Update FLOPs description (#3422)

* Update README.md

* Changing FLOPS to FLOPs.

Co-authored-by: BuildTools <unconfigured@null.spigotmc.org>

utils/torch_utils.py

@@ -18,7 +18,7 @@ import torch.nn.functional as F
 import torchvision
 
 try:
-    import thop  # for FLOPS computation
+    import thop  # for FLOPs computation
 except ImportError:
     thop = None
 logger = logging.getLogger(__name__)
@@ -105,13 +105,13 @@ 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}{'GFLOPS':>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  # device
         m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
         dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
         try:
-            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
+            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
         except:
             flops = 0
 
@@ -219,13 +219,13 @@ def model_info(model, verbose=False, img_size=640):
             print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                   (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
 
-    try:  # FLOPS
+    try:  # FLOPs
         from thop import profile
         stride = max(int(model.stride.max()), 32) 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 GFLOPS
+        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 GFLOPS
+        fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPs
     except (ImportError, Exception):
         fs = ''