o j@s<ddlZddlmZmZddlmZgZejdddZdS)N)core framework)gradients_with_optimizerFcCsdd}||d}t|tt|ksJd|dur=t|ttfs%|g}|D]}|dur;t|tjtjjfs;Jdq'ng}t|dkrQt|t|ksQJdt|t sZJd tj |||dS) a Compute the backward gradients of given tensors. Args: tensors(list of Tensors): the tensors which the gradient to be computed. The tensors can not contain the same tensor. grad_tensors(list of Tensors of None, optional): the init gradients of the `tensors`` .If not None, it must have the same length with ``tensors`` , and if any of the elements is None, then the init gradient is the default value which is filled with 1.0. If None, all the gradients of the ``tensors`` is the default value which is filled with 1.0. Defaults to None. retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter :code:`retain_graph` to True, then the grads will be retained. Thus, setting it to False is much more memory-efficient. Defaults to False. Returns: NoneType: None Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32', stop_gradient=False) >>> y = paddle.to_tensor([[3, 2], [3, 4]], dtype='float32') >>> grad_tensor1 = paddle.to_tensor([[1,2], [2, 3]], dtype='float32') >>> grad_tensor2 = paddle.to_tensor([[1,1], [1, 1]], dtype='float32') >>> z1 = paddle.matmul(x, y) >>> z2 = paddle.matmul(x, y) >>> paddle.autograd.backward([z1, z2], [grad_tensor1, grad_tensor2], True) >>> print(x.grad) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[12., 18.], [17., 25.]]) >>> x.clear_grad() >>> paddle.autograd.backward([z1, z2], [grad_tensor1, None], True) >>> print(x.grad) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[12., 18.], [17., 25.]]) >>> x.clear_grad() >>> paddle.autograd.backward([z1, z2]) >>> print(x.grad) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[10., 14.], [10., 14.]]) cSs|dus J|dt|ttfr8t|dksJ|d|D]}t|tjtjjfs5Jd|dq!|St|tjtjjfsIJ|d|gS)Nz should not be Nonerz connot be emptyz Elements of z must be paddle.Tensorz! must be Tensor or list of Tensor) isinstancelisttuplelenpaddleTensorreager)Z in_out_listnameZeach_varr ^/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/autograd/backward_mode.py check_tensorsSs  zbackward..check_tensorstensorsz[The argument 'tensors' of paddle.autograd.backward contains duplicate paddle.Tensor object.NzThe argument 'grad_tensors' of paddle.autograd.backward is invalid, it can be 'None', 'paddle.Tensor' or 'list[None/paddle.Tensor]'.rz3The length of grad_tensors must be equal to tensorsz"retain_graph must be True or False) rsetrrrr r rr boolZ run_backward)rZ grad_tensorsZ retain_graphrZ each_tensorr r rbackwards8=  r)NF) r Z paddle.baserrZpaddle.base.backwardr__all__Z dygraph_onlyrr r r rs