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# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
LoggerHook, ParamSchedulerHook)
from vlm.engine.runner.loops import AnnoLoop
from xtuner.dataset import ConcatDataset
from projects.mllm_labeling.models import MLLM_Annotor
from projects.mllm_labeling.datasets.demo_dataset import DemoImageCap
#######################################################################
# PART 1 Settings #
#######################################################################
llm_name_or_path = './pretrained/internvl/InternVL2-Llama3-76B-AWQ/' # Please change to your own path
save_folder = './1215_demos/mllm_object_cap/'
model = dict(
type=MLLM_Annotor,
model=llm_name_or_path,
save_folder=save_folder,
)
test_dataset = [dict(
type=DemoImageCap,
image_folder='./1215_demos/mask_outs/out/',
bs=8,
)]
test_dataloader = dict(
batch_size=1,
num_workers=1,
drop_last=False,
sampler=dict(type=DefaultSampler, shuffle=False),
dataset=dict(type=ConcatDataset, datasets=test_dataset),
)
test_evaluator = dict()
test_cfg = dict(type=AnnoLoop, select_metric='first')
custom_hooks = []
# configure default hooks
default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
sampler_seed=dict(type=DistSamplerSeedHook),
)
# configure environment
env_cfg = dict(
# whether to enable cudnn benchmark
cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),
)
# set visualizer
visualizer = None
# set log level
log_level = 'INFO'
# load from which checkpoint
load_from = None
# whether to resume training from the loaded checkpoint
resume = False
# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)
# set log processor
log_processor = dict(by_epoch=False)
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