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Frequently Asked Questions (FAQ)

We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the provided templates and make sure you fill in all required information in the template.

Installation

The compatible MMSegmentation and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.

MMSegmentation version MMCV version MMClassification version
master mmcv-full>=1.5.0, <1.8.0 mmcls>=0.20.1, <=1.0.0
0.29.1 mmcv-full>=1.5.0, <1.8.0 mmcls>=0.20.1, <=1.0.0
0.29.0 mmcv-full>=1.5.0, <1.7.0 mmcls>=0.20.1, <=1.0.0
0.28.0 mmcv-full>=1.5.0, <1.7.0 mmcls>=0.20.1, <=1.0.0
0.27.0 mmcv-full>=1.5.0, <1.7.0 mmcls>=0.20.1, <=1.0.0
0.26.0 mmcv-full>=1.5.0, <=1.6.0 mmcls>=0.20.1, <=1.0.0
0.25.0 mmcv-full>=1.5.0, <=1.6.0 mmcls>=0.20.1, <=1.0.0
0.24.1 mmcv-full>=1.4.4, <=1.6.0 mmcls>=0.20.1, <=1.0.0
0.23.0 mmcv-full>=1.4.4, <=1.6.0 mmcls>=0.20.1, <=1.0.0
0.22.0 mmcv-full>=1.4.4, <=1.6.0 mmcls>=0.20.1, <=1.0.0
0.21.1 mmcv-full>=1.4.4, <=1.6.0 Not required
0.20.2 mmcv-full>=1.3.13, <=1.6.0 Not required
0.19.0 mmcv-full>=1.3.13, <1.3.17 Not required
0.18.0 mmcv-full>=1.3.13, <1.3.17 Not required
0.17.0 mmcv-full>=1.3.7, <1.3.17 Not required
0.16.0 mmcv-full>=1.3.7, <1.3.17 Not required
0.15.0 mmcv-full>=1.3.7, <1.3.17 Not required
0.14.1 mmcv-full>=1.3.7, <1.3.17 Not required
0.14.0 mmcv-full>=1.3.1, <1.3.2 Not required
0.13.0 mmcv-full>=1.3.1, <1.3.2 Not required
0.12.0 mmcv-full>=1.1.4, <1.3.2 Not required
0.11.0 mmcv-full>=1.1.4, <1.3.0 Not required
0.10.0 mmcv-full>=1.1.4, <1.3.0 Not required
0.9.0 mmcv-full>=1.1.4, <1.3.0 Not required
0.8.0 mmcv-full>=1.1.4, <1.2.0 Not required
0.7.0 mmcv-full>=1.1.2, <1.2.0 Not required
0.6.0 mmcv-full>=1.1.2, <1.2.0 Not required

You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

  • "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'".

    1. Uninstall existing mmcv in the environment using pip uninstall mmcv.
    2. Install mmcv-full following the installation instruction.

How to know the number of GPUs needed to train the model

  • Infer from the name of the config file of the model. You can refer to the Config Name Style part of Learn about Configs. For example, for config file with name segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py, 8x1 means training the model corresponding to it needs 8 GPUs, and the batch size of each GPU is 1.
  • Infer from the log file. Open the log file of the model and search nGPU in the file. The number of figures following nGPU is the number of GPUs needed to train the model. For instance, searching for nGPU in the log file yields the record nGPU 0,1,2,3,4,5,6,7, which indicates that eight GPUs are needed to train the model.

What does the auxiliary head mean

Briefly, it is a deep supervision trick to improve the accuracy. In the training phase, decode_head is for decoding semantic segmentation output, auxiliary_head is just adding an auxiliary loss, the segmentation result produced by it has no impact to your model's result, it just works in training. You may read this paper for more information.

Why is the log file not created

In the train script, we call get_root_loggerat Line 167, and get_root_logger in mmseg calls get_logger in mmcv, mmcv will return the same logger which has been initialized in 'mmsegmentation/tools/train.py' with the parameter log_file. There is only one logger (initialized with log_file) during training. Ref: https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16

If you find the log file not been created, you might check if mmcv.utils.get_logger is called elsewhere.

How to output the image for painting the segmentation mask when running the test script

In the test script, we provide show-dir argument to control whether output the painted images. Users might run the following command:

python tools/test.py {config} {checkpoint} --show-dir {/path/to/save/image} --opacity 1

How to handle binary segmentation task

MMSegmentation uses num_classes and out_channels to control output of last layer self.conv_seg. More details could be found here.

num_classes should be the same as number of types of labels, in binary segmentation task, dataset only has two types of labels: foreground and background, so num_classes=2. out_channels controls the output channel of last layer of model, it usually equals to num_classes. But in binary segmentation task, there are two solutions:

  • Set out_channels=2, using Cross Entropy Loss in training, using F.softmax() and argmax() to get prediction of each pixel in inference.

  • Set out_channels=1, using Binary Cross Entropy Loss in training, using F.sigmoid() and threshold to get prediction of each pixel in inference. threshold is set 0.3 as default.

In summary, to implement binary segmentation methods users should modify below parameters in the decode_head and auxiliary_head configs. Here is a modification example of pspnet_unet_s5-d16.py:

  • (1) num_classes=2, out_channels=2 and use_sigmoid=False in CrossEntropyLoss.
decode_head=dict(
    type='PSPHead',
    in_channels=64,
    in_index=4,
    num_classes=2,
    out_channels=2,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=dict(
    type='FCNHead',
    in_channels=128,
    in_index=3,
    num_classes=2,
    out_channels=2,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
  • (2) num_classes=2, out_channels=1 and use_sigmoid=True in CrossEntropyLoss.
decode_head=dict(
    type='PSPHead',
    in_channels=64,
    in_index=4,
    num_classes=2,
    out_channels=1,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
auxiliary_head=dict(
    type='FCNHead',
    in_channels=128,
    in_index=3,
    num_classes=2,
    out_channels=1,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),

What does reduce_zero_label work for?

When loading annotation in MMSegmentation, reduce_zero_label (bool) is provided to determine whether reduce all label value by 1:

if self.reduce_zero_label:
    # avoid using underflow conversion
    gt_semantic_seg[gt_semantic_seg == 0] = 255
    gt_semantic_seg = gt_semantic_seg - 1
    gt_semantic_seg[gt_semantic_seg == 254] = 255

Noted: Please pay attention to label numbers of dataset when using reduce_zero_label. If dataset only has two types of labels (i.e., label 0 and 1), it needs to close reduce_zero_label, i.e., set reduce_zero_label=False.