# Visualization¶

## Pipeline Visualization¶

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 \
--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'. Note 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_8xb32_in1k.py --show --mode pipeline  1. 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  1. 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_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb  ## Learning Rate Schedule Visualization¶ python tools/visualizations/vis_lr.py \${CONFIG_FILE} \
--dataset-size ${DATASET_SIZE} \ --ngpus${NUM_GPUs}
--save-path ${SAVE_PATH} \ --title${TITLE} \
--style ${STYLE} \ --window-size${WINDOW_SIZE}
--cfg-options


Description of all arguments

• config : The path of a model config file.

• dataset-size : The size of the datasets. If set，build_dataset will be skipped and \${DATASET_SIZE} will be used as the size. Default to use the function build_dataset.

• ngpus : The number of GPUs used in training, default to be 1.

• save-path : The learning rate curve plot save path, default not to save.

• title : Title of figure. If not set, default to be config file name.

• style : Style of plt. If not set, default to be whitegrid.

• 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'.

• cfg-options : Modifications to the configuration file, refer to Tutorial 1: Learn about Configs.

Note

Loading annotations maybe consume much time, you can directly specify the size of the dataset with dataset-size to save time.

Examples

python tools/visualizations/vis_lr.py configs/resnet/resnet50_b16x8_cifar100.py


When using ImageNet, directly specify the size of ImageNet, as below:

python tools/visualizations/vis_lr.py configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py --dataset-size 1281167 --ngpus 4 --save-path ./repvgg-B3g4_4xb64-lr.jpg


• None