# Waymo Open Dataset: Tensorflow 2 Object Detection Development Record
contributed by < `gyes00205` >
###### tags: `waymo`
## Download Dataset
[Waymo Open Dataset](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0)
**domain_adaptation** directory doesn't have label data, so please download the data in **training** directory
## tfrecord details
* 5 kinds of camera photos and Lidar informations
* num_classes: 0: Unknown, 1: Vehicle, 2: Pedestrian, 3: Sign, 4: Cyclist
In this project, we don't need sign and Unknown classes, so we should modify label_map.pbtxt :
```pbtxt
item {
id: 1
name: 'vehicle'
}
item {
id: 2
name: 'pedestrian'
}
item {
id: 4
name: 'cyclist'
}
```
* camera categories: FRONT, FRONT_LEFT, FRONT_RIGHT, SIDE_LEFT, SIDE_RIGHT
<img src="https://i.imgur.com/Q68Lepf.jpg">
* bbox (x, y, w, h) coordinate: (x, y) represents center coordinate of bbox, (w, h) represents width and height.
## Setup Environment
### Install Waymo open dataset
```shell
pip3 install waymo-open-dataset-tf-2-1-0==1.2.0
```
### Install COCO API
```shell
pip install cython
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
```
### Install Tensorflow 2 Object Detection API
Refer to [TensorFlow 2 Object Detection API tutorial](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/index.html) to install toolkits
* git clone Tensorflow 2 Object Detection API
```shell
git clone https://github.com/tensorflow/models.git
```
* go to models/research/ and run
```shell
protoc object_detection/protos/*.proto --python_out=.
```
* add API to your environment path
```
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
```
* copy setup.py to models/research/
```shell
cp object_detection/packages/tf2/setup.py ./
```
* install setup.py
```shell
python -m pip install .
```
* Test whether the installation is successful
```
python object_detection/builders/model_builder_tf2_test.py
```
### Build directory structure
```
Waymo
├───models #Tensorflow Object Detection API
├───training_configs #training config
├───pre-trained-models #pretrained model
├───exported-models #exported model
└───data #training data
└───segment-???.tfrecord
```
## Convert tfrecord format
Besides of Lidar informations in waymo's tfrecord, the below is its bbox format:
(x0, y0): is center coordinate. (w, h): is width and height.
Our goal is to filter out Lidar and convert bbox to the following format:
(x1, y1): is left-top coordinate. (x2, y2): is right-down coordinate.
The reference code that convert tfrecord is [LevinJ/tf_obj_detection_api](https://github.com/LevinJ/tf_obj_detection_api), and make some minor changes.
**create_record.py:**
filepath: the path of tfrecord
data_dir: the converted tfrecord will be stored in the data_dir/processed directory
Execute code:
```shell
python create_record.py \
--filepath=data/segment-???.tfrecord \
--data_dir=data/
```
After executing th code, the processed tfrecord will appear in data/processed directory.
```
Waymo
├───models
├───training_configs
├───pre-trained-models
├───exported-models
└───data
├───processed
│ └───segment-???.tfrecord # processed tfrecord
└───segment-???.tfrecord
```
## Download pretrained model
go to [Tensorflow Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md) and download pretrained model.
I download `SSD ResNet50 V1 FPN 640x640 (RetinaNet50)` pretrained model.
* go to pre-trained-models directory.
`cd pre-trained-models`
* download SSD ResNet50 pretrained model
```
wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz
```
* unzip the file
`tar zxvf ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz`
```
Waymo
├───models
├───training_configs
├───pre-trained-models
│ └───ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
│ ├─ checkpoint/
│ ├─ saved_model/
│ └─ pipeline.config
├───exported-models
└───data
├───processed
│ └───segment-???.tfrecord #processed tfrecord
└───segment-???.tfrecord
```
## Modify training config
Go to [configs/tf2](https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2), and find corresponding config that is ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.config
* Create folder in training_configs directory
```shell
cd training_configs
mkdir ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
```
* Create pipeline.config in ssd_resnet50_v1_fpn_640x640_coco17_tpu-8 directory. Copy and paste the config content you just found, and make some modifications.
* num_classes: number of classes
* batch_size: according to your computer memory
* fine_tune_checkpoint: modify to pretrained model ckpt-0 path
* num_steps: training steps
* use_bfloat16: whether to use tpu, if not used, set to false
* label_map_path: label_map.pbtxt path
* train_input_reader: set input_path to the tfrecord path for training
* metrics_set: "coco_detection_metrics"
* use_moving_averages: false
* eval_input_reader: Set input_path to the tfrecord path for evaluating
```config
# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
# loss (a.k.a Retinanet).
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 34.3 mAP on COCO17 Val
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 3 # 3 kinds of classes
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 2
}
}
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 256
class_prediction_bias_init: -4.6
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.0004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.997,
epsilon: 0.001,
}
}
num_layers_before_predictor: 4
kernel_size: 3
}
}
feature_extractor {
type: 'ssd_resnet50_v1_fpn_keras'
fpn {
min_level: 3
max_level: 7
}
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.0004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.997,
epsilon: 0.001,
}
}
override_base_feature_extractor_hyperparams: true
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 2.0
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint_version: V2
#pretrained model ckpt-0 path
fine_tune_checkpoint: "pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection" # set to detection
batch_size: 2
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: false # if not use tpu, set to false
num_steps: 6000 # training steps
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_object_covered: 0.0
min_aspect_ratio: 0.75
max_aspect_ratio: 3.0
min_area: 0.75
max_area: 1.0
overlap_thresh: 0.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 25000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "./label_map.pbtxt"
tf_record_input_reader {
input_path: "data/processed/*.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader: {
label_map_path: "./label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "data/processed/*.tfrecord"
}
}
```
```
Waymo
├───models
├───training_configs
│ └───ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
│ └───pipeline.config # create pipeline.config
├───pre-trained-models
│ └───ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
│ ├─ checkpoint/
│ ├─ saved_model/
│ └─ pipeline.config
├───exported-models
└───data
├───processed
│ └───segment-???.tfrecord
└───segment-???.tfrecord
```
## Train Model
**model_main_tf2.py**
model_dir: the training checkpoint will be stored in the model_dir directory
pipeline_config_path: pipeline.config path
Execute code:
```shell
python model_main_tf2.py \
--model_dir=training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8 \
--pipeline_config_path=training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config
```
Execution results: it will be printed every 100 steps.
```
Step 2100 per-step time 0.320s
INFO:tensorflow:{'Loss/classification_loss': 0.121629156,
'Loss/localization_loss': 0.16370133,
'Loss/regularization_loss': 0.2080817,
'Loss/total_loss': 0.4934122,
'learning_rate': 0.039998136}
I0605 08:29:04.605577 139701982308224 model_lib_v2.py:700] {'Loss/classification_loss': 0.121629156,
'Loss/localization_loss': 0.16370133,
'Loss/regularization_loss': 0.2080817,
'Loss/total_loss': 0.4934122,
'learning_rate': 0.039998136}
```
## Evaluate Model (Optional)
**model_main_tf2.py**
checkpoint_dir: the directory to read checkpoint.
Execute code:
```shell
python model_main_tf2.py \
--model_dir=training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8 \
--pipeline_config_path=training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config \
--checkpoint_dir=training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/
```
Execution results: calculate AP and AR
$AP^{small}:$ AP for small object : area < $32^2$
$AP^{medium}:$ AP for medium object : $32^2$ < area < $96^2$
$AP^{large}:$ AP for large object : $96^2$ < area
## Export Model
**exporter_main_v2.py**
input_type: image_tensor
pipeline_config_path: pipeline.config path
trained_checkpoint_dir: the path to store checkpoint
output_directory: exported model path
Execute code:
```shell
!python exporter_main_v2.py \
--input_type image_tensor \
--pipeline_config_path training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config \
--trained_checkpoint_dir training_configs/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ \
--output_directory exported-models/my_model_6000steps
```
Execution results:
```shell
INFO:tensorflow:Assets written to: exported-models/my_model_6000steps/saved_model/assets
I0605 09:07:21.034602 139745385867136 builder_impl.py:775] Assets written to: exported-models/my_model_6000steps/saved_model/assets
INFO:tensorflow:Writing pipeline config file to exported-models/my_model_6000steps/pipeline.config
I0605 09:07:22.310333 139745385867136 config_util.py:254] Writing pipeline config file to exported-models/my_model_6000steps/pipeline.config
```
```
Waymo
├───models
├───training_configs
│ └───ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
│ └─pipeline.config # create pipeline.config
├───pre-trained-models
│ └───ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
│ ├─ checkpoint/
│ ├─ saved_model/
│ └─ pipeline.config
├───exported-models
│ └───my_model_6000steps
└───data
├───processed
│ └─segment-???.tfrecord
└───segment-???.tfrecord
```
## Use model to predict
**detect.py**
saved_model_path: exported model path
test_path: image path
output_path: output predicted image path
min_score_thresh: confidience
Execute code:
```shell
!python detect.py \
--saved_model_path=exported-models/my_model_6000steps \
--test_path=test_image \
--output_path=output_image \
--min_score_thresh=.1
```
Execution results:
<img src="https://i.imgur.com/NNE6OuI.png" width=250px height=200px>
<img src="https://i.imgur.com/dyRuUpA.png" width=300px height=200px>
<img src="https://i.imgur.com/vICSrnI.png" width=250px height=200px>
<img src="https://i.imgur.com/it53kPf.png" width=300px height=200px>
## Reference
1. [LevinJ/tf_obj_detection_api](https://github.com/LevinJ/tf_obj_detection_api)
2. [Waymo Open Dataset](https://waymo.com/open/)
3. [Waymo quick start tutorial](https://colab.research.google.com/github/waymo-research/waymo-open-dataset/blob/r1.0/tutorial/tutorial.ipynb)
4. [Tensorflow Object Detection API Tutorial](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html)
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