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.

Data Input for EfficientDet#

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.

Pre-processing the Dataset#

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:

Field	Description	Data Type and Constraints	Recommended/Typical Value
image_dir	The path to the directory where raw images are stored	String	–
annotations_file	The path to the annotation JSON file	String	–
output_dir	The output directory where TFRecords are saved	String	–
tag	The number of shards for the converted TFRecords	Integer	256
num_shards	The path to a TAO pruned model for re-training, if any	String	–
include_mask	Whether to include segmentation groundtruth during conversion	Boolean	False

Note

A log file named <tag>_warnings.json will be generated in the output_dir if the bounding box of an object is out of bounds with respect to the image frame or if an object mask is out of bounds with respect to its bounding box. The log file records the image_id that has problematic object IDs. For example, {"200365": {"box": [918], "mask": []} means the bounding box of object 918 is out of bounds in image 200365.

The following example shows how to use the command:

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

Creating a Configuration File#

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:

Field	Description
dataset	Configuration related to data sources and dataloader
model	Configuration related to model construction
train	Configuration related to the training process
evaluate	Configuration related to the standalone evaluation process
prune	Configuration for pruning a trained model
inference	Configuration for running model inference
export	Configuration for exporting a trained model
encryption_key	Global encryption key
results_dir	Directory where experiment results and status logging are saved

Training Config#

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
batch_size	The batch size for each GPU, so the effective batch size is batch_size_per_gpu * num_gpus.	Unsigned int, positive	16
num_epochs	The number of epochs to train the network	Unsigned int, positive	300
num_examples_per _epoch	Total number of images in the training set	Unsigned int, positive	–
checkpoint	The path to the pretrained model, if any	String	–
pruned_model_path	The path to a TAO pruned model for re-training, if any	String	–
checkpoint_interval	The number of training epochs that should run per model checkpoint/validation	Unsigned int, positive	10
amp	Whether to use mixed precision training	Boolean	–
moving_average_decay	Moving average decay	Float	0.9999
l2_weight_decay	L2 weight decay	Float	–
l1_weight_decay	L1 weight decay	Float	–
random_seed	Random seed	Unsigned int, positive	42
clip_gradients_norm	Clip gradients by the norm value	Float	5
qat	Enabled quantization aware training	Boolean	False
optimizer	Optimizer configuration
lr_schedule	Learning rate scheduler configuration

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.

Field	Description	Data Type and Constraints	Recommended/Typical Value
checkpoint	The path to the .tlt model to be evaluated	String	–
max_detections_per_image	The maximum number of detections to visualize	Unsigned int, positive	100
batch_size	The batch size for each GPU, so the effective batch size is batch_size_per_gpu * num_gpus	Unsigned int, positive	16
num_samples	The number of samples for evaluation	Unsigned int	–
label_map	YAML file that stores index to label name mapping. (Optional) If set, per class AP metric will be calculated	String	–
start_eval_epoch	Evaluation will not start until this epoch (Default: 1)	Unsigned int	–
results_dir	The directory where the evaluation result is stored (Optional)	Unsigned int	–

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.

Field	Description	Data Type and Constraints	Recommended/Typical Value
checkpoint	The path to the .tlt model to run inference with	String	–
image_dir	The path to the image directory	String	–
output_dir	The path to the output directory where annotated images will be saved	String	–
dump_label	Whether to dump label files in KITTI format	Boolean	–
batch_size	Batch size to run inference with	Unsigned int	–
min_score_thresh	Minimum confidence threshold to render the predicted bounding boxes	String	–
label_map	YAML file that stores index to label name mapping (Optional) If set, annotated images will have class labels associated with bounding boxes	String	–
max_boxes_to_draw	The maximum number of bounding boxes that will be rendered in the annotated images	String	–
results_dir	The directory where the inference result is stored (Optional)	Unsigned int	–

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.

Field	Description	Data Type and Constraints	Recommended/Typical Value
train_tfrecords	The TFRecord path for training	String	–
val_tfrecords	The TFRecord path for validation	String	–
val_json_file	The annotation file path for validation	String	–
num_classes	The number of classes. If there are N categories in the annotation, num_classes should be N+1 (background class)	Unsigned int	–
max_instances_per_image	The maximum number of object instances to parse (default: 100)	Unsigned int	100
skip_crowd_during_training	Specifies whether to skip crowd during training	Boolean	True
loader	Data loader configuration
augmentation	Data augmentation configuration

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
prefetch_size	The image dimension in “WxH” format, where W and H indicates the dimension of the resized and padded input.	String	“512x512”
shuffle_file	The TFRecord path for training	String	–
shuffle_buffer	The image dimension in “WxH” format, where W and H indicates the dimension of the resized and padded input.	String	“512x512”
cycle_length	The TFRecord path for training	String	–
block_length	The TFRecord path for training	String	–

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
rand_hflip	A flag specifying whether to perform random horizontal flip	Boolean	–
random_crop_min_scale	The minimum scale of RandomCrop augmentation (default: 0.1)	Float	0.1
random_crop_max_scale	The maximum scale of RandomCrop augmentation (default: 2.0)	Float	2.0
auto_color_distortion	A flag to enable automatic color augmentation	Boolean	False
auto_translate_xy	A flag to enable automatic image translation on the X/Y axis	Boolean	False

Model Config#

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
model_name	EfficientDet model name	string	“efficientdet_d0”
min_level	The minimum level of the output feature pyramid	Unsigned int	3 (only 3 is supported)
max_level	The maximum level of the output feature pyramid	Unsigned int	7 (only 7 is supported)
num_scales	The number of anchor octave scales on each pyramid level (e.g. if set to 3, the anchor scales are [2^0, 2^(1/3), 2^(2/3)])	Unsigned int	3
max_instances_per_image	The maximum number of object instances to parse (default: 100)	Unsigned int	100
aspect_ratios	A list of tuples representing the aspect ratios of anchors on each pyramid level	string	“[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]”
anchor_scale	Scale of the base-anchor size to the feature-pyramid stride	Unsigned int	4
input_width	Input width	Unsigned int	512
input_height	Input height	Unsigned int	512

Pruning Config#

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
normalizer	Normalization method. Specify `max` to normalize by dividing each norm by the maximum norm within a layer or `L2` to normalize by dividing by the L2 norm of the vector comprising all kernel norms	String	max
equalization_criterion	The criteria to equalize the stats of inputs to an element-wise op layer or depth-wise conv layer. Options are `arithmetic_mean` `geometric_mean`,``union``, and `intersection`.	String	union
granularity	The number of filters to remove at a time	Integer	8
threshold	Pruning threshold	Float	–
min_num_filters	The minimum number of filters to keep per layer. Default: 16	Integer	16
excluded_layers	A list of layers to be excluded from pruning	List	–
checkpoint	The path to the `.tlt` model file to be pruned	String	–

Export Config#

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

Field	Description	Data Type and Constraints	Recommended/Typical Value
batch_size	The maximum batch size of the `.onnx` model if `dynamic_batch_size` is set to False	Boolean	–
dynamic_batch_size	A flag specifying whether to use dynamic batch size in the exported `.onnx` model	Boolean	True
checkpoint	The path to the `.tlt` model file to be exported	String	–
onnx_file	The path to save the exported `.onnx` model	String	False
min_score_thresh	The confidence threshold in the NMS layer (default: 0.01)	float	–

Training the Model#

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

Evaluating the Model#

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

Running Inference with an EfficientDet Model#

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 the Model#

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.

Note

Due to the complexity of larger EfficientDet models, the pruning process will take significantly longer to finish. For example, pruning the EfficientDet-D5 model may take at least 25 minutes on a V100 server.

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

Re-training the Pruned Model#

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#

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

TensorRT Engine Generation, Validation, and int8 Calibration#

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

Deploying to DeepStream#

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