From 20c74db918a1a30a7416b64711acfb203a262a06 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 25 Oct 2021 13:56:13 +0200 Subject: [PATCH] Add `autobatch` feature for best `batch-size` estimation (#5092) * Autobatch * fix mem * fix mem2 * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update train.py * print result * Cleanup print result * swap fix in call * to 64 * use total * fix * fix * fix * fix * fix * Update * Update * Update * Update * Update * Update * Update * Cleanup printing * Update final printout * Update autobatch.py * Update autobatch.py * Update autobatch.py --- train.py | 17 +++++++++----- utils/autobatch.py | 56 ++++++++++++++++++++++++++++++++++++++++++++ utils/torch_utils.py | 2 +- 3 files changed, 68 insertions(+), 7 deletions(-) create mode 100644 utils/autobatch.py diff --git a/train.py b/train.py index da7346be77ab..d83f3cd1863c 100644 --- a/train.py +++ b/train.py @@ -36,6 +36,7 @@ from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size from utils.datasets import create_dataloader from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \ @@ -131,6 +132,14 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary print(f'freezing {k}') v.requires_grad = False + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz) + # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing @@ -190,11 +199,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary del ckpt, csd - # Image sizes - gs = max(int(model.stride.max()), 32) # grid size (max stride) - nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) - imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple - # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n' @@ -242,6 +246,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) # Model parameters + nl = model.model[-1].nl # number of detection layers (to scale hyps) 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 @@ -440,7 +445,7 @@ def parse_opt(known=False): parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300) - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') diff --git a/utils/autobatch.py b/utils/autobatch.py new file mode 100644 index 000000000000..168b16f691ab --- /dev/null +++ b/utils/autobatch.py @@ -0,0 +1,56 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Auto-batch utils +""" + +from copy import deepcopy + +import numpy as np +import torch +from torch.cuda import amp + +from utils.general import colorstr +from utils.torch_utils import profile + + +def check_train_batch_size(model, imgsz=640): + # Check YOLOv5 training batch size + with amp.autocast(): + return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size + + +def autobatch(model, imgsz=640, fraction=0.9, batch_size=16): + # Automatically estimate best batch size to use `fraction` of available CUDA memory + # Usage: + # import torch + # from utils.autobatch import autobatch + # 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}') + 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}') + return batch_size + + d = str(device).upper() # 'CUDA:0' + t = torch.cuda.get_device_properties(device).total_memory / 1024 ** 3 # (GB) + r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GB) + a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GB) + f = t - (r + a) # free inside reserved + print(f'{prefix}{d} {t:.3g}G total, {r:.3g}G reserved, {a:.3g}G allocated, {f:.3g}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}') + + 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 colorstr(batch-size {b}) for {d} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)') + return b diff --git a/utils/torch_utils.py b/utils/torch_utils.py index d1c48f73ea72..6f52f9a3728d 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -126,7 +126,7 @@ def profile(input, ops, n=10, device=None): _ = (sum([yi.sum() for yi in y]) if isinstance(y, list) else y).sum().backward() t[2] = time_sync() except Exception as e: # no backward method - print(e) + # print(e) # for debug t[2] = float('nan') tf += (t[1] - t[0]) * 1000 / n # ms per op forward tb += (t[2] - t[1]) * 1000 / n # ms per op backward