MaskRCNN
MaskRCNN supports the following tasks:
dataset_convert
train
evaluate
prune
inference
export
These tasks may be invoked from the TAO Toolkit 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.NoteA log file named
<tag>_warnings.json
will be generated in theoutput_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 theimage_id
that has problematic object IDs. For example,{"200365": {"box": [918], "mask": []}
means the bounding box ofobject 918
is out of bounds inimage 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
The id
under categories
in the annotation file should start from 1.
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
}
Field | Description | Data Type and Constraints | Recommended/Typical Value |
seed | The random seed for the experiment | Unsigned int | 123 |
warmup_steps | The steps taken for learning rate to ramp up to the init_learning_rate | Unsigned int | – |
warmup_learning_rate | The initial learning rate during the warmup phase | float | – |
learning_rate_steps | A list of steps at which the learning rate decays by the factor specified in learning_rate_decay_levels | string | – |
learning_rate_decay_levels | A list of decay factors. The length should match the length of learning_rate_steps. | string | – |
total_steps | The total number of training iterations | Unsigned int | – |
train_batch_size | The batch size during training | Unsigned int | 4 |
eval_batch_size | The batch size during validation or evaluation | Unsigned int | 8 |
num_steps_per_eval | Save a checkpoint and run evaluation every N steps. | Unsigned int | – |
momentum | Momentum of the SGD optimizer | float | 0.9 |
l1_weight_decay | L1 weight decay | float | 0.0001 |
l2_weight_decay | L2 weight decay | float | 0.0001 |
use_amp | Specifies whether to use Automatic Mixed Precision training | boolean | False |
checkpoint | The path to a pretrained model | string | – |
maskrcnn_config | The architecture of the model | message | – |
data_config | The input data configuration | message | – |
skip_checkpoint_variables | If specified, the weights of the layers with matching regular expressions will not be loaded. This is especially helpful for transfer learning. | string | – |
pruned_model_path | The path to a pruned MaskRCNN model | string | – |
num_examples_per_epoch | The total number of images in the training set divided by the number of GPUs | Unsigned int | – |
num_epochs | The number of epochs to train the network | Unsigned int | – |
visualize_images_summary | Whether to preview images and inference results in TensorBoard. If enabled, 10 images with predicted bounding boxes from the validation set will be saved in the TensorBoard events file. | boolean | – |
The num_examples_per_epoch
parameter must be specified, and either num_epochs
or total_steps
should be specified. If both are set, total_steps
will be overwritten by
num_epochs * num_examples_per_epoch / train_batch_size
When using skip_checkpoint_variables
, you can first find the model structure in the
training log (Part of the MaskRCNN+ResNet50 model structure is shown below). If, for example,
you want to retrain all prediction heads, you can set skip_checkpoint_variables
to
“head”. TAO Toolkit uses the Python re library to check whether “head” matches any layer name or
re.search($skip_checkpoint_variables, $layer_name)
.
[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.
Field | Description | Data Type and Constraints | Recommended/Typical Value |
nlayers | The number of layers in ResNet arch | Unsigned int | 50 |
arch | The backbone feature extractor name | string | resnet |
freeze_bn | Whether to freeze all BatchNorm layers in the backbone | boolean | False |
freeze_blocks | A list of conv blocks in the backbone to freeze | string ResNet: For the ResNet series, the block IDs valid for freezing are any subset of [0, 1, 2, 3] (inclusive) | – |
gt_mask_size | The groundtruth mask size | Unsigned int | 112 |
rpn_positive_overlap | The lower-bound threshold to assign positive labels for anchors | float | 0.7 |
rpn_negative_overlap | The upper-bound threshold to assign negative labels for anchors | float | 0.3 |
rpn_batch_size_per_im | The number of sampled anchors per image in RPN | Unsigned int | 256 |
rpn_fg_fraction | The desired fraction of positive anchors in a batch | Unsigned int | 0.5 |
rpn_min_size | The minimum proposal height and width | 0 | |
batch_size_per_im | The RoI minibatch size per image | Unsigned int | 512 |
fg_fraction | The target fraction of RoI minibatch that is labeled as foreground | float | 0.25 |
fast_rcnn_mlp_head_dim | The Fast-RCNN classification head dimension | Unsigned int | 1024 |
bbox_reg_weights | The bounding-box regularization weights | string | “(10, 10, 5, 5)” |
include_mask | Specifies whether to include a mask head | boolean | True (currently only True is supported) |
mrcnn_resolution | The mask-head resolution (must be multiple of 4) | Unsigned int | 28 |
train_rpn_pre_nms_topn | The number of top-scoring RPN proposals to keep before applying NMS (per FPN level) during training | Unsigned int | 2000 |
train_rpn_post_nms_topn | The number of top-scoring RPN proposals to keep after applying NMS (total number produced) during training | Unsigned int | 1000 |
train_rpn_nms_threshold | The NMS IOU threshold in RPN during training | float | 0.7 |
test_detections_per_image | The number of bounding box candidates after NMS | Unsigned int | 100 |
test_nms | The NMS IOU threshold during test | float | 0.5 |
test_rpn_pre_nms_topn | The number of top-scoring RPN proposals to keep before applying NMS (per FPN level) during test | Unsigned int | 1000 |
test_rpn_post_nms_topn | The number of top scoring RPN proposals to keep after applying NMS (total number produced) during test | Unsigned int | 1000 |
test_rpn_nms_threshold | The NMS IOU threshold in RPN during test | float | 0.7 |
min_level | The minimum level of the output feature pyramid | Unsigned int | 2 |
max_level | The maximum level of the output feature pyramid | Unsigned int | 6 |
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 | 1 |
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 | 8 |
rpn_box_loss_weight | The weight for adjusting RPN box loss in the total loss | float | 1.0 |
fast_rcnn_box_loss_weight | The weight for adjusting FastRCNN box regression loss in the total loss | float | 1.0 |
mrcnn_weight_loss_mask | The weight for adjusting mask loss in the total loss | float | 1.0 |
The min_level
, max_level
, num_scales
, aspect_ratios
,
and anchor_scale
are used to determine anchor generation for MaskRCNN.
anchor_scale
is the base anchor scale, while min_level
and
max_level
set the range of the scales on different feature maps. For example,
the actual anchor scale for the feature map at min_level
will be anchor_scale * 2^min_level
and the actual anchor scale for the feature map at max_level
will be
anchor_scale * 2^max_level. And it will generate anchors of different
aspect_ratios
based on the actual anchor scale.
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.
Field | Description | Data Type and Constraints | Recommended/Typical Value |
image_size | The image dimension as a tuple within quote marks. “(height, width)” indicates the dimension of the resized and padded input. | string | “(832, 1344)” |
augment_input_data | Specifies whether to augment the data | boolean | True |
eval_samples | The number of samples for evaluation | Unsigned int | – |
training_file_pattern | The TFRecord path for training | string | – |
validation_file_pattern | 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 | – |
skip_crowd_during_training | Specifies whether to skip crowd during training | boolean | True |
prefetch_buffer_size | The prefetch buffer size used by tf.data.Dataset (default: AUTOTUNE) | Unsigned int | – |
shuffle_buffer_size | The shuffle buffer size used by tf.data.Dataset (default: 4096) | Unsigned int | 4096 |
n_workers | The number of workers to parse and preprocess data (default: 16) | Unsigned int | 16 |
max_num_instances | The maximum number of object instances to parse (default: 200) | Unsigned int | 200 |
If an out-of-memory error occurs during training, try to set a smaller image_size
or batch_size
first. If the error persists, try reducing
the n_workers
, shuffle_buffer_size
, and prefetch_buffer_size
values. Lastly, if the original images have a very large resolution,
resize the images offline and create new tfrecords to avoid loading large images to GPU memory.
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 isstdout
.-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 isstdout
.-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 asmodel.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 arearithmetic_mean
,geometric_mean
,union
, andintersection
. (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 tostdout
.
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 isstdout
.-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 usingexport
.-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 theoutput_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 isstdout
.-h, --help
: Show this help message and exit.
MaskRCNN does not support QAT.
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.