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Issue 239 and 189 solution #252

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9c51284
Issue 239 and 189 solution
Sep 9, 2022
616c8f6
create a blank cache folder instead of a soft link
Sep 14, 2022
250fd80
cache combined with cache config to speed up data reading
Sep 16, 2022
c58fd06
lint test
Sep 22, 2022
4941e4f
lint testing
Sep 22, 2022
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20 changes: 16 additions & 4 deletions docs/install.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,11 @@

<!-- TOC -->

- [Requirements](#requirements)
- [Prepare environment](#prepare-environment)
- [Install MMHuman3D](#install-mmhuman3d)
- [A from-scratch setup script](#a-from-scratch-setup-script)
- [Installation](#installation)
- [Requirements](#requirements)
- [Prepare environment](#prepare-environment)
- [Install MMHuman3D](#install-mmhuman3d)
- [A from-scratch setup script](#a-from-scratch-setup-script)

<!-- TOC -->

Expand Down Expand Up @@ -54,6 +55,12 @@ conda install pytorch=1.8.0 torchvision cudatoolkit=10.2 -c pytorch

**Important:** Make sure that your compilation CUDA version and runtime CUDA version match.
Besides, for RTX 30 series GPU, cudatoolkit>=11.0 is required.
To make sure that your installed the right pytorch version, you should check if you get `True` when running the following commands:
```
import torch
torch.cuda.is_available()
```
If you get `False`, you should choose to install other pytorch versions.

d. Install PyTorch3D from source.

Expand Down Expand Up @@ -150,6 +157,11 @@ cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .
```
Here, to check that your mmdet is compatible with pytorch, check if you have problems with running the following command:
```
from mmdet.apis import inference_detector, init_detector
```
If you meet errors, please check your pytorch and mmcv versions and install other versions of pytorch.

- mmpose (optional)
```shell
Expand Down
57 changes: 36 additions & 21 deletions docs/preprocess_dataset.md
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Expand Up @@ -4,26 +4,35 @@
<!-- * [Overview](#overview)
* [Generate dataset files](#generate-dataset-files)
* [Obtain preprocessed datasets](#obtain-preprocessed-datasets) -->
- [Datasets for supported algorithms](#datasets-for-supported-algorithms)
- [Folder structure](#folder-structure)
* [AGORA](#agora)
* [COCO](#coco)
* [COCO-WholeBody](#coco-wholebody)
* [CrowdPose](#crowdpose)
* [EFT](#eft)
* [GTA-Human](#gta-human)
* [Human3.6M](#human36m)
* [Human3.6M Mosh](#human36m-mosh)
* [HybrIK](#hybrik)
* [LSP](#lsp)
* [LSPET](#lspet)
* [MPI-INF-3DHP](#mpi-inf-3dhp)
* [MPII](#mpii)
* [PoseTrack18](#posetrack18)
* [Penn Action](#penn-action)
* [PW3D](#pw3d)
* [SPIN](#spin)
* [SURREAL](#surreal)
- [Data preparation](#data-preparation)
- [Overview](#overview)
- [Datasets for supported algorithms](#datasets-for-supported-algorithms)
- [Folder structure](#folder-structure)
- [AGORA](#agora)
- [AMASS](#amass)
- [COCO](#coco)
- [COCO-WholeBody](#coco-wholebody)
- [CrowdPose](#crowdpose)
- [EFT](#eft)
- [GTA-Human](#gta-human)
- [Human3.6M](#human36m)
- [Human3.6M Mosh](#human36m-mosh)
- [HybrIK](#hybrik)
- [LSP](#lsp)
- [LSPET](#lspet)
- [MPI-INF-3DHP](#mpi-inf-3dhp)
- [MPII](#mpii)
- [PoseTrack18](#posetrack18)
- [Penn Action](#penn-action)
- [PW3D](#pw3d)
- [SPIN](#spin)
- [SURREAL](#surreal)
- [VIBE](#vibe)
- [FreiHand](#freihand)
- [EHF](#ehf)
- [FFHQ](#ffhq)
- [ExPose](#expose)
- [Stirling](#stirling)


## Overview
Expand Down Expand Up @@ -131,7 +140,8 @@ DATASET_CONFIGS = dict(

## Datasets for supported algorithms

For all algorithms, the root path for our datasets and output path for our preprocessed npz files are stored in `data/datasets` and `data/preprocessed_datasets`. As such, use this command with the listed `dataset-names`:
For all algorithms, the root path for our datasets and output path for our preprocessed npz files are stored in `data/datasets` and `data/preprocessed_datasets`.
As such, use this command with the listed `dataset-names`:

```bash
python tools/convert_datasets.py \
Expand Down Expand Up @@ -188,6 +198,11 @@ mmhuman3d
├── mpii_train.npz
└── pw3d_test.npz
```
Note that, to avoid generating npz files every iteration during training, please create a blank cache directory. To do so, run the following command:
```
cp -r data/preprocessed_datasets data/cache
```
Also, remember to use the *_cache.py config during training.

For SPIN training, the following datasets are required:
- [COCO](#coco)
Expand Down