EfficientDet (TF2)

With EfficientDet, the following tasks are supported:

• dataset_convert

• train

• evaluate

• prune

• inference

• export

These tasks may be invoked from the TAO Launcher by following the below convention from command line:

            

            
tao model efficientdet_tf2 <sub_task> <args_per_subtask>
        

Where args_per_subtask are the command line arguments required for a given subtask. Each of these sub-tasks are explained in detail below.

EfficientDet expects directories of images for training or validation and annotation JSON files in COCO format. See the Data Annotation Format page for more information about the data format for EfficientDet.

The raw image data and the corresponding annotation file need to be converted to TFRecords before training and evaluation. The dataset_convert tool helps to achieve seamless conversion while providing insight on potential issues in an annotation file. The following sections detail how to use dataset_convert.

Sample Usage of the Dataset Converter Tool

The dataset_convert tool is described below:

            

            
tao model efficientdet_tf2 dataset-convert [-h] -e <conversion spec file>
        

Below is a sample for the data conversion spec file. The format of the spec file is YAML, with configuration parameters under dataset_convert.

            

            
dataset_convert:
  image_dir: '/workspace/tao-experiments/data/raw-data/train2017/'
  annotations_file: '/workspace/tao-experiments/data/raw-data/annotations/instances_train2017.json'
  output_dir: '/workspace/tao-experiments/data'
  tag: 'train'
  num_shards: 256
  include_masks: True
        

The details of each parameter are summarized in the table below:

The following example shows how to use the command:

            

            
tao model efficientdet_tf2 dataset_convert -i /path/to/convert.yaml
        

Below is a sample for the EfficientDet spec file. It has 7 major components: dataset, model, train, evaluate, inference, prune and export config as well as the encryption key (encryption_key) and the results directory (results_dir).

            

            
dataset:
  loader:
    prefetch_size: 4
    shuffle_file: False
    shuffle_buffer: 10000
    cycle_length: 32
    block_length: 16
  max_instances_per_image: 100
  skip_crowd_during_training: True
  num_classes: 91
  train_tfrecords:
    - '/datasets/coco/train-*'
  val_tfrecords:
    - '/datasets/coco/val-*'
  val_json_file: '/datasets/coco/annotations/instances_val2017.json'
  augmentation:
    rand_hflip: True
    random_crop_min_scale: 0.1
    random_crop_max_scale: 2
    auto_color_distortion: False
    auto_translate_xy: False
train:
  optimizer:
    name: 'sgd'
    momentum: 0.9
  lr_schedule:
    name: 'cosine'
    warmup_epoch: 5
    warmup_init: 0.0001
    learning_rate: 0.2
  amp: True
  checkpoint: "/weights/efficientnet-b0_500.tlt"
  num_examples_per_epoch: 100
  moving_average_decay: 0.999
  batch_size: 20
  checkpoint_interval: 5
  l2_weight_decay: 0.00004
  l1_weight_decay: 0.0
  clip_gradients_norm: 10.0
  image_preview: True
  qat: False
  random_seed: 42
  pruned_model_path: ''
  num_epochs: 200
model:
  name: 'efficientdet-d0'
  aspect_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]'
  anchor_scale: 4
  min_level: 3
  max_level: 7
  num_scales: 3
  freeze_bn: False
  freeze_blocks: []
  input_width: 512
  input_height: 512
evaluate:
  batch_size: 8
  num_samples: 5000
  max_detections_per_image: 100
  checkpoint: ''
export:
  batch_size: 8
  dynamic_batch_size: True
  min_score_thresh: 0.4
  checkpoint: ""
  onnx_file: ""
inference:
  checkpoint: ""
  image_dir: ""
  dump_label: False
  batch_size: 1
prune:
  checkpoint: ""
  normalizer: 'max'
  output_path: ""
  equalization_criterion: 'union'
  granularity: 8
  threshold: 0.5
  min_num_filters: 16
  excluded_layers: []
encryption_key: 'nvidia_tlt'
results_dir: '/workspace/results_dir'
num_gpus: 1
gpu_ids: [0]
        

The format of the spec file is YAML. The top level structure of the spec file is summarized in the table below:

Training Config

The training configuration(train) defines the parameters needed for training, evaluation. Details are summarized in the table below.

The optimizer configuration(train.optimizer) specifies the type and parameters of an optimizer. +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | Field | Description | Data Type and Constraints | Recommended/Typical Value | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | name | Optimizer name (only sgd is supported) | String | ‘sgd’ | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | momentum | Momentum | float | 0.9 | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+

The learning rate scheduler configuration(train.lr_schedule) specifies the type and parameters of a learning rate scheduler. +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | Field | Description | Data Type and Constraints | Recommended/Typical Value | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | name | The name of the learning rate scheduler. Available options are cosine and soft_anneal | String | ‘cosine’ | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | warmup_epoch | The number of warmup epochs in the learning rate schedule | Unsigned int, positive | – | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | warmup_init | The initial learning rate in the warmup period | Float | – | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | learning_rate | The maximum learning rate | Float | – | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+ | annealing_epoch | Start annealing to warmup_init at this point | Unsigned int, positive | – | +———————+——————————————————————————————————-+——————————-+————————————————————————————–+

Evaluation Config

The evaluation configuration (evaluate) defines the parameters needed for the evaluation either during training or standalone evaluation. Details are summarized in the table below.

Inference Config

The inference configuration (inference) defines the parameters needed for the standalone inference with the trained .tlt model. Details are summarized in the table below.

Dataset Config

The dataset configuration (dataset) specifies the input data source and format. This is used for training, evaluation. A detailed description is summarized in the table below.

The dataloader configuration (dataset.loader) specifies how batches of data are fed into the model.

The dataset.augmentation configuration specifies the image augmentation methods used after preprocessing.

Model Config

The model configuration (model) specifies the model structure. A detailed description is summarized in the table below.

Pruning Config

The prune configuration defines the pruning process for a trained model. A detailed description is summarized in the table below.

Export Config

The export configuration contains the parameters for exporting a .tlt model to an .onnx model, which can be used for deployment.

Train the EfficientDet model using this command:

            

            
tao model efficientdet_tf2 train [-h] -e <experiment_spec>
                           [results_dir=<global_results_dir>]
                           [model.<model_option>=<model_option_value>]
                           [dataset.<dataset_option>=<dataset_option_value>]
                           [train.<train_option>=<train_option_value>]
                           [num_gpus=<num GPUs>]
                           [gpu_ids=<gpu_index>]
        

Required Arguments

• -e, --experiment_spec: The experiment specification file to set up the training experiment.

Optional Arguments

• model.<model_option>: The model options.

• dataset.<dataset_option>: The dataset options.

• train.<train_option>: The train options.

• num_gpus: The number of GPUs to be used for training in a multi-GPU scenario. The default value is 1.

• gpu_ids: The indices of the GPUs to use for training. This argument can be used when the machine has multiple GPUs installed.

• -h, --help: Show this help message and exit.

Input Requirement

• Input size: C * W * H (where C = 1 or 3, W >= 128, H >= 128; W, H are multiples of 32)

• Image format: JPG

• Label format: COCO detection

Sample Usage

Here’s an example of the train command:

            

            
tao model efficientdet_tf2 train -e /path/to/spec.yaml num_gpus=2
        

To run evaluation with an EfficientDet model, use this command:

            

            
tao model efficientdet_tf2 evaluate [-h] -e <experiment_spec>
                           evaluate.checkpoint=<model to be evaluated>
                           [evaluate.<evaluate_option>=<evaluate_option_value>]
                           [gpu_ids=<gpu_index>]
        

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up the evaluation experiment. This should be the same as the training specification file.

• evaluate.checkpoint: The .pth model to evaluate.

Optional Arguments

• evaluate.<evaluate_option>: The evaluate options.

• gpu_ids: The index of the GPU to use for evaluation. This argument can be used when the machine has multiple GPUs installed. Note that evaluation can only run on a single GPU.

• -h, --help: Show this help message and exit.

Sample Usage

Here’s an example of using the evaluate command:

            

            
tao model efficientdet_tf2 evaluate -e /path/to/spec.yaml
        

The inference tool for EfficientDet models can be used to visualize bboxes and generate frame-by- frame KITTI format labels on a directory of images.

            

            
tao model efficientdet_tf2 inference [-h] -e <experiment spec file>
                           inference.checkpoint=<model to be inferenced>
                           [inference.<inference_option>=<inference_option_value>]
                           [gpu_ids=<gpu_index>]
        

Required Arguments

• -e, --experiment_spec: The path to an experiment spec file

• inference.checkpoint: The .pth model to inference.

Optional Arguments

• inference.<inference_option>: The inference options.

• gpu_ids: The index of the GPU to run inference on. This argument can be used when the machine has multiple GPUs installed. Note that inference can only run on a single GPU.

• -h, --help: Show this help message and exit

Sample Usage

Here’s an example of using the inference command:

            

            
tao model efficientdet_tf2 inference -e /path/to/spec.yaml
        

Pruning removes parameters from the model to reduce the model size without compromising the integrity of the model itself using the tao model efficientdet_tf2 prune command.

The tao model efficientdet_tf2 prune command includes these parameters:

            

            
tao model efficientdet_tf2 prune [-h] -e <experiment spec file>
                           prune.checkpoint=<model to be pruned>
                           [prune.<prune_option>=<prune_option_value>]
        

Required Arguments

• -e, --experiment_spec: The path to an experiment spec file

• prune.checkpoint: The .pth model to prune.

Optional Arguments

• prune.<prune_option>: The prune options.

• gpu_ids: The index of the GPU to run pruning on. This argument can be used when the machine has multiple GPUs installed. Note that inference can only run on a single GPU.

• -h, --help: Show this help message and exit.

After pruning, the model needs to be retrained. See Re-training the Pruned Model for more details.

Using the Prune Command

Here’s an example of using the tao model efficientdet_tf2 prune command:

            

            
tao model efficientdet_tf2 prune -e /path/to/spec.yaml
        

Once the model has been pruned, there might be a slight decrease in accuracy because some previously useful weights may have been removed. To regain the accuracy, we recommend that you retrain this pruned model over the same dataset. To do this, use the tao model efficientdet_tf2 train command as documented in Training the model, with an updated spec file that points to the newly pruned model as the pretrained model file.

We recommend turning off the regularizer or reducing the weight decay in the training_config for EfficientDet to recover the accuracy when retraining a pruned model. To do this, set the regularizer type to NO_REG as mentioned in the Training config section. All the other parameters may be retained in the spec file from the previous training.

Exporting the model decouples the training process from deployment and allows conversion to TensorRT engines outside the TAO environment. TensorRT engines are specific to each hardware configuration and should be generated for each unique inference environment. The exported model may be used universally across training and deployment hardware.

Exporting the EfficientDet Model

Here’s an example of the command line arguments of the tao model efficientdet_tf2 export command:

            

            
tao model efficientdet_tf2 export [-h] -e <path to experiment spec>
                           export.checkpoint=<model to export>
                           export.onnx_file=<onnx path>
                           [export.<export_option>=<export_option_value>]
                           [gpu_ids=<gpu_index>]
        

Required Arguments

• -e, --experiment_spec: The path to the spec file

• export.checkpoint: The .pth model to export.

• export.onnx_file: The path where the .etlt or .onnx model is saved.

Optional Arguments

• export.<export_option>: The export options.

• gpu_ids: The index of (discrete) GPUs used for exporting the model. You can specify the index of the GPU to run export if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

• -h, --help: Show this help message and exits.

Sample usage

Here’s a sample command to export an EfficientDet model in INT8 mode.

            

            
tao model efficientdet_tf2 export -e /path/to/spec.yaml
        

For TensorRT engine generation, validation, and int8 calibration, refer to the TAO Deploy documentation.

Refer to the Integrating an EfficientDet (TF1/TF2) Model page to learn more about deploying an EfficientDet TF2 model to Deepsteram.