import argparse
import logging as log
from pprint import pformat
import sys
import time
sys.path.insert(0, '.')
from engine._gain_pagesize import GainPageSize
from utils.envs import initEnv
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='patent spider: an excellent spider program')
parser.add_argument('patent_class', help='patent class', default=None, choices=['publish','authorization','utility_model','design'])
args = parser.parse_args()
config = initEnv(patent_class=args.patent_class)
log.info('config\n\n%s\n' % pformat(config))
eng = GainPageSize(config)
# run eng
eng.start_spider()
import time
from statistics import mean
import numpy as np
import torch
from torchvision import transforms as tf
from pprint import pformat
import sys
sys.path.insert(0, '.')
import brambox.boxes as bbb
import vedanet as vn
from utils.envs import initEnv
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='OneDet: an one stage framework based on PyTorch')
parser.add_argument('model_name', help='model name', default=None)
args = parser.parse_args()
train_flag = 0
config = initEnv(train_flag=train_flag, model_name=args.model_name)
log.info('Config\n\n%s\n' % pformat(config))
# init env
hyper_params = vn.hyperparams.HyperParams(config, train_flag=train_flag)
# init and run eng
vn.engine.speed(hyper_params)
resize = 1
thresh = 0.3
print(result)
for label in result.keys():
for value in result[label]:
score = value[0]
if score>thresh:
bbox = np.int0(value[1])
cv2.rectangle(src,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(255,255,0),4,2)
cv2.putText(src,'%s %.2f'%(label,score),(bbox[0], bbox[1]-2), cv2.FONT_HERSHEY_COMPLEX,1, (255,255,0), 3,1)
end2_time = time.time()
print("net:",end1_time-start_time)
print("net+draw:",end2_time-start_time)
dst = cv2.resize(src,(src.shape[1]//resize,src.shape[0]//resize),cv2.INTER_CUBIC)
cv2.imshow("dst",dst)
cv2.waitKey(0)
if __name__ == '__main__':
# parser = argparse.ArgumentParser(description='OneDet: an one stage framework based on PyTorch')
# parser.add_argument('model_name', help='model name', default='Yolov3')
# args = parser.parse_args()
train_flag = 2
config = initEnv(train_flag=train_flag, model_name='Yolov3')
log.info('Config\n\n%s\n' % pformat(config))
# init env
hyper_params = vn.hyperparams.HyperParams(config, train_flag=train_flag)
# init and run eng
mytest(hyper_params)
import sys
sys.path.insert(0, '.')
import brambox.boxes as bbb
import vedanet as vn
from utils.envs import initEnv, randomSeeding
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='OneDet: an one stage framework based on PyTorch')
parser.add_argument('model_name', help='model name', default=None)
args = parser.parse_args()
train_flag = 1
config = initEnv(train_flag=train_flag, model_name=args.model_name)
#randomSeeding(0)
log.info('Config\n\n%s\n' % pformat(config))
# init env
hyper_params = vn.hyperparams.HyperParams(config, train_flag=train_flag)
# int eng
eng = vn.engine.VOCTrainingEngine(hyper_params)
# run eng
b1 = eng.batch
t1 = time.time()
eng()
t2 = time.time()
test_loss / len(cifar100_test_loader.dataset), epoch)
writer.add_scalar('Test/Accuracy',
correct.float() / len(cifar100_test_loader.dataset),
epoch)
return correct.float() / len(cifar100_test_loader.dataset)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('model_name', help='model name', default=None)
args = parser.parse_args()
config = initEnv(args.model_name, train_flag=1)
net = get_network(args, use_gpu=True)
cifar100_training_loader = get_training_dataloader(
config['CIFAR100_TRAIN_MEAN'],
config['CIFAR100_TRAIN_STD'],
num_workers=config['nworkers'],
batch_size=config['batch_size'],
shuffle=config['shuffle'])
cifar100_test_loader = get_test_dataloader(config['CIFAR100_TRAIN_MEAN'],
config['CIFAR100_TRAIN_STD'],
num_workers=config['nworkers'],
batch_size=config['batch_size'],
shuffle=config['shuffle'])
