Context manager `open(file) as f` fixes (#7289)

data/VOC.yaml

@@ -72,7 +72,8 @@ download: |
       imgs_path.mkdir(exist_ok=True, parents=True)
       lbs_path.mkdir(exist_ok=True, parents=True)
 
-      image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
+      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+          image_ids = f.read().strip().split()
       for id in tqdm(image_ids, desc=f'{image_set}{year}'):
           f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
           lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path

export.py

@@ -407,7 +407,8 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
               f'--output_node_names="Identity,Identity_1,Identity_2,Identity_3" {f_pb} {f}'
         subprocess.run(cmd, shell=True)
 
-        json = open(f_json).read()
+        with open(f_json) as j:
+            json = j.read()
         with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
             subst = re.sub(
                 r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '

models/common.py

@@ -378,7 +378,8 @@ class DetectMultiBackend(nn.Module):
                     return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
 
                 gd = tf.Graph().as_graph_def()  # graph_def
-                gd.ParseFromString(open(w, 'rb').read())
+                with open(w, 'rb') as f:
+                    gd.ParseFromString(f.read())
                 frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
             elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
                 try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu

utils/flask_rest_api/example_request.py

-"""Perform test request"""
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Perform test request
+"""
+
 import pprint
 
 import requests
 
 DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-TEST_IMAGE = "zidane.jpg"
+IMAGE = "zidane.jpg"
 
-image_data = open(TEST_IMAGE, "rb").read()
+# Read image
+with open(IMAGE, "rb") as f:
+    image_data = f.read()
 
 response = requests.post(DETECTION_URL, files={"image": image_data}).json()
 

utils/flask_rest_api/restapi.py

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
 Run a Flask REST API exposing a YOLOv5s model
 """
+
 import argparse
 import io