llava_phi3_mini_4k_instruct_full_clip_vit_large_lora_p14_336_anyres_e1_gpu8_internvl_finetune.py

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                            LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          CLIPImageProcessor, CLIPVisionModel)

from xtuner.dataset import AnyResLLaVADataset, AnyResLLaVAProxyEvalDataset
from xtuner.dataset.collate_fns import mm_collate_fn
from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory
from xtuner.dataset.samplers import LengthGroupedSampler
from xtuner.engine.hooks import DatasetInfoHook
from xtuner.model import AnyResLLaVAModel
from xtuner.utils import PROMPT_TEMPLATE
from peft import LoraConfig
from xtuner.dataset.evaluation import MMEDataset, MultipleChoiceDataset, POPEDataset, \
    HallusionDataset, TextVQADataset, GQADataset, VQAv2Dataset, ChartQADataset, GeneralVQADataset
from xtuner.dataset import ConcatDataset
from xtuner.engine.runner import TrainLoop, ValLoop, TestLoop
from mmengine.dataset import DefaultSampler

#######################################################################
#                          PART 1  Settings                           #
#######################################################################
# Model
llm_name_or_path = '/mnt/petrelfs/share_data/gaojianfei/Phi-3-mini-4k-instruct/models--microsoft--Phi-3-mini-4k-instruct/snapshots/3a811845d89f3c1b3f41b341d0f9f05104769f35'
visual_encoder_name_or_path = 'model/models--openai--clip-vit-large-patch14-336/snapshots/ce19dc912ca5cd21c8a653c79e251e808ccabcd1'
# Specify the pretrained pth
pretrained_pth = './work_dirs/llava_phi3_mini_4k_instruct_clip_vit_large_p14_336_e1_gpu8_pretrain/iter_2181.pth'  # noqa: E501

# Data
data_root = '/mnt/petrelfs/share_data/linzhihao/dataset/internvl_sft/'

sharegpt4v_caption_data_path = data_root + 'sharegpt4v_instruct_gpt4-vision_cap100k.jsonl'
sharegpt4v_caption_image_folder = data_root + 'data'

llava_data_path = data_root + 'llava_instruct_150k_zh.jsonl'
llava_image_folder = data_root + 'data/coco'

sharegpt4v_data_path = data_root + 'sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.jsonl'
sharegpt4v_image_folder = data_root + 'data'

dvqa_data_path = data_root + 'dvqa_train_200k.jsonl'
dvqa_image_folder = data_root + 'data/dvqa'

chartqa_data_path = data_root + 'chartqa_train_18k.jsonl'
chartqa_image_folder = data_root + 'data/chartqa'

ai2d_data_path = data_root + 'ai2d_train_12k.jsonl'
ai2d_image_folder = data_root + 'data/ai2d'

docvqa_data_path = data_root + 'docvqa_train_10k.jsonl'
docvqa_image_folder = data_root + 'data/docvqa'

geoqa_data_path = data_root + 'geoqa+.jsonl'
geoqa_image_folder = data_root + 'data/geoqa+'

synthdog_data_path = data_root + 'synthdog_en.jsonl'
synthdog_image_folder = data_root + 'data/synthdog-en'

prompt_template = PROMPT_TEMPLATE.phi3_chat
max_length = int(2048 - (336 // 14) ** 2)

# Scheduler & Optimizer
batch_size = 8  # per_device
accumulative_counts = 1
dataloader_num_workers = 4
max_epochs = 1
optim_type = AdamW
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1  # grad clip
warmup_ratio = 0.03

# Save
save_steps = 1500
save_total_limit = 1  # Maximum checkpoints to keep (-1 means unlimited)

# Evaluate the generation performance during the training
evaluation_freq = 1500
SYSTEM = ''
evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
evaluation_inputs = ['Please describe this picture']

image_grid_pinpoints = [[336, 672], [672, 336], [672, 672], [1008, 336],
                        [336, 1008]]

#######################################################################
#            PART 2  Model & Tokenizer & Image Processor              #
#######################################################################
tokenizer = dict(
    type=AutoTokenizer.from_pretrained,
    pretrained_model_name_or_path=llm_name_or_path,
    trust_remote_code=True,
    padding_side='right')

image_processor = dict(
    type=CLIPImageProcessor.from_pretrained,
    pretrained_model_name_or_path=visual_encoder_name_or_path,
    trust_remote_code=True)

model = dict(
    type=AnyResLLaVAModel,
    image_grid_pinpoints=image_grid_pinpoints,
    token_merge_ratio=-1,
    tokenizer=tokenizer,
    template=prompt_template,
    image_processor=image_processor,
    freeze_llm=False,
    freeze_visual_encoder=True,
    pretrained_pth=pretrained_pth,
    llm=dict(
        type=AutoModelForCausalLM.from_pretrained,
        pretrained_model_name_or_path=llm_name_or_path,
        trust_remote_code=True),
    visual_encoder=dict(
        type=CLIPVisionModel.from_pretrained,
        pretrained_model_name_or_path=visual_encoder_name_or_path),
    visual_encoder_lora=dict(
        type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05, bias='none')
)

pad_image_to_square = False

#######################################################################
#                      PART 3  Dataset & Dataloader                   #
#######################################################################
cache_root='/mnt/petrelfs/share_data/huanghaian/internvl_finetune_phi3/'

sharegpt4v_caption_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'sharegpt4v_caption_dataset',
    data_path=sharegpt4v_caption_data_path,
    image_folder=sharegpt4v_caption_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

llava_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'llava_dataset',
    data_path=llava_data_path,
    image_folder=llava_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

sharegpt4v_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'sharegpt4v_dataset',
    data_path=sharegpt4v_data_path,
    image_folder=sharegpt4v_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)


dvqa_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'dvqa_dataset',
    data_path=dvqa_data_path,
    image_folder=dvqa_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

chartqa_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'chartqa_dataset',
    data_path=chartqa_data_path,
    image_folder=chartqa_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

ai2d_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'ai2d_dataset',
    data_path=ai2d_data_path,
    image_folder=ai2d_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

docvqa_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'docvqa_dataset',
    data_path=docvqa_data_path,
    image_folder=docvqa_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

geoqa_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'geoqa_dataset',
    data_path=geoqa_data_path,
    image_folder=geoqa_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

synthdog_dataset = dict(
    type=AnyResLLaVADataset,
    image_grid_pinpoints=image_grid_pinpoints,
    offline_processed_text_folder=cache_root+'synthdog_dataset',
    data_path=synthdog_data_path,
    image_folder=synthdog_image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=pad_image_to_square)

train_dataset = dict(
    type=ConcatDataset,
    datasets=[sharegpt4v_caption_dataset, llava_dataset, sharegpt4v_dataset,
              dvqa_dataset, chartqa_dataset, ai2d_dataset, docvqa_dataset,
              geoqa_dataset, synthdog_dataset])

train_dataloader = dict(
    batch_size=batch_size,
    num_workers=dataloader_num_workers,
    pin_memory=False,
    dataset=train_dataset,
    sampler=dict(
        type=LengthGroupedSampler,
        length_property='modality_length',
        per_device_batch_size=batch_size * accumulative_counts),
    collate_fn=dict(type=mm_collate_fn, extra_collate_keys=['orig_size']))


#######################################################################
#                    PART 4  Scheduler & Optimizer                    #
#######################################################################
# optimizer
optim_wrapper = dict(
    type=AmpOptimWrapper,
    optimizer=dict(
        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
    accumulative_counts=accumulative_counts,
    loss_scale='dynamic',
    dtype='float16')

# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
param_scheduler = [
    dict(
        type=LinearLR,
        start_factor=1e-5,
        by_epoch=True,
        begin=0,
        end=warmup_ratio * max_epochs,
        convert_to_iter_based=True),
    dict(
        type=CosineAnnealingLR,
        eta_min=0.0,
        by_epoch=True,
        begin=warmup_ratio * max_epochs,
        end=max_epochs,
        convert_to_iter_based=True)
]

# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs, val_interval=save_steps)

#######################################################################
#                           PART 5  Runtime                           #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
    dict(type=DatasetInfoHook, tokenizer=tokenizer),
    # dict(
    #     type=EvaluateChatHook,
    #     tokenizer=tokenizer,
    #     image_processor=image_processor,
    #     every_n_iters=evaluation_freq,
    #     evaluation_inputs=evaluation_inputs,
    #     evaluation_images=evaluation_images,
    #     system=SYSTEM,
    #     prompt_template=prompt_template)
]

# 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`.
    checkpoint=dict(
        type=CheckpointHook,
        save_optimizer=False,
        by_epoch=False,
        interval=save_steps,
        max_keep_ckpts=save_total_limit),
    # set sampler seed in distributed evrionment.
    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)

# ==================== val and test cfg =======================
proxy_eval_dataset = dict(type=AnyResLLaVAProxyEvalDataset,
                          image_grid_pinpoints=image_grid_pinpoints)

val_dataset = [
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMBench_DEV_EN.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
]

test_dataset = [
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMBench_DEV_EN.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/SEEDBench_IMG.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/ScienceQA_VAL.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/ScienceQA_TEST.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMMU_DEV_VAL.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/AI2D_TEST.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=TextVQADataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/share_data/huanghaian/orig_llava_eval/textvqa/llava_textvqa_val_v051_ocr.jsonl',
        ann_file='/mnt/petrelfs/share_data/huanghaian/text_vqa/TextVQA_0.5.1_val.json',
        image_folder='/mnt/petrelfs/share_data/huanghaian/text_vqa/train_images',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MMEDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MME.tsv',
        image_folder='/mnt/petrelfs/share_data/duanhaodong/data/mme/MME_Benchmark_release',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        # for_llava_prompt=True, # 开了后，perception 会掉
        pad_image_to_square=pad_image_to_square),
    dict(
        type=HallusionDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/HallusionBench.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=POPEDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file=[
            '/mnt/petrelfs/share_data/linzhihao/dataset/POPE/coco_pope_adversarial.json',
            '/mnt/petrelfs/share_data/linzhihao/dataset/POPE/coco_pope_popular.json',
            '/mnt/petrelfs/share_data/linzhihao/dataset/POPE/coco_pope_random.json'
        ],
        coco_val_path='/mnt/petrelfs/share_data/linzhihao/dataset/coco/val2014/',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=GQADataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/share_data/zhaoxiangyu/gqa_llava_eval/llava_gqa_testdev_balanced.jsonl',
        ann_file='/mnt/petrelfs/share_data/zhaoxiangyu/gqa_llava_eval/testdev_balanced_questions.json',
        image_folder='/mnt/petrelfs/share_data/basemodel/dataset/multimodality/gqa/images',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/share_data/zhaoxiangyu/datasets--Lin-Chen--MMStar/snapshots/mmstar/MMStar.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMBench_DEV_CN.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    dict(
        type=MultipleChoiceDataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/CCBench.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square),
    # dict(
    #     type=MultipleChoiceDataset,
    #     proxy_eval_dataset = proxy_eval_dataset,
    #     data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMBench_TEST_CN.tsv',
    #     prompt_template=prompt_template,
    #     tokenizer=tokenizer,
    #     image_processor=image_processor,
    #     pad_image_to_square=pad_image_to_square),
    # dict(
    #     type=MultipleChoiceDataset,
    #     proxy_eval_dataset = proxy_eval_dataset,
    #     data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/MMBench_TEST_EN.tsv',
    #     prompt_template=prompt_template,
    #     tokenizer=tokenizer,
    #     image_processor=image_processor,
    #     pad_image_to_square=pad_image_to_square),
    # dict(
    #     type=VQAv2Dataset,
    #     proxy_eval_dataset = proxy_eval_dataset,
    #     data_file='/mnt/petrelfs/share_data/zhaoxiangyu/vqav2_llava_eval/llava_vqav2_mscoco_test-dev2015.jsonl',
    #     test_file='/mnt/petrelfs/share_data/zhaoxiangyu/vqav2_llava_eval/llava_vqav2_mscoco_test2015.jsonl',
    #     image_folder='/mnt/petrelfs/share_data/zhaoxiangyu/vqav2_test2015',
    #     prompt_template=PROMPT_TEMPLATE.vicuna,
    #     tokenizer=tokenizer,
    #     image_processor=image_processor,
    #     pad_image_to_square=pad_image_to_square),
    dict(
        type=ChartQADataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file=['LMUData/ChartQA/ChartQA Dataset/test/test_human.json',
                   'LMUData/ChartQA/ChartQA Dataset/test/test_augmented.json'],
        image_folder='LMUData/ChartQA/ChartQA Dataset/test/png',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square
    ),
    dict(
        type=GeneralVQADataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/DocVQA_VAL.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square
    ),
    dict(
        type=GeneralVQADataset,
        proxy_eval_dataset=proxy_eval_dataset,
        data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/InfoVQA_VAL.tsv',
        prompt_template=prompt_template,
        tokenizer=tokenizer,
        image_processor=image_processor,
        pad_image_to_square=pad_image_to_square
    ),
    # 有问题，需要图片
    # dict(
    #     type=GeneralVQADataset,
    #     proxy_eval_dataset=proxy_eval_dataset,
    #     data_file='/mnt/petrelfs/huanghaian/code/xtuner/LMUData/OCRVQA_TEST.tsv',
    #     prompt_template=prompt_template,
    #     tokenizer=tokenizer,
    #     image_processor=image_processor,
    #     pad_image_to_square=pad_image_to_square
    # ),
]

# TODO: We are not currently using val_evaluator
# Don't support num_workers > 0
val_dataloader = dict(
    batch_size=1,
    num_workers=0,
    drop_last=False,
    sampler=dict(type=DefaultSampler, shuffle=False),
    dataset=dict(type=ConcatDataset, datasets=val_dataset),
    collate_fn=dict(type=mm_collate_fn, extra_collate_keys=['orig_size', 'img_id']))
val_evaluator = dict()
val_cfg = dict(type=ValLoop)

# TODO: We are not currently using test_evaluator
test_dataloader = dict(
    batch_size=1,
    num_workers=0,
    drop_last=False,
    sampler=dict(type=DefaultSampler, shuffle=False),
    dataset=dict(type=ConcatDataset, datasets=test_dataset),
    collate_fn=dict(type=mm_collate_fn, extra_collate_keys=['orig_size', 'img_id'])
)

test_evaluator = val_evaluator
test_cfg = dict(type=TestLoop, select_metric='first')