Component to finetune MMTracking models for video multi-object tracking task.

Version: 0.0.10

View in Studio: https://ml.azure.com/registries/azureml/components/mmtracking_video_multi_object_tracking_finetune/version/0.0.10

component input: model path

Name	Description	Type	Default	Optional	Enum
model_path	Output folder of model selector containing model metadata like config, checkpoints, tokenizer config.	uri_folder		False

component input: training mltable

Name	Description	Type	Default	Optional	Enum
training_data	Path to the mltable of the training dataset.	mltable		False

optional component input: validation mltable

Name	Description	Type	Default	Optional	Enum
validation_data	Path to the mltable of the validation dataset.	mltable		True
image_width	Image width that is input to the network. Default is -1 which means it would be overwritten by image_scale in model config.	integer	-1	True
image_height	Image height that is input to the network. Default is -1 which means it would be overwritten by image_scale in model config.	integer	-1	True
task_name	Which task the model is solving.	string			['video-multi-object-tracking']
number_of_workers	Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.	integer	8	True

Training parameters

Name	Description	Type	Default	Optional	Enum
number_of_epochs	Number of training epochs.	integer	15	True
max_steps	If set to a positive number, the total number of training steps to perform. Overrides 'number_of_epochs'. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted.	integer	-1	True
training_batch_size	Train batch size.	integer	1	True
auto_find_batch_size	Flag to enable auto finding of batch size. If the provided 'per_device_train_batch_size' goes into Out Of Memory (OOM) enabling auto_find_batch_size will find the correct batch size by iteratively reducing 'per_device_train_batch_size' by a factor of 2 till the OOM is fixed.	boolean	False	True

learning rate and learning rate scheduler

Name	Description	Type	Default	Optional	Enum
learning_rate	Start learning rate. Defaults to linear scheduler.	number	0.0001	True
learning_rate_scheduler	The scheduler type to use.	string	warmup_cosine_with_restarts	True	['warmup_linear', 'warmup_cosine', 'warmup_cosine_with_restarts', 'warmup_polynomial', 'constant', 'warmup_constant']
warmup_steps	Number of steps used for a linear warmup from 0 to learning_rate.	integer	5	True

optimizer

Name	Description	Type	Default	Optional	Enum
optimizer	optimizer to be used while training.	string	sgd	True	['adamw_hf', 'adamw', 'sgd', 'adafactor', 'adagrad']
weight_decay	The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer.	number	0.0	True
extra_optim_args	Optional additional arguments that are supplied to SGD Optimizer. The arguments should be semi-colon separated key value pairs and should be enclosed in double quotes. For example, "momentum=0.5; nesterov=True" for sgd. Please make sure to use a valid parameter names for the chosen optimizer. For exact parameter names, please refer https://pytorch.org/docs/1.13/generated/torch.optim.SGD.html#torch.optim.SGD for SGD. Parameters supplied in extra_optim_args will take precedence over the parameter supplied via other arguments such as weight_decay. If weight_decay is provided via "weight_decay" parameter and via extra_optim_args both, values specified in extra_optim_args will be used.	string		True

gradient accumulation

Name	Description	Type	Default	Optional	Enum
gradient_accumulation_step	Number of update steps to accumulate the gradients for, before performing a backward/update pass.	integer	1	True

mixed precision training

Name	Description	Type	Default	Optional	Enum
precision	Apply mixed precision training. This can reduce memory footprint by performing operations in half-precision.	string	32	True	['32', '16']

primary metric

Name	Description	Type	Default	Optional	Enum
metric_for_best_model	Specify the metric to use to compare two different models.	string	mean_average_precision	True	['mean_average_precision', 'precision', 'recall', 'MOTA', 'MOTP', 'IDF1']

metric thresholds

Name	Description	Type	Default	Optional	Enum
iou_threshold	IOU threshold used during inference in non-maximum suppression post processing.	number		True
box_score_threshold	During inference, only return proposals with a score greater than box_score_threshold. The score is the multiplication of the objectness score and classification probability.	number		True

random seed

Name	Description	Type	Default	Optional	Enum
random_seed	Random seed that will be set at the beginning of training.	integer	42	True

evaluation strategy parameters

Name	Description	Type	Default	Optional	Enum
evaluation_strategy	The evaluation strategy to adopt during training. Please note that the save_strategy and evaluation_strategy should match.	string	epoch	True	['epoch', 'steps']
evaluation_steps	Number of update steps between two evals if evaluation_strategy='steps'. Please note that the saving steps should be a multiple of the evaluation steps.	integer	500	True

logging strategy parameters

Name	Description	Type	Default	Optional	Enum
logging_strategy	The logging strategy to adopt during training.	string	epoch	True	['epoch', 'steps']
logging_steps	Number of update steps between two logs if logging_strategy='steps'.	integer	500	True

Save strategy

Name	Description	Type	Default	Optional	Enum
save_strategy	The checkpoint save strategy to adopt during training. Please note that the save_strategy and evaluation_strategy should match.	string	epoch	True	['epoch', 'steps']
save_steps	Number of updates steps before two checkpoint saves if save_strategy="steps". Please note that the saving steps should be a multiple of the evaluation steps.	integer	500	True

model checkpointing limit

Name	Description	Type	Default	Optional	Enum
save_total_limit	If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. If the value is -1 saves all checkpoints".	integer	5	True

Early Stopping Parameters

Name	Description	Type	Default	Optional	Enum
early_stopping	Enable early stopping.	boolean	False	True
early_stopping_patience	Stop training when the specified metric worsens for early_stopping_patience evaluation calls.	integer	1	True

Grad Norm

Name	Description	Type	Default	Optional	Enum
max_grad_norm	Maximum gradient norm (for gradient clipping)	number	1.0	True

resume from the input model

Name	Description	Type	Default	Optional	Enum
resume_from_checkpoint	Loads optimizer, Scheduler and Trainer state for finetuning if true.	boolean	False	True
save_as_mlflow_model	Save as mlflow model with pyfunc as flavour.	boolean	True	True

Name	Description	Type
mlflow_model_folder	Output dir to save the finetune model as mlflow model.	mlflow_model
pytorch_model_folder	Output dir to save the finetune model as torch model.	custom_model

azureml://registries/azureml/environments/acft-mmtracking-video-gpu/versions/33