MAINT: Typos and misc bugs fixed for load_from_cache

pylids/select_frames.py

@@ -67,12 +67,11 @@ def select_augmentations(trn_fls, tst_fls, aug_fls,
     Returns:
         TYPE: Description
     """
-    # expects .png folders for test and train files
-    # returns a list with path to frames to label
-
-    print('This code has a run time of about 40 mins, go make yourself some tea! \n')
-    print('Run time and RAM required scales with dataset size! \n')
-    print('Make sure you have enough RAM for this process, else, downsample your data using k-means clustering... \n')
+
+    print('This can take some time to run, go make yourself some tea!')
+    print('\n Run time and RAM required scales with dataset size!')
+    print('\n Track memory usage and make sure you have enough RAM for this process.')
+    print('\n else, downsample your data using k-means clustering within participants...')
     model = ResNet50(weights="imagenet", include_top=False)
     if not os.path.exists(cache_loc):
         os.makedirs(cache_loc)
@@ -215,17 +214,16 @@ def select_frames_to_label(trn_fls=None, tst_fls=None,
         num_frames (int, optional): Description
         kmeans_batch_size (int, optional): Description
         kmeans_type (str, optional): default, batch
-        load_from_cache (bool, optional): set to True if you want to load trn_fls or tst_fls from cache
+        load_from_cache (bool, optional): set to True if you want to load/save
+                                         trn_fls and tst_fls from cache
 
     Returns:
         TYPE: Description
     """
-    # expects .png folders for test and train files
-    # returns a list with path to frames to label
-
-    print('This code has a run time of about 40 mins, go make yourself some tea!')
+    print('This can take some time to run, go make yourself some tea!')
     print('\n Run time and RAM required scales with dataset size!')
-    print('\n Make sure you have enough RAM for this process else, downsample your data using k-means clustering within participants...')
+    print('\n Track memory usage and make sure you have enough RAM for this process.')
+    print('\n else, downsample your data using k-means clustering within participants...')
 
     model = ResNet50(weights="imagenet", include_top=False)
     if not os.path.exists(cache_loc) and load_from_cache:
@@ -245,22 +243,28 @@ def select_frames_to_label(trn_fls=None, tst_fls=None,
                 np.save(os.path.join(cache_loc, trn_data+'.npy'), trn_rnfs)
         else:
             trn_rnfs = get_rnfs_from_list(trn_fls, model)
-            np.save(os.path.join(cache_loc, trn_data+'.npy'), trn_rnfs)
+
         if return_min_rand_frames:
             av_trn_fs = np.mean(trn_rnfs,axis=0)
 
     if tst_fls is None:
         assert trn_fls is not None, 'Test files should be provided'
     else:
         # extracting resnet features for test dataset images
-        tst_data = input("Enter the name of the test dataset:\n")
-        if os.path.isfile(os.path.join(cache_loc,tst_data+'.npy')):
-            print('Loading test features from cache')
-            tst_rnfs = np.load(os.path.join(cache_loc,tst_data+'.npy'))
+        if load_from_cache:
+            print('Files in cache: \n')
+            print(glob(cache_loc+'*.npy'))
+
+            tst_data = input("Enter the name of the test dataset:\n")
+            if os.path.isfile(os.path.join(cache_loc,tst_data+'.npy')):
+                print('Loading test features from cache')
+                tst_rnfs = np.load(os.path.join(cache_loc,tst_data+'.npy'))
+            else:
+                tst_rnfs = get_rnfs_from_list(tst_fls, model)
+                np.save(os.path.join(cache_loc,tst_data+'.npy'), tst_rnfs)
         else:
             tst_rnfs = get_rnfs_from_list(tst_fls, model)
-            np.save(os.path.join(cache_loc,tst_data+'.npy'), tst_rnfs)
-
+
     # Iterative k means which keeps running till we find a given number of frames to label
     # from the test dataset / set of augmented images
     n_clusters = n_frames