fix seed

ppcls/engine/engine.py

@@ -63,7 +63,7 @@ def __init__(self, config, mode="train"):
 
         # set seed
         seed = self.config["Global"].get("seed", False)
-        if seed or seed == 0:
+        if seed or not isinstance(seed, bool):
             assert isinstance(seed, int), "The 'seed' must be a integer!"
             paddle.seed(seed)
             np.random.seed(seed)
@@ -123,26 +123,6 @@ def __init__(self, config, mode="train"):
             "epochs": self.config["Global"]["epochs"]
         })
 
-        # build dataloader
-        if self.mode == 'train':
-            self.train_dataloader = build_dataloader(
-                self.config["DataLoader"], "Train", self.device, self.use_dali)
-            if self.config["DataLoader"].get('UnLabelTrain', None) is not None:
-                self.unlabel_train_dataloader = build_dataloader(
-                    self.config["DataLoader"], "UnLabelTrain", self.device,
-                    self.use_dali)
-            else:
-                self.unlabel_train_dataloader = None
-
-            self.iter_per_epoch = len(
-                self.train_dataloader) - 1 if platform.system(
-                ) == "Windows" else len(self.train_dataloader)
-            if self.config["Global"].get("iter_per_epoch", None):
-                # set max iteration per epoch mannualy, when training by iteration(s), such as XBM, FixMatch.
-                self.iter_per_epoch = self.config["Global"].get(
-                    "iter_per_epoch")
-            self.iter_per_epoch = self.iter_per_epoch // self.update_freq * self.update_freq
-
         if self.mode == "eval" or (self.mode == "train" and
                                    self.config["Global"]["eval_during_train"]):
             if self.eval_mode in ["classification", "adaface"]:
@@ -183,21 +163,6 @@ def __init__(self, config, mode="train"):
             else:
                 self.eval_loss_func = None
 
-        # build metric
-        if self.mode == 'train' and "Metric" in self.config and "Train" in self.config[
-                "Metric"] and self.config["Metric"]["Train"]:
-            metric_config = self.config["Metric"]["Train"]
-            if hasattr(self.train_dataloader, "collate_fn"
-                       ) and self.train_dataloader.collate_fn is not None:
-                for m_idx, m in enumerate(metric_config):
-                    if "TopkAcc" in m:
-                        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)
-            self.train_metric_func = build_metrics(metric_config)
-        else:
-            self.train_metric_func = None
-
         if self.mode == "eval" or (self.mode == "train" and
                                    self.config["Global"]["eval_during_train"]):
             if self.eval_mode == "classification":
@@ -231,13 +196,6 @@ def __init__(self, config, mode="train"):
                     [self.model, getattr(self, 'train_loss_func', None)],
                     self.config["Global"]["pretrained_model"])
 
-        # build optimizer
-        if self.mode == 'train':
-            self.optimizer, self.lr_sch = build_optimizer(
-                self.config["Optimizer"], self.config["Global"]["epochs"],
-                self.iter_per_epoch // self.update_freq,
-                [self.model, self.train_loss_func])
-
         # AMP training and evaluating
         self.amp = "AMP" in self.config and self.config["AMP"] is not None
         self.amp_eval = False
@@ -331,6 +289,48 @@ def __init__(self, config, mode="train"):
             paddle.seed(int(seed) + dist.get_rank())
             np.random.seed(int(seed) + dist.get_rank())
             random.seed(int(seed) + dist.get_rank())
+
+        # build dataloader
+        if self.mode == 'train':
+            self.train_dataloader = build_dataloader(
+                self.config["DataLoader"], "Train", self.device, self.use_dali)
+            if self.config["DataLoader"].get('UnLabelTrain', None) is not None:
+                self.unlabel_train_dataloader = build_dataloader(
+                    self.config["DataLoader"], "UnLabelTrain", self.device,
+                    self.use_dali)
+            else:
+                self.unlabel_train_dataloader = None
+
+            self.iter_per_epoch = len(
+                self.train_dataloader) - 1 if platform.system(
+                ) == "Windows" else len(self.train_dataloader)
+            if self.config["Global"].get("iter_per_epoch", None):
+                # set max iteration per epoch mannualy, when training by iteration(s), such as XBM, FixMatch.
+                self.iter_per_epoch = self.config["Global"].get(
+                    "iter_per_epoch")
+            self.iter_per_epoch = self.iter_per_epoch // self.update_freq * self.update_freq
+
+        # build optimizer
+        if self.mode == 'train':
+            self.optimizer, self.lr_sch = build_optimizer(
+                self.config["Optimizer"], self.config["Global"]["epochs"],
+                self.iter_per_epoch // self.update_freq,
+                [self.model, self.train_loss_func])
+
+        # build metric
+        if self.mode == 'train' and "Metric" in self.config and "Train" in self.config[
+                "Metric"] and self.config["Metric"]["Train"]:
+            metric_config = self.config["Metric"]["Train"]
+            if hasattr(self.train_dataloader, "collate_fn"
+                       ) and self.train_dataloader.collate_fn is not None:
+                for m_idx, m in enumerate(metric_config):
+                    if "TopkAcc" in m:
+                        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)
+            self.train_metric_func = build_metrics(metric_config)
+        else:
+            self.train_metric_func = None
 
         # build postprocess for infer
         if self.mode == 'infer':