Reduce column length

yolort/v5/models/common.py

@@ -414,7 +414,8 @@ def forward(self, imgs, size=640, augment=False, profile=False):
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
             imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
+        # inference shape
+        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]
         x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
         x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
@@ -441,7 +442,8 @@ class Detections:
     def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
         super().__init__()
         d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
+        # normalizations
+        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
@@ -496,14 +498,15 @@ def display(self, pprint=False, show=False, save=False, crop=False, render=False
 
     def print(self):
         self.display(pprint=True)  # print results
-        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
-                    self.t)
+        LOGGER.info(f'Speed: {self.t[0]:.1f}ms pre-process, {self.t[1]:.1f}ms inference, '
+                    f'{self.t[2]:.1f}ms NMS per image at shape {tuple(self.s)}')
 
     def show(self):
         self.display(show=True)  # show results
 
     def save(self, save_dir='runs/detect/exp'):
-        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)  # increment save_dir
+        # increment save_dir
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)
         self.display(save=True, save_dir=save_dir)  # save results
 
     def crop(self, save=True, save_dir='runs/detect/exp'):
@@ -520,7 +523,8 @@ def pandas(self):
         ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
         cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
         for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            # update
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]
             setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
         return new
 

yolort/v5/utils/autoanchor.py

@@ -14,7 +14,8 @@
 
 
 def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    # Check anchor order against stride order for
+    # YOLOv5 Detect() module m, and correct if necessary
     a = m.anchor_grid.prod(-1).view(-1)  # anchor area
     da = a[-1] - a[0]  # delta a
     ds = m.stride[-1] - m.stride[0]  # delta s
@@ -31,7 +32,8 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
     m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
     shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
     scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
+    wh = torch.tensor(
+        np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()
 
     def metric(k):  # compute metric
         r = wh[:, None] / k[None]
@@ -55,22 +57,29 @@ def metric(k):  # compute metric
         if new_bpr > bpr:  # replace anchors
             anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
             m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            # loss
+            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)
             check_anchor_order(m)
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+            print(f'{prefix}New anchors saved to model. Update model *.yaml '
+                  'to use these anchors in the future.')
         else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
+            print(f'{prefix}Original anchors better than new anchors. '
+                  'Proceeding with original anchors.')
     print('')  # newline
 
 
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
+def kmean_anchors(dataset='./data/coco128.yaml', n=9,
+                  img_size=640, thr=4.0, gen=1000,
+                  verbose=True):
+    """
+    Creates kmeans-evolved anchors from training dataset
 
-        Arguments:
+        Args:
             dataset: path to data.yaml, or a loaded dataset
             n: number of anchors
             img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            thr: anchor-label wh ratio threshold hyperparameter
+                hyp['anchor_t'] used for training, default=4.0
             gen: generations to evolve anchors using genetic algorithm
             verbose: print all results
 
@@ -98,12 +107,15 @@ def anchor_fitness(k):  # mutation fitness
     def print_results(k):
         k = k[np.argsort(k.prod(1))]  # sort small to large
         x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        # best possible recall, anch > thr
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n
         print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
+        print(f'{prefix}n={n}, img_size={img_size}, '
+              f'metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
               f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
         for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+            # use in *.cfg
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')
         return k
 
     if isinstance(dataset, str):  # *.yaml file
@@ -119,32 +131,22 @@ def print_results(k):
     # Filter
     i = (wh0 < 3.0).any(1).sum()
     if i:
-        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
+        print(f'{prefix}WARNING: Extremely small objects found. '
+              f'{i} of {len(wh0)} labels are < 3 pixels in size.')
     wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
     # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
 
     # Kmeans calculation
     print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
     s = wh.std(0)  # sigmas for whitening
     k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
+    assert len(k) == n, (f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points '
+                         f'but returned only {len(k)}')
     k *= s
     wh = torch.tensor(wh, dtype=torch.float32)  # filtered
     wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
     k = print_results(k)
 
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
     # Evolve
     npr = np.random
     f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma

yolort/v5/utils/general.py

@@ -104,20 +104,28 @@ def get_latest_run(search_dir='.'):
 
 
 def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
-    # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
+    # Return path of user configuration directory. Prefer environment
+    # variable if exists. Make dir if required.
     env = os.getenv(env_var)
     if env:
         path = Path(env)  # use environment variable
     else:
-        cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'}  # 3 OS dirs
+        # 3 OS dirs
+        cfg = {
+            'Windows': 'AppData/Roaming',
+            'Linux': '.config',
+            'Darwin': 'Library/Application Support',
+        }
         path = Path.home() / cfg.get(platform.system(), '')  # OS-specific config dir
-        path = (path if is_writeable(path) else Path('/tmp')) / dir  # GCP and AWS lambda fix, only /tmp is writeable
+        # GCP and AWS lambda fix, only /tmp is writeable
+        path = (path if is_writeable(path) else Path('/tmp')) / dir
     path.mkdir(exist_ok=True)  # make if required
     return path
 
 
 def is_writeable(dir, test=False):
-    # Return True if directory has write permissions, test opening a file with write permissions if test=True
+    # 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:
@@ -243,7 +251,8 @@ def check_requirements(requirements='requirements.txt', exclude=(), install=True
     if n:  # if packages updated
         source = file.resolve() if 'file' in locals() else requirements
         s = (f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n"
-             f"{prefix} WARNING {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n")
+             f"{prefix} WARNING "
+             f"{colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n")
         print(emojis(s))
 
 
@@ -254,7 +263,8 @@ def check_img_size(imgsz, s=32, floor=0):
     else:  # list i.e. img_size=[640, 480]
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:
-        print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
+        print(f'WARNING: --img-size {imgsz} must be multiple of '
+              f'max stride {s}, updating to {new_size}')
     return new_size
 
 
@@ -285,7 +295,8 @@ def check_file(file):
         file = Path(urllib.parse.unquote(file)).name.split('?')[0]
         print(f'Downloading {url} to {file}...')
         torch.hub.download_url_to_file(url, file)
-        assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'
+        assert Path(file).exists() and Path(file).stat().st_size > 0, (
+            f'File download failed: {url}')
         return file
     else:  # search
         files = glob.glob('./**/' + file, recursive=True)  # find file
@@ -461,7 +472,8 @@ def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
     # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
     # 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
+    # # coco to darknet
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]
     x = [
         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,
@@ -600,9 +612,13 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
     xc = prediction[..., 4] > conf_thres  # candidates
 
     # Checks
-    assert 0 <= conf_thres <= 1, (f'Invalid Confidence threshold {conf_thres}, '
-                                  'valid values are between 0.0 and 1.0')
-    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
+    assert 0 <= conf_thres <= 1, (
+        f'Invalid Confidence threshold {conf_thres}, '
+        'valid values are between 0.0 and 1.0'
+    )
+    assert 0 <= iou_thres <= 1, (
+        f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
+    )
 
     # Settings
     min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height

yolort/v5/utils/metrics.py

@@ -19,7 +19,8 @@ def fitness(x):
 
 
 def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
-    """ Compute the average precision, given the recall and precision curves.
+    """
+    Compute the average precision, given the recall and precision curves.
     Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
         tp:  True positives (nparray, nx1 or nx10).
@@ -285,7 +286,9 @@ def box_area(box):
     area2 = box_area(box2.T)
 
     # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    inter = (
+        torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])
+    ).clamp(0).prod(2)
     return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
 
 
@@ -304,8 +307,8 @@ def bbox_ioa(box1, box2, eps=1E-7):
     b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
 
     # Intersection area
-    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
+    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (
+        np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
 
     # box2 area
     box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
@@ -357,7 +360,8 @@ def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
     plt.close()
 
 
-def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
+def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(),
+                  xlabel='Confidence', ylabel='Metric'):
     # Metric-confidence curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
     # display per-class legend if < 21 classes