update bash script

README.md

@@ -10,22 +10,26 @@
 
    ```bash
    git clone https://lab.compute.dtu.dk/s161488/region_active_learning.git
-   cd Region_Active_Learning
+   cd region_active_learning
+   chmod +x requirement.sh
+   chmod +x produce_figure.sh
    ```
 
-2. Prepare the Dataset and pertained resnet-50 ckpt 
+2. Create a virtual env with the required packages
 
    ```bash
-   ./requirement.sh
+   conda env create -f active_learning.yaml
+   source activate act
    ```
 
-3. Create a virtual env with the required packages
+
+2. Prepare the Dataset and pertained resnet-50 ckpt 
 
    ```bash
-   conda env create --act active_learning.yaml
-   source activate act
+   ./requirement.sh
    ```
 
+
 #### Evaluate the model
 
 In order to evaluate the model at each acquisition step, run

data_utils/glanddata.py

@@ -17,7 +17,7 @@ from skimage.morphology import dilation, disk
 from scipy import ndimage
 import matplotlib.pyplot as plt
 
-path_mom = "/home/blia/Region_Active_Learning/DATA/"  # the directory for saving data
+path_mom = "DATA/"  # the directory for saving data
 # path_mom = "/Users/bo/Documents/Exp_Data/"
 path_use = path_mom + 'Data'
 if not os.path.exists(path_use):
@@ -167,6 +167,7 @@ def transfer_data_to_dict():
         print("Saving the data in path:", filename.split(".")[0])
     else:
         print("Oh, this is the first time of creating this file")
+        print("Creating the training data npy for GlaS")
 
     np.save(filename.split(".")[0], data)
 
@@ -235,12 +236,10 @@ def transfer_data_to_dict_test():
         print("Remove the existing data file", os.remove(filename))
         print("Saving the data in path:", filename.split(".")[0])
     else:
-        print("The file is not existed")
+        print("Creating GlaS test data (benign)")
 
     print("Saving the data in path:", filename.split(".")[0])
-
     np.save(filename.split(".")[0], data)
-
     data1 = defaultdict(list)
     data1['image'] = image_mali
     data1['label'] = label_mali
@@ -252,6 +251,6 @@ def transfer_data_to_dict_test():
         print("Remove the existing data file", os.remove(filename))
         print("Saving the data in path:", filename.split(".")[0])
     else:
-        print("The file is not existed")
-
+        print("Creating GlaS test data (malignant)")
+    print("Saving the data in path:", filename.split(".")[0])
     np.save(filename.split(".")[0], data1)

data_utils/prepare_data.py

@@ -10,7 +10,7 @@ import tensorflow as tf
 import cv2
 import matplotlib.pyplot as plt
 
-path_mom = "/home/blia/Region_Active_Learning/DATA/"  # NOTE, NEED TO BE MANUALLY DEFINED
+path_mom = "DATA/"  # NOTE, NEED TO BE MANUALLY DEFINED
 
 
 def prepare_train_data(path, select_benign_train, select_mali_train):

produce_figure.sh

 #!/bin/bash
-cd Exp_Stat
+expdir=Exp_Stat
+if [ -d "$expdir" ]; then
+    echo "folder $expdir already exists"
+    echo "next step, download the statistics to reproduce figures"
+else
+    mkdir $expdir
+    echo "make folder $expdir for saving statistics"
+fi
+cd $expdir
 filename=calibration_score
 if [ -d "$filename" ]; then
-    echo "$filename exists"
-    echo "-----next step, reproducing the figures...................."
+    echo "YEAH! $filename exists"
 else
     echo "$filename does not exist"
-    echo "Download the file-------------------"
-    wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /t$
+    echo "Download the file..................................."
+    wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1fu7RcqOMCCjz65VwFlTPg5qlLg2K7l1g' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1fu7RcqOMCCjz65VwFlTPg5qlLg2K7l1g" -O calibration_score.tar.gz && rm -rf /tmp/cookies.txt
     echo "-----unziping the datafile"
     tar -xzvf calibration_score.tar.gz
     mv use_stat calibration_score
     rm calibration_score.tar.gz
 fi
 cd ..
+echo "Start to reproduce figures"
 python3 visualize_calibration_score.py --save True --path Exp_Stat/calibration_score/
+echo "-----------------------------------"
+echo "YEAH, FINISH REPRODUCING FIGURES :)"
+echo "-----------------------------------"
\ No newline at end of file

requirement.sh

 #!/bin/bash
 cd DATA
-wget https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/warwick_qu_dataset_released_2016_07_08.zip
-unzip warwick_qu_dataset_released_2016_07_08.zip
-mv 'Warwick QU Dataset (Released 2016_07_08)' gland_data
-rm -rf warwick_qu_dataset_released_2016_07_08.zip
-rm -rf __MACOSX
+datafolder=gland_data
+if [ -d "$datafolder" ]; then
+    echo "YEAH, $datafolder exist"
+    echo "next download the resnet-v2-50 pretrained weight from tensorflow website..............."
+else
+    echo "download the GlaS dataset ...................................."
+    wget https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/warwick_qu_dataset_released_2016_07_08.zip
+    unzip warwick_qu_dataset_released_2016_07_08.zip
+    mv 'Warwick QU Dataset (Released 2016_07_08)' gland_data
+    rm -rf warwick_qu_dataset_released_2016_07_08.zip
+    rm -rf __MACOSX
+fi
 cd ..
+pwd
+echo "download the resnet-v2-50 pretrained weight............."
+pretrainfolder=pretrain_model
+if [ -d "$pretrainfolder" ]; then
+    echo "YEAH, folder $pretrainfolder exists"
+else
+    echo "create the folder to save resnet-v2-50 pretrained weight"
+    mkdir $pretrainfolder
+fi
+resnetname=resnet_v2_50.ckpt
 cd pretrain_model
-wget http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
-tar -xvf resnet_v2_50_2017_04_14.tar.gz
-rm resnet_v2_50_2017_04_14.tar.gz
-rm train.graph
-rm eval.graph
+if [ -f "$resnetname" ]; then
+    echo "YEAH, $resnetname exists"
+    echo "next prepare the dataset ...................."
+else
+    echo "download the resnet-v2-50 pretrained weight............."
+    wget http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
+    tar -xvf resnet_v2_50_2017_04_14.tar.gz
+    rm resnet_v2_50_2017_04_14.tar.gz
+    rm train.graph
+    rm eval.graph    
+fi
 cd ..
+echo "print current directory"
+pwd
+echo "prepare the dataset"
 python3 -c 'import data_utils.glanddata as gd;gd.transfer_data_to_dict()'
 python3 -c 'import data_utils.glanddata as gd;gd.transfer_data_to_dict_test()'
+echo "-------------------------------"
+echo "YEAH, FINISH PREPARING THE DATA"
+echo "-------------------------------"