def save_checkpoint(state, is_best, filename='*_checkpoint.pth.tar'):
work_dir = os.path.basename(os.path.dirname(os.path.realpath(__file__)))
save_dir = os.path.join('./checkpoints/', work_dir)
cine(save_dir)
filename = filename.replace('*', time_string)
filename = os.path.join(save_dir, filename)
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, os.path.join(save_dir, time_string+'_model_best.pth.tar'))
def save_checkpoint(state, filename='*_checkpoint.pth.tar'):
work_dir = os.path.basename(os.path.dirname(os.path.realpath(__file__)))
save_dir = os.path.join('/mv_users/peiguo/checkpoints/', work_dir)
cine(save_dir)
symlink = './checkpoints'
if not os.path.exists(symlink):
os.symlink(save_dir, symlink)
filename = filename.replace('*', time_string)
filename = os.path.join(save_dir, filename)
torch.save(state, filename)
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--lr_decay', default='50', type=int,
help='lr decay frequency')
parser.add_argument('--crop_size', default='256', type=int,
help='size of cropped image')
parser.add_argument('--visualize', dest='visualize', action='store_true',
help='visualize middle output')
parser.add_argument('--nparts', default='15', type=int,
help='number of keypoints')
best_prec1 = 0
time_string = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
cine('logs')
Tee('logs/cmd_log_{}'.format(time_string), 'w')
unisize = 256
outsize = 64
def main():
global args, best_prec1
args = parser.parse_args()
print(args)
global fig, ax1, ax2, ax3, ax4
if args.visualize:
plt.ion()
plt.show()
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2)