Final changes before submission :) :)

Author: rtgunti

exec.py

@@ -132,7 +132,7 @@ def test(logger):
     test_predictor = Predictor(cf, net, logger, mode='test')
     test_evaluator = Evaluator(cf, logger, mode='test')
     batch_gen = data_loader.get_test_generator(cf, logger)
-    # test_results_list = test_predictor.predict_test_set(batch_gen, return_results=True)
+    test_results_list = test_predictor.predict_test_set(batch_gen, return_results=True)
 
     # # print("Test without WBC")
     # wbc_bool = False
@@ -151,9 +151,9 @@ def test(logger):
     # load_pred_and_eval()
     # return
     fold = cf.fold
-    bool_predict_test_set = False
-    bool_predicted = False
-    for wbc_bool in [False, True]:
+    bool_predict_test_set = False   #set to True to re run the predictions
+    bool_predicted = False          #bool used to avoid repeating predictions
+    for wbc_bool in [True]:  #[False, True]
         cf.fold = fold
         test_evaluator = Evaluator(cf, logger, mode='test')        
         if bool_predict_test_set and not bool_predicted:
@@ -175,9 +175,9 @@ def test(logger):
 
         for bool_low_lesion in [False, True]:
             print("wbc_bool : {} bool_low_lesion : {}".format(wbc_bool, bool_low_lesion))
-            plot_test_prediction(copy.deepcopy(test_results_list), cf, wbc_bool, bool_low_lesion)#@rtgunti
+            # plot_test_prediction(copy.deepcopy(test_results_list), cf, wbc_bool, bool_low_lesion)#@rtgunti
             break
-        # break
+        break
 
 
     # for test_result in test_results_list:

experiments/lidc_exp/configs.py

@@ -14,11 +14,14 @@
 # limitations under the License.
 # ==============================================================================
 
-from default_configs import DefaultConfigs
+
 import numpy as np
 import sys
 import os
 sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+sys.path.append("/home/rgunti/medkit/")
+print(sys.path)
+from default_configs import DefaultConfigs
 
 
 class configs(DefaultConfigs):
@@ -31,7 +34,7 @@ def __init__(self, server_env=None):
 
         self.data = ["Thesis", "LiTS"]
 
-        self.root_dir = '/home/ravi/Thesis'
+        self.root_dir = '/home/rgunti/data/Thesis'
         self.raw_data_dir = '{}/data_raw/data/'.format(self.root_dir)
         self.raw_seg_dir = '{}/data_raw/seg/'.format(self.root_dir)
 
@@ -55,7 +58,7 @@ def __init__(self, server_env=None):
         self.dim = 3
 
         # one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn', 'decoupled_refinement'].
-        self.model = 'retina_net'
+        self.model = 'decoupled_refinement'
 
         DefaultConfigs.__init__(self, self.model, server_env, self.dim)
 
@@ -67,10 +70,10 @@ def __init__(self, server_env=None):
         self.data_dest = ''
 
         # path to preprocessed data.
-        # self.pp_name = 'data_pp'
+        self.pp_name = 'data_pp'
         # self.pp_name = 'data_pp_int'
         # self.pp_name = 'data_pp_wocrop'
-        self.pp_name = 'data_pp_int_wocrop'
+        # self.pp_name = 'data_pp_int_wocrop'
 
         self.input_df_name = 'info_df.pickle'
         self.pp_data_path = '{}/{}/'.format(self.root_dir, self.pp_name)

plotting.py

@@ -221,7 +221,8 @@ def plot_test_prediction(results_list, cf, wbc_bool, bool_low_lesion, outfile=No
         except:
             raise Warning('failed to save plot.')
         plt.close(fig)
-        # break
+        print(outfile)
+        break
 
 
 def plot_batch_prediction(batch, results_dict, cf, outfile= None):
@@ -523,10 +524,13 @@ def plot_stat_curves(stats, outfile):
         plt.figure()
         for s in stats:
             if s[c] is not None:
-                s[c][1][0] = s[c][1][1]
-                s[c][0][0] = 0
+                # plt.plot(s[c][0], s[c][1], label=s['name'] + '_' + c)     #line from original code. Plots look flipped somehow. 
+                # plt.plot(s[c][1], s[c][0], label=s['name'] + '_' + c)     # If axes are interchanged, plot looks ok. But recall is forced to reach to 1 (since false negatives are marked at score 0). Plots look weird
+                # To remove the last point from recall, 
+                s[c][1][0] = s[c][1][1]                                     # Assign x coordinate of second value of recall to first value
+                s[c][0][0] = 0                                              # Assign y coordinate of first point to 0 so that the curve drops to x axis after max recall is reached
                 plt.plot(s[c][1], s[c][0], label=s['name'] + '_' + c )
-                plt.plot([0,1], [1,0])
+                plt.plot([0,1], [1,0])                                      #Add a diagonal line for reference
             break
         title += '\n Average Precision : ' + str(np.round(s['ap'], 3))
         plt.title(title)

predictor.py

@@ -273,6 +273,9 @@ def load_saved_predictions(self, apply_wbc=True):
             pool.close()
             pool.join()
         else:
+            '''
+            If not using WBC, apply NMS for comparison
+            '''
             apply_nms = True
             if apply_nms:
                 self.logger.info('applying NMS to test set predictions with iou = {} and n_ens = {}.'.format(

utils/plot_comparison.py

@@ -29,8 +29,8 @@
 exp_dir1 = exp_dir_62
 exp_dir2 = exp_dir_633_int_dcr
 
-bool_plot_all_folds = False
-bool_plot_prc = False
+bool_plot_all_folds = True
+bool_plot_prc = True
 bool_plot_overall_with_wbc = True
 
 exp_pairs = [