add default metrics

ppcls/engine/trainer.py

@@ -93,37 +93,17 @@ class Trainer(object):
         self.train_metric_func = None
         self.eval_metric_func = None
 
-    def _build_metric_info(self, metric_config, mode="train"):
-        """
-        _build_metric_info: build metrics according to current mode
-        Return:
-            metric: dict of the metrics info
-        """
-        metric = None
-        mode = mode.capitalize()
-        if mode in metric_config and metric_config[mode] is not None:
-            metric = build_metrics(metric_config[mode])
-        return metric
-
-    def _build_loss_info(self, loss_config, mode="train"):
-        """
-        _build_loss_info: build loss according to current mode
-        Return:
-            loss_dict: dict of the loss info
-        """
-        loss = None
-        mode = mode.capitalize()
-        if mode in loss_config and loss_config[mode] is not None:
-            loss = build_loss(loss_config[mode])
-        return loss
-
     def train(self):
         # build train loss and metric info
         if self.train_loss_func is None:
-            self.train_loss_func = self._build_loss_info(self.config["Loss"])
-        if "Metric" in self.config and self.train_metric_func is None:
-            self.train_metric_func = self._build_metric_info(self.config[
-                "Metric"])
+            self.train_loss_func = build_loss(self.config["Loss"])
+        if self.train_metric_func is None:
+            metric_config = self.config.get("Metric", None)
+            if metric_config is None:
+                metric_config = [{"name": "TopkAcc", "topk": (1, 5)}]
+            else:
+                metric_config = metric_config["Train"]
+            self.train_metric_func = build_metrics(metric_config)
 
         if self.train_dataloader is None:
             self.train_dataloader = build_dataloader(self.config["DataLoader"],
@@ -241,10 +221,26 @@ class Trainer(object):
     @paddle.no_grad()
     def eval(self, epoch_id=0):
         self.model.eval()
+        if self.eval_loss_func is None:
+            loss_info = self.config.get("Loss", None)
+            if loss_info is None:
+                loss_info = [{"CELoss": {"weight": 1.0}}]
+            else:
+                loss_info = loss_info["Eval"]
+            self.eval_loss_func = build_loss(loss_info)
         if self.eval_mode == "classification":
             if self.eval_dataloader is None:
                 self.eval_dataloader = build_dataloader(
                     self.config["DataLoader"], "Eval", self.device)
+
+            if self.eval_metric_func is None:
+                metric_config = self.config.get("Metric", None)
+                if metric_config is None:
+                    metric_config = [{"name": "TopkAcc", "topk": (1, 5)}]
+                else:
+                    metric_config = metric_config["Eval"]
+                self.eval_metric_func = build_metrics(metric_config)
+
             eval_result = self.eval_cls(epoch_id)
 
         elif self.eval_mode == "retrieval":
@@ -255,13 +251,14 @@ class Trainer(object):
             if self.query_dataloader is None:
                 self.query_dataloader = build_dataloader(
                     self.config["DataLoader"], "Query", self.device)
-            # build train loss and metric info
-            if self.eval_loss_func is None:
-                self.eval_loss_func = self._build_loss_info(
-                    self.config["Loss"], "eval")
+            # build metric info
             if self.eval_metric_func is None:
-                self.eval_metric_func = self._build_metric_info(
-                    self.config["Metric"], "eval")
+                metric_config = self.config.get("Metric", None)
+                if metric_config is None:
+                    metric_config = [{"name": "Recallk", "topk": (1, 5)}]
+                else:
+                    metric_config = metric_config["Eval"]
+                self.eval_metric_func = build_metrics(metric_config)
             eval_result = self.eval_retrieval(epoch_id)
         else:
             logger.warning("Invalid eval mode: {}".format(self.eval_mode))

ppcls/metric/__init__.py

@@ -16,7 +16,7 @@ from paddle import nn
 import copy
 from collections import OrderedDict
 
-from .metrics import Topk, mAP, mINP, Recallk
+from .metrics import TopkAcc, mAP, mINP, Recallk
 
 
 class CombinedMetrics(nn.Layer):

ppcls/metric/metrics.py

@@ -18,7 +18,7 @@ import paddle.nn as nn
 
 
 # TODO: fix the format
-class Topk(nn.Layer):
+class TopkAcc(nn.Layer):
     def __init__(self, topk=(1, 5)):
         super().__init__()
         assert isinstance(topk, (int, list, tuple))