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interpolate pure-torch

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-04-20 14:29:39 -04:00
parent 3b39a5b5a4
commit de3cb6b85f
1 changed files with 37 additions and 3 deletions
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modules/rife/__init__.py
+37 -3
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@@ -82,13 +82,13 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
for _i in range(pad): # fill starting frames
buffer.put(frame)
I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.)
I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.0)
with torch.no_grad():
with tqdm(total=len(images), desc='Interpolate', unit='frame') as pbar:
for image in images:
frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
I0 = I1
I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.)
I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.0)
I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False).to(torch.float32)
I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False).to(torch.float32)
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
@@ -104,7 +104,7 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
else:
output = execute(I0, I1, count-1)
for mid in output:
mid = (((mid[0] * 255.).byte().cpu().numpy().transpose(1, 2, 0)))
mid = (((mid[0] * 255.0).byte().cpu().numpy().transpose(1, 2, 0)))
buffer.put(mid[:h, :w])
buffer.put(frame)
pbar.update(1)
@@ -116,3 +116,37 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
t1 = time.time()
shared.log.info(f'Video interpolate: input={len(images)} frames={len(interpolated)} buffer={buffer.qsize()} duplicate={duplicate} width={w} height={h} interpolate={count} scale={scale} pad={pad} change={change} time={round(t1 - t0, 2)}')
return interpolated
def interpolate_nchw(images: list, count: int = 2, scale: float = 1.0):
if images is None or len(images) < 2:
return images
if model is None:
load()
interpolated = []
_n, _c, h, w = images.shape
t0 = time.time()
def f_pad(img):
return F.pad(img, padding).to(device=devices.device, dtype=devices.dtype) # pylint: disable=not-callable
tmp = max(128, int(128 / scale))
ph = ((h - 1) // tmp + 1) * tmp
pw = ((w - 1) // tmp + 1) * tmp
padding = (0, pw - w, 0, ph - h)
I1 = f_pad(images[0].unsqueeze(0))
with torch.no_grad():
with tqdm(total=len(images), desc='Interpolate', unit='frame') as pbar:
for frame in images:
I0 = I1
I1 = f_pad(frame.unsqueeze(0))
for i in range(count-1):
output = model.inference(I0, I1, (i+1) * 1. / (count), scale)
interpolated.append(output)
interpolated.append(I1)
pbar.update(1)
t1 = time.time()
shared.log.info(f'Video interpolate: input={len(images)} frames={len(interpolated)} width={w} height={h} interpolate={count} scale={scale} time={round(t1 - t0, 2)}')
return interpolated