PyTorch convertor for Seg SwimTransformer (#637)

* Update stargan.md

* fix the paddle_type

* add docs

* add docs

* add acknowledge

* fix the docs

* fix the docs

* add docs

* fix

* add docs

* add docs

* Update README.md

* fix onnx inputs

* fix

* fix

* remove

* remove numpy input

* add for seg swin transformer

* add pad

* add pad

* fix onnx

Author: SunAhong1993

x2paddle/core/program.py

@@ -109,7 +109,7 @@ def add_layer(self, kernel, inputs, outputs, scope_name="", **kwargs):
         layer = PaddleLayer(
             layer_id, kernel, inputs, outputs, scope_name=scope_name, **kwargs)
         self.layers[layer_id] = layer
-        if layer.kernel in ["prim.list_unpack" or "prim.tuple_unpack"]:
+        if layer.kernel in ["prim.list_unpack" , "prim.tuple_unpack"]:
             self.has_unpack = True
         return layer_id
 

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -1514,7 +1514,7 @@ def PRelu(self, node):
                 "paddle.minimum",
                 inputs={"x": val_x.name,
                         "y": output_name + "__zeros"},
-                outputs=[output_name + "__max"])
+                outputs=[output_name + "__min"])
             self.paddle_graph.add_layer(
                 "paddle.multiply",
                 inputs={"x": val_slope.name,

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -983,23 +983,31 @@ def aten_constant_pad_nd(mapper, graph, node):
     mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
                         scope_name)
     layer_inputs["input"] = inputs_name[0]
+    # 处理输入1，即%4876
+    is_padding_tensor = False
+    if inputs_name[1] in mapper.attrs:
+        layer_attrs["padding"] = mapper.attrs[inputs_name[1]]
+    else:
+        mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
+        layer_inputs["pad"] = inputs_name[1]
+        is_padding_tensor = True
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
-    # 处理输入1，即%4876
-    layer_attrs["padding"] = mapper.attrs[inputs_name[1]]
     # 处理输入2，即%42
     layer_attrs["value"] = mapper.attrs[inputs_name[2]]
 
-    graph.add_layer(
-        "prim.shape",
-        inputs={"input": inputs_name[0]},
-        outputs=[inputs_name[0] + "_shape"],
-        scope_name=scope_name)
-    graph.add_layer(
-        "prim.len",
-        inputs={"input": inputs_name[0] + "_shape"},
-        outputs=[inputs_name[0] + "_len"],
-        scope_name=scope_name)
+    if not is_padding_tensor:
+        graph.add_layer(
+            "prim.shape",
+            inputs={"input": inputs_name[0]},
+            outputs=[inputs_name[0] + "_shape"],
+            scope_name=scope_name)
+        graph.add_layer(
+            "prim.len",
+            inputs={"input": inputs_name[0] + "_shape"},
+            outputs=[inputs_name[0] + "_len"],
+            scope_name=scope_name)
 
     def add_pad_layers(kernel, dim):
         graph.add_layer(
@@ -1020,6 +1028,7 @@ def add_pad_layers(kernel, dim):
             inputs={"y": inputs_name[0] + "_len"},
             outputs=[inputs_name[0] + "_len0"],
             scope_name=scope_name,
+            alpha=1.0,
             x=dim)
         block.add_layer(
             "prim.len2list",
@@ -1058,17 +1067,25 @@ def add_pad_layers(kernel, dim):
         if_layer.inputs["input-0"] = inputs_name[0]
         if_layer.inputs["input-1"] = inputs_name[0] + "_len"
 
-    if len(layer_attrs["padding"]) == 2:
-        layer_outputs[0] = layer_outputs[0].raplace("pad", "pad1d")
-        add_pad_layers("paddle.nn.Pad1D", 3)
-    elif len(layer_attrs["padding"]) == 4:
-        layer_outputs[0] = layer_outputs[0].raplace("pad", "pad2d")
-        add_pad_layers("paddle.nn.Pad2D", 4)
-    elif len(layer_attrs["padding"]) == 6:
-        layer_outputs[0] = layer_outputs[0].raplace("pad", "pad3d")
-        add_pad_layers("paddle.nn.Pad3D", 5)
+    if not is_padding_tensor:
+        if len(layer_attrs["padding"]) == 2:
+            layer_outputs[0] = layer_outputs[0].replace("pad", "pad1d")
+            add_pad_layers("paddle.nn.Pad1D", 3)
+        elif len(layer_attrs["padding"]) == 4:
+            layer_outputs[0] = layer_outputs[0].replace("pad", "pad2d")
+            add_pad_layers("paddle.nn.Pad2D", 4)
+        elif len(layer_attrs["padding"]) == 6:
+            layer_outputs[0] = layer_outputs[0].replace("pad", "pad3d")
+            add_pad_layers("paddle.nn.Pad3D", 5)
+        else:
+            raise Exception("The lenght of padding list must be 2, 4 or 6!")
     else:
-        raise Exception("The lenght of padding list must be 2, 4 or 6!")
+        graph.add_layer(
+            "custom_layer:Pad",
+            inputs=layer_inputs,
+            outputs=[output_name],
+            scope_name=scope_name,
+            **layer_attrs)
     return current_inputs, current_outputs
 
 
@@ -4191,10 +4208,45 @@ def aten_relu6(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_remainder(mapper, graph, node):
+    """ 构造取余数的PaddleLayer。
+    TorchScript示例:
+        %701 : Tensor = aten::remainder(%661, %139)
+        参数含义:
+        %701 (Tensor): 输出，取余结果的Tensor。
+        %661 (Tensor): 需要取余的Tensor。
+        %139 (Tensor): 除数Tensor。
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    inputs_name, inputs_node = mapper._get_inputs_name(node)
+    # 获取当前节点输出的list
+    current_outputs = [output_name]
+    # 处理输入0，即%661
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
+                        scope_name)
+    layer_inputs["x"] = inputs_name[0]
+    # 处理输入1，即%139
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
+    layer_inputs["y"] = inputs_name[1]
+    # 获取当前节点输入、输出的list
+    current_inputs = list(layer_inputs.values())
+    
+    graph.add_layer(
+        "prim.remainder",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name)
+    return current_inputs, current_outputs
+
+
 def aten_repeat(mapper, graph, node):
     """ 构造根据参数对输入各维度进行复制的PaddleLayer。
     TorchScript示例:
-        701 : Tensor = aten::repeat(%699, %700)
+        %701 : Tensor = aten::repeat(%699, %700)
         参数含义:
         %701 (Tensor): 输出，复制后的Tensor。
         %699 (Tensor): 需要复制的Tensor。

x2paddle/op_mapper/pytorch2paddle/prim2code.py

@@ -609,6 +609,21 @@ def prim_or(layer,
     if is_return_line:
         return line.split(" = ")[1]
     forward_func.extend(gen_codes([line], indent=indent))
+    
+    
+def prim_remainder(layer,
+                   indent=1,
+                   init_func=[],
+                   forward_func=[],
+                   layer_id=None,
+                   different_attrs=None,
+                   is_return_line=False):
+    line = "{} = {} % {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs),
+                                  get_value(layer, "y", different_attrs))
+    if is_return_line:
+        return line.split(" = ")[1]
+    forward_func.extend(gen_codes([line], indent=indent))
 
 
 def prim_replaceitem(layer,

x2paddle/op_mapper/pytorch2paddle/pytorch_custom_layer/__init__.py

@@ -14,3 +14,4 @@
 
 from .gather import Gather
 from .instance_norm import InstanceNorm
+from .pad import Pad

x2paddle/op_mapper/pytorch2paddle/pytorch_custom_layer/pad.py

@@ -0,0 +1,48 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+from x2paddle.core.util import *
+
+
+class Pad(object):
+    def __init__(self, value):
+        self.value = value
+
+    def __call__(self, input, pad):
+        shape = input.shape
+        dim = len(shape)
+        if len(pad) == 2:
+            data_format = "NCL"
+        elif len(pad) == 4:
+            data_format = "NCHW"
+        elif len(pad) == 6:
+            data_format = "NCDHW"
+        if dim == 3 and len(pad) == 4:
+            input = paddle.unsqueeze(input, [0])
+            output = paddle.nn.functional.pad(input,
+                                              pad,
+                                              data_format=data_format)
+            output = paddle.squeeze(output, [0])
+        elif dim == 4 and len(pad) == 6:
+            input = paddle.unsqueeze(input, [0])
+            output = paddle.nn.functional.pad(input,
+                                              pad,
+                                              data_format=data_format)
+            output = paddle.squeeze(output, [0])
+        else:
+            output = paddle.nn.functional.pad(input,
+                                              pad,
+                                              data_format=data_format)
+        return output