useful_tools.md 10.1 KB
Newer Older
twang's avatar
twang committed
1
2
3
4
5
6
7
8
9
We provide lots of useful tools under `tools/` directory.

# Log Analysis

You can plot loss/mAP curves given a training log file. Run `pip install seaborn` first to install the dependency.

![loss curve image](../resources/loss_curve.png)

```shell
Ziyi Wu's avatar
Ziyi Wu committed
10
python tools/analysis_tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}] [--mode ${MODE}] [--interval ${INTERVAL}]
twang's avatar
twang committed
11
12
```

13
14
**Notice**: If the metric you want to plot is calculated in the eval stage, you need to add the flag `--mode eval`. If you perform evaluation with an interval of `${INTERVAL}`, you need to add the args `--interval ${INTERVAL}`.

twang's avatar
twang committed
15
16
Examples:

Ziyi Wu's avatar
Ziyi Wu committed
17
-   Plot the classification loss of some run.
twang's avatar
twang committed
18

Ziyi Wu's avatar
Ziyi Wu committed
19
20
21
    ```shell
    python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls
    ```
twang's avatar
twang committed
22

Ziyi Wu's avatar
Ziyi Wu committed
23
-   Plot the classification and regression loss of some run, and save the figure to a pdf.
twang's avatar
twang committed
24

Ziyi Wu's avatar
Ziyi Wu committed
25
26
27
    ```shell
    python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --out losses.pdf
    ```
twang's avatar
twang committed
28

Ziyi Wu's avatar
Ziyi Wu committed
29
-   Compare the bbox mAP of two runs in the same figure.
twang's avatar
twang committed
30

Ziyi Wu's avatar
Ziyi Wu committed
31
32
33
34
35
36
    ```shell
    # evaluate PartA2 and second on KITTI according to Car_3D_moderate_strict
    python tools/analysis_tools/analyze_logs.py plot_curve tools/logs/PartA2.log.json tools/logs/second.log.json --keys KITTI/Car_3D_moderate_strict --legend PartA2 second --mode eval --interval 1
    # evaluate PointPillars for car and 3 classes on KITTI according to Car_3D_moderate_strict
    python tools/analysis_tools/analyze_logs.py plot_curve tools/logs/pp-3class.log.json tools/logs/pp.log.json --keys KITTI/Car_3D_moderate_strict --legend pp-3class pp --mode eval --interval 2
    ```
twang's avatar
twang committed
37
38
39
40

You can also compute the average training speed.

```shell
Ziyi Wu's avatar
Ziyi Wu committed
41
python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--include-outliers]
twang's avatar
twang committed
42
43
44
45
46
47
48
49
50
51
52
53
```

The output is expected to be like the following.

```
-----Analyze train time of work_dirs/some_exp/20190611_192040.log.json-----
slowest epoch 11, average time is 1.2024
fastest epoch 1, average time is 1.1909
time std over epochs is 0.0028
average iter time: 1.1959 s/iter
```

54
55
 

twang's avatar
twang committed
56
57
# Visualization

58
59
## Results

60
To see the prediction results of trained models, you can run the following command
twang's avatar
twang committed
61
62
63
64
65

```bash
python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --show --show-dir ${SHOW_DIR}
```

66
After running this command, plotted results including input data and the output of networks visualized on the input (e.g. `***_points.obj` and `***_pred.obj` in single-modality 3D detection task) will be saved in `${SHOW_DIR}`.
twang's avatar
twang committed
67

68
To see the prediction results during evaluation time, you can run the following command
Ziyi Wu's avatar
Ziyi Wu committed
69

twang's avatar
twang committed
70
```bash
71
python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --eval-options 'show=True' 'out_dir=${SHOW_DIR}'
twang's avatar
twang committed
72
```
Ziyi Wu's avatar
Ziyi Wu committed
73

74
After running this command, you will obtain the input data, the output of networks and ground-truth labels visualized on the input (e.g. `***_points.obj`, `***_pred.obj`, `***_gt.obj`, `***_img.png` and `***_pred.png` in multi-modality detection task) in `${SHOW_DIR}`. When `show` is enabled, [Open3D](http://www.open3d.org/) will be used to visualize the results online. You need to set `show=False` while running test in remote server without GUI.
twang's avatar
twang committed
75

76
77
As for offline visualization, you will have two options.
To visualize the results with `Open3D` backend, you can run the following command
Ziyi Wu's avatar
Ziyi Wu committed
78

79
```bash
80
python tools/misc/visualize_results.py ${CONFIG_FILE} --result ${RESULTS_PATH} --show-dir ${SHOW_DIR}
81
```
Ziyi Wu's avatar
Ziyi Wu committed
82

83
84
![Open3D_visualization](../resources/open3d_visual.gif)

85
Or you can use 3D visualization software such as the [MeshLab](http://www.meshlab.net/) to open the these files under `${SHOW_DIR}` to see the 3D detection output. Specifically, open `***_points.obj` to see the input point cloud and open `***_pred.obj` to see the predicted 3D bounding boxes. This allows the inference and results generation be done in remote server and the users can open them on their host with GUI.
twang's avatar
twang committed
86
87
88

**Notice**: The visualization API is a little unstable since we plan to refactor these parts together with MMDetection in the future.

89
90
## Dataset

91
We also provide scripts to visualize the dataset without inference. You can use `tools/misc/browse_dataset.py` to show loaded data and ground-truth online and save them on the disk. Currently we support single-modality 3D detection and 3D segmentation on all the datasets, multi-modality 3D detection on KITTI and SUN RGB-D, as well as monocular 3D detection on nuScenes. To browse the KITTI dataset, you can run the following command
92
93

```shell
94
python tools/misc/browse_dataset.py configs/_base_/datasets/kitti-3d-3class.py --task det --output-dir ${OUTPUT_DIR} --online
95
96
```

97
**Notice**: Once specifying `--output-dir`, the images of views specified by users will be saved when pressing `_ESC_` in open3d window. If you don't have a monitor, you can remove the `--online` flag to only save the visualization results and browse them offline.
98

99
If you also want to show 2D images with 3D bounding boxes projected onto them, you need to find a config that supports multi-modality data loading, and then change the `--task` args to `multi_modality-det`. An example is showed below
100
101

```shell
102
python tools/misc/browse_dataset.py configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py --task multi_modality-det --output-dir ${OUTPUT_DIR} --online
103
104
```

105
106
107
108
109
![Open3D_visualization](../resources/browse_dataset_multi_modality.png)

You can simply browse different datasets using different configs, e.g. visualizing the ScanNet dataset in 3D semantic segmentation task

```shell
110
python tools/misc/browse_dataset.py configs/_base_/datasets/scannet_seg-3d-20class.py --task seg --output-dir ${OUTPUT_DIR} --online
111
112
113
```

![Open3D_visualization](../resources/browse_dataset_seg.png)
114

115
116
117
118
119
120
121
122
And browsing the nuScenes dataset in monocular 3D detection task

```shell
python tools/misc/browse_dataset.py configs/_base_/datasets/nus-mono3d.py --task mono-det --output-dir ${OUTPUT_DIR} --online
```

![Open3D_visualization](../resources/browse_dataset_mono.png)

123
124
 

twang's avatar
twang committed
125
126
# Model Complexity

Ziyi Wu's avatar
Ziyi Wu committed
127
You can use `tools/analysis_tools/get_flops.py` in MMDetection, a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch), to compute the FLOPs and params of a given model.
twang's avatar
twang committed
128
129

```shell
Ziyi Wu's avatar
Ziyi Wu committed
130
python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
twang's avatar
twang committed
131
132
133
134
135
136
137
138
139
140
141
142
143
```

You will get the results like this.

```text
==============================
Input shape: (3, 1280, 800)
Flops: 239.32 GFLOPs
Params: 37.74 M
==============================
```

**Note**: This tool is still experimental and we do not guarantee that the
Ziyi Wu's avatar
Ziyi Wu committed
144
145
number is absolutely correct. You may well use the result for simple
comparisons, but double check it before you adopt it in technical reports or papers.
twang's avatar
twang committed
146
147

1. FLOPs are related to the input shape while parameters are not. The default
Ziyi Wu's avatar
Ziyi Wu committed
148
   input shape is (1, 3, 1280, 800).
twang's avatar
twang committed
149
150
151
2. Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
3. The FLOPs of two-stage detectors is dependent on the number of proposals.

152
153
 

twang's avatar
twang committed
154
155
156
157
# Model Conversion

## RegNet model to MMDetection

Ziyi Wu's avatar
Ziyi Wu committed
158
159
`tools/model_converters/regnet2mmdet.py` convert keys in pycls pretrained RegNet models to
MMDetection style.
twang's avatar
twang committed
160
161

```shell
Ziyi Wu's avatar
Ziyi Wu committed
162
python tools/model_converters/regnet2mmdet.py ${SRC} ${DST} [-h]
twang's avatar
twang committed
163
164
165
166
167
```

## Detectron ResNet to Pytorch

`tools/detectron2pytorch.py` in MMDetection could convert keys in the original detectron pretrained
Ziyi Wu's avatar
Ziyi Wu committed
168
ResNet models to PyTorch style.
twang's avatar
twang committed
169
170
171
172
173
174
175

```shell
python tools/detectron2pytorch.py ${SRC} ${DST} ${DEPTH} [-h]
```

## Prepare a model for publishing

Ziyi Wu's avatar
Ziyi Wu committed
176
`tools/model_converters/publish_model.py` helps users to prepare their model for publishing.
twang's avatar
twang committed
177
178
179
180
181
182

Before you upload a model to AWS, you may want to

1. convert model weights to CPU tensors
2. delete the optimizer states and
3. compute the hash of the checkpoint file and append the hash id to the
Ziyi Wu's avatar
Ziyi Wu committed
183
   filename.
twang's avatar
twang committed
184
185

```shell
Ziyi Wu's avatar
Ziyi Wu committed
186
python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
twang's avatar
twang committed
187
188
189
190
191
```

E.g.,

```shell
Ziyi Wu's avatar
Ziyi Wu committed
192
python tools/model_converters/publish_model.py work_dirs/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth
twang's avatar
twang committed
193
194
195
196
```

The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`.

197
198
 

twang's avatar
twang committed
199
200
201
202
203
204
205
206
207
208
209
# Dataset Conversion

`tools/data_converter/` contains tools to convert datasets to other formats. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. Waymo converter is used to reorganize waymo raw data like KITTI style. Users could refer to them for our approach to converting data format. It is also convenient to modify them to use as scripts like nuImages converter.

To convert the nuImages dataset into COCO format, please use the command below:

```shell
python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERIONS} \
                                                    --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
```

Ziyi Wu's avatar
Ziyi Wu committed
210
211
212
213
214
-   `--data-root`: the root of the dataset, defaults to `./data/nuimages`.
-   `--version`: the version of the dataset, defaults to `v1.0-mini`. To get the full dataset, please use `--version v1.0-train v1.0-val v1.0-mini`
-   `--out-dir`: the output directory of annotations and semantic masks, defaults to `./data/nuimages/annotations/`.
-   `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
-   `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.
twang's avatar
twang committed
215

216
More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/) for nuImages dataset.
twang's avatar
twang committed
217

218
219
 

twang's avatar
twang committed
220
221
222
223
# Miscellaneous

## Print the entire config

Ziyi Wu's avatar
Ziyi Wu committed
224
225
`tools/misc/print_config.py` prints the whole config verbatim, expanding all its
imports.
twang's avatar
twang committed
226
227

```shell
Ziyi Wu's avatar
Ziyi Wu committed
228
python tools/misc/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]
twang's avatar
twang committed
229
```