add combined_metrics

ppcls/engine/trainer.py

@@ -31,7 +31,7 @@ from ppcls.utils import logger
 from ppcls.data import build_dataloader
 from ppcls.arch import build_model
 from ppcls.loss import build_loss
-from ppcls.arch.loss_metrics import build_metrics
+from ppcls.metric import build_metrics
 from ppcls.optimizer import build_optimizer
 from ppcls.utils.save_load import load_dygraph_pretrain
 from ppcls.utils.save_load import init_model
@@ -379,6 +379,11 @@ class Trainer(object):
                                                 query_img_id, gallery_img_id)
         else:
             metric_dict = {metric_key: 0.}
+        metric_msg = ", ".join([
+            "{}: {:.5f}".format(key, metric_dict[key].avg)
+            for key in metric_dict
+        ])
+        logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
 
         return metric_dict[metric_key]
 

ppcls/metric/__init__.py

+#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from paddle import nn
+import copy
+from collections import OrderedDict
+
+from .metrics import Topk, mAP, mINP, Recallk
+
+
+class CombinedMetrics(nn.Layer):
+    def __init__(self, config_list):
+        super().__init__()
+        self.metric_func_list = []
+        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]
+            metric_params = config[metric_name]
+            self.metric_func_list.append(eval(metric_name)(**metric_params))
+
+    def __call__(self,
+                 similarities_matrix,
+                 query_img_id,
+                 gallery_img_id,
+                 x=None,
+                 label=None):
+        metric_dict = OrderedDict()
+        for idx, metric_func in enumerate(self.metric_func_list):
+            if x is None:
+                metric_dict.update(metric_func(x, label))
+            else:
+                metric_dict.update(
+                    metric_func(similarities_matrix, query_img_id,
+                                gallery_img_id))
+        return metric_dict
+
+
+def build_metrics(config):
+    metrics_list = CombinedMetrics(copy.deepcopy(config))
+    return metrics_list