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test_HGPU.py
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test_HGPU.py
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import os
import glob
import torch
from PIL import Image
from tqdm import tqdm
from scipy.misc import imresize
from torchvision import transforms
from libs.utils.utils import check_parallel
from libs.utils.utils import load_checkpoint_epoch
from libs.model.HGPU import EncoderNet, DecoderNet
def func(listTemp, n):
for i in range(0, len(listTemp), n):
yield listTemp[i:i + n]
def print_list_davis(imagefile):
temp = []
imagefiles = []
temp.extend(imagefile[::])
temp.extend(imagefile[1: -1:])
temp.sort()
li = func(temp, 2)
for i in li:
imagefiles.append(i)
return imagefiles
def flip(x, dim):
if x.is_cuda:
return torch.index_select(x, dim, torch.arange(x.size(dim) - 1, -1, -1).\
long().cuda())
else:
return torch.index_select(x, dim, torch.arange(x.size(dim) - 1, -1, -1).\
long())
def test():
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
tr = transforms.ToTensor()
image_transforms = transforms.Compose([tr, normalize])
encoder_dict, decoder_dict = load_checkpoint_epoch(model_name, epoch, use_gpu=True, load_opt=False)
encoder = EncoderNet()
decoder = DecoderNet()
encoder_dict, decoder_dict = check_parallel(encoder_dict, decoder_dict)
encoder.load_state_dict(encoder_dict)
decoder.load_state_dict(decoder_dict)
encoder.cuda()
decoder.cuda()
encoder.train(False)
decoder.train(False)
for video in tqdm(seqs):
im_dir = os.path.join(davis_image_dir, video)
flow_dir = os.path.join(davis_flow_dir, video)
imagefile = sorted(glob.glob(os.path.join(im_dir, '*.jpg')))
imagefiles = []
imagefiles.extend(print_list_davis(imagefile))
flowfiles = sorted(glob.glob(os.path.join(flow_dir, '*.png')))
with torch.no_grad():
for imagefile, flowfile in zip(imagefiles, flowfiles):
im1 = Image.open(imagefile[0]).convert('RGB')
im2 = Image.open(imagefile[1]).convert('RGB')
flow = Image.open(flowfile).convert('RGB')
width, height = im1.size
im1 = imresize(im1, img_size)
im2 = imresize(im2, img_size)
flow = imresize(flow, img_size)
im1 = image_transforms(im1)
im2 = image_transforms(im2)
flow = image_transforms(flow)
im1 = im1.unsqueeze(0)
im2 = im2.unsqueeze(0)
flow = flow.unsqueeze(0)
im1, im2, flow = im1.cuda(), im2.cuda(), flow.cuda()
h5_1, h4_1, h3_1, h2_1, \
h5_2, h4_2, h3_2, h2_2, \
h5_3, h4_3, h3_3, h2_3 = encoder(im1, im2, flow)
mask_1, mask_2 = decoder(h5_1, h4_1, h3_1, h2_1,
h5_2, h4_2, h3_2, h2_2,
h5_3, h4_3, h3_3, h2_3)
if use_flip:
im1_flip = flip(im1, 3)
im2_flip = flip(im2, 3)
flow_flip = flip(flow, 3)
h5_1, h4_1, h3_1, h2_1, \
h5_2, h4_2, h3_2, h2_2, \
h5_3, h4_3, h3_3, h2_3 = encoder(im1_flip, im2_flip, flow_flip)
mask_flip_1, mask_flip_2 = decoder(h5_1, h4_1, h3_1, h2_1,
h5_2, h4_2, h3_2, h2_2,
h5_3, h4_3, h3_3, h2_3)
mask_flip_1 = flip(mask_flip_1, 3)
mask_flip_2 = flip(mask_flip_2, 3)
mask_1 = (mask_1 + mask_flip_1) / 2.0
mask_2 = (mask_2 + mask_flip_2) / 2.0
mask_1 = mask_1[0, 0, :, :]
mask_2 = mask_2[0, 0, :, :]
mask_1 = Image.fromarray(mask_1.cpu().detach().numpy() * 255).convert('L')
mask_2 = Image.fromarray(mask_2.cpu().detach().numpy() * 255).convert('L')
save_mask_folder = '{}/{}_epoch{}/{}'.format(davis_mask_dir, model_name, epoch, video)
if not os.path.exists(save_mask_folder):
os.makedirs(save_mask_folder)
save_file1 = os.path.join(save_mask_folder,
os.path.basename(imagefile[0])[:-4] + '.png')
save_file2 = os.path.join(save_mask_folder,
os.path.basename(imagefile[1])[:-4] + '.png')
mask_1 = mask_1.resize((width, height))
mask_2 = mask_2.resize((width, height))
mask_1.save(save_file1)
mask_2.save(save_file2)
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
img_size = (512, 512)
use_flip = True
model_name = 'HGPU'
epoch = 0.8394088008745166
val_set_v1 = './libs/dataset/val_v1.txt'
val_set_v2 = './libs/dataset/val_v2.txt'
davis_image_dir = '/YourPath/DAVIS/JPEGImages/480p'
davis_flow_dir = '/YourPath/DAVIS/davis-flow'
davis_mask_dir = './outputs/DAVIS-16'
EncoderNet.flag = 'pre'
with open(val_set_v1) as f:
seqs = f.readlines()
seqs = [seq.strip() for seq in seqs]
test()
EncoderNet.flag = 'main'
with open(val_set_v2) as f:
seqs = f.readlines()
seqs = [seq.strip() for seq in seqs]
test()