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model_zoo/deeplabv3/README.md

@@ -0,0 +1,64 @@
+Deeplab-V3 Example
+
+Description
+This is an example of training DeepLabv3 with PASCAL VOC 2012 dataset in MindSpore.
+Paper Rethinking Atrous Convolution for Semantic Image Segmentation
+Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam
+
+
+Requirements
+Install MindSpore.
+Download the VOC 2012 dataset for training.
+For more information, please check the resources below： 
+MindSpore tutorials
+MindSpore API
+
+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 EPOCH_SIZE DATA_DIR
+Run run_distribute_train.sh for distributed training.
+sh scripts/run_distribute_train.sh DEVICE_NUM EPOCH_SIZE DATA_DIR MINDSPORE_HCCL_CONFIG_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_DIR
+
+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.
+	
+
+
+Parameters:
+Parameters for dataset and network:
+    distribute						Run distribute, default is false.
+	epoch_size						Epoch size, default is 6.
+    batch_size                      batch size of input dataset: N, default is 2.
+	data_url						Train/Evaluation data url, required.
+	checkpoint_url					Checkpoint path, default is None.
+	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.

model_zoo/deeplabv3/evaluation.py

@@ -26,7 +26,7 @@ parser = argparse.ArgumentParser(description="Deeplabv3 evaluation")
 parser.add_argument('--epoch_size', type=int, default=2, help='Epoch size.')
 parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
 parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
-parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument('--data_url', required=True, default=None, help='Evaluation data url')
 parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
 
 args_opt = parser.parse_args()

model_zoo/deeplabv3/scripts/run_eval.sh

@@ -15,8 +15,8 @@
 # ============================================================================
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_eval.sh DEVICE_ID EPOCH_SIZE DATA_DIR"
+echo "bash run_eval.sh DEVICE_ID DATA_DIR"
-echo "for example: bash run_eval.sh 0 /path/zh-wiki/ "
+echo "for example: bash run_eval.sh /path/zh-wiki/ "
 echo "=============================================================================================================="
  
 DEVICE_ID=$1

model_zoo/deeplabv3/train.py

@@ -27,13 +27,12 @@ from src.config import config
 
 parser = argparse.ArgumentParser(description="Deeplabv3 training")
 parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
-parser.add_argument('--epoch_size', type=int, default=2, help='Epoch size.')
+parser.add_argument('--epoch_size', type=int, default=6, help='Epoch size.')
 parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
 parser.add_argument('--data_url', required=True, default=None, help='Train data url')
 parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
 parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
 parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
-parser.add_argument('--max_checkpoint_num', type=int, default=5, help='Max checkpoint number.')
 parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
 parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
 args_opt = parser.parse_args()
@@ -80,7 +79,7 @@ if __name__ == "__main__":
                                      keep_checkpoint_max=args_opt.save_checkpoint_num)
         ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
         callback.append(ckpoint_cb)
-    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+    net = deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
                               infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
                               decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
                               fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)