Update FLOPs description (#3422)

* Update README.md

* Changing FLOPS to FLOPs.

Co-authored-by: BuildTools <[email protected]>

README.md

@@ -30,7 +30,7 @@ This repository represents Ultralytics open-source research into future object d
 
 [assets]: https://github.com/ultralytics/yolov5/releases
 
-Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
+Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |<br><sup>640 (B)
 ---   |---  |---        |---         |---             |---                |---|---              |---
 [YOLOv5s][assets]    |640  |36.7     |36.7     |55.4     |**2.0** | |7.3   |17.0
 [YOLOv5m][assets]    |640  |44.5     |44.5     |63.1     |2.7     | |21.4  |51.3
@@ -112,7 +112,7 @@ Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, devi
 YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)
 
 Fusing layers... 
-Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
+Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs
 image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
 image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
 Results saved to runs/detect/exp2

models/yolo.py

@@ -21,7 +21,7 @@ from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, s
     select_device, copy_attr
 
 try:
-    import thop  # for FLOPS computation
+    import thop  # for FLOPs computation
 except ImportError:
     thop = None
 
@@ -140,13 +140,13 @@ class Model(nn.Module):
                 x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
 
             if profile:
-                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
+                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
                 t = time_synchronized()
                 for _ in range(10):
                     _ = m(x)
                 dt.append((time_synchronized() - t) * 100)
                 if m == self.model[0]:
-                    logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s}  {'module'}")
+                    logger.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
                 logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
 
             x = m(x)  # run

requirements.txt

@@ -27,4 +27,4 @@ pandas
 # extras --------------------------------------
 # Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
 pycocotools>=2.0  # COCO mAP
-thop  # FLOPS computation
+thop  # FLOPs computation

tutorial.ipynb

@@ -611,7 +611,7 @@
             "YOLOv5 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS\n",
+            "Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs\n",
             "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.008s)\n",
             "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n",
             "Results saved to runs/detect/exp\n",
@@ -734,7 +734,7 @@
             "100% 168M/168M [00:05<00:00, 32.3MB/s]\n",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n",
+            "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPs\n",
             "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3102.29it/s]\n",
             "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n",
             "               Class      Images      Labels           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:23<00:00,  1.87it/s]\n",
@@ -964,7 +964,7 @@
             " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
             " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
             " 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
-            "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n",
+            "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",
             "\n",
             "Transferred 362/362 items from yolov5s.pt\n",
             "Scaled weight_decay = 0.0005\n",

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 = ''