Image Classification (TF1)
See the Data Annotation Format page for more information about the data format for image classification.
Here is an example of a specification file for model classification:
model_config {
# Model Architecture can be chosen from:
# ['resnet', 'vgg', 'googlenet', 'alexnet']
arch: "resnet"
# for resnet --> n_layers can be [10, 18, 50]
# for vgg --> n_layers can be [16, 19]
n_layers: 101
use_batch_norm: True
use_bias: False
all_projections: False
use_pooling: True
retain_head: True
resize_interpolation_method: BICUBIC
# if you want to use the pretrained model,
# image size should be "3,224,224"
# otherwise, it can be "3, X, Y", where X,Y >= 16
input_image_size: "3,224,224"
}
train_config {
train_dataset_path: "/path/to/your/train/data"
val_dataset_path: "/path/to/your/val/data"
pretrained_model_path: "/path/to/your/pretrained/model"
# Only ['sgd', 'adam'] are supported for optimizer
optimizer {
sgd {
lr: 0.01
decay: 0.0
momentum: 0.9
nesterov: False
}
}
batch_size_per_gpu: 50
n_epochs: 150
# Number of CPU cores for loading data
n_workers: 16
# regularizer
reg_config {
# regularizer type can be "L1", "L2" or "None".
type: "L2"
# if the type is not "None",
# scope can be either "Conv2D" or "Dense" or both.
scope: "Conv2D,Dense"
# 0 < weight decay < 1
weight_decay: 0.000015
}
# learning_rate
lr_config {
cosine {
learning_rate: 0.04
soft_start: 0.0
}
}
enable_random_crop: True
enable_center_crop: True
enable_color_augmentation: True
mixup_alpha: 0.2
label_smoothing: 0.1
preprocess_mode: "caffe"
image_mean {
key: 'b'
value: 103.9
}
image_mean {
key: 'g'
value: 116.8
}
image_mean {
key: 'r'
value: 123.7
}
}
eval_config {
eval_dataset_path: "/path/to/your/test/data"
model_path: "/workspace/tao-experiments/classification/weights/resnet_080.tlt"
top_k: 3
batch_size: 256
n_workers: 8
enable_center_crop: True
}
The classification experiment specification consists of three main components:
model_config
eval_config
train_config
Model Config
The table below describes the configurable parameters in the model_config
.
Parameter | Datatype | Typical value | Description | Supported Values |
|
bool |
|
For templates with shortcut connections, this parameter defines whether or not all shortcuts should be instantiated with 1x1 |
True or False (only to be used in ResNet templates) |
|
string |
|
This defines the architecture of the back bone feature extractor to be used to train. |
* resnet |
|
int |
|
Depth of the feature extractor for scalable templates. |
* resnets: 10, 18, 34, 50, 101 |
|
Boolean |
|
Choose between using strided convolutions or MaxPooling while downsampling. When True, MaxPooling is used to down sample, however |
True or False |
use_batch_norm |
Boolean | False |
Boolean variable to use batch normalization layers or not. | True or False |
|
float |
– |
This parameter defines which blocks may be frozen from the instantiated feature extractor template, and is different for different |
* ResNet series: For the ResNet series, the block ID’s valid for freezing is any subset of {0, 1, 2, 3}(inclusive) |
|
Boolean |
|
You can choose to freeze the Batch |
True or False |
|
string |
|
The dimension of the input layer of the model. Images in the dataset will be resized to this shape by the dataloader |
C,X,Y, where C=1 or C=3 and X,Y >=16 and X,Y are integers. |
resize_interpolation_method |
enum | BILEANER |
The interpolation method for resizing the input images. | BILINEAR, BICUBIC |
|
Boolean |
|
Whether or not to use the header layers as in the original implementation on ImageNet. Set this to True to reproduce the |
True or False |
dropout |
float | 0.0 |
Dropout rate for Dropout layers in the model. This is only valid for VGG and SqueezeNet. | Float in the interval [0, 1) |
batch_norm_config |
proto message | – | Parameters for BatchNormalization layers. | – |
activation |
proto message | – | Parameters for the activation functions in the model. | – |
BatchNormalization Parameters
The parameter batch_norm_config
defines parameters for BatchNormalization layers in the model (momentum and epsilon).
Parameter | Datatype | Typical value | Description | Supported Values |
momentum |
float | 0.9 |
Momentum of BatchNormalization layers. | float in the interval (0, 1), usually close to 1.0. |
epsilon |
float | 1e-5 |
Epsilon to avoid zero division. | float that is close to 0.0. |
Activation functions
The parameter activation
defines the parameters for activation functions in the model.
Parameter | Datatype | Default | Description | Supported Values |
activation_type |
String | – | Type of the activation function. | Only relu and swish are supported. |
Eval Config
The table below defines the configurable parameters for evaluating a classification model.
Parameter | Datatype | Typical value | Description | Supported Values |
eval_dataset_path |
string | UNIX format path to the root directory of the evaluation dataset. | UNIX format path. | |
model_path |
string | UNIX format path to the root directory of the model file you would like to evaluate. | UNIX format path. | |
top_k |
int | 5 |
The number elements to look at when calculating the top-K classification categorical accuracy metric. | 1, 3, 5 |
batch_size |
int | 256 |
Number of images per batch when evaluating the model. | >1 (bound by the number of images that can be fit in the GPU memory) |
n_workers |
int | 8 |
Number of workers fetching batches of images in the evaluation dataloader. | >1 |
enable_center_crop |
Boolean | True |
Enable center crop for input images or not. Usually this parameter is set to True to achieve better accuracy. |
True or False |
Training Config
This section defines the configurable parameters for the classification model trainer.
Parameter | Datatype | Default | Description | Supported Values |
val_dataset_path |
string | UNIX format path to the root directory of the validation dataset. | UNIX format path. | |
train_dataset_path |
string | UNIX format path to the root directory of the training dataset. | UNIX format path. | |
pretrained_model_path |
string | UNIX format path to the model file containing the pretrained weights to initialize the model from. | UNIX format path. | |
batch_size_per_gpu |
int | 32 |
This parameter defines the number of images per batch per gpu. | >1 |
n_epochs |
int | 120 |
This parameter defines the total number of epochs to run the experiment. | |
n_workers |
int | 10 |
Number of workers fetching batches of images in the training/validation dataloader. | >1 |
lr_config |
proto message | – | The parameters for learning rate scheduler. | – |
reg_config |
proto message | – | The parameters for regularizers. | – |
optimizer |
proto message | – | This parameter defines which optimizer to use for training. Can be chosen from sgd , adam , or rmsprop |
– |
random_seed |
int | – | Random seed for training. | – |
enable_random_crop |
Boolean | True |
A flag to enable random crop during training. | True or False |
enable_center_crop |
Boolean | True |
A flag to enable center crop during validation. | True or False |
enable_color_augmentation |
Boolean | True |
A flag to enable color augmentation during training. | True or False |
disable_horizontal_flip |
Boolean | False |
A flag to disable horizontal flip. | True or False |
label_smoothing |
float | 0.1 |
A factor used for label smoothing. | in the interval (0, 1) |
mixup_alpha |
float | 0.2 |
A factor used for mixup augmentation. | in the interval (0, 1) |
preprocess_mode |
string | 'caffe' |
Mode for input image preprocessing. Defaults to ‘caffe’. | ‘caffe’, ‘torch’, ‘tf’ |
model_parallelism |
repeated float | – | List of fractions to indicate how we split the model on multiple GPUs for model parallelism. | – |
image_mean |
dict | ‘b’: 103.939 ‘g’: 116.779 ‘r’: 123.68 | A key/value pair to specify image mean values. It’s only applicable when preprocess_mode is caffe .
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. |
– |
Learning Rate Scheduler
The parameter lr_config
defines the parameters for learning rate scheduler
The learning rate scheduler can be either step
, soft_anneal
or cosine
.
Step
The parameter step
defines the step learning rate scheduler.
Parameter | Datatype | Typical value | Description | Supported Values |
learning_rate |
float | – | The base(maximum) learning rate value. | Positive, usually in the interval (0, 1). |
step_size |
int | – | The progress (percentage of the entire training duration) after which the learning rate will be decreased. | Less than 100. |
gamma |
float | – | The multiplicative factor used to decrease the learning rate. | In the interval (0, 1). |
The learning rate is automatically scaled with the number of GPUs used during training, or the effective learning rate is learning_rate * n_gpu
.
Soft Annealing
The parameter soft_anneal
defines the soft annealing learning rate scheduler.
Parameter | Datatype | Typical value | Description | Supported Values |
learning_rate |
float | – | The base (maximum) learning rate value. | Positive, usually in the interval (0, 1). |
soft_start |
float | – | The progress at which learning rate achieves the base learning rate. | In the interval (0, 1). |
annealing_divider |
float | – | The divider by which the learning rate will be scaled down. | Greater than 1.0. |
annealing_points |
repeated float | – | Points of progress at which the learning rate will be decreased. | List of floats. Each will be in the interval (0, 1). |
Cosine
The parameter cosine
defines the cosine learning rate scheduler.
Parameter | Datatype | Typical value | Description | Supported Values |
learning_rate |
float | – | The base (maximum) learning rate. | Usually less than 1.0 |
min_lr_ratio |
float | – | The ratio of minimum learning rate to the base learning rate. | Less than 1.0 |
soft_start |
float | – | The progress at which learning rate achieves the base learning rate. | In the interval (0, 1). |
![learning_rate_schedules.png](https://docscontent.nvidia.com/dims4/default/f43c984/2147483647/strip/true/crop/798x419+0+0/resize/798x419!/quality/90/?url=https%3A%2F%2Fk3-prod-nvidia-docs.s3.us-west-2.amazonaws.com%2Fbrightspot%2Fsphinx%2F00000191-a484-d6d8-a5fb-a7a71a290000%2Ftao%2Ftao-toolkit%2F_images%2Flearning_rate_schedules.png)
Optimizer
Three types of optimizers are supported: Adam, SGD and RMSProp. Only one type should be specified in
the spec file. No matter which type is chosen, it will be wrapped in an optimizer
proto, as
shown in the following example:
optimizer {
sgd {
lr: 0.01
decay: 0.0
momentum: 0.9
nesterov: False
}
}
The Adam optimizer parameters are summarized in the table below.
Parameter | Description | Data Type and Constraints | Default/Suggested Value |
lr |
The learning rate. This parameter is overridden by the learning rate scheduler and hence not useful. | float | 0.01 |
beta_1 |
The momentum for the means of the model parameters | float | 0.9 |
beta_2 |
The momentum for the variances of the model parameters | float | 0.999 |
decay |
Th decay factor for the learning rate. This parameter is not useful. | float | 0.0 |
epsilon |
A small constant for numerical stability | float | 1e-7 |
The SGD optimizer parameters are summarized in the table below.
Parameter | Description | Data Type and Constraints | Default/Suggested Value |
lr |
The learning rate. This parameter is overridden by the learning rate scheduler and hence not useful. | float | 0.01 |
momentum |
The momentum of SGD | float | 0.9 |
decay |
The decay factor of the learning rate. This parameter is not useful because it is overridden by the learning rate scheduler. | float | 0.0 |
nesterov |
A flag to enable Nesterov momentum for SGD | Boolean | False |
The RMSProp optimizer parameters are summarized in the table below.
Parameter | Description | Data Type and Constraints | Default/Suggested Value |
lr |
The learning rate. This parameter is overridden by the learning rate scheduler and hence not useful. | float | 0.01 |
Use the tao model classification_tf1 train
command to tune a pre-trained model:
tao model classification_tf1 train [-h] -e <spec file>
-k <encoding key>
-r <result directory>
[--gpus <num GPUs>]
[--num_processes <number_of_processes>]
[--gpu_index <gpu_index>]
[--use_amp]
[--log_file <log_file_path>]
Required Arguments
-r, --results_dir
: Path to a folder where the experiment outputs should be written.-k, --key
: User specific encoding key to save or load a.tlt
model.-e, --experiment_spec_file
: Path to the experiment spec file.
Optional Arguments
--gpus
: Number of GPUs to use and processes to launch for training. The default value is 1.--num_processes, -np
: Number of processes to be spawned for training. It defaults to be -1(equal to--gpus
, for the use case of data parallelism). In the case of model parallelism, this argument should be explicitly set to 1 or more, depending on the actual scenario. Setting--gpus
to be larger than 1 and--num_processes
to 1 corresponding to the model parallelism use case; while setting both--gpus
andnum_processes
to be larger than 1 corresponding to the case of enabling both model parallelism and data parallelism. For example,--gpus=4
and--num_processes=2
means 2 horovod processes will be spawned and each of them will occupy 2 GPUs for model parallelism.--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.--use_amp
: A flag to enable AMP training.--log_file
: Path to the log file. Defaults to stdout.-h, --help
: Print the help message.
See the Specification File for Classification section for more details.
Input Requirement
Input size: 3 * H * W (W, H >= 32)
Input format: JPG, JPEG, PNG
Classification input images do not need to be manually resized. The input dataloader
automatically resizes images to input size
.
Sample Usage
Here’s an example of using the tao model classification_tf1 train
command:
tao model classification_tf1 train -e /workspace/tlt_drive/spec/spec.cfg -r /workspace/output -k $YOUR_KEY
Model parallelism
Image classification supports model parallelism. Model parallelism is a technique that we split the entire model
on multiple GPUs and each GPU will hold a part of the model. A model is split by layers. For example,
if a model has 100 layers, then we can place the layer 0-49 on GPU 0 and layer 50-99 on GPU 1.
Model parallelism will be useful when the model is huge and cannot fit into a single GPU even with
batch size 1. Model parallelism is also useful if we want to increase the batch size that is seen
by BatchNormalization layers and hence potentially improve the accuracy. This feature can be enabled
by setting model_parallelism
in training_config
. For example,
model_parallelism: 0.3
model_parallelism: 0.7
will enable a 2-GPU model parallelism where the first GPU will hold 30% of the model layers and the second GPU will hold 70% of the model layers. The percentage of model layers can be adjusted with some trial-and-error so all GPUs consumes almost the same GPU memory size and in that case we can use the largest batch size for this model-parallelised training.
Model parallelism can be enabled jointly with data parallelism. For example, in above case we enabled a 2-GPU model parallelism, at the same time we can also enable 4 horovod processes for it. In this case, we have 4 horovod processes for data parallelism and each process will have the model split on 2 GPUs.
After the model has been trained, using the experiment config file, and by following the steps to
train a model, the next step is to evaluate this model on a test set to measure the
accuracy of the model. TAO includes the tao model classification_tf1 evaluate
command to do this.
The classification app computes evaluation loss, Top-k accuracy, precision, and recall as metrics.
When training is complete, the model is stored in the output directory of your choice in
$OUTPUT_DIR. Evaluate a model using the tao model classification_tf1 evaluate
command:
tao model classification_tf1 evaluate [-h] -e <experiment_spec_file>
-k <key>
[--gpu_index <gpu_index>]
[--log_file <log_file>]
Required Arguments
-e, --experiment_spec_file
: Path to the experiment spec file.-k, –key
: Provide the encryption key to decrypt the model.
Optional Arguments
-h, --help
: Show this help message and exit.--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.--log_file
: Path to the log file. Defaults to stdout.
If you followed the example in training a classification model, run the evaluation:
tao model classification_tf1 evaluate -e classification_spec.cfg -k $YOUR_KEY
TAO evaluates for classification and produces the following metrics:
Loss
Top-K accuracy
Precision (P): TP / (TP + FP)
Recall (R): TP / (TP + FN)
Confusion Matrix
The tao model classification_tf1 inference
command runs the inference on a specified set of input images.
For classification, tao model classification_tf1 inference
provides class label output over
the command-line for a single image or a csv file containing the image path and the corresponding labels for multiple
images. TensorRT Python inference can also be enabled.
Execute tao model classification_tf1 inference
on a classification model trained on TAO.
tao model classification_tf1 inference [-h] -m <model>
-i <image>
-d <image dir>
-k <key>
-cm <classmap>
-e <experiment_spec_file>
[-b <batch size>]
[--gpu_index <gpu_index>]
[--log_file <log_file>]
Here are the arguments of the tao model classification_tf1 inference
tool:
Required arguments
-m, --model
: Path to the pretrained model (TAO model).-i, --image
: A single image file for inference.-d, --image_dir
: The directory of input images for inference.-k, --key
: Key to load model.-cm, --class_map
: The json file that specifies the class index and label mapping.-e, --experiment_spec_file
: Path to the experiment spec file.
Optional arguments
--batch_size
: Inference batch size, default: 1-h, --help
: Show this help message and exit.--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.--log_file
: Path to the log file. Defaults to stdout.
The inference tool requires a cluster_params.json file to configure the post processing
block. When executing with -d
, or directory mode, a result.csv
file
is created and stored in the directory you specify using -d
. The
result.csv
has the file path in the first column and predicted labels in
the second.
In both single image and directory modes, a classmap (-cm
) is required, which
should be a by product (-classmap.json
) of your training process.
Pruning removes parameters from the model to reduce the model size without compromising the
integrity of the model itself using the tao model classification_tf1 prune
command.
The tao model classification_tf1 prune
command includes these parameters:
tao model classification_tf1 prune [-h] -m <model>
-o <output_file>
-k <key>
[-n <normalizer>
[-eq <equalization_criterion>]
[-pg <pruning_granularity>]
[-pth <pruning threshold>]
[-nf <min_num_filters>]
[-el <excluded_list>]
[--gpu_index <gpu_index>]
[--log_file <log_file>]
Required Arguments
-m, --model
: Path to pretrained model-o, --output_file
: Path to output checkpoints-k, --key
: Key to load a .tlt model
Optional Arguments
-h, --help
: Show this help message and exit.-n, –normalizer
:max
to normalize by dividing each norm by the maximum norm within a layer;L2
to normalize by dividing by the L2 norm of the vector comprising all kernel norms. (default:max
)-eq, --equalization_criterion
: Criteria to equalize the stats of inputs to an elementwise op layer, or depth-wise convolutional layer. This parameter is useful for ResNet and MobileNet. Options arearithmetic_mean
,geometric_mean
,union
, andintersection
. (default:union
)-pg, -pruning_granularity
: Number of filters to remove at a time (default: 8)-pth
: Threshold to compare normalized norm against (default: 0.1)-nf, --min_num_filters
: Minimum number of filters to keep per layer (default: 16)-el, --excluded_layers
: List of excluded_layers. Examples: -i item1 item2 (default: [])--gpu_index
: The GPU indices used to run the training. We can specify the GPU indices used to run training when the machine has multiple GPUs installed.--log_file
: Path to the log file. Defaults to stdout.
After pruning, the model needs to be retrained. See Re-training the Pruned Model for more details.
Using the Prune Command
Here’s an example of using the tao model classification_tf1 prune
command:
tao model classification_tf1 prune -m /workspace/output/weights/resnet_003.tlt
-o /workspace/output/weights/resnet_003_pruned.tlt
-eq union
-pth 0.7 -k $KEY
After the model has been pruned, there might be a slight decrease in accuracy. This happens
because some previously useful weights may have been removed. In order to regain the accuracy,
NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, use
the tao model classification_tf1 train
command as documented in Training the model, 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 training_config for classification to recover
the accuracy when retraining a pruned model. You may do this by setting the regularizer type
to NO_REG
. All the other parameters may be retained in the spec file from the previous training.
Exporting the model decouples the training process from inference and allows conversion to
TensorRT engines outside the TAO environment. TensorRT engines are specific to each hardware
configuration and should be generated for each unique inference environment.
The exported model may be used universally across training and deployment hardware.
The exported model format is referred to as .etlt
. Like .tlt
, the .etlt
model
format is also a encrypted model format with the same key of the .tlt
model that it is
exported from. This key is required when deploying this model.
Here’s an example of the tao model classification_tf1 export
command:
tao model classification_tf1 export [-h] -m <path to the .tlt model file generated by training>
-e <experiment_spec_file>
[-k <key>]
[-o <path to output file>]
[--gen_ds_config <Flag to generate ds config and label file>]
[--verbose]
[--gpu_index <gpu_index>]
[--log_file <log_file_path>]
[--classmap_json CLASSMAP_JSON]
Required Arguments
-m, --model
: Path to the.tlt
model file to be exported.-e, --experiment_spec_file
: Path to the experiment spec file.
Optional Arguments
-k, --key
: Key used to save the.tlt
model file.-o, --output_file
: Path to save the exported model to. The default is./<input_file>.etlt
.--gen_ds_config
: A Boolean flag indicating whether to generate the template DeepStream related configuration (“nvinfer_config.txt”) as well as a label file (“labels.txt”) in the same directory as theoutput_file
. Note that the config file is NOT a complete configuration file and requires the user to update the sample config files in DeepStream with the parameters generated.--classmap_json
: Path to the classmap_json file. It is already generated in training result folder. This file is required if gen_ds_config is enabled.--gpu_index
: The index of (discrete) 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.--log_file
: Path to the log file. Defaults to stdout.-v, --verbose
: Verbose log.
Sample usage
Here’s a sample command.
tao model classification_tf1 export -m /ws/output_retrain/weights/resnet_001.tlt
-o /ws/export/final_model.etlt
-k $KEY
For TensorRT engine generation, validation, and int8 calibration, refer to the TAO Deploy documentation.
Refer to the Integrating a Classification (TF1/TF2) Model page for more information about deploying a classification model with DeepStream.