adding my own flops calculation

linear/linear_run.py

@@ -1,5 +1,5 @@
 import time
-from ptflops import get_model_complexity_info
+#from ptflops import get_model_complexity_info
 
 def run(point):
    start = time.time()
@@ -24,11 +24,10 @@ def run(point):
       print('torch version: ',torch.__version__,' torch file: ',torch.__file__)
 
       inputs = torch.arange(batch_size * in_features,dtype=torch.float).view((batch_size,in_features))
-
+      # using flops from here:
+      # https://machinethink.net/blog/how-fast-is-my-model/
+      total_flop = batch_size * (2*in_features - 1) * out_features
       layer = torch.nn.Linear(in_features,out_features,bias=bias)
-      flops, params = get_model_complexity_info(layer, tuple(inputs.shape),as_strings=False)
-      print(flops)
-
       outputs = layer(inputs)
 
       runs = 5
@@ -41,11 +40,11 @@ def run(point):
 
       ave_time = tot_time / runs
 
-      print('flop = ',flops,'ave_time = ',ave_time)
+      print('flop = ',total_flop,'ave_time = ',ave_time)
 
-      ave_flops = flops / ave_time  #* batch_size
+      ave_flops = total_flop / ave_time
 
-      print('runtime=',time.time() - start)
+      print('runtime=',time.time() - start,'ave_flops=',ave_flops)
       return ave_flops
    except Exception as e:
       import traceback