Pickle FileNotFoundError in evaluation phase of distributed training

[austinmw]

Describe the bug
I get an MMEngine error in MMDetection distributed training (2 instances each with 8 gpus) at the first evaluation interval phase. It seems a node can't find evaluation pickle file parts.

Reproduction

I used torchrun to kick off tools/train.py

Here's my config (yolox_s_8xb8-300e_c0c0.py):

_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']

img_scale = (640, 640)  # height, width

# model settings
model = dict(
    type='YOLOX',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        pad_size_divisor=32,
        batch_augments=[
            dict(
                type='BatchSyncRandomResize',
                random_size_range=(480, 800),
                size_divisor=32,
                interval=10)
        ]),
    backbone=dict(
        type='CSPDarknet',
        deepen_factor=0.33,
        widen_factor=0.5,
        out_indices=(2, 3, 4),
        use_depthwise=False,
        spp_kernal_sizes=(5, 9, 13),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish'),
    ),
    neck=dict(
        type='YOLOXPAFPN',
        in_channels=[128, 256, 512],
        out_channels=128,
        num_csp_blocks=1,
        use_depthwise=False,
        upsample_cfg=dict(scale_factor=2, mode='nearest'),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish')),
    bbox_head=dict(
        type='YOLOXHead',
        num_classes=80,
        in_channels=128,
        feat_channels=128,
        stacked_convs=2,
        strides=(8, 16, 32),
        use_depthwise=False,
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish'),
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_bbox=dict(
            type='IoULoss',
            mode='square',
            eps=1e-16,
            reduction='sum',
            loss_weight=5.0),
        loss_obj=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
    # In order to align the source code, the threshold of the val phase is
    # 0.01, and the threshold of the test phase is 0.001.
    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))

# dataset settings
data_root = '/opt/ml/input/data/coco/'
dataset_type = 'CocoDataset'

# file_client_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
file_client_args = dict(backend='disk')

train_pipeline = [
    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
    dict(
        type='RandomAffine',
        scaling_ratio_range=(0.1, 2),
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='MixUp',
        img_scale=img_scale,
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    # According to the official implementation, multi-scale
    # training is not considered here but in the
    # 'mmdet/models/detectors/yolox.py'.
    # Resize and Pad are for the last 15 epochs when Mosaic,
    # RandomAffine, and MixUp are closed by YOLOXModeSwitchHook.
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        # If the image is three-channel, the pad value needs
        # to be set separately for each channel.
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
    dict(type='PackDetInputs')
]

train_dataset = dict(
    # use MultiImageMixDataset wrapper to support mosaic and mixup
    type='MultiImageMixDataset',
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        pipeline=[
            dict(type='LoadImageFromFile', file_client_args=file_client_args),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
    pipeline=train_pipeline)

test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

train_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=train_dataset)
val_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'annotations/instances_val2017.json',
    metric='bbox')
test_evaluator = val_evaluator

# training settings
max_epochs = 3
num_last_epochs = 1
interval = 1

train_cfg = dict(max_epochs=max_epochs, val_interval=interval)

# optimizer
# default 8 gpu
base_lr = 0.01
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4,
        nesterov=True),
    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))

# learning rate
param_scheduler = [
    dict(
        # use quadratic formula to warm up 5 epochs
        # and lr is updated by iteration
        # TODO: fix default scope in get function
        type='mmdet.QuadraticWarmupLR',
        by_epoch=True,
        begin=0,
        #end=5,
        end=1,
        convert_to_iter_based=True),
    dict(
        # use cosine lr from 5 to 285 epoch
        type='CosineAnnealingLR',
        eta_min=base_lr * 0.05,
        #begin=5,
        begin=1,
        T_max=max_epochs - num_last_epochs,
        end=max_epochs - num_last_epochs,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        # use fixed lr during last 15 epochs
        type='ConstantLR',
        by_epoch=True,
        factor=1,
        begin=max_epochs - num_last_epochs,
        end=max_epochs,
    )
]


default_hooks = dict(
    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3, save_best='auto')

    # checkpoint=dict(
    #     interval=interval,
    #     max_keep_ckpts=3,  # only keep latest 3 checkpoints
    # )
    )

custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        num_last_epochs=num_last_epochs,
        priority=48),
    dict(type='SyncNormHook', priority=48),
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        priority=49)
]

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64, enable=True)

log_config = dict(  # config to register logger hook
    interval=50,  # Interval to print the log
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook', log_dir='/opt/ml/checkpoints')
    ]
)

load_from='https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'

# fp16 settings
fp16 = dict(loss_scale=512.)

2. Did you make any modifications on the code or config? Did you understand what you have modified?
   Yes, above. I think so.

3. What dataset did you use?
   A small subset of the COCO dataset.

Environment

The environment script seems to be broken. I'm running in a Docker container on Amazon SageMaker with the latest version of MMEngine and MMDetection 3.0.

Error traceback

File "/opt/ml/code/mmdetection/tools/train.py", line 120, in <module>
main()
File "/opt/ml/code/mmdetection/tools/train.py", line 116, in main
runner.train()
File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1631, in train
model = self.train_loop.run()  # type: ignore
File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 94, in run
self.runner.val_loop.run()
File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 346, in run
metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
File "/opt/conda/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 79, in evaluate
_results = metric.evaluate(size)
File "/opt/conda/lib/python3.8/site-packages/mmengine/evaluator/metric.py", line 105, in evaluate
results = collect_results(self.results, size, self.collect_device)
File "/opt/conda/lib/python3.8/site-packages/mmengine/dist/dist.py", line 920, in collect_results
return collect_results_cpu(results, size, tmpdir)
File "/opt/conda/lib/python3.8/site-packages/mmengine/dist/dist.py", line 979, in collect_results_cpu
with open(osp.join(tmpdir, f'part_{rank}.pkl'), 'wb') as f:  # type: ignore
FileNotFoundError: [Errno 2] No such file or directory: '.dist_test/tmpx8oxbsxt/part_13.pkl'

[C1rN09]

Thanks for reporting the bug! I checked the code and find it's a bug in our dist.collect_results_cpu.

The bug occurs when you are evaluating a model in multiple instances without shared storage. We'll fix it ASAP. Some workarounds now:

• Make sure .dist_test folder that is shared across instances exists just in the directory you are running torchrun. If you have shared storage in other directories (e.g. /mnt/your_shared), you may create a soft link via ln -s. If you don't have one, you may try mounting through nfs or something similar.
• Or, you may add collect_device='gpu' to your metrics' config to enable GPU collecting. This is currently experimental and may not be that stable.

[austinmw]

Yep, that matches my scenario (multiple AWS p3.16xlarge instances without shared storage), thanks!