dbg

ppcls/engine/trainer.py

@@ -97,16 +97,12 @@ class Trainer(object):
         # build train loss and metric info
         if self.train_loss_func is None:
             loss_info = self.config.get("Loss", None)
-            if loss_info is None:
-                loss_info = [{"CELoss": {"weight": 1.0}}]
-            else:
+            if loss_info is not None:
                 loss_info = loss_info["Train"]
             self.train_loss_func = build_loss(loss_info)
         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:
+            if metric_config is not None:
                 metric_config = metric_config["Train"]
             self.train_metric_func = build_metrics(metric_config)
 
@@ -228,11 +224,9 @@ class Trainer(object):
         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:
+            if loss_info is not None:
                 loss_info = loss_info["Eval"]
-            self.eval_loss_func = build_loss(loss_info)
+                self.eval_loss_func = build_loss(loss_info)
         if self.eval_mode == "classification":
             if self.eval_dataloader is None:
                 self.eval_dataloader = build_dataloader(
@@ -240,11 +234,9 @@ class Trainer(object):
 
             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:
+                if metric_config is not None:
                     metric_config = metric_config["Eval"]
-                self.eval_metric_func = build_metrics(metric_config)
+                    self.eval_metric_func = build_metrics(metric_config)
 
             eval_result = self.eval_cls(epoch_id)
 
@@ -358,14 +350,11 @@ class Trainer(object):
         for block_idx, block_fea in enumerate(fea_blocks):
             similarity_matrix = paddle.matmul(
                 block_fea, gallery_feas, transpose_y=True)
-            # image_id_block = image_id_blocks[block_idx]
-            # image_id_mask = (image_id_block != gallery_img_id)
-            # similarity_matrix = similarity_matrix.masked_select(image_id_mask)
-            # if query_camera_id is not None:
-            #     camera_id_block = camera_id_blocks[block_idx]
-            #     camera_id_mask = (camera_id_block != gallery_camera_id)
-            #     similarity_matrix = similarity_matrix.masked_select(
-            #         camera_id_mask)
+            if query_camera_id is not None:
+                camera_id_block = camera_id_blocks[block_idx]
+                camera_id_mask = (camera_id_block != gallery_camera_id)
+                similarity_matrix = similarity_matrix.masked_select(
+                    camera_id_mask)
             if cum_similarity_matrix is None:
                 cum_similarity_matrix = similarity_matrix
             else:

ppcls/metric/__init__.py

@@ -26,7 +26,6 @@ class CombinedMetrics(nn.Layer):
         assert isinstance(config_list, list), (
             'operator config should be a list')
         for config in config_list:
-            print(config)
             assert isinstance(config,
                               dict) and len(config) == 1, "yaml format error"
             metric_name = list(config)[0]