multi-card eval support

ppcls/data/dataloader/multilabel_dataset.py

@@ -42,7 +42,7 @@ class MultiLabelDataset(CommonDataset):
                 self.labels.append(labels)
                 assert os.path.exists(self.images[-1])
         if label_ratio:
-            return np.array(self.labels).mean(0)
+            return np.array(self.labels).mean(0).astype("float32")
 
     def __getitem__(self, idx):
         try:
@@ -53,7 +53,7 @@ class MultiLabelDataset(CommonDataset):
             img = img.transpose((2, 0, 1))
             label = np.array(self.labels[idx]).astype("float32")
             if self.label_ratio is not None:
-                return (img, [label, self.label_ratio])
+                return (img, np.array([label, self.label_ratio]))
             else:
                 return (img, label)
 

ppcls/engine/evaluation/classification.py

@@ -84,6 +84,7 @@ def classification_eval(engine, epoch_id=0):
         # gather Tensor when distributed
         if paddle.distributed.get_world_size() > 1:
             label_list = []
+
             paddle.distributed.all_gather(label_list, batch[1])
             labels = paddle.concat(label_list, 0)
 

ppcls/loss/multilabelloss.py

@@ -38,7 +38,7 @@ class MultiLabelLoss(nn.Layer):
 
     def _binary_crossentropy(self, input, target, class_num):
         if self.weight_ratio:
-            target, label_ratio = target
+            target, label_ratio = target[:, 0, :], target[:, 1, :]
         if self.epsilon is not None:
             target = self._labelsmoothing(target, class_num)
         cost = F.binary_cross_entropy_with_logits(

ppcls/metric/metrics.py

@@ -363,6 +363,6 @@ class ATTRMetric(nn.Layer):
         self.threshold = threshold
 
     def forward(self, output, target):
-        metric_dict = get_attr_metrics(target[0].numpy(),
+        metric_dict = get_attr_metrics(target[:, 0, :].numpy(),
                                        output.numpy(), self.threshold)
         return metric_dict