[Docs]Docs about useful_tools (#1791)

* fix usetools

* fix comments

docs/en/user_guides/useful_tools.md

@@ -55,81 +55,6 @@ average iter time: 1.1959 s/iter
 
  
 
-## Visualization
-
-### Results
-
-To see the prediction results of trained models, you can run the following command
-
-```bash
-python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --show --show-dir ${SHOW_DIR}
-```
-
-After running this command, plotted results including input data and the output of networks visualized on the input (e.g. `***_points.obj` and `***_pred.obj` in single-modality 3D detection task) will be saved in `${SHOW_DIR}`.
-
-To see the prediction results during evaluation, you can run the following command
-
-```bash
-python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --eval-options 'show=True' 'out_dir=${SHOW_DIR}'
-```
-
-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. If you are running test in remote server without GUI, the online visualization is not supported, you can set `show=False` to only save the output results in `{SHOW_DIR}`.
-
-As for offline visualization, you will have two options.
-To visualize the results with `Open3D` backend, you can run the following command
-
-```bash
-python tools/misc/visualize_results.py ${CONFIG_FILE} --result ${RESULTS_PATH} --show-dir ${SHOW_DIR}
-```
-
-![](../../resources/open3d_visual.*)
-
-Or you can use 3D visualization software such as the [MeshLab](http://www.meshlab.net/) to open these files under `${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 to be done in remote server and the users can open them on their host with GUI.
-
-**Notice**: The visualization API is a little unstable since we plan to refactor these parts together with MMDetection in the future.
-
-### Dataset
-
-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
-
-```shell
-python tools/misc/browse_dataset.py configs/_base_/datasets/kitti-3d-3class.py --task det --output-dir ${OUTPUT_DIR} --online
-```
-
-**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.
-
-To verify the data consistency and the effect of data augmentation, you can also add `--aug` flag to visualize the data after data augmentation using the command as below:
-
-```shell
-python tools/misc/browse_dataset.py configs/_base_/datasets/kitti-3d-3class.py --task det --aug --output-dir ${OUTPUT_DIR} --online
-```
-
-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
-
-```shell
-python tools/misc/browse_dataset.py configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py --task multi_modality-det --output-dir ${OUTPUT_DIR} --online
-```
-
-![](../../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
-python tools/misc/browse_dataset.py configs/_base_/datasets/scannet-seg.py --task seg --output-dir ${OUTPUT_DIR} --online
-```
-
-![](../../resources/browse_dataset_seg.png)
-
-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
-```
-
-![](../../resources/browse_dataset_mono.png)
-
- 
-
 ## Model Serving
 
 **Note**: This tool is still experimental now, only SECOND is supported to be served with [`TorchServe`](https://pytorch.org/serve/). We'll support more models in the future.

tools/analysis_tools/analyze_logs.py

@@ -173,16 +173,18 @@ def load_json_logs(json_logs):
     log_dicts = [dict() for _ in json_logs]
     for json_log, log_dict in zip(json_logs, log_dicts):
         with open(json_log, 'r') as log_file:
+            epoch = 1
             for line in log_file:
                 log = json.loads(line.strip())
-                # skip lines without `epoch` field
-                if 'epoch' not in log:
+                # skip lines only contains one key
+                if not len(log) > 1:
                     continue
-                epoch = log.pop('epoch')
                 if epoch not in log_dict:
                     log_dict[epoch] = defaultdict(list)
                 for k, v in log.items():
                     log_dict[epoch][k].append(v)
+                if 'epoch' in log.keys():
+                    epoch = log['epoch'] + 1
     return log_dicts
 
 

tools/analysis_tools/get_flops.py

@@ -2,9 +2,10 @@
 import argparse
 
 import torch
-from mmcv import Config, DictAction
+from mmengine import Config, DictAction
 
-from mmdet3d.models import build_model
+from mmdet3d.registry import MODELS
+from mmdet3d.utils import register_all_modules
 
 try:
     from mmcv.cnn import get_model_complexity_info
@@ -42,7 +43,7 @@ def parse_args():
 
 
 def main():
-
+    register_all_modules()
     args = parse_args()
 
     if args.modality == 'point':
@@ -64,21 +65,11 @@ def main():
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
-    model = build_model(
-        cfg.model,
-        train_cfg=cfg.get('train_cfg'),
-        test_cfg=cfg.get('test_cfg'))
+    model = MODELS.build(cfg.model)
     if torch.cuda.is_available():
         model.cuda()
     model.eval()
 
-    if hasattr(model, 'forward_dummy'):
-        model.forward = model.forward_dummy
-    else:
-        raise NotImplementedError(
-            'FLOPs counter is currently not supported for {}'.format(
-                model.__class__.__name__))
-
     flops, params = get_model_complexity_info(model, input_shape)
     split_line = '=' * 30
     print(f'{split_line}\nInput shape: {input_shape}\n'