Add GaussianNLLLoss API.#50843
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| 你的PR提交成功，感谢你对开源项目的贡献! |
| @GGBond8488 你好，重新提交了pr |
python/paddle/nn/functional/loss.py Outdated
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| # Entries of var must be non-negative | ||
| # print(paddle.any(var < 0)) | ||
| # if paddle.any(var < 0): |
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此处静态图时判断var返回为LoDTensor
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这是一个比较老的概念，但是应该不会影响这一段的检查
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可能是我对静态图不了解，paddle.any(var < 0)是否在静态图时可能输出的是节点信息？我在测试静态图时这段检查会进入到判断语句内层返回Error。相同代码的动态图可以通过测试。
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测试返回的错误代码
if paddle.any(var < 0): \ print('var',var) \ print(paddle.any(var < 0)) \ raise ValueError("var has negative entry/entries")
输出的结果：
var var Var : LOD_TENSOR.shape(10, 2).dtype(float32).stop_gradient(True) E.E var any_1.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False) var var Var : LOD_TENSOR.shape(10, 2).dtype(float32).stop_gradient(True) var any_3.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False)
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这是一个比较老的概念，但是应该不会影响这一段的检查
尝试了cond()函数，发现在组网时都会调用cond()函数里设计的tru_func()和false_func()并抛出函数内的错误。
然后我找了一下其他人是否有在lossfunc中使用到raise ValueError，可以发现在python\paddle\nn\functional\loss.py中的triplet_margin_with_distance_loss内line 3526有关于节点内参数的值的判断。如您所说的不会影响到检查。
但是使用Print()进行检查发现节点内的数据无误。
print(paddle.any(var < 0)) var_res = paddle.static.Print(paddle.any(var < 0)) # if paddle.any(var < 0): # raise ValueError("var has negative entry/entries") ================================================ Variable: any_1.tmp_0 - lod: {} - place: Place(cpu) - shape: [1] - layout: NCHW - dtype: bool - data: [0] Variable: any_3.tmp_0 - lod: {} - place: Place(cpu) - shape: [1] - layout: NCHW - dtype: bool - data: [0] 进程已结束,退出代码0 var any_0.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False) var any_2.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False) I0227 16:33:39.522938 20040 interpretercore.cc:273] New Executor is Running. Ran 1 test in 0.204s OK 如果不添加判断的代码，则可以正常通过测试
如果添加了判断代码，仍会进入到判断语句中返回错误。
Error Traceback (most recent call last): File "D:\PyWorkspace\Paddle\python\paddle\fluid\tests\unittests\test_gaussian_nll_loss.py", line 130, in test_static_case out1,var_res = F.gaussian_nll_loss( File "D:\Anaconda\envs\paddle_devcpu\lib\site-packages\paddle\nn\functional\loss.py", line 4003, in gaussian_nll_loss raise ValueError("var has negative entry/entries") ValueError: var has negative entry/entries There was a problem hiding this comment.
如果语句修改为
if not paddle.all(var > 0): raise ValueError("var has negative entry/entries") 也可以通过测试。。但是会不会还是判断的是节点
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你的判断是正确的，在静态图里面，组网阶段是没有办法拿到var的数据的，所以这个检查在静态图下会报错，现在有两种解决方案：
- 增加c++ 层 的kernel，在kernel层实现计算，并实现对数据的检查，kernel运行在计算阶段，可以拿到对应的数据
- https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/nn/control_flow.py#L43，利用这里 的Assert OP进行数值判断和提示
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这是一个比较老的概念，但是应该不会影响这一段的检查
尝试了
cond()函数，发现在组网时都会调用cond()函数里设计的tru_func()和false_func()并抛出函数内的错误。 然后我找了一下其他人是否有在lossfunc中使用到raise ValueError，可以发现在python\paddle\nn\functional\loss.py中的triplet_margin_with_distance_loss内line 3526有关于节点内参数的值的判断。如您所说的不会影响到检查。 但是使用Print()进行检查发现节点内的数据无误。print(paddle.any(var < 0)) var_res = paddle.static.Print(paddle.any(var < 0)) # if paddle.any(var < 0): # raise ValueError("var has negative entry/entries") ================================================ Variable: any_1.tmp_0 - lod: {} - place: Place(cpu) - shape: [1] - layout: NCHW - dtype: bool - data: [0] Variable: any_3.tmp_0 - lod: {} - place: Place(cpu) - shape: [1] - layout: NCHW - dtype: bool - data: [0] 进程已结束,退出代码0 var any_0.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False) var any_2.tmp_0 : LOD_TENSOR.shape(1,).dtype(bool).stop_gradient(False) I0227 16:33:39.522938 20040 interpretercore.cc:273] New Executor is Running. Ran 1 test in 0.204s OK如果不添加判断的代码，则可以正常通过测试 如果添加了判断代码，仍会进入到判断语句中返回错误。
Error Traceback (most recent call last): File "D:\PyWorkspace\Paddle\python\paddle\fluid\tests\unittests\test_gaussian_nll_loss.py", line 130, in test_static_case out1,var_res = F.gaussian_nll_loss( File "D:\Anaconda\envs\paddle_devcpu\lib\site-packages\paddle\nn\functional\loss.py", line 4003, in gaussian_nll_loss raise ValueError("var has negative entry/entries") ValueError: var has negative entry/entries
这里的cond，控制流会对控制流的分支都进行组网，所以会发现true_fn以及false_fn都会抛出异常
而Print()实际上也是一个op，也在进行组网，只是在计算阶段会执行和打印
python/paddle/nn/functional/loss.py Outdated
| 'gaussian_nll_loss', | ||
| ) | ||
| condition = paddle.all(var > 0) | ||
| Assert(condition) |
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补充Assert的参数，把var的名字的数据填进去，提示更友好一点
python/paddle/nn/layer/loss.py Outdated
| loss = F.multi_label_soft_margin_loss(input, target, var, reduction='none') | ||
| print(loss) | ||
| | ||
| loss = F.multi_label_soft_margin_loss(input, target, var, reduction='mean') |
python/paddle/nn/functional/loss.py Outdated
| loss = F.multi_label_soft_margin_loss(input, target, var, reduction='none') | ||
| print(loss) | ||
| | ||
| loss = F.multi_label_soft_margin_loss(input, target, var, reduction='mean') |
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抱歉，git流程还是不熟悉，之前的例子丢失了，我现在来补充
| return [np.mean(loss)] | ||
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| class TestGaussianNLLLossAPI(unittest.TestCase): |
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其他的没问题了，这个单测不同的场景分写成不同的test_case吧（把这些用例写到单独的class里面），方便后续直接定位是哪个case不通过。
| code is fine, but rfc need to be modified in chapter 5: |
Yes, sorry for my carelessness. |
| @Atlantisming 可以提个 PR 修改下 RFC |
| output (Tensor): If ``reduction`` is ``'none'``, the shape of output is same as ``input`` , else the shape of output is [1]. | ||
| | ||
| Examples:: | ||
| .. code-block:: python |
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code-block这行下空一行吧，否则解析会出错
| A callable object of GaussianNLLLoss. | ||
| | ||
| Examples:: | ||
| .. code-block:: python |
Atlantisming dismissed stale reviews from jeff41404, GGBond8488, and PaddlePaddle-Gardener via 2cb2432 python/paddle/nn/layer/loss.py Outdated
| class GaussianNLLLoss(Layer): | ||
| r"""Gaussian negative log likelihood loss. | ||
| | ||
| The targets are treated as samples from Gaussian distributions with |
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英文文档这里的描述参考一下其他loss的描述，重新组织一下文案，可以参考 BCELoss，上面functional下的也一样
python/paddle/nn/functional/loss.py Outdated
| The ``label`` is treated as samples from Gaussian distributions with | ||
| expectations ``input`` and ``variance`` predicted by the neural network. For a | ||
| ``label`` tensor modelled as having Gaussian distribution with a tensor | ||
| of expectations ``input`` and a tensor of positive ``variance`` the loss is: |
python/paddle/nn/functional/loss.py Outdated
| | ||
| Gaussian negative log likelihood loss among ``input``, ``variance`` and | ||
| ``label``. Note that the ``label`` is treated as samples from Gaussian distributions. | ||
| One of the interpretations is this class is used to train a neural network predicts |
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这里应该是function 不是class， One of the interpretations 是不是去掉更好点 @sunzhongkai588 再看看
| 同时请修复下 PR-CI-Codestyle-Check 失败的问题 |
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| import paddle | ||
| import paddle.fluid.core as core | ||
| import paddle.fluid as core |
| variance (Tensor): tensor of positive variance(s), :math:`(N, *)` or :math:`(*)`, same shape as the input, or same shape as the input but | ||
| with one dimension equal to 1, or same shape as the input but with one fewer | ||
| dimension (to allow for broadcasting). One for each of the expectations | ||
| in the input (heteroscedastic), or a single one (homoscedastic), available dtype is float32, float64. |
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该行和上一行进行左对齐，否则会解析错误
python/paddle/nn/layer/loss.py Outdated
| class GaussianNLLLoss(Layer): | ||
| r"""Create a callable object of 'GaussianNLLLoss' to calculate Gaussian negative log likelihood loss. | ||
| | ||
| This class create a callable object of Gaussian negative log likelihood loss among ``input``,``variance`` and |
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rfc 文档链接：PaddlePaddle/community#372
中文文档链接：PaddlePaddle/docs#5623