Update Actions with Lychee and GitHub Token (#12592)

* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/segment/augmentations.py

@@ -1,7 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Image augmentation functions
-"""
+"""Image augmentation functions."""
 
 import math
 import random
@@ -22,15 +20,9 @@ def mixup(im, labels, segments, im2, labels2, segments2):
     return im, labels, segments
 
 
-def random_perspective(im,
-                       targets=(),
-                       segments=(),
-                       degrees=10,
-                       translate=.1,
-                       scale=.1,
-                       shear=10,
-                       perspective=0.0,
-                       border=(0, 0)):
+def random_perspective(
+    im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)
+):
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]
 
@@ -62,8 +54,8 @@ def random_perspective(im,
 
     # Translation
     T = np.eye(3)
-    T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width)  # x translation (pixels)
-    T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height)  # y translation (pixels)
+    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
+    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
 
     # Combined rotation matrix
     M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
@@ -89,7 +81,7 @@ def random_perspective(im,
             xy = np.ones((len(segment), 3))
             xy[:, :2] = segment
             xy = xy @ M.T  # transform
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2])  # perspective rescale or affine
+            xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
 
             # clip
             new[i] = segment2box(xy, width, height)

utils/segment/dataloaders.py

@@ -1,7 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Dataloaders
-"""
+"""Dataloaders."""
 
 import os
 import random
@@ -17,30 +15,32 @@ from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn
 from ..torch_utils import torch_distributed_zero_first
 from .augmentations import mixup, random_perspective
 
-RANK = int(os.getenv('RANK', -1))
+RANK = int(os.getenv("RANK", -1))
 
 
-def create_dataloader(path,
-                      imgsz,
-                      batch_size,
-                      stride,
-                      single_cls=False,
-                      hyp=None,
-                      augment=False,
-                      cache=False,
-                      pad=0.0,
-                      rect=False,
-                      rank=-1,
-                      workers=8,
-                      image_weights=False,
-                      quad=False,
-                      prefix='',
-                      shuffle=False,
-                      mask_downsample_ratio=1,
-                      overlap_mask=False,
-                      seed=0):
+def create_dataloader(
+    path,
+    imgsz,
+    batch_size,
+    stride,
+    single_cls=False,
+    hyp=None,
+    augment=False,
+    cache=False,
+    pad=0.0,
+    rect=False,
+    rank=-1,
+    workers=8,
+    image_weights=False,
+    quad=False,
+    prefix="",
+    shuffle=False,
+    mask_downsample_ratio=1,
+    overlap_mask=False,
+    seed=0,
+):
     if rect and shuffle:
-        LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+        LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = LoadImagesAndLabelsAndMasks(
@@ -58,7 +58,8 @@ def create_dataloader(path,
             prefix=prefix,
             downsample_ratio=mask_downsample_ratio,
             overlap=overlap_mask,
-            rank=rank)
+            rank=rank,
+        )
 
     batch_size = min(batch_size, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
@@ -81,7 +82,6 @@ def create_dataloader(path,
 
 
 class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
-
     def __init__(
         self,
         path,
@@ -96,14 +96,29 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         stride=32,
         pad=0,
         min_items=0,
-        prefix='',
+        prefix="",
         downsample_ratio=1,
         overlap=False,
         rank=-1,
         seed=0,
     ):
-        super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls,
-                         stride, pad, min_items, prefix, rank, seed)
+        super().__init__(
+            path,
+            img_size,
+            batch_size,
+            augment,
+            hyp,
+            rect,
+            image_weights,
+            cache_images,
+            single_cls,
+            stride,
+            pad,
+            min_items,
+            prefix,
+            rank,
+            seed,
+        )
         self.downsample_ratio = downsample_ratio
         self.overlap = overlap
 
@@ -111,7 +126,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp['mosaic']
+        mosaic = self.mosaic and random.random() < hyp["mosaic"]
         masks = []
         if mosaic:
             # Load mosaic
@@ -119,7 +134,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
             shapes = None
 
             # MixUp augmentation
-            if random.random() < hyp['mixup']:
+            if random.random() < hyp["mixup"]:
                 img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))
 
         else:
@@ -147,30 +162,36 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
                 labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
 
             if self.augment:
-                img, labels, segments = random_perspective(img,
-                                                           labels,
-                                                           segments=segments,
-                                                           degrees=hyp['degrees'],
-                                                           translate=hyp['translate'],
-                                                           scale=hyp['scale'],
-                                                           shear=hyp['shear'],
-                                                           perspective=hyp['perspective'])
+                img, labels, segments = random_perspective(
+                    img,
+                    labels,
+                    segments=segments,
+                    degrees=hyp["degrees"],
+                    translate=hyp["translate"],
+                    scale=hyp["scale"],
+                    shear=hyp["shear"],
+                    perspective=hyp["perspective"],
+                )
 
         nl = len(labels)  # number of labels
         if nl:
             labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
             if self.overlap:
-                masks, sorted_idx = polygons2masks_overlap(img.shape[:2],
-                                                           segments,
-                                                           downsample_ratio=self.downsample_ratio)
+                masks, sorted_idx = polygons2masks_overlap(
+                    img.shape[:2], segments, downsample_ratio=self.downsample_ratio
+                )
                 masks = masks[None]  # (640, 640) -> (1, 640, 640)
                 labels = labels[sorted_idx]
             else:
                 masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio)
 
-        masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] //
-                                                                        self.downsample_ratio, img.shape[1] //
-                                                                        self.downsample_ratio))
+        masks = (
+            torch.from_numpy(masks)
+            if len(masks)
+            else torch.zeros(
+                1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio
+            )
+        )
         # TODO: albumentations support
         if self.augment:
             # Albumentations
@@ -180,17 +201,17 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
             nl = len(labels)  # update after albumentations
 
             # HSV color-space
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+            augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
 
             # Flip up-down
-            if random.random() < hyp['flipud']:
+            if random.random() < hyp["flipud"]:
                 img = np.flipud(img)
                 if nl:
                     labels[:, 2] = 1 - labels[:, 2]
                     masks = torch.flip(masks, dims=[1])
 
             # Flip left-right
-            if random.random() < hyp['fliplr']:
+            if random.random() < hyp["fliplr"]:
                 img = np.fliplr(img)
                 if nl:
                     labels[:, 1] = 1 - labels[:, 1]
@@ -254,16 +275,18 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         # img4, labels4 = replicate(img4, labels4)  # replicate
 
         # Augment
-        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
-        img4, labels4, segments4 = random_perspective(img4,
-                                                      labels4,
-                                                      segments4,
-                                                      degrees=self.hyp['degrees'],
-                                                      translate=self.hyp['translate'],
-                                                      scale=self.hyp['scale'],
-                                                      shear=self.hyp['shear'],
-                                                      perspective=self.hyp['perspective'],
-                                                      border=self.mosaic_border)  # border to remove
+        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"])
+        img4, labels4, segments4 = random_perspective(
+            img4,
+            labels4,
+            segments4,
+            degrees=self.hyp["degrees"],
+            translate=self.hyp["translate"],
+            scale=self.hyp["scale"],
+            shear=self.hyp["shear"],
+            perspective=self.hyp["perspective"],
+            border=self.mosaic_border,
+        )  # border to remove
         return img4, labels4, segments4
 
     @staticmethod
@@ -312,8 +335,10 @@ def polygons2masks(img_size, polygons, color, downsample_ratio=1):
 
 def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
     """Return a (640, 640) overlap mask."""
-    masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
-                     dtype=np.int32 if len(segments) > 255 else np.uint8)
+    masks = np.zeros(
+        (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
+        dtype=np.int32 if len(segments) > 255 else np.uint8,
+    )
     areas = []
     ms = []
     for si in range(len(segments)):

utils/segment/general.py

@@ -6,8 +6,7 @@ import torch.nn.functional as F
 
 def crop_mask(masks, boxes):
     """
-    "Crop" predicted masks by zeroing out everything not in the predicted bbox.
-    Vectorized by Chong (thanks Chong).
+    "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong).
 
     Args:
         - masks should be a size [n, h, w] tensor of masks
@@ -35,7 +34,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
 
     c, mh, mw = protos.shape  # CHW
     masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
-    masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     masks = crop_mask(masks, bboxes)  # CHW
     return masks.gt_(0.5)
 
@@ -63,7 +62,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
 
     masks = crop_mask(masks, downsampled_bboxes)  # CHW
     if upsample:
-        masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+        masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     return masks.gt_(0.5)
 
 
@@ -85,7 +84,7 @@ def process_mask_native(protos, masks_in, bboxes, shape):
     bottom, right = int(mh - pad[1]), int(mw - pad[0])
     masks = masks[:, top:bottom, left:right]
 
-    masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     masks = crop_mask(masks, bboxes)  # CHW
     return masks.gt_(0.5)
 
@@ -144,17 +143,17 @@ def masks_iou(mask1, mask2, eps=1e-7):
     return intersection / (union + eps)
 
 
-def masks2segments(masks, strategy='largest'):
+def masks2segments(masks, strategy="largest"):
     # Convert masks(n,160,160) into segments(n,xy)
     segments = []
-    for x in masks.int().cpu().numpy().astype('uint8'):
+    for x in masks.int().cpu().numpy().astype("uint8"):
         c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
         if c:
-            if strategy == 'concat':  # concatenate all segments
+            if strategy == "concat":  # concatenate all segments
                 c = np.concatenate([x.reshape(-1, 2) for x in c])
-            elif strategy == 'largest':  # select largest segment
+            elif strategy == "largest":  # select largest segment
                 c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
         else:
             c = np.zeros((0, 2))  # no segments found
-        segments.append(c.astype('float32'))
+        segments.append(c.astype("float32"))
     return segments

utils/segment/loss.py

@@ -18,14 +18,14 @@ class ComputeLoss:
         h = model.hyp  # hyperparameters
 
         # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device))
+        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))
 
         # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
+        self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0))  # positive, negative BCE targets
 
         # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
+        g = h["fl_gamma"]  # focal loss gamma
         if g > 0:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
@@ -82,7 +82,7 @@ class ComputeLoss:
 
                 # Mask regression
                 if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
-                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
+                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
                 marea = xywhn[i][:, 2:].prod(1)  # mask width, height normalized
                 mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
                 for bi in b.unique():
@@ -100,10 +100,10 @@ class ComputeLoss:
 
         if self.autobalance:
             self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        lseg *= self.hyp['box'] / bs
+        lbox *= self.hyp["box"]
+        lobj *= self.hyp["obj"]
+        lcls *= self.hyp["cls"]
+        lseg *= self.hyp["box"] / bs
 
         loss = lbox + lobj + lcls + lseg
         return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
@@ -111,7 +111,7 @@ class ComputeLoss:
     def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
         # Mask loss for one image
         pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n,32) @ (32,80,80) -> (n,80,80)
-        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
+        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
 
     def build_targets(self, p, targets):
@@ -132,16 +132,20 @@ class ComputeLoss:
         targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2)  # append anchor indices
 
         g = 0.5  # bias
-        off = torch.tensor(
-            [
-                [0, 0],
-                [1, 0],
-                [0, 1],
-                [-1, 0],
-                [0, -1],  # j,k,l,m
-                # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-            ],
-            device=self.device).float() * g  # offsets
+        off = (
+            torch.tensor(
+                [
+                    [0, 0],
+                    [1, 0],
+                    [0, 1],
+                    [-1, 0],
+                    [0, -1],  # j,k,l,m
+                    # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
+                ],
+                device=self.device,
+            ).float()
+            * g
+        )  # offsets
 
         for i in range(self.nl):
             anchors, shape = self.anchors[i], p[i].shape
@@ -152,7 +156,7 @@ class ComputeLoss:
             if nt:
                 # Matches
                 r = t[..., 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"]  # compare
                 # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                 t = t[j]  # filter
 

utils/segment/metrics.py

@@ -1,7 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Model validation metrics
-"""
+"""Model validation metrics."""
 
 import numpy as np
 
@@ -15,14 +13,14 @@ def fitness(x):
 
 
 def ap_per_class_box_and_mask(
-        tp_m,
-        tp_b,
-        conf,
-        pred_cls,
-        target_cls,
-        plot=False,
-        save_dir='.',
-        names=(),
+    tp_m,
+    tp_b,
+    conf,
+    pred_cls,
+    target_cls,
+    plot=False,
+    save_dir=".",
+    names=(),
 ):
     """
     Args:
@@ -30,41 +28,33 @@ def ap_per_class_box_and_mask(
         tp_m: tp of masks.
         other arguments see `func: ap_per_class`.
     """
-    results_boxes = ap_per_class(tp_b,
-                                 conf,
-                                 pred_cls,
-                                 target_cls,
-                                 plot=plot,
-                                 save_dir=save_dir,
-                                 names=names,
-                                 prefix='Box')[2:]
-    results_masks = ap_per_class(tp_m,
-                                 conf,
-                                 pred_cls,
-                                 target_cls,
-                                 plot=plot,
-                                 save_dir=save_dir,
-                                 names=names,
-                                 prefix='Mask')[2:]
+    results_boxes = ap_per_class(
+        tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box"
+    )[2:]
+    results_masks = ap_per_class(
+        tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask"
+    )[2:]
 
     results = {
-        'boxes': {
-            'p': results_boxes[0],
-            'r': results_boxes[1],
-            'ap': results_boxes[3],
-            'f1': results_boxes[2],
-            'ap_class': results_boxes[4]},
-        'masks': {
-            'p': results_masks[0],
-            'r': results_masks[1],
-            'ap': results_masks[3],
-            'f1': results_masks[2],
-            'ap_class': results_masks[4]}}
+        "boxes": {
+            "p": results_boxes[0],
+            "r": results_boxes[1],
+            "ap": results_boxes[3],
+            "f1": results_boxes[2],
+            "ap_class": results_boxes[4],
+        },
+        "masks": {
+            "p": results_masks[0],
+            "r": results_masks[1],
+            "ap": results_masks[3],
+            "f1": results_masks[2],
+            "ap_class": results_masks[4],
+        },
+    }
     return results
 
 
 class Metric:
-
     def __init__(self) -> None:
         self.p = []  # (nc, )
         self.r = []  # (nc, )
@@ -74,7 +64,9 @@ class Metric:
 
     @property
     def ap50(self):
-        """AP@0.5 of all classes.
+        """
+        AP@0.5 of all classes.
+
         Return:
             (nc, ) or [].
         """
@@ -90,7 +82,9 @@ class Metric:
 
     @property
     def mp(self):
-        """mean precision of all classes.
+        """
+        Mean precision of all classes.
+
         Return:
             float.
         """
@@ -98,7 +92,9 @@ class Metric:
 
     @property
     def mr(self):
-        """mean recall of all classes.
+        """
+        Mean recall of all classes.
+
         Return:
             float.
         """
@@ -106,7 +102,9 @@ class Metric:
 
     @property
     def map50(self):
-        """Mean AP@0.5 of all classes.
+        """
+        Mean AP@0.5 of all classes.
+
         Return:
             float.
         """
@@ -114,18 +112,20 @@ class Metric:
 
     @property
     def map(self):
-        """Mean AP@0.5:0.95 of all classes.
+        """
+        Mean AP@0.5:0.95 of all classes.
+
         Return:
             float.
         """
         return self.all_ap.mean() if len(self.all_ap) else 0.0
 
     def mean_results(self):
-        """Mean of results, return mp, mr, map50, map"""
+        """Mean of results, return mp, mr, map50, map."""
         return (self.mp, self.mr, self.map50, self.map)
 
     def class_result(self, i):
-        """class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+        """Class-aware result, return p[i], r[i], ap50[i], ap[i]"""
         return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
 
     def get_maps(self, nc):
@@ -159,8 +159,8 @@ class Metrics:
         Args:
             results: Dict{'boxes': Dict{}, 'masks': Dict{}}
         """
-        self.metric_box.update(list(results['boxes'].values()))
-        self.metric_mask.update(list(results['masks'].values()))
+        self.metric_box.update(list(results["boxes"].values()))
+        self.metric_mask.update(list(results["masks"].values()))
 
     def mean_results(self):
         return self.metric_box.mean_results() + self.metric_mask.mean_results()
@@ -178,33 +178,35 @@ class Metrics:
 
 
 KEYS = [
-    'train/box_loss',
-    'train/seg_loss',  # train loss
-    'train/obj_loss',
-    'train/cls_loss',
-    'metrics/precision(B)',
-    'metrics/recall(B)',
-    'metrics/mAP_0.5(B)',
-    'metrics/mAP_0.5:0.95(B)',  # metrics
-    'metrics/precision(M)',
-    'metrics/recall(M)',
-    'metrics/mAP_0.5(M)',
-    'metrics/mAP_0.5:0.95(M)',  # metrics
-    'val/box_loss',
-    'val/seg_loss',  # val loss
-    'val/obj_loss',
-    'val/cls_loss',
-    'x/lr0',
-    'x/lr1',
-    'x/lr2', ]
+    "train/box_loss",
+    "train/seg_loss",  # train loss
+    "train/obj_loss",
+    "train/cls_loss",
+    "metrics/precision(B)",
+    "metrics/recall(B)",
+    "metrics/mAP_0.5(B)",
+    "metrics/mAP_0.5:0.95(B)",  # metrics
+    "metrics/precision(M)",
+    "metrics/recall(M)",
+    "metrics/mAP_0.5(M)",
+    "metrics/mAP_0.5:0.95(M)",  # metrics
+    "val/box_loss",
+    "val/seg_loss",  # val loss
+    "val/obj_loss",
+    "val/cls_loss",
+    "x/lr0",
+    "x/lr1",
+    "x/lr2",
+]
 
 BEST_KEYS = [
-    'best/epoch',
-    'best/precision(B)',
-    'best/recall(B)',
-    'best/mAP_0.5(B)',
-    'best/mAP_0.5:0.95(B)',
-    'best/precision(M)',
-    'best/recall(M)',
-    'best/mAP_0.5(M)',
-    'best/mAP_0.5:0.95(M)', ]
+    "best/epoch",
+    "best/precision(B)",
+    "best/recall(B)",
+    "best/mAP_0.5(B)",
+    "best/mAP_0.5:0.95(B)",
+    "best/precision(M)",
+    "best/recall(M)",
+    "best/mAP_0.5(M)",
+    "best/mAP_0.5:0.95(M)",
+]

utils/segment/plots.py

@@ -14,7 +14,7 @@ from ..plots import Annotator, colors
 
 
 @threaded
-def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):
+def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None):
     # Plot image grid with labels
     if isinstance(images, torch.Tensor):
         images = images.cpu().float().numpy()
@@ -27,7 +27,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
     max_subplots = 16  # max image subplots, i.e. 4x4
     bs, _, h, w = images.shape  # batch size, _, height, width
     bs = min(bs, max_subplots)  # limit plot images
-    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
+    ns = np.ceil(bs**0.5)  # number of subplots (square)
     if np.max(images[0]) <= 1:
         images *= 255  # de-normalise (optional)
 
@@ -38,7 +38,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
             break
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         im = im.transpose(1, 2, 0)
-        mosaic[y:y + h, x:x + w, :] = im
+        mosaic[y : y + h, x : x + w, :] = im
 
     # Resize (optional)
     scale = max_size / ns / max(h, w)
@@ -60,7 +60,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
             ti = targets[idx]  # image targets
 
             boxes = xywh2xyxy(ti[:, 2:6]).T
-            classes = ti[:, 1].astype('int')
+            classes = ti[:, 1].astype("int")
             labels = ti.shape[1] == 6  # labels if no conf column
             conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
 
@@ -77,7 +77,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                 color = colors(cls)
                 cls = names[cls] if names else cls
                 if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+                    label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}"
                     annotator.box_label(box, label, color=color)
 
             # Plot masks
@@ -103,41 +103,44 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                         else:
                             mask = image_masks[j].astype(bool)
                         with contextlib.suppress(Exception):
-                            im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
+                            im[y : y + h, x : x + w, :][mask] = (
+                                im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
+                            )
                 annotator.fromarray(im)
     annotator.im.save(fname)  # save
 
 
-def plot_results_with_masks(file='path/to/results.csv', dir='', best=True):
+def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
     # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
     save_dir = Path(file).parent if file else Path(dir)
     fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
     ax = ax.ravel()
-    files = list(save_dir.glob('results*.csv'))
-    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
+    files = list(save_dir.glob("results*.csv"))
+    assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
     for f in files:
         try:
             data = pd.read_csv(f)
-            index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
-                              0.1 * data.values[:, 11])
+            index = np.argmax(
+                0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11]
+            )
             s = [x.strip() for x in data.columns]
             x = data.values[:, 0]
             for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
                 y = data.values[:, j]
                 # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2)
+                ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
                 if best:
                     # best
-                    ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3)
-                    ax[i].set_title(s[j] + f'\n{round(y[index], 5)}')
+                    ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
+                    ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
                 else:
                     # last
-                    ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3)
-                    ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}')
+                    ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
+                    ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
         except Exception as e:
-            print(f'Warning: Plotting error for {f}: {e}')
+            print(f"Warning: Plotting error for {f}: {e}")
     ax[1].legend()
-    fig.savefig(save_dir / 'results.png', dpi=200)
+    fig.savefig(save_dir / "results.png", dpi=200)
     plt.close()