fix attribute error numpy.int (#3655)

Author: eaidova

demos/image_inpainting_demo/python/image_inpainting_demo.py

@@ -78,8 +78,8 @@ def create_random_mask(parts, max_vertex, max_length, max_brush_width, h, w, max
             next_y = start_y + length * np.cos(angle)
             next_x = start_x + length * np.sin(angle)
 
-            next_y = np.clip(next_y, 0, h - 1).astype(np.int)
-            next_x = np.clip(next_x, 0, w - 1).astype(np.int)
+            next_y = np.clip(next_y, 0, h - 1).astype(np.int32)
+            next_x = np.clip(next_x, 0, w - 1).astype(np.int32)
             cv2.line(mask, (start_y, start_x), (next_y, next_x), 1, brush_width)
             cv2.circle(mask, (start_y, start_x), brush_width // 2, 1)
 

demos/image_translation_demo/python/image_translation_demo/preprocessing.py

@@ -18,7 +18,7 @@
 def scatter(source, classes, axis=1, base=0, value=1):
     shape = [1, 1, *source.shape]
     shape[axis] = classes
-    label_map = np.full(shape, base, np.int)
+    label_map = np.full(shape, base, np.int32)
     ndim = len(shape)
     expanded_index = []
     for i in range(ndim):

tools/accuracy_checker/openvino/tools/accuracy_checker/adapters/text_detection.py

@@ -131,7 +131,7 @@ def get_valid_y(y_coord):
 
             rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])
             points = cv2.boxPoints(rect)
-            points = points.astype(np.int0)
+            points = points.astype(np.intp)
             for i_xy, (x_coord, y_coord) in enumerate(points):
                 x_coord = get_valid_x(x_coord)
                 y_coord = get_valid_y(y_coord)
@@ -677,10 +677,10 @@ def get_mini_boxes(contour):
     def box_score_fast(bitmap, _box):
         h, w = bitmap.shape[:2]
         box = _box.copy()
-        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
-        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
-        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
-        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
         mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
         box[:, 0] = box[:, 0] - xmin
         box[:, 1] = box[:, 1] - ymin

tools/accuracy_checker/openvino/tools/accuracy_checker/annotation_converters/imagenet.py

@@ -15,7 +15,6 @@
 """
 
 from pathlib import Path
-import numpy as np
 
 from ..config import PathField, BoolField
 from ..representation import ClassificationAnnotation
@@ -71,7 +70,7 @@ def convert(self, check_content=False, progress_callback=None, progress_interval
                 if not check_file_existence(self.images_dir / image_name):
                     content_errors.append('{}: does not exist'.format(self.images_dir / image_name))
 
-            label = np.int64(label) if not self.has_background else np.int64(label) + 1
+            label = int(label) if not self.has_background else int(label) + 1
             annotation.append(ClassificationAnnotation(image_name, label))
             if progress_callback is not None and image_id % progress_interval == 0:
                 progress_callback(image_id / num_iterations * 100)

tools/accuracy_checker/openvino/tools/accuracy_checker/evaluators/custom_evaluators/asr_encoder_prediction_joint_evaluator.py

@@ -358,7 +358,7 @@ def predict(self, identifiers, input_data, callback=None):
 class EncoderONNXModel(CommonONNXModel):
     def fit_to_input(self, input_data):
         frames, _, _ = input_data.shape
-        return {self.input_blob.name: input_data, '1': np.array([frames], dtype=np.int64)}
+        return {self.input_blob.name: input_data, '1': np.array([frames], dtype=int)}
 
 
 class PredictionONNXModel(CommonONNXModel):

tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/input_feeder.py

@@ -68,7 +68,7 @@
     'I8': np.int8,  # signed char
     'I16': np.int16,  # signed short
     'I32': np.int32,  # signed int
-    'I64': np.int64,  # signed long int
+    'I64': int,  # signed long int
     'STR': str,  # string'
     'BOOL': bool,
     'f32': np.float32,

tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/openvino_launcher.py

@@ -46,7 +46,7 @@
 
 
 format_map = {
-      'f32': np.float32, 'i32': np.int32, 'i64': np.int64,
+      'f32': np.float32, 'i32': np.int32, 'i64': int,
       'fp16': np.float16, 'f16': np.float16, 'i16': np.int16, 'u16': np.uint16,
       'i8': np.int8, 'u8': np.uint8, 'boolean': np.uint8
 }

tools/accuracy_checker/openvino/tools/accuracy_checker/metrics/background_matting.py

@@ -118,7 +118,7 @@ def gauss_gradient(self, img):
 
     def gauss_filter(self, sigma, epsilon=1e-2):
         half_size = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon)))
-        size = np.int(2 * half_size + 1)
+        size = np.int32(2 * half_size + 1)
         # create filter in x axis
         filter_x = np.zeros((size, size))
         for i in range(size):

tools/accuracy_checker/openvino/tools/accuracy_checker/preprocessor/inpainting_preprocessor.py

@@ -68,8 +68,8 @@ def _free_form_mask(mask, max_vertex, max_length, max_brush_width, h, w, max_ang
             next_y = start_y + length * np.cos(angle)
             next_x = start_x + length * np.sin(angle)
 
-            next_y = np.clip(next_y, 0, h - 1).astype(np.int)
-            next_x = np.clip(next_x, 0, w - 1).astype(np.int)
+            next_y = np.clip(next_y, 0, h - 1).astype(np.int32)
+            next_x = np.clip(next_x, 0, w - 1).astype(np.int32)
             cv2.line(mask, (start_y, start_x), (next_y, next_x), 1, brush_width)
             cv2.circle(mask, (start_y, start_x), brush_width // 2, 1)
 

tools/accuracy_checker/openvino/tools/accuracy_checker/preprocessor/one_hot_encoding.py

@@ -62,7 +62,7 @@ def process_data(data, classes, axis, value, base):
             shapes = list(data.shape)
             ndim = len(shapes)
             shapes[axis] = classes
-            base_arr = np.full(shapes, base, np.int)
+            base_arr = np.full(shapes, base, np.int32)
             expanded_index = []
             for i in range(ndim):
                 arr = (data if axis == i