type=int, default=10000, help='number of evaluation') parser.add_argument('--vis_num', type=int, default=60, help='number of visible evaluation') parser.add_argument('--multiscale', type=bool, default=False, help='enable multiscale_search') args = parser.parse_args() Config.set_model_name(args.model_name) Config.set_model_type(Config.MODEL[args.model_type]) Config.set_model_backbone(Config.BACKBONE[args.model_backbone]) Config.set_dataset_type(Config.DATA[args.dataset_type]) Config.set_dataset_path(args.dataset_path) Config.set_dataset_version(args.dataset_version) config = Config.get_config() model = Model.get_model(config) evaluate = Model.get_evaluate(config) dataset = Dataset.get_dataset(config) evaluate(model, dataset, vis_num=args.vis_num, total_eval_num=args.eval_num, enable_multiscale_search=args.multiscale)
import pathlib import tensorflow as tf from functools import partial from hyperpose import Config,Model,Dataset #load model weights from hyperpose Config.set_model_name("new_pifpaf") Config.set_model_type(Config.MODEL.Pifpaf) Config.set_dataset_type(Config.DATA.MSCOCO) config=Config.get_config() model=Model.get_model(config) model.load_weights(f"{config.model.model_dir}/newest_model.npz") model.eval() #construct representative dataset used for quantization(here using the first 100 validate images) scale_image_func=partial(Model.common.scale_image,hin=model.hin,win=model.win,scale_rate=0.95) def decode_image(image_file,image_id): image = tf.io.read_file(image_file) image = tf.image.decode_jpeg(image, channels=3) # get RGB with 0~1 image = tf.image.convert_image_dtype(image, dtype=tf.float32) scaled_image,pad = tf.py_function(scale_image_func,[image],[tf.float32,tf.float32]) return scaled_image dataset=Dataset.get_dataset(config) val_dataset=dataset.get_eval_dataset() rep_dataset=val_dataset.enumerate() rep_dataset=rep_dataset.filter(lambda i,image_data : i<=100) rep_dataset=rep_dataset.map(lambda i,image_data: image_data) rep_dataset=rep_dataset.map(decode_image).batch(1) print(f"test rep_dataset:{rep_dataset}") #covert to tf-lite using int8-only quantization input_signature=tf.TensorSpec(shape=(None,3,None,None)) converter=tf.lite.TFLiteConverter.from_concrete_functions([model.infer.get_concrete_function(x=input_signature)])