提交 82297644 编写于 作者: D Dario Pavllo 提交者: Michael Auli

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# Code of Conduct
Facebook has adopted a Code of Conduct that we expect project participants to adhere to. Please [read the full text](https://code.facebook.com/codeofconduct) so that you can understand what actions will and will not be tolerated.
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# Contributing
We want to make contributing to this project as easy and transparent as
possible.
## Pull Requests
We actively welcome your pull requests.
1. Fork the repo and create your branch from `master`.
2. If you've added code that should be tested, add tests.
3. If you've changed APIs, update the documentation.
4. Ensure the test suite passes.
5. Make sure your code lints.
6. If you haven't already, complete the Contributor License Agreement ("CLA").
## Contributor License Agreement ("CLA")
In order to accept your pull request, we need you to submit a CLA. You only need
to do this once to work on any of Facebook's open source projects.
Complete your CLA here: <https://code.facebook.com/cla>
## Issues
We use GitHub issues to track public bugs. Please ensure your description is
clear and has sufficient instructions to be able to reproduce the issue.
## Coding Style
We follow the [PEP 8](https://www.python.org/dev/peps/pep-0008/) style guidelines.
## License
By contributing to this project, you agree that your contributions will be licensed
under the LICENSE file in the root directory of this source tree.
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# Dataset setup
## Human3.6M
We provide two ways to set up the Human3.6M dataset on our pipeline. You can either use the [dataset preprocessed by Martinez et al.](https://github.com/una-dinosauria/3d-pose-baseline) (fastest way) or convert the original dataset from scratch. The two methods produce the same result. After this step, you should end up with two files in the `data` directory: `data_3d_h36m.npz` for the 3D poses, and `data_2d_h36m_gt.npz` for the ground-truth 2D poses.
### Setup from preprocessed dataset
Download the [h36m.zip archive](https://www.dropbox.com/s/e35qv3n6zlkouki/h36m.zip) (source: [3D pose baseline repository](https://github.com/una-dinosauria/3d-pose-baseline)) to the `data` directory, and run the conversion script from the same directory. This step does not require any additional dependency.
```sh
cd data
wget https://www.dropbox.com/s/e35qv3n6zlkouki/h36m.zip
python prepare_data_h36m.py --from-archive h36m.zip
cd ..
```
### Setup from original source
Alternatively, you can download the dataset from the [Human3.6m website](http://vision.imar.ro/human3.6m/) and convert it from its original format. This is useful if the other link goes down, or if you want to be sure to use the original source. MATLAB is required for this step.
First, we need to convert the 3D poses from `.cdf` to `.mat`, so they can be loaded from Python scripts. To this end, we have provided the MATLAB script `convert_cdf_to_mat.m` in the `data` directory. Extract the archives named `Poses_D3_Positions_S*.tgz` (subjects 1, 5, 6, 7, 8, 9, 11) to a directory named `pose`, and set up your directory tree so that it looks like this:
```
/path/to/dataset/convert_cdf_to_mat.m
/path/to/dataset/pose/S1/MyPoseFeatures/D3_Positions/Directions 1.cdf
/path/to/dataset/pose/S1/MyPoseFeatures/D3_Positions/Directions.cdf
...
```
Then run `convert_cdf_to_mat.m` from MATLAB.
Finally, as before, run the Python conversion script specifying the dataset path:
```sh
cd data
python prepare_data_h36m.py --from-source /path/to/dataset/pose
cd ..
```
## 2D detections for Human3.6M
We provide support for the following 2D detections:
- `gt`: ground-truth 2D poses, extracted through the camera projection parameters.
- `sh_pt_mpii`: Stacked Hourglass detections, pretrained on MPII.
- `sh_ft_h36m`: Stacked Hourglass detections, fine-tuned on Human3.6M.
- `detectron_ft_h36m`: Detectron (Mask R-CNN) detections, fine-tuned on Human3.6M.
- `cpn_ft_h36m_dbb`: Cascaded Pyramid Network detections, fine-tuned on Human3.6M. Bounding boxes from `detectron_ft_h36m`.
- User-supplied (see below).
The 2D detection source is specified through the `--keypoints` parameter, which loads the file `data_2d_DATASET_DETECTION.npz` from the `data` directory, where `DATASET` is the dataset name (e.g. `h36m`) and `DETECTION` is the 2D detection source (e.g. `sh_pt_mpii`). Since all the files are encoded according to the same format, it is trivial to create a custom set of 2D detections.
Ground-truth poses (`gt`) have already been extracted by the previous step. The other detections must be downloaded manually (see instructions below). You only need to download the detections you want to use. For reference, our best results on Human3.6M are achieved by `cpn_ft_h36m_dbb`.
### Mask R-CNN and CPN detections
You can download these from AWS. You just have to put `data_2d_h36m_cpn_ft_h36m_dbb.npz` and `data_2d_h36m_detectron_ft_h36m.npz` in the `data` directory.
```sh
cd data
wget https://s3.amazonaws.com/video-pose-3d/data_2d_h36m_cpn_ft_h36m_dbb.npz
wget https://s3.amazonaws.com/video-pose-3d/data_2d_h36m_detectron_ft_h36m.npz
cd ..
```
These detections have been produced by models fine-tuned on Human3.6M. We adopted the usual protocol of fine-tuning on 5 subjects (S1, S5, S6, S7, and S8). We also included detections from the unlabeled subjects S2, S3, S4, which can be loaded by our framework for semi-supervised experimentation.
### Stacked Hourglass detections
These detections (both pretrained and fine-tuned) are provided by [Martinez et al.](https://github.com/una-dinosauria/3d-pose-baseline) in their repository on 3D human pose estimation. The 2D poses produced by the pretrained model are in the same archive as the dataset ([h36m.zip](https://www.dropbox.com/s/e35qv3n6zlkouki/h36m.zip)). The fine-tuned poses can be downloaded [here](https://drive.google.com/open?id=0BxWzojlLp259S2FuUXJ6aUNxZkE). Put the two archives in the `data` directory and run:
```sh
cd data
python prepare_data_2d_h36m_sh.py -pt h36m.zip
python prepare_data_2d_h36m_sh.py -ft stacked_hourglass_fined_tuned_240.tar.gz
cd ..
```
## HumanEva-I
For HumanEva, you need the original dataset and MATLAB. We provide a MATLAB script to extract the revelant parts of the dataset automatically.
1. Download the [HumanEva-I dataset](http://humaneva.is.tue.mpg.de/datasets_human_1) and extract it.
2. Download the official [source code v1.1 beta](http://humaneva.is.tue.mpg.de/main/download?file=Release_Code_v1_1_beta.zip) and extract it where you extracted the dataset.
3. Copy the contents of the directory `Release_Code_v1_1_beta\HumanEva_I` to the root of the source tree (`Release_Code_v1_1_beta/`).
4. Download the [critical dataset update](http://humaneva.is.tue.mpg.de/main/download?file=Critical_Update_OFS_files.zip) and apply it.
5. **Important:** for visualization purposes, the original code requires an old library named *dxAvi*, which is used for decoding XVID videos. A precompiled binary for 32-bit architectures is already included, but if you are running MATLAB on a 64-bit system, the code will not work. You can either recompile *dxAvi* library for x64, or bypass it entirely, since we are not using visualization features in our conversion script. To this end, you can patch `@sync_stream/sync_stream.m`, replacing line 202: `ImageStream(I) = image_stream(image_paths{I}, start_image_offset(I));` with `ImageStream(I) = 0;`
6. Now you can copy our script `ConvertHumanEva.m` (from `data/`) to `Release_Code_v1_1_beta/`, and run it. It will create a directory named `converted_15j`, which contains the converted 2D/3D ground-truth poses on a 15-joint skeleton.
7. **Optional:** if you want to experiment with a 20-joint skeleton, change `N_JOINTS` to 20 in `ConvertHumanEva.m`, and repeat the process. It will create a directory named `converted_20j`. Adapt next steps accordingly.
If you get warnings about mocap errors or dropped frames, this is normal. The HumanEva dataset contains some invalid frames due to occlusions, which are simply discarded. Since we work with videos (and not individual frames), we try to minimize the impact of this issue by grouping valid sequences into contiguous chunks.
Finally, run the Python script to produce the final files:
```
python prepare_data_humaneva.py -p /path/to/dataset/Release_Code_v1_1_beta/converted_15j --convert-3d
```
You should end up with two files in the `data` directory: `data_3d_humaneva15.npz` for the 3D poses, and `data_2d_humaneva15_gt.npz` for the ground-truth 2D poses.
### 2D detections for HumanEva-I
We provide support for the following 2D detections:
- `gt`: ground-truth 2D poses, extracted through camera projection.
- `detectron_pt_coco`: Detectron (Mask R-CNN) detections, pretrained on COCO.
Since HumanEva is very small, we do not fine-tune the pretrained models. As before, you can download Mask R-CNN detections from AWS (`data_2d_humaneva15_detectron_pt_coco.npz`, which must be copied to `data/`). As before, we have included detections for unlabeled subjects/actions. These begin with the prefix `Unlabeled/`. Chunks that correspond to corrupted motion capture streams are also marked as unlabeled.
```sh
cd data
wget https://s3.amazonaws.com/video-pose-3d/data_2d_humaneva15_detectron_pt_coco.npz
cd ..
```
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# Documentation
This guide explains in depth all the features of this framework. Make sure you have read the quick start guide in [`README.md`](README.md) before proceeding.
## Training
By default, the script `run.py` runs in training mode. The list of command-line arguments is defined in `common/arguments.py`.
- `-h`: shows the help / list of parameters.
- `-d` or `--dataset`: specifies the dataset to use (`h36m` or `humaneva15`). Default: `h36m`. If you converted the 20-joint HumanEva skeleton, you can also use `humaneva20`.
- `-k` or `--keypoints`: specifies the 2D detections to use. Default: `cpn_ft_h36m_dbb` (CPN fine-tuned on Human 3.6M).
- `-c` or `--checkpoint`: specifies the directory where checkpoints are saved/read. Default: `checkpoint`.
- `--checkpoint-frequency`: save checkpoints every N epochs. Default: `10`.
- `-r` or `--resume`: resume training from a particular checkpoint (you should only specify the file name, not the path), e.g. `epoch_10.bin`.
- `-str` or `--subjects-train`: specifies the list of subjects on which the model is trained, separated by commas. Default: `S1,S5,S6,S7,S8`. For HumanEva, you may want to specify these manually.
- `-ste` or `--subjects-test`: specifies the list of subjects on which the model is tested at the end of each epoch (and in the final evaluation), separated by comma. Default: `S9,S11`. For HumanEva, you may want to specify these manually.
- `-a` or `--actions`: select only a subset of actions, separated by commas. E.g. `Walk,Jog`. By default, all actions are used.
- `-e` or `--epochs`: train for N epochs, i.e. N passes over the entire training set. Default: `60`.
- `--no-eval`: disable testing at the end of each epoch (marginal speed up). By default, testing is enabled.
- `--export-training-curves`: export training curves as PNG images after every epoch. They are saved in the checkpoint directory. Default: disabled.
If `--no-eval` is not specified, the model is tested at the end of each epoch, although the reported metric is merely an approximation of the final result (for performance reasons). Once training is over, the model is automatically tested using the full procedure. This means that you can also specify the testing parameters when training.
Here is a description of the model hyperparameters:
- `-s` or `--stride`: the chunk size used for training, i.e. the number of frames that are predicted at once from each sequence. Increasing this value improves training speed at the expense of the error (due to correlated batch statistics). Default: `1` frame, which ensures maximum decorrelation. When this value is set to `1`, we also employ an optimized implementation of the model (see implementation details).
- `-b` or `--batch-size`: the batch size used for training the model, in terms of *output frames* (regardless of the stride/chunk length). Default: `1024` frames.
- `-drop` or `--dropout`: dropout probability. Default: `0.25`.
- `-lr` or `--learning-rate`: initial learning rate. Default: `0.001`.
- `-lrd` or `--lr-decay`: learning rate decay after every epoch (multiplicative coefficient). Default: `0.95`.
- `-no-tta` or `--no-test-time-augmentation`: disable test-time augmentation (which is enabled by default), i.e. do not flip poses horizontally when testing the model. Only effective when combined with data augmentation, so if you disable this you should also disable train-time data augmentation.
- `-no-da` or `--no-data-augmentation`: disable train-time data augmentation (which is enabled by default), i.e. do not flip poses horizontally to double the training data.
- `-arc` or `--architecture`: filter widths (only odd numbers supported) separated by commas. This parameter also specifies the number of residual blocks, and determines the receptive field of the model. The first number refers to the input layer, and is followed by the filter widths of the residual blocks. For instance, `3,5,5` uses `3x1` convolutions in the first layer, followed by two residual blocks with `5x1` convolutions. Default: `3,3,3`. Some valid examples are:
-- `-arc 3,3,3` (27 frames)
-- `-arc 3,3,7` (63 frames)
-- `-arc 3,3,3,3` (81 frames)
-- `-arc 3,3,3,3,3` (243 frames)
- `--causal`: use causal (i.e. asymmetric) convolutions instead of symmetric convolutions. Causal convolutions are suitable for real-time applications because they do not exploit future frames (they only look in the past), but symmetric convolutions result in a better error since they can consider both past and future data. See below for more details. Default: disabled.
- `-ch` or `--channels`: number of channels in convolutions. Default: `1024`.
- `--dense`: use dense convolutions instead of dilated convolutions. This is only useful for benchmarks and ablation experiments.
- `--disable-optimizations`: disable the optimized implementation when `--stride` == `1`. This is only useful for benchmarks.
## Semi-supervised training
Semi-supervised learning is only implemented for Human3.6M.
- `-sun` or `--subjects-unlabeled`: specifies the list of unlabeled subjects that are used for semi-supervision (separated by commas). Semi-supervised learning is automatically enabled when this parameter is set.
- `--warmup`: number of supervised training epochs before attaching the semi-supervised loss. Default: `1` epoch. You may want to increase this when downsampling the dataset.
- `--subset`: reduce the size of the training set by a given factor (a real number). E.g. `0.1` uses one tenth of the training data. Subsampling is achieved by extracting a random contiguous chunk from each video, while preserving the original frame rate. Default: `1` (i.e. disabled). This parameter can also be used in a supervised setting, but it is especially useful to simulate data scarcity in a semi-supervised setting.
- `--downsample`: reduce the dataset frame rate by an integer factor. Default: `1` (i.e. disabled).
- `--no-bone-length`: do not add the bone length term to the unsupervised loss function (only useful for ablation experiments).
- `--linear-projection`: ignore non-linear camera distortion parameters when performing projection to 2D, i.e. use only focal length and principal point.
- `--no-proj`: do not add the projection consistency term to the loss function (only useful for ablations).
## Testing
To test a particular model, you need to specify the checkpoint file via the `--evaluate` parameter, which will be loaded from the checkpoint directory (default: `checkpoint/`, but you can change it using the `-c` parameter). You also need to specify the same settings/hyperparameters that you used for training (e.g. input keypoints, architecture, etc.). The script will not run any compatibility checks -- this is a design choice to facilitate ablation experiments.
## Visualization
You can render videos by specifying both `--evaluate` and `--render`. The script generates a visualization which contains three viewports: the 2D input keypoints (and optionally, a video overlay), the 3D reconstruction, and the 3D ground truth.
Note that when you specify a video, the 2D detections are still loaded from the dataset according to the given parameters. It is up to you to choose the correct video. You can also visualize unlabeled videos -- in this case, the ground truth will not be shown.
Here is a list of the command-line arguments related to visualization:
- `--viz-subject`: subject to render, e.g. `S1`.
- `--viz-action`: action to render, e.g. `Walking` or `Walking 1`.
- `--viz-camera`: camera to render (integer), from 0 to 3 for Human3.6M, 0 to 2 for HumanEva. Default: `0`.
- `--viz-video`: path to the 2D video to show. If specified, the script will render a skeleton overlay on top of the video. If not specified, a black background will be rendered instead (but the 2D detections will still be shown).
- `--viz-skip`: skip the first N frames from the specified video. Useful for HumanEva. Default: `0`.
- `--viz-output`: output file name (either a `.mp4` or `.gif` file).
- `--viz-bitrate`: bitrate for MP4 videos. Default: `3000`.
- `--viz-no-ground-truth`: by default, the videos contain three viewports: the 2D input pose, the 3D reconstruction, and the 3D ground truth. This flags removes the last one.
- `--viz-limit`: render only first N frames. By default, all frames are rendered.
- `--viz-downsample`: downsample videos by the specified factor, i.e. reduce the frame rate. E.g. if set to `2`, the frame rate is reduced from 50 FPS to 25 FPS. Default: `1` (no downsampling).
- `--viz-size`: output resolution multiplier. Higher = larger images. Default: `5`.
Example:
```
python run.py -k cpn_ft_h36m_dbb -arc 3,3,3,3,3 -c checkpoint --evaluate pretrained_h36m_cpn.bin --render --viz-subject S11 --viz-action Walking --viz-camera 0 --viz-video "/path/to/videos/S11/Videos/Walking.54138969.mp4" --viz-output output.gif --viz-size 3 --viz-downsample 2 --viz-limit 60
```
![](images/demo_h36m.gif)
Generates a visualization for S11/Walking from camera 0, and exports the first frames to a GIF animation with a frame rate of 25 FPS. If you remove the `--viz-video` parameter, the skeleton overlay will be rendered on a blank background.
While Human3.6M visualization works out of the box, HumanEva visualization is trickier because the original videos must be segmented manually. Additionally, invalid frames and software synchronization complicate matters. Nonetheless, you can get decent visualizations by selecting the chunk 0 of validation sequences (which start at the beginning of each video) and discarding the first frames using `--viz-skip`. For a suggestion on the number of frames to skip, take a look at `sync_data` in `data/prepare_data_humaneva.py`.
Example:
```
python run.py -d humaneva15 -k detectron_pt_coco -str Train/S1,Train/S2,Train/S3 -ste Validate/S1,Validate/S2,Validate/S3 -c checkpoint --evaluate pretrained_humaneva15_detectron.bin --render --viz-subject Validate/S2 --viz-action "Walking 1 chunk0" --viz-camera 0 --viz-output output_he.gif --viz-size 3 --viz-downsample 2 --viz-video "/path/to/videos/S2/Walking_1_(C1).avi" --viz-skip 115 --viz-limit 60
```
![](images/demo_humaneva.gif)
Unlabeled videos are easier to visualize because they do not require synchronization with the ground truth. In this case, visualization works out of the box even for HumanEva.
Example:
```
python run.py -d humaneva15 -k detectron_pt_coco -str Train/S1,Train/S2,Train/S3 -ste Validate/S1,Validate/S2,Validate/S3 -c checkpoint --evaluate pretrained_humaneva15_detectron.bin --render --viz-subject Unlabeled/S4 --viz-action "Box 2" --viz-camera 0 --viz-output output_he.gif --viz-size 3 --viz-downsample 2 --viz-video "/path/to/videos/S4/Box_2_(C1).avi" --viz-limit 60
```
![](images/demo_humaneva_unlabeled.gif)
## Implementation details
### Batch generation during training
Some details of our training procedure are better understood visually.
![](images/batching.png)
The figure above shows how training batches are generated, depending on the value of `--stride` (from left to right: 1, 2, and 4). This example shows a sequence of 2D poses which has a length of N = 8 frames. The 3D poses (blue boxes in the figure) are inferred using a model that has a receptive field F = 5 frames. Therefore, because of valid padding, an input sequence of length N results in an output sequence of length N - F + 1, i.e. N - 4 in this example.
When `--stride=1`, we generate one training example for each frame. This ensures that the batches are maximally uncorrelated, which helps batch normalization as well as generalization. As `--stride` increases, training becomes faster because the model can reutilize intermediate computations, at the cost of biased batch statistics. However, we provide an optimized implementation when `--stride=1`, which replaces dilated convolutions with strided convolutions (only while training), so in principle you should not touch this parameter unless you want to run specific experiments. To understand how it works, see the figures below:
![](images/convolutions_1f_naive.png)
The figure above shows the information flow for a model with a receptive field of 27 frames, and a single-frame prediction, i.e. from N = 27 input frames we end up with one output frame. You can observe that this regular implementation tends to waste some intermediate results when a small number of frames are predicted. However, for inference of very long sequences, this approach is very efficient as intermediate results are shared among successive frames.
![](images/convolutions_1f_optimized.png)
Therefore, for training *only*, we use the implementation above, which replaces dilated convolutions with strided convolutions. It achieves the same result, but avoids computing unnecessary intermediate results.
### Symmetric convolutions vs causal convolutions
The figures below show the information flow from input (bottom) to output (top). In this example, we adopt a model with a receptive field of 27 frames.
![](images/convolutions_normal.png)
With symmetric convolutions, both past and future information is exploited, resulting in a better reconstruction.
![](images/convolutions_causal.png)
With causal convolutions, only past data is exploited. This approach is suited to real-time applications where future data cannot be exploited, at the cost of a slightly higher error.
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# 3D human pose estimation in video with temporal convolutions and semi-supervised training