Change the default value of the label indices from [1] to [1:]

mindocr/utils/callbacks.py

@@ -26,7 +26,7 @@ class Evaluator:
         metrics: metrics to evaluate network performance
         pred_cast_fp32: whehter to cast network prediction to float 32. Set True if AMP is used.
         input_indices: The indices of the data tuples which will be fed into the network. If it is None, then the first item will be fed only.
-        label_indices: The indices of the data tuples which will be marked as label. If it is None, then the second item will be marked as label.
+        label_indices: The indices of the data tuples which will be marked as label. If it is None, then the remaining items will be marked as label.
         meta_data_indices: The indices for the data tuples which will be marked as meta data. If it is None, then the item indices not in input or label indices are marked as meta data.
     """
 
@@ -96,7 +96,7 @@ def eval(self):
             if self.label_indices is not None:
                 gt = [data[x] for x in self.label_indices]
             else:
-                gt = [data[1]]
+                gt = data[1:]
 
             net_preds = self.net(*inputs)
 
@@ -113,7 +113,7 @@ def eval(self):
                 else:
                     # assume the indices not in input_indices or label_indices are all meta_data_indices
                     input_indices = set(self.input_indices) if self.input_indices is not None else {0}
-                    label_indices = set(self.label_indices) if self.label_indices is not None else {1}
+                    label_indices = set(self.label_indices) if self.label_indices is not None else set(range(1, len(data), 1))
                     meta_data_indices = sorted(set(range(len(data))) - input_indices - label_indices)
                     meta_info = [data[x] for x in meta_data_indices]
 

mindocr/utils/model_wrapper.py

@@ -14,7 +14,7 @@ class NetWithLossWrapper(nn.Cell):
         net (nn.Cell): network
         loss_fn: loss function
         input_indices: The indices of the data tuples which will be fed into the network. If it is None, then the first item will be fed only.
-        label_indices: The indices of the data tuples which will be fed into the loss function. If it is None, then the second item will be fed only.
+        label_indices: The indices of the data tuples which will be fed into the loss function. If it is None, then the remaining items will be fed.
     '''
     def __init__(self, net, loss_fn, pred_cast_fp32=False, input_indices=None, label_indices=None):
         super().__init__(auto_prefix=False)
@@ -44,7 +44,7 @@ def construct(self, *args):
                 pred = [F.cast(p, mstype.float32) for p in pred]
 
         if self.label_indices is None:
-            loss_val = self._loss_fn(pred, args[1])
+            loss_val = self._loss_fn(pred, *args[1:])
         else:
             loss_val = self._loss_fn(pred, *select_inputs_by_indices(args, self.label_indices))
 
@@ -60,7 +60,7 @@ class NetWithEvalWrapper(nn.Cell):
         net (nn.Cell): network
         loss_fn: loss function, if None, will not compute loss for evaluation dataset
         input_indices: The indices of the data tuples which will be fed into the network. If it is None, then the first item will be fed only.
-        label_indices: The indices of the data tuples which will be fed into the loss function. If it is None, then the second item will be fed only.
+        label_indices: The indices of the data tuples which will be fed into the loss function. If it is None, then the remaining items will be fed.
     '''
     def __init__(self, net, loss_fn=None, input_indices=None, label_indices=None):
         super().__init__(auto_prefix=False)
@@ -84,7 +84,7 @@ def construct(self, *args):
             pred = self._net(*select_inputs_by_indices(args, self.input_indices))
 
         if self.label_indices is None:
-            labels = [args[1]]
+            labels = args[1:]
         else:
             labels = select_inputs_by_indices(args, self.label_indices)