Merge remote-tracking branch 'upstream/develop' into rm_fluid

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -1097,6 +1097,12 @@ class OpSet9():
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         starts, ends, axes, steps = None, None, None, None
         layer_attrs = {}
+        if val_x.dtype == 'uint8':
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": val_x.name},
+                outputs=[val_x.name],
+                dtype=string('int32'))
         if len(node.inputs) > 1:
             starts = self.graph.get_input_node(node, idx=1, copy=True)
             ends = self.graph.get_input_node(node, idx=2, copy=True)
@@ -1125,7 +1131,8 @@ class OpSet9():
                 starts_value = starts_value.copy()
                 ends_value = ends_value.copy()
                 for idx in range(len(ends_value)):
-                    if starts_value[idx] >= val_x.out_shapes[0][axes[
+                    if len(val_x.out_shapes[0]) != 0 and starts_value[
+                            idx] >= val_x.out_shapes[0][axes[
                                 idx]] and val_x.out_shapes[0][axes[idx]] > 0:
                         starts_value[idx] = val_x.out_shapes[0][axes[idx]] - 1
                         ends_value[idx] = val_x.out_shapes[0][axes[idx]]
@@ -1182,6 +1189,12 @@ class OpSet9():
                 inputs={"input": val_x.name},
                 outputs=[node.name],
                 **layer_attrs)
+        if val_x.dtype == 'uint8':
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                dtype=string('uint8'))
 
     @print_mapping_info
     def ConstantOfShape(self, node):
@@ -1812,7 +1825,11 @@ class OpSet9():
     def Squeeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
-        if len(val_x.out_shapes[0]) == 1:
+        if axes is None:
+            axes_node = self.graph.get_input_node(node, idx=1, copy=True)
+            axes = _const_weight_or_none(axes_node, necessary=True)
+        # deal with scalar(0D) tensor
+        if len(val_x.out_shapes[0]) <= 1 and len(axes) == 1 and axes[0] == 0:
             self.paddle_graph.add_layer(
                 "paddle.cast",
                 inputs={"x": val_x.name},