pruning and sparsity initial commit

models/yolo.py

@@ -48,7 +48,7 @@ class Model(nn.Module):
         if type(model_cfg) is dict:
             self.md = model_cfg  # model dict
         else:  # is *.yaml
-            import yaml
+            import yaml  # for torch hub
             with open(model_cfg) as f:
                 self.md = yaml.load(f, Loader=yaml.FullLoader)  # model dict
 

utils/torch_utils.py

@@ -76,6 +76,26 @@ def find_modules(model, mclass=nn.Conv2d):
     return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
 
 
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0., 0.
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+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, torch.nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    print(' %.3g global sparsity' % sparsity(model))
+
+
 def fuse_conv_and_bn(conv, bn):
     # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
     with torch.no_grad():