Useful tools

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 test_pipeline.
--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

We provide tools/deploy_test.py to evaluate ONNX model with different backend.

Prerequisite

Install onnx and onnxruntime-gpu
```
pip install onnx onnxruntime-gpu
```
Install TensorRT following how-to-build-tensorrt-plugins-in-mmcv(optional)

Usage

python tools/deploy_test.py \
    ${CONFIG_FILE} \
    ${MODEL_FILE} \
    ${BACKEND} \
    --out ${OUTPUT_FILE} \
    --eval ${EVALUATION_METRICS} \
    --show \
    --show-dir ${SHOW_DIRECTORY} \
    --cfg-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 converted model file.
backend: Backend of the inference, options: onnxruntime, tensorrt.
--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
--cfg-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	TensorRT-fp32	TensorRT-fp16
FCN	fcn_r50-d8_512x1024_40k_cityscapes.py	cityscapes	mIoU	72.2	72.2	72.2	72.2
PSPNet	pspnet_r50-d8_512x1024_40k_cityscapes.py	cityscapes	mIoU	77.8	77.8	77.8	77.8
deeplabv3	deeplabv3_r50-d8_512x1024_40k_cityscapes.py	cityscapes	mIoU	79.0	79.0	79.0	79.0
deeplabv3+	deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py	cityscapes	mIoU	79.6	79.5	79.5	79.5
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

:::{note} TensorRT is only available on configs with whole mode. :::

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 \
    --show

Description of arguments:

config : The path of a pytorch model config file.
--checkpoint : The path of a pytorch model checkpoint file.
--output-file: The path of output TorchScript model. If not specified, it will be set to tmp.pt.
--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 512 512.
--show: Determines whether to print the traced graph 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.

:::{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. :::

Examples:

Convert the cityscapes PSPNet pytorch model.

python tools/pytorch2torchscript.py configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
--checkpoint checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
--output-file checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pt \
--shape 512 1024

Convert to TensorRT (experimental)

A script to convert ONNX model to TensorRT format.

Prerequisite

install mmcv-full with ONNXRuntime custom ops and TensorRT plugins follow ONNXRuntime in mmcv and TensorRT plugin in mmcv.
Use pytorch2onnx to convert the model from PyTorch to ONNX.

Usage

python ${MMSEG_PATH}/tools/onnx2tensorrt.py \
    ${CFG_PATH} \
    ${ONNX_PATH} \
    --trt-file ${OUTPUT_TRT_PATH} \
    --min-shape ${MIN_SHAPE} \
    --max-shape ${MAX_SHAPE} \
    --input-img ${INPUT_IMG} \
    --show \
    --verify

Description of all arguments

config : Config file of the model.
model : Path to the input ONNX model.
--trt-file : Path to the output TensorRT engine.
--max-shape : Maximum shape of model input.
--min-shape : Minimum shape of model input.
--fp16 : Enable fp16 model conversion.
--workspace-size : Max workspace size in GiB.
--input-img : Image for visualize.
--show : Enable result visualize.
--dataset : Palette provider, CityscapesDataset as default.
--verify : Verify the outputs of ONNXRuntime and TensorRT.
--verbose : Whether to verbose logging messages while creating TensorRT engine. Defaults to False.

:::{note} Only tested on whole mode. :::

Miscellaneous

Print the entire config

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

python tools/print_config.py \
  ${CONFIG} \
  --graph \
  --cfg-options ${OPTIONS [OPTIONS...]} \

Description of arguments:

config : The path of a pytorch model config file.
--graph : Determines whether to print the models graph.
--cfg-options: Custom options to replace the config file.

Plot training logs

tools/analyze_logs.py plots 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

Model conversion

tools/model_converters/ provide several scripts to convert pretrain models released by other repos to MMSegmentation style.

ViT Swin MiT Transformer Models

ViT

tools/model_converters/vit2mmseg.py convert keys in timm pretrained vit models to MMSegmentation style.
```
python tools/model_converters/vit2mmseg.py ${SRC} ${DST}
```
Swin

tools/model_converters/swin2mmseg.py convert keys in official pretrained swin models to MMSegmentation style.
```
python tools/model_converters/swin2mmseg.py ${SRC} ${DST}
```
SegFormer

tools/model_converters/mit2mmseg.py convert keys in official pretrained mit models to MMSegmentation style.
```
python tools/model_converters/mit2mmseg.py ${SRC} ${DST}
```

Model Serving

In order to serve an MMSegmentation model with TorchServe, you can follow the steps:

1. Convert model from MMSegmentation to TorchServe

python tools/torchserve/mmseg2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}

:::{note} ${MODEL_STORE} needs to be an absolute path to a folder. :::

2. Build `mmseg-serve` docker image

docker build -t mmseg-serve:latest docker/serve/

3. Run `mmseg-serve`

Check the official docs for running TorchServe with docker.

In order to run in GPU, you need to install nvidia-docker. You can omit the --gpus argument in order to run in CPU.

Example:

docker run --rm \
--cpus 8 \
--gpus device=0 \
-p8080:8080 -p8081:8081 -p8082:8082 \
--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
mmseg-serve:latest

Read the docs about the Inference (8080), Management (8081) and Metrics (8082) APIs

4. Test deployment

curl -O https://raw.githubusercontent.com/open-mmlab/mmsegmentation/master/resources/3dogs.jpg
curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg -o 3dogs_mask.png

The response will be a ".png" mask.

You can visualize the output as follows:

import matplotlib.pyplot as plt
import mmcv
plt.imshow(mmcv.imread("3dogs_mask.png", "grayscale"))
plt.show()

You should see something similar to:

And you can use test_torchserve.py to compare result of torchserve and pytorch, and visualize them.

python tools/torchserve/test_torchserve.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--result-image ${RESULT_IMAGE}] [--device ${DEVICE}]

Example:

python tools/torchserve/test_torchserve.py \
demo/demo.png \
configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py \
checkpoint/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth \
fcn

Confusion Matrix

In order to generate and plot a nxn confusion matrix where n is the number of classes, you can follow the steps:

1.Generate a prediction result in pkl format using `test.py`

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${PATH_TO_RESULT_FILE}]

Note that the argument for --eval should be None so that the result file contains numpy type of prediction results. The usage for distribution test is just the same.

Example:

python tools/test.py \
configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py \
checkpoint/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth \
--out result/pred_result.pkl

2. Use `confusion_matrix.py` to generate and plot a confusion matrix

python tools/confusion_matrix.py ${CONFIG_FILE} ${PATH_TO_RESULT_FILE} ${SAVE_DIR} --show

Description of arguments:

config: Path to the test config file.
prediction_path: Path to the prediction .pkl result.
save_dir: Directory where confusion matrix will be saved.
--show: Enable result visualize.
--color-theme: Theme of the matrix color map.
--cfg_options: Custom options to replace the config file.

Example:

python tools/confusion_matrix.py \
configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py \
result/pred_result.pkl \
result/confusion_matrix \
--show

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useful_tools.md

useful_tools.md

Useful tools

Get the FLOPs and params (experimental)

Publish a model

Convert to ONNX (experimental)

Evaluate ONNX model

Prerequisite

Usage

Results and Models

Convert to TorchScript (experimental)

Convert to TensorRT (experimental)

Miscellaneous

Print the entire config

Plot training logs

Model conversion

ViT Swin MiT Transformer Models

Model Serving

1. Convert model from MMSegmentation to TorchServe

2. Build `mmseg-serve` docker image

3. Run `mmseg-serve`

4. Test deployment

Confusion Matrix

1.Generate a prediction result in pkl format using `test.py`

2. Use `confusion_matrix.py` to generate and plot a confusion matrix

Files

useful_tools.md

Latest commit

History

useful_tools.md

File metadata and controls

Useful tools

Get the FLOPs and params (experimental)

Publish a model

Convert to ONNX (experimental)

Evaluate ONNX model

Prerequisite

Usage

Results and Models

Convert to TorchScript (experimental)

Convert to TensorRT (experimental)

Miscellaneous

Print the entire config

Plot training logs

Model conversion

ViT Swin MiT Transformer Models

Model Serving

1. Convert model from MMSegmentation to TorchServe

2. Build mmseg-serve docker image

3. Run mmseg-serve

4. Test deployment

Confusion Matrix

1.Generate a prediction result in pkl format using test.py

2. Use confusion_matrix.py to generate and plot a confusion matrix

2. Build `mmseg-serve` docker image

3. Run `mmseg-serve`

1.Generate a prediction result in pkl format using `test.py`

2. Use `confusion_matrix.py` to generate and plot a confusion matrix