Hackathon 5th No.70】DET重点模型支持实例分割

configs/mask_rtdetr/README.md

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+English | [简体中文](README_cn.md)
+
+# Mask RT-DETR
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Model Zoo](#model-zoo)
+- [Getting Start](#getting-start)
+- [More Usage](#more-usage)
+
+## Introduction
+Mask RT-DETR is an instance segmentation version of [RT DETR](../rtdetr/README.md).
+
+## Model Zoo
+|     Model      | Epoch | Backbone | Input shape | Box AP | Mask AP | Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS) |                                    Pretrained Model                                    |                   config                    |
+|:--------------:|:-----:|:--------:|:-----------:|:------:|:-------:|:---------:|:--------:|:---------------------:|:--------------------------------------------------------------------------------------:|:-------------------------------------------:|
+| Mask-RT-DETR-L |  6x   | HGNetv2  |     640     |  51.2  |  45.7   |    32     |   120    |          90           | [model](https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams) | [config](mask_rtdetr_hgnetv2_l_6x_coco.yml) |
+
+
+## Getting Start
+
+### Datasets and Metrics
+
+PaddleDetection team provides **COCO dataset** , decompress and place it under `PaddleDetection/dataset/`:
+
+```
+wget https://bj.bcebos.com/v1/paddledet/data/coco.tar
+# tar -xvf coco.tar
+```
+
+**Note:**
+  - For the format of COCO style dataset, please refer to [format-data](https://cocodataset.org/#format-data) and [format-results](https://cocodataset.org/#format-results).
+  - For the evaluation metric of COCO, please refer to [detection-eval](https://cocodataset.org/#detection-eval), and install  [cocoapi](https://github.com/cocodataset/cocoapi) at first.
+
+### Custom dataset
+
+1.For the annotation of custom dataset, please refer to [DetAnnoTools](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/DetAnnoTools_en.md);
+
+2.For training preparation of custom dataset，please refer to [PrepareDataSet](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/PrepareDetDataSet_en.md).
+
+
+### Training
+
+Training Mask RT-DETR with following command
+
+```bash
+# training on a single GPU
+export CUDA_VISIBLE_DEVICES=0
+python tools/train.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml --amp --eval
+
+# training on multi GPUs
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml --amp --eval
+```
+**Notes:**
+- If you need to evaluate while training, please add `--eval`.
+- Mask RT-DETR supports mixed precision training, please add `--amp`.
+- PaddleDetection supports multi-machine distributed training, you can refer to [DistributedTraining tutorial](../../docs/tutorials/DistributedTraining_en.md).
+
+
+### Evaluation
+
+Evaluating Mask RT-DETR on COCO val2017 dataset in single GPU with following commands:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams 
+```
+
+For evaluation on COCO test-dev2017 dataset, please download COCO test-dev2017 dataset from [COCO dataset download](https://cocodataset.org/#download) and decompress to COCO dataset directory and configure `EvalDataset` like `configs/ppyolo/ppyolo_test.yml`.
+
+### Inference
+
+Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.
+
+
+```bash
+# inference single image
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
+
+# inference all images in the directory
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams --infer_dir=demo
+```
+
+### Exporting models
+
+For deployment on GPU or speed testing, model should be first exported to inference model using `tools/export_model.py`.
+
+**Exporting Mask RT-DETR for Paddle Inference without TensorRT**, use following command
+
+```bash
+python tools/export_model.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams
+```
+
+**Exporting Mask RT-DETR for Paddle Inference with TensorRT** for better performance, use following command with extra `-o trt=True` setting.
+
+```bash
+python tools/export_model.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams trt=True
+```
+
+If you want to export Mask RT-DETR model to **ONNX format**, use following command refer to [PaddleDetection Model Export as ONNX Format Tutorial](../../deploy/EXPORT_ONNX_MODEL_en.md).
+
+```bash
+
+# export inference model
+python tools/export_model.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams trt=True
+
+# install paddle2onnx
+pip install paddle2onnx
+
+# convert to onnx
+paddle2onnx --model_dir output_inference/mask_rtdetr_hgnetv2_l_6x_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 16 --save_file mask_rtdetr_hgnetv2_l_6x_coco.onnx
+```
+
+**Notes:** ONNX model only supports batch_size=1 now
+
+### Speed testing
+
+**Using Paddle Inference with TensorRT** to test speed, run following command
+
+```bash
+# export inference model with trt=True
+python tools/export_model.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams trt=True
+
+# convert to onnx
+paddle2onnx --model_dir output_inference/mask_rtdetr_hgnetv2_l_6x_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 16 --save_file mask_rtdetr_hgnetv2_l_6x_coco.onnx
+
+```
+
+Fix the previously exported ONNX model's `im_shape` and `scale_factor` two input data, code as follows：
+```python
+# onnx_edit.py
+
+import copy
+
+import onnx
+
+if __name__ == '__main__':
+    model_path = './mask_rtdetr_hgnetv2_l_6x_coco.onnx'
+    model = onnx.load_model(model_path)
+
+    im_shape = onnx.helper.make_tensor(
+        name='im_shape',
+        data_type=onnx.helper.TensorProto.FLOAT,
+        dims=[1, 2],
+        vals=[640, 640])
+    scale_factor = onnx.helper.make_tensor(
+        name='scale_factor',
+        data_type=onnx.helper.TensorProto.FLOAT,
+        dims=[1, 2],
+        vals=[1, 1])
+
+    new_model = copy.deepcopy(model)
+
+    for input in model.graph.input:
+        if input.name == 'im_shape':
+            new_model.graph.input.remove(input)
+            new_model.graph.initializer.append(im_shape)
+
+        if input.name == 'scale_factor':
+            new_model.graph.input.remove(input)
+            new_model.graph.initializer.append(scale_factor)
+
+    onnx.checker.check_model(new_model, full_check=True)
+    onnx.save_model(new_model, model_path)
+```
+
+Simplify the onnx model using onnxsim：
+
+```shell
+pip install onnxsim
+onnxsim mask_rtdetr_hgnetv2_l_6x_coco.onnx mask_rtdetr_hgnetv2_l_6x_coco.onnx --overwrite-input-shape "image:1,3,640,640"
+```
+
+```shell
+# trt inference using fp16 and batch_size=1
+trtexec --onnx=./mask_rtdetr_hgnetv2_l_6x_coco.onnx --saveEngine=./mask_rtdetr_hgnetv2_l_6x_coco.engine --workspace=4096 --avgRuns=1000 --fp16
+```
+
+
+### Deployment
+
+Mask RT-DETR can be deployed by following approaches:
+  - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+
+Next, we will introduce how to use Paddle Inference to deploy Mask RT-DETR models in TensorRT FP16 mode.
+
+First, refer to [Paddle Inference Docs](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python), download and install packages corresponding to CUDA, CUDNN and TensorRT version.
+
+Then, Exporting Mask RT-DETR for Paddle Inference **with TensorRT**, use following command.
+
+```bash
+python tools/export_model.py -c configs/mask_rtdetr/mask_rtdetr_hgnetv2_l_6x_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/mask_rtdetr_hgnetv2_l_6x_coco.pdparams trt=True
+```
+
+Finally, inference in TensorRT FP16 mode.
+
+```bash
+# inference single image
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/mask_rtdetr_hgnetv2_l_6x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16
+
+# inference all images in the directory
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/mask_rtdetr_hgnetv2_l_6x_coco --image_dir=demo/ --device=gpu  --run_mode=trt_fp16
+
+```
+
+**Notes:**
+- TensorRT will perform optimization for the current hardware platform according to the definition of the network, generate an inference engine and serialize it into a file. This inference engine is only applicable to the current hardware hardware platform. If your hardware and software platform has not changed, you can set `use_static=True` in [enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660). In this way, the serialized file generated will be saved in the `output_inference` folder, and the saved serialized file will be loaded the next time when TensorRT is executed.
+
+
+## More Usage
+
+### Setting the Model Weight Saving Metric
+When using the COCO-style evaluation metric for instance segmentation tasks, if the inference results contain both `box` and `mask`, the default metric for saving the best model weight is `box ap`. You can change the saving metric to `mask ap` by adding the `target_metrics` parameter in the configuration file and assigning it to `mask`. For a specific configuration file example, please refer to [mask_rtdetr_r50vd.yml](./_base_/mask_rtdetr_r50vd.yml).
+
+### Setting the Number of Queries
+- The number of queries in the post-processing can be adjusted by modifying the `num_top_queries` parameter of `DETRPostProcess`. The default value of `num_top_queries` for Mask RT-DETR post-processing is 100.
+- The number of queries in the Query Selection part can be adjusted by modifying the `num_queries` parameter of `MaskRTDETR`. The default value of `num_queries` for Mask RT-DETR is 300. Since the classification loss of Mask RT-DETR aligns the mask IoU and the confidence, we can set the `num_queries` of Mask RT-DETR to 100 and directly load the weights trained with `num_queries` of 300. This can improve the inference speed with little loss of accuracy.