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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
This commit is contained in:
Glenn Jocher
GitHub
parent
692be757b6
commit
ca19df5f7f
17
train.py
17
train.py
@ -36,6 +36,7 @@ import val # for end-of-epoch mAP
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from models.experimental import attempt_load
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from models.yolo import Model
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from utils.autoanchor import check_anchors
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from utils.autobatch import check_train_batch_size
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from utils.datasets import create_dataloader
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
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strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
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@ -131,6 +132,14 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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print(f'freezing {k}')
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v.requires_grad = False
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# Image size
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
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# Batch size
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if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
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batch_size = check_train_batch_size(model, imgsz)
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# Optimizer
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nbs = 64 # nominal batch size
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accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
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@ -190,11 +199,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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del ckpt, csd
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# Image sizes
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
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imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
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# DP mode
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if cuda and RANK == -1 and torch.cuda.device_count() > 1:
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logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n'
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@ -242,6 +246,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
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# Model parameters
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nl = model.model[-1].nl # number of detection layers (to scale hyps)
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hyp['box'] *= 3. / nl # scale to layers
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hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
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hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
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@ -440,7 +445,7 @@ def parse_opt(known=False):
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
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parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
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parser.add_argument('--epochs', type=int, default=300)
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
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56
utils/autobatch.py
Normal file
56
utils/autobatch.py
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@ -0,0 +1,56 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Auto-batch utils
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"""
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from copy import deepcopy
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import numpy as np
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import torch
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from torch.cuda import amp
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from utils.general import colorstr
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from utils.torch_utils import profile
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def check_train_batch_size(model, imgsz=640):
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# Check YOLOv5 training batch size
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with amp.autocast():
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return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
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def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
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# Automatically estimate best batch size to use `fraction` of available CUDA memory
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# Usage:
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# import torch
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# from utils.autobatch import autobatch
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# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
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# print(autobatch(model))
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prefix = colorstr('autobatch: ')
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print(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
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device = next(model.parameters()).device # get model device
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if device.type == 'cpu':
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print(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
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return batch_size
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d = str(device).upper() # 'CUDA:0'
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t = torch.cuda.get_device_properties(device).total_memory / 1024 ** 3 # (GB)
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r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GB)
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a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GB)
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f = t - (r + a) # free inside reserved
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print(f'{prefix}{d} {t:.3g}G total, {r:.3g}G reserved, {a:.3g}G allocated, {f:.3g}G free')
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batch_sizes = [1, 2, 4, 8, 16]
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try:
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img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
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y = profile(img, model, n=3, device=device)
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except Exception as e:
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print(f'{prefix}{e}')
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y = [x[2] for x in y if x] # memory [2]
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batch_sizes = batch_sizes[:len(y)]
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p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit
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b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
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print(f'{prefix}Using colorstr(batch-size {b}) for {d} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)')
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return b
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@ -126,7 +126,7 @@ def profile(input, ops, n=10, device=None):
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_ = (sum([yi.sum() for yi in y]) if isinstance(y, list) else y).sum().backward()
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t[2] = time_sync()
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except Exception as e: # no backward method
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print(e)
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# print(e) # for debug
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t[2] = float('nan')
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tf += (t[1] - t[0]) * 1000 / n # ms per op forward
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tb += (t[2] - t[1]) * 1000 / n # ms per op backward
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