def main():
model = Model()
loader = model.load_model('./train_log')
model.eval()
model.device()
fixargs(args)
return
img0 = cv2.imread(args.img[0])
img1 = cv2.imread(args.img[1])
img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) /
255.).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) /
255.).unsqueeze(0)
n, c, h, w = img0.shape
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
img0 = F.pad(img0, padding)
img1 = F.pad(img1, padding)
img_list = [img0, img1]
for i in range(args.exp):
tmp = []
for j in range(len(img_list) - 1):
mid = model.inference(img_list[j], img_list[j + 1])
tmp.append(img_list[j])
tmp.append(mid)
tmp.append(img1)
img_list = tmp
if not os.path.exists('output'):
os.mkdir('output')
for i in range(len(img_list)):
cv2.imwrite('output/img{}.png'.format(i),
(img_list[i][0] * 255).byte().cpu().numpy().transpose(
1, 2, 0)[:h, :w])
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.load_model('train_log')
model.eval()
model.device()
name = [
'Beanbags', 'Dimetrodon', 'DogDance', 'Grove2', 'Grove3', 'Hydrangea',
'MiniCooper', 'RubberWhale', 'Urban2', 'Urban3', 'Venus', 'Walking'
]
IE_list = []
for i in name:
i0 = cv2.imread('other-data/{}/frame10.png'.format(i)).transpose(2, 0,
1) / 255.
i1 = cv2.imread('other-data/{}/frame11.png'.format(i)).transpose(2, 0,
1) / 255.
gt = cv2.imread('other-gt-interp/{}/frame10i11.png'.format(i))
h, w = i0.shape[1], i0.shape[2]
imgs = torch.zeros([1, 6, 480, 640]).to(device)
import os
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.load_model('./train_log')
model.eval()
model.device()
path = 'vimeo_interp_test/'
f = open(path + 'tri_testlist.txt', 'r')
psnr_list = []
ssim_list = []
for i in f:
name = str(i).strip()
if (len(name) <= 1):
continue
print(path + 'target/' + name + '/im1.png')
I0 = cv2.imread(path + 'target/' + name + '/im1.png')
I1 = cv2.imread(path + 'target/' + name + '/im2.png')
I2 = cv2.imread(path + 'target/' + name + '/im3.png')
I0 = (torch.tensor(I0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
I2 = (torch.tensor(I2.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
import cv2
import sys
sys.path.append('.')
import time
import torch
import torch.nn as nn
from model.RIFE import Model
model = Model()
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
I0 = torch.rand(1, 3, 480, 640).to(device)
I1 = torch.rand(1, 3, 480, 640).to(device)
with torch.no_grad():
for i in range(100):
pred = model.inference(I0, I1)
if torch.cuda.is_available():
torch.cuda.synchronize()
time_stamp = time.time()
for i in range(100):
pred = model.inference(I0, I1)
if torch.cuda.is_available():
torch.cuda.synchronize()
print((time.time() - time_stamp) / 100)
np.array(loss_flow_list).mean(), nr_eval)
writer_val.add_scalar('loss_cons',
np.array(loss_cons_list).mean(), nr_eval)
writer_val.add_scalar('loss_ter',
np.array(loss_ter_list).mean(), nr_eval)
writer_val.add_scalar('psnr', np.array(psnr_list).mean(), nr_eval)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='slomo')
parser.add_argument('--epoch', default=300, type=int)
parser.add_argument('--batch_size',
default=16,
type=int,
help='minibatch size')
parser.add_argument('--local_rank', default=0, type=int, help='local rank')
parser.add_argument('--world_size', default=4, type=int, help='world size')
args = parser.parse_args()
torch.distributed.init_process_group(backend="nccl",
world_size=args.world_size)
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
seed = 1234
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = True
model = Model(args.local_rank)
train(model, args.local_rank)
parser.add_argument('--png',
dest='png',
action='store_true',
help='whether to vid_out png format vid_outs')
parser.add_argument('--ext',
dest='ext',
type=str,
default='mp4',
help='vid_out video extension')
parser.add_argument('--exp', dest='exp', type=int, default=1)
args = parser.parse_args()
assert (args.exp == 1 or args.exp == 2)
args.exp = 2**args.exp
from model.RIFE import Model
model = Model()
model.load_model('./train_log')
model.eval()
model.device()
videoCapture = cv2.VideoCapture(args.video)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
videoCapture.release()
if args.fps is None:
fpsNotAssigned = True
args.fps = fps * args.exp
else:
fpsNotAssigned = False
videogen = skvideo.io.vreader(args.video)
lastframe = next(videogen)
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.load_model('train_log')
model.eval()
model.device()
path = 'UCF101/ucf101_interp_ours/'
dirs = os.listdir(path)
psnr_list = []
ssim_list = []
print(len(dirs))
for d in dirs:
img0 = (path + d + '/frame_00.png')
img1 = (path + d + '/frame_02.png')
gt = (path + d + '/frame_01_gt.png')
img0 = (torch.tensor(cv2.imread(img0).transpose(2, 0, 1) /
255.)).to(device).float().unsqueeze(0)
img1 = (torch.tensor(cv2.imread(img1).transpose(2, 0, 1) /
255.)).to(device).float().unsqueeze(0)
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from pytorch_msssim import ssim_matlab
from model.RIFE import Model
from skimage.color import rgb2yuv, yuv2rgb
from yuv_frame_io import YUV_Read,YUV_Write
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.load_model('train_log')
model.eval()
model.device()
name_list = [
('HD_dataset/HD720p_GT/parkrun_1280x720_50.yuv', 720, 1280),
('HD_dataset/HD720p_GT/shields_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD720p_GT/stockholm_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD1080p_GT/BlueSky.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/Kimono1_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/ParkScene_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/sunflower_1080p25.yuv', 1080, 1920),
('HD_dataset/HD544p_GT/Sintel_Alley2_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Market5_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Temple1_1280x544.yuv', 544, 1280),
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.load_model(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'train_log'))
model.eval()
model.device()
name = [
'Beanbags', 'Dimetrodon', 'DogDance', 'Grove2', 'Grove3', 'Hydrangea',
'MiniCooper', 'RubberWhale', 'Urban2', 'Urban3', 'Venus', 'Walking'
]
IE_list = []
for i in name:
i0 = cv2.imread('other-data/{}/frame10.png'.format(i)).transpose(2, 0,
1) / 255.
i1 = cv2.imread('other-data/{}/frame11.png'.format(i)).transpose(2, 0,
1) / 255.
gt = cv2.imread('other-gt-interp/{}/frame10i11.png'.format(i))
h, w = i0.shape[1], i0.shape[2]
import os
import cv2
import torch
import argparse
from torch.nn import functional as F
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
parser = argparse.ArgumentParser(
description='Interpolation for a pair of images')
parser.add_argument('--img', dest='img', nargs=2, required=True)
parser.add_argument('--times', default=4, type=int)
args = parser.parse_args()
model = Model()
model.load_model('./train_log')
model.eval()
model.device()
img0 = cv2.imread(args.img[0])
img1 = cv2.imread(args.img[1])
img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
n, c, h, w = img0.shape
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
img0 = F.pad(img0, padding)
img1 = F.pad(img1, padding)
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
from skimage.color import rgb2yuv, yuv2rgb
from yuv_frame_io import YUV_Read,YUV_Write
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model(arbitrary=True)
model.load_model('RIFE_m_train_log')
model.eval()
model.device()
name_list = [
('HD_dataset/HD720p_GT/parkrun_1280x720_50.yuv', 720, 1280),
('HD_dataset/HD720p_GT/shields_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD720p_GT/stockholm_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD1080p_GT/BlueSky.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/Kimono1_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/ParkScene_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/sunflower_1080p25.yuv', 1080, 1920),
('HD_dataset/HD544p_GT/Sintel_Alley2_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Market5_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Temple1_1280x544.yuv', 544, 1280),