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!")
