mindspore/model_zoo/deeplabv3

Deeplab-V3 Example

Description

This is an example of training DeepLabv3 with PASCAL VOC 2012 dataset in MindSpore.

Requirements

• Install MindSpore.
• Download the VOC 2012 dataset for training.

Notes: If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.

Running the Example

Training

• Set options in config.py.
• Run run_standalone_train.sh for non-distributed training.

  sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
  

• Run run_distribute_train.sh for distributed training.

  sh scripts/run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH DATA_PATH
  

Evaluation

Set options in evaluation_config.py. Make sure the 'data_file' and 'finetune_ckpt' are set to your own path.

• Run run_eval.sh for evaluation.

  sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
  

Options and Parameters

It contains of parameters of Deeplab-V3 model and options for training, which is set in file config.py.

Options:

config.py:
    learning_rate                   Learning rate, default is 0.0014.
    weight_decay                	Weight decay, default is 5e-5.
    momentum                    	Momentum, default is 0.97.
    crop_size                       Image crop size [height, width] during training, default is 513.
    eval_scales                     The scales to resize images for evaluation, default is [0.5, 0.75, 1.0, 1.25, 1.5, 1.75].
	output_stride					The ratio of input to output spatial resolution, default is 16.
	ignore_label					Ignore label value,	default is 255.
	seg_num_classes					Number of semantic classes, including the background class (if exists). 
									foreground classes + 1 background class in the PASCAL VOC 2012 dataset, default is 21.
	fine_tune_batch_norm			Fine tune the batch norm parameters or not, default is False.
	atrous_rates					Atrous rates for atrous spatial pyramid pooling, default is None.
	decoder_output_stride			The ratio of input to output spatial resolution when employing decoder
									to refine segmentation results, default is None.
	image_pyramid					Input scales for multi-scale feature extraction, default is None.
	epoch_size						Epoch size, default is 6.
    batch_size                      batch size of input dataset: N, default is 2.
	enable_save_ckpt				Enable save checkpoint, default is true.
	save_checkpoint_steps			Save checkpoint steps, default is 1000.
	save_checkpoint_num				Save checkpoint numbers, default is 1.

Parameters:

Parameters for dataset and network:
    distribute						Run distribute, default is false.
	data_url						Train/Evaluation data url, required.
	checkpoint_url					Checkpoint path, default is None.