={"x": input1_name},
                     outputs=[node.name + '_mul1'],
-                    scale=coeff[2])
+                    scale=coeff[1])
                 inputs_dict = {}
                 inputs_dict['x'] = node.name + '_mul0'
                 inputs_dict['y'] = node.name + '_mul1'
@@ -1024,12 +1026,12 @@ class CaffeOpMapper(OpMapper):
         # operation = MEAN
         else: 
             layer_attrs = {
-                "dim": dim[axis:],
-                "keep_dim": False,
+                "axis": dim[axis:],
+                "keepdim": False,
             }
             self.paddle_graph.add_layer(
                 "paddle.mean",
-                inputs={"input": input.name},
+                inputs={"x": input.name},
                 outputs=[node.name],
                 **layer_attrs)
         self.paddle_graph.add_layer(