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| from pathlib import Path | |
| import subprocess | |
| import sys | |
| import torch | |
| import torchvision.transforms as tvf | |
| from ..utils.base_model import BaseModel | |
| from .. import logger | |
| fire_path = Path(__file__).parent / "../../third_party/fire" | |
| sys.path.append(str(fire_path)) | |
| import fire_network | |
| from lib.how.how.stages.evaluate import eval_asmk_fire, load_dataset_fire | |
| from lib.asmk import asmk | |
| from asmk import io_helpers, asmk_method, kernel as kern_pkg | |
| EPS = 1e-6 | |
| class FIRe(BaseModel): | |
| default_conf = { | |
| "global": True, | |
| "asmk": False, | |
| "model_name": "fire_SfM_120k.pth", | |
| "scales": [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25], # default params | |
| "features_num": 1000, | |
| "asmk_name": "asmk_codebook.bin", | |
| "config_name": "eval_fire.yml", | |
| } | |
| required_inputs = ["image"] | |
| # Models exported using | |
| fire_models = { | |
| "fire_SfM_120k.pth": "http://download.europe.naverlabs.com/ComputerVision/FIRe/official/fire.pth", | |
| "fire_imagenet.pth": "http://download.europe.naverlabs.com/ComputerVision/FIRe/pretraining/fire_imagenet.pth", | |
| } | |
| def _init(self, conf): | |
| assert conf["model_name"] in self.fire_models.keys() | |
| # Config paths | |
| model_path = fire_path / "model" / conf["model_name"] | |
| config_path = fire_path / conf["config_name"] | |
| asmk_bin_path = fire_path / "model" / conf["asmk_name"] | |
| # Download the model. | |
| if not model_path.exists(): | |
| model_path.parent.mkdir(exist_ok=True) | |
| link = self.fire_models[conf["model_name"]] | |
| cmd = ["wget", link, "-O", str(model_path)] | |
| logger.info(f"Downloading the FIRe model with `{cmd}`.") | |
| subprocess.run(cmd, check=True) | |
| logger.info(f"Loading fire model...") | |
| # Load net | |
| state = torch.load(model_path) | |
| state["net_params"]["pretrained"] = None | |
| net = fire_network.init_network(**state["net_params"]) | |
| net.load_state_dict(state["state_dict"]) | |
| self.net = net | |
| self.norm_rgb = tvf.Normalize( | |
| **dict(zip(["mean", "std"], net.runtime["mean_std"])) | |
| ) | |
| # params | |
| self.scales = conf["scales"] | |
| self.features_num = conf["features_num"] | |
| def _forward(self, data): | |
| image = self.norm_rgb(data["image"]) | |
| local_desc = self.net.forward_local( | |
| image, features_num=self.features_num, scales=self.scales | |
| ) | |
| logger.info(f"output[0].shape = {local_desc[0].shape}\n") | |
| return { | |
| # 'global_descriptor': desc | |
| "local_descriptor": local_desc | |
| } | |