mmocr: [Errno 21] Is a directory: 'data/mixture/Syn90k/label.lmdb'

When I tried to train MASTER on GPUs, it raised the error as below, however, I had orgnaized my data right and the directory “label.lmdb” surely had two files named “data.mdb” and “lock.mdb”

$RCS){OT{P@`2LUI@_Z4~SQ7$

%ZWF28 )OY@@`@2R%T4$)TJ

About this issue

Original URL
State: closed
Created 2 years ago
Comments: 22 (12 by maintainers)

Most upvoted comments

@MingyuLau I am confused too. Is it possible that you specified two dataset configs at the same time, and the correct one is replaced by the wrong one. E.g.

_base_ = [
    '../../_base_/recog_datasets/ST_SA_MJ_train.py',
    '../../_base_/recog_datasets/ST_MJ_train.py',
]

Are you training with master_r31_12e_ST_MJ_SA.py ?

Mountchicken on Jun 24, 2022

@Mountchicken I know where the error lies in ，I debug and find in the ST_SA_MJ_train.py ,the parameters of train3 cover the parameters of train1 Can you help me to fix this error? 7TJIH3GB HMHCO49FD(D~DO

MingyuLau on Jun 27, 2022

@Mountchicken Yes，I am training with master_r31_12e_ST_MJ_SA.py And this is my training file:

_base_ = [
    '../../_base_/default_runtime.py', '../../_base_/recog_models/master.py',
    '../../_base_/schedules/schedule_adam_step_12e.py',
    '../../_base_/recog_pipelines/master_pipeline.py',
    '../../_base_/recog_datasets/ST_SA_MJ_train.py',
    '../../_base_/recog_datasets/academic_test.py'
]

train_list = {{_base_.train_list}}
test_list = {{_base_.test_list}}

train_pipeline = {{_base_.train_pipeline}}
test_pipeline = {{_base_.test_pipeline}}

data = dict(
    samples_per_gpu=8,
    workers_per_gpu=4,
    val_dataloader=dict(samples_per_gpu=8),
    test_dataloader=dict(samples_per_gpu=8),
    train=dict(
        type='UniformConcatDataset',
        datasets=train_list,
        pipeline=train_pipeline),
    val=dict(
        type='UniformConcatDataset',
        datasets=test_list,
        pipeline=test_pipeline),
    test=dict(
        type='UniformConcatDataset',
        datasets=test_list,
        pipeline=test_pipeline))

evaluation = dict(interval=1, metric='acc')

MingyuLau on Jun 24, 2022