Show processing information when evaluating the model

paddlehub/finetune/trainer.py

@@ -23,7 +23,7 @@ import paddle
 from paddle.distributed import ParallelEnv
 from visualdl import LogWriter
 
-from paddlehub.utils.log import logger
+from paddlehub.utils.log import logger, processing
 from paddlehub.utils.utils import Timer
 
 
@@ -111,14 +111,14 @@ class Trainer(object):
                 self.current_epoch, metric_msg))
 
         # load model checkpoint
-        model_params_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch), 'model.pdparmas')
+        model_params_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch), 'model.pdparams')
         state_dict = paddle.load(model_params_path)
-        self.model.set_dict(state_dict)
+        self.model.set_state_dict(state_dict)
 
         # load optimizer checkpoint
         optim_params_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch), 'model.pdopt')
         state_dict = paddle.load(optim_params_path)
-        self.optimizer.set_dict(state_dict)
+        self.optimizer.set_state_dict(state_dict)
 
     def _save_checkpoint(self):
         '''Save model checkpoint and state dict'''
@@ -131,7 +131,7 @@ class Trainer(object):
         model_params_path = os.path.join(save_dir, 'model.pdparams')
         optim_params_path = os.path.join(save_dir, 'model.pdopt')
         paddle.save(self.model.state_dict(), model_params_path)
-        paddle.save(self.model.state_dict(), optim_params_path)
+        paddle.save(self.optimizer.state_dict(), optim_params_path)
 
     def _save_metrics(self):
         with open(os.path.join(self.checkpoint_dir, 'metrics.pkl'), 'wb') as file:
@@ -162,10 +162,6 @@ class Trainer(object):
             log_interval(int) : Log the train infomation every `log_interval` steps.
             save_interval(int) : Save the checkpoint every `save_interval` epochs.
         '''
-        use_gpu = True
-        place = 'gpu' if use_gpu else 'cpu'
-        paddle.set_device(place)
-
         batch_sampler = paddle.io.DistributedBatchSampler(
             train_dataset, batch_size=batch_size, shuffle=True, drop_last=False)
         loader = paddle.io.DataLoader(
@@ -253,10 +249,6 @@ class Trainer(object):
             batch_size(int) : Batch size of per step, default is 1.
             num_workers(int) : Number of subprocess to load data, default is 0.
         '''
-        use_gpu = True
-        place = 'gpu' if use_gpu else 'cpu'
-        paddle.set_device(place)
-
         batch_sampler = paddle.io.DistributedBatchSampler(
             eval_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
 
@@ -268,6 +260,7 @@ class Trainer(object):
         sum_metrics = defaultdict(int)
         avg_metrics = defaultdict(int)
 
+        with processing('Evaluation on validation dataset'):
             for batch_idx, batch in enumerate(loader):
                 result = self.validation_step(batch, batch_idx)
                 loss = result.get('loss', None)

paddlehub/utils/log.py

@@ -13,11 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import copy
 import functools
 import logging
 import sys
 import time
+import threading
 from typing import List
 
 import colorlog
@@ -102,6 +104,13 @@ class Logger(object):
 
         self.logger.log(log_level, msg)
 
+    @contextlib.contextmanager
+    def use_terminator(self, terminator: str):
+        old_terminator = self.handler.terminator
+        self.handler.terminator = terminator
+        yield
+        self.handler.terminator = old_terminator
+
 
 class ProgressBar(object):
     '''
@@ -161,6 +170,28 @@ class ProgressBar(object):
             sys.stdout.write('\n')
 
 
+@contextlib.contextmanager
+def processing(msg: str, interval: float = 0.1):
+    '''
+    '''
+    end = False
+
+    def _printer():
+        index = 0
+        flags = ['\\', '|', '/', '-']
+        while not end:
+            flag = flags[index % len(flags)]
+            with logger.use_terminator('\r'):
+                logger.info('{}: {}'.format(msg, flag))
+            time.sleep(interval)
+            index += 1
+
+    t = threading.Thread(target=_printer)
+    t.start()
+    yield
+    end = True
+
+
 class FormattedText(object):
     '''
     Cross-platform formatted string