[DOC] change clip grad api, test=develop #2758
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TCChenlong
reviewed
Oct 9, 2020
| ClipGradByValue | ||
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| .. py:class:: paddle.nn.ClipGradByValue(max, min=None, need_clip=None) |
| ClipGradByGlobalNorm | ||
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| .. py:class:: paddle.nn.ClipGradByGlobalNorm(clip_norm, group_name='default_group', need_clip=None) |
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应该只有clip_norm吧 没有group_name、need_clip
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确认过，保留group_name，删除need_clip
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| 参数: | ||
| - **clip_norm** (float) - 所允许的范数最大值 | ||
| - **group_name** (str, optional) - 剪切的组名 |
| ClipGradByNorm | ||
| ------------------------------- | ||
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| .. py:class:: paddle.nn.ClipGradByNorm(clip_norm, need_clip=None) |
RichardWooSJTU
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to RichardWooSJTU/docs
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Apr 8, 2022
This fix should allow for visualizing YOLOv5 model graphs correctly in Tensorboard by uncommenting line 335 in train.py:
```python
if tb_writer:
tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
```
The problem was that the detect() layer checks the input size to adapt the grid if required, and tracing does not seem to like this shape check (even if the shape is fine and no grid recomputation is required). The following will warn:
https://github.com/ultralytics/yolov5/blob/0cae7576a9241110157cd154fc2237e703c2719e/train.py#L335
Solution is below. This is a YOLOv5s model displayed in TensorBoard. You can see the Detect() layer merging the 3 layers into a single output for example, and everything appears to work and visualize correctly.
```python
tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), [])
```
<img width="893" alt="Screenshot 2021-04-11 at 01 10 09" src="https://user-images.githubusercontent.com/26833433/114286928-349bd600-9a63-11eb-941f-7139ee6cd602.png">
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Change doc of 3 Clip apis.
Paddle PR: PaddlePaddle/Paddle#27767