Improved FLOPS computation (#1398)

* Improved FLOPS computation

* update comment

models/yolo.py

@@ -192,8 +192,8 @@ class Model(nn.Module):
         copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
         return m
 
-    def info(self, verbose=False):  # print model information
-        model_info(self, verbose)
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
 
 
 def parse_model(d, ch):  # model_dict, input_channels(3)

utils/torch_utils.py

@@ -139,8 +139,8 @@ def fuse_conv_and_bn(conv, bn):
     return fusedconv
 
 
-def model_info(model, verbose=False):
-    # Plots a line-by-line description of a PyTorch model
+def model_info(model, verbose=False, img_size=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
     n_p = sum(x.numel() for x in model.parameters())  # number parameters
     n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
     if verbose:
@@ -152,8 +152,10 @@ def model_info(model, verbose=False):
 
     try:  # FLOPS
         from thop import profile
-        flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, 64, 64),), verbose=False)[0] / 1E9 * 2
-        fs = ', %.1f GFLOPS' % (flops * 100)  # 640x640 FLOPS
+        stride = int(model.stride.max())
+        flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, stride, stride),), verbose=False)[0] / 1E9 * 2
+        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
     except ImportError:
         fs = ''