update to 2.0beta

dygraph/paddleseg/core/val.py

@@ -17,10 +17,9 @@ import os
 import numpy as np
 import tqdm
 import cv2
-from paddle.fluid.dygraph.base import to_variable
-import paddle.fluid as fluid
-import paddle.nn.functional as F
 import paddle
+import paddle.nn.functional as F
+from paddle import to_variable
 
 import paddleseg.utils.logger as logger
 from paddleseg.utils import ConfusionMatrix
@@ -34,7 +33,7 @@ def evaluate(model,
              ignore_index=255,
              iter_id=None):
     ckpt_path = os.path.join(model_dir, 'model')
-    para_state_dict, opti_state_dict = fluid.load_dygraph(ckpt_path)
+    para_state_dict, opti_state_dict = paddle.load(ckpt_path)
     model.set_dict(para_state_dict)
     model.eval()
 

dygraph/paddleseg/cvlibs/param_init.py

@@ -12,19 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import paddle.fluid as fluid
+import paddle.nn as nn
 
 
 def constant_init(param, **kwargs):
-    initializer = fluid.initializer.Constant(**kwargs)
+    initializer = nn.initializer.Constant(**kwargs)
     initializer(param, param.block)
 
 
 def normal_init(param, **kwargs):
-    initializer = fluid.initializer.Normal(**kwargs)
+    initializer = nn.initializer.Normal(**kwargs)
     initializer(param, param.block)
 
 
 def msra_init(param, **kwargs):
-    initializer = fluid.initializer.MSRA(**kwargs)
+    initializer = nn.initializer.MSRA(**kwargs)
     initializer(param, param.block)

dygraph/paddleseg/datasets/dataset.py

@@ -14,7 +14,7 @@
 
 import os
 
-import paddle.fluid as fluid
+import paddle
 import numpy as np
 from PIL import Image
 
@@ -23,7 +23,7 @@ from paddleseg.transforms import Compose
 
 
 @manager.DATASETS.add_component
-class Dataset(fluid.io.Dataset):
+class Dataset(paddle.io.Dataset):
     """Pass in a custom dataset that conforms to the format.
 
     Args:

dygraph/paddleseg/models/losses/cross_entroy_loss.py

@@ -15,7 +15,6 @@
 import paddle
 from paddle import nn
 import paddle.nn.functional as F
-import paddle.fluid as fluid
 
 from paddleseg.cvlibs import manager
 '''
@@ -107,34 +106,15 @@ class CrossEntropyLoss(nn.Layer):
         if len(label.shape) != len(logit.shape):
             label = paddle.unsqueeze(label, 1)
 
-        # logit = paddle.transpose(logit, [0, 2, 3, 1])
-        # label = paddle.transpose(label, [0, 2, 3, 1])
-        # loss = F.softmax_with_cross_entropy(
-        #     logit, label, ignore_index=self.ignore_index, axis=-1)
-        # loss = paddle.reduce_mean(loss)
+        logit = paddle.transpose(logit, [0, 2, 3, 1])
+        label = paddle.transpose(label, [0, 2, 3, 1])
+        loss = F.softmax_with_cross_entropy(
+            logit, label, ignore_index=self.ignore_index, axis=-1)
 
-        # mask = label != self.ignore_index
-        # mask = paddle.cast(mask, 'float32')
-        # avg_loss = loss / (paddle.mean(mask) + self.EPS)
-
-        # label.stop_gradient = True
-        # mask.stop_gradient = True
-        # return avg_loss
-
-        logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
-        label = fluid.layers.transpose(label, [0, 2, 3, 1])
         mask = label != self.ignore_index
-        mask = fluid.layers.cast(mask, 'float32')
-        loss, probs = fluid.layers.softmax_with_cross_entropy(
-            logit,
-            label,
-            ignore_index=self.ignore_index,
-            return_softmax=True,
-            axis=-1)
-
+        mask = paddle.cast(mask, 'float32')
         loss = loss * mask
-        avg_loss = fluid.layers.mean(loss) / (
-            fluid.layers.mean(mask) + self.EPS)
+        avg_loss = paddle.reduce_mean(loss) / (paddle.mean(mask) + self.EPS)
 
         label.stop_gradient = True
         mask.stop_gradient = True

dygraph/paddleseg/utils/get_environ_info.py

@@ -19,8 +19,8 @@ import subprocess
 import glob
 
 import paddle
-import paddle.fluid as fluid
 import cv2
+from paddle.distributed import ParallelEnv
 
 IS_WINDOWS = sys.platform == 'win32'
 
@@ -96,13 +96,14 @@ def get_environ_info():
 
     env_info['Python'] = sys.version.replace('\n', '')
 
+    # todo is_compiled_with_cuda() has not been moved
     compiled_with_cuda = paddle.fluid.is_compiled_with_cuda()
     env_info['Paddle compiled with cuda'] = compiled_with_cuda
 
     if compiled_with_cuda:
         cuda_home = _find_cuda_home()
         env_info['NVCC'] = _get_nvcc_info(cuda_home)
-        gpu_nums = fluid.core.get_cuda_device_count()
+        gpu_nums = ParallelEnv().nranks
         env_info['GPUs used'] = gpu_nums
         env_info['CUDA_VISIBLE_DEVICES'] = os.environ.get(
             'CUDA_VISIBLE_DEVICES')

dygraph/paddleseg/utils/logger.py

@@ -16,7 +16,7 @@ import time
 import os
 import sys
 
-from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.distributed import ParallelEnv
 
 levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
 log_level = 2

dygraph/paddleseg/utils/utils.py

@@ -14,11 +14,12 @@
 
 import contextlib
 import os
-import numpy as np
 import math
+
+import numpy as np
 import cv2
 import tempfile
-import paddle.fluid as fluid
+import paddle
 from urllib.parse import urlparse, unquote
 
 import filelock
@@ -74,10 +75,7 @@ def load_pretrained_model(model, pretrained_model):
 
         if os.path.exists(pretrained_model):
             ckpt_path = os.path.join(pretrained_model, 'model')
-            try:
-                para_state_dict, _ = fluid.load_dygraph(ckpt_path)
-            except:
-                para_state_dict = fluid.load_program_state(pretrained_model)
+            para_state_dict, _ = paddle.load(ckpt_path)
 
             model_state_dict = model.state_dict()
             keys = model_state_dict.keys()
@@ -115,7 +113,7 @@ def resume(model, optimizer, resume_model):
         if os.path.exists(resume_model):
             resume_model = os.path.normpath(resume_model)
             ckpt_path = os.path.join(resume_model, 'model')
-            para_state_dict, opti_state_dict = fluid.load_dygraph(ckpt_path)
+            para_state_dict, opti_state_dict = paddle.load(ckpt_path)
             model.set_dict(para_state_dict)
             optimizer.set_dict(opti_state_dict)
             epoch = resume_model.split('_')[-1]