For Paddle2.1 (#596)

* Update stargan.md

* fix the type

* fix

* for 2.0.0

* fix

docs/pytorch_project_convertor/after_convert.md

@@ -24,19 +24,18 @@ class VocDataset(paddle.io.Dataset):
     ...
 ```
 
-3. 若存在Tensor对比操作（包含==、!=、<、<=、>、>=操作符）,在对比操作符前添加对Tensor类型的判断，如果为bool型强转为int型，并在对比后转换回bool型。
+3. 若存在Tensor对比操作（包含==、!=、<、<=、>、>=操作符）,在对比操作符前添加对Tensor类型的判断，如果为非bool型强转为bool型，并在对比后转换回bool型。
 
 ```
-# 原始代码（其中c_trg是Tensor）
+# 原始代码（其中c_trg是非bool型的Tensor）
 c_trg = c_trg == 0
 # 替换后代码
-is_bool = False
-if str(c_trg.dtype) == "VarType.BOOL":
-    c_trg = c_trg.cast("int32")
-    is_bool = True
-c_trg = c_trg == 0
-if is_bool:
-    c_trg = c_trg.cast("bool")
+c_trg = c_trg.cast("int32")
+c_trg_tmp = paddle.zeros_like(c_trg)
+paddle.assign(c_trg, c_trg_tmp)
+c_trg_tmp = c_trg_tmp.cast("bool")
+c_trg_tmp[:, i] = c_trg[:, i] == 0
+c_trg = c_trg_tmp 
 ```
 
 4. 如若转换后的运行代码的入口为sh脚本文件，且其中有预训练模型路径，应将其中的预训练模型的路径字符串中的“.pth”、“.pt”、“.ckpt”替换为“.pdiparams”。

docs/pytorch_project_convertor/demo/stargan.md

@@ -80,17 +80,15 @@ class Solver(object):
                         if j != i:
                             c_trg[:, j] = 0
             else:
-                # 如果为bool型，需要强转为int32，
-                # 在17-20行实现
-                is_bool = False
-                if str(c_trg.dtype) == "VarType.BOOL":
-                    c_trg = c_trg.cast("int32")
-                    is_bool = True
-                c_trg[:, i] = (c_trg[:, i] == 0)
-                # 如果为bool类型转换为原类型
-                # 在23-24行实现
-                if is_bool:
-                    c_trg = c_trg.cast("bool")
+                # 如果为非int型，需要强转为int32，
+                # 在18-22行实现
+                # c_trg[:, i] = (c_trg[:, i] == 0)
+                c_trg = c_trg.cast("int32")
+                c_trg_tmp = paddle.zeros_like(c_trg)
+                paddle.assign(c_trg, c_trg_tmp)
+                c_trg_tmp = c_trg_tmp.cast("bool")
+                c_trg_tmp[:, i] = c_trg[:, i] == 0
+                c_trg = c_trg_tmp 
             ...
         ...
     ...

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -15,7 +15,8 @@
 
 import copy
 import numpy as np
-from x2paddle.core.util import *
+from x2paddle.core.util import name_generator, string
+from x2paddle.utils import paddle_dtypes
 from x2paddle.core.program import PaddleGraph
 
 dtype_dict = {
@@ -182,13 +183,8 @@ def aten_addmm(mapper, graph, node):
     # 获取当前节点输出的list
     current_outputs = [output_name]
     # 处理输入0，即%150
-    mapper._check_input(
-        graph,
-        inputs_node[0],
-        inputs_name[0],
-        current_outputs,
-        scope_name,
-        add_dim=True)
+    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%input.3
     mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
@@ -247,13 +243,8 @@ def aten_add(mapper, graph, node):
                         scope_name)
     layer_inputs["x"] = inputs_name[0]
     # 处理输入1，即%288
-    mapper._check_input(
-        graph,
-        inputs_node[1],
-        inputs_name[1],
-        current_outputs,
-        scope_name,
-        add_dim=True)
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
     layer_inputs["y"] = inputs_name[1]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
@@ -289,13 +280,8 @@ def aten_add_(mapper, graph, node):
                         scope_name)
     layer_inputs["x"] = inputs_name[0]
     # 处理输入1，即%150
-    mapper._check_input(
-        graph,
-        inputs_node[1],
-        inputs_name[1],
-        current_outputs,
-        scope_name,
-        add_dim=True)
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
     layer_inputs["y"] = inputs_name[1]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
@@ -745,13 +731,8 @@ def aten_bmm(mapper, graph, node):
                         scope_name)
     layer_inputs["x"] = inputs_name[0]
     # 处理输入1，即%288
-    mapper._check_input(
-        graph,
-        inputs_node[1],
-        inputs_name[1],
-        current_outputs,
-        scope_name,
-        add_dim=True)
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
     layer_inputs["y"] = inputs_name[1]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
@@ -1854,17 +1835,12 @@ def aten_expand_as(mapper, graph, node):
         inputs={"input": inputs_name[0]},
         outputs=[inputs_name[0] + "_type"],
         scope_name=scope_name)
-    graph.add_layer(
-        "prim.str",
-        inputs={"input": inputs_name[0] + "_type"},
-        outputs=[inputs_name[0] + "_type"],
-        scope_name=scope_name)
     graph.add_layer(
         "prim.eq",
         inputs={"x": inputs_name[0] + "_type"},
         outputs=[inputs_name[0] + "_cond"],
         scope_name=scope_name,
-        y=string("VarType.BOOL"))
+        y=paddle_dtypes.t_bool)
     graph.add_layer(
         "prim.if", {'input': inputs_name[0] + "_cond"},
         outputs=[inputs_name[0] + "_if1"],
@@ -2101,10 +2077,11 @@ def aten_floor(mapper, graph, node):
         outputs=[inputs_name[0] + "_type"],
         scope_name=scope_name)
     graph.add_layer(
-        "prim.startswith", {'input': inputs_name[0] + "_type"},
+        "prim.eq",
+        inputs={"x": inputs_name[0] + "_type"},
         outputs=[inputs_name[0] + "_cond"],
         scope_name=scope_name,
-        start_str=string("VarType"))
+        y=paddle_dtypes.t_bool)
     graph.add_layer(
         "prim.if", {'input': inputs_name[0] + "_cond"},
         outputs=[inputs_name[0] + "_if"],
@@ -5004,13 +4981,8 @@ def aten_sub(mapper, graph, node):
                         scope_name)
     layer_inputs["x"] = inputs_name[0]
     # 处理输入1，即%836
-    mapper._check_input(
-        graph,
-        inputs_node[1],
-        inputs_name[1],
-        current_outputs,
-        scope_name,
-        add_dim=True)
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
     layer_inputs["y"] = inputs_name[1]
     # 处理输入2，即%3
     if len(inputs_node) > 2:

x2paddle/op_mapper/dygraph/pytorch2paddle/prim.py

@@ -15,7 +15,7 @@
 
 import torch
 import numpy as np
-from x2paddle.core.util import *
+from x2paddle.core.util import string
 
 
 def prim_Constant(mapper, graph, node):

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/gather.py

@@ -13,13 +13,13 @@
 # limitations under the License.
 
 import paddle
+from x2paddle.core.util import *
+
 
 class Gather(object):
     def __init__(self, dim):
         self.dim = dim
-        self.dtype_mapping = {"VarType.INT32": "int32", 
-                              "VarType.INT64": "int64"}
-        
+
     def __call__(self, x, index):
         if self.dim < 0:
             self.dim += len(x.shape)
@@ -31,27 +31,27 @@ class Gather(object):
         index_range[0] = self.dim
         index_range[self.dim] = 0
         index_swaped = paddle.transpose(index, perm=index_range)
-        dtype = self.dtype_mapping[str(index.dtype)]
-        
+        dtype = index.dtype
+
         x_shape = paddle.shape(x_swaped)
         index_shape = paddle.shape(index_swaped)
-        
-        prod = paddle.prod(x_shape, dtype=dtype) / x_shape[0]
-        
+
+        prod = paddle.cast(paddle.prod(x_shape), dtype=dtype) / x_shape[0]
+
         x_swaped_flattend = paddle.flatten(x_swaped)
         index_swaped_flattend = paddle.flatten(index_swaped)
         index_swaped_flattend *= prod
-        
+
         bias = paddle.arange(start=0, end=prod, dtype=dtype)
         bias = paddle.reshape(bias, x_shape[1:])
         bias = paddle.crop(bias, index_shape[1:])
         bias = paddle.flatten(bias)
         bias = paddle.tile(bias, [index_shape[0]])
         index_swaped_flattend += bias
-        
+
         gathered = paddle.index_select(x_swaped_flattend, index_swaped_flattend)
         gathered = paddle.reshape(gathered, index_swaped.shape)
-        
+
         out = paddle.transpose(gathered, perm=x_range)
 
         return out

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py

@@ -16,7 +16,7 @@
 import torch
 import numpy as np
 from x2paddle.core.op_mapper import OpMapper
-from x2paddle.core.util import *
+from x2paddle.core.util import string
 from x2paddle.core.program import PaddleGraph
 from x2paddle.op_mapper.dygraph.pytorch2paddle import prim
 from x2paddle.op_mapper.dygraph.pytorch2paddle import aten
@@ -169,18 +169,10 @@ class PyTorchOpMapper(OpMapper):
             outputs_name.append(output_name)
         return outputs_name
 
-    def _check_input(self,
-                     graph,
-                     node,
-                     output_name,
-                     node_outputs,
-                     scope_name,
-                     add_dim=False):
+    def _check_input(self, graph, node, output_name, node_outputs, scope_name):
         if node.kind() == "prim::GetAttr":
             param = self.pytorch_params[output_name]
             if isinstance(param, np.ndarray):
-                if add_dim:
-                    param = param[np.newaxis, :]
                 self.paddle_params[output_name] = param
                 layer_id = graph.add_layer(
                     "self.create_parameter",
@@ -208,8 +200,6 @@ class PyTorchOpMapper(OpMapper):
                                 else:
                                     if id1_part[i] == "0" and id2_part[
                                             i] == "1":
-                                        if add_dim:
-                                            param = param[np.newaxis, :]
                                         self.paddle_params[output_name] = param
                                         layer_id = graph.add_layer(
                                             "self.create_parameter",

x2paddle/project_convertor/pytorch/mapper.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from x2paddle.project_convertor.pytorch.api_mapper import *
-from x2paddle.utils import *
+from x2paddle.utils import is_new_version
 
 OPTIMIZER_MAPPER = {
     "torch.optim": ["paddle.optimizer", None],
@@ -25,7 +25,6 @@ OPTIMIZER_MAPPER = {
     ["paddle.optimizer.lr.MultiStepDecay", LRScheculerMapper],
     "torch.optim.Adam": ["x2paddle.torch2paddle.Adam", None],
     "torch.optim.SGD": ["x2paddle.torch2paddle.Momentum", None]
-    
 }
 
 NN_MAPPER = {
@@ -169,11 +168,42 @@ DIST_MAPPER = {
     ["x2paddle.torch2paddle.init_process_group", None]
 }
 
-DTYPE_MAPPER = {
-    "torch.float32": [string("float32"), None],
-    "torch.long": [string("int64"), None],
-    "torch.bool": [string("bool"), None]
-}
+if is_new_version:
+    DTYPE_MAPPER = {
+        "torch.float16": ["paddle.float16", None],
+        "torch.half": ["paddle.float16", None],
+        "torch.float32": ["paddle.float32", None],
+        "torch.float": ["paddle.float32", None],
+        "torch.float64": ["paddle.float64", None],
+        "torch.double": ["paddle.float64", None],
+        "torch.uint8": ["paddle.uint8", None],
+        "torch.int8": ["paddle.int8", None],
+        "torch.int16": ["paddle.int16", None],
+        "torch.short": ["paddle.int16", None],
+        "torch.int32": ["paddle.int32", None],
+        "torch.int": ["paddle.int32", None],
+        "torch.int64": ["paddle.int64", None],
+        "torch.long": ["paddle.int64", None],
+        "torch.bool": ["paddle.bool", None],
+    }
+else:
+    DTYPE_MAPPER = {
+        "torch.float16": [string("float16"), None],
+        "torch.half": [string("float16"), None],
+        "torch.float32": [string("float32"), None],
+        "torch.float": [string("float32"), None],
+        "torch.float64": [string("float64"), None],
+        "torch.double": [string("float64"), None],
+        "torch.uint8": [string("uint8"), None],
+        "torch.int8": [string("int8"), None],
+        "torch.int16": [string("int16"), None],
+        "torch.short": [string("int16"), None],
+        "torch.int32": [string("int32"), None],
+        "torch.int": [string("int32"), None],
+        "torch.int64": [string("int64"), None],
+        "torch.long": [string("int64"), None],
+        "torch.bool": [string("bool"), None],
+    }
 
 TORCHVISION_MAPPER = {
     "torchvision": ["paddle.vision", None],

x2paddle/project_convertor/pytorch/torch2paddle/nn_init.py

@@ -19,6 +19,7 @@ from paddle.fluid import framework
 from paddle.fluid.core import VarDesc
 from paddle.fluid.initializer import XavierInitializer, MSRAInitializer
 from paddle.fluid.data_feeder import check_variable_and_dtype
+from x2paddle.utils import paddle_dtypes
 
 
 def _calculate_fan_in_and_fan_out(var):
@@ -101,8 +102,8 @@ class KaimingNormal(MSRAInitializer):
             self._seed = block.program.random_seed
 
         # to be compatible of fp16 initalizers
-        if var.dtype == VarDesc.VarType.FP16:
-            out_dtype = VarDesc.VarType.FP32
+        if var.dtype == paddle_dtypes.t_float16:
+            out_dtype = paddle_dtypes.t_float32
             out_var = block.create_var(
                 name=unique_name.generate(".".join(
                     ['masra_init', var.name, 'tmp'])),
@@ -169,8 +170,8 @@ class XavierNormal(XavierInitializer):
             self._seed = block.program.random_seed
 
         # to be compatible of fp16 initalizers
-        if var.dtype == VarDesc.VarType.FP16:
-            out_dtype = VarDesc.VarType.FP32
+        if var.dtype == paddle_dtypes.t_float16:
+            out_dtype = paddle_dtypes.t_float32
             out_var = block.create_var(
                 name=unique_name.generate(".".join(
                     ['xavier_init', var.name, 'tmp'])),
@@ -195,7 +196,7 @@ class XavierNormal(XavierInitializer):
                 "seed": self._seed
             },
             stop_gradient=True)
-        if var.dtype == VarDesc.VarType.FP16:
+        if var.dtype == paddle_dtypes.t_float16:
             block.append_op(
                 type="cast",
                 inputs={"X": out_var},
@@ -233,8 +234,8 @@ class XavierUniform(XavierInitializer):
             self._seed = block.program.random_seed
 
         # to be compatible of fp16 initalizers
-        if var.dtype == VarDesc.VarType.FP16:
-            out_dtype = VarDesc.VarType.FP32
+        if var.dtype == paddle_dtypes.t_float16:
+            out_dtype = paddle_dtypes.t_float32
             out_var = block.create_var(
                 name=unique_name.generate(".".join(
                     ['xavier_init', var.name, 'tmp'])),
@@ -260,7 +261,7 @@ class XavierUniform(XavierInitializer):
                 "seed": self._seed
             },
             stop_gradient=True)
-        if var.dtype == VarDesc.VarType.FP16:
+        if var.dtype == paddle_dtypes.t_float16:
             block.append_op(
                 type="cast",
                 inputs={"X": out_var},

x2paddle/project_convertor/pytorch/torch2paddle/varbase.py

@@ -14,6 +14,7 @@
 
 import paddle
 from paddle.fluid.core import VarBase
+from x2paddle.utils import paddle_dtypes
 
 
 def is_condition_one(idx):
@@ -23,8 +24,8 @@ def is_condition_one(idx):
     a[mask, :]
     a[mask, ...]
     """
-    if not (isinstance(idx[0], paddle.Tensor) and
-            str(idx[0].dtype) == "VarType.BOOL"):
+    if not (isinstance(idx[0], paddle.Tensor) and \
+            idx[0].dtype == paddle_dtypes.t_bool):
         return False
     if len(idx) == 1:
         return False
@@ -57,13 +58,13 @@ VarBase.tmp = VarBase.__getitem__
 
 def __getitem__(self, idx):
     is_bool = False
-    if str(self.dtype) == "VarType.BOOL":
+    if self.dtype == paddle_dtypes.t_bool:
         self = self.cast("int32")
         is_bool = True
     if isinstance(idx, paddle.Tensor) and len(idx.shape) == 1:
         out = paddle.gather(self, idx)
         return out.cast("bool") if is_bool else out
-    elif isinstance(idx, paddle.Tensor) and str(idx.dtype) == "VarType.BOOL":
+    elif isinstance(idx, paddle.Tensor) and idx.dtype == paddle_dtypes.t_bool:
         idx = paddle.cast(idx, "int32")
         idx = paddle.nonzero(idx)
         out = paddle.gather_nd(self, idx)
@@ -100,7 +101,7 @@ VarBase.setitem_tmp = VarBase.__setitem__
 
 
 def __setitem__(self, idx, value):
-    if isinstance(idx, paddle.Tensor) and str(idx.dtype) == "VarType.BOOL":
+    if isinstance(idx, paddle.Tensor) and idx.dtype == paddle_dtypes.t_bool:
         """
         a = paddle.to_tensor(np.array([1,2,3]).astype("float32"))
         mask = paddle.to_tensor(np.array([True, False, True]).astype("bool"))

x2paddle/utils.py

@@ -13,8 +13,49 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import paddle
+
 
 def string(param):
     """ 生成字符串。
     """
     return "\'{}\'".format(param)
+
+
+def check_version():
+    version = paddle.__version__
+    v0, v1, v2 = version.split('.')
+    if not ((v0 == '0' and v1 == '0' and v2 == '0') or
+            (int(v0) >= 2 and int(v1) >= 1)):
+        return False
+    else:
+        return True
+
+
+class PaddleDtypes():
+    def __init__(self, is_new_version=True):
+        if is_new_version:
+            self.t_float16 = paddle.float16
+            self.t_float32 = paddle.float32
+            self.t_float64 = paddle.float64
+            self.t_uint8 = paddle.uint8
+            self.t_int8 = paddle.int8
+            self.t_int16 = paddle.int16
+            self.t_int32 = paddle.int32
+            self.t_int64 = paddle.int64
+            self.t_bool = paddle.bool
+        else:
+            from paddle.fluid.core import VarDesc
+            self.t_float16 = VarDesc.VarType.FP16
+            self.t_float32 = VarDesc.VarType.FP32
+            self.t_float64 = VarDesc.VarType.FP64
+            self.t_uint8 = VarDesc.VarType.UINT8
+            self.t_int8 = VarDesc.VarType.INT8
+            self.t_int16 = VarDesc.VarType.INT16
+            self.t_int32 = VarDesc.VarType.INT32
+            self.t_int64 = VarDesc.VarType.INT64
+            self.t_bool = VarDesc.VarType.BOOL
+
+
+is_new_version = check_version()
+paddle_dtypes = PaddleDtypes(is_new_version)