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arcface

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arcface_torch/docs/eval.md
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## Eval on ICCV2021-MFR
coming soon.
## Eval IJBC
You can eval ijbc with pytorch or onnx.
1. Eval IJBC With Onnx
```shell
CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50
```
2. Eval IJBC With Pytorch
```shell
CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
--model-prefix ms1mv3_arcface_r50/backbone.pth \
--image-path IJB_release/IJBC \
--result-dir ms1mv3_arcface_r50 \
--batch-size 128 \
--job ms1mv3_arcface_r50 \
--target IJBC \
--network iresnet50
```
## Inference
```shell
python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50
```
arcface_torch/docs/install.md
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## v1.8.0
### Linux and Windows
```shell
# CUDA 11.0
pip --default-timeout=100 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 10.2
pip --default-timeout=100 install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0
# CPU only
pip --default-timeout=100 install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
```
## v1.7.1
### Linux and Windows
```shell
# CUDA 11.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 10.2
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
# CUDA 10.1
pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 9.2
pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# CPU only
pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
```
## v1.6.0
### Linux and Windows
```shell
# CUDA 10.2
pip install torch==1.6.0 torchvision==0.7.0
# CUDA 10.1
pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 9.2
pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
# CPU only
pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
```
arcface_torch/docs/install_dali.md
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TODO
arcface_torch/docs/modelzoo.md
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arcface_torch/docs/prepare_webface42m.md
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## 1. Download Datasets and Unzip
Download WebFace42M from [https://www.face-benchmark.org/download.html](https://www.face-benchmark.org/download.html).
## 2. Create **Pre-shuffle** Rec File for DALI
Note: preshuffled rec is very important to DALI, and rec without preshuffled can cause performance degradation, origin insightface style rec file
do not support Nvidia DALI, you must follow this command [mxnet.tools.im2rec](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) to generate a pre-shuffle rec file.
```shell
# 1) create train.lst using follow command
python -m mxnet.tools.im2rec --list --recursive train "Your WebFace42M Root"
# 2) create train.rec and train.idx using train.lst using following command
python -m mxnet.tools.im2rec --num-thread 16 --quality 100 train "Your WebFace42M Root"
```
Finally, you will get three files: `train.lst`, `train.rec`, `train.idx`. which `train.idx`, `train.rec` are using for training.
arcface_torch/docs/speed_benchmark.md
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## Test Training Speed
- Test Commands
You need to use the following two commands to test the Partial FC training performance.
The number of identites is **3 millions** (synthetic data), turn mixed precision training on, backbone is resnet50,
batch size is 1024.
```shell
# Model Parallel
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions
# Partial FC 0.1
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions_pfc
```
- GPU Memory
```
# (Model Parallel) gpustat -i
[0] Tesla V100-SXM2-32GB | 64'C, 94 % | 30338 / 32510 MB
[1] Tesla V100-SXM2-32GB | 60'C, 99 % | 28876 / 32510 MB
[2] Tesla V100-SXM2-32GB | 60'C, 99 % | 28872 / 32510 MB
[3] Tesla V100-SXM2-32GB | 69'C, 99 % | 28872 / 32510 MB
[4] Tesla V100-SXM2-32GB | 66'C, 99 % | 28888 / 32510 MB
[5] Tesla V100-SXM2-32GB | 60'C, 99 % | 28932 / 32510 MB
[6] Tesla V100-SXM2-32GB | 68'C, 100 % | 28916 / 32510 MB
[7] Tesla V100-SXM2-32GB | 65'C, 99 % | 28860 / 32510 MB
# (Partial FC 0.1) gpustat -i
[0] Tesla V100-SXM2-32GB | 60'C, 95 % | 10488 / 32510 MB │·······················
[1] Tesla V100-SXM2-32GB | 60'C, 97 % | 10344 / 32510 MB │·······················
[2] Tesla V100-SXM2-32GB | 61'C, 95 % | 10340 / 32510 MB │·······················
[3] Tesla V100-SXM2-32GB | 66'C, 95 % | 10340 / 32510 MB │·······················
[4] Tesla V100-SXM2-32GB | 65'C, 94 % | 10356 / 32510 MB │·······················
[5] Tesla V100-SXM2-32GB | 61'C, 95 % | 10400 / 32510 MB │·······················
[6] Tesla V100-SXM2-32GB | 68'C, 96 % | 10384 / 32510 MB │·······················
[7] Tesla V100-SXM2-32GB | 64'C, 95 % | 10328 / 32510 MB │·······················
```
- Training Speed
```python
# (Model Parallel) trainging.log
Training: Speed 2271.33 samples/sec Loss 1.1624 LearningRate 0.2000 Epoch: 0 Global Step: 100
Training: Speed 2269.94 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150
Training: Speed 2272.67 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200
Training: Speed 2266.55 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250
Training: Speed 2272.54 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300
# (Partial FC 0.1) trainging.log
Training: Speed 5299.56 samples/sec Loss 1.0965 LearningRate 0.2000 Epoch: 0 Global Step: 100
Training: Speed 5296.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150
Training: Speed 5304.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200
Training: Speed 5274.43 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250
Training: Speed 5300.10 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300
```
In this test case, Partial FC 0.1 only use1 1/3 of the GPU memory of the model parallel,
and the training speed is 2.5 times faster than the model parallel.
## Speed Benchmark
1. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better)
| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
| :--- | :--- | :--- | :--- |
|125000 | 4681 | 4824 | 5004 |
|250000 | 4047 | 4521 | 4976 |
|500000 | 3087 | 4013 | 4900 |
|1000000 | 2090 | 3449 | 4803 |
|1400000 | 1672 | 3043 | 4738 |
|2000000 | - | 2593 | 4626 |
|4000000 | - | 1748 | 4208 |
|5500000 | - | 1389 | 3975 |
|8000000 | - | - | 3565 |
|16000000 | - | - | 2679 |
|29000000 | - | - | 1855 |
2. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better)
| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
| :--- | :--- | :--- | :--- |
|125000 | 7358 | 5306 | 4868 |
|250000 | 9940 | 5826 | 5004 |
|500000 | 14220 | 7114 | 5202 |
|1000000 | 23708 | 9966 | 5620 |
|1400000 | 32252 | 11178 | 6056 |
|2000000 | - | 13978 | 6472 |
|4000000 | - | 23238 | 8284 |
|5500000 | - | 32188 | 9854 |
|8000000 | - | - | 12310 |
|16000000 | - | - | 19950 |
|29000000 | - | - | 32324 |