Update torch_utils.py

FLOPS to GFLOPS

utils/torch_utils.py

@@ -86,7 +86,7 @@ def profile(x, ops, n=100, device=None):
     x = x.to(device)
     x.requires_grad = True
     print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
-    print(f"\n{'Params':>12s}{'FLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
+    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
     for m in ops if isinstance(ops, list) else [ops]:
         m = m.to(device) if hasattr(m, 'to') else m
         dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
@@ -197,9 +197,9 @@ def model_info(model, verbose=False, img_size=640):
         from thop import profile
         stride = int(model.stride.max()) if hasattr(model, 'stride') else 32
         img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
-        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride FLOPS
+        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
         img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
-        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 FLOPS
+        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
     except (ImportError, Exception):
         fs = ''