update numpy type conversion argument string to np value

Author: inzapp

generator.py

@@ -69,8 +69,8 @@ def load(self):
             if dir_name != 'unknown':
                 y[self.class_names.index(dir_name)] = 1.0
             batch_y.append(y)
-        batch_x = np.asarray(batch_x).reshape((self.batch_size,) + self.input_shape).astype('float32')
-        batch_y = np.asarray(batch_y).reshape((self.batch_size, self.num_classes)).astype('float32')
+        batch_x = np.asarray(batch_x).reshape((self.batch_size,) + self.input_shape).astype(np.float32)
+        batch_y = np.asarray(batch_y).reshape((self.batch_size, self.num_classes)).astype(np.float32)
         return batch_x, batch_y
 
     def preprocess(self, img, aug=False):
@@ -79,7 +79,7 @@ def preprocess(self, img, aug=False):
             img = self.transform(image=img)['image']
         if self.input_shape[-1] == 3:
             img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # swap rb
-        x = np.asarray(img).reshape(self.input_shape).astype('float32') / 255.0
+        x = np.asarray(img).reshape(self.input_shape).astype(np.float32) / 255.0
         return x
 
     def get_next_image_path(self):

sigmoid_classifier.py

@@ -219,7 +219,7 @@ def draw_cam(self, x, label, window_size_h=512, alpha=0.6):
             cam -= np.min(cam)
             cam /= np.max(cam)
             cam *= 255.0
-            cam = cam.astype('uint8')
+            cam = cam.astype(np.uint8)
             cam = cv2.resize(cam, (img_w, img_h))
             cam = cam[..., np.newaxis]
             cam = np.concatenate([cam, cam, cam], axis=-1)
@@ -228,8 +228,8 @@ def draw_cam(self, x, label, window_size_h=512, alpha=0.6):
             cam_blended = cv2.addWeighted((org_image * 255).astype(np.uint8), alpha, cam_jet, (1 - alpha), 0)
 
             label_box = np.zeros((img_h, 20, 3), dtype=np.float32) + float(label == idx)
-            label_box = (label_box * 255.0).astype('uint8')
-            org_image = (org_image * 255.0).astype('uint8')
+            label_box = (label_box * 255.0).astype(np.uint8)
+            org_image = (org_image * 255.0).astype(np.uint8)
             grid_row = np.concatenate([label_box, org_image, cam, cam_jet, cam_blended], axis=1)
             image_grid = np.append(image_grid, grid_row, axis=0) if image_grid is not None else grid_row.copy()
         if window_size_h is not None: