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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import logging | |
| from detectron2.utils.file_io import PathHandler, PathManager | |
| class ModelCatalog: | |
| """ | |
| Store mappings from names to third-party models. | |
| """ | |
| S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron" | |
| # MSRA models have STRIDE_IN_1X1=True. False otherwise. | |
| # NOTE: all BN models here have fused BN into an affine layer. | |
| # As a result, you should only load them to a model with "FrozenBN". | |
| # Loading them to a model with regular BN or SyncBN is wrong. | |
| # Even when loaded to FrozenBN, it is still different from affine by an epsilon, | |
| # which should be negligible for training. | |
| # NOTE: all models here uses PIXEL_STD=[1,1,1] | |
| # NOTE: Most of the BN models here are no longer used. We use the | |
| # re-converted pre-trained models under detectron2 model zoo instead. | |
| C2_IMAGENET_MODELS = { | |
| "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", | |
| "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", | |
| "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", | |
| "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", | |
| "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", | |
| "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", | |
| "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl", | |
| } | |
| C2_DETECTRON_PATH_FORMAT = ( | |
| "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl" # noqa B950 | |
| ) | |
| C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival" | |
| C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival" | |
| # format: {model_name} -> part of the url | |
| C2_DETECTRON_MODELS = { | |
| "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW", # noqa B950 | |
| "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I", # noqa B950 | |
| "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7", # noqa B950 | |
| "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ", # noqa B950 | |
| "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB", # noqa B950 | |
| "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC", # noqa B950 | |
| "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT", # noqa B950 | |
| "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI", # noqa B950 | |
| "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q", # noqa B950 | |
| "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao", # noqa B950 | |
| "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L", # noqa B950 | |
| "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179", # noqa B950 | |
| "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2", # noqa B950 | |
| } | |
| def get(name): | |
| if name.startswith("Caffe2Detectron/COCO"): | |
| return ModelCatalog._get_c2_detectron_baseline(name) | |
| if name.startswith("ImageNetPretrained/"): | |
| return ModelCatalog._get_c2_imagenet_pretrained(name) | |
| raise RuntimeError("model not present in the catalog: {}".format(name)) | |
| def _get_c2_imagenet_pretrained(name): | |
| prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX | |
| name = name[len("ImageNetPretrained/") :] | |
| name = ModelCatalog.C2_IMAGENET_MODELS[name] | |
| url = "/".join([prefix, name]) | |
| return url | |
| def _get_c2_detectron_baseline(name): | |
| name = name[len("Caffe2Detectron/COCO/") :] | |
| url = ModelCatalog.C2_DETECTRON_MODELS[name] | |
| if "keypoint_rcnn" in name: | |
| dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS | |
| else: | |
| dataset = ModelCatalog.C2_DATASET_COCO | |
| if "35998355/rpn_R-50-C4_1x" in name: | |
| # this one model is somehow different from others .. | |
| type = "rpn" | |
| else: | |
| type = "generalized_rcnn" | |
| # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`. | |
| url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format( | |
| prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset | |
| ) | |
| return url | |
| class ModelCatalogHandler(PathHandler): | |
| """ | |
| Resolve URL like catalog://. | |
| """ | |
| PREFIX = "catalog://" | |
| def _get_supported_prefixes(self): | |
| return [self.PREFIX] | |
| def _get_local_path(self, path, **kwargs): | |
| logger = logging.getLogger(__name__) | |
| catalog_path = ModelCatalog.get(path[len(self.PREFIX) :]) | |
| logger.info("Catalog entry {} points to {}".format(path, catalog_path)) | |
| return PathManager.get_local_path(catalog_path, **kwargs) | |
| def _open(self, path, mode="r", **kwargs): | |
| return PathManager.open(self._get_local_path(path), mode, **kwargs) | |
| PathManager.register_handler(ModelCatalogHandler()) | |