From e60afe4993cab9174683be400b46375f7edaddee Mon Sep 17 00:00:00 2001
From: RE-OWOD <95522332+RE-OWOD@users.noreply.github.com>
Date: Tue, 4 Jan 2022 13:54:26 +0800
Subject: [PATCH] Add files via upload
---
projects/DensePose/dev/README.md | 7 +
projects/DensePose/dev/run_inference_tests.sh | 33 +++
projects/DensePose/dev/run_instant_tests.sh | 28 ++
projects/DensePose/doc/GETTING_STARTED.md | 58 ++++
projects/DensePose/doc/MODEL_ZOO.md | 277 ++++++++++++++++++
projects/DensePose/doc/TOOL_APPLY_NET.md | 131 +++++++++
projects/DensePose/doc/TOOL_QUERY_DB.md | 105 +++++++
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.../DensePose/doc/images/res_bbox_dp_segm.jpg | Bin 0 -> 156777 bytes
.../DensePose/doc/images/res_bbox_dp_u.jpg | Bin 0 -> 158784 bytes
.../DensePose/doc/images/res_bbox_dp_v.jpg | Bin 0 -> 158204 bytes
.../DensePose/doc/images/vis_bbox_dp_i.jpg | Bin 0 -> 86262 bytes
.../DensePose/doc/images/vis_bbox_dp_pts.jpg | Bin 0 -> 83397 bytes
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projects/DensePose/tests/common.py | 124 ++++++++
.../tests/test_combine_data_loader.py | 46 +++
.../DensePose/tests/test_frame_selector.py | 60 ++++
.../tests/test_image_resize_transform.py | 16 +
projects/DensePose/tests/test_model_e2e.py | 43 +++
projects/DensePose/tests/test_setup.py | 36 +++
projects/DensePose/tests/test_structures.py | 25 ++
.../tests/test_video_keyframe_dataset.py | 92 ++++++
24 files changed, 1081 insertions(+)
create mode 100644 projects/DensePose/dev/README.md
create mode 100644 projects/DensePose/dev/run_inference_tests.sh
create mode 100644 projects/DensePose/dev/run_instant_tests.sh
create mode 100644 projects/DensePose/doc/GETTING_STARTED.md
create mode 100644 projects/DensePose/doc/MODEL_ZOO.md
create mode 100644 projects/DensePose/doc/TOOL_APPLY_NET.md
create mode 100644 projects/DensePose/doc/TOOL_QUERY_DB.md
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create mode 100644 projects/DensePose/doc/images/vis_bbox_dp_v.jpg
create mode 100644 projects/DensePose/tests/common.py
create mode 100644 projects/DensePose/tests/test_combine_data_loader.py
create mode 100644 projects/DensePose/tests/test_frame_selector.py
create mode 100644 projects/DensePose/tests/test_image_resize_transform.py
create mode 100644 projects/DensePose/tests/test_model_e2e.py
create mode 100644 projects/DensePose/tests/test_setup.py
create mode 100644 projects/DensePose/tests/test_structures.py
create mode 100644 projects/DensePose/tests/test_video_keyframe_dataset.py
diff --git a/projects/DensePose/dev/README.md b/projects/DensePose/dev/README.md
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+
+## Some scripts for developers to use, include:
+
+- `run_instant_tests.sh`: run training for a few iterations.
+- `run_inference_tests.sh`: run inference on a small dataset.
+- `../../dev/linter.sh`: lint the codebase before commit
+- `../../dev/parse_results.sh`: parse results from log file.
diff --git a/projects/DensePose/dev/run_inference_tests.sh b/projects/DensePose/dev/run_inference_tests.sh
new file mode 100644
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+#!/bin/bash -e
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+BIN="python train_net.py"
+OUTPUT="inference_test_output"
+NUM_GPUS=2
+IMS_PER_GPU=2
+IMS_PER_BATCH=$(( NUM_GPUS * IMS_PER_GPU ))
+
+CFG_LIST=( "${@:1}" )
+
+if [ ${#CFG_LIST[@]} -eq 0 ]; then
+ CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
+fi
+
+echo "========================================================================"
+echo "Configs to run:"
+echo "${CFG_LIST[@]}"
+echo "========================================================================"
+
+for cfg in "${CFG_LIST[@]}"; do
+ echo "========================================================================"
+ echo "Running $cfg ..."
+ echo "========================================================================"
+ $BIN \
+ --eval-only \
+ --num-gpus $NUM_GPUS \
+ --config-file "$cfg" \
+ OUTPUT_DIR "$OUTPUT" \
+ SOLVER.IMS_PER_BATCH $IMS_PER_BATCH
+ rm -rf $OUTPUT
+done
+
diff --git a/projects/DensePose/dev/run_instant_tests.sh b/projects/DensePose/dev/run_instant_tests.sh
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+#!/bin/bash -e
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+BIN="python train_net.py"
+OUTPUT="instant_test_output"
+NUM_GPUS=2
+SOLVER_IMS_PER_BATCH=$((NUM_GPUS * 2))
+
+CFG_LIST=( "${@:1}" )
+if [ ${#CFG_LIST[@]} -eq 0 ]; then
+ CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml )
+fi
+
+echo "========================================================================"
+echo "Configs to run:"
+echo "${CFG_LIST[@]}"
+echo "========================================================================"
+
+for cfg in "${CFG_LIST[@]}"; do
+ echo "========================================================================"
+ echo "Running $cfg ..."
+ echo "========================================================================"
+ $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \
+ SOLVER.IMS_PER_BATCH $SOLVER_IMS_PER_BATCH \
+ OUTPUT_DIR "$OUTPUT"
+ rm -rf "$OUTPUT"
+done
+
diff --git a/projects/DensePose/doc/GETTING_STARTED.md b/projects/DensePose/doc/GETTING_STARTED.md
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+# Getting Started with DensePose
+
+## Inference with Pre-trained Models
+
+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)
+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:
+```bash
+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
+```
+Please see [Apply Net](TOOL_APPLY_NET.md) for more details on the tool.
+
+## Training
+
+First, prepare the [dataset](http://densepose.org/#dataset) into the following structure under the directory you'll run training scripts:
+
+datasets/coco/
+ annotations/
+ densepose_{train,minival,valminusminival}2014.json
+ densepose_minival2014_100.json (optional, for testing only)
+ {train,val}2014/
+ # image files that are mentioned in the corresponding json
+
+
+To train a model one can use the [train_net.py](../train_net.py) script.
+This script was used to train all DensePose models in [Model Zoo](MODEL_ZOO.md).
+For example, to launch end-to-end DensePose-RCNN training with ResNet-50 FPN backbone
+on 8 GPUs following the s1x schedule, one can run
+```bash
+python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml --num-gpus 8
+```
+The configs are made for 8-GPU training. To train on 1 GPU, one can apply the
+[linear learning rate scaling rule](https://arxiv.org/abs/1706.02677):
+```bash
+python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
+ SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
+```
+
+## Evaluation
+
+Model testing can be done in the same way as training, except for an additional flag `--eval-only` and
+model location specification through `MODEL.WEIGHTS model.pth` in the command line
+```bash
+python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
+ --eval-only MODEL.WEIGHTS model.pth
+```
+
+## Tools
+
+We provide tools which allow one to:
+ - easily view DensePose annotated data in a dataset;
+ - perform DensePose inference on a set of images;
+ - visualize DensePose model results;
+
+`query_db` is a tool to print or visualize DensePose data in a dataset.
+Please refer to [Query DB](TOOL_QUERY_DB.md) for more details on this tool
+
+`apply_net` is a tool to print or visualize DensePose results.
+Please refer to [Apply Net](TOOL_APPLY_NET.md) for more details on this tool
diff --git a/projects/DensePose/doc/MODEL_ZOO.md b/projects/DensePose/doc/MODEL_ZOO.md
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+# Model Zoo and Baselines
+
+# Introduction
+
+We provide baselines trained with Detectron2 DensePose. The corresponding
+configuration files can be found in the [configs](../configs) directory.
+All models were trained on COCO `train2014` + `valminusminival2014` and
+evaluated on COCO `minival2014`. For the details on common settings in which
+baselines were trained, please check [Detectron 2 Model Zoo](../../../MODEL_ZOO.md).
+
+## License
+
+All models available for download through this document are licensed under the
+[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/)
+
+## COCO DensePose Baselines with DensePose-RCNN
+
+### Legacy Models
+
+Baselines trained using schedules from [Güler et al, 2018](https://arxiv.org/pdf/1802.00434.pdf)
+
+
+
+### Improved Baselines, Original Fully Convolutional Haad
+
+These models use an improved training schedule and Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446).
+
+
+
+### Improved Baselines, DeepLabV3 Head
+
+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).
+
+
+
+### Baselines with Confidence Estimation
+
+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).
+
+
+
+## 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.
diff --git a/projects/DensePose/doc/TOOL_APPLY_NET.md b/projects/DensePose/doc/TOOL_APPLY_NET.md
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+# 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 ]
+```
+
+There are three mandatory arguments:
+ - ``, configuration file for a given model;
+ - ``, model file with trained parameters
+ - ``, 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': },
+ {'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': }]
+```
+
+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 ] [--nms_thresh ] [--output ]
+```
+
+There are four mandatory arguments:
+ - ``, configuration file for a given model;
+ - ``, model file with trained parameters
+ - ``, input image file name, pattern or folder
+ - ``, 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
+```
+
diff --git a/projects/DensePose/doc/TOOL_QUERY_DB.md b/projects/DensePose/doc/TOOL_QUERY_DB.md
new file mode 100644
index 0000000..b0a764b
--- /dev/null
+++ b/projects/DensePose/doc/TOOL_QUERY_DB.md
@@ -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]
+```
+
+There are two mandatory arguments:
+ - ``, DensePose dataset specification, from which to select
+ the entries (e.g. `densepose_coco_2014_train`).
+ - ``, 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 ]
+```
+
+There are three mandatory arguments:
+ - ``, DensePose dataset specification, from which to select
+ the entries (e.g. `densepose_coco_2014_train`).
+ - ``, 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 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
+```
+
+
+
diff --git a/projects/DensePose/doc/images/res_bbox_dp_contour.jpg b/projects/DensePose/doc/images/res_bbox_dp_contour.jpg
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