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import sys |
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from pathlib import Path |
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import subprocess |
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import torch |
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from ..utils.base_model import BaseModel |
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lanet_path = Path(__file__).parent / "../../third_party/lanet" |
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sys.path.append(str(lanet_path)) |
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from network_v0.model import PointModel |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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class LANet(BaseModel): |
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default_conf = { |
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"model_name": "v0", |
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"keypoint_threshold": 0.1, |
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} |
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required_inputs = ["image"] |
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def _init(self, conf): |
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model_path = lanet_path / "checkpoints" / f'PointModel_{conf["model_name"]}.pth' |
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if not model_path.exists(): |
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print(f"No model found at {model_path}") |
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self.net = PointModel(is_test=True) |
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state_dict = torch.load(model_path, map_location="cpu") |
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self.net.load_state_dict(state_dict["model_state"]) |
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def _forward(self, data): |
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image = data["image"] |
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keypoints, scores, descriptors = self.net(image) |
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_, _, Hc, Wc = descriptors.shape |
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kpts_score = ( |
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torch.cat([keypoints, scores], dim=1).view(3, -1).t().cpu().detach().numpy() |
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) |
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descriptors = ( |
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descriptors.view(256, Hc, Wc).view(256, -1).t().cpu().detach().numpy() |
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) |
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descriptors = descriptors[kpts_score[:, 0] > self.conf["keypoint_threshold"], :] |
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kpts_score = kpts_score[kpts_score[:, 0] > self.conf["keypoint_threshold"], :] |
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keypoints = kpts_score[:, 1:] |
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scores = kpts_score[:, 0] |
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return { |
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"keypoints": torch.from_numpy(keypoints)[None], |
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"scores": torch.from_numpy(scores)[None], |
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"descriptors": torch.from_numpy(descriptors.T)[None], |
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} |
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