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import torch |
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import numpy as np |
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import torchvision.transforms.functional as F |
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from torchvision.models.optical_flow import Raft_Large_Weights, raft_large |
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class RAFT: |
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def __init__(self): |
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weights = Raft_Large_Weights.DEFAULT |
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self.transforms = weights.transforms() |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.model = raft_large(weights=weights, progress=False).to(self.device).eval() |
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def predict(self, image1, image2, num_flow_updates:int = 50): |
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img1 = F.to_tensor(image1) |
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img2 = F.to_tensor(image2) |
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img1_batch, img2_batch = img1.unsqueeze(0), img2.unsqueeze(0) |
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img1_batch, img2_batch = self.transforms(img1_batch, img2_batch) |
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with torch.no_grad(): |
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flow = self.model(image1=img1_batch.to(self.device), image2=img2_batch.to(self.device), num_flow_updates=num_flow_updates)[-1].cpu().numpy()[0] |
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flow = np.transpose(flow, (1, 2, 0)) |
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return flow |
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def delete_model(self): |
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del self.model |