import models from config import DefaultConfig from data import DatasetPrep from data.DatasetImpl import FashionDataset from utils import Visualizer import numpy as np import torch as t from torch.utils.data import DataLoader from torch.autograd import Variable from torchnet import meter opt = DefaultConfig() # DatasetPrep.fashionDatasetPrep(root=opt.data_root) viz = Visualizer(opt.env) assert viz.check_connection() viz.close() lm = eval_lm = meter.AverageValueMeter() cm = eval_cm = meter.ConfusionMeter(10) criterion = t.nn.CrossEntropyLoss() def train(**kwargs): opt.parse(kwargs) # Instantialize model model = getattr(models, opt.model)() print('====================================================') print('CURRENT MODEL:') print(model) if opt.load_model_path: model.load(opt.load_model_path) # Instantialize train and eval dataset and dataloader
avaliables = detector.availiable_obejct_keys if pressed_key in avaliables: target = avaliables[pressed_key] elif (pressed_key == ord("q")) or (target not in object_d.keys()): target = None if args.render: pressed_key = v.draw(ret["map_n"], objects=object_d, target=target or list(avaliables.values())) if pressed_key == ESC: break push_data = build_push_data(detector.object_dict, target) push_detection_dev.pub_set('det.data', push_data) except Exception as e: pass finally: # import cv2 # cv2.destroyWindow("Detection") # cv2.destroyAllWindows() v.close() for _ in range(3): push_detection_dev.pub_set("det.data", [-99] * 7) time.sleep(5) push_detection_dev.close() logging.info("Everything Closed!")