import numpy as np import torch import os.path as osp from training_utils.engine_pointnet import EnginePointnet from training_utils.find_top_models import find_top_models from kaolin.models.PointNet import PointNetClassifier as Pointnet # this must be changed to the model that was used... from config.config_triumf_pointnet_adam_all_features import config if __name__ == '__main__': # Initialization model = Pointnet(**config.model_kwargs) engine = EnginePointnet(model, config) #Validation dump_path = "/home/dgreen/training_outputs/pointnet/all_features/adam/20200302_002018/" models = find_top_models(dump_path, 5) print(models) for model in models: engine.load_state(osp.join(dump_path, model)) engine.validate("validation", name=osp.splitext(model)[0])
import torch import numpy as np from models.selector import Model from training_utils.engine_graph import EngineGraph from training_utils.find_top_models import find_top_models from config.config_triumf import config import os.path as osp if __name__ == '__main__': # Initialization model = Model(name=config.model_name, **config.model_kwargs) engine = EngineGraph(model, config) # Training engine.train() # Save network engine.save_state() #Validation models = find_top_models(engine.dirpath, 5) for model in models: engine.load_state(osp.join(engine.dirpath, model)) engine.validate("validation", name=osp.splitext(model)[0])