model name update

Author: hankuipeng

.gitignore

@@ -0,0 +1 @@
+*.tar

analysis_multi_nC.py

@@ -33,9 +33,9 @@
 
 
 ## input arguments 
-parser = argparse.ArgumentParser(description='Hierarchical Entropy Rate Superpixel (HERS) Segmentation on a folder of images',
+parser = argparse.ArgumentParser(description='DAL-HERS Superpixel Segmentation on a folder of images',
                                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('--pretrained', default='./pretrained/DGSS_loss=bce-rgb_date=23Feb2021.tar', help='path to the pretrained model')
+parser.add_argument('--pretrained', default='./pretrained/DAL_loss=bce-rgb_date=23Feb2021.tar', help='path to the pretrained model')
 parser.add_argument('--input_dir', default='./sample_imgs/input/', help='path to images folder')
 parser.add_argument('--output_dir', default='./sample_imgs/output/', help='path to output folder')
 parser.add_argument('--output_suff', default='', help='suffix to the output file')
@@ -57,7 +57,7 @@ def main():
 
     # load the model 
     network_data = torch.load(args.pretrained, map_location=args.device)
-    model = DGSS(nr_channel=8, conv1_size=7)
+    model = DAL(nr_channel=8, conv1_size=7)
     model.load_state_dict(network_data['state_dict'])
     model.eval()
 

analysis_multi_nC.sh

@@ -3,7 +3,7 @@
 MAIN_DIR="."
 INPUT_DIR="${MAIN_DIR}/sample_imgs/input/"
 OUTPUT_DIR="${MAIN_DIR}/sample_imgs/output/"
-PRETRAINED="${MAIN_DIR}/pretrained/DGSS_loss=bce-rgb_date=23Feb2021.tar"
+PRETRAINED="${MAIN_DIR}/pretrained/DAL_loss=bce-rgb_date=23Feb2021.tar"
 
 python3 analysis_multi_nC.py \
 --pretrained $PRETRAINED \

analysis_single_nC.py

@@ -33,7 +33,7 @@
 
 
 ## input arguments 
-parser = argparse.ArgumentParser(description='Hierarchical Entropy Rate Superpixel (HERS) Segmentation on a folder of images',
+parser = argparse.ArgumentParser(description='DAL-HERS Superpixel Segmentation on a folder of images',
                                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 parser.add_argument('--nC', default=200, type=int, help='the number of desired superpixels')
 parser.add_argument('--pretrained', default='./pretrained/DGSS_loss=bce-rgb_date=23Feb2021.tar', help='path to the pretrained model')
@@ -58,7 +58,7 @@ def main():
 
     # load the model 
     network_data = torch.load(args.pretrained, map_location=args.device)
-    model = DGSS(nr_channel=8, conv1_size=7)
+    model = DAL(nr_channel=8, conv1_size=7)
     model.load_state_dict(network_data['state_dict'])
     model.eval()
 

analysis_single_nC.sh

@@ -3,7 +3,7 @@
 MAIN_DIR="."
 INPUT_DIR="${MAIN_DIR}/sample_imgs/input/"
 OUTPUT_DIR="${MAIN_DIR}/sample_imgs/output/"
-PRETRAINED="${MAIN_DIR}/pretrained/DGSS_loss=bce-rgb_date=23Feb2021.tar"
+PRETRAINED="${MAIN_DIR}/pretrained/DAL_loss=bce-rgb_date=23Feb2021.tar"
 
 python3 analysis_single_nC.py \
 --pretrained $PRETRAINED \

model/__pycache__/network.cpython-38.pyc

@@ -139,11 +139,11 @@ def forward(self, x):
         return out
 
 
-class DGSS(nn.Module):
+class DAL(nn.Module):
 
     def __init__(self, nr_channel, conv1_size):
 
-        super(DGSS, self).__init__()
+        super(DAL, self).__init__()
 
         pad_size = int((conv1_size - 1) / 2)
         self.pad1 = nn.ReplicationPad2d((pad_size, pad_size, pad_size, pad_size))     # left, right, top, bottom