Visual ChangeNet-Classification#

Visual ChangeNet-Classification is an NVIDIA-developed classification change detection model and is included in the TAO. Visual ChangeNet supports the following tasks:

  • train

  • evaluate

  • inference

  • export

Each task is explained in detail in the following sections.

Note

  • Throughout this documentation, you will see references to $EXPERIMENT_ID and $DATASET_ID in the FTMS Client sections.

    • For instructions on creating a dataset using the remote client, see the Creating a dataset section in the Remote Client documentation.

    • For instructions on creating an experiment using the remote client, see the Creating an experiment section in the Remote Client documentation.

  • The spec format is YAML for TAO Launcher and JSON for FTMS Client.

  • File-related parameters, such as dataset paths or pretrained model paths, are required only for TAO Launcher and not for FTMS Client.

Data Input for VisualChangeNet#

Single Golden Data Format#

VisualChangeNet-Classification requires the data to be provided as image and CSV files. Refer to the Data Annotation Format page for more information about the input data format for VisualChangeNet-Classification, which follows the same input data format as Optical Inspection.

Multiple Golden Data Format#

To enable Multiple Golden mode, set num_golden > 1 in the Dataset Configuration. This mode requires a different data format to support multiple golden reference images per sample. Refer to the Data Annotation Format page for more information about the input data format for Multiple-Golden-VisualChangeNet-Classification.

Creating a Training Experiment Spec File#

Configuring a Custom Dataset#

This section provides example configuration and commands to retrieve configuration for training VisualChangeNet-Classification using the dataset format described above.

Note

Make sure to set task=classify in SPECS for all task specs.

SPECS=$(tao-client visual_changenet get-spec --action train --job_type experiment --id $EXPERIMENT_ID)

Parameter

Data Type

Default

Description

Supported Values

model

dict config

The configuration of the model architecture.

dataset

dict config

The configuration of the dataset.

train

dict config

The configuration of the training task.

evaluate

dict config

The configuration of the evaluation task.

inference

dict config

The configuration of the inference task.

encryption_key

string

None

The encryption key to encrypt and decrypt model files.

results_dir

string

/results

The directory where experiment results are saved.

export

dict config

The configuration of the ONNX export task.

task

str

classify

A flag to indicate the change detection task. Supports two tasks: ‘segment’ and ‘classify’ for segmentation and classification.

classify, segment

train#

Parameter

Datatype

Default

Description

Supported Values

num_gpus

unsigned int

1

The number of GPUs to use for distributed training.

>0

gpu_ids

List[int]

[0]

The indices of the GPU’s to use for distributed training.

seed

unsigned int

1234

The random seed for random, NumPy, and torch.

>0

num_epochs

unsigned int

10

The total number of epochs to run the experiment.

>0

checkpoint_interval

unsigned int

1

The epoch interval at which the checkpoints are saved.

>0

validation_interval

unsigned int

1

The epoch interval at which the validation is run.

>0

resume_training_checkpoint_path

string

The intermediate PyTorch Lightning checkpoint from which to resume training.

results_dir

string

/results/train

The directory in which to save training results.

classify


Dict
str
list
None
ce

The classify dict contains configurable parameters for the VisualChangeNet Classification pipeline with the following parameters:
* loss: The loss function used for classification training.
* cls_weights: Weights for Cross-Entropy Loss for unbalanced dataset distributions.



segment


Dict
str
list
None
ce
[0.5, 0.5, 0.5, 0.8, 1.0]
The segment dict contains configurable parameters for the VisualChangeNet Segmentation pipeline with the following parameters:
* loss: The loss function used for segmentation training.




num_nodes

unsigned int

1

The number of nodes. If larger than 1, multi-node is enabled.

pretrained_model_path

string

The path to the pretrained model checkpoint to initialize the end-end model weights.

optim

dict
config
None

Contains the configurable parameters for the VisualChangeNet optimizer detailed in
the optim section.


tensorboard

dict config
bool
None
True
Enable TensorBoard visualisation using a dict with configurable parameters:
* enabled: If set to True, enables TensorBoard.


optim#

optim:
  lr: 0.0001
  optim: "adamw"
  policy: "linear"
  momentum: 0.9
  weight_decay: 0.01

Parameter

Datatype

Default

Description

Supported Values

lr

float

0.0005

The learning rate.

>=0.0

optim

str

adamw

The optimizer.

policy


str


linear


The learning scheduler:
* linear : LambdaLR decreases the lr by a multiplicative factor.
* step : StepLR decrease the lr by 0.1 at every num_epochs // 3 steps.
linear/step


momentum

float

0.9

The momentum for the AdamW optimizer.

weight_decay

float

0.1

The weight decay coefficient.

monitor_name

str

val_loss

The name of the monitor used for saving the top-k checkpoints.

Model#

The following example model config provides options to change the VisualChangeNet-Classification architecture for training. VisualChangeNet-Classification supports two model architectures. Architecture 1 leverages only the last feature maps from the FAN backbone using Euclidean difference to perform contrastive learning. Architecture 2 leverages the VisualChangeNet-Classification learnable difference modules for 4 different features at 3 feature resolutions to minimize Cross-Entropy loss.

model:
  backbone:
    type: "fan_small_12_p4_hybrid"
    pretrained_backbone_path: null
    freeze_backbone: False
  decode_head:
    feature_strides: [4, 8, 16, 16]
    align_corner: False
    use_summary_token: True
  classify:
    train_margin_euclid: 2.0
    eval_margin: 0.005
    embedding_vectors: 5
    embed_dec: 30
    difference_module: 'learnable'
    learnable_difference_modules: 4

Parameter

Datatype

Default

Description

Supported Values

backbone










Dict
string







bool
bool
None







None
False
False
A dictionary containing the following configurable parameters for VisualChangeNet-Classification backbone:
* type: The name of the backbone to be used.






* pretrained_backbone_path: The path to pre-trained backbone weights file.
* freeze_backbone: If set to True, freezes the backbone weights during training.
* feat_downsample: If set to True, downsamples the last feature map in FAN backbone configurations.
fan_tiny_8_p4_hybrid
fan_large_16_p4_hybrid
fan_small_12_p4_hybrid
fan_base_16_p4_hybrid
vit_large_nvdinov2
c_radio_v2_vit_huge_patch16_224
c_radio_v2_vit_large_patch16_224
c_radio_v2_vit_base_patch16_224



decode_head






Dict
bool
bool
list
Dict
int

None
False
True
[4, 8, 16, 16]

256

A dictionary containing the following configurable parameters for the decoder:
* align_corners: If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels.
* use_summary_token: If set to True, uses the summary token of the backbone.
* feature_strides: The downsampling feature strides for different backbones.
* decoder_params: Contains the following network parameters:
embed_dims: The embedding dimensions.


True, False
True, False


>0

classify






Dict
string





None
2.0

5
30
learnable
4
A dictionary containing the following configurable parameters for VisualChangeNet-Classification model:
* train_margin_euclid: The training margin threshold for contrastive learning (applicable for Architecture 1).
* eval_margin: The evaluation margin threshold.
* embedding_vectors: The output embedding dimension for each input image before computing Euclidean distance (applicable to Architecture 1).
* embed_dec: The transformer decoder MLP embedding dimension (applicable to Architecture 2).
* difference_module: The type of difference module used (applicable to both architectures).
* learnable_difference_modules: The number of learnable difference modules (applicable to Architecture 2).

>0
>0
>0
>0
Euclidean, learnable
<4

Dataset#

The dataset parameter defines the dataset source, training batch size, augmentation, and pre-processing. An example dataset is provided below.

dataset:
  classify:
    train_dataset:
      csv_path: /path/to/train.csv
      images_dir: /path/to/img_dir
    validation_dataset:
      csv_path: /path/to/val.csv
      images_dir: /path/to/img_dir
    test_dataset:
      csv_path: /path/to/test.csv
      images_dir: /path/to/img_dir
    infer_dataset:
      csv_path: /path/to/infer.csv
      images_dir: /path/to/img_dir
    image_ext: .jpg
    batch_size: 16
    workers: 2
    fpratio_sampling: 0.2
    num_input: 4
    input_map:
      LowAngleLight: 0
      SolderLight: 1
      UniformLight: 2
      WhiteLight: 3
    concat_type: linear
    grid_map:
      x: 2
      y: 2
    image_width: 128
    image_height: 128
    augmentation_config:
      rgb_input_mean: [0.485, 0.456, 0.406]
      rgb_input_std: [0.229, 0.224, 0.225]
    num_classes: 2

* Refer to the Dataset Annotation Format definition for more information about specifying lighting conditions.

Parameter

Datatype

Default

Description

Supported Values

segment

Dict

The segment contains dataset config for the segmentation dataloader.

classify

Dict

The classify contains dataset config for the classification dataloader detailed in the classify section.

classify#

Parameter

Datatype

Default

Description

Supported Values

train_dataset

Dict

The paths to the image directory and CSV files for the training dataset.

validation_dataset

Dict

The paths to the image directory and CSV files for the validation dataset.

test_dataset

Dict

The paths to the image directory and CSV files for the test dataset.

infer_dataset

Dict

The paths to the image directory and CSV files for the inference dataset.

image_ext

str

.jpg

The file extension of the images in the dataset.

string

batch_size

int

32

The number of samples per batch.

string

workers

int

8

The number of worker processes for data loading.

fpratio_sampling

int

0.1

The ratio of false-positive examples to sample.

>0

num_input

int

4

The number of lighting conditions for each input image*.

>0

input_map

Dict

The mapping of lighting conditions to indices specifying concatenation ordering*.

concat_type

string

linear

Type of concatenation to use for different image lighting conditions.

linear, grid

grid_map


Dict
Dict
Dict
None
None
None
The parameters to define the grid dimensions to concatenate images as a grid:
* x: The number of images along the x-axis.
* y: The number of images along the y-axis.
Dict


input_width

int

100

The width of the input image.

>0

input_height

int

100

The height of the input image.

>0

num_classes

int

2

The number of classes in the dataset.

>1

augmentation_config

Dict

None

Dictionary containing various data augmentation settings, which is detailed in the augmentation section.

num_golden


int


1


Number of golden images to use per input image. Setting this value greater than 1 enables Multiple Golden mode.
Multiple Golden mode is only supported with ViT backbones, using input_width = input_height = 224 and input_map = None.
In Multiple Golden mode, the dataset must follow the multiple golden data format.
>0


augmentation_config#

Parameter

Datatype

Default

Description

Supported Values

random_flip



Dict
float
float
bool
None
0.5
0.5
True
Random vertical and horizontal flipping augmentation settings.
* vflip_probability: Probability of vertical flipping.
* hflip_probability: Probability of horizontal flipping.
* enable: If set to True, enables random flipping augmentation.

>=0.0
>=0.0

random_rotate



Dict
float
list
bool
None
0.5
[90, 180, 270]
True
Random rotation augmentation settings.
* rotate_probability: Probability of applying random rotation.
* angle_list: List of rotation angles to choose from.
* enable: If set to True, enables random rotation augmentation.

>=0.0
>=0.0

random_color






Dict
float
float
float
float
bool
float
None
0.3
0.3
0.3
0.3
True
0.5
Random color augmentation settings.
* brightness: Maximum brightness change factor.
* contrast: Maximum contrast change factor.
* saturation: Maximum saturation change factor.
* hue: Maximum hue change factor.
* enabled: If set to True, enables random color augmentation.
* color_probability: Probability of applying color augmentation.

>=0.0
>=0.0
>=0.0
>=0.0

>=0.0

with_random_crop

bool

True

If set to True, applies random crop augmentation.

True, False

with_random_blur

bool

True

If set to True, applies random blurring augmentation.

True, False

rgb_input_mean

List[float]

[0.485, 0.456, 0.406]

The mean to be subtracted for pre-processing.

rgb_input_std

List[float]

[0.229, 0.224, 0.225]

The standard deviation to divide the image by.

augment

bool

False

If set to True, applies data augmentations.

True, False

Example spec File for ViT Backbones#

Note

The following spec file is only relevant for TAO versions 5.3 and later.

SPECS=$(tao-client visual_changenet get-spec --action train --job_type experiment --id $EXPERIMENT_ID)

Training the Model#

Use the following command to run VisualChangeNet-Classification training:

TRAIN_JOB_ID=$(tao-client visual_changenet experiment-run-action --action train --id $EXPERIMENT_ID --specs "$SPECS")

Creating a Testing Experiment Spec File#

Here is an example spec file for testing evaluation and inference of a trained VisualChangeNet-Classification model.

SPECS=$(tao-client visual_changenet get-spec --action evaluate --job_type experiment --id $EXPERIMENT_ID)

Inference/Evaluate#

Parameter

Datatype

Default

Description

Supported Values

checkpoint

string

Path to PyTorch model to evaluate/inference.

trt_engine

string

Path to TensorRT model to inference/evaluate. Should be only used with TAO Deploy.

num_gpus

unsigned int

1

The number of GPUs to use.

>0

gpu_ids

unsigned int

[0]

The GPU IDs to use.

results_dir

string

The path to a folder where the experiment outputs should be written.

vis_after_n_batches

unsigned int

1

Number of batches after which to save inference/evaluate visualization results.

>0

batch_size

unsigned int

The batch size of inference/evaluate.

Evaluating the Model#

Use the following command to run VisualChangeNet-Classification evaluation:

EVALUATE_JOB_ID=$(tao-client visual_changenet experiment-run-action --action evaluate --id $EXPERIMENT_ID --specs "$SPECS" --parent_job_id $TRAIN_JOB_ID)

Multi-GPU evaluation is currently not supported for Visual ChangeNet Classify.

Running Inference on the Model#

Use the following command to run inference on VisualChangeNet-Classification with the .pth model:

INFERENCE_JOB_ID=$(tao-client visual_changenet experiment-run-action --action inference --id $EXPERIMENT_ID --specs "$SPECS" --parent_job_id $TRAIN_JOB_ID)

Exporting the Model#

Here is an example spec file for exporting the trained VisualChangeNet model:

SPECS=$(tao-client visual_changenet get-spec --action export --job_type experiment --id $EXPERIMENT_ID)

Parameter

Datatype

Default

Description

Supported Values

checkpoint

string

The path to the PyTorch model to export.

onnx_file

string

The path to the .onnx file.

opset_version

unsigned int

12

The opset version of the exported ONNX.

>0

input_channel

unsigned int

3

The input channel size. Only the value 3 is supported.

3

input_width

unsigned int

128

The input width.

>0

input_height

unsigned int

512

The input height.

>0

batch_size

unsigned int

-1

The batch size of the ONNX model. If this value is set to -1, the export uses dynamic batch size.

>=-1

gpu_id

unsigned int

0

The GPU ID to use.

on_cpu

bool

False

If set to True, exports the model on CPU.

verbose

bool

False

If set to True, prints a human-readable representation of the network.

Use the following command to export the model:

EXPORT_JOB_ID=$(tao-client visual_changenet experiment-run-action --action export --id $EXPERIMENT_ID --specs "$SPECS" --parent_job_id $TRAIN_JOB_ID)

TensorRT Engine Generation, Validation, and int8 Calibration#

For deployment, refer to the TAO Deploy Documentation for VisualChangeNet-Classification.