Revert "refactor: iter_per_epoch -> max_iter"

This reverts commit a38e42f6.

ppcls/data/__init__.py

@@ -204,8 +204,6 @@ class DataIterator(object):
         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
@@ -236,7 +234,7 @@ def build_dataloader(engine):
         "epochs": engine.config["Global"]["epochs"]
     })
 
-    use_dali = engine.use_dali
+    use_dali = engine.config['Global'].get("use_dali", False)
     dataloader_dict = {
         "Train": None,
         "UnLabelTrain": None,
@@ -248,15 +246,18 @@ def build_dataloader(engine):
     if engine.mode == 'train':
         train_dataloader = build(
             engine.config["DataLoader"], "Train", use_dali, seed=None)
-
-        if engine.config["DataLoader"]["Train"].get("max_iter", None):
+        iter_per_epoch = len(train_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(train_dataloader)
+        if engine.config["Global"].get("iter_per_epoch", None):
+            # TODO(gaotingquan): iter_per_epoch should be set in Dataloader.Train, not Global
             # set max iteration per epoch mannualy, when training by iteration(s), such as XBM, FixMatch.
-            max_iter = engine.config["Train"].get("max_iter")
-        max_iter = train_dataloader.max_iter // engine.update_freq * engine.update_freq
-        train_dataloader.max_iter = max_iter
-        if engine.config["DataLoader"]["Train"].get("convert_iterator", True):
-            train_dataloader = DataIterator(train_dataloader, use_dali)
+            iter_per_epoch = engine.config["Global"].get("iter_per_epoch")
+        iter_per_epoch = iter_per_epoch // engine.update_freq * engine.update_freq
+        # engine.iter_per_epoch = iter_per_epoch
+        train_dataloader.iter_per_epoch = iter_per_epoch
         dataloader_dict["Train"] = train_dataloader
+        # TODO(gaotingquan): set the iterator field in config, such as Dataloader.Train.convert_iterator=True
+        dataloader_dict["TrainIter"] = DataIterator(train_dataloader, use_dali)
 
     if engine.config["DataLoader"].get('UnLabelTrain', None) is not None:
         dataloader_dict["UnLabelTrain"] = build(

ppcls/engine/engine.py

@@ -72,7 +72,6 @@ class Engine(object):
         self.update_freq = self.config["Global"].get("update_freq", 1)
 
         # build dataloader
-        self.use_dali = self.config["Global"].get("use_dali", False)
         self.dataloader_dict = build_dataloader(self)
 
         # build loss

ppcls/engine/evaluation/classification.py

@@ -92,7 +92,8 @@ def classification_eval(engine, epoch_id=0):
                 paddle.distributed.all_gather(pred_list, out)
                 preds = paddle.concat(pred_list, 0)
 
-            if accum_samples > total_samples and not engine.use_dali:
+            if accum_samples > total_samples and not engine.config[
+                    "Global"].get("use_dali", False):
                 if isinstance(preds, list):
                     preds = [
                         pred[:total_samples + current_samples - accum_samples]
@@ -151,7 +152,7 @@ def classification_eval(engine, epoch_id=0):
                 epoch_id, iter_id, max_iter, metric_msg, time_msg, ips_msg))
 
         tic = time.time()
-    if engine.use_dali:
+    if engine.config["Global"].get("use_dali", False):
         engine.dataloader_dict["Eval"].reset()
 
     if "ATTRMetric" in engine.config["Metric"]["Eval"][0]:

ppcls/engine/train/regular_train_epoch.py

@@ -22,8 +22,8 @@ from ppcls.utils import profiler
 def regular_train_epoch(engine, epoch_id, print_batch_step):
     tic = time.time()
 
-    for iter_id in range(engine.dataloader_dict["Train"].max_iter):
-        batch = engine.dataloader_dict["Train"].get_batch()
+    for iter_id in range(engine.dataloader_dict["Train"].iter_per_epoch):
+        batch = engine.dataloader_dict["TrainIter"].get_batch()
 
         profiler.add_profiler_step(engine.config["profiler_options"])
         if iter_id == 5:

ppcls/engine/train/utils.py

@@ -55,13 +55,13 @@ def log_info(trainer, batch_size, epoch_id, iter_id):
         batch_size / trainer.time_info["batch_cost"].avg)
 
     eta_sec = ((trainer.config["Global"]["epochs"] - epoch_id + 1
-                ) * trainer.dataloader_dict["Train"].max_iter - iter_id
+                ) * trainer.dataloader_dict["Train"].iter_per_epoch - iter_id
                ) * trainer.time_info["batch_cost"].avg
     eta_msg = "eta: {:s}".format(str(datetime.timedelta(seconds=int(eta_sec))))
     logger.info("[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".format(
         epoch_id, trainer.config["Global"][
             "epochs"], iter_id, trainer.dataloader_dict["Train"]
-        .max_iter, lr_msg, metric_msg, time_msg, ips_msg, eta_msg))
+        .iter_per_epoch, lr_msg, metric_msg, time_msg, ips_msg, eta_msg))
 
     for i, lr in enumerate(trainer.lr_sch):
         logger.scaler(

ppcls/optimizer/__init__.py

@@ -48,12 +48,12 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
 def build_optimizer(engine):
     if engine.mode != "train":
         return None, None
-    config, max_iter, model_list = engine.config, engine.dataloader_dict[
-        "Train"].max_iter, [engine.model, engine.train_loss_func]
+    config, iter_per_epoch, model_list = engine.config, engine.dataloader_dict[
+        "Train"].iter_per_epoch, [engine.mode, engine.train_loss_func]
     optim_config = copy.deepcopy(config["Optimizer"])
     epochs = config["Global"]["epochs"]
-    update_freq = engine.update_freq
-    step_each_epoch = max_iter // update_freq
+    update_freq = config["Global"].get("update_freq", 1)
+    step_each_epoch = iter_per_epoch // update_freq
     if isinstance(optim_config, dict):
         # convert {'name': xxx, **optim_cfg} to [{name: {scope: xxx, **optim_cfg}}]
         optim_name = optim_config.pop("name")