colorstr() updates (#1909)

test.py

@@ -12,7 +12,7 @@ from tqdm import tqdm
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
-    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path
+    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
 from utils.loss import compute_loss
 from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import plot_images, output_to_target, plot_study_txt
@@ -86,7 +86,8 @@ def test(data,
         img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
         _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
         path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images
-        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
+        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True,
+                                       prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]
 
     seen = 0
     confusion_matrix = ConfusionMatrix(nc=nc)

train.py

@@ -36,15 +36,9 @@ from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_di
 
 logger = logging.getLogger(__name__)
 
-try:
-    import wandb
-except ImportError:
-    wandb = None
-    logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
-
 
 def train(hyp, opt, device, tb_writer=None, wandb=None):
-    logger.info(colorstr('blue', 'bold', 'Hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
+    logger.info(colorstr('Hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
     save_dir, epochs, batch_size, total_batch_size, weights, rank = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
 
@@ -189,7 +183,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                             hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
                                             world_size=opt.world_size, workers=opt.workers,
-                                            image_weights=opt.image_weights, quad=opt.quad)
+                                            image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
     nb = len(dataloader)  # number of batches
     assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
@@ -198,8 +192,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     if rank in [-1, 0]:
         ema.updates = start_epoch * nb // accumulate  # set EMA updates
         testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,  # testloader
-                                       hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
-                                       rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0]
+                                       hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
+                                       world_size=opt.world_size, workers=opt.workers,
+                                       pad=0.5, prefix=colorstr('val: '))[0]
 
         if not opt.resume:
             labels = np.concatenate(dataset.labels, 0)
@@ -514,6 +509,12 @@ if __name__ == '__main__':
 
     # Train
     logger.info(opt)
+    try:
+        import wandb
+    except ImportError:
+        wandb = None
+        prefix = colorstr('wandb: ')
+        logger.info(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
     if not opt.evolve:
         tb_writer = None  # init loggers
         if opt.global_rank in [-1, 0]:

utils/autoanchor.py

@@ -22,7 +22,7 @@ def check_anchor_order(m):
 
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
-    prefix = colorstr('blue', 'bold', 'autoanchor') + ': '
+    prefix = colorstr('autoanchor: ')
     print(f'\n{prefix}Analyzing anchors... ', end='')
     m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
     shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
@@ -73,7 +73,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
             from utils.autoanchor import *; _ = kmean_anchors()
     """
     thr = 1. / thr
-    prefix = colorstr('blue', 'bold', 'autoanchor') + ': '
+    prefix = colorstr('autoanchor: ')
 
     def metric(k, wh):  # compute metrics
         r = wh[:, None] / k[None]

utils/datasets.py

@@ -56,7 +56,7 @@ def exif_size(img):
 
 
 def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
-                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False):
+                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
     # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
     with torch_distributed_zero_first(rank):
         dataset = LoadImagesAndLabels(path, imgsz, batch_size,
@@ -67,8 +67,8 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                       single_cls=opt.single_cls,
                                       stride=int(stride),
                                       pad=pad,
-                                      rank=rank,
-                                      image_weights=image_weights)
+                                      image_weights=image_weights,
+                                      prefix=prefix)
 
     batch_size = min(batch_size, len(dataset))
     nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
@@ -129,7 +129,7 @@ class LoadImages:  # for inference
         elif os.path.isfile(p):
             files = [p]  # files
         else:
-            raise Exception('ERROR: %s does not exist' % p)
+            raise Exception(f'ERROR: {p} does not exist')
 
         images = [x for x in files if x.split('.')[-1].lower() in img_formats]
         videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
@@ -144,8 +144,8 @@ class LoadImages:  # for inference
             self.new_video(videos[0])  # new video
         else:
             self.cap = None
-        assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
-                            (p, img_formats, vid_formats)
+        assert self.nf > 0, f'No images or videos found in {p}. ' \
+                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
 
     def __iter__(self):
         self.count = 0
@@ -171,14 +171,14 @@ class LoadImages:  # for inference
                     ret_val, img0 = self.cap.read()
 
             self.frame += 1
-            print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='')
+            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')
 
         else:
             # Read image
             self.count += 1
             img0 = cv2.imread(path)  # BGR
             assert img0 is not None, 'Image Not Found ' + path
-            print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
+            print(f'image {self.count}/{self.nf} {path}: ', end='')
 
         # Padded resize
         img = letterbox(img0, new_shape=self.img_size)[0]
@@ -238,9 +238,9 @@ class LoadWebcam:  # for inference
                         break
 
         # Print
-        assert ret_val, 'Camera Error %s' % self.pipe
+        assert ret_val, f'Camera Error {self.pipe}'
         img_path = 'webcam.jpg'
-        print('webcam %g: ' % self.count, end='')
+        print(f'webcam {self.count}: ', end='')
 
         # Padded resize
         img = letterbox(img0, new_shape=self.img_size)[0]
@@ -271,15 +271,15 @@ class LoadStreams:  # multiple IP or RTSP cameras
         self.sources = [clean_str(x) for x in sources]  # clean source names for later
         for i, s in enumerate(sources):
             # Start the thread to read frames from the video stream
-            print('%g/%g: %s... ' % (i + 1, n, s), end='')
+            print(f'{i + 1}/{n}: {s}... ', end='')
             cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
-            assert cap.isOpened(), 'Failed to open %s' % s
+            assert cap.isOpened(), f'Failed to open {s}'
             w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
             h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
             fps = cap.get(cv2.CAP_PROP_FPS) % 100
             _, self.imgs[i] = cap.read()  # guarantee first frame
             thread = Thread(target=self.update, args=([i, cap]), daemon=True)
-            print(' success (%gx%g at %.2f FPS).' % (w, h, fps))
+            print(f' success ({w}x{h} at {fps:.2f} FPS).')
             thread.start()
         print('')  # newline
 
@@ -336,7 +336,7 @@ def img2label_paths(img_paths):
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
     def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
-                 cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
+                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
         self.img_size = img_size
         self.augment = augment
         self.hyp = hyp
@@ -358,11 +358,11 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                         parent = str(p.parent) + os.sep
                         f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
                 else:
-                    raise Exception('%s does not exist' % p)
+                    raise Exception(f'{prefix}{p} does not exist')
             self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
-            assert self.img_files, 'No images found'
+            assert self.img_files, f'{prefix}No images found'
         except Exception as e:
-            raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url))
+            raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
 
         # Check cache
         self.label_files = img2label_paths(self.img_files)  # labels
@@ -370,15 +370,15 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if cache_path.is_file():
             cache = torch.load(cache_path)  # load
             if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache:  # changed
-                cache = self.cache_labels(cache_path)  # re-cache
+                cache = self.cache_labels(cache_path, prefix)  # re-cache
         else:
-            cache = self.cache_labels(cache_path)  # cache
+            cache = self.cache_labels(cache_path, prefix)  # cache
 
         # Display cache
         [nf, nm, ne, nc, n] = cache.pop('results')  # found, missing, empty, corrupted, total
         desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
-        tqdm(None, desc=desc, total=n, initial=n)
-        assert nf > 0 or not augment, f'No labels found in {cache_path}. Can not train without labels. See {help_url}'
+        tqdm(None, desc=prefix + desc, total=n, initial=n)
+        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
 
         # Read cache
         cache.pop('hash')  # remove hash
@@ -432,9 +432,9 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             for i, x in pbar:
                 self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # img, hw_original, hw_resized = load_image(self, i)
                 gb += self.imgs[i].nbytes
-                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
+                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
 
-    def cache_labels(self, path=Path('./labels.cache')):
+    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
         # Cache dataset labels, check images and read shapes
         x = {}  # dict
         nm, nf, ne, nc = 0, 0, 0, 0  # number missing, found, empty, duplicate
@@ -466,18 +466,18 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 x[im_file] = [l, shape]
             except Exception as e:
                 nc += 1
-                print('WARNING: Ignoring corrupted image and/or label %s: %s' % (im_file, e))
+                print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
 
-            pbar.desc = f"Scanning '{path.parent / path.stem}' for images and labels... " \
+            pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' for images and labels... " \
                         f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
 
         if nf == 0:
-            print(f'WARNING: No labels found in {path}. See {help_url}')
+            print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
 
         x['hash'] = get_hash(self.label_files + self.img_files)
         x['results'] = [nf, nm, ne, nc, i + 1]
         torch.save(x, path)  # save for next time
-        logging.info(f"New cache created: {path}")
+        logging.info(f'{prefix}New cache created: {path}')
         return x
 
     def __len__(self):

utils/general.py

@@ -118,7 +118,7 @@ def one_cycle(y1=0.0, y2=1.0, steps=100):
 
 def colorstr(*input):
     # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *prefix, string = input  # color arguments, string
+    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
     colors = {'black': '\033[30m',  # basic colors
               'red': '\033[31m',
               'green': '\033[32m',
@@ -138,8 +138,7 @@ def colorstr(*input):
               'end': '\033[0m',  # misc
               'bold': '\033[1m',
               'underline': '\033[4m'}
-
-    return ''.join(colors[x] for x in prefix) + f'{string}' + colors['end']
+    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
 
 
 def labels_to_class_weights(labels, nc=80):

utils/plots.py

@@ -245,9 +245,9 @@ def plot_study_txt(path='study/', x=None):  # from utils.plots import *; plot_st
              'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
 
     ax2.grid()
+    ax2.set_yticks(np.arange(30, 60, 5))
     ax2.set_xlim(0, 30)
     ax2.set_ylim(29, 51)
-    ax2.set_yticks(np.arange(30, 55, 5))
     ax2.set_xlabel('GPU Speed (ms/img)')
     ax2.set_ylabel('COCO AP val')
     ax2.legend(loc='lower right')