Spaces:
Running
on
A10G
Running
on
A10G
File size: 12,095 Bytes
320e465 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
# -*- coding: utf-8 -*-
import sys
sys.path.append(".")
import os
import cv2
import numpy as np
import argparse
from PIL import Image
import torch.nn.functional as F
import torch
from torch.utils.data import DataLoader
from model.modules.flow_comp_raft import RAFT_bi
from model.recurrent_flow_completion import RecurrentFlowCompleteNet
from model.propainter import InpaintGenerator
# from core.dataset import TestDataset
from core.dataset import TestDataset
from core.metrics import calc_psnr_and_ssim, calculate_i3d_activations, calculate_vfid, init_i3d_model
from time import time
import warnings
warnings.filterwarnings("ignore")
# sample reference frames from the whole video
def get_ref_index(neighbor_ids, length, ref_stride=10):
ref_index = []
for i in range(0, length, ref_stride):
if i not in neighbor_ids:
ref_index.append(i)
return ref_index
def main_worker(args):
args.size = (args.width, args.height)
w, h = args.size
# set up datasets and data loader
assert (args.dataset == 'davis') or args.dataset == 'youtube-vos', \
f"{args.dataset} dataset is not supported"
test_dataset = TestDataset(vars(args))
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=args.num_workers)
# set up models
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
fix_raft = RAFT_bi(args.raft_model_path, device)
fix_flow_complete = RecurrentFlowCompleteNet(args.fc_model_path)
for p in fix_flow_complete.parameters():
p.requires_grad = False
fix_flow_complete.to(device)
fix_flow_complete.eval()
model = InpaintGenerator(model_path=args.propainter_model_path).to(device)
model.eval()
time_all = []
print('Start evaluation ...')
if args.task == 'video_completion':
result_path = os.path.join(f'results_eval',
f'{args.dataset}_rs_{args.ref_stride}_nl_{args.neighbor_length}_video_completion')
if not os.path.exists(result_path):
os.makedirs(result_path, exist_ok=True)
eval_summary = open(os.path.join(result_path, f"{args.dataset}_metrics.txt"),"w")
total_frame_psnr = []
total_frame_ssim = []
output_i3d_activations = []
real_i3d_activations = []
i3d_model = init_i3d_model('weights/i3d_rgb_imagenet.pt')
else:
result_path = os.path.join(f'results_eval',
f'{args.dataset}_rs_{args.ref_stride}_nl_{args.neighbor_length}_object_removal')
if not os.path.exists(result_path):
os.makedirs(result_path, exist_ok=True)
if not os.path.exists(result_path):
os.makedirs(result_path)
for index, items in enumerate(test_loader):
torch.cuda.empty_cache()
# frames, masks, video_name, frames_PIL = items
frames, masks, flows_f, flows_b, video_name, frames_PIL = items
video_name = video_name[0]
print('Processing:', video_name)
video_length = frames.size(1)
frames, masks = frames.to(device), masks.to(device)
masked_frames = frames * (1 - masks)
torch.cuda.synchronize()
time_start = time()
with torch.no_grad():
# ---- compute flow ----
if args.load_flow:
gt_flows_bi = (flows_f.to(device), flows_b.to(device))
else:
short_len = 60
if frames.size(1) > short_len:
gt_flows_f_list, gt_flows_b_list = [], []
for f in range(0, video_length, short_len):
end_f = min(video_length, f + short_len)
if f == 0:
flows_f, flows_b = fix_raft(frames[:,f:end_f], iters=args.raft_iter)
else:
flows_f, flows_b = fix_raft(frames[:,f-1:end_f], iters=args.raft_iter)
gt_flows_f_list.append(flows_f)
gt_flows_b_list.append(flows_b)
gt_flows_f = torch.cat(gt_flows_f_list, dim=1)
gt_flows_b = torch.cat(gt_flows_b_list, dim=1)
gt_flows_bi = (gt_flows_f, gt_flows_b)
else:
gt_flows_bi = fix_raft(frames, iters=args.raft_iter)
# ---- complete flow ----
pred_flows_bi, _ = fix_flow_complete.forward_bidirect_flow(gt_flows_bi, masks)
pred_flows_bi = fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, masks)
# ---- temporal propagation ----
prop_imgs, updated_local_masks = model.img_propagation(masked_frames, pred_flows_bi, masks, 'nearest')
b, t, _, _, _ = masks.size()
updated_masks = updated_local_masks.view(b, t, 1, h, w)
updated_frames = frames * (1-masks) + prop_imgs.view(b, t, 3, h, w) * masks # merge
del gt_flows_bi, frames, updated_local_masks
if not args.load_flow:
torch.cuda.empty_cache()
ori_frames = frames_PIL
ori_frames = [
ori_frames[i].squeeze().cpu().numpy() for i in range(video_length)
]
comp_frames = [None] * video_length
# complete holes by our model
neighbor_stride = args.neighbor_length // 2
for f in range(0, video_length, neighbor_stride):
neighbor_ids = [
i for i in range(max(0, f - neighbor_stride),
min(video_length, f + neighbor_stride + 1))
]
ref_ids = get_ref_index(neighbor_ids, video_length, args.ref_stride)
selected_imgs = updated_frames[:, neighbor_ids + ref_ids, :, :, :]
selected_masks = masks[:, neighbor_ids + ref_ids, :, :, :]
selected_update_masks = updated_masks[:, neighbor_ids + ref_ids, :, :, :]
selected_pred_flows_bi = (pred_flows_bi[0][:, neighbor_ids[:-1], :, :, :], pred_flows_bi[1][:, neighbor_ids[:-1], :, :, :])
with torch.no_grad():
l_t = len(neighbor_ids)
pred_img = model(selected_imgs, selected_pred_flows_bi, selected_masks, selected_update_masks, l_t)
pred_img = pred_img.view(-1, 3, h, w)
pred_img = (pred_img + 1) / 2
pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255
binary_masks = masks[0, neighbor_ids, :, :, :].cpu().permute(
0, 2, 3, 1).numpy().astype(np.uint8)
for i in range(len(neighbor_ids)):
idx = neighbor_ids[i]
img = np.array(pred_img[i]).astype(np.uint8) * binary_masks[i] \
+ ori_frames[idx] * (1 - binary_masks[i])
if comp_frames[idx] is None:
comp_frames[idx] = img
else:
comp_frames[idx] = comp_frames[idx].astype(
np.float32) * 0.5 + img.astype(np.float32) * 0.5
torch.cuda.synchronize()
time_i = time() - time_start
time_i = time_i*1.0/video_length
time_all.append(time_i)
if args.task == 'video_completion':
# calculate metrics
cur_video_psnr = []
cur_video_ssim = []
comp_PIL = [] # to calculate VFID
frames_PIL = []
for ori, comp in zip(ori_frames, comp_frames):
psnr, ssim = calc_psnr_and_ssim(ori, comp)
cur_video_psnr.append(psnr)
cur_video_ssim.append(ssim)
total_frame_psnr.append(psnr)
total_frame_ssim.append(ssim)
frames_PIL.append(Image.fromarray(ori.astype(np.uint8)))
comp_PIL.append(Image.fromarray(comp.astype(np.uint8)))
# saving i3d activations
frames_i3d, comp_i3d = calculate_i3d_activations(frames_PIL,
comp_PIL,
i3d_model,
device=device)
real_i3d_activations.append(frames_i3d)
output_i3d_activations.append(comp_i3d)
cur_psnr = sum(cur_video_psnr) / len(cur_video_psnr)
cur_ssim = sum(cur_video_ssim) / len(cur_video_ssim)
avg_psnr = sum(total_frame_psnr) / len(total_frame_psnr)
avg_ssim = sum(total_frame_ssim) / len(total_frame_ssim)
avg_time = sum(time_all) / len(time_all)
print(
f'[{index+1:3}/{len(test_loader)}] Name: {str(video_name):25} | PSNR/SSIM: {cur_psnr:.4f}/{cur_ssim:.4f} \
| Avg PSNR/SSIM: {avg_psnr:.4f}/{avg_ssim:.4f} | Time: {avg_time:.4f}'
)
eval_summary.write(
f'[{index+1:3}/{len(test_loader)}] Name: {str(video_name):25} | PSNR/SSIM: {cur_psnr:.4f}/{cur_ssim:.4f} \
| Avg PSNR/SSIM: {avg_psnr:.4f}/{avg_ssim:.4f} | Time: {avg_time:.4f}\n'
)
else:
avg_time = sum(time_all) / len(time_all)
print(
f'[{index+1:3}/{len(test_loader)}] Name: {str(video_name):25} | Time: {avg_time:.4f}'
)
# saving images for evaluating warpping errors
if args.save_results:
save_frame_path = os.path.join(result_path, video_name)
if not os.path.exists(save_frame_path):
os.makedirs(save_frame_path, exist_ok=False)
for i, frame in enumerate(comp_frames):
cv2.imwrite(
os.path.join(save_frame_path,
str(i).zfill(5) + '.png'),
cv2.cvtColor(frame.astype(np.uint8), cv2.COLOR_RGB2BGR))
if args.task == 'video_completion':
avg_frame_psnr = sum(total_frame_psnr) / len(total_frame_psnr)
avg_frame_ssim = sum(total_frame_ssim) / len(total_frame_ssim)
fid_score = calculate_vfid(real_i3d_activations, output_i3d_activations)
print('Finish evaluation... Average Frame PSNR/SSIM/VFID: '
f'{avg_frame_psnr:.2f}/{avg_frame_ssim:.4f}/{fid_score:.3f} | Time: {avg_time:.4f}')
eval_summary.write(
'Finish evaluation... Average Frame PSNR/SSIM/VFID: '
f'{avg_frame_psnr:.2f}/{avg_frame_ssim:.4f}/{fid_score:.3f} | Time: {avg_time:.4f}')
eval_summary.close()
else:
print('Finish evaluation... Time: {avg_time:.4f}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--height', type=int, default=240)
parser.add_argument('--width', type=int, default=432)
parser.add_argument("--ref_stride", type=int, default=10)
parser.add_argument("--neighbor_length", type=int, default=20)
parser.add_argument("--raft_iter", type=int, default=20)
parser.add_argument('--task', default='video_completion', choices=['object_removal', 'video_completion'])
parser.add_argument('--raft_model_path', default='weights/raft-things.pth', type=str)
parser.add_argument('--fc_model_path', default='weights/recurrent_flow_completion.pth', type=str)
parser.add_argument('--propainter_model_path', default='weights/ProPainter.pth', type=str)
parser.add_argument('--dataset', choices=['davis', 'youtube-vos'], type=str)
parser.add_argument('--video_root', default='dataset_root', type=str)
parser.add_argument('--mask_root', default='mask_root', type=str)
parser.add_argument('--flow_root', default='flow_ground_truth_root', type=str)
parser.add_argument('--load_flow', default=False, type=bool)
parser.add_argument('--save_results', action='store_true')
parser.add_argument('--num_workers', default=4, type=int)
args = parser.parse_args()
main_worker(args)
|