Add `autobatch` feature for best `batch-size` estimation (#5092)

train.py

@@ -36,6 +36,7 @@ import val  # for end-of-epoch mAP
 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')

utils/autobatch.py

+# 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

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