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update README.md

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59
recognition/arcface_paddle/README.md
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@@ -12,13 +12,13 @@
Note: Many thanks to [GuoQuanhao](https://github.com/GuoQuanhao) for the reproduction of the [Arcface basline using PaddlePaddle](https://github.com/GuoQuanhao/arcface-Paddle).
## 2. Environment preparation
## 2. Environment Preparation
Please refer to [Installation](./install_en.md) to setup environment at first.
## 3. Data preparation
## 3. Data Preparation
### 3.1 Download dataset
### 3.1 Download Dataset
Download the dataset from [insightface datasets](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_).
@@ -59,7 +59,7 @@ If you want to use customed dataset, you can arrange your data according to the
## 4. How to Training
### 4.1 Single node, single GPU
### 4.1 Single Node, Single GPU
```bash
export CUDA_VISIBLE_DEVICES=1
@@ -76,7 +76,7 @@ python tools/train.py \
--fp16 False
```
### 4.2 Single node, 8 GPUs:
### 4.2 Single Node, 8 GPUs:
#### Static Mode
@@ -94,7 +94,7 @@ sh scripts/train_dynamic.sh
During training, you can view loss changes in real time through `VisualDL`, For more information, please refer to [VisualDL](https://github.com/PaddlePaddle/VisualDL/).
## 5. Model evaluation
## 5. Model Evaluation
The model evaluation process can be started as follows.
@@ -110,7 +110,7 @@ sh scripts/validation_static.sh
sh scripts/validation_dynamic.sh
```
## 6. Export model
## 6. Export Model
PaddlePaddle supports inference using prediction engines. Firstly, you should export inference model.
#### Static Mode
@@ -127,7 +127,7 @@ sh scripts/export_dynamic.sh
We also support export to onnx model, you only need to set `--export_type onnx`.
## 7. Model inference
## 7. Model Inference
The model inference process supports paddle save inference model and onnx model.
@@ -135,7 +135,7 @@ The model inference process supports paddle save inference model and onnx model.
sh scripts/inference.sh
```
## 8. Model performance
## 8. Model Performance
### 8.1 Performance of Lighting Model
@@ -199,37 +199,32 @@ sh scripts/inference.sh
For more experimental results see [PLSC](https://github.com/PaddlePaddle/PLSC), which is an open source Paddle Large Scale Classification Tools powered by PaddlePaddle. It supports 60 million classes on 8 NVIDIA V100 (32G).
## 9. Inference using PaddleInference
## 9. Inference Combined with Face Detection Model
Firstly, use the following commands to download the index gallery, demo image and font file for visualization.
### 9.1 Install insightface-paddle
```bash
# install insightface-paddle
pip install --upgrade insightface-paddle
```
### 9.2 Download the index gallery, demo image.
```bash
mkdir -p images/gallery/
mkdir -p images/query/
# Index library for the recognition process
wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/index.bin -P images/gallery/
wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/index.bin
# Demo image
wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/query/friends2.jpg -P images/query/
wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/query/friends2.jpg
# Font file for visualization
wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/SourceHanSansCN-Medium.otf
```
### 9.3 Inference using MobileFace
The demo image is shown as follows.
```bash
# default using MobileFace
insightfacepaddle \
--det \
--rec \
--index=images/gallery/index.bin \
--input=images/query/friends2.jpg \
--output="./output"
<div align="center">
<img src="https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/query/friends2.jpg" width = "800" />
</div>
Use the following command to run the whole face recognition demo.
```shell
# detection + recogniotion process
python3.7 test_recognition.py --det --rec --index=index.bin --input=friends2.jpg --output="./output"
```
The final result is save in folder `output/`, which is shown as follows.

183
recognition/arcface_paddle/tools/test_recognition.py
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@@ -32,6 +32,8 @@ from paddle.inference import Config
from paddle.inference import create_predictor
__all__ = ["InsightFace", "parser"]
BASE_INFERENCE_MODEL_DIR = os.path.expanduser("~/.insightface/ppmodels/")
BASE_DOWNLOAD_URL = "https://paddle-model-ecology.bj.bcebos.com/model/insight-face/{}.tar"
def parser(add_help=True):
@@ -43,27 +45,17 @@ def parser(add_help=True):
"--det", action="store_true", help="Whether to detect.")
parser.add_argument(
"--rec", action="store_true", help="Whether to recognize.")
parser.add_argument(
"--det_model_file_path",
"--det_model",
type=str,
default="models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdmodel",
help="The detection model file path.")
default="BlazeFace",
help="The detection model.")
parser.add_argument(
"--det_params_file_path",
"--rec_model",
type=str,
default="models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdiparams",
help="The detection params file path.")
parser.add_argument(
"--rec_model_file_path",
type=str,
default="models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdmodel",
help="The detection model file path.")
parser.add_argument(
"--rec_params_file_path",
type=str,
default="models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdiparams",
help="The detection params file path.")
default="MobileFace",
help="The recognition model.")
parser.add_argument(
"--use_gpu",
type=str2bool,
@@ -86,10 +78,7 @@ def parser(add_help=True):
type=str,
help="The path or directory of image(s) or video to be predicted.")
parser.add_argument(
"--output",
type=str,
default="./output/",
help="The directory of prediction result.")
"--output", type=str, default="./output/", help="The directory of prediction result.")
parser.add_argument(
"--det_thresh",
type=float,
@@ -100,7 +89,7 @@ def parser(add_help=True):
parser.add_argument(
"--cdd_num",
type=int,
default=10,
default=5,
help="The number of candidates in the recognition retrieval. Default by 10."
)
parser.add_argument(
@@ -113,21 +102,6 @@ def parser(add_help=True):
type=int,
default=1,
help="The maxium of batch_size to recognize. Default by 1.")
parser.add_argument(
"--build_index",
type=str,
default=None,
help="The path of index to be build.")
parser.add_argument(
"--img_dir",
type=str,
default=None,
help="The img(s) dir used to build index.")
parser.add_argument(
"--label",
type=str,
default=None,
help="The label file path used to build index.")
return parser
@@ -145,6 +119,86 @@ def print_config(args):
print("{}".format("-" * width))
def download_with_progressbar(url, save_path):
"""Download from url with progressbar.
"""
if os.path.isfile(save_path):
os.remove(save_path)
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
with open(save_path, "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes or not os.path.isfile(
save_path):
raise Exception(
f"Something went wrong while downloading model/image from {url}")
def check_model_file(model):
"""Check the model files exist and download and untar when no exist.
"""
model_map = {
"ArcFace": "arcface_iresnet50_v1.0_infer",
"BlazeFace": "blazeface_fpn_ssh_1000e_v1.0_infer",
"MobileFace": "mobileface_v1.0_infer"
}
if os.path.isdir(model):
model_file_path = os.path.join(model, "inference.pdmodel")
params_file_path = os.path.join(model, "inference.pdiparams")
if not os.path.exists(model_file_path) or not os.path.exists(
params_file_path):
raise Exception(
f"The specifed model directory error. The drectory must include 'inference.pdmodel' and 'inference.pdiparams'."
)
elif model in model_map:
storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR,
model)
url = BASE_DOWNLOAD_URL.format(model_map[model])
tar_file_name_list = [
"inference.pdiparams", "inference.pdiparams.info",
"inference.pdmodel"
]
model_file_path = storage_directory("inference.pdmodel")
params_file_path = storage_directory("inference.pdiparams")
if not os.path.exists(model_file_path) or not os.path.exists(
params_file_path):
tmp_path = storage_directory(url.split("/")[-1])
logging.info(f"Download {url} to {tmp_path}")
os.makedirs(storage_directory(), exist_ok=True)
download_with_progressbar(url, tmp_path)
with tarfile.open(tmp_path, "r") as tarObj:
for member in tarObj.getmembers():
filename = None
for tar_file_name in tar_file_name_list:
if tar_file_name in member.name:
filename = tar_file_name
if filename is None:
continue
file = tarObj.extractfile(member)
with open(storage_directory(filename), "wb") as f:
f.write(file.read())
os.remove(tmp_path)
if not os.path.exists(model_file_path) or not os.path.exists(
params_file_path):
raise Exception(
f"Something went wrong while downloading and unzip the model[{model}] files!"
)
else:
raise Exception(
f"The specifed model name error. Support 'BlazeFace' for detection and 'ArcFace' and 'MobileFace' for recognition. And support local directory that include model files ('inference.pdmodel' and 'inference.pdiparams')."
)
return model_file_path, params_file_path
def normalize_image(img, scale=None, mean=None, std=None, order='chw'):
if isinstance(scale, str):
scale = eval(scale)
@@ -516,7 +570,9 @@ class InsightFace(object):
if print_info:
print_config(args)
self.font_path = "assets/SourceHanSansCN-Medium.otf"
self.font_path = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"SourceHanSansCN-Medium.otf")
self.args = args
predictor_config = {
@@ -525,13 +581,17 @@ class InsightFace(object):
"cpu_threads": args.cpu_threads
}
if args.det:
model_file_path, params_file_path = check_model_file(
args.det_model)
det_config = {"thresh": args.det_thresh, "target_size": [640, 640]}
predictor_config["model_file"] = args.det_model_file_path
predictor_config["params_file"] = args.det_params_file_path
predictor_config["model_file"] = model_file_path
predictor_config["params_file"] = params_file_path
self.det_predictor = Detector(det_config, predictor_config)
self.color_map = ColorMap(100)
if args.rec:
model_file_path, params_file_path = check_model_file(
args.rec_model)
rec_config = {
"max_batch_size": args.max_batch_size,
"resize": 112,
@@ -539,8 +599,8 @@ class InsightFace(object):
"index": args.index,
"cdd_num": args.cdd_num
}
predictor_config["model_file"] = args.rec_model_file_path
predictor_config["params_file"] = args.rec_params_file_path
predictor_config["model_file"] = model_file_path
predictor_config["params_file"] = params_file_path
self.rec_predictor = Recognizer(rec_config, predictor_config)
def preprocess(self, img):
@@ -644,34 +704,6 @@ class InsightFace(object):
}
logging.info(f"Predict complete!")
def build_index(self):
img_dir = self.args.img_dir
label_path = self.args.label
with open(label_path, "r") as f:
sample_list = f.readlines()
feature_list = []
label_list = []
for idx, sample in enumerate(sample_list):
name, label = sample.strip().split("\t")
img = cv2.imread(os.path.join(img_dir, name))
if img is None:
logging.warning(f"Error in reading img {name}! Ignored.")
continue
box_list, np_feature = self.predict_np_img(img)
feature_list.append(np_feature[0])
label_list.append(label)
if idx % 100 == 0:
logging.info(f"Build idx: {idx}")
with open(self.args.build_index, "wb") as f:
pickle.dump({"label": label_list, "feature": feature_list}, f)
logging.info(
f"Build done. Total {len(label_list)}. Index file has been saved in \"{self.args.build_index}\""
)
# for CLI
def main(args=None):
@@ -679,13 +711,10 @@ def main(args=None):
args = parser().parse_args()
predictor = InsightFace(args)
if args.build_index:
predictor.build_index()
else:
res = predictor.predict(args.input, print_info=True)
for _ in res:
pass
res = predictor.predict(args.input, print_info=True)
for _ in res:
pass
if __name__ == "__main__":
main()
main()