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import sys |
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import torch |
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from ..utils.base_model import BaseModel |
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from ..utils import do_system |
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from pathlib import Path |
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import subprocess |
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import logging |
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logger = logging.getLogger(__name__) |
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sys.path.append(str(Path(__file__).parent / "../../third_party")) |
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from ASpanFormer.src.ASpanFormer.aspanformer import ASpanFormer as _ASpanFormer |
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from ASpanFormer.src.config.default import get_cfg_defaults |
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from ASpanFormer.src.utils.misc import lower_config |
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from ASpanFormer.demo import demo_utils |
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aspanformer_path = Path(__file__).parent / "../../third_party/ASpanFormer" |
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class ASpanFormer(BaseModel): |
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default_conf = { |
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"weights": "outdoor", |
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"match_threshold": 0.2, |
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"config_path": aspanformer_path / "configs/aspan/outdoor/aspan_test.py", |
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"model_name": "weights_aspanformer.tar", |
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} |
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required_inputs = ["image0", "image1"] |
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proxy = "http://localhost:1080" |
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aspanformer_models = { |
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"weights_aspanformer.tar": "https://drive.google.com/uc?id=1eavM9dTkw9nbc-JqlVVfGPU5UvTTfc6k&confirm=t" |
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} |
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def _init(self, conf): |
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model_path = aspanformer_path / "weights" / Path(conf["weights"] + ".ckpt") |
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if not model_path.exists(): |
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tar_path = aspanformer_path / conf["model_name"] |
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if not tar_path.exists(): |
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link = self.aspanformer_models[conf["model_name"]] |
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cmd = ["gdown", link, "-O", str(tar_path), "--proxy", self.proxy] |
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cmd_wo_proxy = ["gdown", link, "-O", str(tar_path)] |
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logger.info(f"Downloading the Aspanformer model with `{cmd_wo_proxy}`.") |
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try: |
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subprocess.run(cmd_wo_proxy, check=True) |
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except subprocess.CalledProcessError as e: |
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logger.info(f"Downloading the Aspanformer model with `{cmd}`.") |
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try: |
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subprocess.run(cmd, check=True) |
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except subprocess.CalledProcessError as e: |
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logger.error(f"Failed to download the Aspanformer model.") |
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raise e |
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do_system(f"cd {str(aspanformer_path)} & tar -xvf {str(tar_path)}") |
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logger.info(f"Loading Aspanformer model...") |
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config = get_cfg_defaults() |
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config.merge_from_file(conf["config_path"]) |
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_config = lower_config(config) |
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self.net = _ASpanFormer(config=_config["aspan"]) |
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weight_path = model_path |
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state_dict = torch.load(str(weight_path), map_location="cpu")["state_dict"] |
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self.net.load_state_dict(state_dict, strict=False) |
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def _forward(self, data): |
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data_ = { |
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"image0": data["image0"], |
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"image1": data["image1"], |
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} |
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self.net(data_, online_resize=True) |
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corr0 = data_["mkpts0_f"] |
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corr1 = data_["mkpts1_f"] |
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pred = {} |
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pred["keypoints0"], pred["keypoints1"] = corr0, corr1 |
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return pred |
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