updates

Author: Crispy13

.gitignore

@@ -0,0 +1,9 @@
+
+*.log
+*.pyc
+logs/
+examples/.ipynb_checkpoints/guided_grad_cam-checkpoint.ipynb
+core/.ipynb_checkpoints/ecf-checkpoint.py
+core/.ipynb_checkpoints/eckeras-checkpoint.py
+core/.ipynb_checkpoints/emri-checkpoint.py
+core/.ipynb_checkpoints/guided_grad_cam-checkpoint.py

core/ecf.py

@@ -124,7 +124,8 @@ def get_savepath(save_path=None):
 
     # Search files with file name
     gl1 = glob.glob(f"{file_name}*{file_ext}") ; logger.debug(f"gl1: {gl1}")
-    re1 = list(filter(lambda x:re.search(f"{file_name}(?:_[0-9]+){file_ext}$", x) is not None, gl1)) ; logger.debug(f"re1: {re1}")
+    fn = file_name.replace(os.sep, "/")
+    re1 = list(filter(lambda x:re.search(f"{fn}(?:_[0-9]+){file_ext}$", os.path.normpath(x).replace(os.sep, "/")) is not None, gl1)) ; logger.debug(f"re1: {re1}")
     re2 = re1 + glob.glob(save_path)
 
     if len(re2) == 0:

core/eckeras.py

@@ -17,31 +17,35 @@
 def get_model_memory_usage(batch_size, model):
     """
     https://stackoverflow.com/questions/43137288/how-to-determine-needed-memory-of-keras-model
+    
     """
-
+    
     shapes_mem_count = 0
     internal_model_mem_count = 0
     for l in model.layers:
         layer_type = l.__class__.__name__
         if layer_type == 'Model':
             internal_model_mem_count += get_model_memory_usage(batch_size, l)
         single_layer_mem = 1
-        for s in l.output_shape:
+        out_shape = l.output_shape
+        if type(out_shape) is list:
+            out_shape = out_shape[0]
+        for s in out_shape:
             if s is None:
                 continue
             single_layer_mem *= s
         shapes_mem_count += single_layer_mem
 
-    trainable_count = np.sum([K.count_params(p) for p in set(model.trainable_weights)])
-    non_trainable_count = np.sum([K.count_params(p) for p in set(model.non_trainable_weights)])
+    trainable_count = np.sum([K.count_params(p) for p in model.trainable_weights])
+    non_trainable_count = np.sum([K.count_params(p) for p in model.non_trainable_weights])
 
     number_size = 4.0
     if K.floatx() == 'float16':
-         number_size = 2.0
+        number_size = 2.0
     if K.floatx() == 'float64':
-         number_size = 8.0
-            
-    total_memory = number_size*(batch_size*shapes_mem_count + trainable_count + non_trainable_count)
+        number_size = 8.0
+
+    total_memory = number_size * (batch_size * shapes_mem_count + trainable_count + non_trainable_count)
     gbytes = np.round(total_memory / (1024.0 ** 3), 3) + internal_model_mem_count
     return gbytes
 

core/emri.py

@@ -784,6 +784,40 @@ def get_index_by_patient_number_old(plist, df1, seed_n = 42, mode = 'random', co
 
 # Plot prediction result
 def plot_prediction_result(data, labels, preds, logged = None, chunk_size = 80, save_path = None, arrangement = ['d1', 'd1-l1', 'd1-p1', 'd1-ph', 'd1-l1-ph', 'd1-p1-ph'], **kwargs):
+    """
+    Plots a comparison figure with arrangement.
+    
+    Parameters
+    ----------
+    N : data numbers
+    C : channels
+    H : Height
+    W : Width
+    D : Depth
+    
+    data : a numpy array
+        (N, C, H, W, D) array, image data
+    
+    labels : a numpy array
+        (N, C, H, W, D) array, label data
+    
+    preds : a numpy array
+        (N, C, H, W, D) array, preds data
+        
+    chunk_size : the number of data for one png file.
+    
+    save_path : png save path.
+    
+    arrangement : a list of strings
+        e.g.
+            'd1' means data channel 1
+            'd1-l2' means overlaying label channel 2 on data channel 1 
+            'd1-p1-ph' means the height is what has the widest prediction area, pred channel 1 overlayed on data channel 1
+    
+        If you set arrangement to ['d1', 'd1-l1', 'd1-p1', 'd1-ph', 'd1-l1-ph', 'd1-p1-ph'], the figure will be (samples, 6) plot.
+            
+        
+    """
     ca = arange(0, len(data), chunk_size)
 
     for i in range(len(ca)):
@@ -1066,15 +1100,37 @@ def show_mri(img, label, pred = None, logged = None, arrangement = ['d1', 'd1-l1
              height=None, cmap='Greys_r', data_order=['x_test', 'y_test', 'y_pred', 'x_test', 'y_pred'], nums = None,
              thresh=0.5, save_path = None, pad = 5, subplot_adjust_left = 0.33, max_samples = 1000, img_types = None, initial_num = 1, **kwargs):
     """
-    Paramters
-    ---------
-    arrangement : a list.
-        e.g. ['d1', 'd2', 'd1-l1', 'd1-ph', 'd1-ph-p1']
-            d1 = data - channel 1
-            l2 = label - channel 2
-            p2 = pred - channel 2
-            ph = height based on prediction(not label)
-                       
+    
+    Parameters
+    ----------
+    N : data numbers
+    C : channels
+    H : Height
+    W : Width
+    D : Depth
+    
+    data : a numpy array
+        (N, C, H, W, D) array, image data
+    
+    labels : a numpy array
+        (N, C, H, W, D) array, label data
+    
+    preds : a numpy array
+        (N, C, H, W, D) array, preds data
+        
+    chunk_size : the number of data for one png file.
+    
+    save_path : png save path.
+    
+    arrangement : a list of strings
+        e.g.
+            'd1' means data channel 1
+            'd1-l2' means overlaying label channel 2 on data channel 1 
+            'd1-p1-ph' means the height is what has the widest prediction area, pred channel 1 overlayed on data channel 1
+    
+        If you set arrangement to ['d1', 'd1-l1', 'd1-p1', 'd1-ph', 'd1-l1-ph', 'd1-p1-ph'], the figure will be (samples, 6) plot.
+            
+        
     """
 
     # Set paramters
@@ -1254,14 +1310,14 @@ def truncate_trainset_size(*args, batch_size):
 
 
 def make_model(build_model, input_shape, output_channels, n_gpu, test_mode = True, seed_number = 42):
-    tf.set_random_seed(seed_number)
+    tf.random.set_seed(seed_number)
     np.random.seed(seed_number)
     logger.info(f"{seed_number} was set to seed number, and seed of tensorflow and numpy was set to the number.")
 
-    model, template_model, opt, lg, lv, dc = build_model(input_shape=input_shape,
+    model, opt, lg, lv, dc = build_model(input_shape=input_shape,
                                                     output_channels=output_channels, n_gpu=n_gpu, test_mode=True)
 
-    return model, template_model, opt, lg, lv, dc
+    return model, opt, lg, lv, dc
 
 
 def set_callbacks(model, save_dir, fold_number = None, min_delta = 0.01, patience = 10, baseline = None, a0 = 1e-5, lr_schedule_total_epoch = 300,