AutoAnchor and AutoBatch LOGGER

utils/autoanchor.py

@@ -10,7 +10,9 @@
 import yaml
 from tqdm import tqdm
 
-from utils.general import colorstr
+from utils.general import LOGGER, colorstr, emojis
+
+PREFIX = colorstr('AutoAnchor: ')
 
 
 def check_anchor_order(m):
@@ -19,14 +21,12 @@ def check_anchor_order(m):
     da = a[-1] - a[0]  # delta a
     ds = m.stride[-1] - m.stride[0]  # delta s
     if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
+        LOGGER.info(f'{PREFIX}Reversing anchor order')
         m.anchors[:] = m.anchors.flip(0)
 
 
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
-    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)
     scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
@@ -42,23 +42,24 @@ def metric(k):  # compute metric
 
     anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1)  # current anchors
     bpr, aat = metric(anchors.cpu().view(-1, 2))
-    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...')
+    s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
+    if bpr > 0.98:  # threshold to recompute
+        LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
+    else:
+        LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
         na = m.anchors.numel() // 2  # number of anchors
         try:
             anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
         except Exception as e:
-            print(f'{prefix}ERROR: {e}')
+            LOGGER.info(f'{PREFIX}ERROR: {e}')
         new_bpr = metric(anchors)[0]
         if new_bpr > bpr:  # replace anchors
             anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
             m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
             check_anchor_order(m)
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+            LOGGER.info(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('')  # newline
+            LOGGER.info(f'{PREFIX}Original anchors better than new anchors. Proceeding with original anchors.')
 
 
 def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
@@ -81,7 +82,6 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
     from scipy.cluster.vq import kmeans
 
     thr = 1 / thr
-    prefix = colorstr('autoanchor: ')
 
     def metric(k, wh):  # compute metrics
         r = wh[:, None] / k[None]
@@ -93,15 +93,17 @@ def anchor_fitness(k):  # mutation fitness
         _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
         return (best * (best > thr).float()).mean()  # fitness
 
-    def print_results(k):
+    def print_results(k, verbose=True):
         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(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='')
+        s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
+            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: '
         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
+            s += '%i,%i, ' % (round(x[0]), round(x[1]))
+        if verbose:
+            LOGGER.info(s[:-2])
         return k
 
     if isinstance(dataset, str):  # *.yaml file
@@ -117,19 +119,19 @@ 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.')
+        LOGGER.info(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(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
+    LOGGER.info(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, 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 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)
+    k = print_results(k, verbose=False)
 
     # Plot
     # k, d = [None] * 20, [None] * 20
@@ -146,7 +148,7 @@ def print_results(k):
     # 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=f'{prefix}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)
@@ -155,8 +157,8 @@ def print_results(k):
         fg = anchor_fitness(kg)
         if fg > f:
             f, k = fg, kg.copy()
-            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
+            pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
             if verbose:
-                print_results(k)
+                print_results(k, verbose)
 
     return print_results(k)

utils/autobatch.py

@@ -9,7 +9,7 @@
 import torch
 from torch.cuda import amp
 
-from utils.general import colorstr
+from utils.general import LOGGER, colorstr
 from utils.torch_utils import profile
 
 
@@ -27,11 +27,11 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
     #     model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
     #     print(autobatch(model))
 
-    prefix = colorstr('autobatch: ')
-    print(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
+    prefix = colorstr('AutoBatch: ')
+    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
     device = next(model.parameters()).device  # get model device
     if device.type == 'cpu':
-        print(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
+        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
         return batch_size
 
     d = str(device).upper()  # 'CUDA:0'
@@ -40,18 +40,18 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
     r = torch.cuda.memory_reserved(device) / 1024 ** 3  # (GiB)
     a = torch.cuda.memory_allocated(device) / 1024 ** 3  # (GiB)
     f = t - (r + a)  # free inside reserved
-    print(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
+    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
 
     batch_sizes = [1, 2, 4, 8, 16]
     try:
         img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
         y = profile(img, model, n=3, device=device)
     except Exception as e:
-        print(f'{prefix}{e}')
+        LOGGER.warning(f'{prefix}{e}')
 
     y = [x[2] for x in y if x]  # memory [2]
     batch_sizes = batch_sizes[:len(y)]
     p = np.polyfit(batch_sizes, y, deg=1)  # first degree polynomial fit
     b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
-    print(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)')
+    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)')
     return b

utils/plots.py

@@ -17,8 +17,8 @@
 import torch
 from PIL import Image, ImageDraw, ImageFont
 
-from utils.general import (Timeout, check_requirements, clip_coords, increment_path, is_ascii, is_chinese, try_except,
-                           user_config_dir, xywh2xyxy, xyxy2xywh)
+from utils.general import (LOGGER, Timeout, check_requirements, clip_coords, increment_path, is_ascii, is_chinese,
+                           try_except, user_config_dir, xywh2xyxy, xyxy2xywh)
 from utils.metrics import fitness
 
 # Settings
@@ -328,7 +328,7 @@ def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_
 @Timeout(30)  # known issue https://github.com/ultralytics/yolov5/issues/5611
 def plot_labels(labels, names=(), save_dir=Path('')):
     # plot dataset labels
-    print('Plotting labels... ')
+    LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
     nc = int(c.max() + 1)  # number of classes
     x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])