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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import logging | |
| import os | |
| import pickle | |
| import torch | |
| import torch.nn as nn | |
| from termcolor import colored | |
| from collections import defaultdict | |
| from typing import Any, Dict, Iterable, List, NamedTuple, Optional, Tuple | |
| from fvcore.common.checkpoint import Checkpointer, _IncompatibleKeys | |
| from torch.nn.parallel import DistributedDataParallel | |
| import detectron2.utils.comm as comm | |
| from detectron2.utils.file_io import PathManager | |
| from .c2_model_loading import align_and_update_state_dicts | |
| 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, **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 | |
| 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 ...") | |
| 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} | |
| loaded = super()._load_file(filename) # load native pth checkpoint | |
| if "model" not in loaded: | |
| loaded = {"model": loaded} | |
| loaded["matching_heuristics"] = True | |
| return loaded | |
| def _load_model(self, checkpoint): | |
| if checkpoint.get("matching_heuristics", False): | |
| self._convert_ndarray_to_tensor(checkpoint["model"]) | |
| # convert weights by name-matching heuristics | |
| 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 | |
| incompatible = super()._load_model(checkpoint) | |
| 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 | |
| for k in incompatible.unexpected_keys[:]: | |
| # Ignore unexpected keys about cell anchors. They exist in old checkpoints | |
| # but now they are non-persistent buffers and will not be in new checkpoints. | |
| if "anchor_generator.cell_anchors" in k: | |
| incompatible.unexpected_keys.remove(k) | |
| return incompatible | |
| def _log_incompatible_keys(self, incompatible: _IncompatibleKeys) -> None: | |
| """ | |
| Log information about the incompatible keys returned by ``_load_model``. | |
| """ | |
| for k, shape_checkpoint, shape_model in incompatible.incorrect_shapes: | |
| self.logger.warning( | |
| "Skip loading parameter '{}' to the model due to incompatible " | |
| "shapes: {} in the checkpoint but {} in the " | |
| "model! You might want to double check if this is expected.".format( | |
| k, shape_checkpoint, shape_model | |
| ) | |
| ) | |
| if incompatible.missing_keys: | |
| missing_keys = _filter_reused_missing_keys( | |
| self.model, incompatible.missing_keys | |
| ) | |
| if missing_keys: | |
| self.logger.warning(get_missing_parameters_message(missing_keys)) | |
| if incompatible.unexpected_keys: | |
| self.logger.warning( | |
| get_unexpected_parameters_message(incompatible.unexpected_keys) | |
| ) | |
| def _filter_reused_missing_keys(model: nn.Module, keys: List[str]) -> List[str]: | |
| """ | |
| Filter "missing keys" to not include keys that have been loaded with another name. | |
| """ | |
| keyset = set(keys) | |
| param_to_names = defaultdict(set) # param -> names that points to it | |
| for module_prefix, module in _named_modules_with_dup(model): | |
| for name, param in list(module.named_parameters(recurse=False)) + list( | |
| module.named_buffers(recurse=False) | |
| ): | |
| full_name = (module_prefix + "." if module_prefix else "") + name | |
| param_to_names[param].add(full_name) | |
| for names in param_to_names.values(): | |
| # if one name appears missing but its alias exists, then this | |
| # name is not considered missing | |
| if any(n in keyset for n in names) and not all(n in keyset for n in names): | |
| [keyset.remove(n) for n in names if n in keyset] | |
| return list(keyset) | |
| def get_missing_parameters_message(keys: List[str]) -> str: | |
| """ | |
| Get a logging-friendly message to report parameter names (keys) that are in | |
| the model but not found in a checkpoint. | |
| Args: | |
| keys (list[str]): List of keys that were not found in the checkpoint. | |
| Returns: | |
| str: message. | |
| """ | |
| groups = _group_checkpoint_keys(keys) | |
| msg_per_group = sorted(k + _group_to_str(v) for k, v in groups.items()) | |
| msg = "Some model parameters or buffers are not found in the checkpoint:\n" | |
| msg += "\n".join([colored(x, "blue") for x in msg_per_group]) | |
| return msg | |
| def _group_checkpoint_keys(keys: List[str]) -> Dict[str, List[str]]: | |
| """ | |
| Group keys based on common prefixes. A prefix is the string up to the final | |
| "." in each key. | |
| Args: | |
| keys (list[str]): list of parameter names, i.e. keys in the model | |
| checkpoint dict. | |
| Returns: | |
| dict[list]: keys with common prefixes are grouped into lists. | |
| """ | |
| groups = defaultdict(list) | |
| for key in keys: | |
| pos = key.rfind(".") | |
| if pos >= 0: | |
| head, tail = key[:pos], [key[pos + 1 :]] | |
| else: | |
| head, tail = key, [] | |
| groups[head].extend(tail) | |
| return groups | |
| def _group_to_str(group: List[str]) -> str: | |
| """ | |
| Format a group of parameter name suffixes into a loggable string. | |
| Args: | |
| group (list[str]): list of parameter name suffixes. | |
| Returns: | |
| str: formated string. | |
| """ | |
| if len(group) == 0: | |
| return "" | |
| if len(group) == 1: | |
| return "." + group[0] | |
| return ".{" + ", ".join(sorted(group)) + "}" | |
| def get_unexpected_parameters_message(keys: List[str]) -> str: | |
| """ | |
| Get a logging-friendly message to report parameter names (keys) that are in | |
| the checkpoint but not found in the model. | |
| Args: | |
| keys (list[str]): List of keys that were not found in the model. | |
| Returns: | |
| str: message. | |
| """ | |
| groups = _group_checkpoint_keys(keys) | |
| msg = "The checkpoint state_dict contains keys that are not used by the model:\n" | |
| msg += "\n".join( | |
| " " + colored(k + _group_to_str(v), "magenta") for k, v in groups.items() | |
| ) | |
| return msg | |
| def _named_modules_with_dup( | |
| model: nn.Module, prefix: str = "" | |
| ) -> Iterable[Tuple[str, nn.Module]]: | |
| """ | |
| The same as `model.named_modules()`, except that it includes | |
| duplicated modules that have more than one name. | |
| """ | |
| yield prefix, model | |
| for name, module in model._modules.items(): | |
| if module is None: | |
| continue | |
| submodule_prefix = prefix + ("." if prefix else "") + name | |
| yield from _named_modules_with_dup(module, submodule_prefix) |