mmclassification/docs/tools/visualization.md

Visualization

• Visualization
  • Pipeline Visualization
    • Usage
  • FAQs

pipeline visualization

Usage

python tools/visualizations/vis_pipeline.py \
    ${CONFIG_FILE} \
    --output-dir ${OUTPUT_DIR} \
    --phase ${DATASET_PHASE} \
    --number ${BUNBER_IMAGES_DISPLAY} \
    --skip-type ${SKIP_TRANSFORM_TYPE}
    --mode ${DISPLAY_MODE} \
    --show \
    --adaptive \
    --min-edge-length ${MIN_EDGE_LENGTH} \
    --max-edge-length ${MAX_EDGE_LENGTH} \
    --bgr2rgb \
    --window-size ${WINDOW_SIZE}

Description of all arguments：

• config : The path of a model config file.
• --output-dir: The output path for visualized images. If not specified, it will be set to '', which means not to save.
• --phase: Phase of visualizing dataset，must be one of [train, val, test]. If not specified, it will be set to train.
• --number: The number of samples to visualize. If not specified, display all images in the dataset.
• --skip-type: The pipelines to be skipped. If not specified, it will be set to ['ToTensor', 'Normalize', 'ImageToTensor', 'Collect'].
• --mode: The display mode, can be one of [original, pipeline, concat]. If not specified, it will be set to concat.
• --show: If set, display pictures in pop-up windows.
• --adaptive: If set, automatically adjust the size of the visualization images.
• --min-edge-length: The minimum edge length, used when --adaptive is set. When any side of the picture is smaller than ${MIN_EDGE_LENGTH}, the picture will be enlarged while keeping the aspect ratio unchanged, and the short side will be aligned to ${MIN_EDGE_LENGTH}. If not specified, it will be set to 200.
• --max-edge-length: The maximum edge length, used when --adaptive is set. When any side of the picture is larger than ${MAX_EDGE_LENGTH}, the picture will be reduced while keeping the aspect ratio unchanged, and the long side will be aligned to ${MAX_EDGE_LENGTH}. If not specified, it will be set to 1000.
• --bgr2rgb: If set, flip the color channel order of images.
• --window-size: The shape of the display window. If not specified, it will be set to 12*7. If used, it must be in the format 'W*H'.

1. If the `--mode` is not specified, it will be set to `concat` as default, get the pictures stitched together by original pictures and transformed pictures; if the `--mode` is set to `original`, get the original pictures; if the `--mode` is set to `pipeline`, get the transformed pictures.

2. When `--adaptive` option is set, images that are too large or too small will be automatically adjusted, you can use `--min-edge-length` and `--max-edge-length` to set the adjust size.

Examples：

1. Visualize all the transformed pictures of the ImageNet training set and display them in pop-up windows：

python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_b32x8_imagenet.py --show --mode pipeline

2. Visualize 10 comparison pictures in the ImageNet train set and save them in the ./tmp folder：

python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive

3. Visualize 100 original pictures in the CIFAR100 validation set, then display and save them in the ./tmp folder：

python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_b16x8_cifar100.py --phase val --output-dir tmp --mode original --number 100  --show --adaptive --bgr2rgb

FAQs

• None