o * i @sUddlmZddlmZddlmZddlmZddlm Z ddl m Z erQddlm Z m Z mZddlmZe d Zd ed <e d Zd ed <eeefZd ed<ddddZdS)) annotations) TYPE_CHECKING)_C_ops)check_variable_and_dtype) LayerHelper)in_dynamic_or_pir_mode)Literal TypeAliasUnion)Tensor)meansumnoner _ReduceModeStringLiteral)r_ReduceModeNumberLiteral _ReduceModerxr reductionreturncCst|trdddd|}|durtdtr!t||St|ddd gd d |i}t dit }|j dd }| |}|j d d |id|i|d|S)aMarks a tensor as being part of the loss calculation for IPU. This operator is used to handle on the (final) loss of a model so that it is used as the start of backpropagation. When `reduction` is `none`, return raw `Out`. When `reduction` is `mean`, return .. math:: Out = MEAN(Out) When `reduction` is `sum`, return .. math:: Out = SUM(Out) Parameters: x (Variable): The input tensor. The shapes is [N, *], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64 on CPU and float16, float32 on IPU. reduction(str|int, optional): Reduce the loss output. Supported string values are: 'sum', 'mean', 'none' the corresponding int values are 0, 1, 2 respectively. The default value is "none". Returns: Variable: The loss ``Tensor`` with the specified reduction applied. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> loss = paddle.static.data(name="loss", shape=[-1, 1], dtype="float32") >>> out = paddle.incubate.identity_loss(loss, reduction=1) rrr)r r rNzUnsupported reduction type.rZfloat32Zfloat64 identity_lossr)Zinput_param_nameXZOut)typeZinputsZoutputsattrs)r) isinstancestrgetlower ExceptionrrrrrlocalsZ input_dtypeZ"create_variable_for_type_inferenceZ append_op)rrrhelperZdtypeoutr#c/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/incubate/nn/loss.pyr$s $   rN)r)rr rrrr ) __future__rtypingrZpaddlerZpaddle.base.data_feederrZpaddle.base.layer_helperrZpaddle.frameworkrrr r r r__annotations__rrrr#r#r#r$s