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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
import pickle
import torch
# from typing import Any
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer, _IncompatibleKeys
from fvcore.common.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
from torch.nn.parallel import DistributedDataParallel
import uniperceiver.utils.comm as comm
from uniperceiver.utils.env import TORCH_VERSION
from uniperceiver.utils.file_io import PathManager
from collections import defaultdict
import copy
import io
from .c2_model_loading import align_and_update_state_dicts
from typing import Any, Dict, Iterable, List, NamedTuple, Optional, Tuple
# import deepspeed
# from deepspeed.runtime.engine import DeepSpeedEngine
import shutil
from timm.utils import ModelEma
class PeriodicEpochCheckpointer(PeriodicCheckpointer):
def step(self, iteration: int, epoch: int, **kwargs: Any) -> None:
"""
Perform the appropriate action at the given iteration.
Args:
iteration (int): the current iteration, ranged in [0, max_iter-1].
kwargs (Any): extra data to save, same as in
:meth:`Checkpointer.save`.
"""
iteration = int(iteration)
epoch = int(epoch)
additional_state = {"iteration": iteration}
additional_state.update(kwargs)
if (iteration + 1) % self.period == 0:
self.checkpointer.save(
"{}_Epoch_{:05d}_Iter_{:07d}".format(self.file_prefix, epoch,
iteration),
**additional_state)
if self.max_to_keep is not None:
self.recent_checkpoints.append(
self.checkpointer.get_checkpoint_file())
# pyre-fixme[58]: `>` is not supported for operand types `int` and
# `Optional[int]`.
if len(self.recent_checkpoints) > self.max_to_keep:
file_to_delete = self.recent_checkpoints.pop(0)
if self.path_manager.exists(
file_to_delete) and not file_to_delete.endswith(
f"{self.file_prefix}_final.pth"):
self.path_manager.rm(file_to_delete)
class TorchCheckpointer(Checkpointer):
"""
Same as :class:`Checkpointer`, but is able to handle models in uniperceiver
model zoo, and apply conversions for legacy models.
"""
def __init__(
self,
model,
model_ema: ModelEma,
save_dir="",
*,
save_to_disk=None,
checkpoint_mapping=None,
mapping=False,
resume_tau=True,
ceph_save=False,
ceph_config=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
if checkpoint_mapping is None:
self.checkpoint_mapping = None
else:
self.checkpoint_mapping = defaultdict(list)
for mapping_pair in checkpoint_mapping:
self.checkpoint_mapping[mapping_pair['ORIGIN']].append(
mapping_pair['DEST'])
self.mapping = mapping
self.resume_tau = resume_tau
self.ceph_save = ceph_save
if self.ceph_save:
self.path_prefix = 's3://checkpoints_zjg/'
self.client = PetrelBackend(path_mapping={},
tcs_conf_path=ceph_config)
# if self.ceph_save and is_main_process:
# # for local machine debug
# if os.path.relpath(self.save_dir, os.getcwd()).startswith('outputs'):
# self.client.remove(self.save_dir)
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
}
if self.ceph_save:
relpath = os.path.relpath(filename, os.getcwd())
s3url = os.path.join(self.path_prefix, relpath)
with io.BytesIO(self.client.get(s3url)) as buffer:
loaded = torch.load(buffer, map_location=torch.device("cpu"))
else:
loaded = super()._load_file(filename) # load native pth checkpoint
if "model" not in loaded:
loaded = {"model": loaded}
return loaded
def save(self, name: str, **kwargs: Any) -> None:
"""
Dump model and checkpointables to a file.
Args:
name (str): name of the file.
kwargs (dict): extra arbitrary data to save.
"""
if not self.save_dir or not self.save_to_disk:
return
data = {}
data["model"] = self.model.state_dict()
for key, obj in self.checkpointables.items():
data[key] = obj.state_dict()
data.update(kwargs)
basename = "{}.pth".format(name)
if self.ceph_save:
local_save_file = os.path.join(self.save_dir, basename)
relpath = os.path.relpath(local_save_file, os.getcwd())
save_file = os.path.join(self.path_prefix, relpath)
assert os.path.basename(save_file) == basename, basename
self.logger.info("Saving checkpoint to {}".format(save_file))
with io.BytesIO() as f:
torch.save(data, f)
self.client.put(f.getvalue(), save_file)
else:
save_file = os.path.join(self.save_dir, basename)
assert os.path.basename(save_file) == basename, basename
self.logger.info("Saving checkpoint to {}".format(save_file))
with self.path_manager.open(save_file, "wb") as f:
torch.save(data, f)
self.tag_last_checkpoint(basename)
def load(self,
path: str,
checkpointables: Optional[List[str]] = None) -> Dict[str, Any]:
"""
Load from the given checkpoint.
Args:
path (str): path or url to the checkpoint. If empty, will not load
anything.
checkpointables (list): List of checkpointable names to load. If not
specified (None), will load all the possible checkpointables.
Returns:
dict:
extra data loaded from the checkpoint that has not been
processed. For example, those saved with
:meth:`.save(**extra_data)`.
"""
if not path:
# no checkpoint provided
self.logger.info(
"No checkpoint found. Initializing model from scratch")
return {}
self.logger.info("[Checkpointer] Loading from {} ...".format(path))
if not self.ceph_save:
if not os.path.isfile(path):
path = self.path_manager.get_local_path(path)
assert os.path.isfile(path), "Checkpoint {} not found!".format(
path)
else:
relpath = os.path.relpath(path, os.getcwd())
s3url = os.path.join(self.path_prefix, relpath)
#TODO: dev branch is needed
# if not self.client.exists(s3url):
# assert self.client.exists(s3url), "Checkpoint {} not found!".format(s3url)
checkpoint = self._load_file(path)
incompatible = self._load_model(checkpoint)
if (incompatible is not None
): # handle some existing subclasses that returns None
self._log_incompatible_keys(incompatible)
for key in self.checkpointables if checkpointables is None else checkpointables:
if key in checkpoint:
self.logger.info("Loading {} from {} ...".format(key, path))
obj = self.checkpointables[key]
obj.load_state_dict(checkpoint.pop(key))
# return any further checkpoint data
return checkpoint
def _convert_checkpoint(self, checkpoint):
# for multitask pretrain and fintune
if self.checkpoint_mapping is not None and self.mapping:
pretrain_checkpoint = checkpoint["model"]
for origin_task in self.checkpoint_mapping.keys():
for k in list(pretrain_checkpoint.keys()):
if origin_task in k:
# mapping to downstrean task
state_dict_temp = copy.deepcopy(
pretrain_checkpoint.pop(k))
for subtask in self.checkpoint_mapping[origin_task]:
new_key = k.replace(origin_task, subtask)
pretrain_checkpoint[new_key] = state_dict_temp
checkpoint["model"] = pretrain_checkpoint
if not self.resume_tau:
pretrain_checkpoint = checkpoint["model"]
for k in list(pretrain_checkpoint.keys()):
if "logit_scale" in k:
pretrain_checkpoint.pop(k)
checkpoint["model"] = pretrain_checkpoint
return checkpoint
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
# convert checkpoint for pretrained model between different tasks
checkpoint = self._convert_checkpoint(checkpoint)
# 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
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:
self.logger.info(
get_missing_parameters_message(incompatible.missing_keys))
if incompatible.unexpected_keys:
self.logger.info(
get_unexpected_parameters_message(
incompatible.unexpected_keys))
def resume_or_load(self, path, resume: bool = True, **kwargs):
super().resume_or_load(path, resume=resume)
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