clean fluid

x2paddle/op_mapper/caffe2paddle/caffe_custom_layer/detectionoutput.py

@@ -34,7 +34,7 @@ class DetectionOutput(object):
         pbv = priorbox_list[1]
         pb = paddle.reshape(x=pb, shape=[-1, 4])
         pbv = paddle.reshape(x=pbv, shape=[-1, 4])
-        pb_dim = fluid.layers.shape(pb)[0]
+        pb_dim = paddle.shape(pb)[0]
         loc = paddle.reshape(x0, shape=[-1, pb_dim, 4])
         conf_flatten = paddle.reshape(x1, shape=[0, pb_dim, -1])
         out = fluid.layers.detection_output(

x2paddle/op_mapper/caffe2paddle/caffe_custom_layer/normalize.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import paddle
-import paddle.fluid as fluid
 
 
 class Normalize(object):
@@ -21,7 +20,7 @@ class Normalize(object):
         self.axis = axis
 
     def __call__(self, x, param):
-        l2_norm = fluid.layers.l2_normalize(x=x, axis=1)
+        l2_norm = paddle.norm(x=x, p=2, axis=1)
         param = paddle.reshape(param, [param.shape[-1]])
         perm = list(range(len(l2_norm.shape)))
         perm.pop(self.axis)

x2paddle/op_mapper/caffe2paddle/caffe_custom_layer/select.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import paddle
-import paddle.fluid as fluid
 
 
 class Select(object):

x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py

@@ -435,7 +435,7 @@ class CaffeOpMapper():
             "beta": params.beta,
         }
         self.paddle_graph.add_layer(
-            "paddle.fluid.layers.lrn",
+            "paddle.nn.LocalResponseNorm",
             inputs={"input": input.name},
             outputs=[node.layer_name],
             **layer_attrs)

x2paddle/op_mapper/prim2code.py

@@ -612,7 +612,7 @@ def prim_shape_dim(layer,
                    forward_func=[],
                    layer_id=None,
                    different_attrs=None):
-    line = "{} = fluid.layers.shape({})[{}]".format(
+    line = "{} = paddle.shape({})[{}]".format(
         layer.outputs[0],
         get_value(layer, "input", different_attrs),
         get_value(layer, "dim", different_attrs))

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -6025,7 +6025,7 @@ def aten_upsample_bilinear2d(mapper, graph, node):
             inputs={"input": inputs_name[1]},
             outputs=[inputs_name[1] + "_isinstance"],
             scope_name=scope_name,
-            cls="paddle.fluid.Variable")
+            cls="paddle.static.Variable")
         # TODO(syf): paddle.Variable
         graph.add_layer(
             "prim.if", {"input": inputs_name[1] + "_isinstance"},
@@ -6103,7 +6103,7 @@ def aten_upsample_nearest2d(mapper, graph, node):
             inputs={"input": inputs_name[1]},
             outputs=[inputs_name[1] + "_isinstance"],
             scope_name=scope_name,
-            cls="paddle.fluid.Variable")
+            cls="paddle.static.Variable")
         # TODO(syf): paddle.Variable
         graph.add_layer(
             "prim.if", {"input": inputs_name[1] + "_isinstance"},

x2paddle/op_mapper/pytorch2paddle/pytorch_custom_layer/instance_norm.py

@@ -14,7 +14,7 @@
 
 import paddle
 from paddle.nn.functional import instance_norm
-from paddle.fluid.initializer import Constant
+from paddle.nn.initializer import Constant
 
 
 class InstanceNorm(paddle.nn.Layer):

x2paddle/optimizer/fusion/interpolate_bilinear_fuser.py

@@ -46,7 +46,7 @@ class InterpolateBilinearFuser(FuseBase):
                 if x2271 :
                     x2274 = x2197[0]
                     x2275 = x2197[1]
-                    x2233_isinstance = isinstance(x2233, paddle.fluid.Variable)
+                    x2233_isinstance = isinstance(x2233, paddle.static.Variable)
                     if x2233_isinstance :
                         x2233 = x2233.numpy().tolist()
                     x2276 = paddle.nn.functional.interpolate(x=x2181, size=x2233, scale_factor=x2274, align_corners=False, align_mode=0, mode='bilinear')
@@ -146,7 +146,7 @@ class InterpolateBilinearFuser(FuseBase):
             "prim.isinstance",
             inputs={"input": "interpolate-input-3"},
             outputs=["interpolate-input-0_isinstance"],
-            cls="paddle.fluid.Variable")
+            cls="paddle.static.Variable")
         pattern_block_block.add_layer(
             "prim.if", {"input": "interpolate-input-0_isinstance"},
             outputs=["interpolate-input-0_if1"])

x2paddle/utils.py

@@ -103,15 +103,7 @@ class PaddleDtypes():
             self.t_int64 = paddle.int64
             self.t_bool = paddle.bool
         else:
-            self.t_float16 = "paddle.fluid.core.VarDesc.VarType.FP16"
-            self.t_float32 = "paddle.fluid.core.VarDesc.VarType.FP32"
-            self.t_float64 = "paddle.fluid.core.VarDesc.VarType.FP64"
-            self.t_uint8 = "paddle.fluid.core.VarDesc.VarType.UINT8"
-            self.t_int8 = "paddle.fluid.core.VarDesc.VarType.INT8"
-            self.t_int16 = "paddle.fluid.core.VarDesc.VarType.INT16"
-            self.t_int32 = "paddle.fluid.core.VarDesc.VarType.INT32"
-            self.t_int64 = "paddle.fluid.core.VarDesc.VarType.INT64"
-            self.t_bool = "paddle.fluid.core.VarDesc.VarType.BOOL"
+            raise Exception("Paddle>=2.0.0 is required, Please update version!")
 
 
 is_new_version = check_version()