Add colorstr() (#1887)

train.py

@@ -216,8 +216,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
 
     # Model parameters
-    hyp['cls'] *= nc / 80.  # scale hyp['cls'] to class count
-    hyp['obj'] *= imgsz ** 2 / 640. ** 2 * 3. / nl  # scale hyp['obj'] to image size and output layers
+    hyp['box'] *= 3. / nl  # scale to layers
+    hyp['cls'] *= nc / 80. * 3. / nl  # scale to classes and layers
+    hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl  # scale to image size and layers
     model.nc = nc  # attach number of classes to model
     model.hyp = hyp  # attach hyperparameters to model
     model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)

utils/autoanchor.py

@@ -6,6 +6,8 @@ import yaml
 from scipy.cluster.vq import kmeans
 from tqdm import tqdm
 
+from utils.general import colorstr
+
 
 def check_anchor_order(m):
     # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
@@ -20,7 +22,8 @@ def check_anchor_order(m):
 
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
-    print('\nAnalyzing anchors... ', end='')
+    prefix = colorstr('blue', 'bold', '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)
     scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
@@ -35,7 +38,7 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
         return bpr, aat
 
     bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
-    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
+    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
     if bpr < 0.98:  # threshold to recompute
         print('. Attempting to improve anchors, please wait...')
         na = m.anchor_grid.numel() // 2  # number of anchors
@@ -46,9 +49,9 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
             m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
             m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
             check_anchor_order(m)
-            print('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('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
 
 
@@ -70,6 +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') + ': '
 
     def metric(k, wh):  # compute metrics
         r = wh[:, None] / k[None]
@@ -85,9 +89,9 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
         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
-        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
-        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
-              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
+        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, '
+              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
         return k
@@ -107,13 +111,12 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     # Filter
     i = (wh0 < 3.0).any(1).sum()
     if i:
-        print('WARNING: Extremely small objects found. '
-              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
+        print(f'{prefix}WARNING: Extremely small objects found. {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('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
+    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
     k *= s
@@ -136,7 +139,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     # Evolve
     npr = np.random
     f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
+    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
     for _ in pbar:
         v = np.ones(sh)
         while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
@@ -145,7 +148,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
         fg = anchor_fitness(kg)
         if fg > f:
             f, k = fg, kg.copy()
-            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
+            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
             if verbose:
                 print_results(k)
 

utils/general.py

@@ -47,7 +47,7 @@ def get_latest_run(search_dir='.'):
 
 def check_git_status():
     # Suggest 'git pull' if repo is out of date
-    if platform.system() in ['Linux', 'Darwin'] and not os.path.isfile('/.dockerenv'):
+    if Path('.git').exists() and platform.system() in ['Linux', 'Darwin'] and not Path('/.dockerenv').is_file():
         s = subprocess.check_output('if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8')
         if 'Your branch is behind' in s:
             print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n')
@@ -115,6 +115,32 @@ def one_cycle(y1=0.0, y2=1.0, steps=100):
     return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
 
 
+def colorstr(*input):
+    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
+    *prefix, str = input  # color arguments, string
+    colors = {'black': '\033[30m',  # basic colors
+              'red': '\033[31m',
+              'green': '\033[32m',
+              'yellow': '\033[33m',
+              'blue': '\033[34m',
+              'magenta': '\033[35m',
+              'cyan': '\033[36m',
+              'white': '\033[37m',
+              'bright_black': '\033[90m',  # bright colors
+              'bright_red': '\033[91m',
+              'bright_green': '\033[92m',
+              'bright_yellow': '\033[93m',
+              'bright_blue': '\033[94m',
+              'bright_magenta': '\033[95m',
+              'bright_cyan': '\033[96m',
+              'bright_white': '\033[97m',
+              'end': '\033[0m',  # misc
+              'bold': '\033[1m',
+              'undelrine': '\033[4m'}
+
+    return ''.join(colors[x] for x in prefix) + str + colors['end']
+
+
 def labels_to_class_weights(labels, nc=80):
     # Get class weights (inverse frequency) from training labels
     if labels[0] is None:  # no labels loaded

utils/loss.py

@@ -105,7 +105,6 @@ def compute_loss(p, targets, model):  # predictions, targets, model
 
     # Losses
     nt = 0  # number of targets
-    no = len(p)  # number of outputs
     balance = [4.0, 1.0, 0.3, 0.1, 0.03]  # P3-P7
     for i, pi in enumerate(p):  # layer index, layer predictions
         b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
@@ -138,10 +137,9 @@ def compute_loss(p, targets, model):  # predictions, targets, model
 
         lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
 
-    s = 3 / no  # output count scaling
-    lbox *= h['box'] * s
+    lbox *= h['box']
     lobj *= h['obj']
-    lcls *= h['cls'] * s
+    lcls *= h['cls']
     bs = tobj.shape[0]  # batch size
 
     loss = lbox + lobj + lcls

utils/torch_utils.py

@@ -3,9 +3,11 @@
 import logging
 import math
 import os
+import subprocess
 import time
 from contextlib import contextmanager
 from copy import deepcopy
+from pathlib import Path
 
 import torch
 import torch.backends.cudnn as cudnn
@@ -41,9 +43,17 @@ def init_torch_seeds(seed=0):
         cudnn.benchmark, cudnn.deterministic = True, False
 
 
+def git_describe():
+    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    if Path('.git').exists():
+        return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1]
+    else:
+        return ''
+
+
 def select_device(device='', batch_size=None):
     # device = 'cpu' or '0' or '0,1,2,3'
-    s = f'Using torch {torch.__version__} '  # string
+    s = f'YOLOv5 {git_describe()} torch {torch.__version__} '  # string
     cpu = device.lower() == 'cpu'
     if cpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
@@ -61,9 +71,9 @@ def select_device(device='', batch_size=None):
             p = torch.cuda.get_device_properties(i)
             s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
     else:
-        s += 'CPU'
+        s += 'CPU\n'
 
-    logger.info(f'{s}\n')  # skip a line
+    logger.info(s)  # skip a line
     return torch.device('cuda:0' if cuda else 'cpu')