Update autobatch.py (#5538)

* Update autobatch.py

* Update autobatch.py

* Update autobatch.py

utils/autobatch.py

@@ -35,11 +35,12 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
         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)
+    properties = torch.cuda.get_device_properties(device)  # device properties
+    t = properties.total_memory / 1024 ** 3  # (GiB)
+    r = torch.cuda.memory_reserved(device) / 1024 ** 3  # (GiB)
+    a = torch.cuda.memory_allocated(device) / 1024 ** 3  # (GiB)
     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')
+    print(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
 
     batch_sizes = [1, 2, 4, 8, 16]
     try:
@@ -52,5 +53,5 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
     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 batch-size {b} for {d} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)')
+    print(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)')
     return b