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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import logging | |
| import os | |
| import pickle | |
| import torch | |
| from fvcore.common.checkpoint import Checkpointer | |
| from torch.nn.parallel import DistributedDataParallel | |
| import detectron2.utils.comm as comm | |
| from detectron2.utils.env import TORCH_VERSION | |
| from detectron2.utils.file_io import PathManager | |
| from .c2_model_loading import align_and_update_state_dicts | |
| from .clip_model_loading import align_and_update_state_dicts_for_CLIP | |
| class DetectionCheckpointer(Checkpointer): | |
| """ | |
| Same as :class:`Checkpointer`, but is able to: | |
| 1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models. | |
| 2. correctly load checkpoints that are only available on the master worker | |
| """ | |
| def __init__(self, model, save_dir="", *, save_to_disk=None, bb_rpn_weights=False, **checkpointables): | |
| is_main_process = comm.is_main_process() | |
| super().__init__( | |
| model, | |
| save_dir, | |
| save_to_disk=is_main_process if save_to_disk is None else save_to_disk, | |
| **checkpointables, | |
| ) | |
| self.path_manager = PathManager | |
| self.bb_rpn_weights = bb_rpn_weights | |
| def load(self, path, *args, **kwargs): | |
| need_sync = False | |
| if path and isinstance(self.model, DistributedDataParallel): | |
| logger = logging.getLogger(__name__) | |
| path = self.path_manager.get_local_path(path) | |
| has_file = os.path.isfile(path) | |
| all_has_file = comm.all_gather(has_file) | |
| if not all_has_file[0]: | |
| raise OSError(f"File {path} not found on main worker.") | |
| if not all(all_has_file): | |
| logger.warning( | |
| f"Not all workers can read checkpoint {path}. " | |
| "Training may fail to fully resume." | |
| ) | |
| # TODO: broadcast the checkpoint file contents from main | |
| # worker, and load from it instead. | |
| need_sync = True | |
| if not has_file: | |
| path = None # don't load if not readable | |
| ret = super().load(path, *args, **kwargs) | |
| if need_sync: | |
| logger.info("Broadcasting model states from main worker ...") | |
| if TORCH_VERSION >= (1, 7): | |
| self.model._sync_params_and_buffers() | |
| return ret | |
| def _load_file(self, filename): | |
| if filename.endswith(".pkl"): | |
| with PathManager.open(filename, "rb") as f: | |
| data = pickle.load(f, encoding="latin1") | |
| if "model" in data and "__author__" in data: | |
| # file is in Detectron2 model zoo format | |
| self.logger.info("Reading a file from '{}'".format(data["__author__"])) | |
| return data | |
| else: | |
| # assume file is from Caffe2 / Detectron1 model zoo | |
| if "blobs" in data: | |
| # Detection models have "blobs", but ImageNet models don't | |
| data = data["blobs"] | |
| data = {k: v for k, v in data.items() if not k.endswith("_momentum")} | |
| return {"model": data, "__author__": "Caffe2", "matching_heuristics": True} | |
| elif filename.endswith(".pyth"): | |
| # assume file is from pycls; no one else seems to use the ".pyth" extension | |
| with PathManager.open(filename, "rb") as f: | |
| data = torch.load(f) | |
| assert ( | |
| "model_state" in data | |
| ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'." | |
| model_state = { | |
| k: v | |
| for k, v in data["model_state"].items() | |
| if not k.endswith("num_batches_tracked") | |
| } | |
| return {"model": model_state, "__author__": "pycls", "matching_heuristics": True} | |
| elif "OAI_CLIP" in filename: | |
| # assume file is from OpenAI CLIP pre-trained model | |
| loaded = super()._load_file(filename) # load native pth checkpoint | |
| if "model" not in loaded: | |
| loaded = {"model": loaded} | |
| return {"model": loaded["model"], "__author__": "OAI_CLIP", "matching_heuristics": True} | |
| loaded = super()._load_file(filename) # load native pth checkpoint | |
| if "model" not in loaded: | |
| loaded = {"model": loaded} | |
| return loaded | |
| def _load_model(self, checkpoint): | |
| # if checkpoint.get("matching_heuristics", False) or self.bb_rpn_weights: | |
| # self._convert_ndarray_to_tensor(checkpoint["model"]) | |
| # # convert weights by name-matching heuristics | |
| # if checkpoint.get("__author__", "NA") == "OAI_CLIP" or self.bb_rpn_weights: # for OAI_CLIP or 2nd ckpt (offline modules) | |
| # checkpoint["model"] = align_and_update_state_dicts_for_CLIP( | |
| # self.model.state_dict(), | |
| # checkpoint["model"], | |
| # bb_rpn_weights=self.bb_rpn_weights, | |
| # ) | |
| # else: # default loading | |
| # checkpoint["model"] = align_and_update_state_dicts( | |
| # self.model.state_dict(), | |
| # checkpoint["model"], | |
| # c2_conversion=checkpoint.get("__author__", None) == "Caffe2", | |
| # ) | |
| # for non-caffe2 models, use standard ways to load it | |
| # if not self.bb_rpn_weights: | |
| # checkpoint = {'model': {'backbone.' + key: val for key, val in checkpoint['model'].items()}} | |
| incompatible = super()._load_model(checkpoint) | |
| del checkpoint # try saving memory | |
| model_buffers = dict(self.model.named_buffers(recurse=False)) | |
| for k in ["pixel_mean", "pixel_std"]: | |
| # Ignore missing key message about pixel_mean/std. | |
| # Though they may be missing in old checkpoints, they will be correctly | |
| # initialized from config anyway. | |
| if k in model_buffers: | |
| try: | |
| incompatible.missing_keys.remove(k) | |
| except ValueError: | |
| pass | |
| return incompatible |