from torch.autograd import Variable
from davis import DAVIS
from model import generate_model
import time
import subprocess as sp
import pickle
class Object():
pass
opt = Object()
opt.crop_size = 512
opt.double_size = True if opt.crop_size == 512 else False
########## DAVIS
DAVIS_ROOT = './rgb_new'#'./image_2' 480p/inpaint.txt
DTset = DAVIS(DAVIS_ROOT, imset='480p/inpaint.txt', size=(opt.crop_size, opt.crop_size))
DTloader = data.DataLoader(DTset, batch_size=1, shuffle=False, num_workers=1)
opt.search_range = 4 # fixed as 4: search range for flow subnetworks
opt.pretrain_path = 'results/vinet_agg_rec/save_agg_rec_512.pth'
opt.result_path = 'results/vinet_agg_rec'
opt.model = 'vinet_final'
opt.batch_norm = False
opt.no_cuda = False # use GPU
opt.no_train = True
opt.test = True
opt.t_stride = 3
opt.loss_on_raw = False
opt.prev_warp = True
opt.save_image = True
import subprocess as sp
import pickle
import pdb
class Object():
pass
opt = Object()
opt.crop_size = 512
opt.double_size = True if opt.crop_size == 512 else False
########## DAVIS
DAVIS_ROOT = './DAVIS_demo'
DTset = DAVIS(DAVIS_ROOT,
imset='2016/demo_davis.txt',
size=(opt.crop_size, opt.crop_size))
DTloader = data.DataLoader(DTset, batch_size=1, shuffle=False, num_workers=1)
opt.search_range = 4 # fixed as 4: search range for flow subnetworks
opt.pretrain_path = 'results/vinet_agg_rec/save_agg_rec_512.pth'
opt.result_path = 'results/vinet_agg_rec'
opt.model = 'vinet_final'
opt.batch_norm = False
opt.no_cuda = False # use GPU
opt.no_train = True
opt.test = True
opt.t_stride = 3
opt.loss_on_raw = False
opt.prev_warp = True
args = get_arguments()
class Object:
pass
opt = Object()
opt.crop_size = 512
opt.double_size = True if opt.crop_size == 512 else False
########## DAVIS
DAVIS_ROOT = args.input_root_path
mask = args.input_mask_path
DTset = DAVIS(DAVIS_ROOT,
mask,
imset='example.txt',
size=(opt.crop_size, opt.crop_size))
DTloader = data.DataLoader(DTset, batch_size=1, shuffle=False, num_workers=1)
opt.search_range = 4 # fixed as 4: search range for flow subnetworks
opt.pretrain_path = 'results/vinet_agg_rec/save_agg_rec_512.pth'
opt.result_path = 'results/vinet_agg_rec'
opt.model = 'vinet_final'
opt.batch_norm = False
opt.no_cuda = False # use GPU
opt.no_train = True
opt.test = True
opt.t_stride = 3
opt.loss_on_raw = False
opt.prev_warp = True