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])