GFLOPs computation fix for classification models (ultralytics#8954)

* GFLOPs computation fix for classification models

Improved robustness in reading input channel count

* Update torch_utils.py

* Update torch_utils.py

utils/torch_utils.py

@@ -199,12 +199,11 @@ def sparsity(model):
 def prune(model, amount=0.3):
     # Prune model to requested global sparsity
     import torch.nn.utils.prune as prune
-    print('Pruning model... ', end='')
     for name, m in model.named_modules():
         if isinstance(m, nn.Conv2d):
             prune.l1_unstructured(m, name='weight', amount=amount)  # prune
             prune.remove(m, 'weight')  # make permanent
-    print(' %.3g global sparsity' % sparsity(model))
+    LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')
 
 
 def fuse_conv_and_bn(conv, bn):
@@ -230,7 +229,7 @@ def fuse_conv_and_bn(conv, bn):
     return fusedconv
 
 
-def model_info(model, verbose=False, img_size=640):
+def model_info(model, verbose=False, imgsz=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
@@ -242,12 +241,12 @@ def model_info(model, verbose=False, img_size=640):
                   (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
 
     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
-        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
+        p = next(model.parameters())
+        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
+        im = torch.zeros((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
+        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
+        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
+        fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
     except Exception:
         fs = ''