update to beta version

dygraph/paddleseg/core/train.py

@@ -15,11 +15,9 @@
 import os
 
 import paddle
-import paddle.fluid as fluid
-from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import DataLoader
-# from paddle.incubate.hapi.distributed import DistributedBatchSampler
+from paddle.distributed import ParallelEnv
 from paddle.io import DistributedBatchSampler
+from paddle.io import DataLoader
 import paddle.nn.functional as F
 
 import paddleseg.utils.logger as logger
@@ -79,8 +77,8 @@ def train(model,
         os.makedirs(save_dir)
 
     if nranks > 1:
-        strategy = fluid.dygraph.prepare_context()
-        ddp_model = fluid.dygraph.DataParallel(model, strategy)
+        strategy = paddle.distributed.prepare_context()
+        ddp_model = paddle.DataParallel(model, strategy)
 
     batch_sampler = DistributedBatchSampler(
         train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
@@ -130,7 +128,7 @@ def train(model,
             optimizer.minimize(loss)
             model.clear_gradients()
             avg_loss += loss.numpy()[0]
-            lr = optimizer.current_step_lr()
+            lr = optimizer.get_lr()
             train_batch_cost += timer.elapsed_time()
             if (iter) % log_iters == 0 and ParallelEnv().local_rank == 0:
                 avg_loss /= log_iters
@@ -160,10 +158,10 @@ def train(model,
                                                 "iter_{}".format(iter))
                 if not os.path.isdir(current_save_dir):
                     os.makedirs(current_save_dir)
-                fluid.save_dygraph(model.state_dict(),
-                                   os.path.join(current_save_dir, 'model'))
-                fluid.save_dygraph(optimizer.state_dict(),
-                                   os.path.join(current_save_dir, 'model'))
+                paddle.save(model.state_dict(),
+                            os.path.join(current_save_dir, 'model'))
+                paddle.save(optimizer.state_dict(),
+                            os.path.join(current_save_dir, 'model'))
 
                 if eval_dataset is not None:
                     mean_iou, avg_acc = evaluate(
@@ -177,9 +175,8 @@ def train(model,
                         best_mean_iou = mean_iou
                         best_model_iter = iter
                         best_model_dir = os.path.join(save_dir, "best_model")
-                        fluid.save_dygraph(
-                            model.state_dict(),
-                            os.path.join(best_model_dir, 'model'))
+                        paddle.save(model.state_dict(),
+                                    os.path.join(best_model_dir, 'model'))
                     logger.info(
                         'Current evaluated best model in eval_dataset is iter_{}, miou={:4f}'
                         .format(best_model_iter, best_mean_iou))

dygraph/paddleseg/utils/config.py

@@ -15,11 +15,14 @@
 import codecs
 import os
 from typing import Any, Callable
+import pprint
 
 import yaml
-import paddle.fluid as fluid
+import paddle
+import paddle.nn.functional as F
 
 import paddleseg.cvlibs.manager as manager
+from paddleseg.utils import logger
 
 
 class Config(object):
@@ -36,7 +39,7 @@ class Config(object):
 
         if path.endswith('yml') or path.endswith('yaml'):
             dic = self._parse_from_yaml(path)
-            print(dic)
+            logger.info('\n' + pprint.pformat(dic))
             self._build(dic)
         else:
             raise RuntimeError('Config file should in yaml format!')
@@ -127,24 +130,18 @@ class Config(object):
             lr = self._learning_rate
             args = self.decay_args
             args.setdefault('decay_steps', self.iters)
-            return fluid.layers.polynomial_decay(lr, **args)
+            return paddle.optimizer.PolynomialLR(lr, **args)
         else:
             raise RuntimeError('Only poly decay support.')
 
     @property
-    def optimizer(self) -> fluid.optimizer.Optimizer:
+    def optimizer(self) -> paddle.optimizer.Optimizer:
         if self.optimizer_type == 'sgd':
             lr = self.learning_rate
             args = self.optimizer_args
             args.setdefault('momentum', 0.9)
-            weight_decay = args.get('weight_decay', None)
-            if weight_decay is not None:
-                args.pop('weight_decay')
-                regularization = fluid.regularizer.L2DecayRegularizer(
-                    regularization_coeff=weight_decay)
-            args.setdefault('regularization', None)
-            return fluid.optimizer.Momentum(
-                lr, parameter_list=self.model.parameters(), **args)
+            return paddle.optimizer.Momentum(
+                lr, parameters=self.model.parameters(), **args)
         else:
             raise RuntimeError('Only sgd optimizer support.')
 

dygraph/train.py

@@ -14,8 +14,8 @@
 
 import argparse
 
-import paddle.fluid as fluid
-from paddle.fluid.dygraph.parallel import ParallelEnv
+import paddle
+from paddle.distributed import ParallelEnv
 
 import paddleseg
 from paddleseg.cvlibs import manager
@@ -91,41 +91,40 @@ def main(args):
                      ['-' * 48])
     logger.info(info)
 
-    places = fluid.CUDAPlace(ParallelEnv().dev_id) \
+    places = paddle.CUDAPlace(ParallelEnv().dev_id) \
         if env_info['Paddle compiled with cuda'] and env_info['GPUs used'] \
-        else fluid.CPUPlace()
-
-    with fluid.dygraph.guard(places):
-        if not args.cfg:
-            raise RuntimeError('No configuration file specified.')
-
-        cfg = Config(args.cfg)
-        train_dataset = cfg.train_dataset
-        if not train_dataset:
-            raise RuntimeError(
-                'The training dataset is not specified in the configuration file.'
-            )
-
-        val_dataset = cfg.val_dataset if args.do_eval else None
-
-        losses = cfg.loss
-
-        train(
-            cfg.model,
-            train_dataset,
-            places=places,
-            eval_dataset=val_dataset,
-            optimizer=cfg.optimizer,
-            save_dir=args.save_dir,
-            iters=cfg.iters,
-            batch_size=cfg.batch_size,
-            save_interval_iters=args.save_interval_iters,
-            log_iters=args.log_iters,
-            num_classes=train_dataset.num_classes,
-            num_workers=args.num_workers,
-            use_vdl=args.use_vdl,
-            losses=losses,
-            ignore_index=losses['types'][0].ignore_index)
+        else paddle.CPUPlace()
+
+    paddle.disable_static(places)
+    if not args.cfg:
+        raise RuntimeError('No configuration file specified.')
+
+    cfg = Config(args.cfg)
+    train_dataset = cfg.train_dataset
+    if not train_dataset:
+        raise RuntimeError(
+            'The training dataset is not specified in the configuration file.')
+
+    val_dataset = cfg.val_dataset if args.do_eval else None
+
+    losses = cfg.loss
+
+    train(
+        cfg.model,
+        train_dataset,
+        places=places,
+        eval_dataset=val_dataset,
+        optimizer=cfg.optimizer,
+        save_dir=args.save_dir,
+        iters=cfg.iters,
+        batch_size=cfg.batch_size,
+        save_interval_iters=args.save_interval_iters,
+        log_iters=args.log_iters,
+        num_classes=train_dataset.num_classes,
+        num_workers=args.num_workers,
+        use_vdl=args.use_vdl,
+        losses=losses,
+        ignore_index=losses['types'][0].ignore_index)
 
 
 if __name__ == '__main__':

dygraph/val.py

@@ -14,8 +14,8 @@
 
 import argparse
 
-import paddle.fluid as fluid
-from paddle.fluid.dygraph.parallel import ParallelEnv
+import paddle
+from paddle.distributed import ParallelEnv
 
 import paddleseg
 from paddleseg.cvlibs import manager
@@ -41,25 +41,25 @@ def parse_args():
 
 def main(args):
     env_info = get_environ_info()
-    places = fluid.CUDAPlace(ParallelEnv().dev_id) \
+    places = paddle.CUDAPlace(ParallelEnv().dev_id) \
         if env_info['Paddle compiled with cuda'] and env_info['GPUs used'] \
-        else fluid.CPUPlace()
+        else paddle.CPUPlace()
 
-    with fluid.dygraph.guard(places):
-        if not args.cfg:
-            raise RuntimeError('No configuration file specified.')
+    paddle.disable_static(places)
+    if not args.cfg:
+        raise RuntimeError('No configuration file specified.')
 
-        cfg = Config(args.cfg)
-        val_dataset = cfg.val_dataset
-        if not val_dataset:
-            raise RuntimeError(
-                'The verification dataset is not specified in the configuration file.'
-            )
-        evaluate(
-            cfg.model,
-            val_dataset,
-            model_dir=args.model_dir,
-            num_classes=val_dataset.num_classes)
+    cfg = Config(args.cfg)
+    val_dataset = cfg.val_dataset
+    if not val_dataset:
+        raise RuntimeError(
+            'The verification dataset is not specified in the configuration file.'
+        )
+    evaluate(
+        cfg.model,
+        val_dataset,
+        model_dir=args.model_dir,
+        num_classes=val_dataset.num_classes)
 
 
 if __name__ == '__main__':