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add config doc

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darkliang
2021-11-03 15:35:03 +08:00
parent 01935aac2f
commit 6e71c7ac34
3 changed files with 194 additions and 10 deletions
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README.md
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@@ -14,13 +14,13 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
# Model Zoo
| Model | NM | BG | CL | Configuration | Input Size | Inference Time | Model Size |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :--------: | :--------: | :------------------------------------------------------------------------------------------- | :--------: | :------------: | :--------------: |
| Baseline | 96.3 | 92.2 | 77.6 | [baseline.yaml](config/baseline.yaml) | 64x44 | 12s | 3.78M |
| [GaitSet(AAAI2019)](https://arxiv.org/pdf/1811.06186.pdf) | 95.8(95.0) | 90.0(87.2) | 75.4(70.4) | [gaitset.yaml](config/gaitset.yaml) | 64x44 | 11s | 2.59M |
| [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf) | 96.1(96.2) | 90.7(91.5) | 78.7(78.7) | [gaitpart.yaml](config/gaitpart.yaml) | 64x44 | 22s | 1.20M |
| Model | NM | BG | CL | Configuration | Input Size | Inference Time | Model Size |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :--------: | :--------: | :------------------------------------------------------------------------------------------- | :--------: | :------------: | :------------: |
| Baseline | 96.3 | 92.2 | 77.6 | [baseline.yaml](config/baseline.yaml) | 64x44 | 12s | 3.78M |
| [GaitSet(AAAI2019)](https://arxiv.org/pdf/1811.06186.pdf) | 95.8(95.0) | 90.0(87.2) | 75.4(70.4) | [gaitset.yaml](config/gaitset.yaml) | 64x44 | 11s | 2.59M |
| [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf) | 96.1(96.2) | 90.7(91.5) | 78.7(78.7) | [gaitpart.yaml](config/gaitpart.yaml) | 64x44 | 22s | 1.20M |
| [GLN*(ECCV2020)](http://home.ustc.edu.cn/~saihui/papers/eccv2020_gln.pdf) | 96.4(95.6) | 93.1(92.0) | 81.0(77.2) | [gln_phase1.yaml](config/gln/gln_phase1.yaml), [gln_phase2.yaml](config/gln/gln_phase2.yaml) | 128x88 | 14s | 8.54M / 14.70M |
| [GaitGL(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | 97.4(97.4) | 94.5(94.5) | 83.8(83.6) | [gaitgl.yaml](config/gaitgl.yaml) | 64x44 | 31s | 3.10M |
| [GaitGL(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | 97.4(97.4) | 94.5(94.5) | 83.8(83.6) | [gaitgl.yaml](config/gaitgl.yaml) | 64x44 | 31s | 3.10M |
The results in the parentheses are mentioned in the papers
@@ -60,7 +60,7 @@ It's inference process just cost about 90 secs(Baseline & 8 RTX6000).
## Prepare dataset
See [prepare dataset](doc/prepare_dataset.md).
## Get pretrained model
## Get trained model
- Option 1:
```
python misc/download_pretrained_model.py
@@ -93,12 +93,13 @@ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 l
You can run commands in [test.sh](test.sh) for testing different models.
## Customize
If you want customize your own model, see [here](doc/how_to_create_your_model.md).
1. First, you need to read the [config documentation](doc/detailed_config.md) to figure out the usage of every item.
2. If you want create your own model, see [here](doc/how_to_create_your_model.md).
# Warning
- Some models may not be compatible with `AMP`, you can disable it by setting `enable_float16` **False**.
- In `DDP` mode, zombie processes may occur when the program terminates abnormally. You can use this command `kill $(ps aux | grep main.py | grep -v grep | awk '{print $2}')` to clear them.
- We implemented the functionality of testing while training, but it slightly affected the results. None of our published models use this functionality. You can disable it by setting `with_test` **False**.
- In `DDP` mode, zombie processes may be generated when the program terminates abnormally. You can use this command `kill $(ps aux | grep main.py | grep -v grep | awk '{print $2}')` to clear them.
- We implemented the functionality about testing while training, but it slightly affected the results. None of our published models use this functionality. You can disable it by setting `with_test` **False**.
# Authors:
**Open Gait Team (OGT)**
doc/detailed_config.md
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# Configuration item
### data_cfg
* Data configuration
>
> * Args
> * dataset_name: Dataset name. Only support `CASIA-B`.
> * dataset_root: The path of storing your dataset.
> * num_workers: The number of workers to collect data.
> * dataset_partition: The path of storing your dataset partition file. It splits the dataset to two parts, including train set and test set.
> * cache: If `True`, load all data to memory during buiding dataset.
> * test_dataset_name: The name of test dataset.
----
### loss_cfg
* Loss function
> * Args
> * type: Loss function type, support `TripletLoss` and `CrossEntropyLoss`
> * loss_term_weights: loss weight.
> * log_prefix: the prefix of loss log.
----
### optimizer_cfg
* Optimizer
> * Args
> * solver: Optimizer type, example: `SGD`, `Adam`
> * **others**: Please refer to `torch.optim`
### scheduler_cfg
* Learning rate scheduler
> * Args
> * scheduler : Learning rate scheduler, example: `MultiStepLR`
> * **others** : Please refer to `torch.optim.lr_scheduler`
----
### model_cfg
* Model to be trained
> * Args
> * model : Model type, please refer to [Model Library](../lib/modeling/models) for the supported values
> * **others** : Please refer to [Training Configuration File of Corresponding Model](../config)
----
### evaluator_cfg
* Evaluator configuration
> * Args
> * enable_float16: If `True`, enable auto mixed precision.
> * restore_ckpt_strict: If `True`, check whether the checkpoint is the same as the model.
> * restore_hint: `int` value indicates the iteration number of restored checkpoint; `str` value indicates the path of restored checkpoint.
> * save_name: The name of the experiment.
> * eval_func: The function name of evaluation. For `CASIA-B`, choose `identification`.
> * sampler:
> - type: The name of sampler. Choose `InferenceSampler`
> - sample_type: In general, we use `all_ordered` to input all frames by its natural order, which makes sure the tests are consistent.
> - batch_size: In general, it should equal to the number of utilized GPU.
> - **others**: Please refer to [data.sampler](../lib/data/sampler.py) and [data.collate_fn](../lib/data/collate_fn.py)
> * transform: support `BaseSilCuttingTransform`, `BaseSilTransform`. The difference between them is `BaseSilCuttingTransform` cut the pixels on both sides horizontally.
> * metric: `euc` or `cos`, generally, `euc` performs better.
----
### trainer_cfg
* Trainer configuration
> * Args
> * fix_BN: If `True`, we fix the weight of all `BatchNorm` layers.
> * log_iter: Every `log_iter` iterations, log the information.
> * save_iter: Every `save_iter` iterations, save the model.
> * with_test: If `True`, we test the model every `save_iter` iterations. A bit of performance impact.(*To Be Fixed*)
> * optimizer_reset: If `True` and `restore_hint!=0`, reset the optimizer while restoring the model.
> * scheduler_reset: If `True` and `restore_hint!=0`, reset the scheduler while restoring the model.
> * sync_BN: If `True`, applies Batch Normalization as described in the paper [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167).
> * total_iter: The total number of training iterations.
> * sampler:
> - type: The name of sampler. Choose `TripletSampler`
> - sample_type: `[all, fixed, unfixed]` indicates the number of frames used to test, while `[unordered, ordered]` means whether input sequence by its natural order. Example: `fixed_unordered` means selecting fixed number of frames randomly.
> - batch_size: *[P,K]*\
> **example**:
> - 8
> - 16
> - **others**: Please refer to [data.sampler](../lib/data/sampler.py) and [data.collate_fn](../lib/data/collate_fn.py)
> * **others**: Please refer to `evaluator_cfg`
---
**Note**: All configuatrarion items will merged into [default.yaml](../config/default.yaml), and the current configuration is preferable.
# Example
```yaml
data_cfg:
dataset_name: CASIA-B
dataset_root: your_path
dataset_partition: ./misc/partitions/CASIA-B_include_005.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: CASIA-B
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 60000
save_name: Baseline
eval_func: identification
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
loss_cfg:
- loss_term_weights: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weights: 0.1
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
in_channels: 1
layers_cfg: # Layers configuration for automatically model construction
- BC-64
- BC-64
- M
- BC-128
- BC-128
- M
- BC-256
- BC-256
type: Plain
SeparateFCs:
in_channels: 256
out_channels: 256
parts_num: 31
SeparateBNNecks:
class_num: 74
in_channels: 256
parts_num: 31
bin_num:
- 16
- 8
- 4
- 2
- 1
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 20000
- 40000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 10000
save_name: Baseline
sync_BN: true
total_iter: 60000
sampler:
batch_shuffle: true
batch_size:
- 8 # TripletSampler, batch_size[0] indicates Number of Identity
- 16 # batch_size[1] indicates Samples sequqnce for each Identity
frames_num_fixed: 30 # fixed frames number for training
frames_num_max: 50 # max frames number for unfixed training
frames_num_min: 25 # min frames number for unfixed traing
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
```
misc/download_pretrained_model.py
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@@ -101,6 +101,8 @@ def download_file_and_uncompress(url,
if not os.path.exists(savepath):
_download_file(url, savepath, print_progress)
if print_progress:
print("Uncompress %s" % os.path.basename(savepath))
for total_num, index, rootpath in _uncompress_file_zip(savepath, extrapath):
if print_progress:
done = int(50 * float(index) / total_num)