ReIdentificationNet

ReIdentificationNet takes cropped images of a person from different perspectives as network input and outputs the embedding features for that person. The embeddings are used to perform similarity matching to re-identify the same person. The model supported in the current version is based on ResNet, which is the most commonly used baseline for re-identification due to its high accuracy.

The expected time to train ReIdentificationNet is as follows:

The ReIdentificationNet apps in TAO expect data in Market-1501 format for training and evaluation.

See the Data Annotation Format page for more information about the Market-1501 data format.

The spec file for ReIdentificationNet includes model, dataset, re_ranking, and train parameters. Here is an example spec for training a ResNet model on Market-1501 that contains 751 identities in the training set.

            

            
results_dir: "/path/to/experiment_results"
encryption_key: nvidia_tao
model:
  backbone: resnet_50
  last_stride: 1
  pretrain_choice: imagenet
  pretrained_model_path: "/path/to/pretrained_model.pth"
  input_channels: 3
  input_width: 128
  input_height: 256
  neck: bnneck
  feat_dim: 256
  neck_feat: after
  metric_loss_type: triplet
  with_center_loss: False
  with_flip_feature: False
  label_smooth: True
dataset:
  train_dataset_dir: "/path/to/train_dataset_dir"
  test_dataset_dir: "/path/to/test_dataset_dir"
  query_dataset_dir: "/path/to/query_dataset_dir"
  num_classes: 751
  batch_size: 64
  val_batch_size: 128
  num_workers: 1
  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_instances: 4
re_ranking:
  re_ranking: True
  k1: 20
  k2: 6
  lambda_value: 0.3
train:
  results_dir: "${results_dir}/train"
  optim:
    name: Adam
    lr_monitor: val_loss
    steps: [40, 70]
    gamma: 0.1
    bias_lr_factor: 1
    weight_decay: 0.0005
    weight_decay_bias: 0.0005
    warmup_factor: 0.01
    warmup_iters: 10
    warmup_method: linear
    base_lr: 0.00035
    momentum: 0.9
    center_loss_weight: 0.0005
    center_lr: 0.5
    triplet_loss_margin: 0.3
  num_epochs: 10
  checkpoint_interval: 5
  validation_interval: 5
  seed: 1234
        

model

The model parameter provides options to change the ReIdentificationNet architecture.

            

            
model:
  backbone: resnet_50
  last_stride: 1
  pretrain_choice: imagenet
  pretrained_model_path: "/path/to/pretrained_model.pth"
  input_channels: 3
  input_width: 128
  input_height: 256
  neck: bnneck
  feat_dim: 256
  neck_feat: after
  metric_loss_type: triplet
  with_center_loss: False
  with_flip_feature: False
  label_smooth: True
        

dataset

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

            

            
dataset:
  train_dataset_dir: "/path/to/train_dataset_dir"
  test_dataset_dir: "/path/to/test_dataset_dir"
  query_dataset_dir: "/path/to/query_dataset_dir"
  num_classes: 751
  batch_size: 64
  val_batch_size: 128
  num_workers: 1
  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_instances: 4
        

re_ranking

The re_ranking parameter defines the settings for the re-ranking module.

            

            
re_ranking:
  re_ranking: True
  k1: 20
  k2: 6
  lambda_value: 0.3
        

train

The train parameter defines the hyperparameters of the training process.

            

            
train:
  optim:
    name: Adam
    lr_monitor: val_loss
    steps: [40, 70]
    gamma: 0.1
    bias_lr_factor: 1
    weight_decay: 0.0005
    weight_decay_bias: 0.0005
    warmup_factor: 0.01
    warmup_iters: 10
    warmup_method: linear
    base_lr: 0.00035
    momentum: 0.9
    center_loss_weight: 0.0005
    center_lr: 0.5
    triplet_loss_margin: 0.3
  num_epochs: 10
  checkpoint_interval: 5
  validation_interval: 5
  seed: 1234
        

optim

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

            

            
optim:
  name: Adam
  lr_monitor: val_loss
  lr_steps: [40, 70]
  gamma: 0.1
  bias_lr_factor: 1
  weight_decay: 0.0005
  weight_decay_bias: 0.0005
  warmup_factor: 0.01
  warmup_iters: 10
  warmup_method: linear
  base_lr: 0.00035
  momentum: 0.9
  center_loss_weight: 0.0005
  center_lr: 0.5
  triplet_loss_margin: 0.3
        

Use the following command to run ReIdentificationNet training:

            

            
tao model re_identification train [-h] -e <experiment_spec>
                            [results_dir=<global_results_dir>]
                            [model.<model_option>=<model_option_value>]
                            [dataset.<dataset_option>=<dataset_option_value>]
                            [train.<train_option>=<train_option_value>]
                            [train.gpu_ids=<gpu indices>]
                            [train.num_gpus=<number of gpus>]
        

Required Arguments

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

Optional Arguments

You can set optional arguments to override the option values in the experiment spec file.

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

• model.<model_option>: The model options.

• dataset.<dataset_option>: The dataset options.

• re_ranking.<rerank_option>: The re-ranking options.

• train.<train_option>: The train options.

• train.optim.<optim_option>: The optimizer options

Checkpointing and Resuming Training

At every train.checkpoint_interval, a PyTorch Lightning checkpoint is saved. It is called model_epoch_<epoch_num>.pth. These are saved in train.results_dir, like so:

            

            
$ ls /results/train

'model_epoch_000.pth'
'model_epoch_001.pth'
'model_epoch_002.pth'
'model_epoch_003.pth'
'model_epoch_004.pth'
        

The latest checkpoint is saved as reid_model_latest.pth. Training automatically resumes from reid_model_latest.pth, if it exists in train.results_dir. This is superseded by train.resume_training_checkpoint_path, if it is provided.

The major implication of this logic is that, if you wish to trigger fresh training from scratch, either:

• Specify a new, empty results directory (Recommended)

• Remove the latest checkpoint from the results directory

The evaluation metric of ReIdentificationNet is the mean average precision and ranked accuracy. The plots of sampled matches and the cumulative matching characteristic (CMC) curve can be obtained using the evaluate.output_sampled_matches_plot and evaluate.output_cmc_curve_plot parameters, respectively.

Use the following command to run ReIdentificationNet evaluation:

            

            
tao model re_identification evaluate [-h] -e <experiment_spec_file>
                            evaluate.checkpoint=<model to be evaluated>
                            evaluate.output_sampled_matches_plot=<path to the output sampled matches plot>
                            evaluate.output_cmc_curve_plot=<path to the output CMC curve plot>
                            evaluate.test_dataset=<path to test data>
                            evaluate.query_dataset=<path to query data>
                            [evaluate.<evaluate_option>=<evaluate_option_value>]
                            [evaluate.gpu_ids=<gpu indices>]
                            [evaluate.num_gpus=<number of gpus>]
        

Multi-GPU evaluation is not supported for Re-Identification.

Required Arguments

• -e, --experiment_spec_file: The experiment spec file to set up the evaluation experiment

• evaluate.checkpoint: The .pth model

• evaluate.output_sampled_matches_plot: The path to the plotted file of sampled matches

• evaluate.output_cmc_curve_plot: The path to the plotted file of the CMC curve

• evaluate.test_dataset: The path to the test data

• evaluate.query_dataset: The path to the query data

Optional Argument

• evaluate.gpu_ids: The GPU indices to run evaluation. Defaults to [0].

• evaluate.num_gpus: The number of GPUs to run evaluation. Defualts to 1.

• evaluate.results_dir: The directory to save the evaluation results. Defaults to /results/evaluate.

Use the following command to run inference on ReIdentificationNet with the .tlt model.

            

            
tao model re_identification inference [-h] -e <experiment_spec>
                            inference.checkpoint=<inference model>
                            inference.output_file=<path to output file>
                            inference.test_dataset=<path to gallery data>
                            inference.query_dataset=<path to query data>
                            [inference.<infer_option>=<infer_option_value>]
                            [inference.gpu_ids=<gpu indices>]
                            [inference.num_gpus=<number of gpus>]
        

The output is a JSON file that contains the feature embeddings of all the test and query data.

Multi-GPU inference is currently not supported for Re-Identification.

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up inference

• inference.checkpoint: The .pth model to perform inference with

• inference.output_file: The path to the output JSON file

• inference.test_dataset: The path to the test data

• inference.query_dataset: The path to the query data

Optional Argument

• inference.gpu_ids: The GPU indices to run inference. Defaults to [0].

• inference.num_gpus: The number of GPUs to run inference. Defualts to 1.

• inference.results_dir: The directory to save the inference results. Defaults to /results/inference.

The expected output would be as follows:

            

            
[
  {
    "img_path": "/path/to/img1.jpg",
    "embedding": [-0.30, 0.12, 0.13,...]
  },
  {
    "img_path": "/path/to/img2.jpg",
    "embedding": [-0.10, -0.06, -1.85,...]
  },
  ...
  {
    "img_path": "/path/to/imgN.jpg",
    "embedding": [1.41, 0.63, -0.15,...]
  }
]
        

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

            

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

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up export.

• export.checkpoint: The .pth model to be exported.

• export.onnx_file: The path to save the exported model to. The default path is in the same directory as the \*.pth model.

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.

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

You can deploy the trained deep -earning and computer-vision models on edge devices–such as a Jetson Xavier, Jetson Nano, or Tesla–or in the cloud with NVIDIA GPUs. The exported \*.onnx model can also be used with TAO Triton Apps.

Running ReIdentificationNet Inference on the Triton Sample

The TAO Triton Apps provide an inference sample for ReIdentificationNet. It consumes a TensorRT engine and supports running with a directory of query (probe) images and a directory of test (gallery) images containing the same identities.

To use this sample, you need to generate the TensorRT engine from an \*.onnx model using trtexec.

Generating TensorRT Engine Using trtexec

For instructions on generating a TensorRT engine using the trtexec command, refer to the trtexec guide for ReIdentificationNet.

Running the Triton Inference Sample

You can generate the TensorRT engine when starting the Triton server using the following command:

            

            
bash scripts/start_server.sh
        

When the server is running, you can get results from a directory of query images and a directory of test images using the following command with a client:

            

            
python tao_client.py <path_to_query_directory> \
                    --test_dir <path_to_test_directory>
                    -m re_identification_tao model \
                    -x 1 \
                    -b 16 \
                    --mode Re_identification \
                    -i https \
                    -u localhost:8000 \
                    --async \
                    --output_path <path_to_output_directory>
        

            

            
[
  ...,
  {
    "img_path": "/localhome/Data/market1501/query/1121_c3s2_156744_00.jpg",
    "embedding": [-1.1530249118804932, -1.8521332740783691,..., 0.380886435508728]
  },...
  {
    "img_path": "/localhome/Data/market1501/bounding_box_test/1377_c2s3_038007_05.jpg",
    "embedding": [0.09496910870075226, 0.26107653975486755,..., 0.2835155725479126]
  },...
]
        

End-to-End Inference Using Triton

The TAO Triton Apps provides a sample for end-to-end inference from a directory of query images and a directory of test images. The sample downloads the Market-1501 dataset and randomly samples a subset of 100 identities. The client implicitly converts the image samples into arrays and sends them to the Triton server. The feature embedding for each image is returned and saved to the JSON output. An image of sampled matches and a figure of the CMC curve is also generated for visualization.

You can start the Triton server using the following command (only the ReIdentificationNet model will be downloaded and converted into a TensorRT engine):

            

            
bash scripts/re_id_e2e_inference/start_server.sh
        

Once the Triton server has started, open another terminal and use the following command to run re-identification on the query and test images using the Triton server instance that you have previously spun up:

            

            
bash scripts/re_id_e2e_inference/start_client.sh