initial project version!

ultralytics/utils/autobatch.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.
+"""
+
+from copy import deepcopy
+
+import numpy as np
+import torch
+
+from ultralytics.utils import DEFAULT_CFG, LOGGER, colorstr
+from ultralytics.utils.torch_utils import profile
+
+
+def check_train_batch_size(model, imgsz=640, amp=True):
+    """
+    Check YOLO training batch size using the autobatch() function.
+
+    Args:
+        model (torch.nn.Module): YOLO model to check batch size for.
+        imgsz (int): Image size used for training.
+        amp (bool): If True, use automatic mixed precision (AMP) for training.
+
+    Returns:
+        (int): Optimal batch size computed using the autobatch() function.
+    """
+
+    with torch.cuda.amp.autocast(amp):
+        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
+
+
+def autobatch(model, imgsz=640, fraction=0.60, batch_size=DEFAULT_CFG.batch):
+    """
+    Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.
+
+    Args:
+        model (torch.nn.module): YOLO model to compute batch size for.
+        imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
+        fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67.
+        batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.
+
+    Returns:
+        (int): The optimal batch size.
+    """
+
+    # 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)
+
+        # 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.info(f'{prefix}WARNING ⚠️ CUDA anomaly detected, using default batch-size {batch_size}.')
+
+        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
+    except Exception as e:
+        LOGGER.warning(f'{prefix}WARNING ⚠️ error detected: {e},  using default batch-size {batch_size}.')
+        return batch_size