def run():
"""
Run the code.
"""
# Main parameters
gpu_devices = [3]
# Model path
model_path = "/home/andrew/projects/htracking/notebooks/posture_estimation/face_model.pth"
# Set CUDA devices
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, gpu_devices))
num_gpus = len(gpu_devices)
if num_gpus > 0 and torch.cuda.is_available():
use_cuda = True
device = torch.device("cuda:0")
else:
use_cuda = False
device = torch.device("cpu")
print("gpu_devices: {}, num_gpus: {}".format(gpu_devices, num_gpus))
print("Running on the device: {}".format(device))
cwd = "/home/andrew/projects/htracking/tools/detect_faces"
config_name = "config.yaml"
config_path = os.path.join(cwd, config_name)
config = read_config(config_path)
training = False
# Model with data parallel
model = YOLO3_MODEL(config, device, training=training)
# Load the trained models
print("Loading the trained model from {}".format(model_path))
model.load_state(model_path)
# Generate the annotation files
ann_dir = "/home/andrew/projects/datasets/face_datasets/MYFACES/buffer/xml"
image_dir = "/home/andrew/projects/datasets/face_datasets/MYFACES/buffer/images"
labeler = ImageLabeler(ann_dir, image_dir, model)
labeler.run()
default='config.yaml',
help="Configuaration file")
ap.add_argument("-a",
"--anchors",
required=False,
default=9,
help="Number of anchors")
args = vars(ap.parse_args())
config_name = args['config']
num_anchors = args['anchors']
# Read the configuration file
cwd = os.getcwd()
config_path = os.path.join(cwd, config_name)
config = read_config(config_path)
print("Finding the anchors:")
# Dataset
train_images_path = config['train_images_path']
train_ann_path = config['train_ann_path']
transform = transforms.Compose([
transforms.Resize(416),
transforms.ToTensor(),
])
target_transform = torchvision.transforms.Compose(
[ListToNumpy(), NumpyToTensor()])
dataset = VOCDetection(train_images_path,
train_ann_path,