Python DAVISの例

コード例 #1

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

コード例 #2

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

コード例 #3

ファイル: demo_vi.py プロジェクト: just-sparta/Deep-Video-Inpainting

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