Update loss.py

python/paddle/nn/functional/loss.py

@@ -1804,41 +1804,8 @@ def cross_entropy(input,
             weight_gather_reshape = reshape(weight_gather, shape=out_shape)
             out = paddle.cast(out, weight_gather_reshape.dtype)
         else:
-            if input.shape[-1] != weight.shape[-1]:
-                raise ValueError("input's class_dimension({}) must equal to \
-                        weight's class_dimension({}) \
-                            when weight is provided"
-                                 .format(input.shape[-1], weight.shape[-1]))
-            valid_label = paddle.where(
-                label == ignore_index,
-                paddle.zeros(
-		            [1], dtype=label.dtype),
-	    	    label)
-            if (paddle.numel(paddle.nonzero(valid_label < 0)) > 0) or (
-                    paddle.numel(
-						paddle.nonzero(valid_label >= input.shape[-1])) > 0):
-                invalid_label = paddle.gather_nd(
-                    input, paddle.nonzero(valid_label < 0))
-                if paddle.numel(invalid_label) > 0:
-                    raise ValueError(
-                        "Target({}) is out of class_dimension's lower bound({})".
-                        format(invalid_label[0], 0))
-                invalid_label = paddle.gather_nd(
-                    input, paddle.nonzero(valid_label >= input.shape[-1]))
-                if paddle.numel(invalid_label) > 0:
-                    raise ValueError(
-                        "Target({}) is out of class_dimension's upper bound({})".
-                        format(invalid_label[0], input.shape[-1]))
-
-            ignore_weight_mask = paddle.cast((label != ignore_index), out.dtype)
-            if ignore_weight_mask.ndim > 1 and ignore_weight_mask.shape[
-                    -1] == 1:
-                ignore_weight_mask = paddle.squeeze(ignore_weight_mask, -1)
-
             weight_gather = paddle.gather_nd(
-                weight,
-                valid_label)  #trans weight from class to sample, shape:N
-            weight_gather = paddle.multiply(weight_gather, ignore_weight_mask)
+                weight, label)  #trans weight from class to sample, shape:N
             input_shape = list(label.shape)
             weight_gather_reshape = reshape(weight_gather, shape=input_shape)
         out = paddle.multiply(out, weight_gather_reshape, name=weight_name)