revert for running

ppcls/data/__init__.py

@@ -207,25 +207,3 @@ def build_dataloader(config, mode, seed=None):
 
     logger.debug("build data_loader({}) success...".format(data_loader))
     return data_loader
-
-
-# # TODO(gaotingquan): the length of dataloader should be determined by sampler
-# class DataIterator(object):
-#     def __init__(self, dataloader, use_dali=False):
-#         self.dataloader = dataloader
-#         self.use_dali = use_dali
-#         self.iterator = iter(dataloader)
-#         self.max_iter = dataloader.max_iter
-#         self.total_samples = dataloader.total_samples
-
-#     def get_batch(self):
-#         # fetch data batch from dataloader
-#         try:
-#             batch = next(self.iterator)
-#         except Exception:
-#             # NOTE: reset DALI dataloader manually
-#             if self.use_dali:
-#                 self.dataloader.reset()
-#             self.iterator = iter(self.dataloader)
-#             batch = next(self.iterator)
-#         return batch

ppcls/engine/evaluation/classification.py

@@ -31,7 +31,7 @@ class ClassEval(object):
         self.model = model
         self.print_batch_step = self.config["Global"]["print_batch_step"]
         self.use_dali = self.config["Global"].get("use_dali", False)
-        self.eval_metric_func = build_metrics(self.config, "eval")
+        self.eval_metric_func = build_metrics(self.config, "Eval")
         self.eval_dataloader = build_dataloader(self.config, "Eval")
         self.eval_loss_func = build_loss(self.config, "Eval")
         self.output_info = dict()

ppcls/engine/train/classification.py

@@ -48,12 +48,13 @@ class ClassTrainer(object):
         # build dataloader
         self.use_dali = self.config["Global"].get("use_dali", False)
         self.dataloader = build_dataloader(self.config, "Train")
+        self.dataloader_iter = iter(self.dataloader)
 
         # build loss
         self.loss_func = build_loss(config, "Train")
 
         # build metric
-        self.train_metric_func = build_metrics(config, "train")
+        self.train_metric_func = build_metrics(config, "Train")
 
         # build optimizer
         self.optimizer, self.lr_sch = build_optimizer(
@@ -174,7 +175,17 @@ class ClassTrainer(object):
         self.model.train()
         tic = time.time()
 
-        for iter_id, batch in enumerate(self.dataloader):
+        for iter_id in range(self.dataloader.max_iter):
+            # fetch data batch from dataloader
+            try:
+                batch = next(self.dataloader_iter)
+            except Exception:
+                # NOTE: reset DALI dataloader manually
+                if self.use_dali:
+                    self.dataloader.reset()
+                self.dataloader_iter = iter(self.dataloader)
+                batch = next(self.dataloader_iter)
+
             profiler.add_profiler_step(self.config["profiler_options"])
             if iter_id == 5:
                 for key in self.time_info:

ppcls/metric/__init__.py

@@ -66,7 +66,7 @@ class CombinedMetrics(AvgMetrics):
 
 
 def build_metrics(config, mode):
-    if mode == 'train' and "Metric" in config and "Train" in config[
+    if mode == 'Train' and "Metric" in config and "Train" in config[
             "Metric"] and config["Metric"]["Train"]:
         metric_config = config["Metric"]["Train"]
         if config["DataLoader"]["Train"]["dataset"].get("batch_transform_ops",
@@ -76,22 +76,17 @@ def build_metrics(config, mode):
                     msg = f"Unable to calculate accuracy when using \"batch_transform_ops\". The metric \"{m}\" has been removed."
                     logger.warning(msg)
                     metric_config.pop(m_idx)
-        train_metric_func = CombinedMetrics(copy.deepcopy(metric_config))
-        return train_metric_func
+        return CombinedMetrics(copy.deepcopy(metric_config))
 
-    if mode == "eval" or (mode == "train" and
-                          config["Global"]["eval_during_train"]):
-        eval_mode = config["Global"].get("eval_mode", "classification")
-        if eval_mode == "classification":
+    if mode == "Eval":
+        task = config["Global"].get("task", "classification")
+        assert task in ["classification", "retrieval"]
+        if task == "classification":
             if "Metric" in config and "Eval" in config["Metric"]:
-                eval_metric_func = CombinedMetrics(
-                    copy.deepcopy(config["Metric"]["Eval"]))
-            else:
-                eval_metric_func = None
-        elif eval_mode == "retrieval":
+                return CombinedMetrics(copy.deepcopy(config["Metric"]["Eval"]))
+        elif task == "retrieval":
             if "Metric" in config and "Eval" in config["Metric"]:
                 metric_config = config["Metric"]["Eval"]
             else:
                 metric_config = [{"name": "Recallk", "topk": (1, 5)}]
-            eval_metric_func = CombinedMetrics(copy.deepcopy(metric_config))
-        return eval_metric_func
+            return CombinedMetrics(copy.deepcopy(metric_config))