'''
config = Configs(models_dir='/models')
iters = 100
#model_name = 'retinaface_mnet025_v2'
model_name = 'retinaface_r50_v1'
input_shape = [1024, 768]
# 'plan' for TensorRT or 'onnx' for ONNX
backend = 'plan'
retina_backends = {'onnx': RetinaInferORT, 'plan': RetinaInferTRT}
model_dir, model_path = config.build_model_paths(model_name, backend)
retina_backend = retina_backends[backend](rec_name=model_path)
detector = FaceDetector(inference_backend=retina_backend)
detector.prepare(fix_image_size=(input_shape[1], input_shape[0]))
tt0 = time.time()
image = cv2.imread('test_images/Stallone.jpg.jpg', cv2.IMREAD_COLOR)
image = ImageData(image, input_shape)
image.resize_image(mode='pad')
tt1 = time.time()
print(f"Preparing image took: {tt1 - tt0}")
t0 = time.time()
os.remove(intermediate_symbol)
os.remove(intermediate_params)
if __name__ == '__main__':
config = Configs(models_dir='/models')
models_to_convert = [
name for name in config.mxnet_models
if config.in_official_package(name)
]
custom_shape = (1, 3, 480, 640)
for model in models_to_convert:
print(f"Downloading model: {model}...")
get_model_file(model, root=config.mxnet_models_dir)
for model in models_to_convert:
mxnet_symbol, mxnet_params = config.get_mxnet_model_paths(model)
reshape = config.mxnet_models[model].get('reshape')
shape = config.mxnet_models[model].get('shape', (1, 3, 112, 112))
if custom_shape and reshape == True:
shape = custom_shape
output_onnx_dir, output_onnx_model = config.build_model_paths(
model, "onnx")
os.makedirs(output_onnx_dir, exist_ok=True)
print(f'Converting "{model}" model to ONNX, shape {shape}...')
convert_insight_model(mxnet_symbol, mxnet_params, output_onnx_model,
shape)
'''
ATTENTION!!! This script is for testing purposes only. Work in progress.
'''
def normalize(embedding):
embedding_norm = norm(embedding)
normed_embedding = embedding / embedding_norm
return normed_embedding
config = Configs()
model_name = 'arcface_r100_v1'
engine = config.build_model_paths(model_name, 'plan')[1]
model_onnx = config.build_model_paths(model_name, 'onnx')[1]
model = InsightTRT(rec_name=engine)
#model = InsightTriton(rec_name=engine)
#model = InsightORT(rec_name=model_onnx)
model.prepare()
model_orig = model_zoo.get_model(model_name, root=config.mxnet_models_dir)
model_orig.prepare(-1)
im = cv2.imread('test_images/crop.jpg', cv2.IMREAD_COLOR)
iters = 100
t0 = time.time()
for i in range(iters):
if __name__ == '__main__':
configs = Configs(models_dir='/models')
#model_name = 'retinaface_r50_v1'
model_name = 'retinaface_mnet025_v2'
im_size = [1024, 768] # W, H
prepare_folders([
configs.mxnet_models_dir, configs.onnx_models_dir,
configs.trt_engines_dir
])
mx_symbol, mx_params = configs.get_mxnet_model_paths(model_name)
output_onnx_model_path, output_onnx = configs.build_model_paths(
model_name, 'onnx')
output_trt_engine_path, output_engine = configs.build_model_paths(
model_name, 'plan')
prepare_folders([output_onnx_model_path, output_trt_engine_path])
prepare_retina_engine(mx_symbol,
mx_params,
output_onnx,
output_engine,
model_name,
configs.mxnet_models_dir,
shape=(1, 3, im_size[1], im_size[0]),
force=True)