components mmdetection_image_objectdetection_instancesegmentation_finetune

Image Object Detection and Instance Segmentation MMDetection Model Finetune

mmdetection_image_objectdetection_instancesegmentation_finetune

Overview

Component to finetune MMDetection models for image object detection and instance segmentation.

Version: 0.0.11

View in Studio: https://ml.azure.com/registries/azureml/components/mmdetection_image_objectdetection_instancesegmentation_finetune/version/0.0.11

Inputs

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_min_size	Minimum image size after augmentation that is input to the network. If left empty, it would either be overwritten by image_scale in model config or would be chosen based on the task type and model selected. The image will be rescaled as large as possible within the range [image_min_size, image_max_size]. The image size will be constraint so that the max edge is no longer than image_max_size and short edge is no longer than image_min_size.	integer		True
image_max_size	Maximum image size after augmentation that is input to the network. If left empty, it would either be overwritten by image_scale in model config or would be chosen based on the task type and model selected. The image will be rescaled as large as possible within the range [image_min_size, image_max_size]. The image size will be constraint so that the max edge is no longer than image_max_size and short edge is no longer than image_min_size.	integer		True
task_name	Which task the model is solving.	string			['image-object-detection', 'image-instance-segmentation']

primary metric

Name	Description	Type	Default	Optional	Enum
metric_for_best_model	Specify the metric to use to compare two different models. If left empty, will be chosen automatically based on the task type and model selected.	string		True	['mean_average_precision', 'precision', 'recall']

Augmentation parameters

Name	Description	Type	Default	Optional	Enum
apply_augmentations	If set to true, will enable data augmentations for training.	boolean	True	True
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

Deepspeed Parameters

Name	Description	Type	Default	Optional	Enum
apply_deepspeed	If set to true, will enable deepspeed for training. Please note deepspeed is not yet supported for MMDetection, will be enabled in future.	boolean	False	True

optional component input: deepspeed config

Name	Description	Type	Default	Optional	Enum
deepspeed_config	Deepspeed config to be used for finetuning. Please note deepspeed is not yet supported for MMDetection, will be enabled in future.	uri_file		True
apply_ort	If set to true, will use the ONNXRunTime training. Please note ONNXRunTime is not yet supported for MMDetection, will be enabled in future.	boolean	False	True

Training parameters

Name	Description	Type	Default	Optional	Enum
number_of_epochs	Number of training epochs. If left empty, will be chosen automatically based on the task type and model selected.	integer		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. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
training_batch_size	Train batch size. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
validation_batch_size	Validation batch size. If left empty, will be chosen automatically based on the task type and model selected.	integer		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. If left empty, will be chosen automatically based on the task type and model selected.	number		True
learning_rate_scheduler	The scheduler type to use. If left empty, will be chosen automatically based on the task type and model selected.	string		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. If left empty, will be chosen automatically based on the task type and model selected.	integer		True

optimizer

Name	Description	Type	Default	Optional	Enum
optimizer	optimizer to be used while training. If left empty, will be chosen automatically based on the task type and model selected.	string		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 Adam, AdamW & SGD optimizer. If left empty, will be chosen automatically based on the task type and model selected.	number		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. If left empty, will be chosen automatically based on the task type and model selected.	integer		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']

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	-1	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). If left empty, will be chosen automatically based on the task type and model selected.	number		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

Outputs

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

Environment

azureml://registries/azureml/environments/acft-mmdetection-image-gpu/versions/18