update docs for WOD

README.md

@@ -26,7 +26,7 @@ configs and results of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Datase
 [2020-07-30] `OpenPCDet` v0.3.0 is released with the following features:
    * The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now.
    * The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)).
-   * High efficiency than last version, support `PyTorch 1.1~1.5` and `spconv 1.0~1.2` simultaneously.
+   * High efficiency than last version, support **PyTorch 1.1~1.7** and **spconv 1.0~1.2** simultaneously.
    
 [2020-07-17]  Add simple visualization codes and a quick demo to test with custom data. 
 
@@ -112,12 +112,12 @@ All models are trained with 8 GTX 1080Ti GPUs and are available for download.
 We provide the setting of `DATA_CONFIG.SAMPLED_INTERVAL` on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation, 
 so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources. 
 
-By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set.    
+By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
 
 |                                             | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
 | [SECOND](tools/cfgs/waymo_models/second.yaml) | 68.03/67.44	| 59.57/59.04 | 61.14/50.33	| 53.00/43.56 | 54.66/53.31 | 52.67/51.37 | 
-| [PartA2](tools/cfgs/waymo_models/PartA2.yaml) | 71.82/71.29 | 64.33/63.82 | 63.15/54.96 | 54.24/47.11 | 65.23/63.92 | 62.61/61.35 |
+| [Part-A^2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 71.82/71.29 | 64.33/63.82 | 63.15/54.96 | 54.24/47.11 | 65.23/63.92 | 62.61/61.35 |
 | [PV-RCNN](tools/cfgs/waymo_models/pv_rcnn.yaml) | 74.06/73.38 | 64.99/64.38 |	62.66/52.68 | 53.80/45.14 |	63.32/61.71	| 60.72/59.18 | 
 
 We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), 

docs/DEMO.md

@@ -23,7 +23,7 @@ y-axis points towards to the left direction, and z-axis points towards to the to
    ...
    
    # Save it to the file. 
-   # The shape of points should be (num_points, 4), that is [x, y, z, intensity],  
+   # The shape of points should be (num_points, 4), that is [x, y, z, intensity] (Only for KITTI dataset).  
    # If you doesn't have the intensity information, just set them to zeros. 
    # If you have the intensity information, you should normalize them to [0, 1].
    points[:, 3] = 0 

docs/GETTING_STARTED.md

@@ -81,13 +81,11 @@ OpenPCDet
 * Install the official `waymo-open-dataset` by running the following command: 
 ```shell script
 pip3 install --upgrade pip
-# tf 2.1.0.
-pip3 install waymo-open-dataset-tf-2-1-0==1.2.0 --user
 # tf 2.0.0
-# pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
+pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
 ```
 
-* Generate the data infos by running the following command (it takes several hours): 
+* Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours): 
 ```python 
 python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \ -
     -cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

pcdet/datasets/waymo/waymo_dataset.py

@@ -78,8 +78,8 @@ class WaymoDataset(DatasetTemplate):
         import concurrent.futures as futures
         from functools import partial
         from . import waymo_utils
-        # tf.enable_eager_execution()
-        print('The waymo sample interval is %d, total sequecnes is %d' % (sampled_interval, len(self.sample_sequence_list)))
+        print('---------------The waymo sample interval is %d, total sequecnes is %d-----------------'
+              % (sampled_interval, len(self.sample_sequence_list)))
 
         process_single_sequence = partial(
             waymo_utils.process_single_sequence,
@@ -90,7 +90,7 @@ class WaymoDataset(DatasetTemplate):
             for sequence_file in self.sample_sequence_list
         ]
 
-        # process_single_sequence(sample_sequence_file_list[0])
+        process_single_sequence(sample_sequence_file_list[0])
         with futures.ThreadPoolExecutor(num_workers) as executor:
             sequence_infos = executor.map(process_single_sequence, sample_sequence_file_list)
         sequence_infos = list(sequence_infos)
@@ -325,7 +325,7 @@ def create_waymo_infos(dataset_cfg, class_names, data_path, save_path,
     )
     with open(train_filename, 'wb') as f:
         pickle.dump(waymo_infos_train, f)
-    print('Waymo info train file is saved to %s' % train_filename)
+    print('----------------Waymo info train file is saved to %s----------------' % train_filename)
 
     dataset.set_split(val_split)
     waymo_infos_val = dataset.get_infos(
@@ -335,7 +335,7 @@ def create_waymo_infos(dataset_cfg, class_names, data_path, save_path,
     )
     with open(val_filename, 'wb') as f:
         pickle.dump(waymo_infos_val, f)
-    print('Waymo info val file is saved to %s' % val_filename)
+    print('----------------Waymo info val file is saved to %s----------------' % val_filename)
 
     print('---------------Start create groundtruth database for data augmentation---------------')
     dataset.set_split(train_split)

pcdet/datasets/waymo/waymo_utils.py

@@ -12,6 +12,11 @@ import tensorflow as tf
 from waymo_open_dataset.utils import frame_utils, transform_utils, range_image_utils
 from waymo_open_dataset import dataset_pb2
 
+try:
+    tf.enable_eager_execution()
+except:
+    pass
+
 WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Sign', 'Cyclist']
 
 
@@ -167,7 +172,7 @@ def process_single_sequence(sequence_file, save_path, sampled_interval, has_labe
 
     # print('Load record (sampled_interval=%d): %s' % (sampled_interval, sequence_name))
     if not sequence_file.exists():
-        print('NotFoundError: %s' % sequence_name)
+        print('NotFoundError: %s' % sequence_file)
         return []
 
     dataset = tf.data.TFRecordDataset(str(sequence_file), compression_type='')