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| import argparse | |
| import sys | |
| from pathlib import Path | |
| import numpy as np | |
| import torch | |
| from torchvision.transforms import ToPILImage | |
| from hloc import DEVICE, MODEL_REPO_ID | |
| from ..utils.base_model import BaseModel | |
| sys.path.append(str(Path(__file__).parent / "../../third_party/COTR")) | |
| from COTR.inference.sparse_engine import SparseEngine | |
| from COTR.models import build_model | |
| from COTR.options.options import * # noqa: F403 | |
| from COTR.options.options_utils import * # noqa: F403 | |
| from COTR.utils import utils as utils_cotr | |
| utils_cotr.fix_randomness(0) | |
| torch.set_grad_enabled(False) | |
| class COTR(BaseModel): | |
| default_conf = { | |
| "weights": "out/default", | |
| "match_threshold": 0.2, | |
| "max_keypoints": -1, | |
| "model_name": "checkpoint.pth.tar", | |
| } | |
| required_inputs = ["image0", "image1"] | |
| def _init(self, conf): | |
| parser = argparse.ArgumentParser() | |
| set_COTR_arguments(parser) # noqa: F405 | |
| opt = parser.parse_args() | |
| opt.command = " ".join(sys.argv) | |
| opt.load_weights_path = self._download_model( | |
| repo_id=MODEL_REPO_ID, | |
| filename="{}/{}".format( | |
| Path(__file__).stem, self.conf["model_name"] | |
| ), | |
| ) | |
| layer_2_channels = { | |
| "layer1": 256, | |
| "layer2": 512, | |
| "layer3": 1024, | |
| "layer4": 2048, | |
| } | |
| opt.dim_feedforward = layer_2_channels[opt.layer] | |
| model = build_model(opt) | |
| model = model.to(DEVICE) | |
| weights = torch.load(opt.load_weights_path, map_location="cpu")[ | |
| "model_state_dict" | |
| ] | |
| utils_cotr.safe_load_weights(model, weights) | |
| self.net = model.eval() | |
| self.to_pil_func = ToPILImage(mode="RGB") | |
| def _forward(self, data): | |
| img_a = np.array(self.to_pil_func(data["image0"][0].cpu())) | |
| img_b = np.array(self.to_pil_func(data["image1"][0].cpu())) | |
| corrs = SparseEngine( | |
| self.net, 32, mode="tile" | |
| ).cotr_corr_multiscale_with_cycle_consistency( | |
| img_a, | |
| img_b, | |
| np.linspace(0.5, 0.0625, 4), | |
| 1, | |
| max_corrs=self.conf["max_keypoints"], | |
| queries_a=None, | |
| ) | |
| pred = { | |
| "keypoints0": torch.from_numpy(corrs[:, :2]), | |
| "keypoints1": torch.from_numpy(corrs[:, 2:]), | |
| } | |
| return pred | |