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# Copyright (c) Facebook, Inc. and its affiliates.
import pickle
from fvcore.common.checkpoint import Checkpointer
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 handle models in detectron & detectron2
model zoo, and apply conversions for legacy models.
"""
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,
)
if hasattr(self, "path_manager"):
self.path_manager = PathManager
else:
# This could only happen for open source
# TODO remove after upgrading fvcore
from fvcore.common.file_io import PathManager as g_PathManager
for handler in PathManager._path_handlers.values():
try:
g_PathManager.register_handler(handler)
except KeyError:
pass
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}
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):
self._convert_ndarray_to_tensor(checkpoint["model"])
# convert weights by name-matching heuristics
model_state_dict = self.model.state_dict()
align_and_update_state_dicts(
model_state_dict,
checkpoint["model"],
c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
)
checkpoint["model"] = model_state_dict
# for non-caffe2 models, use standard ways to load it
incompatible = super()._load_model(checkpoint)
if incompatible is None: # support older versions of fvcore
return None
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
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