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## Some scripts for developers to use, include:
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- `run_instant_tests.sh`: run training for a few iterations.
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- `run_inference_tests.sh`: run inference on a small dataset.
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- `../../dev/linter.sh`: lint the codebase before commit
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- `../../dev/parse_results.sh`: parse results from log file.
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#!/bin/bash -e
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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BIN="python train_net.py"
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OUTPUT="inference_test_output"
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NUM_GPUS=2
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IMS_PER_GPU=2
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IMS_PER_BATCH=$(( NUM_GPUS * IMS_PER_GPU ))
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CFG_LIST=( "${@:1}" )
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if [ ${#CFG_LIST[@]} -eq 0 ]; then
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CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
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fi
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echo "========================================================================"
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echo "Configs to run:"
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echo "${CFG_LIST[@]}"
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echo "========================================================================"
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for cfg in "${CFG_LIST[@]}"; do
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echo "========================================================================"
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echo "Running $cfg ..."
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echo "========================================================================"
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$BIN \
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--eval-only \
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--num-gpus $NUM_GPUS \
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--config-file "$cfg" \
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OUTPUT_DIR "$OUTPUT" \
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SOLVER.IMS_PER_BATCH $IMS_PER_BATCH
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rm -rf $OUTPUT
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done
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#!/bin/bash -e
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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BIN="python train_net.py"
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OUTPUT="instant_test_output"
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NUM_GPUS=2
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SOLVER_IMS_PER_BATCH=$((NUM_GPUS * 2))
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CFG_LIST=( "${@:1}" )
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if [ ${#CFG_LIST[@]} -eq 0 ]; then
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CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml )
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fi
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echo "========================================================================"
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echo "Configs to run:"
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echo "${CFG_LIST[@]}"
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echo "========================================================================"
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for cfg in "${CFG_LIST[@]}"; do
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echo "========================================================================"
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echo "Running $cfg ..."
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echo "========================================================================"
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$BIN --num-gpus $NUM_GPUS --config-file "$cfg" \
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SOLVER.IMS_PER_BATCH $SOLVER_IMS_PER_BATCH \
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OUTPUT_DIR "$OUTPUT"
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rm -rf "$OUTPUT"
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done
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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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# Model Zoo and Baselines
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# Introduction
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We provide baselines trained with Detectron2 DensePose. The corresponding
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configuration files can be found in the [configs](../configs) directory.
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All models were trained on COCO `train2014` + `valminusminival2014` and
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evaluated on COCO `minival2014`. For the details on common settings in which
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baselines were trained, please check [Detectron 2 Model Zoo](../../../MODEL_ZOO.md).
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## License
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All models available for download through this document are licensed under the
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[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/)
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## COCO DensePose Baselines with DensePose-RCNN
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### Legacy Models
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Baselines trained using schedules from [Güler et al, 2018](https://arxiv.org/pdf/1802.00434.pdf)
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="bottom">Name</th>
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<th valign="bottom">lr<br/>sched</th>
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<th valign="bottom">train<br/>time<br/>(s/iter)</th>
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<th valign="bottom">inference<br/>time<br/>(s/im)</th>
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<th valign="bottom">train<br/>mem<br/>(GB)</th>
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<th valign="bottom">box<br/>AP</th>
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<th valign="bottom">dp. AP<br/>GPS</th>
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<th valign="bottom">dp. AP<br/>GPSm</th>
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<th valign="bottom">model id</th>
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<th valign="bottom">download</th>
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<!-- TABLE BODY -->
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<!-- ROW: densepose_rcnn_R_50_FPN_s1x_legacy -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml">R_50_FPN_s1x_legacy</a></td>
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<td align="center">s1x</td>
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<td align="center">0.307</td>
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<td align="center">0.051</td>
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<td align="center">3.2</td>
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<td align="center">58.1</td>
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<td align="center">52.1</td>
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<td align="center">54.9</td>
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<td align="center">164832157</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/model_final_d366fa.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: densepose_rcnn_R_101_FPN_s1x_legacy -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml">R_101_FPN_s1x_legacy</a></td>
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<td align="center">s1x</td>
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<td align="center">0.390</td>
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<td align="center">0.063</td>
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<td align="center">4.3</td>
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<td align="center">59.5</td>
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<td align="center">53.2</td>
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<td align="center">56.1</td>
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<td align="center">164832182</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/model_final_10af0e.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/metrics.json">metrics</a></td>
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</tr>
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</tbody></table>
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### Improved Baselines, Original Fully Convolutional Haad
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These models use an improved training schedule and Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446).
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="bottom">Name</th>
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<th valign="bottom">lr<br/>sched</th>
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<th valign="bottom">train<br/>time<br/>(s/iter)</th>
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<th valign="bottom">inference<br/>time<br/>(s/im)</th>
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<th valign="bottom">train<br/>mem<br/>(GB)</th>
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<th valign="bottom">box<br/>AP</th>
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<th valign="bottom">dp. AP<br/>GPS</th>
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<th valign="bottom">dp. AP<br/>GPSm</th>
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<th valign="bottom">model id</th>
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<th valign="bottom">download</th>
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<!-- TABLE BODY -->
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<!-- ROW: densepose_rcnn_R_50_FPN_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x.yaml">R_50_FPN_s1x</a></td>
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<td align="center">s1x</td>
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<td align="center">0.359</td>
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<td align="center">0.066</td>
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<td align="center">4.5</td>
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<td align="center">61.2</td>
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<td align="center">63.7</td>
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<td align="center">65.3</td>
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<td align="center">165712039</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: densepose_rcnn_R_101_FPN_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x.yaml">R_101_FPN_s1x</a></td>
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<td align="center">s1x</td>
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<td align="center">0.428</td>
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<td align="center">0.079</td>
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<td align="center">5.8</td>
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<td align="center">62.3</td>
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<td align="center">64.5</td>
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<td align="center">66.4</td>
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<td align="center">165712084</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/model_final_c6ab63.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/metrics.json">metrics</a></td>
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</tr>
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</tbody></table>
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### Improved Baselines, DeepLabV3 Head
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These models use an improved training schedule, Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446) and DeepLabV3 head from [Chen et al, 2017](https://arxiv.org/abs/1706.05587).
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="bottom">Name</th>
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<th valign="bottom">lr<br/>sched</th>
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<th valign="bottom">train<br/>time<br/>(s/iter)</th>
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<th valign="bottom">inference<br/>time<br/>(s/im)</th>
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<th valign="bottom">train<br/>mem<br/>(GB)</th>
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<th valign="bottom">box<br/>AP</th>
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<th valign="bottom">dp. AP<br/>GPS</th>
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<th valign="bottom">dp. AP<br/>GPSm</th>
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<th valign="bottom">model id</th>
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<th valign="bottom">download</th>
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<!-- TABLE BODY -->
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<!-- ROW: densepose_rcnn_R_50_FPN_DL_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml">R_50_FPN_DL_s1x</a></td>
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<td align="center">s1x</td>
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<td align="center">0.392</td>
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<td align="center">0.070</td>
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<td align="center">6.7</td>
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<td align="center">61.1</td>
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<td align="center">65.6</td>
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<td align="center">66.8</td>
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<td align="center">165712097</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/model_final_0ed407.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: densepose_rcnn_R_101_FPN_DL_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml">R_101_FPN_DL_s1x</a></td>
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<td align="center">s1x</td>
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<td align="center">0.478</td>
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<td align="center">0.083</td>
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<td align="center">7.0</td>
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<td align="center">62.3</td>
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<td align="center">66.3</td>
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<td align="center">67.7</td>
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<td align="center">165712116</td>
|
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/model_final_844d15.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/metrics.json">metrics</a></td>
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</tr>
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</tbody></table>
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### Baselines with Confidence Estimation
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These models perform additional estimation of confidence in regressed UV coodrinates, along the lines of [Neverova et al., 2019](https://papers.nips.cc/paper/8378-correlated-uncertainty-for-learning-dense-correspondences-from-noisy-labels).
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<table><tbody>
|
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<!-- START TABLE -->
|
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<!-- TABLE HEADER -->
|
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<th valign="bottom">Name</th>
|
||||
<th valign="bottom">lr<br/>sched</th>
|
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<th valign="bottom">train<br/>time<br/>(s/iter)</th>
|
||||
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
|
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<th valign="bottom">train<br/>mem<br/>(GB)</th>
|
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<th valign="bottom">box<br/>AP</th>
|
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<th valign="bottom">dp. AP<br/>GPS</th>
|
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<th valign="bottom">dp. AP<br/>GPSm</th>
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<th valign="bottom">model id</th>
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<th valign="bottom">download</th>
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<!-- TABLE BODY -->
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<!-- ROW: densepose_rcnn_R_50_FPN_WC1_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC1_s1x.yaml">R_50_FPN_WC1_s1x</a></td>
|
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<td align="center">s1x</td>
|
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<td align="center">0.353</td>
|
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<td align="center">0.064</td>
|
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<td align="center">4.6</td>
|
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<td align="center">60.5</td>
|
||||
<td align="center">64.2</td>
|
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<td align="center">65.6</td>
|
||||
<td align="center">173862049</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/model_final_289019.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: densepose_rcnn_R_50_FPN_WC2_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC2_s1x.yaml">R_50_FPN_WC2_s1x</a></td>
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<td align="center">s1x</td>
|
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<td align="center">0.364</td>
|
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<td align="center">0.066</td>
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<td align="center">4.8</td>
|
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<td align="center">60.7</td>
|
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<td align="center">64.2</td>
|
||||
<td align="center">65.7</td>
|
||||
<td align="center">173861455</td>
|
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/model_final_3abe14.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC1_s1x -->
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<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml">R_50_FPN_DL_WC1_s1x</a></td>
|
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<td align="center">s1x</td>
|
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<td align="center">0.397</td>
|
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<td align="center">0.068</td>
|
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<td align="center">6.7</td>
|
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<td align="center">61.1</td>
|
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<td align="center">65.8</td>
|
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<td align="center">67.1</td>
|
||||
<td align="center">173067973</td>
|
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<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/model_final_b1e525.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/metrics.json">metrics</a></td>
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</tr>
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||||
<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC2_s1x -->
|
||||
<tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml">R_50_FPN_DL_WC2_s1x</a></td>
|
||||
<td align="center">s1x</td>
|
||||
<td align="center">0.410</td>
|
||||
<td align="center">0.070</td>
|
||||
<td align="center">6.8</td>
|
||||
<td align="center">60.8</td>
|
||||
<td align="center">65.6</td>
|
||||
<td align="center">66.7</td>
|
||||
<td align="center">173859335</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/model_final_60fed4.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/metrics.json">metrics</a></td>
|
||||
</tr>
|
||||
<!-- ROW: densepose_rcnn_R_101_FPN_WC1_s1x -->
|
||||
<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml">R_101_FPN_WC1_s1x</a></td>
|
||||
<td align="center">s1x</td>
|
||||
<td align="center">0.435</td>
|
||||
<td align="center">0.076</td>
|
||||
<td align="center">5.7</td>
|
||||
<td align="center">62.5</td>
|
||||
<td align="center">64.9</td>
|
||||
<td align="center">66.5</td>
|
||||
<td align="center">171402969</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/model_final_9e47f0.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/metrics.json">metrics</a></td>
|
||||
</tr>
|
||||
<!-- ROW: densepose_rcnn_R_101_FPN_WC2_s1x -->
|
||||
<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml">R_101_FPN_WC2_s1x</a></td>
|
||||
<td align="center">s1x</td>
|
||||
<td align="center">0.450</td>
|
||||
<td align="center">0.078</td>
|
||||
<td align="center">5.7</td>
|
||||
<td align="center">62.3</td>
|
||||
<td align="center">64.8</td>
|
||||
<td align="center">66.6</td>
|
||||
<td align="center">173860702</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/model_final_5ea023.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/metrics.json">metrics</a></td>
|
||||
</tr>
|
||||
<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC1_s1x -->
|
||||
<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml">R_101_FPN_DL_WC1_s1x</a></td>
|
||||
<td align="center">s1x</td>
|
||||
<td align="center">0.479</td>
|
||||
<td align="center">0.081</td>
|
||||
<td align="center">7.9</td>
|
||||
<td align="center">62.0</td>
|
||||
<td align="center">66.2</td>
|
||||
<td align="center">67.4</td>
|
||||
<td align="center">173858525</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/model_final_f359f3.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/metrics.json">metrics</a></td>
|
||||
</tr>
|
||||
<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC2_s1x -->
|
||||
<tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml">R_101_FPN_DL_WC2_s1x</a></td>
|
||||
<td align="center">s1x</td>
|
||||
<td align="center">0.491</td>
|
||||
<td align="center">0.082</td>
|
||||
<td align="center">7.6</td>
|
||||
<td align="center">61.7</td>
|
||||
<td align="center">65.9</td>
|
||||
<td align="center">67.3</td>
|
||||
<td align="center">173294801</td>
|
||||
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/model_final_6e1ed1.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/metrics.json">metrics</a></td>
|
||||
</tr>
|
||||
</tbody></table>
|
||||
|
||||
## Old Baselines
|
||||
|
||||
It is still possible to use some baselines from [DensePose 1](https://github.com/facebookresearch/DensePose).
|
||||
Below are evaluation metrics for the baselines recomputed in the current framework:
|
||||
|
||||
| Model | bbox AP | AP | AP50 | AP75 | APm |APl |
|
||||
|-----|-----|-----|--- |--- |--- |--- |
|
||||
| [`ResNet50_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl) | 54.673 | 48.894 | 84.963 | 50.717 | 43.132 | 50.433 |
|
||||
| [`ResNet101_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl) | 56.032 | 51.088 | 86.250 | 55.057 | 46.542 | 52.563 |
|
||||
|
||||
Note: these scores are close, but not strictly equal to the ones reported in the [DensePose 1 Model Zoo](https://github.com/facebookresearch/DensePose/blob/master/MODEL_ZOO.md),
|
||||
which is due to small incompatibilities between the frameworks.
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
# Apply Net
|
||||
|
||||
`apply_net` is a tool to print or visualize DensePose results on a set of images.
|
||||
It has two modes: `dump` to save DensePose model results to a pickle file
|
||||
and `show` to visualize them on images.
|
||||
|
||||
## Dump Mode
|
||||
|
||||
The general command form is:
|
||||
```bash
|
||||
python apply_net.py dump [-h] [-v] [--output <dump_file>] <config> <model> <input>
|
||||
```
|
||||
|
||||
There are three mandatory arguments:
|
||||
- `<config>`, configuration file for a given model;
|
||||
- `<model>`, model file with trained parameters
|
||||
- `<input>`, input image file name, pattern or folder
|
||||
|
||||
One can additionally provide `--output` argument to define the output file name,
|
||||
which defaults to `output.pkl`.
|
||||
|
||||
|
||||
Examples:
|
||||
|
||||
1. Dump results of a DensePose model with ResNet-50 FPN backbone for images
|
||||
in a folder `images` to file `dump.pkl`:
|
||||
```bash
|
||||
python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl images --output dump.pkl -v
|
||||
```
|
||||
|
||||
2. Dump results of a DensePose model with ResNet-50 FPN backbone for images
|
||||
with file name matching a pattern `image*.jpg` to file `results.pkl`:
|
||||
```bash
|
||||
python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl "image*.jpg" --output results.pkl -v
|
||||
```
|
||||
|
||||
If you want to load the pickle file generated by the above command:
|
||||
```
|
||||
# make sure DensePose is in your PYTHONPATH, or use the following line to add it:
|
||||
sys.path.append("/your_detectron2_path/detectron2_repo/projects/DensePose/")
|
||||
|
||||
f = open('/your_result_path/results.pkl', 'rb')
|
||||
data = pickle.load(f)
|
||||
```
|
||||
|
||||
The file `results.pkl` contains the list of results per image, for each image the result is a dictionary:
|
||||
```
|
||||
data: [{'file_name': '/your_path/image1.jpg',
|
||||
'scores': tensor([0.9884]),
|
||||
'pred_boxes_XYXY': tensor([[ 69.6114, 0.0000, 706.9797, 706.0000]]),
|
||||
'pred_densepose': <densepose.structures.DensePoseResult object at 0x7f791b312470>},
|
||||
{'file_name': '/your_path/image2.jpg',
|
||||
'scores': tensor([0.9999, 0.5373, 0.3991]),
|
||||
'pred_boxes_XYXY': tensor([[ 59.5734, 7.7535, 579.9311, 932.3619],
|
||||
[612.9418, 686.1254, 612.9999, 704.6053],
|
||||
[164.5081, 407.4034, 598.3944, 920.4266]]),
|
||||
'pred_densepose': <densepose.structures.DensePoseResult object at 0x7f7071229be0>}]
|
||||
```
|
||||
|
||||
We can use the following code, to parse the outputs of the first
|
||||
detected instance on the first image.
|
||||
```
|
||||
from densepose.data.structures import DensePoseResult
|
||||
img_id, instance_id = 0, 0 # Look at the first image and the first detected instance
|
||||
bbox_xyxy = data[img_id]['pred_boxes_XYXY'][instance_id]
|
||||
result_encoded = data[img_id]['pred_densepose'].results[instance_id]
|
||||
iuv_arr = DensePoseResult.decode_png_data(*result_encoded)
|
||||
```
|
||||
The array `bbox_xyxy` contains (x0, y0, x1, y1) of the bounding box.
|
||||
|
||||
The shape of `iuv_arr` is `[3, H, W]`, where (H, W) is the shape of the bounding box.
|
||||
- `iuv_arr[0,:,:]`: The patch index of image points, indicating which of the 24 surface patches the point is on.
|
||||
- `iuv_arr[1,:,:]`: The U-coordinate value of image points.
|
||||
- `iuv_arr[2,:,:]`: The V-coordinate value of image points.
|
||||
|
||||
|
||||
## Visualization Mode
|
||||
|
||||
The general command form is:
|
||||
```bash
|
||||
python apply_net.py show [-h] [-v] [--min_score <score>] [--nms_thresh <threshold>] [--output <image_file>] <config> <model> <input> <visualizations>
|
||||
```
|
||||
|
||||
There are four mandatory arguments:
|
||||
- `<config>`, configuration file for a given model;
|
||||
- `<model>`, model file with trained parameters
|
||||
- `<input>`, input image file name, pattern or folder
|
||||
- `<visualizations>`, visualizations specifier; currently available visualizations are:
|
||||
* `bbox` - bounding boxes of detected persons;
|
||||
* `dp_segm` - segmentation masks for detected persons;
|
||||
* `dp_u` - each body part is colored according to the estimated values of the
|
||||
U coordinate in part parameterization;
|
||||
* `dp_v` - each body part is colored according to the estimated values of the
|
||||
V coordinate in part parameterization;
|
||||
* `dp_contour` - plots contours with color-coded U and V coordinates
|
||||
|
||||
|
||||
One can additionally provide the following optional arguments:
|
||||
- `--min_score` to only show detections with sufficient scores that are not lower than provided value
|
||||
- `--nms_thresh` to additionally apply non-maximum suppression to detections at a given threshold
|
||||
- `--output` to define visualization file name template, which defaults to `output.png`.
|
||||
To distinguish output file names for different images, the tool appends 1-based entry index,
|
||||
e.g. output.0001.png, output.0002.png, etc...
|
||||
|
||||
|
||||
The following examples show how to output results of a DensePose model
|
||||
with ResNet-50 FPN backbone using different visualizations for image `image.jpg`:
|
||||
|
||||
1. Show bounding box and segmentation:
|
||||
```bash
|
||||
python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_segm -v
|
||||
```
|
||||
|
||||
|
||||
2. Show bounding box and estimated U coordinates for body parts:
|
||||
```bash
|
||||
python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_u -v
|
||||
```
|
||||
|
||||
|
||||
3. Show bounding box and estimated V coordinates for body parts:
|
||||
```bash
|
||||
python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_v -v
|
||||
```
|
||||
|
||||
|
||||
4. Show bounding box and estimated U and V coordinates via contour plots:
|
||||
```bash
|
||||
python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg dp_contour,bbox -v
|
||||
```
|
||||
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
|
||||
# Query Dataset
|
||||
|
||||
`query_db` is a tool to print or visualize DensePose data from a dataset.
|
||||
It has two modes: `print` and `show` to output dataset entries to standard
|
||||
output or to visualize them on images.
|
||||
|
||||
## Print Mode
|
||||
|
||||
The general command form is:
|
||||
```bash
|
||||
python query_db.py print [-h] [-v] [--max-entries N] <dataset> <selector>
|
||||
```
|
||||
|
||||
There are two mandatory arguments:
|
||||
- `<dataset>`, DensePose dataset specification, from which to select
|
||||
the entries (e.g. `densepose_coco_2014_train`).
|
||||
- `<selector>`, dataset entry selector which can be a single specification,
|
||||
or a comma-separated list of specifications of the form
|
||||
`field[:type]=value` for exact match with the value
|
||||
or `field[:type]=min-max` for a range of values
|
||||
|
||||
One can additionally limit the maximum number of entries to output
|
||||
by providing `--max-entries` argument.
|
||||
|
||||
Examples:
|
||||
|
||||
1. Output at most 10 first entries from the `densepose_coco_2014_train` dataset:
|
||||
```bash
|
||||
python query_db.py print densepose_coco_2014_train \* --max-entries 10 -v
|
||||
```
|
||||
|
||||
2. Output all entries with `file_name` equal to `COCO_train2014_000000000036.jpg`:
|
||||
```bash
|
||||
python query_db.py print densepose_coco_2014_train file_name=COCO_train2014_000000000036.jpg -v
|
||||
```
|
||||
|
||||
3. Output all entries with `image_id` between 36 and 156:
|
||||
```bash
|
||||
python query_db.py print densepose_coco_2014_train image_id:int=36-156 -v
|
||||
```
|
||||
|
||||
## Visualization Mode
|
||||
|
||||
The general command form is:
|
||||
```bash
|
||||
python query_db.py show [-h] [-v] [--max-entries N] [--output <image_file>] <dataset> <selector> <visualizations>
|
||||
```
|
||||
|
||||
There are three mandatory arguments:
|
||||
- `<dataset>`, DensePose dataset specification, from which to select
|
||||
the entries (e.g. `densepose_coco_2014_train`).
|
||||
- `<selector>`, dataset entry selector which can be a single specification,
|
||||
or a comma-separated list of specifications of the form
|
||||
`field[:type]=value` for exact match with the value
|
||||
or `field[:type]=min-max` for a range of values
|
||||
- `<visualizations>`, visualizations specifier; currently available visualizations are:
|
||||
* `bbox` - bounding boxes of annotated persons;
|
||||
* `dp_i` - annotated points colored according to the containing part;
|
||||
* `dp_pts` - annotated points in green color;
|
||||
* `dp_segm` - segmentation masks for annotated persons;
|
||||
* `dp_u` - annotated points colored according to their U coordinate in part parameterization;
|
||||
* `dp_v` - annotated points colored according to their V coordinate in part parameterization;
|
||||
|
||||
One can additionally provide one of the two optional arguments:
|
||||
- `--max_entries` to limit the maximum number of entries to visualize
|
||||
- `--output` to provide visualization file name template, which defaults
|
||||
to `output.png`. To distinguish file names for different dataset
|
||||
entries, the tool appends 1-based entry index to the output file name,
|
||||
e.g. output.0001.png, output.0002.png, etc.
|
||||
|
||||
The following examples show how to output different visualizations for image with `id = 322`
|
||||
from `densepose_coco_2014_train` dataset:
|
||||
|
||||
1. Show bounding box and segmentation:
|
||||
```bash
|
||||
python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v
|
||||
```
|
||||
|
||||
|
||||
2. Show bounding box and points colored according to the containing part:
|
||||
```bash
|
||||
python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_i -v
|
||||
```
|
||||
|
||||
|
||||
3. Show bounding box and annotated points in green color:
|
||||
```bash
|
||||
python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v
|
||||
```
|
||||
|
||||
|
||||
4. Show bounding box and annotated points colored according to their U coordinate in part parameterization:
|
||||
```bash
|
||||
python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_u -v
|
||||
```
|
||||
|
||||
|
||||
5. Show bounding box and annotated points colored according to their V coordinate in part parameterization:
|
||||
```bash
|
||||
python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_v -v
|
||||
```
|
||||
|
||||
|
||||
|
||||
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|
|
@ -0,0 +1,124 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import os
|
||||
import torch
|
||||
|
||||
from detectron2.config import get_cfg
|
||||
from detectron2.engine import default_setup
|
||||
from detectron2.modeling import build_model
|
||||
|
||||
from densepose import add_densepose_config
|
||||
|
||||
_BASE_CONFIG_DIR = "configs"
|
||||
_EVOLUTION_CONFIG_SUB_DIR = "evolution"
|
||||
_HRNET_CONFIG_SUB_DIR = "HRNet"
|
||||
_QUICK_SCHEDULES_CONFIG_SUB_DIR = "quick_schedules"
|
||||
_BASE_CONFIG_FILE_PREFIX = "Base-"
|
||||
_CONFIG_FILE_EXT = ".yaml"
|
||||
|
||||
|
||||
def _get_base_config_dir():
|
||||
"""
|
||||
Return the base directory for configurations
|
||||
"""
|
||||
return os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", _BASE_CONFIG_DIR)
|
||||
|
||||
|
||||
def _get_evolution_config_dir():
|
||||
"""
|
||||
Return the base directory for evolution configurations
|
||||
"""
|
||||
return os.path.join(_get_base_config_dir(), _EVOLUTION_CONFIG_SUB_DIR)
|
||||
|
||||
|
||||
def _get_hrnet_config_dir():
|
||||
"""
|
||||
Return the base directory for HRNet configurations
|
||||
"""
|
||||
return os.path.join(_get_base_config_dir(), _HRNET_CONFIG_SUB_DIR)
|
||||
|
||||
|
||||
def _get_quick_schedules_config_dir():
|
||||
"""
|
||||
Return the base directory for quick schedules configurations
|
||||
"""
|
||||
return os.path.join(_get_base_config_dir(), _QUICK_SCHEDULES_CONFIG_SUB_DIR)
|
||||
|
||||
|
||||
def _collect_config_files(config_dir):
|
||||
"""
|
||||
Collect all configuration files (i.e. densepose_*.yaml) directly in the specified directory
|
||||
"""
|
||||
start = _get_base_config_dir()
|
||||
results = []
|
||||
for entry in os.listdir(config_dir):
|
||||
path = os.path.join(config_dir, entry)
|
||||
if not os.path.isfile(path):
|
||||
continue
|
||||
_, ext = os.path.splitext(entry)
|
||||
if ext != _CONFIG_FILE_EXT:
|
||||
continue
|
||||
if entry.startswith(_BASE_CONFIG_FILE_PREFIX):
|
||||
continue
|
||||
config_file = os.path.relpath(path, start)
|
||||
results.append(config_file)
|
||||
return results
|
||||
|
||||
|
||||
def get_config_files():
|
||||
"""
|
||||
Get all the configuration files (relative to the base configuration directory)
|
||||
"""
|
||||
return _collect_config_files(_get_base_config_dir())
|
||||
|
||||
|
||||
def get_evolution_config_files():
|
||||
"""
|
||||
Get all the evolution configuration files (relative to the base configuration directory)
|
||||
"""
|
||||
return _collect_config_files(_get_evolution_config_dir())
|
||||
|
||||
|
||||
def get_hrnet_config_files():
|
||||
"""
|
||||
Get all the HRNet configuration files (relative to the base configuration directory)
|
||||
"""
|
||||
return _collect_config_files(_get_hrnet_config_dir())
|
||||
|
||||
|
||||
def get_quick_schedules_config_files():
|
||||
"""
|
||||
Get all the quick schedules configuration files (relative to the base configuration directory)
|
||||
"""
|
||||
return _collect_config_files(_get_quick_schedules_config_dir())
|
||||
|
||||
|
||||
def _get_model_config(config_file):
|
||||
"""
|
||||
Load and return the configuration from the specified file (relative to the base configuration
|
||||
directory)
|
||||
"""
|
||||
cfg = get_cfg()
|
||||
add_densepose_config(cfg)
|
||||
path = os.path.join(_get_base_config_dir(), config_file)
|
||||
cfg.merge_from_file(path)
|
||||
if not torch.cuda.is_available():
|
||||
cfg.MODEL_DEVICE = "cpu"
|
||||
return cfg
|
||||
|
||||
|
||||
def get_model(config_file):
|
||||
"""
|
||||
Get the model from the specified file (relative to the base configuration directory)
|
||||
"""
|
||||
cfg = _get_model_config(config_file)
|
||||
return build_model(cfg)
|
||||
|
||||
|
||||
def setup(config_file):
|
||||
"""
|
||||
Setup the configuration from the specified file (relative to the base configuration directory)
|
||||
"""
|
||||
cfg = _get_model_config(config_file)
|
||||
cfg.freeze()
|
||||
default_setup(cfg, {})
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import random
|
||||
import unittest
|
||||
from typing import Any, Iterable, Iterator, Tuple
|
||||
|
||||
from densepose.data import CombinedDataLoader
|
||||
|
||||
|
||||
def _grouper(iterable: Iterable[Any], n: int, fillvalue=None) -> Iterator[Tuple[Any]]:
|
||||
"""
|
||||
Group elements of an iterable by chunks of size `n`, e.g.
|
||||
grouper(range(9), 4) ->
|
||||
(0, 1, 2, 3), (4, 5, 6, 7), (8, None, None, None)
|
||||
"""
|
||||
it = iter(iterable)
|
||||
while True:
|
||||
values = []
|
||||
for _ in range(n):
|
||||
try:
|
||||
value = next(it)
|
||||
except StopIteration:
|
||||
values.extend([fillvalue] * (n - len(values)))
|
||||
yield tuple(values)
|
||||
return
|
||||
values.append(value)
|
||||
yield tuple(values)
|
||||
|
||||
|
||||
class TestCombinedDataLoader(unittest.TestCase):
|
||||
def test_combine_loaders_1(self):
|
||||
loader1 = _grouper([f"1_{i}" for i in range(10)], 2)
|
||||
loader2 = _grouper([f"2_{i}" for i in range(11)], 3)
|
||||
batch_size = 4
|
||||
ratios = (0.1, 0.9)
|
||||
random.seed(43)
|
||||
combined = CombinedDataLoader((loader1, loader2), batch_size, ratios)
|
||||
BATCHES_GT = [
|
||||
["1_0", "1_1", "2_0", "2_1"],
|
||||
["2_2", "2_3", "2_4", "2_5"],
|
||||
["1_2", "1_3", "2_6", "2_7"],
|
||||
["2_8", "2_9", "2_10", None],
|
||||
]
|
||||
for i, batch in enumerate(combined):
|
||||
self.assertEqual(len(batch), batch_size)
|
||||
self.assertEqual(batch, BATCHES_GT[i])
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
||||
import random
|
||||
import unittest
|
||||
|
||||
from densepose.data.video import FirstKFramesSelector, LastKFramesSelector, RandomKFramesSelector
|
||||
|
||||
|
||||
class TestFrameSelector(unittest.TestCase):
|
||||
def test_frame_selector_random_k_1(self):
|
||||
_SEED = 43
|
||||
_K = 4
|
||||
random.seed(_SEED)
|
||||
selector = RandomKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 20, 2))
|
||||
_SELECTED_GT = [0, 8, 4, 6]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
||||
def test_frame_selector_random_k_2(self):
|
||||
_SEED = 43
|
||||
_K = 10
|
||||
random.seed(_SEED)
|
||||
selector = RandomKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 6, 2))
|
||||
_SELECTED_GT = [0, 2, 4]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
||||
def test_frame_selector_first_k_1(self):
|
||||
_K = 4
|
||||
selector = FirstKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 20, 2))
|
||||
_SELECTED_GT = frame_tss[:_K]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
||||
def test_frame_selector_first_k_2(self):
|
||||
_K = 10
|
||||
selector = FirstKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 6, 2))
|
||||
_SELECTED_GT = frame_tss[:_K]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
||||
def test_frame_selector_last_k_1(self):
|
||||
_K = 4
|
||||
selector = LastKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 20, 2))
|
||||
_SELECTED_GT = frame_tss[-_K:]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
||||
def test_frame_selector_last_k_2(self):
|
||||
_K = 10
|
||||
selector = LastKFramesSelector(_K)
|
||||
frame_tss = list(range(0, 6, 2))
|
||||
_SELECTED_GT = frame_tss[-_K:]
|
||||
selected = selector(frame_tss)
|
||||
self.assertEqual(_SELECTED_GT, selected)
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
||||
import unittest
|
||||
import torch
|
||||
|
||||
from densepose.data.transform import ImageResizeTransform
|
||||
|
||||
|
||||
class TestImageResizeTransform(unittest.TestCase):
|
||||
def test_image_resize_1(self):
|
||||
images_batch = torch.ones((3, 100, 100, 3), dtype=torch.uint8) * 100
|
||||
transform = ImageResizeTransform()
|
||||
images_transformed = transform(images_batch)
|
||||
IMAGES_GT = torch.ones((3, 3, 800, 800), dtype=torch.float) * 100
|
||||
self.assertEqual(images_transformed.size(), IMAGES_GT.size())
|
||||
self.assertAlmostEqual(torch.abs(IMAGES_GT - images_transformed).max().item(), 0.0)
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import unittest
|
||||
import torch
|
||||
|
||||
from detectron2.structures import BitMasks, Boxes, Instances
|
||||
|
||||
from .common import get_model
|
||||
|
||||
|
||||
# TODO(plabatut): Modularize detectron2 tests and re-use
|
||||
def make_model_inputs(image, instances=None):
|
||||
if instances is None:
|
||||
return {"image": image}
|
||||
|
||||
return {"image": image, "instances": instances}
|
||||
|
||||
|
||||
def make_empty_instances(h, w):
|
||||
instances = Instances((h, w))
|
||||
instances.gt_boxes = Boxes(torch.rand(0, 4))
|
||||
instances.gt_classes = torch.tensor([]).to(dtype=torch.int64)
|
||||
instances.gt_masks = BitMasks(torch.rand(0, h, w))
|
||||
return instances
|
||||
|
||||
|
||||
class ModelE2ETest(unittest.TestCase):
|
||||
CONFIG_PATH = ""
|
||||
|
||||
def setUp(self):
|
||||
self.model = get_model(self.CONFIG_PATH)
|
||||
|
||||
def _test_eval(self, sizes):
|
||||
inputs = [make_model_inputs(torch.rand(3, size[0], size[1])) for size in sizes]
|
||||
self.model.eval()
|
||||
self.model(inputs)
|
||||
|
||||
|
||||
class DensePoseRCNNE2ETest(ModelE2ETest):
|
||||
CONFIG_PATH = "densepose_rcnn_R_101_FPN_s1x.yaml"
|
||||
|
||||
def test_empty_data(self):
|
||||
self._test_eval([(200, 250), (200, 249)])
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import unittest
|
||||
|
||||
from .common import (
|
||||
get_config_files,
|
||||
get_evolution_config_files,
|
||||
get_hrnet_config_files,
|
||||
get_quick_schedules_config_files,
|
||||
setup,
|
||||
)
|
||||
|
||||
|
||||
class TestSetup(unittest.TestCase):
|
||||
def _test_setup(self, config_file):
|
||||
setup(config_file)
|
||||
|
||||
def test_setup_configs(self):
|
||||
config_files = get_config_files()
|
||||
for config_file in config_files:
|
||||
self._test_setup(config_file)
|
||||
|
||||
def test_setup_evolution_configs(self):
|
||||
config_files = get_evolution_config_files()
|
||||
for config_file in config_files:
|
||||
self._test_setup(config_file)
|
||||
|
||||
def test_setup_hrnet_configs(self):
|
||||
config_files = get_hrnet_config_files()
|
||||
for config_file in config_files:
|
||||
self._test_setup(config_file)
|
||||
|
||||
def test_setup_quick_schedules_configs(self):
|
||||
config_files = get_quick_schedules_config_files()
|
||||
for config_file in config_files:
|
||||
self._test_setup(config_file)
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import unittest
|
||||
|
||||
from densepose.data.structures import normalized_coords_transform
|
||||
|
||||
|
||||
class TestStructures(unittest.TestCase):
|
||||
def test_normalized_coords_transform(self):
|
||||
bbox = (32, 24, 288, 216)
|
||||
x0, y0, w, h = bbox
|
||||
xmin, ymin, xmax, ymax = x0, y0, x0 + w, y0 + h
|
||||
f = normalized_coords_transform(*bbox)
|
||||
# Top-left
|
||||
expected_p, actual_p = (-1, -1), f((xmin, ymin))
|
||||
self.assertEqual(expected_p, actual_p)
|
||||
# Top-right
|
||||
expected_p, actual_p = (1, -1), f((xmax, ymin))
|
||||
self.assertEqual(expected_p, actual_p)
|
||||
# Bottom-left
|
||||
expected_p, actual_p = (-1, 1), f((xmin, ymax))
|
||||
self.assertEqual(expected_p, actual_p)
|
||||
# Bottom-right
|
||||
expected_p, actual_p = (1, 1), f((xmax, ymax))
|
||||
self.assertEqual(expected_p, actual_p)
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
|
||||
import contextlib
|
||||
import os
|
||||
import random
|
||||
import tempfile
|
||||
import unittest
|
||||
import torch
|
||||
import torchvision.io as io
|
||||
|
||||
from densepose.data.transform import ImageResizeTransform
|
||||
from densepose.data.video import RandomKFramesSelector, VideoKeyframeDataset
|
||||
|
||||
try:
|
||||
import av
|
||||
except ImportError:
|
||||
av = None
|
||||
|
||||
|
||||
# copied from torchvision test/test_io.py
|
||||
def _create_video_frames(num_frames, height, width):
|
||||
y, x = torch.meshgrid(torch.linspace(-2, 2, height), torch.linspace(-2, 2, width))
|
||||
data = []
|
||||
for i in range(num_frames):
|
||||
xc = float(i) / num_frames
|
||||
yc = 1 - float(i) / (2 * num_frames)
|
||||
d = torch.exp(-((x - xc) ** 2 + (y - yc) ** 2) / 2) * 255
|
||||
data.append(d.unsqueeze(2).repeat(1, 1, 3).byte())
|
||||
return torch.stack(data, 0)
|
||||
|
||||
|
||||
# adapted from torchvision test/test_io.py
|
||||
@contextlib.contextmanager
|
||||
def temp_video(num_frames, height, width, fps, lossless=False, video_codec=None, options=None):
|
||||
if lossless:
|
||||
if video_codec is not None:
|
||||
raise ValueError("video_codec can't be specified together with lossless")
|
||||
if options is not None:
|
||||
raise ValueError("options can't be specified together with lossless")
|
||||
video_codec = "libx264rgb"
|
||||
options = {"crf": "0"}
|
||||
if video_codec is None:
|
||||
video_codec = "libx264"
|
||||
if options is None:
|
||||
options = {}
|
||||
data = _create_video_frames(num_frames, height, width)
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4") as f:
|
||||
f.close()
|
||||
io.write_video(f.name, data, fps=fps, video_codec=video_codec, options=options)
|
||||
yield f.name, data
|
||||
os.unlink(f.name)
|
||||
|
||||
|
||||
@unittest.skipIf(av is None, "PyAV unavailable")
|
||||
class TestVideoKeyframeDataset(unittest.TestCase):
|
||||
def test_read_keyframes_all(self):
|
||||
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
|
||||
video_list = [fname]
|
||||
dataset = VideoKeyframeDataset(video_list)
|
||||
self.assertEqual(len(dataset), 1)
|
||||
data1 = dataset[0]
|
||||
self.assertEqual(data1.shape, torch.Size((5, 300, 300, 3)))
|
||||
self.assertEqual(data1.dtype, torch.uint8)
|
||||
return
|
||||
self.assertTrue(False)
|
||||
|
||||
def test_read_keyframes_with_selector(self):
|
||||
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
|
||||
video_list = [fname]
|
||||
random.seed(0)
|
||||
frame_selector = RandomKFramesSelector(3)
|
||||
dataset = VideoKeyframeDataset(video_list, frame_selector)
|
||||
self.assertEqual(len(dataset), 1)
|
||||
data1 = dataset[0]
|
||||
self.assertEqual(data1.shape, torch.Size((3, 300, 300, 3)))
|
||||
self.assertEqual(data1.dtype, torch.uint8)
|
||||
return
|
||||
self.assertTrue(False)
|
||||
|
||||
def test_read_keyframes_with_selector_with_transform(self):
|
||||
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
|
||||
video_list = [fname]
|
||||
random.seed(0)
|
||||
frame_selector = RandomKFramesSelector(1)
|
||||
transform = ImageResizeTransform()
|
||||
dataset = VideoKeyframeDataset(video_list, frame_selector, transform)
|
||||
data1 = dataset[0]
|
||||
self.assertEqual(len(dataset), 1)
|
||||
self.assertEqual(data1.shape, torch.Size((1, 3, 800, 800)))
|
||||
self.assertEqual(data1.dtype, torch.float32)
|
||||
return
|
||||
self.assertTrue(False)
|
||||
Loading…
Reference in New Issue