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useful_tools.md

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Apart from training/testing scripts, We provide lots of useful tools under the tools/ directory.

Get the FLOPs and params (experimental)

We provide a script adapted from flops-counter.pytorch to compute the FLOPs and params of a given model.

python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]

You will get the result like this.

==============================
Input shape: (3, 2048, 1024)
Flops: 1429.68 GMac
Params: 48.98 M
==============================

Note: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.

(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 1280, 800). (2) Some operators are not counted into FLOPs like GN and custom operators.

Publish a model

Before you upload a model to AWS, you may want to (1) convert model weights to CPU tensors, (2) delete the optimizer states and (3) compute the hash of the checkpoint file and append the hash id to the filename.

python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}

E.g.,

python tools/publish_model.py work_dirs/pspnet/latest.pth psp_r50_hszhao_200ep.pth

The final output filename will be psp_r50_512x1024_40ki_cityscapes-{hash id}.pth.

Convert to ONNX (experimental)

We provide a script to convert model to ONNX format. The converted model could be visualized by tools like Netron. Besides, we also support comparing the output results between Pytorch and ONNX model.

python tools/pytorch2onnx.py \
    ${CONFIG_FILE} \
    --checkpoint ${CHECKPOINT_FILE} \
    --output-file ${ONNX_FILE} \
    --input-img ${INPUT_IMG} \
    --shape ${INPUT_SHAPE} \
    --rescale-shape ${RESCALE_SHAPE} \
    --show \
    --verify \
    --dynamic-export \
    --cfg-options \
      model.test_cfg.mode="whole"

Description of arguments:

  • config : The path of a model config file.
  • --checkpoint : The path of a model checkpoint file.
  • --output-file: The path of output ONNX model. If not specified, it will be set to tmp.onnx.
  • --input-img : The path of an input image for conversion and visualize.
  • --shape: The height and width of input tensor to the model. If not specified, it will be set to img_scale of testpipeline.
  • --rescale-shape: rescale shape of output, set this value to avoid OOM, only work on slide mode.
  • --show: Determines whether to print the architecture of the exported model. If not specified, it will be set to False.
  • --verify: Determines whether to verify the correctness of an exported model. If not specified, it will be set to False.
  • --dynamic-export: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to False.
  • --cfg-options:Update config options.

Note: This tool is still experimental. Some customized operators are not supported for now.

Evaluate ONNX model with ONNXRuntime

We provide tools/ort_test.py to evaluate ONNX model with ONNXRuntime backend.

Prerequisite

  • Install onnx and onnxruntime-gpu

    pip install onnx onnxruntime-gpu

Usage

python tools/ort_test.py \
    ${CONFIG_FILE} \
    ${ONNX_FILE} \
    --out ${OUTPUT_FILE} \
    --eval ${EVALUATION_METRICS} \
    --show \
    --show-dir ${SHOW_DIRECTORY} \
    --options ${CFG_OPTIONS} \
    --eval-options ${EVALUATION_OPTIONS} \
    --opacity ${OPACITY} \

Description of all arguments

  • config: The path of a model config file.
  • model: The path of a ONNX model file.
  • --out: The path of output result file in pickle format.
  • --format-only : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to False. Note that this argument is mutually exclusive with --eval.
  • --eval: Evaluation metrics, which depends on the dataset, e.g., "mIoU" for generic datasets, and "cityscapes" for Cityscapes. Note that this argument is mutually exclusive with --format-only.
  • --show: Show results flag.
  • --show-dir: Directory where painted images will be saved
  • --options: Override some settings in the used config file, the key-value pair in xxx=yyy format will be merged into config file.
  • --eval-options: Custom options for evaluation, the key-value pair in xxx=yyy format will be kwargs for dataset.evaluate() function
  • --opacity: Opacity of painted segmentation map. In (0, 1] range.

Results and Models

Model Config Dataset Metric PyTorch ONNXRuntime
FCN fcn_r50-d8_512x1024_40k_cityscapes.py cityscapes mIoU 72.2 72.2
PSPNet pspnet_r50-d8_769x769_40k_cityscapes.py cityscapes mIoU 78.2 78.1
deeplabv3 deeplabv3_r50-d8_769x769_40k_cityscapes.py cityscapes mIoU 78.5 78.3
deeplabv3+ deeplabv3plus_r50-d8_769x769_40k_cityscapes.py cityscapes mIoU 78.9 78.7

Convert to TorchScript (experimental)

We also provide a script to convert model to TorchScript format. You can use the pytorch C++ API LibTorch inference the trained model. The converted model could be visualized by tools like Netron. Besides, we also support comparing the output results between Pytorch and TorchScript model.

python tools/pytorch2torchscript.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} --output-file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify]

Note: It's only support PyTorch>=1.8.0 for now.

Note: This tool is still experimental. Some customized operators are not supported for now.

Miscellaneous

Print the entire config

tools/print_config.py prints the whole config verbatim, expanding all its imports.

python tools/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]

Plot training logs

tools/analyze_logs.py plot s loss/mIoU curves given a training log file. pip install seaborn first to install the dependency.

python tools/analyze_logs.py xxx.log.json [--keys ${KEYS}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]

Examples:

  • Plot the mIoU, mAcc, aAcc metrics.

    python tools/analyze_logs.py log.json --keys mIoU mAcc aAcc --legend mIoU mAcc aAcc

  • Plot loss metric.

    python tools/analyze_logs.py log.json --keys loss --legend loss