save best model

tools/program.py

@@ -383,17 +383,21 @@ def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
         tic = time.time()
         for i, m in enumerate(metrics):
             metric_list[i].update(m[0], len(batch[0]))
-        fetchs_str = ''.join([str(m.value)+' '
-                              for m in metric_list]+ [batch_time.value])
+        fetchs_str = ''.join([str(m.value) + ' '
+                              for m in metric_list] + [batch_time.value])
         if epoch != -1:
             logger.info("epoch:{:<3d} {:s} step:{:<4d} {:s}s".format(
-            epoch, mode, idx, fetchs_str))
+                epoch, mode, idx, fetchs_str))
         else:
-            logger.info("{:s} step:{:<4d} {:s}s".format(
-            mode, idx, fetchs_str))
+            logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx, fetchs_str))
 
-    end_str = ''.join([str(m.mean)+' ' for m in metric_list] + [batch_time.total])
-    if epoch!= -1:
+    end_str = ''.join([str(m.mean) + ' '
+                       for m in metric_list] + [batch_time.total])
+    if epoch != -1:
         logger.info("END epoch:{:<3d} {:s} {:s}s".format(epoch, mode, end_str))
     else:
         logger.info("END {:s} {:s}s".format(mode, end_str))
+
+    # save the best model
+    top1_acc = fetchs["top1"][1].avg
+    return top1_acc

tools/train.py

@@ -26,6 +26,7 @@ from paddle.fluid.incubate.fleet.collective import fleet
 from ppcls.data import Reader
 from ppcls.utils.config import get_config
 from ppcls.utils.save_load import init_model, save_model
+from ppcls.utils import logger
 import program
 
 
@@ -61,6 +62,10 @@ def main(args):
     startup_prog = fluid.Program()
     train_prog = fluid.Program()
 
+    # best_top1_acc_list[0]: top1 acc
+    # best_top1_acc_list[1]: epoch id
+    best_top1_acc_list = [0.0, 0]
+
     train_dataloader, train_fetchs = program.build(
         config, train_prog, startup_prog, is_train=True)
 
@@ -94,8 +99,16 @@ def main(args):
                     epoch_id, 'train')
         # 2. validate with validate dataset
         if config.validate and epoch_id % config.valid_interval == 0:
-            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')
+            if top1_acc > best_top1_acc_list[0]:
+                best_top1_acc_list[0] = top1_acc
+                best_top1_acc_list[1] = epoch_id
+                logger.info("Best top1 acc: {}, in epoch: {}".format(
+                    best_top1_acc_list[0], best_top1_acc_list[1]))
+                model_path = os.path.join(config.model_save_dir,
+                                          config.ARCHITECTURE["name"])
+                save_model(train_prog, model_path, "best_model")
 
         # 3. save the persistable model
         if epoch_id % config.save_interval == 0: