UnsupportedOperation: fileno on 1.X MMClassification (Jupyter Notebook)

[lianjie99]

I tried several times and even created a new environment following the guided conda environment setup but I keep on encountering this error below.

Does anyone face this issue on their JupyterLab, and how to solve it? It works fine on Google Colab tho.

The code before the error:

from mmengine import Config, ConfigDict
from mmengine.runner import Runner

base_cfg = Config.fromfile(config_file)

# ------ model settings ------
# Modify the number of classes in the head.
model = base_cfg.model
model.head.num_classes = 30

# Load the pre-trained model's checkpoint.
model.backbone.init_cfg = dict(type='Pretrained', checkpoint=checkpoint_file, prefix='backbone')

# ----- data settings ------
train_dataloader = dict(
    batch_size=8,
    num_workers=2,
    dataset=dict(
        type='CustomDataset',
        data_prefix='JupyterLab/Demo/MMClassification/Agriculture/agriculture/train',
        classes=listFolderName,
        pipeline=base_cfg.train_dataloader.dataset.pipeline,
    ),
    sampler=dict(type='DefaultSampler', shuffle=True),
)

val_dataloader = dict(
    batch_size=8,
    num_workers=2,
    dataset=dict(
        type='CustomDataset',
        data_prefix='JupyterLab/Demo/MMClassification/Agriculture/agriculture/val',
        ann_file="",
        classes=listFolderName,
        pipeline=base_cfg.train_dataloader.dataset.pipeline,
    ),
    sampler=dict(type='DefaultSampler', shuffle=False),
)

# Specify the normalization parameters in the data preprocessor.
data_preprocessor = dict(mean=[124.508, 116.050, 106.438], std=[58.577, 57.310, 57.437], to_rgb=True)

# Specify the validation metric
val_evaluator = dict(type='Accuracy', topk=1)

# ------ schedule settings ------
# Specify the optimizer
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001),
    clip_grad=None,
)

# Specify the learning rate scheduler
param_scheduler = dict(type='StepLR', by_epoch=True, step_size=1, gamma=0.1)

# Specify the training epochs
train_cfg = dict(by_epoch=True, max_epochs=2)
val_cfg = dict()  # To use the default validation settings.

# logger settings
logger = dict(type='LoggerHook', interval=10)

runner = Runner(
    # Common settings
    model=model,
    default_scope='mmcls',
    work_dir=r'JupyterLab/Demo/MMClassification/Agriculture/agriculture/work_dir',
    experiment_name='mobilenet-v2_agriculture_1.XMMclassification',
    default_hooks=dict(logger=logger),
    # Training settings
    train_dataloader=train_dataloader,
    train_cfg=train_cfg,
    optim_wrapper=optim_wrapper,
    param_scheduler=param_scheduler,
    # Validation settings
    val_dataloader=val_dataloader,
    val_cfg=val_cfg,
    val_evaluator=val_evaluator,
)