add print_interval and refine override

ppcls/utils/config.py

@@ -144,9 +144,14 @@ def override(dl, ks, v):
             override(dl[k], ks[1:], v)
     else:
         if len(ks) == 1:
-            assert ks[0] in dl, ('{} is not exist in {}'.format(ks[0], dl))
+            #assert ks[0] in dl, ('{} is not exist in {}'.format(ks[0], dl))
+            if not ks[0] in dl:
+                logger.warning('A new filed ({}) detected!'.format(ks[0], dl))
             dl[ks[0]] = str2num(v)
         else:
+            assert ks[0] in dl, (
+                '({}) doesn\'t exist in {}, a new dict field is invalid'.
+                format(ks[0], dl))
             override(dl[ks[0]], ks[1:], v)
 
 

tools/eval.py

@@ -74,7 +74,7 @@ def main(args):
 
     compiled_valid_prog = program.compile(config, valid_prog)
     program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, -1,
-                'eval')
+                'eval', config)
 
 
 if __name__ == '__main__':

tools/program.py

@@ -410,6 +410,7 @@ def run(dataloader,
         fetchs,
         epoch=0,
         mode='train',
+        config=None,
         vdl_writer=None):
     """
     Feed data to the model and fetch the measures and loss
@@ -443,16 +444,28 @@ def run(dataloader,
             logger.scaler('loss', metrics[0][0], total_step, vdl_writer)
             total_step += 1
         if mode == 'eval':
-            logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx, fetchs_str))
+            if idx % config.get('print_interval', 1) == 0:
+                logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx,
+                                                            fetchs_str))
         else:
             epoch_str = "epoch:{:<3d}".format(epoch)
             step_str = "{:s} step:{:<4d}".format(mode, idx)
 
-            logger.info("{:s} {:s} {:s}".format(
-                logger.coloring(epoch_str, "HEADER")
-                if idx == 0 else epoch_str,
-                logger.coloring(step_str, "PURPLE"),
-                logger.coloring(fetchs_str, 'OKGREEN')))
+            # Keep the first 10 batches statistics, They are important for develop
+            if epoch == 0 and idx < 10:
+                logger.info("{:s} {:s} {:s}".format(
+                    logger.coloring(epoch_str, "HEADER")
+                    if idx == 0 else epoch_str,
+                    logger.coloring(step_str, "PURPLE"),
+                    logger.coloring(fetchs_str, 'OKGREEN')))
+
+            else:
+                if idx % config.get('print_interval', 1) == 0:
+                    logger.info("{:s} {:s} {:s}".format(
+                        logger.coloring(epoch_str, "HEADER")
+                        if idx == 0 else epoch_str,
+                        logger.coloring(step_str, "PURPLE"),
+                        logger.coloring(fetchs_str, 'OKGREEN')))
 
     end_str = ''.join([str(m.mean) + ' '
                        for m in metric_list] + [batch_time.total]) + 's'

tools/run.sh

@@ -5,4 +5,5 @@ export PYTHONPATH=$PWD:$PYTHONPATH
 python -m paddle.distributed.launch \
     --selected_gpus="0,1,2,3" \
     tools/train.py \
-        -c ./configs/ResNet/ResNet50.yaml
+        -c ./configs/ResNet/ResNet50.yaml \
+        -o print_interval=10

tools/train.py

@@ -110,21 +110,21 @@ def main(args):
     for epoch_id in range(config.epochs):
         # 1. train with train dataset
         program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
-                    epoch_id, 'train', vdl_writer)
+                    epoch_id, 'train', config, vdl_writer)
         if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
             # 2. validate with validate dataset
             if config.validate and epoch_id % config.valid_interval == 0:
                 if config.get('use_ema'):
                     logger.info(logger.coloring("EMA validate start..."))
                     with ema.apply(exe):
-                        top1_acc = program.run(valid_dataloader, exe,
-                                               compiled_valid_prog,
-                                               valid_fetchs, epoch_id, 'valid')
+                        top1_acc = program.run(
+                            valid_dataloader, exe, compiled_valid_prog,
+                            valid_fetchs, epoch_id, 'valid', config)
                     logger.info(logger.coloring("EMA validate over!"))
 
                 top1_acc = program.run(valid_dataloader, exe,
                                        compiled_valid_prog, valid_fetchs,
-                                       epoch_id, 'valid')
+                                       epoch_id, 'valid', config)
                 if top1_acc > best_top1_acc:
                     best_top1_acc = top1_acc
                     message = "The best top1 acc {:.5f}, in epoch: {:d}".format(