DSSD ==== .. _dssd: Data Input for Object Detection ------------------------------- The object detection apps in TLT expect data in KITTI format for training and evaluation. See the :ref:`Data Annotation Format ` page for more information about the KITTI data format. Creating a Configuration File ----------------------------- .. _creating_a_configuration_file_dssd: Below is a sample for the DSSD spec file. It has six major components: :code:`dssd_config`, :code:`training_config`, :code:`eval_config`, :code:`nms_config`, :code:`augmentation_config`, and :code:`dataset_config`. 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. .. code:: random_seed: 42 dssd_config { aspect_ratios: "[[1.0, 2.0, 0.5], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5], [1.0, 2.0, 0.5]]" scales: "[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]" two_boxes_for_ar1: true clip_boxes: false variances: "[0.1, 0.1, 0.2, 0.2]" arch: "resnet" nlayers: 18 freeze_bn: false freeze_blocks: 0 } training_config { batch_size_per_gpu: 16 num_epochs: 80 enable_qat: false learning_rate { soft_start_annealing_schedule { min_learning_rate: 5e-5 max_learning_rate: 2e-2 soft_start: 0.15 annealing: 0.8 } } regularizer { type: L1 weight: 3e-5 } } eval_config { validation_period_during_training: 10 average_precision_mode: SAMPLE batch_size: 16 matching_iou_threshold: 0.5 } nms_config { confidence_threshold: 0.01 clustering_iou_threshold: 0.6 top_k: 200 } augmentation_config { output_width: 300 output_height: 300 output_channel: 3 image_mean { key: 'b' value: 103.9 } image_mean { key: 'g' value: 116.8 } image_mean { key: 'r' value: 123.7 } } dataset_config { data_sources: { label_directory_path: "/path/to/train/labels" image_directory_path: "/path/to/train/images" } include_difficult_in_training: true target_class_mapping { key: "car" value: "car" } target_class_mapping { key: "pedestrian" value: "pedestrian" } target_class_mapping { key: "cyclist" value: "cyclist" } target_class_mapping { key: "van" value: "car" } target_class_mapping { key: "person_sitting" value: "pedestrian" } validation_data_sources: { label_directory_path: "/path/to/val/labels" image_directory_path: "/path/to/val/images" } } Training Config ^^^^^^^^^^^^^^^ .. _training_config_dssd: The training configuration (:code:`training_config`) defines the parameters needed for the training, evaluation, and inference. Details are summarized in the table below. +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | **Field** | **Description** | **Data Type and Constraints** | **Recommended/Typical Value** | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | batch_size_per_gpu | The batch size for each GPU, so the effective batch size is | Unsigned int, positive | -- | | | "batch_size_per_gpu * num_gpus" | | | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | num_epochs | The number of epochs to train the network | Unsigned int, positive. | -- | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | enable_qat | Whether to use quantization-aware training | Boolean | **Note**: DSSD does not support loading a pruned non-QAT model and retraining | | | | | it with QAT enabled, or vice versa. For example, to get a pruned QAT model, | | | | | perform the initial training with QAT enabled or :code:`enable_qat=True`. | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | learning_rate | Only soft_start_annealing_schedule with these nested parameters is supported: | Message type. | -- | | | | | | | | 1. min_learning_rate: The minimum learning during the entire experiment | | | | | 2. max_learning_rate: The maximum learning during the entire experiment | | | | | 3. soft_start: Time to lapse before warm up ( expressed in percentage of progress | | | | | between 0 and 1) | | | | | 4. annealing: Time to start annealing the learning rate | | | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | regularizer | This parameter configures the regularizer to be used while training and contains | Message type. | L1 (Note: NVIDIA suggests using the L1 regularizer when training a network before | | | the following nested parameters: | | pruning as L1 regularization helps make the network weights more prunable.) | | | | | | | | 1. type: The type of regularizer to use. NVIDIA supports NO_REG, L1, and L2 | | | | | 2. weight: The floating point value for the regularizer weight | | | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | max_queue_size | The number of prefetch batches in data loading | Unsigned int, positive | -- | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | n_workers | The number of workers for data loading | Unsigned int, positive | -- | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ | use_multiprocessing| Whether to use multiprocessing mode of keras sequence data loader | Boolean | -- | +--------------------+---------------------------------------------------------------------------------------+-------------------------------+---------------------------------------------------------------------------------------+ Evaluation Config ^^^^^^^^^^^^^^^^^ The evaluation configuration (:code:`eval_config`) defines the parameters needed for the evaluation either during training or as a standalone procedure. Details are summarized in the table below. +-----------------------------------+----------------------------------------------------------------------------------------------+----------------------------------+-------------------------------+ | **Field** | **Description** | **Data Type and Constraints** | **Recommended/Typical Value** | +-----------------------------------+----------------------------------------------------------------------------------------------+----------------------------------+-------------------------------+ | validation_period_during_training | The number of training epochs per validation. | Unsigned int, positive | 10 | +-----------------------------------+----------------------------------------------------------------------------------------------+----------------------------------+-------------------------------+ | average_precision_mode | The Average Precision (AP) calculation mode can be either SAMPLE or INTEGRATE. SAMPLE | ENUM type ( SAMPLE or INTEGRATE) | SAMPLE | | | is used as VOC metrics for VOC 2009 or before. INTEGRATE is used for VOC 2010 or after. | | | +-----------------------------------+----------------------------------------------------------------------------------------------+----------------------------------+-------------------------------+ | matching_iou_threshold | The lowest IoU of the predicted box and ground truth box that can be considered a match. | Boolean | 0.5 | +-----------------------------------+----------------------------------------------------------------------------------------------+----------------------------------+-------------------------------+ NMS Config ^^^^^^^^^^ The NMS configuration (:code:`nms_config`) defines the parameters needed for NMS postprocessing. The NMS configuration applies to the NMS layer of the model in training, validation, evaluation, inference, and export. Details are summarized in the table below. +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ | **Field** | **Description** | **Data Type and Constraints** | **Recommended/Typical Value** | +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ | confidence_threshold | Boxes with a confidence score less than confidence_threshold are discarded before applying NMS.| float | 0.01 | +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ | cluster_iou_threshold | The IoU threshold below which boxes will go through the NMS process. | float | 0.6 | +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ | top_k | top_k boxes will be output after the NMS keras layer. If the number of valid boxes is less | Unsigned int | 200 | | | than k, the returned array will be padded with boxes whose confidence score is 0. | | | +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ | infer_nms_score_bits | The number of bits to represent the score values in NMS plugin in TensorRT OSS. The valid range| int. In the interval [1, 10]. | 0 | | | is integers in [1, 10]. Setting it to any other values will make it fall back to ordinary NMS. | | | | | Currently this optimized NMS plugin is only avaible in FP16 but it should also be selected by | | | | | INT8 data type as there is no INT8 NMS in TensorRT OSS and hence this fastest implementation in| | | | | FP16 will be selected. If falling back to ordinary NMS, the actual data type when building the | | | | | engine will decide the exact precision(FP16 or FP32) to run at. | | | +------------------------------------------+------------------------------------------------------------------------------------------------+-------------------------------+-------------------------------+ Augmentation Config ^^^^^^^^^^^^^^^^^^^ The :code:`augmentation_config` parameter defines the image size after preprocessing. The augmentation methods in the SSD `paper`_ will be performed during training, including random flip, zoom-in, zoom-out and color jittering. And the augmented images will be resized to the output shape defined in :code:`augmentation_config`. In evaluation process, only the resize will be performed. .. Note:: The details of augmentation methods can be found in setcion 2.2 and 3.6 of the `paper`_. .. _paper: https://arxiv.org/abs/1512.02325v5 +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | **Field** | **Description** | **Data Type and Constraints** | **Recommended/Typical Value** | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | output_channel | Output image channel of augmentation pipeline. | integer | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | output_width | The width of preprocessed images and the network input. | integer, multiple of 32 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | output_height | The height of preprocessed images and the network input. | integer, multiple of 32 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | random_crop_min_scale | Minimum patch scale of RandomCrop augmentation. Default:0.3 | float >= 1.0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | random_crop_max_scale | Maximum patch scale of RandomCrop augmentation. Default:1.0 | float >= 1.0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | random_crop_min_ar | Minimum aspect ratio of RandomCrop augmentation. Default:0.5 | float > 0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | random_crop_max_ar | Maximum aspect ratio of RandomCrop augmentation. Default:2.0 | float > 0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | zoom_out_min_scale | Minimum scale of ZoomOut augmentation. Default:1.0 | float >= 1.0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | zoom_out_max_scale | Maximum scale of ZoomOut augmentation. Default:4.0 | float >= 1.0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | brightness | Brightness delta in color jittering augmentation. Default:32 | integer >= 0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | contrast | Contrast delta factor in color jitter augmentation. Default:0.5 | float of [0, 1) | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | saturation | Saturation delta factor in color jitter augmentation. Default:0.5 | float of [0, 1) | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | hue | Hue delta in color jittering augmentation. Default:18 | integer >= 0 | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | random_flip | Probablity of performing random horizontal flip. Default:0.5 | float of [0, 1) | -- | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ | image_mean | A key/value pair to specify image mean values. | dict | -- | | | If omitted, ImageNet mean will be used for image preprocessing. | | | | | If set, depending on `output_channel`, either 'r/g/b' or 'l' | | | | | key/value pair must be configured. | | | +------------------------------+----------------------------------------------------------------------+-------------------------------+-------------------------------+ .. Note:: If set `random_crop_min_scale = random_crop_max_scale = 1.0`, RandomCrop augmentation will be disabled. Similarly, set `zoom_out_min_scale = zoom_out_max_scale = 1`, ZoomOut augmentation will be disabled. And all color jitter delta values are set to 0, color jittering augmentation will be disabled. Dataset Config ^^^^^^^^^^^^^^ The dataset configuration (:code:`dataset_config`) defines the parameters needed for the data loader. The configuration is shared with DetectNet_v2. See the :ref:`Dataloader ` section for more information. DSSD Config ^^^^^^^^^^^ The DSSD configuration (:code:`dssd_config`) defines the parameters needed for building the DSSD model. Details are summarized in the table below. +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | **Field** | **Description** | **Data Type and Constraints** | **Recommended/Typical Value** | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | aspect_ratios_global | The anchor boxes of aspect ratios defined in aspect_ratios_global will be generated | string | “[1.0, 2.0, 0.5, 3.0, 0.33]” | | | for each feature layer used for prediction. Note that either the aspect_ratios_global or | | | | | aspect_ratios parameter is required; you don't need to specify both. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | aspect_ratios | The length of the outer list must be equivalent to the number of feature layers | string | “[[1.0,2.0,0.5], | | | used for anchor box generation, and the i-th layer will have anchor boxes with aspect | | [1.0,2.0,0.5], | | | ratios defined in aspect_ratios[i]. Note that either the aspect_ratios_global or | | [1.0,2.0,0.5], | | | aspect_ratios parameter is required; you don't need to specify both. | | [1.0,2.0,0.5], | | | | | [1.0,2.0,0.5], | | | | | [1.0, 2.0, 0.5, 3.0, 0.33]]” | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | two_boxes_for_ar1 | This setting is only relevant for layers that have 1.0 as the aspect ratio. If two_boxes_for_ar1 | Boolean | True | | | is true, two boxes will be generated with an aspect ratio of 1: one with a scale for | | | | | this layer and the other with a scale that is the geometric mean of the scale for this layer and the | | | | | scale for the next layer. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | clip_boxes | If this parameter is True, all corner anchor boxes will be truncated so they are fully inside the feature images. | Boolean | False | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | scales | A list of positive floats containing scaling factors per convolutional predictor layer. | string | “[0.05, 0.1, 0.25, 0.4, 0.55, 0.7, 0.85]” | | | This list must be one element longer than the number of predictor layers so that, if two_boxes_for_ar1 is | | | | | true, the second aspect-ratio 1.0 box for the last layer can have a proper scale. Except for the last | | | | | element in this list, each positive float is the scaling factor for boxes in that layer. For example, | | | | | if for one layer the scale is 0.1, then the generated anchor box with aspect ratio 1 for that layer | | | | | (the first aspect-ratio 1 box if two_boxes_for_ar1 is set to True) will have its height and width as | | | | | 0.1*min (img_h, img_w). | | | | | | | | | | min_scale and max_scale are two positive floats. If both of them appear in the config, the program | | | | | can automatically generate the scales by evenly splitting the space between min_scale and max_scale. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | min_scale/max_scale | If both appear in the config, scales will be generated evenly by splitting the space between min_scale | float | -- | | | and max_scale. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | variances | A list of 4 positive floats. The four floats, in order, represent variances for box | | | | | center x, box center y, log box height, and log box width. The box offset for box center (cx, cy) and log box | | -- | | | size (height/width) w.r.t. anchor will be divided by their respective variance value. Therefore, larger | | | | | variances result in less significant differences between two different boxes on encoded offsets. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | steps | An optional list inside quotation marks with a length that is the number of feature layers for prediction. The | string | -- | | | elements should be floats or tuples/lists of two floats. The steps define how many pixels apart the anchor-box | | | | | center points should be. If the element is a float, both vertical and horizontal margin is the same. Otherwise, | | | | | the first value is step_vertical and the second value is step_horizontal. If steps are not provided, anchor | | | | | boxes will be distributed uniformly inside the image. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | offsets | An optional list of floats inside quotation marks with length equal to the number of feature layers for prediction. | string | -- | | | The first anchor box will have a margin of offsets[i]*steps[i] pixels from the left and top borders. If offsets are | | | | | not provided, 0.5 will be used as default value. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | arch | The backbone for feature extraction. Currently, “resnet”, “vgg”, “darknet”, “googlenet”, “mobilenet_v1”, | string | resnet | | | “mobilenet_v2” and “squeezenet” are supported. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | nlayers | The number of conv layers in a specific arch. For “resnet”, 10, 18, 34, 50 and 101 are supported. For “vgg”, 16 and | Unsigned int | -- | | | 19 are supported. For “darknet”, 19 and 53 are supported. All other networks don’t have this configuration, and | | | | | users should delete this parameter from the config file. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | pred_num_channels | This setting controls the number of channels of the convolutional layers in the DSSD prediction module. Setting | Unsigned int | 512 | | | this value to 0 will disable the DSSD prediction module. Supported values for this setting are 0, 256, 512 and | | | | | 1024. A larger value gives a larger network and usually means the network is harder to train. | | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | freeze_bn | Whether to freeze all batch normalization layers during training. | boolean | False | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ | freeze_blocks | The list of block IDs to be frozen in the model during training. You can choose to freeze some of the CNN blocks | list(repeated integers) | -- | | | in the model to make the training more stable and/or easier to converge. The definition of a block is heuristic | | | | | for a specific architecture. For example, by stride or by logical blocks in the model, etc. However, the block ID | • ResNet series. For the ResNet series, the block IDs valid | | | | numbers identify the blocks in the model in a sequential order so you don't have to know the exact locations of the | for freezing is any subset of [0, 1, 2, 3] (inclusive) | | | | blocks when you do training. As a general principle, the smaller the block ID, the closer it is to | • VGG series. For the VGG series, the block IDs valid for | | | | the model input; the larger the block ID, the closer it is to the model output. | freezing is any subset of[1, 2, 3, 4, 5] (inclusive) | | | | | • GoogLeNet. For the GoogLeNet, the block IDs valid for freezing | | | | You can divide the whole model into several blocks and optionally freeze a subset of it. Note that for FasterRCNN, | is any subset of[0, 1, 2, 3, 4, 5, 6, 7] (inclusive) | | | | you can only freeze the blocks that are before the ROI pooling layer. Any layer after the ROI pooling layer will | • MobileNet V1. For the MobileNet V1, the block IDs valid for freezing | | | | not be frozen anyway. For different backbones, the number of blocks and the block ID for each block are different. | is any subset of [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] (inclusive) | | | | It deserves some detailed explanations on how to specify the block IDs for each backbone. | • MobileNet V2. For the MobileNet V2, the block IDs valid for freezing | | | | | is any subset of [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] (inclusive) | | | | | • DarkNet. For the DarkNet 19 and DarkNet 53, the block IDs valid for freezing | | | | | is any subset of [0, 1, 2, 3, 4, 5] (inclusive) | | +----------------------+---------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------+-------------------------------------------+ Training the Model ------------------ Train the DSSD model using this command: .. code:: tlt dssd train [-h] -e -r -k [--gpus ] [--gpu_index ] [--use_amp] [--log_file ] [-m ] [--initial_epoch ] Required Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-r, --results_dir`: Path to the folder where the experiment output is written. * :code:`-k, --key`: Provide the encryption key to decrypt the model. * :code:`-e, --experiment_spec_file`: Experiment specification file to set up the evaluation experiment. This should be the same as the training specification file. Optional Arguments ^^^^^^^^^^^^^^^^^^ * :code:`--gpus num_gpus`: Number of GPUs to use and processes to launch for training. The default = 1. * :code:`--gpu_index`: The GPU indices used to run the training. We can specify the GPU indices used to run training when the machine has multiple GPUs installed. * :code:`--use_amp`: A flag to enable AMP training. * :code:`--log_file`: The path to the log file. Defaults to :code:`stdout`. * :code:`-m, --resume_model_weights`: Path to a pre-trained model or model to continue training. * :code:`--initial_epoch`: Epoch number to resume from. * :code:`--use_multiprocessing`: Enable multiprocessing mode in data generator. * :code:`-h, --help`: Show this help message and exit. Input Requirement ^^^^^^^^^^^^^^^^^ * **Input size**: C * W * H (where C = 1 or 3, W >= 128, H >= 128) * **Image format**: JPG, JPEG, PNG * **Label format**: KITTI detection Sample Usage ^^^^^^^^^^^^ Here's an example of using the train command on a DSSD model: .. code:: tlt dssd train --gpus 2 -e /path/to/dssd_spec.txt -r /path/to/result -k $KEY Evaluating the Model -------------------- Use following command to run evaluation for a DSSD model: .. code:: tlt dssd evaluate [-h] -m -e [-k ] [--gpu_index ] [--log_file ] Required Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-m, --model`: The :code:`.tlt` model or :code:`TensorRT` engine to be evaluated. * :code:`-e, --experiment_spec_file`: The experiment spec file to set up the evaluation experiment. This should be the same as the training spec file. Optional Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-h, --help`: Show this help message and exit. * :code:`-k, --key`：The encoding key for the :code:`.tlt` model * :code:`--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. * :code:`--log_file`: The path to the log file. The default path is :code:`stdout`. Here is a sample command to evaluate a DSSD model: .. code:: tlt dssd evaluate -m /path/to/trained_tlt_dssd_model -k -e /path/to/dssd_spec.txt Running Inference on the Model ------------------------------ The :code:`inference` tool for DSSD networks can be used to visualize bboxes or generate frame-by-frame KITTI format labels on a directory of images. Here's an example of using this tool: .. code:: tlt dssd inference [-h] -i -o -e -m -k [-l ] [-t ] [--gpu_index ] [--log_file ] Required Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-m, --model`: The path to the pretrained model (TLT model). * :code:`-i, --in_image_dir`: The directory of input images for inference. * :code:`-o, --out_image_dir`: The directory path to output annotated images. * :code:`-k, --key`: The key to the load model. * :code:`-e, --config_path`: The path to an experiment spec file for training. Optional Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-t, --threshold`: The threshold for drawing a bbox and dumping a label file. (default: 0.3) * :code:`-h, --help`: Show this help message and exit. * :code:`-l, --out_label_dir`: The directory to output KITTI labels to. * :code:`--gpu_index`: The index of the GPU to run inference (useful when the machine has multiple GPUs installed). Note that evaluation can only run on a single GPU. * :code:`--log_file`: The path to the log file. The default path is :code:`stdout`. Here is a sample of using inference with the DSSD model: .. code:: tlt dssd inference -i /path/to/input/images_dir -o /path/to/output/dir -m /path/to/trained_tlt_dssd_model -k -e /path/to/dssd_spec.txt Pruning the Model ----------------- .. _pruning_the_model_dssd: Pruning removes parameters from the model to reduce the model size without compromising the integrity of the model itself. The :code:`prune` command includes these parameters: .. code:: tlt dssd prune [-h] -m -o -k [-n ] [-eq ] [-pg ] [-pth ] [-nf ] [-el [] [--gpu_index ] [--log_file ] Required Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-m, --pretrained_model`: The path to the pretrained model. * :code:`-o, --output_file`: The path to output checkpoints to. * :code:`-k, --key`: The key to load a :code:`.tlt` model. Optional Arguments ^^^^^^^^^^^^^^^^^^ * :code:`-h, --help`: Show this help message and exit. * :code:`-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`) * :code:`-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 :code:`arithmetic_mean`,:code:`geometric_mean`, :code:`union`, and :code:`intersection`. (default: :code:`union`) * :code:`-pg, -pruning_granularity`: Number of filters to remove at a time. (default:8) * :code:`-pth`: Threshold to compare normalized norm against. (default:0.1) .. Note: NVIDIA recommends changing the threshold to keep the number of parameters in the model to within 10-20% of the original unpruned model. * :code:`-nf, --min_num_filters`: Minimum number of filters to keep per layer. (default:16) * :code:`-el, --excluded_layers`: List of excluded_layers. Examples: -i item1 item2 (default: []) * :code:`--gpu_index`: The index of the GPU to run pruning (useful when the machine has multiple GPUs installed). Note that evaluation can only run on a single GPU. * :code:`--log_file`: The path to the log file. Defaults to :code:`stdout`. Here's an example of using the :code:`prune` command: .. code:: tlt dssd prune -m /workspace/output/weights/resnet_003.tlt \ -o /workspace/output/weights/resnet_003_pruned.tlt \ -eq union \ -pth 0.7 -k $KEY After pruning, the model needs to be retrained. See :ref:`Re-training the Pruned Model ` for more details. Re-training the Pruned Model ---------------------------- .. _re-training_the_pruned_model_dssd: Once the model has been pruned, there might be a slight 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, use the :code:`tlt dssd train` command with an updated spec file that points to the newly pruned model as the pretrained model file. Users are advised to turn off the regularizer in the :code:`training_config` for DSSD to recover the accuracy when retraining a pruned model. You may do this by setting the regularizer type to NO_REG, as mentioned :ref:`here`. All the other parameters may be retained in the spec file from the previous training. .. Note:: DSSD does not support loading a pruned non-QAT model and retraining it with QAT enabled, or vice versa. For example, to get a pruned QAT model, perform the initial training with QAT enabled or :code:`enable_qat=True`. Exporting the Model ------------------- .. _exporting_the_model_dssd: The Transfer Learning Toolkit includes the :code:`export` command to export and prepare TLT models for :ref:`Deploying to DeepStream `. The :code:`export` command optionally generates the calibration cache for TensorRT INT8 engine calibration. Exporting the model decouples the training process from inference and allows conversion to TensorRT engines outside the TLT environment. TensorRT engines are specific to each hardware configuration and should be generated for each unique inference environment. This may be interchangeably referred to as the :code:`.trt` or :code:`.engine` file. The same exported TLT model may be used universally across training and deployment hardware. This is referred to as the :code:`.etlt` file or encrypted TLT file. During model export, the TLT model is encrypted with a private key. This key is required when you deploy this model for inference. 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 :code:`export` is run with the :code:`--data_type` flag set to :code:`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 an INT8 calibration cache by ingesting training data using the following method: * Pointing 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 :code:`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 model from the :code:`train` step to :code:`export` to convert it into an encrypted TLT model. .. image:: ../../content/fp16_fp32_export.png Exporting command ^^^^^^^^^^^^^^^^^ Use the following command to export a DSSD model: .. code:: tlt dssd export [-h] -m -k -e ] [-o ] [--cal_data_file ] [--cal_image_dir ] [--data_type ] [--batches ] [--max_batch_size ] [--max_workspace_size ] [--engine_file ] [--strict_type_constraints] [--force_ptq] [--gen_ds_config] [--gpu_index ] [--log_file ] [--verbose] Required Arguments ****************** * :code:`-m, --model`: The path to the :code:`.tlt` model file to be exported using :code:`export`. * :code:`-k, --key`: The key used to save the :code:`.tlt` model file. * :code:`-e, --experiment_spec`: The path to the spec file. Optional Arguments ****************** * :code:`-o, --output_file`: The path to save the exported model to. The default path is :code:`./.etlt`. * :code:`--data_type`: The desired engine data type. The options are "fp32", "fp16", "int8". The default value is "fp32". If using int8, the following INT8 arguments are required. * :code:`-s, --strict_type_constraints`: A Boolean flag to indicate whether or not to apply the TensorRT :code:`strict_type_constraints` when building the TensorRT engine. Note this is only for applying the strict type of INT8 mode. * :code:`--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 :code:`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. * :code:`--gpu_index`: The index of (descrete) GPUs used for exporting the model. We can specify the GPU index to run export if the machine has multiple GPUs installed. Note that export can only run on a single GPU. * :code:`--log_file`: Path to the log file. Defaults to stdout. INT8 Export Mode Required Arguments ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * :code:`--cal_data_file`: The tensorfile generated from :code:`tlt-int8-tensorfile` for calibrating the engine. This can also be an output file if used with :code:`--cal_image_dir`. * :code:`--cal_image_dir`: The directory of images to use for calibration .. Note:: The :code:`--cal_image_dir` parameter applies the necessary preprocessing to generate a tensorfile at the path mentioned in the :code:`--cal_data_file` parameter, which is in turn used for calibration. The number of generated batches in the tensorfile is obtained from the value set to the :code:`--batches` parameter, and the :code:`batch_size` is obtained from the value set to the :code:`--batch_size` parameter. Ensure that the directory mentioned in :code:`--cal_image_dir` has at least :code:`batch_size * batches` number of images in it. The valid image extensions are :code:`.jpg`, :code:`.jpeg`, and :code:`.png`. In this case, the :code:`input_dimensions` of the calibration tensors are derived from the input layer of the :code:`.tlt` model. INT8 Export Optional Arguments ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * :code:`--cal_cache_file`: The path to save the calibration cache file to. The default value is :code:`./cal.bin`. * :code:`--batches`: The number of batches to use for calibration and inference testing. The default value is 10. * :code:`--batch_size`: The batch size to use for calibration. The default value is 8. * :code:`--max_batch_size`: The maximum batch size of the TensorRT engine. The default value is 16. * :code:`--max_workspace_size`: The maximum workspace size of the TensorRT engine. The default value is 1073741824 = 1<<30. * :code:`--engine_file`: The path to the serialized TensorRT engine file. Note that this file is hardware specific and cannot be generalized across GPUs. Use this argument to quickly test your model accuracy using TensorRT on the host. As the TensorRT engine file is hardware specific, you cannot use this engine file for deployment unless the deployment GPU is identical to the training GPU. * :code:`--force_ptq`: A Boolean flag to force post-training quantization on the exported :code:`.etlt` model. .. Note:: When exporting a model that was trained with QAT enabled, the tensor scale factors to calibrate the activations are peeled out of the model and serialized to a TensorRT-readable cache file defined by the :code:`cal_cache_file` argument. However, the current version of QAT doesn’t natively support DLA int8 deployment on Jetson. To deploy this model on Jetson with DLA :code:`int8`, use the :code:`--force_ptq` flag to use TensorRT post-training quantization to generate the calibration cache file. Exporting a Model ^^^^^^^^^^^^^^^^^ Here's a sample command using the :code:`--cal_image_dir` option: .. code:: tlt dssd export -m $USER_EXPERIMENT_DIR/data/dssd/dssd_kitti_retrain_epoch12.tlt \ -o $USER_EXPERIMENT_DIR/data/dssd/dssd_kitti_retrain.int8.etlt \ -e $SPECS_DIR/dssd_kitti_retrain_spec.txt \ --key $KEY \ --cal_image_dir $USER_EXPERIMENT_DIR/data/KITTI/val/image_2 \ --data_type int8 \ --batch_size 8 \ --batches 10 \ --cal_data_file $USER_EXPERIMENT_DIR/data/dssd/cal.tensorfile \ --cal_cache_file $USER_EXPERIMENT_DIR/data/dssd/cal.bin \ --engine_file $USER_EXPERIMENT_DIR/data/dssd/detection.trt Deploying to DeepStream ----------------------- .. _deploying_to_deepstream_dssd: .. include:: ../excerpts/deploying_to_deepstream.rst .. _here: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html TensorRT Open Source Software (OSS) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TensorRT OSS build is required for DSSD models. This is required because several TensorRT plugins that are required by these models are only available in TensorRT open source repo and not in the general TensorRT release. Specifically, for DSSD, we need the :code:`batchTilePlugin` and :code:`NMSPlugin`. If the deployment platform is x86 with NVIDIA GPU, follow instructions for x86; if your deployment is on NVIDIA Jetson platform, follow instructions for Jetson. TensorRT OSS on x86 ******************* .. include:: ../excerpts/tensorrt_oss_on_x86.rst TensorRT OSS on Jetson (ARM64) ****************************** .. include:: ../excerpts/tensorrt_oss_on_jetson_arm64.rst Generating an Engine Using tlt-converter ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. _generating_an_engine_using_tlt-converter_dssd: .. include:: ../excerpts/generating_an_engine_using_tlt-converter.rst Instructions for x86 ******************** .. include:: ../excerpts/instructions_for_x86_with_OSS.rst Instructions for Jetson *********************** .. include:: ../excerpts/instructions_for_jetson_with_OSS.rst Using the tlt-converter *********************** .. code:: tlt-converter [-h] -k -d -o [-c ] [-e ] [-b ] [-m ] [-t ] [-w ] [-i ] [-p ] [-s] [-u ] input_file Required Arguments ~~~~~~~~~~~~~~~~~~ * :code:`input_file`: Path to the :code:`.etlt` model exported using :code:`tlt dssd export`. * :code:`-k`: The key used to encode the :code:`.tlt` model when doing the training. * :code:`-d`: Comma-separated list of input dimensions that should match the dimensions used for :code:`tlt dssd export`. * :code:`-o`: Comma-separated list of output blob names that should match the output configuration used for :code:`tlt dssd export`. For DSSD, set this argument to :code:`NMS`. Optional Arguments ~~~~~~~~~~~~~~~~~~ * :code:`-e`: Path to save the engine to. (default: :code:`./saved.engine`) * :code:`-t`: Desired engine data type, generates calibration cache if in INT8 mode. The default value is :code:`fp32`. The options are {:code:`fp32`, :code:`fp16`, :code:`int8`}. * :code:`-w`: Maximum workspace size for the TensorRT engine. The default value is :code:`1073741824(1<<30)`. * :code:`-i`: Input dimension ordering, all other TLT commands use NCHW. The default value is :code:`nchw`. The options are {:code:`nchw`, :code:`nhwc`, :code:`nc`}. For DSSD, we can omit it(defaults to :code:`nchw`). * :code:`-p`: Optimization profiles for :code:`.etlt` models with dynamic shape. Comma separated list of optimization profile shapes in the format :code:`,,,`, where each shape has the format: :code:`xxx`. Can be specified multiple times if there are multiple input tensors for the model. This is only useful for new models introduced in TLT 3.0. This parameter is not required for models that are already existed in TLT 2.0. * :code:`-s`: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints when building the TensorRT engine. * :code:`-u`: Use DLA core. Specifying DLA core index when building the TensorRT engine on Jetson devices. INT8 Mode Arguments ~~~~~~~~~~~~~~~~~~~ * :code:`-c`: Path to calibration cache file, only used in INT8 mode. The default value is :code:`./cal.bin`. * :code:`-b`: Batch size used during the export step for INT8 calibration cache generation. (default: :code:`8`). * :code:`-m`: Maximum batch size for TensorRT engine.(default: :code:`16`). If meet with out-of-memory issue, decrease the batch size accordingly. This parameter is not required for :code:`.etlt` models generated with dynamic shape. (This is only possible for new models introduced in TLT 3.0.) Sample Output Log ~~~~~~~~~~~~~~~~~ Here is a sample log for exporting a DSSD model. .. code:: tlt-converter -k $KEY \ -d 3,384,1248 \ -o NMS \ -e /export/trt.fp16.engine \ -t fp16 \ -i nchw \ -m 1 \ /ws/dssd_resnet18_epoch_100.etlt .. [INFO] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output. [INFO] Detected 1 inputs and 2 output network tensors. Integrating the model to DeepStream ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ There are 2 options to integrate models from TLT with DeepStream: * **Option 1**: Integrate the model (.etlt) with the encrypted key directly in the DeepStream app. The model file is generated by :code:`tlt dssd export`. * **Option 2**: Generate a device specific optimized TensorRT engine, using tlt-converter. The TensorRT engine file can also be ingested by DeepStream. For DSSD, we will need to build TensorRT Open source plugins and custom bounding box parser. The instructions are provided below in the TensorRT OSS section above and the required code can be found in this `GitHub repo`_. .. _GitHub repo: https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps In order to integrate the models with DeepStream, you need the following: 1. Download_ and install DeepStream SDK. The installation instructions for DeepStream are provided in the `DeepStream Development Guide`_. 2. An exported :code:`.etlt` model file and optional calibration cache for INT8 precision. 3. `TensorRT 7+ OSS Plugins`_ . 4. A :code:`labels.txt` file containing the labels for classes in the order in which the networks produces outputs. 5. A sample :code:`config_infer_*.txt` file to configure the nvinfer element in DeepStream. The nvinfer element handles everything related to TensorRT optimization and engine creation in DeepStream. .. _Download: https://developer.nvidia.com/deepstream-download .. _DeepStream Development Guide: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html .. _TensorRT 7+ OSS Plugins : https://github.com/NVIDIA/TensorRT/tree/release/7.0 DeepStream SDK ships with an end-to-end reference application which is fully configurable. Users can configure input sources, inference model and output sinks. The app requires a primary object detection model, followed by an optional secondary classification model. The reference application is installed as :code:`deepstream-app`. The graphic below shows the architecture of the reference application. .. image:: ../../content/arch_ref_appl.png There are typically 2 or more configuration files that are used with this app. In the install directory, the config files are located in :code:`samples/configs/deepstream-app` or :code:`sample/configs/tlt_pretrained_models`. The main config file configures all the high level parameters in the pipeline above. This would set input source and resolution, number of inferences, tracker and output sinks. The other supporting config files are for each individual inference engine. The inference specific config files are used to specify models, inference resolution, batch size, number of classes and other customization. The main config file will call all the supporting config files. Here are some config files in :code:`samples/configs/deepstream-app` for your reference. * :code:`source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt`: Main config file * :code:`config_infer_primary.txt`: Supporting config file for primary detector in the pipeline above * :code:`config_infer_secondary_*.txt`: Supporting config file for secondary classifier in the pipeline above The :code:`deepstream-app` will only work with the main config file. This file will most likely remain the same for all models and can be used directly from the DeepStream SDK will little to no change. User will only have to modify or create :code:`config_infer_primary.txt` and :code:`config_infer_secondary_*.txt`. Integrating an DSSD Model ************************* To run a DSSD model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 7+ Open source software and DSSD bounding box parser for DeepStream. A DeepStream sample with documentation on how to run inference using the trained DSSD models from TLT is provided on GitHub here_. Prerequisite for DSSD Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. DSSD requires batchTilePlugin and NMS_TRT. This plugin is available in the TensorRT open source repo, but not in TensorRT 7.0. Detailed instructions to build TensorRT OSS can be found in `TensorRT Open Source Software (OSS)`. 2. DSSD requires custom bounding box parsers that are not built-in inside the DeepStream SDK. The source code to build custom bounding box parsers for DSSD is available here_. The following instructions can be used to build bounding box parser: **Step1**: Install git-lfs_ (git >= 1.8.2) .. _git-lfs: https://github.com/git-lfs/git-lfs/wiki/Installation .. code:: curl -s https://packagecloud.io/install/repositories/github/git-lfs/ script.deb.sh | sudo bash sudo apt-get install git-lfs git lfs install **Step 2**: Download Source Code with SSH or HTTPS .. code:: git clone -b release/tlt3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps **Step 3**: Build .. code:: // or Path for DS installation export CUDA_VER=10.2 // CUDA version, e.g. 10.2 make This generates :code:`libnvds_infercustomparser_tlt.so` in the directory :code:`post_processor`. Label File ^^^^^^^^^^ The label file is a text file containing the names of the classes that the DSSD model is trained to detect. The order in which the classes are listed here must match the order in which the model predicts the output. During the training, TLT DSSD will specify all class names in lower case and sort them in alphabetical order. For example, if the `dataset_config` is: .. code:: dataset_config { data_sources: { label_directory_path: "/workspace/tlt-experiments/data/training/label_2" image_directory_path: "/workspace/tlt-experiments/data/training/image_2" } target_class_mapping { key: "car" value: "car" } target_class_mapping { key: "person" value: "person" } target_class_mapping { key: "bicycle" value: "bicycle" } validation_data_sources: { label_directory_path: "/workspace/tlt-experiments/data/val/label" image_directory_path: "/workspace/tlt-experiments/data/val/image" } } Then the corresponding `dssd_labels.txt` file would be: .. code:: background bicycle car person DeepStream Configuration File ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The detection model is typically used as a primary inference engine. It can also be used as a secondary inference engine. To run this model in the sample :code:`deepstream-app`, you must modify the existing :code:`config_infer_primary.txt` file to point to this model. .. image:: ../../content/dstream_deploy_options2.png **Option 1**: Integrate the model (:code:`.etlt`) directly in the DeepStream app. For this option, users will need to add the following parameters in the configuration file. The :code:`int8-calib-file` is only required for INT8 precision. .. code:: tlt-encoded-model= tlt-model-key= int8-calib-file= The :code:`tlt-encoded-model` parameter points to the exported model (:code:`.etlt`) from TLT. The :code:`tlt-model-key` is the encryption key used during model export. **Option 2**: Integrate TensorRT engine file with DeepStream app. **Step 1**: Generate TensorRT engine using tlt-converter. Detail instructions are provided in the :ref:`Generating an engine using tlt-converter ` section above. **Step 2**: Once the engine file is generated successfully, modify the following parameters to use this engine with DeepStream. .. code:: model-engine-file= All other parameters are common between the two approaches. To use the custom bounding box parser instead of the default parsers in DeepStream, modify the following parameters in [property] section of primary infer configuration file: .. code:: parse-bbox-func-name=NvDsInferParseCustomNMSTLT custom-lib-path= Add the label file generated above using: .. code:: labelfile-path= For all the options, see the sample configuration file below. To learn about what all the parameters are used for, refer to the `DeepStream Development Guide`_. .. _DeepStream Development Guide: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html .. code:: [property] gpu-id=0 net-scale-factor=1.0 offsets=103.939;116.779;123.68 model-color-format=1 labelfile-path= tlt-encoded-model= tlt-model-key= infer-dims=3;384;1248 uff-input-order=0 maintain-aspect-ratio=1 uff-input-blob-name=Input batch-size=1 ## 0=FP32, 1=INT8, 2=FP16 mode network-mode=0 num-detected-classes=4 interval=0 gie-unique-id=1 is-classifier=0 #network-type=0 output-blob-names=NMS parse-bbox-func-name=NvDsInferParseCustomNMSTLT custom-lib-path= [class-attrs-all] threshold=0.3 roi-top-offset=0 roi-bottom-offset=0 detected-min-w=0 detected-min-h=0 detected-max-w=0 detected-max-h=0