Metric Learning Recognition

Metric Learning Recognition (MLRecogNet) is a classifier that encodes the input image to embedding vectors and predicts their labels based on the embedding vectors in the reference space. MLRecogNet consists of two parts:

Trunk: A backbone network that encodes the input image to a feature vector.
Embedder: A fully connected layer that maps the feature vector to the embedding space.

The embedding space is a high-dimensional space where the distance between the embedding vectors of the same class is small and the distance between the embedding vectors of different classes is large. The embedder is trained to minimize the distance between the embedding vectors of the same class and maximize the distance between the embedding vectors of different classes. The embedding vectors of the query images are compared with the embedding vectors of the reference images to predict the labels of the query images.

The current supported trunk is ResNet, which is the most commonly used baseline for vision classification. And the current supported embedder is a one-layer MLP.

During training, evaluation, and inference, MLRecogNet requires a reference set and a query set for validation or test. The reference set consists of a collection of labeled images, while the query set refers to a group of unlabeled images–the goal is to predict the labels of the unlabeled images by comparing their similarity to the embedding vectors of the reference set generated by trained MLRecogNet.

Preparing the Dataset

MLRecogNet requires cropped images from the detection set or classification set as input. These images are resized to 224x224 by default for model input. Augmentation is applied to each image during training.

The data should be organized in the following structure:

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            /Dataset
    /reference
      /class1
            0001.jpg
            0002.jpg
            ...
            0100.jpg
      /class2
            0001.jpg
            0002.jpg
            ...
            0100.jpg
        ...
    /train
      /reference
        /class1
              0101.jpg
              0102.jpg
              ...
              0200.jpg
        /class2
              0101.jpg
              0102.jpg
              ...
              0200.jpg
    /val
      /class1
            0201.jpg
            0202.jpg
            ...
            0220.jpg
      /class2
            0201.jpg
            0202.jpg
            ...
            0220.jpg
    /test
      /class1
            0301.jpg
            0302.jpg
            ...
            0400.jpg
      /class2
            0301.jpg
            0302.jpg
            ...
            0400.jpg

The root directory of the dataset contains sub-directories for reference, training, validation, and test. The sub-directories are required to be in ImageNet structure, as demonstrated above. Each sub-directory has images of the same class. If the classes in test set are not in the reference set, the queried images cannot be correctly recognized.

Creating an Experiment Spec File

The spec file for MLRecogNet includes model, train, and dataset parameters. Here is an example spec $TRAIN_SPEC for training a MLRecogNet model on a target dataset.

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            results_dir: "???"
model:
  backbone: resnet_101
  pretrain_choice: ""
  pretrained_model_path: /path/to/resnet101_pretrained_mlrecog.pth.tar
  input_width: 224
  input_height: 224
  feat_dim: 2048
train:
  optim:
    name: Adam
    steps: [40, 70]
    gamma: 0.1
    embedder:
      bias_lr_factor: 1
      weight_decay: 0.0001
      weight_decay_bias: 0.0005
      base_lr: 0.000001
      momentum: 0.9
    trunk:
      bias_lr_factor: 1
      weight_decay: 0.0001
      weight_decay_bias: 0.0005
      base_lr: 0.00001
      momentum: 0.9
    warmup_factor: 0.01
    warmup_iters: 10
    warmup_method: linear
    triplet_loss_margin: 0.3
    miner_function_margin: 0.1
  num_epochs: 5
  resume_training_checkpoint_path: null
  checkpoint_interval: 1
  smooth_loss: False
  batch_size: 16
  val_batch_size: 16
dataset:
  train_dataset: /path/to/dataset/train
  val_dataset:
    reference: /path/to/dataset/reference
    query: /path/to/dataset/val
  workers: 12
  pixel_mean: [0.485, 0.456, 0.406]
  pixel_std: [0.226, 0.226, 0.226]
  prob: 0.5
  re_prob: 0.5
  num_instance: 4
  color_augmentation:
    enabled: True
    brightness: 0.5
    contrast: 0.3
    saturation: 0.1
    hue: 0.1
  gaussian_blur:
    enabled: True
    kernel: [15, 15]
    sigma: [0.3, 0.7]
  random_rotation: True
  class_map: /path/to/class_map.yaml

Parameter	Data Type	Default	Description
`model`	dict config	–	The configuration for the model architecture
`train`	dict config	–	The configuration for the training process
`dataset`	dict config	–	The configuration for the dataset
`results_dir`	string	None	The path to the root results directory. It’s not required when the target task has its own `results_dir` specified.

model

The model parameter provides options to change the MetricLearningRecognition architecture.

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            model:
  backbone: resnet_50
  pretrain_choice: imagenet
  pretrained_model_path: "/path/to/pretrained_model.pth"
  input_channels: 3
  input_width: 224
  input_height: 224
  feat_dim: 256

Parameter	Datatype	Default	Description	Supported Values
`backbone`	string	resnet_50	The type of model. Values are currently limited to resnet_50 and resnet_101.	resnet_50
`pretrain_choice`	string	imagenet	The pretrained network	imagenet/””
`pretrained_model_path`	string		The path to the pretrained model. The weights are only loaded to the trunk part.
`input_channels`	unsigned int	3	The number of input channels	>0
`input_width`	int	224	The input width of the images	int
`input_height`	int	224	The input height of the images	int
`feat_dim`	unsigned int	256	The output size of the feature embeddings	>0

train

The train parameter defines the hyperparameters of the training process.

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            train:
  optim:
    name: Adam
    steps: [40, 70]
    gamma: 0.1
    warmup_factor: 0.01
    warmup_iters: 10
    warmup_method: 'linear'
    triplet_loss_margin: 0.3
    miner_function_margin: 0.1
    embedder:
      bias_lr_factor: 1
      base_lr: 0.000001
      momentum: 0.9
      weight_decay: 0.0001
      weight_decay_bias: 0.0005
    trunk:
      bias_lr_factor: 1
      base_lr: 0.00001
      momentum: 0.9
      weight_decay: 0.0001
      weight_decay_bias: 0.0005
  num_epochs: 120
  checkpoint_interval: 10
  clip_grad_norm: 0.0
  resume_training_checkpoint_path: null
  report_accuracy_per_class: True
  smooth_loss: True
  batch_size: 64
  val_batch_size: 64
  results_dir: null

Parameter	Datatype	Default	Description	Supported Values
`optim`	dict config	–	The configuration for the torch optimizer (Optim Config), including the learning rate, learning scheduler, weight decay, etc.
`gpu_ids`	unsigned int list	[0]	The list of indices of GPUs for training task
`num_epoch`	unsigned int	120	The total number of epochs to run the experiment	>0
`checkpoint_interval`	unsigned int	10	The interval at which the checkpoints are saved	>0
`clip_grad_norm`	float	0.0	The amount to clip the gradient by the L2 norm. A value of 0.0 specifies no clipping.	>=0
`resume_training_checkpoint_path`	string		The path to a checkpoint to continue training
`report_accuracy_per_class`	bool	True	If True, the top1 precision of each class will be reported.	True/False
`smooth_loss`	bool	True	If True, the log-exp version of the triplet loss will be used.	True/False
`batch_size`	unsigned int	64	The batch size for training	>0
`val_batch_size`	unsigned int	64	The batch size for validation	>0
`results_dir`	string		The results directory of the train task

optim

The optim parameter defines the configuration for the Torch optimizer in training, including the learning rate, learning scheduler, and weight decay.

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            optim:
  name: Adam
  steps: [40, 70]
  gamma: 0.1
  warmup_factor: 0.01
  warmup_iters: 10
  warmup_method: 'linear'
  triplet_loss_margin: 0.3
  miner_epsilon: 0.1
  embedder:
    bias_lr_factor: 1
    base_lr: 0.00035
    momentum: 0.9
    weight_decay: 0.0005
    weight_decay_bias: 0.0005
  trunk:
    bias_lr_factor: 1
    base_lr: 0.00035
    momentum: 0.9
    weight_decay: 0.0005
    weight_decay_bias: 0.0005

Parameter	Datatype	Default	Description	Supported Values
`name`	string	Adam	The name of the optimizer. The Algorithms in `torch.optim` are supported.	Adam/SGD/Adamax/…
`steps`	int list	[40, 70]	The steps to decrease the learning rate for the `MultiStep` scheduler
`gamma`	float	0.1	The decay rate for the `WarmupMultiStepLR` scheduler	>0.0
`warmup_factor`	float	0.01	The warmup factor for the `WarmupMultiStepLR` scheduler	>0.0
`warmup_iters`	unsigned int	10	The number of warmup iterations for the `WarmupMultiStepLR` scheduler	>0
`warmup_method`	string	linear	The warmup method for the optimizer	constant/linear
`triplet_loss_margin`	float	0.3	The desired difference between the anchor-positive distance and the anchor-negative distance	>0.0
`miner_function_margin`	float	0.1	Negative pairs are chosen if they have similarity greater than the hardest positive pair, minus this margin; positive pairs are chosen if they have similarity less than the hardest negative pair, plus the margin	>0.0
`embedder`	dict config	–	The learning rate configurations (LR Config) for the MLRecogNet embedder
`trunk`	dict config	–	The learning rate configurations (LR Config) for MLRecogNet trunk

LR config

Parameter	Datatype	Default	Description	Supported Values
`base_lr`	float	0.00035	The initial learning rate for the training	>0.0
`bias_lr_factor`	float	1	The bias learning rate factor for the WarmupMultiStepLR	>=1
`momentum`	float	0.9	The momentum for the WarmupMultiStepLR optimizer	>0.0
`weight_decay`	float	0.0005	The weight decay coefficient for the optimizer	>0.0
`weight_decay_bias`	float	0.0005	The weight decay bias for the optimizer	>0.0

dataset

The dataset parameter defines the dataset source, training batch size, and augmentation.

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            dataset:
  train_dataset: /path/to/dataset/train
  val_dataset:
    reference: /path/to/dataset/reference
    query: /path/to/dataset/val
  workers: 8
  pixel_mean: [0.485, 0.456, 0.406]
  pixel_std: [0.226, 0.226, 0.226]
  padding: 10
  prob: 0.5
  re_prob: 0.5
  sampler: softmax_triplet
  num_instance: 4
  gaussian_blur:
    enabled: True
    kernel: [15, 15]
    sigma: [0.3, 0.7]
  color_augmentation:
    enabled: True
    brightness: 0.5
    contrast: 0.3
    saturation: 0.1
    hue: 0.1

Parameter	Datatype	Default	Description	Supported Values
`train_dataset`	string		The path to the train dataset. This field is only required for the train task.
`val_dataset`	dict		The map of reference set and query set addresses. For training and evaluation, both fields are required. For inference, only the reference set address is needed.	{“reference”: /path/to/reference/set, “query”: “”}
`workers`	unsigned int	8	The number of parallel workers processing data	>0
`class_map`	string		In `tao model`, the class_map is a YAML file mapping dataset class names to desired class names. If not specified, by default the reported class names are the folder names in the dataset folder. In `tao deploy`, the class_map is a TXT file listing the class names line by line. And the line index would be the class index. By default the class names are the folder names and the order of the classes are alphanumeric.
`pixel_mean`	float list	[0.485, 0.456, 0.406]	The pixel mean for image normalization	float list
`pixel_std`	float list	[0.226, 0.226, 0.226]	The pixel standard deviation for image normalization	float list
`num_instance`	unsigned int	4	The number of image instances of the same person in a batch	>0
`prob`	float	0.5	The random horizontal flipping probability for image augmentation	>0
`re_prob`	float	0.5	The random erasing probability for image augmentation	>0
`random_rotation`	bool	True	If True, random rotations at 0 ~ 180 degrees to the input data are applied	True/False
`gaussian_blur`	dict config	–	The configuration of the Gaussian blur augmentation on input samples
`color_augmentation`	dict config	–	The configuration of the color augmentation on input samples

Gaussian Blur Config

Parameter	Datatype	Default	Description	Supported Values
`enabled`	bool	True	If True, applies Gaussian blur augmentation to input samples	True/False
`kernel`	unsigned int list	[15, 15]	The kernel size for the Gaussian blur
`sigma`	float list	[0.3, 0.7]	The sigma value range for the Gaussian blur

Color Augmentation Config

Parameter	Datatype	Default	Description	Supported Values
`enabled`	bool	True	If True, applies color augmentation to input samples	True/False
`brightness`	float	0.5	The value of jittering brightness	>=0
`contrast`	float	0.3	The value of jittering contrast	>=0
`saturation`	float	0.1	The value of jittering saturation	>=0
`hue`	float	0.1	The value of jittering hue	>=0, <=0.5

Training the Model

Use the following command to run MLRecogNet training:

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            tao model ml_recog train -e <experiment_spec_file>
                   -r <results_dir>
                  [train.gpu_ids=<gpu id list>]
                  [train.resume_training_checkpoint_path=<absolute path to \*.pth checkpoint>]

Required Arguments

-e, --experiment_spec_file: The path to the experiment spec file
-r, --results_dir: The path to a folder where the experiment outputs should be written

Optional Arguments

train.gpu_ids: The GPU indices list for training. If you set more than one GPU ID, multi-GPU training will be triggered automatically.
train.resume_training_checkpoint_path: The path to a checkpoint to continue training

Here’s an example of using the MLRecogNet training command:

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            tao model ml_recog train -e $TRAIN_SPEC -r $RESULTS_DIR

Here’s an example of output $RESULTS_DIR/train/status.json:

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            {"date": "6/20/2023", "time": "23:11:2", "status": "STARTED", "verbosity": "INFO", "message": "Starting Training Loop."}
...

{"date": "6/20/2023", "time": "23:11:22", "status": "SUCCESS", "verbosity": "INFO", "message": "Train finished successfully."}

Evaluating the Model

Here is an example spec $EVAL_SPEC for evaluating an MLRecogNet model on a test dataset.

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            results_dir: /path/to/root/results/dir
model:
  backbone: resnet_50
  input_width: 224
  input_height: 224
  feat_dim: 256
dataset:
  workers: 8
  val_dataset:
    reference: /path/to/dataset/reference
    query: /path/to/dataset/val
evaluate:
  checkpoint: /path/to/checkpoint
  batch_size: 128
  results_dir: /path/to/results

Parameter	Datatype	Default	Description	Supported Values
`checkpoint`	string	None	The path to the .pth Torch model to be evaluated
`trt_engine`	string	None	The path to the TensorRT (TRT) engine to be evaluated. Currently, only `trt_engine` is supported in TAO Deploy
`gpu_id`	unsigned int	0	The GPU ID for evaluation. Currently, evaluation is only supported on a single GPU.	>=0
`topk`	int	1	If greater than 1, the accuracy will be top-k precision. Currently, only `evaluate.topk` is supported in TAO Deploy	>0
`batch_size`	int	64	The batch size for the evaluation task	>0
`report_accuracy_per_class`	bool	True	If True, the top-1 precision of each class will be reported	True/False
`results_dir`	string	None	The path to the results directory of the evaluation task

The following are evaluation metrics for MLRecogNet:

Adjusted Mutual Information (AMI): A measure used in statistics and information theory to quantify the agreement between two assignments, such as cluster assignments, which is adjusted for chance and therefore provides a more accurate depiction of the similarity between the two compared to raw mutual information.
Normalized Mutual Information (NMI): A normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation).
Mean Average Precision: The average precision achieved by a model across different recall levels, providing a comprehensive evaluation of its performance on information retrieval.
Mean Average Precision at r: A model’s average precision for the top-R ranked results, offering insight into the effectiveness of the retrieval or object detection performance of the model when considering a limited number of results
Mean Reciprocal Rank: The average of the inverse ranks of the first relevant result for a set of queries, emphasizing the importance of retrieving relevant information as early as possible.
Precision at 1: The accuracy of the nearest neighbor retrievals
R Precision: An evaluation metric for information retrieval systems that measures the proportion of relevant documents among the top-R ranked results, where “”R corresponds to the total number of relevant documents for a given query.

When evaluate.report_accuracy_per_class is set to True, the accuracy of each class will be added.

Use the following command to run MLRecogNet evaluation:

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            tao model ml_recog evaluate -e <experiment_spec_file>
                               evaluate.checkpoint=<model to be evaluated>
                               dataset.val_dataset.reference=<path to test reference set>
                               dataset.val_dataset.query=<path to test query set>
                               evaluate.results_dir=<directory of the saved JSON file>
                               [evaluate.gpu_id=<gpu index>]

Required Arguments

-e, --experiment_spec_file: The experiment spec file to set up the evaluation experiment
evaluate.checkpoint: The path to the .pth model to be evaluated
dataset.val_dataset.reference: The path to the test reference set
dataset.val_dataset.query: The path to the test query set
evaluate.results_dir: The path to the directory where the output JSON file is saved. If this value is not specified, the output JSON file will be saved to results_dir.

Optional Argument

evaluate.gpu_id: The GPU index used to run the evaluation. You can specify the GPU index used to run evaluation when the machine has multiple GPUs installed. Note that evaluation can only run on a single GPU.

Here’s an example of using the MLRecogNet evaluation command:

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            tao model ml_recog evaluate -e $EVAL_SPEC evaluate.checkpoint=$TRAINED_PTH_MODEL dataset.val_dataset.reference=$DATA/test/reference dataset.val_dataset.query=$DATA/test/query

Here’s an example of output $RESULTS_DIR/evaluate/status.json:

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            {"date": "6/2/2023", "time": "6:12:16", "status": "STARTED", "verbosity": "INFO", "message": "Starting Metric Learning Recognition evaluate."}
{"date": "6/2/2023", "time": "6:12:17", "status": "STARTED", "verbosity": "INFO", "message": "Loading checkpoint:$RESULTS_DIR/train/ml_model_epoch=000.pth"}
{"date": "6/2/2023", "time": "6:12:17", "status": "RUNNING", "verbosity": "INFO", "message": "Constructing model graph..."}
{"date": "6/2/2023", "time": "6:12:17", "status": "SKIPPED", "verbosity": "INFO", "message": "Skipped loading pretrained model as checkpoint is to load."}
{"date": "6/2/2023", "time": "6:12:23", "status": "SUCCESS", "verbosity": "INFO", "message": "Evaluate finished successfully.", "kpi": {"AMI": 0.8074901483322209, "NMI": 0.8118350536509751, "Mean Average Precision": 0.6876838920302153, "Mean Reciprocal Rank": 0.992727267742157, "r-Precision": 0.666027864375903, "Precision at Rank 1": 0.989090909090909}}

Below is an example of the printouts:

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            Starting Metric Learning Recognition evaluate.
Experiment configuration:
...
results_dir: $RESULTS_DIR

Loading checkpoint: $RESULTS_DIR/train/ml_model_epoch=000.pth
Constructing model graph...
Skipped loading pretrained model as checkpoint is to load.
Evaluating epoch eval mode
...
Computing accuracy for the query split w.r.t ['gallery']
running k-nn with k=106
embedding dimensionality is 256
/usr/local/lib/python3.8/dist-packages/torch/storage.py:315: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.
  warnings.warn(message, UserWarning)
running k-means clustering with k=5
embedding dimensionality is 256
******************* Evaluation results **********************
AMI: 0.8075
NMI: 0.8118
Mean Average Precision: 0.7560
Mean Reciprocal Rank: 0.9922
r-Precision: 0.7421
Precision at Rank 1: 0.9882
*************************************************************

Running Inference on the Model

Here is an example spec $INFERENCE_SPEC for running MLRecogNet model inference on an inference set.

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            results_dir: /path/to/root/results/dir
model:
  backbone: resnet_50
  input_width: 224
  input_height: 224
  feat_dim: 256
dataset:
  workers: 8
  val_dataset:
    reference: /path/to/dataset/reference
    query: ""
inference:
  input_path: /path/to/dataset/test
  inference_input_type: classification_folder
  checkpoint: /path/to/model/checkpoint
  results_dir: /path/to/results/dir
  batch_size: 128

Parameter	Datatype	Default	Description	Supported Values
`checkpoint`	string	None	The path to the .pth torch model to run inference
`trt_engine`	string	None	The path to the TensorRT (TRT) engine to run inference. Currently, only `trt_engine` is supported in TAO Deploy.
`gpu_id`	unsigned int	0	The GPU ID for inference. Currently, inference is only supported on a single GPU.	>=0
`input_path`	string		The path to the data to run inference on	>0
`inference_input_type`	string	“image_folder”	Three options are supported: `image_folder`: Used when `input_path` is a folder of images. `classification_folder`: Used when `input_path` is an `ImageNet` structured folder. `image`: Used when `input_path` is an image file	“image_folder”/”classification_folder”/”image”
`batch_size`	int	64	The batch size for the inference task	>0
`topk`	int	1	The number of top results to be returned	>0
`results_dir`	string	None	The path to the results directory of the inference task

Use the following command to run inference on MLRecogNet with the .pth model:

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            tao model ml_recog inference -e <experiment_spec>
                                inference.checkpoint=<inference model>
                                dataset.val_dataset.reference=<path to gallery data>
                                inference.input_path=<path to query data>
                                inference.results_dir=<path to output file>
                                [inference.gpu_id=<gpu index>]

The output will be a CSV file that contains the feature embeddings of all the query data and their predicted labels.

Required Arguments

-e, --experiment_spec: The experiment spec file to set up inference
inference.checkpoint: The .pth model to perform inference with
dataset.val_dataset.reference: The path to the reference set
inference.input_path: The path to the data to run inference on
inference.results_dir: The directory to the saved output CSV file

Optional Argument

inference.gpu_id: The index of the GPU that will be used to run inference. You can specify this value when the machine has multiple GPUs installed. Note that inference can only run on a single GPU.

Here’s an example of using the MLRecogNet inference command:

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            tao model ml_recog inference -e $INFERENCE_SPEC inference.checkpoint=$TRAINED_PTH_MODEL dataset.val_data.reference=$DATA/test/reference inference.input_path=$DATA/test/query inference.inference_input_type=classification_folder inference.results_dir=$OUTPUT_DIR

The expected output is as follows:

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            /path/to/images/c000001_10.png,"['c000001', 'c000005', 'c000001', 'c000005']","[5.0030694183078595e-06, 5.5495906963187736e-06, 5.976316515443614e-06, 6.004379429214168e-06]"
/path/to/images/c000001_11.png,"['c000001', 'c000005', 'c000001', 'c000001']","[3.968068540416425e-06, 5.043690180173144e-06, 5.885293830942828e-06, 6.030047643434955e-06]"
/path/to/images/c000001_120.png,"['c000001', 'c000001', 'c000005', 'c000003']","[1.9612791675172048e-06, 4.112744136364199e-06, 4.603011802828405e-06, 5.8091877690458205e-06]"

Where the first column contains the inference image paths, the second column the top-k predicted labels, and the third column the embedding vector distances of the top-k results.

Here’s an example of output $RESULTS_DIR/inference/status.json:

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            {"date": "6/2/2023", "time": "6:13:47", "status": "STARTED", "verbosity": "INFO", "message": "Starting Metric Learning Recognition inference."}
{"date": "6/2/2023", "time": "6:13:47", "status": "STARTED", "verbosity": "INFO", "message": "Loading checkpoint:$RESULTS_DIR/train/ml_model_epoch=001.pth"}
{"date": "6/2/2023", "time": "6:13:47", "status": "RUNNING", "verbosity": "INFO", "message": "Constructing model graph..."}
{"date": "6/2/2023", "time": "6:13:48", "status": "SKIPPED", "verbosity": "INFO", "message": "Skipped loading pretrained model as checkpoint is to load."}
{"date": "6/2/2023", "time": "6:14:6", "status": "SUCCESS", "verbosity": "INFO", "message": "result saved at$RESULTS_DIR/inference/result.csv"}
{"date": "6/2/2023", "time": "6:14:6", "status": "SUCCESS", "verbosity": "INFO", "message": "Inference finished successfully."}

Below is an example of the printouts:

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            Starting Metric Learning Recognition inference.
Experiment configuration:
...

Loading checkpoint: $RESULTS_DIR/train/ml_model_epoch=001.pth
Constructing model graph...
Skipped loading pretrained model as checkpoint is to load.
/usr/local/lib/python3.8/dist-packages/torch/storage.py:315: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.
  warnings.warn(message, UserWarning)
...
result saved at $RESULTS_DIR/inference/result.csv
Inference finished successfully.

Exporting the Model

Here is an example spec $EXPORT_SPEC for exporting the MLRecogNet model.

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            results_dir: /path/to/root/results/dir
model:
  backbone: resnet_50
  input_width: 224
  input_height: 224
  feat_dim: 256
export:
  checkpoint: /path/to/checkpoint
  onnx_file: /path/to/results/model.onnx
  results_dir:  /path/to/results
  batch_size: -1
  on_cpu: false
  verbose: true

Parameter	Datatype	Default	Description	Supported Values
`checkpoint`	string	None	the path to the .pth Torch model to be evaluated
`onnx_file`	string	None	The path to the exported ONNX file. If this value is not specified, it defaults to `model.onnx` in `export.results_dir`
`batch_size`	int	-1	The batch size of the exported ONNX model. If `batch_size` is -1, the exported ONNX model has a dynamic batch size.	>0; -1
`gpu_id`	unsigned int	0	The GPU ID for Torch-to-ONNX export. Currently, the export task only supports running on a single GPU	>=0
`on_cpu`	bool	False	If True, the Torch-to-ONNX export will be performed on CPU	True/False
`opset_version`	unsigned int	14	The version of the default (ai.onnx) opset to target	>= 7 and <= 16.
`verbose`	bool	True	If True, prints a description of the model being exported to `stdout`.	True/False
`results_dir`	string	None	The path to the results directory of the export task

Use the following command to export MLRecogNet to the .onnx format for deployment:

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            tao model ml_recog export -e <experiment_spec>
                          export.checkpoint=<.pth checkpoint to be exported>
                          [export.gpu_id=<gpu index>]
                          [export.onnx_file=<path to exported ONNX file>]

Required Arguments

-e, --experiment_spec: The experiment spec file to set up export
export.checkpoint: The .pth model to be exported

Optional Arguments

export.gpu_id: The index of the GPU that will be used to run the export. You can specify this value when the machine has multiple GPUs installed. Note that export can only run on a single GPU.
export.onnx_file: The path to save the exported model to. The default path is in the same directory as the export.results_dir (if any) or results_dir.

Here’s an example of using the MLRecogNet export command:

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            tao model ml_recog export -e $EXPORT_SPEC export.checkpoint=$TRAINED_PTH_MODEL

Here’s an example of output $RESULTS_DIR/export/status.json:

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            {"date": "6/2/2023", "time": "6:17:45", "status": "STARTED", "verbosity": "INFO", "message": "Starting Metric Learning Recognition export."}
{"date": "6/2/2023", "time": "6:17:45", "status": "STARTED", "verbosity": "INFO", "message": "Loading checkpoint:$RESULTS_DIR/train/ml_model_epoch=001.pth"}
{"date": "6/2/2023", "time": "6:17:45", "status": "RUNNING", "verbosity": "INFO", "message": "Constructing model graph..."}
{"date": "6/2/2023", "time": "6:17:46", "status": "SKIPPED", "verbosity": "INFO", "message": "Skipped loading pretrained model as checkpoint is to load."}
{"date": "6/2/2023", "time": "6:17:46", "status": "STARTED", "verbosity": "INFO", "message": "Exporting model to ONNX"}
{"date": "6/2/2023", "time": "6:17:48", "status": "STARTED", "verbosity": "INFO", "message": "Simplifying ONNX model"}
{"date": "6/2/2023", "time": "6:17:50", "status": "SUCCESS", "verbosity": "INFO", "message": "ONNX model saved at$RESULTS_DIR/export/ml_model_epoch=001.onnx"}
{"date": "6/2/2023", "time": "6:17:50", "status": "SUCCESS", "verbosity": "INFO", "message": "Export finished successfully."}

Below is an example of the printouts:

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            Starting Metric Learning Recognition export.
Experiment configuration:
...

Loading checkpoint: $RESULTS_DIR/train/ml_model_epoch=001.pth
Constructing model graph...
Skipped loading pretrained model as checkpoint is to load.
Exporting model to ONNX
Exported graph: graph(%input : Float(*, 3, 224, 224, strides=[150528, 50176, 224, 1], requires_grad=0, device=cuda:0),
  ...

========== Diagnostic Run torch.onnx.export version 1.14.0a0+44dac51 ===========
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================

Simplifying ONNX model
Checking 0/3...
Checking 1/3...
Checking 2/3...
ONNX model saved at $RESULTS_DIR/export/ml_model_epoch=001.onnx
Export finished successfully.

Deploying the Model

You can use TAO Deploy to deploy the trained deep-learning and computer-vision models on edge devices–such as a Jetson Xavier, Jetson Nano, or Tesla–or in the cloud with NVIDIA GPUs. TAO Deploy an application in TAO Toolkit that converts an ONNX model to a TensorRT engine and runs inferences through the TensorRT engine.

Running MLRecogNet Inference on TAO Deploy

The MLRecogNet ONNX file generated from export is taken as input to TAO Deploy to generate an optimized TensorRT engine. For more information about using TAO Deploy to run inference on an MLRecogNet TensorRT engine, refer to the TAO Deploy documentation.