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, MMCV and MMEngine versions are as below. Please install the correct versions of them to avoid installation issues.
MMSegmentation version MMCV version MMEngine version MMClassification (optional) version MMDetection (optional) version dev-1.x branch mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 main branch mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.2.2 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.2.1 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.2.0 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.1.2 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.1.1 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.1.0 mmcv >= 2.0.0 MMEngine >= 0.7.4 mmpretrain>=1.0.0rc7 mmdet >= 3.0.0 1.0.0 mmcv >= 2.0.0rc4 MMEngine >= 0.7.1 mmcls==1.0.0rc6 mmdet >= 3.0.0 1.0.0rc6 mmcv >= 2.0.0rc4 MMEngine >= 0.5.0 mmcls>=1.0.0rc0 mmdet >= 3.0.0rc6 1.0.0rc5 mmcv >= 2.0.0rc4 MMEngine >= 0.2.0 mmcls>=1.0.0rc0 mmdet>=3.0.0rc6 1.0.0rc4 mmcv == 2.0.0rc3 MMEngine >= 0.1.0 mmcls>=1.0.0rc0 mmdet>=3.0.0rc4, \<=3.0.0rc5 1.0.0rc3 mmcv == 2.0.0rc3 MMEngine >= 0.1.0 mmcls>=1.0.0rc0 mmdet>=3.0.0rc4, \<=3.0.0rc5 1.0.0rc2 mmcv == 2.0.0rc3 MMEngine >= 0.1.0 mmcls>=1.0.0rc0 mmdet>=3.0.0rc4, \<=3.0.0rc5 1.0.0rc1 mmcv >= 2.0.0rc1, \<=2.0.0rc3> MMEngine >= 0.1.0 mmcls>=1.0.0rc0 Not required 1.0.0rc0 mmcv >= 2.0.0rc1, \<=2.0.0rc3> MMEngine >= 0.1.0 mmcls>=1.0.0rc0 Not requiredNotes:
MMClassification and MMDetatction are optional for MMSegmentation. If you didn’t install them, ConvNeXt (required MMClassification) and MaskFormer, Mask2Former (required MMDetection) cannot be used. We recommend to install them with source code. Please refer to MMClasssication and MMDetection for more details about their installation.
To install MMSegmentation 0.x and master branch, please refer to the faq 0.x document to check compatible versions of MMCV.
If you have installed an incompatible version of mmcv, please run pip uninstall mmcv to uninstall the installed mmcv first. If you have previously installed mmcv-full (which exists in OpenMMLab 1.x), please run pip uninstall mmcv-full to uninstall it.
If “No module named ‘mmcv’” appears, please follow the steps below;
Use pip uninstall mmcv to uninstall the existing mmcv in the environment.
Install the corresponding mmcv according to the installation instructions.
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_8xb1-160k_cityscapes-1024x1024.py, 8xb1 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.
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.
In the test script, we provide --out argument to control whether output the painted images. Users might run the following command:
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --out ${OUTPUT_DIR}
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)),
Functionality of reduce_zero_label¶
The parameter type of reduce_zero_label in dataset is Boolean, which is default to False. It is used to ignore the dataset label 0. The specific method is to change label 0 to 255, and subtract 1 from the corresponding number of all the remaining labels. At the same time, set 255 as ignore index in the decode head, which means that it will not participate in the loss calculation.
Following is the specific implementation logic of reduce_zero_label:
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
Whether your dataset needs to use reduce_zero_label, there are two types of situations:
On Potsdam dataset, there are six classes: 0-Impervious surfaces, 1-Building, 2-Low vegetation, 3-Tree, 4-Car, 5-Clutter/background. However, this dataset provides two types of RGB labels, one with black pixels at the edges of the images, and the other without. For labels with black edges, in dataset_converters.py, it converts the black edges to label 0, and the other labels are 1-Impervious surfaces, 2-Building, 3-Low vegetation, 4-Tree, 5-Car, 6-Clutter/background. Therefore, in the dataset config potsdam.py reduce_zero_label=True。 If you are using labels without black edges, then there are only class 0-5 in the mask label. At this point, you should use reduce_zero_label=False. reduce_zero_label usage needs to be considered with your actual situation.
On a dataset with class 0 as the background class, if you need to separate the background from the rest of your classes ultimately then you do not need to use reduce_zero_label, which in the dataset config settings should be reduce_zero_label=False
Note: Please confirm the number of original classes in the dataset. If there are only two classes, you should not use reduce_zero_label which is reduce_zero_label=False.
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