train on tiles

dataset/craft_datasets.py

@@ -33,7 +33,7 @@ def py_read_data_and_label(data_fname:str):
         data_label = content["arr_0"]
     data_array = data_label[0]
     label_array = data_label[1]
-    return (tf.convert_to_tensor(data_array, dtype=tf.float32), tf.convert_to_tensor(label_array, dtype=tf.int32))
+    return data_array, label_array
 
 
 def read_data_and_label(patient_id:str, src_folder:str):
@@ -69,6 +69,22 @@ class Array3d_read_and_resize:
     def __init__(self, folder):
         self.folder = folder
 
+    def random_crop(self, data, label, x, y, z):
+        data_shape = np.shape(data)
+        random_range = [data_shape[0] - x, data_shape[1] - y, data_shape[2] - z]
+        random_offset = np.random.randint(0, random_range, size = 3)
+        _data = data[
+                random_offset[0]:random_offset[0] + x,
+                random_offset[1]:random_offset[1] + y, 
+                random_offset[2]:random_offset[2] + z,
+                ...]
+        _label = label[
+                random_offset[0]:random_offset[0] + x,
+                random_offset[1]:random_offset[1] + y,
+                random_offset[2]:random_offset[2] + z,
+                 ...]
+        return _data, _label
+
     def __call__(self):
         self.file_list = FileIterator(self.folder)
         for data_file in self.file_list:
@@ -79,14 +95,16 @@ def __call__(self):
             data, label = read_data_and_label(patient_id, self.folder)
             finish_reading = time.time()
 
+            data, label = self.random_crop(data, label, config.IMAGE_DIMENSION_X, config.IMAGE_DIMENSION_Y, config.IMAGE_DIMENSION_Z)
+
             start_resize = time.time()
             # data, label = borders.cut_and_resize_including_pancreas(data, label, np.random.rand(), np.random.rand())
             finish_resize = time.time()
 
             if DEBUG_DATA_LOADING_PERFORMANCE:
                 print(f"\tDATA_LOADING_PERFORMANCE: reading time: {finish_reading - start_reading:.1f} resize time: {finish_resize - start_resize:.1f}")
 
-            yield data, label
+            yield tf.convert_to_tensor(data, dtype=tf.float32), tf.convert_to_tensor(label, dtype=tf.int8)
 
 
 # def array3d_read_and_resize():
@@ -209,6 +227,10 @@ def __run_through_data_wo_any_action(ds_train, ds_valid):
 
 
 if __name__ == "__main__":
+    # read_and_resize = Array3d_read_and_resize(os.path.join(config.TFRECORD_FOLDER, "train"))
+    # item1 = next(read_and_resize())
+    # print("item1:", item1[0].shape, item1[1].shape)
+
     train_ds = craft_datasets(os.path.join(config.TFRECORD_FOLDER, "train"))
     valid_ds = craft_datasets(os.path.join(config.TFRECORD_FOLDER, "valid"))
     __run_through_data_wo_any_action(train_ds, valid_ds)