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Speedup cpu data transport

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hzwer
2020-11-20 15:04:35 +08:00
parent 71da8d3994
commit 6e18a30b6a
2 changed files with 5 additions and 4 deletions
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4
inference_video.py
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@@ -73,8 +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, 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 = torch.from_numpy(np.transpose(lastframe, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
I1 = torch.from_numpy(np.transpose(frame, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
I0 = F.pad(I0, padding)
I1 = F.pad(I1, padding)
p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)

5
inference_video_parallel.py
View File

@@ -105,8 +105,9 @@ 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, non_blocking=True)
I1 = torch.from_numpy(np.transpose(img_list[1:], (0, 3, 1, 2)).astype('float32') / 255.).to(device, non_blocking=True)
imgs = torch.from_numpy(np.transpose(img_list, (0, 3, 1, 2))).to(device, non_blocking=True).float() / 255.
I0 = imgs[:-1]
I1 = imgs[1:]
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)