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infer.py
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import numpy as np
import os
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
import joint_transforms
from config import VMD_test_root
from misc import check_mkdir
# from networks.TVSD import TVSD
from networks.FusionNet import FusionNet
from dataset.VShadow_crosspairwise_query_other import listdirs_only, CrossPairwiseImg
import argparse
from tqdm import tqdm
from glob import glob
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
args = {
'scale': 512,
'test_adjacent': 1,
'input_folder': 'JPEGImages',
'label_folder': 'Annotations'
}
img_transform = transforms.Compose([
transforms.Resize((args['scale'], args['scale'])),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
val_joint_transform = joint_transforms.Compose([
joint_transforms.Resize((args['scale'], args['scale']))
])
root = VMD_test_root[0]
to_pil = transforms.ToPILImage()
val_set = CrossPairwiseImg([VMD_test_root], val_joint_transform, img_transform, target_transform)
val_loader = DataLoader(val_set, batch_size=1, num_workers=1, shuffle=False)
def main():
net = FusionNet().cuda()
checkpoint = ''
save_dir = ""
check_point = torch.load(checkpoint)
net.load_state_dict(check_point['model'])
net.eval()
with torch.no_grad():
old_temp = ''
val_iterator = tqdm(val_loader)
for i, sample in enumerate(val_iterator):
exemplar, query, other = sample['exemplar'].cuda(), sample['query'].cuda(), sample['other'].cuda()
# exemplar_gt, query_gt, other_gt = sample['exemplar_gt'].cuda(), sample['query_gt'].cuda(), sample['other_gt'].cuda()
# exemplar_gt, query_gt = sample['exemplar_gt'].cuda(), sample['query_gt'].cuda()
video_name = sample['video_name'][0]
if old_temp == video_name:
# exemplar_index = query_index
query_index = query_index + 1
else:
# query_index = 1
query_index = 0
# exemplar_index = 1
# #
# if flag is False:
# cv2.imwrite(os.path.join(save_dir, "exemplar_results", str(old_temp), query_save_name),
# exemplar_prediction)
# flag = True
# else:
# Image.fromarray(exemplar_prediction).save(
# os.path.join(seg_save_dir, "exemplar_results", str(old_temp), query_save_name))
# print(exemplar_index)
# print(query_index)
# print()
_, _, _, query_final, _ = net(exemplar, query, other)
res = (query_final.data > 0).to(torch.float32).squeeze(0)
# exemplar_res = (exemplar_pre.data > 0).to(torch.float32).squeeze(0)
# res = torch.sigmoid(exemplar_pre.squeeze())
# query_index1 = str(query_index).zfill(4)
query_index1 = str(query_index).zfill(5)
# exemplar_index1 = str(exemplar_index).zfill(4)
first_image = np.array(Image.open(root + '/JPEGImages/' + str(video_name) + '/' + query_index1 + '.jpg'))
# print(first_image.shape)
h, w, _ = first_image.shape
prediction = np.array(
transforms.Resize((h, w))(to_pil(res.cpu())))
# exemplar_prediction = np.array(
# transforms.Resize((h, w))(to_pil(exemplar_res.cpu())))
check_mkdir(os.path.join(save_dir, str(video_name)))
# check_mkdir(os.path.join(seg_save_dir, "exemplar_results", str(video_name)))
query_save_name = f"{query_index1}.png"
# exemplar_save_name = f"{exemplar_index1}.png"
# print(os.path.join(seg_save_dir, "results", video, save_name))
Image.fromarray(prediction).save(os.path.join(save_dir, str(video_name), query_save_name))
# Image.fromarray(exemplar_prediction).save(
# os.path.join(seg_save_dir, "exemplar_results", str(video_name), exemplar_save_name))
old_temp = video_name
def sortImg(img_list):
img_int_list = [int(f) for f in img_list]
sort_index = [i for i, v in sorted(enumerate(img_int_list), key=lambda x: x[1])] # sort img to 001,002,003...
return [img_list[i] for i in sort_index]
def getAdjacentIndex(current_index, start_index, video_length, adjacent_length):
if current_index + adjacent_length < start_index + video_length:
query_index_list = [current_index+i+1 for i in range(adjacent_length)]
else:
query_index_list = [current_index-i-1 for i in range(adjacent_length)]
return query_index_list
if __name__ == '__main__':
main()