From 8a79868537fc43676ddace634bc3b22dc56a1da8 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 13 Nov 2021 15:40:18 +0100 Subject: [PATCH] AutoAnchor and AutoBatch `LOGGER` (#5635) * AutoBatch, AutoAnchor `LOGGER` * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update autoanchor.py Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- utils/autoanchor.py | 50 +++++++++++++++++++++++---------------------- utils/autobatch.py | 14 ++++++------- utils/plots.py | 6 +++--- 3 files changed, 36 insertions(+), 34 deletions(-) diff --git a/utils/autoanchor.py b/utils/autoanchor.py index af0aa7de65ac..eef8f6499194 100644 --- a/utils/autoanchor.py +++ b/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) diff --git a/utils/autobatch.py b/utils/autobatch.py index 3f2b4d1a4c38..cb94f041e95d 100644 --- a/utils/autobatch.py +++ b/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 diff --git a/utils/plots.py b/utils/plots.py index a5b20803c7be..9919e4d9d88f 100644 --- a/utils/plots.py +++ 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'])