mirror of https://github.com/RE-OWOD/RE-OWOD
59 lines
2.6 KiB
Markdown
59 lines
2.6 KiB
Markdown
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# Getting Started with DensePose
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## Inference with Pre-trained Models
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1. Pick a model and its config file from [Model Zoo](MODEL_ZOO.md), for example [densepose_rcnn_R_50_FPN_s1x.yaml](../configs/densepose_rcnn_R_50_FPN_s1x.yaml)
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2. Run the [Apply Net](TOOL_APPLY_NET.md) tool to visualize the results or save the to disk. For example, to use contour visualization for DensePose, one can run:
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```bash
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python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml densepose_rcnn_R_50_FPN_s1x.pkl image.jpg dp_contour,bbox --output image_densepose_contour.png
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```
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Please see [Apply Net](TOOL_APPLY_NET.md) for more details on the tool.
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## Training
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First, prepare the [dataset](http://densepose.org/#dataset) into the following structure under the directory you'll run training scripts:
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<pre>
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datasets/coco/
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annotations/
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densepose_{train,minival,valminusminival}2014.json
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<a href="https://dl.fbaipublicfiles.com/detectron2/densepose/densepose_minival2014_100.json">densepose_minival2014_100.json </a> (optional, for testing only)
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{train,val}2014/
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# image files that are mentioned in the corresponding json
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</pre>
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To train a model one can use the [train_net.py](../train_net.py) script.
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This script was used to train all DensePose models in [Model Zoo](MODEL_ZOO.md).
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For example, to launch end-to-end DensePose-RCNN training with ResNet-50 FPN backbone
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on 8 GPUs following the s1x schedule, one can run
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```bash
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python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml --num-gpus 8
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```
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The configs are made for 8-GPU training. To train on 1 GPU, one can apply the
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[linear learning rate scaling rule](https://arxiv.org/abs/1706.02677):
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```bash
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python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
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SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
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```
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## Evaluation
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Model testing can be done in the same way as training, except for an additional flag `--eval-only` and
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model location specification through `MODEL.WEIGHTS model.pth` in the command line
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```bash
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python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
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--eval-only MODEL.WEIGHTS model.pth
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```
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## Tools
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We provide tools which allow one to:
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- easily view DensePose annotated data in a dataset;
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- perform DensePose inference on a set of images;
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- visualize DensePose model results;
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`query_db` is a tool to print or visualize DensePose data in a dataset.
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Please refer to [Query DB](TOOL_QUERY_DB.md) for more details on this tool
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`apply_net` is a tool to print or visualize DensePose results.
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Please refer to [Apply Net](TOOL_APPLY_NET.md) for more details on this tool
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