Config file dataset problem

[hannachoum]

Hello there, I think i may have a problem in my dataset file config, i have the felling that i missed settings but i dont know what to add.

import os.path as osp import numpy as np from mmseg.registry import DATASETS from .basesegdataset import BaseSegDataset

@DATASETS.register_module() class milvueDataset(BaseSegDataset): palette = [ .... ]

METAINFO = dict(
    classes=(
        ....
    ),
)
def __init__(
    self,
    data_root,
    **kwargs
):
    super().__init__(
        data_root=data_root,
        **kwargs
    )

[hannachoum]

My file is mmseg/datasets/milvue.py , and i have the following error : ValueError: class IterBasedTrainLoop in mmengine/runner/loops.py: class milvueDataset in mmseg/datasets/milvue.py: need at least one array to concatenate

[MatCorr]

I am facing the same error when using BaseSegDataset.

Here's my config file:

_base_ = [
    '../_base_/models/setr_mla.py', '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]

crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
norm_cfg = dict(type='SyncBN', requires_grad=True)

num_classes=1

metainfo = dict(classes = ('test'))

dataset_type = 'BaseSegDataset'
data_root = 'path_to_dataset_folder'
img_suffix='.png'
seg_map_suffix='.png'

pre_trained_weights_path = 'path_to_weights/weights.pth'

reduce_zero_label = True

model = dict(
    data_preprocessor=data_preprocessor,
    pretrained=None,
    backbone=dict(
        img_size=(512, 512),
        drop_rate=0.,
        init_cfg=dict(
            type='Pretrained', checkpoint=pre_trained_weights_path)),
    decode_head=dict(num_classes=num_classes),
    auxiliary_head=[
        dict(
            type='FCNHead',
            in_channels=256,
            channels=256,
            in_index=0,
            dropout_ratio=0,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='ReLU'),
            num_convs=0,
            kernel_size=1,
            concat_input=False,
            num_classes=num_classes,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
            type='FCNHead',
            in_channels=256,
            channels=256,
            in_index=1,
            dropout_ratio=0,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='ReLU'),
            num_convs=0,
            kernel_size=1,
            concat_input=False,
            num_classes=num_classes,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
            type='FCNHead',
            in_channels=256,
            channels=256,
            in_index=2,
            dropout_ratio=0,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='ReLU'),
            num_convs=0,
            kernel_size=1,
            concat_input=False,
            num_classes=num_classes,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
            type='FCNHead',
            in_channels=256,
            channels=256,
            in_index=3,
            dropout_ratio=0,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='ReLU'),
            num_convs=0,
            kernel_size=1,
            concat_input=False,
            num_classes=num_classes,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    ],
    test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)),
)

optimizer = dict(lr=0.001, weight_decay=0.0)
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=optimizer,
    paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))

train_dataloader = dict(
    batch_size=8,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(
            img_path='images/training', 
            seg_map_path='annotations/training'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(
                type='RandomResize',
                scale=(2048, 512),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs')
        ]))

val_dataloader = dict(
    batch_size=8,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(
            img_path='images/validation',
            seg_map_path='annotations/validation'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', scale=(2048, 512), keep_ratio=True),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(type='PackSegInputs')
        ]))

test_dataloader = dict(
    batch_size=8,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(
            img_path='images/test',
            seg_map_path='annotations/test'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', scale=(2048, 512), keep_ratio=True),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(type='PackSegInputs')
        ]))

[ruirui-coder]

你好，这个问题你解决了吗，是哪里的原因呀

[StoneMau]

You may try setup again, use "pip install -v -e ." blablabla in the instruction, for it needs setup again every time you change files in configs or something. It works for me.