Code refactor (#7923)

* Code refactor for general.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update restapi.py

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/flask_rest_api/restapi.py

@@ -17,7 +17,7 @@ DETECTION_URL = "/v1/object-detection/yolov5s"
 
 @app.route(DETECTION_URL, methods=["POST"])
 def predict():
-    if not request.method == "POST":
+    if request.method != "POST":
         return
 
     if request.files.get("image"):

utils/general.py

@@ -67,17 +67,16 @@ def is_kaggle():
 
 def is_writeable(dir, test=False):
     # Return True if directory has write permissions, test opening a file with write permissions if test=True
-    if test:  # method 1
-        file = Path(dir) / 'tmp.txt'
-        try:
-            with open(file, 'w'):  # open file with write permissions
-                pass
-            file.unlink()  # remove file
-            return True
-        except OSError:
-            return False
-    else:  # method 2
+    if not test:
         return os.access(dir, os.R_OK)  # possible issues on Windows
+    file = Path(dir) / 'tmp.txt'
+    try:
+        with open(file, 'w'):  # open file with write permissions
+            pass
+        file.unlink()  # remove file
+        return True
+    except OSError:
+        return False
 
 
 def set_logging(name=None, verbose=VERBOSE):
@@ -244,7 +243,7 @@ def is_ascii(s=''):
 
 def is_chinese(s='人工智能'):
     # Is string composed of any Chinese characters?
-    return True if re.search('[\u4e00-\u9fff]', str(s)) else False
+    return bool(re.search('[\u4e00-\u9fff]', str(s)))
 
 
 def emojis(str=''):
@@ -417,7 +416,7 @@ def check_file(file, suffix=''):
     # Search/download file (if necessary) and return path
     check_suffix(file, suffix)  # optional
     file = str(file)  # convert to str()
-    if Path(file).is_file() or file == '':  # exists
+    if Path(file).is_file() or not file:  # exists
         return file
     elif file.startswith(('http:/', 'https:/')):  # download
         url = str(Path(file)).replace(':/', '://')  # Pathlib turns :// -> :/
@@ -481,28 +480,26 @@ def check_dataset(data, autodownload=True):
         val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
         if not all(x.exists() for x in val):
             LOGGER.info(emojis('\nDataset not found ⚠, missing paths %s' % [str(x) for x in val if not x.exists()]))
-            if s and autodownload:  # download script
-                t = time.time()
-                root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
-                if s.startswith('http') and s.endswith('.zip'):  # URL
-                    f = Path(s).name  # filename
-                    LOGGER.info(f'Downloading {s} to {f}...')
-                    torch.hub.download_url_to_file(s, f)
-                    Path(root).mkdir(parents=True, exist_ok=True)  # create root
-                    ZipFile(f).extractall(path=root)  # unzip
-                    Path(f).unlink()  # remove zip
-                    r = None  # success
-                elif s.startswith('bash '):  # bash script
-                    LOGGER.info(f'Running {s} ...')
-                    r = os.system(s)
-                else:  # python script
-                    r = exec(s, {'yaml': data})  # return None
-                dt = f'({round(time.time() - t, 1)}s)'
-                s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
-                LOGGER.info(emojis(f"Dataset download {s}"))
-            else:
+            if not s or not autodownload:
                 raise Exception(emojis('Dataset not found ❌'))
-
+            t = time.time()
+            root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
+            if s.startswith('http') and s.endswith('.zip'):  # URL
+                f = Path(s).name  # filename
+                LOGGER.info(f'Downloading {s} to {f}...')
+                torch.hub.download_url_to_file(s, f)
+                Path(root).mkdir(parents=True, exist_ok=True)  # create root
+                ZipFile(f).extractall(path=root)  # unzip
+                Path(f).unlink()  # remove zip
+                r = None  # success
+            elif s.startswith('bash '):  # bash script
+                LOGGER.info(f'Running {s} ...')
+                r = os.system(s)
+            else:  # python script
+                r = exec(s, {'yaml': data})  # return None
+            dt = f'({round(time.time() - t, 1)}s)'
+            s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
+            LOGGER.info(emojis(f"Dataset download {s}"))
     check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
     return data  # dictionary
 
@@ -531,8 +528,7 @@ def check_amp(model):
 def url2file(url):
     # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
     url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/
-    file = Path(urllib.parse.unquote(url)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth
-    return file
+    return Path(urllib.parse.unquote(url)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth
 
 
 def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):
@@ -645,10 +641,9 @@ def labels_to_class_weights(labels, nc=80):
 
 def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
     # Produces image weights based on class_weights and image contents
+    # Usage: index = random.choices(range(n), weights=image_weights, k=1)  # weighted image sample
     class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
-    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
-    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
-    return image_weights
+    return (class_weights.reshape(1, nc) * class_counts).sum(1)
 
 
 def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
@@ -657,11 +652,10 @@ def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
     # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
     # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
     # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
-    x = [
+    return [
         1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-    return x
 
 
 def xyxy2xywh(x):
@@ -883,7 +877,7 @@ def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_op
         p.requires_grad = False
     torch.save(x, s or f)
     mb = os.path.getsize(s or f) / 1E6  # filesize
-    LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
+    LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
 
 
 def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
@@ -946,10 +940,9 @@ def apply_classifier(x, model, img, im0):
             # Classes
             pred_cls1 = d[:, 5].long()
             ims = []
-            for j, a in enumerate(d):  # per item
+            for a in d:
                 cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
                 im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('example%i.jpg' % j, cutout)
 
                 im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                 im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32