Mirrors/ECCV2022-RIFE
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Will be faster

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hzwer
2020-11-20 14:27:47 +08:00
parent a3a0973d1d
commit 324949a068
2 changed files with 9 additions and 7 deletions
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5
inference_video.py
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@@ -13,6 +13,7 @@ if torch.cuda.is_available():
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(4)
parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
parser.add_argument('--video', dest='video', required=True)
@@ -72,8 +73,8 @@ while success:
if success:
if args.montage:
frame = frame[:, left: left + w]
I0 = torch.from_numpy(np.transpose(lastframe, (2,0,1)).astype("float32") / 255.).to(device).unsqueeze(0)
I1 = torch.from_numpy(np.transpose(frame, (2,0,1)).astype("float32") / 255.).to(device).unsqueeze(0)
I0 = torch.from_numpy(np.transpose(lastframe, (2,0,1)).astype('float32') / 255.).to(device, non_blocking=True).unsqueeze(0)
I1 = torch.from_numpy(np.transpose(frame, (2,0,1)).astype('float32') / 255.).to(device, non_blocking=True).unsqueeze(0)
I0 = F.pad(I0, padding)
I1 = F.pad(I1, padding)
p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)

11
inference_video_parallel.py
View File

@@ -13,6 +13,7 @@ if torch.cuda.is_available():
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(4)
parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
parser.add_argument('--video', dest='video', required=True)
@@ -104,16 +105,16 @@ while success:
if success:
img_list.append(frame)
if len(img_list) == 5 or (not success and len(img_list) > 1):
I0 = torch.from_numpy(np.transpose(img_list[:-1], (0, 3, 1, 2)).astype("float32") / 255.).to(device)
I1 = torch.from_numpy(np.transpose(img_list[1:], (0, 3, 1, 2)).astype("float32") / 255.).to(device)
I0 = torch.from_numpy(np.transpose(img_list[:-1], (0, 3, 1, 2)).astype('float32') / 255.).to(device, non_blocking=True)
I1 = torch.from_numpy(np.transpose(img_list[1:], (0, 3, 1, 2)).astype('float32') / 255.).to(device, non_blocking=True)
p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)
- F.interpolate(I1, (16, 16), mode='bilinear', align_corners=False)).abs()
I0 = F.pad(I0, padding)
I1 = F.pad(I1, padding)
inferences = make_inference(model, I0, I1, exp=args.exp)
I0 = ((I0[:, :, :h, :w] * 255.).byte().cpu().detach().numpy().transpose(0, 2, 3, 1))
I1 = ((I1[:, :, :h, :w] * 255.).byte().cpu().detach().numpy().transpose(0, 2, 3, 1))
I0 = np.array(img_list[:-1])
I1 = np.array(img_list[1:])
inferences = list(map(lambda x: ((x[:, :, :h, :w] * 255.).byte().cpu().detach().numpy().transpose(0, 2, 3, 1)), inferences))
write_frame(vid_out, I0, inferences, I1, p.mean(3).mean(2).mean(1), args)