MaskRCNN

MaskRCNN supports the following tasks:

• dataset_convert

• train

• evaluate

• prune

• inference

• export

These tasks may be invoked from the TAO Launcher using the following convention on the command line:

            

            
tao model mask_rcnn <sub_task> <args_per_subtask>
        

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

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 mask_rcnn dataset-convert [-h] -i <image_directory>
                                   -a <annotation_json_file>
                                   -o <tfrecords_output_directory>
                                   [-t <tag>]
                                   [-s <num_shards>]
                                   [--include_mask]
        

You can use the following arguments:

• -i, --image_dir: The path to the directory where raw images are stored

• -a, --annotations_file: The annotation JSON file

• -o, --output_dir: The output directory where TFRecords are saved

• -t, --tag: The tag for the converted TFRecords (e.g. “train”). The tag defaults to the name of the annotation file.

• -s, --num_shards: The number of shards for the converted TFRecords. The default value is 256.

• --include_mask: Whether to include segmentation groundtruth during conversion. The default value is True.

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

  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 with the dataset:

            

            
tao model mask_rcnn dataset_convert -i /path/to/image_dir
                              -a /path/to/train.json
                              -o /path/to/output_dir
        

Below is a sample MaskRCNN spec file. It has three major components: top level experiment configs, data_config, and maskrcnn_config, explained below in detail. The format of the spec file is a protobuf text (prototxt) message and each of its fields can be either a basic data type or a nested message. The top level structure of the spec file is summarized in the table below.

Here’s a sample of the MaskRCNN spec file:

            

            
seed: 123
use_amp: False
warmup_steps: 0
checkpoint: "/workspace/tao-experiments/maskrcnn/pretrained_resnet50/tlt_instance_segmentation_vresnet50/resnet50.hdf5"
learning_rate_steps: "[60000, 80000, 100000]"
learning_rate_decay_levels: "[0.1, 0.02, 0.002]"
total_steps: 120000
train_batch_size: 2
eval_batch_size: 4
num_steps_per_eval: 10000
momentum: 0.9
l2_weight_decay: 0.0001
l1_weight_decay: 0.0
warmup_learning_rate: 0.0001
init_learning_rate: 0.02
num_examples_per_epoch: 14700
# pruned_model_path: "/workspace/tao-experiments/maskrcnn/pruned_model/model.tlt"

data_config{
        image_size: "(832, 1344)"
        augment_input_data: True
        eval_samples: 500
        training_file_pattern: "/workspace/tao-experiments/data/train*.tfrecord"
        validation_file_pattern: "/workspace/tao-experiments/data/val*.tfrecord"
        val_json_file: "/workspace/tao-experiments/data/annotations/instances_val2017.json"

        # dataset specific parameters
        num_classes: 91
        skip_crowd_during_training: True
        max_num_instances: 200
}

maskrcnn_config {
        nlayers: 50
        arch: "resnet"
        freeze_bn: True
        freeze_blocks: "[0,1]"
        gt_mask_size: 112

        # Region Proposal Network
        rpn_positive_overlap: 0.7
        rpn_negative_overlap: 0.3
        rpn_batch_size_per_im: 256
        rpn_fg_fraction: 0.5
        rpn_min_size: 0.

        # Proposal layer.
        batch_size_per_im: 512
        fg_fraction: 0.25
        fg_thresh: 0.5
        bg_thresh_hi: 0.5
        bg_thresh_lo: 0.

        # Faster-RCNN heads.
        fast_rcnn_mlp_head_dim: 1024
        bbox_reg_weights: "(10., 10., 5., 5.)"

        # Mask-RCNN heads.
        include_mask: True
        mrcnn_resolution: 28

        # training
        train_rpn_pre_nms_topn: 2000
        train_rpn_post_nms_topn: 1000
        train_rpn_nms_threshold: 0.7

        # evaluation
        test_detections_per_image: 100
        test_nms: 0.5
        test_rpn_pre_nms_topn: 1000
        test_rpn_post_nms_topn: 1000
        test_rpn_nms_thresh: 0.7

        # model architecture
        min_level: 2
        max_level: 6
        num_scales: 1
        aspect_ratios: "[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]"
        anchor_scale: 8

        # localization loss
        rpn_box_loss_weight: 1.0
        fast_rcnn_box_loss_weight: 1.0
        mrcnn_weight_loss_mask: 1.0
}
        

            

            
[MaskRCNN] INFO    : ================ TRAINABLE VARIABLES ==================
[MaskRCNN] INFO    : [#0001] conv1/kernel:0 => (7, 7, 3, 64)
[MaskRCNN] INFO    : [#0002] bn_conv1/gamma:0 => (64,)
[MaskRCNN] INFO    : [#0003] bn_conv1/beta:0 => (64,)
[MaskRCNN] INFO    : [#0004] block_1a_conv_1/kernel:0 => (1, 1, 64, 64)
[MaskRCNN] INFO    : [#0005] block_1a_bn_1/gamma:0 => (64,)
[MaskRCNN] INFO    : [#0006] block_1a_bn_1/beta:0 => (64,)
[MaskRCNN] INFO    : [#0007] block_1a_conv_2/kernel:0 => (3, 3, 64, 64)
[MaskRCNN] INFO    : [#0008] block_1a_bn_2/gamma:0 => (64,)
[MaskRCNN] INFO    : [#0009] block_1a_bn_2/beta:0 => (64,)
[MaskRCNN] INFO    : [#0010] block_1a_conv_3/kernel:0 => (1, 1, 64, 256)
[MaskRCNN] INFO    : [#0011] block_1a_bn_3/gamma:0 => (256,)
[MaskRCNN] INFO    : [#0012] block_1a_bn_3/beta:0 => (256,)
[MaskRCNN] INFO    : [#0110] block_3d_bn_3/gamma:0 => (1024,)
[MaskRCNN] INFO    : [#0111] block_3d_bn_3/beta:0 => (1024,)
[MaskRCNN] INFO    : [#0112] block_3e_conv_1/kernel:0 => (1, 1, 1024, [MaskRCNN] INFO : [#0144] block_4b_bn_1/beta:0 => (512,)
                                …     …       …    …                                                                       ...
[MaskRCNN] INFO    : [#0174] post_hoc_d5/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0175] post_hoc_d5/bias:0 => (256,)
[MaskRCNN] INFO    : [#0176] rpn/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0177] rpn/bias:0 => (256,)
[MaskRCNN] INFO    : [#0178] rpn-class/kernel:0 => (1, 1, 256, 3)
[MaskRCNN] INFO    : [#0179] rpn-class/bias:0 => (3,)
[MaskRCNN] INFO    : [#0180] rpn-box/kernel:0 => (1, 1, 256, 12)
[MaskRCNN] INFO    : [#0181] rpn-box/bias:0 => (12,)
[MaskRCNN] INFO    : [#0182] fc6/kernel:0 => (12544, 1024)
[MaskRCNN] INFO    : [#0183] fc6/bias:0 => (1024,)
[MaskRCNN] INFO    : [#0184] fc7/kernel:0 => (1024, 1024)
[MaskRCNN] INFO    : [#0185] fc7/bias:0 => (1024,)
[MaskRCNN] INFO    : [#0186] class-predict/kernel:0 => (1024, 91)
[MaskRCNN] INFO    : [#0187] class-predict/bias:0 => (91,)
[MaskRCNN] INFO    : [#0188] box-predict/kernel:0 => (1024, 364)
[MaskRCNN] INFO    : [#0189] box-predict/bias:0 => (364,)
[MaskRCNN] INFO    : [#0190] mask-conv-l0/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0191] mask-conv-l0/bias:0 => (256,)
[MaskRCNN] INFO    : [#0192] mask-conv-l1/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0193] mask-conv-l1/bias:0 => (256,)
[MaskRCNN] INFO    : [#0194] mask-conv-l2/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0195] mask-conv-l2/bias:0 => (256,)
[MaskRCNN] INFO    : [#0196] mask-conv-l3/kernel:0 => (3, 3, 256, 256)
[MaskRCNN] INFO    : [#0197] mask-conv-l3/bias:0 => (256,)
[MaskRCNN] INFO    : [#0198] conv5-mask/kernel:0 => (2, 2, 256, 256)
[MaskRCNN] INFO    : [#0199] conv5-mask/bias:0 => (256,)
[MaskRCNN] INFO    : [#0200] mask_fcn_logits/kernel:0 => (1, 1, 256, 91)
[MaskRCNN] INFO    : [#0201] mask_fcn_logits/bias:0 => (91,)
        

MaskRCNN Config

The MaskRCNN configuration (maskrcnn_config) defines the model structure. This model is used for training, evaluation, and inference. A detailed description is included in the table below. Currently, MaskRCNN only supports ResNet10/18/34/50/101 as its backbone.

Data Config

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

Train the MaskRCNN model using this command:

            

            
tao model mask_rcnn train [-h] -e <experiment_spec>
                         -d <output_dir>
                         -k <key>
                         [--gpus <num_gpus>]
                         [--gpu_index <gpu_index>]
                         [--log_file <log_file_path>]
        

Required Arguments

• -d, --model_dir: The path to the folder where the experiment output is written.

• -k, --key: The encryption key to decrypt the model.

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

Optional Arguments

• --gpus num_gpus: The number of GPUs to use and processes to launch for training. The default value is 1.

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

• --log_file: The path to the log file. The default path is stdout.

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

Input Requirement

• Input size: C * W * H (where C = 3, W >= 128, H >= 128 and W, H are multiples of 2^ max_level)

• Image format: JPG

• Label format: COCO detection

Sample Usage

Here’s an example of using the train command on a MaskRCNN model:

            

            
tao model mask_rcnn train --gpus 2 -e /path/to/spec.txt -d /path/to/result -k $KEY
        

To run evaluation for a MaskRCNN model, use this command:

            

            
tao model mask_rcnn evaluate [-h] -e <experiment_spec_file>
                            -m <model_file>
                            -k <key>
                            [--gpu_index <gpu_index>]
                            [--log_file <log_file_path>]
        

Required Arguments

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

• -m, --model: The path to the model file to use for evaluation (only .tlt model is supported).

• -k, --key: The key to load the model.

Optional Arguments

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

• --log_file: The path to the log file. The default path is stdout.

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

Pruning removes parameters from the model to reduce the model size. Retraining is necessary to regain the performance of the unpruned model.

The prune command includes these parameters:

            

            
tao model mask_rcnn prune [-h] -m <pretrained_model>
                         -o <output_dir>
                         -k <key>
                         [-n <normalizer>]
                         [-eq <equalization_criterion>]
                         [-pg <pruning_granularity>]
                         [-pth <pruning threshold>]
                         [-nf <min_num_filters>]
                         [-el [<excluded_list>]
                         [--gpu_index <gpu_index>]
                         [--log_file <log_file>]
        

Required Arguments

• -m, --pretrained_model: The path to the pretrained model.

• -o, --output_dir: The output directory which contains the pruned model, named as model.tlt.

• -k, --key: The key to load a .tlt model.

Optional Arguments

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

• -n, --normalizer: max to normalize by dividing each norm by the maximum norm within a layer; L2 to normalize by dividing by the L2 norm of the vector comprising all kernel norms. (default: max)

• -eq, --equalization_criterion: Criteria to equalize the stats of inputs to an element wise op layer, or depth-wise convolutional layer. This parameter is useful for resnets and mobilenets. Options are arithmetic_mean, geometric_mean, union, and intersection. (default: union)

• -pg, --pruning_granularity: Number of filters to remove at a time. (default:8)

• -pth: Threshold to compare normalized norm against. (default:0.1)

• -nf, --min_num_filters: Minimum number of filters to keep per layer (default:16)

• -el, --excluded_layers: List of excluded_layers. Examples: -i item1 item2 (default: [])

• --gpu_index: The index of the GPU to run evaluation (useful when the machine has multiple GPUs installed). Note that evaluation can only run on a single GPU.

• --log_file: The path to the log file. Defaults to stdout.

Here’s an example of using the prune command:

            

            
tao model mask_rcnn prune -m /workspace/model.step-100.tlt
                    -o /workspace/output
                    -eq union
                    -pth 0.7 -k $KEY
        

After pruning, the model needs to be retrained first before it can be used for inference or evaluation.

Once the model has been pruned, there might be a decrease in accuracy. This happens because some previously useful weights may have been removed. To regain accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, run the tao model mask_rcnn train command with an updated spec file that points to the newly pruned model by setting pruned_model_path.

Users are advised to turn off the regularizer during retraining. You may do this by setting the regularizer weights to 0 for both l1_weight_decay and l2_weight_decay. The other parameters may be retained in the spec file from the previous training. train_batch_size and eval_batch_size must be kept unchanged.

The inference tool for MaskRCNN networks can be used to visualize bboxes or generate frame-by-frame COCO-format labels on a directory of images. Here’s an example of using this tool:

            

            
tao model mask_rcnn inference [-h] -i <input directory>
                             -r <results directory>
                             -e <experiment spec file>
                             -m <model file>
                             [-k <key>]
                             [-t <bbox confidence threshold>]
                             [--include_mask]
                             [--gpu_index <gpu_index>]
                             [--log_file <log_file_path>]
        

Required Arguments

• -m, --model_path: The path to the trained MaskRCNN model (either a .tlt model or a converted TensorRT engine).

• -i, --image_dir: The directory of input images for inference. Supported image formats include PNG, JPG and JPEG.

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

• -r, --results_dir: The directory path to output annotated images.

Optional Arguments

• -k, --key: The key to load a .tlt model (not required if TensorRT engine is used).

• -t, --threshold: The threshold for drawing a bbox (default: 0.6)

• -c, --class_map: A text file containing class names, which should match the category names in the annotation file in ascending order of category IDs. If the label file is omitted, the annotated image will not display category names next to bounding boxes. This argument is only supported with the TensorRT engine.

• --include_mask: Specifies whether to draw masks on the annotated output.

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

• --log_file: The path to the log file. The default path is stdout.

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

Exporting the model decouples the training process from inference 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.

The exported model format is referred to as .etlt. Like the .tlt model format, .etlt is an encrypted model format, and it uses the same key as the .tlt model that it is exported from. This key is required when deploying this model.

INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to improve performance, but require a calibration cache at engine creation-time. The calibration cache is generated using a calibration tensor file, if export is run with the --data_type flag set to int8. Pre-generating the calibration information and caching it removes the need for calibrating the model on the inference machine. Moving the calibration cache is usually much more convenient than moving the calibration tensorfile, since it is a much smaller file and can be moved with the exported model. Using the calibration cache also speeds up engine creation as building the cache can take several minutes to generate depending on the size of the Tensorfile and the model itself.

The export tool can generate the INT8 calibration cache by ingesting training data using one of these options:

• Option 1: Use the training data loader to load the training images for INT8 calibration. This option is now the recommended approach to support multiple image directories by leveraging the training dataset loader. This also ensures two important aspects of data during calibration:

  • Data pre-processing in the INT8 calibration step is the same as in the training process.

  • The data batches are sampled randomly across the entire training dataset, thereby improving the accuracy of the INT8 model.

• Option 2: Point the tool to a directory of images that you want to use to calibrate the model. For this option, make sure to create a sub-sampled directory of random images that best represent your training dataset.

FP16/FP32 Model

The calibration.bin is only required if you need to run inference at INT8 precision. For FP16/FP32-based inference, the export step is much simpler: All you need to do is provide a .tlt model from the training/retraining step to be converted into .etlt format.

Exporting the MaskRCNN Model

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

            

            
tao model mask_rcnn export [-h] -m <path to the .tlt model file generated by tao model train>
                          -k <key>
                          --experiment_spec <path to experiment spec file>
                          [-o <path to output file>]
                          [--gen_ds_config <Flag to generate ds config and label file>]
                          [--gpu_index <gpu_index>]
                          [--log_file <log_file_path>]
        

Required Arguments

• -m, --model: The path to the .tlt model file to be exported using export.

• -k, --key: The key used to save the .tlt model file.

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

Optional Arguments

• -o, --output_file: The path to save the exported model to. The default path is ./<input_file>.etlt.

• --gen_ds_config: A Boolean flag indicating whether to generate the template DeepStream related configuration (“nvinfer_config.txt”) as well as a label file (“labels.txt”) in the same directory as the output_file. Note that the config file is NOT a complete configuration file and requires the user to update the sample config files in DeepStream with the parameters generated.

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

• --log_file: The path to the log file. The default path is stdout.

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

Sample usage

Here’s a sample command to export a MaskRCNN model in INT8 mode:

            

            
tao model mask_rcnn export -m /ws/model.step-25000.tlt \
                     -k nvidia_tlt \
                     -e /ws/maskrcnn_train_resnet50.txt
        

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

Refer to Integrating a MaskRCNN Model to learn more about deploying a MaskRCNN model to DeepStream.