rename lib to opengait

README.md

@@ -98,7 +98,7 @@ See [prepare dataset](docs/0.prepare_dataset.md).
 ### Train
 Train a model by
 ```
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase train
 ```
 - `python -m torch.distributed.launch` [DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) launch instruction.
 - `--nproc_per_node` The number of gpus to use, and it must equal the length of `CUDA_VISIBLE_DEVICES`.
@@ -112,7 +112,7 @@ You can run commands in [train.sh](train.sh) for training different models.
 ### Test
 Evaluate the trained model by
 ```
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase test
 ```
 - `--phase` Specified as `test`.
 - `--iter` Specify a iteration checkpoint.

docs/1.detailed_config.md

@@ -36,7 +36,7 @@
 ### model_cfg
 * Model to be trained
 >  * Args
->     * model : Model type, please refer to [Model Library](../lib/modeling/models) for the supported values.
+>     * model : Model type, please refer to [Model Library](../opengait/modeling/models) for the supported values.
 >     * **others** : Please refer to the [Training Configuration File of Corresponding Model](../config).
 ----
 ### evaluator_cfg
@@ -51,7 +51,7 @@
 >       - 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: `int` values.
->       - **others**: Please refer to [data.sampler](../lib/data/sampler.py) and [data.collate_fn](../lib/data/collate_fn.py)
+>       - **others**: Please refer to [data.sampler](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py)
 >     * transform: Support `BaseSilCuttingTransform`, `BaseSilTransform`. The difference between them is `BaseSilCuttingTransform` cut out the black pixels on both sides horizontally.
 >     * metric: `euc` or `cos`, generally, `euc` performs better.
 
@@ -73,7 +73,7 @@
 >       - batch_size: *[P,K]* where `P` denotes the subjects in training batch while the `K` represents the sequences every subject owns. **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 [data.sampler](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py).
 >     * **others**: Please refer to `evaluator_cfg`.
 ---
 **Note**: 

docs/2.how_to_create_your_model.md

@@ -50,11 +50,11 @@ class NewModel(BaseModel):
 > 
 > `embeddings`, `logits` and `labels` are the input arguments of the loss function.
 
-More information should be seen in [base_model.py](../lib/modeling/base_model.py) and [loss_aggregator.py](../lib/modeling/loss_aggregator.py).
+More information should be seen in [base_model.py](../opengait/modeling/base_model.py) and [loss_aggregator.py](../opengait/modeling/loss_aggregator.py).
 
 After finishing the model file, you have two steps left to do:
 
-**Step 1**: Put your newmodel.py under `lib/modeling/models`.
+**Step 1**: Put your newmodel.py under `opengait/modeling/models`.
 
 **Step 2**: Specify the model name in a yaml file:
 ```yaml
@@ -67,7 +67,7 @@ model_cfg:
 
 
 ## A new loss
-If you want to write a new loss, you need to write a class inherited from `lib/modeling/losses`, like this
+If you want to write a new loss, you need to write a class inherited from `opengait/modeling/losses`, like this
 ```python
 from .base import BaseLoss
 
@@ -81,6 +81,6 @@ class NewLoss(BaseLoss):
 ```
 Remember to use `gather_and_scale_wrapper` to wrap your forward function if your loss is computed by pairs like `triplet`. By this, we gather all features to one GPU card and scale the loss by the number of GPUs.
 
-Then, put your loss in `lib/modeling/losses` so that you can use it in config file. 
+Then, put your loss in `opengait/modeling/losses` so that you can use it in config file. 
 
-Moreover, refer to [loss_aggregator.py](../lib/modeling/loss_aggregator.py) to explore how does your defined loss work in the model.
+Moreover, refer to [loss_aggregator.py](../opengait/modeling/loss_aggregator.py) to explore how does your defined loss work in the model.

docs/3.advanced_usages.md

@@ -32,10 +32,10 @@
 > * If your path structure is similar to [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) (the 3-flod style: `id-type-view`), we recommand you to  -->
 
 ### Data Augmentation
-> In OpenGait, there is a basic transform class almost called by all the models, this is [BaseSilCuttingTransform](../lib/data/transform.py#L20), which is used to cut the input silhouettes.
+> In OpenGait, there is a basic transform class almost called by all the models, this is [BaseSilCuttingTransform](../opengait/data/transform.py#L20), which is used to cut the input silhouettes.
 >
 > Accordingly, by referring to this implementation, you can easily customize the data agumentation in just two steps:
-> * *Step1*: Define the transform function or class in [transform.py](../lib/data/transform.py), and make sure it callable. The style of [torchvision.transforms](https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html) is recommanded, and following shows a demo;
+> * *Step1*: Define the transform function or class in [transform.py](../opengait/data/transform.py), and make sure it callable. The style of [torchvision.transforms](https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html) is recommanded, and following shows a demo;
 >> ```python
 >> import torchvision.transforms as T
 >> class demo1():
@@ -77,9 +77,9 @@
 ### Visualization
 > To learn how does the model work, sometimes, you need to visualize the intermediate result.
 > 
-> For this purpose, we have defined a built-in instantiation of [`torch.utils.tensorboard.SummaryWriter`](https://pytorch.org/docs/stable/tensorboard.html), that is [`self.msg_mgr.writer`](../lib/utils/msg_manager.py#L24), to make sure you can log the middle information everywhere you want.
+> For this purpose, we have defined a built-in instantiation of [`torch.utils.tensorboard.SummaryWriter`](https://pytorch.org/docs/stable/tensorboard.html), that is [`self.msg_mgr.writer`](../opengait/utils/msg_manager.py#L24), to make sure you can log the middle information everywhere you want.
 > 
-> Demo: if we want to visualize the output feature of [baseline's backbone](../lib/modeling/models/baseline.py#L27), we could just insert the following codes at [baseline.py#L28](../lib/modeling/models/baseline.py#L28):
+> Demo: if we want to visualize the output feature of [baseline's backbone](../opengait/modeling/models/baseline.py#L27), we could just insert the following codes at [baseline.py#L28](../opengait/modeling/models/baseline.py#L28):
 >> ```python
 >> summary_writer = self.msg_mgr.writer
 >> if torch.distributed.get_rank() == 0 and self.training and self.iteration % 100==0:

misc/GREW/README.md

@@ -64,13 +64,13 @@ Then you will see the structure like:
 ## Train the dataset
 Modify the `dataset_root` in `./config/baseline_GREW.yaml`, and then run this command:
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/baseline_GREW.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline_GREW.yaml --phase train
 ```
 <!-- You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping. -->
 
 ## Get the submission file
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/baseline_GREW.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline_GREW.yaml --phase test
 ```
 The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.
 

misc/HID/README.md

@@ -12,13 +12,13 @@ python misc/HID/pretreatment_HID.py --input_train_path="train" --input_gallery_p
 ## Train the dataset
 Modify the `dataset_root` in `./misc/HID/baseline_hid.yaml`, and then run this command:
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase train
 ```
 You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping.
 
 ## Get the submission file
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase test
 ```
 The result will be generated in your working directory.
 

opengait/modeling/base_model.py

@@ -2,7 +2,7 @@
 
 This module defines the abstract meta model class and base model class. In the base model,
  we define the basic model functions, like get_loader, build_network, and run_train, etc.
- The api of the base model is run_train and run_test, they are used in `lib/main.py`.
+ The api of the base model is run_train and run_test, they are used in `opengait/main.py`.
 
 Typical usage:
 

test.sh

@@ -1,32 +1,32 @@
 # # **************** For CASIA-B ****************
 # # Baseline
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase test
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/gaitset.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset.yaml --phase test
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/gaitpart.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart.yaml --phase test
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --master_port 12345 --nproc_per_node=4 lib/main.py --cfgs ./config/gaitgl.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --master_port 12345 --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl.yaml --phase test
 
 # # GLN 
 # # Phase 1
-# CUDA_VISIBLE_DEVICES=3,4 python -m torch.distributed.launch --master_port 12345  --nproc_per_node=2 lib/main.py --cfgs ./config/gln/gln_phase1.yaml --phase test
+# CUDA_VISIBLE_DEVICES=3,4 python -m torch.distributed.launch --master_port 12345  --nproc_per_node=2 opengait/main.py --cfgs ./config/gln/gln_phase1.yaml --phase test
 # # Phase 2
-# CUDA_VISIBLE_DEVICES=2,5 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/gln/gln_phase2.yaml --phase test
+# CUDA_VISIBLE_DEVICES=2,5 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gln/gln_phase2.yaml --phase test
 
 
 # # **************** For OUMVLP ****************
 # # Baseline
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase test
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase test
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase test
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase test

train.sh

@@ -1,32 +1,32 @@
 # # **************** For CASIA-B ****************
 # # Baseline
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/baseline.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase train
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/gaitset.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset.yaml --phase train
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/main.py --cfgs ./config/gaitpart.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart.yaml --phase train
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/gaitgl.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl.yaml --phase train
 
 # # GLN 
 # # Phase 1
-# CUDA_VISIBLE_DEVICES=2,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/gln/gln_phase1.yaml --phase train
+# CUDA_VISIBLE_DEVICES=2,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/gln/gln_phase1.yaml --phase train
 # # Phase 2
-# CUDA_VISIBLE_DEVICES=2,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/gln/gln_phase2.yaml --phase train
+# CUDA_VISIBLE_DEVICES=2,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/gln/gln_phase2.yaml --phase train
 
 
 # # **************** For OUMVLP ****************
 # # Baseline
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase train
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase train
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase train
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase train