Add AutoBatch from YOLOv5 (#145)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/trainer.py

@@ -27,6 +27,7 @@ from ultralytics.yolo.configs import get_config
 from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
 from ultralytics.yolo.utils import (DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
                                     yaml_save)
+from ultralytics.yolo.utils.autobatch import check_train_batch_size
 from ultralytics.yolo.utils.checks import check_file, print_args
 from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
 from ultralytics.yolo.utils.files import get_latest_run, increment_path
@@ -135,7 +136,7 @@ class BaseTrainer:
         self.fitness = None
         self.loss = None
         self.tloss = None
-        self.loss_names = None
+        self.loss_names = ['Loss']
         self.csv = self.save_dir / 'results.csv'
         self.plot_idx = [0, 1, 2]
 
@@ -192,6 +193,15 @@ class BaseTrainer:
         self.set_model_attributes()
         if world_size > 1:
             self.model = DDP(self.model, device_ids=[rank])
+
+        # Batch size
+        if self.batch_size == -1:
+            if RANK == -1:  # single-GPU only, estimate best batch size
+                self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
+            else:
+                SyntaxError('batch=-1 to use AutoBatch is only available in Single-GPU training. '
+                            'Please pass a valid batch size value for Multi-GPU DDP training, i.e. batch=16')
+
         # Optimizer
         self.accumulate = max(round(self.args.nbs / self.batch_size), 1)  # accumulate loss before optimizing
         self.args.weight_decay *= self.batch_size * self.accumulate / self.args.nbs  # scale weight_decay

ultralytics/yolo/engine/validator.py

@@ -78,7 +78,7 @@ class BaseValidator:
             self.device = trainer.device
             self.data = trainer.data
             model = trainer.ema.ema or trainer.model
-            self.args.half &= self.device.type != 'cpu'
+            self.args.half = self.device.type != 'cpu'  # force FP16 val during training
             model = model.half() if self.args.half else model.float()
             self.model = model
             self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)

ultralytics/yolo/utils/autobatch.py

@@ -0,0 +1,72 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Auto-batch utils
+"""
+
+from copy import deepcopy
+
+import numpy as np
+import torch
+
+from ultralytics.yolo.utils import LOGGER, colorstr
+from ultralytics.yolo.utils.torch_utils import profile
+
+
+def check_train_batch_size(model, imgsz=640, amp=True):
+    # Check YOLOv5 training batch size
+    with torch.cuda.amp.autocast(amp):
+        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
+
+
+def autobatch(model, imgsz=640, fraction=0.7, batch_size=16):
+    # Automatically estimate best YOLOv5 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))
+
+    # Check device
+    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':
+        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
+        return batch_size
+    if torch.backends.cudnn.benchmark:
+        LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
+        return batch_size
+
+    # Inspect CUDA memory
+    gb = 1 << 30  # bytes to GiB (1024 ** 3)
+    d = str(device).upper()  # 'CUDA:0'
+    properties = torch.cuda.get_device_properties(device)  # device properties
+    t = properties.total_memory / gb  # GiB total
+    r = torch.cuda.memory_reserved(device) / gb  # GiB reserved
+    a = torch.cuda.memory_allocated(device) / gb  # GiB allocated
+    f = t - (r + a)  # GiB free
+    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
+
+    # Profile batch sizes
+    batch_sizes = [1, 2, 4, 8, 16]
+    try:
+        img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
+        results = profile(img, model, n=3, device=device)
+    except Exception as e:
+        LOGGER.warning(f'{prefix}{e}')
+
+    # Fit a solution
+    y = [x[2] for x in results if x]  # memory [2]
+    p = np.polyfit(batch_sizes[:len(y)], y, deg=1)  # first degree polynomial fit
+    b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
+    if None in results:  # some sizes failed
+        i = results.index(None)  # first fail index
+        if b >= batch_sizes[i]:  # y intercept above failure point
+            b = batch_sizes[max(i - 1, 0)]  # select prior safe point
+    if b < 1 or b > 1024:  # b outside of safe range
+        b = batch_size
+        LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
+
+    fraction = (np.polyval(p, b) + r + a) / t  # actual fraction predicted
+    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
+    return b

ultralytics/yolo/utils/torch_utils.py

@@ -299,3 +299,54 @@ def guess_task_from_head(head):
         raise SyntaxError("task or model not recognized! Please refer the docs at : ")  # TODO: add docs links
 
     return task
+
+
+def profile(input, ops, n=10, device=None):
+    """ YOLOv5 speed/memory/FLOPs profiler
+    Usage:
+        input = torch.randn(16, 3, 640, 640)
+        m1 = lambda x: x * torch.sigmoid(x)
+        m2 = nn.SiLU()
+        profile(input, [m1, m2], n=100)  # profile over 100 iterations
+    """
+    results = []
+    if not isinstance(device, torch.device):
+        device = select_device(device)
+    print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
+          f"{'input':>24s}{'output':>24s}")
+
+    for x in input if isinstance(input, list) else [input]:
+        x = x.to(device)
+        x.requires_grad = True
+        for m in ops if isinstance(ops, list) else [ops]:
+            m = m.to(device) if hasattr(m, 'to') else m  # device
+            m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
+            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
+            try:
+                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
+            except Exception:
+                flops = 0
+
+            try:
+                for _ in range(n):
+                    t[0] = time_sync()
+                    y = m(x)
+                    t[1] = time_sync()
+                    try:
+                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
+                        t[2] = time_sync()
+                    except Exception:  # no backward method
+                        # 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
+                mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
+                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
+                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
+                print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
+                results.append([p, flops, mem, tf, tb, s_in, s_out])
+            except Exception as e:
+                print(e)
+                results.append(None)
+            torch.cuda.empty_cache()
+    return results

ultralytics/yolo/v8/detect/train.py

@@ -74,9 +74,16 @@ class DetectionTrainer(BaseTrainer):
         return self.compute_loss(preds, batch)
 
     def label_loss_items(self, loss_items=None, prefix="train"):
-        # We should just use named tensors here in future
+        """
+        Returns a loss dict with labelled training loss items tensor
+        """
+        # Not needed for classification but necessary for segmentation & detection
         keys = [f"{prefix}/{x}" for x in self.loss_names]
-        return dict(zip(keys, loss_items)) if loss_items is not None else keys
+        if loss_items is not None:
+            loss_items = [round(float(x), 5) for x in loss_items]  # convert tensors to 5 decimal place floats
+            return dict(zip(keys, loss_items))
+        else:
+            return keys
 
     def progress_string(self):
         return ('\n' + '%11s' *