rename

square_images/square_run.py

@@ -0,0 +1,68 @@
+import time
+
+def run(point):
+   start = time.time()
+   try:
+      batch_size = point['batch_size']
+      image_size = point['image_size']
+      in_channels = point['in_channels']
+      out_channels = point['out_channels']
+      kernel_size = point['kernel_size']
+      omp_num_threads = point['omp_num_threads']
+
+      import os
+      os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
+      os.environ['MKL_NUM_THREADS'] = str(omp_num_threads)
+      os.environ['KMP_HW_SUBSET'] = '1s,%sc,2t' % str(omp_num_threads)
+      os.environ['KMP_AFFINITY'] = 'granularity=fine,verbose,compact,1,0'
+      os.environ['KMP_BLOCKTIME'] = str(0)
+      #os.environ['MKLDNN_VERBOSE'] = str(1)
+      import torch
+
+      print('torch version: ',torch.__version__,' torch file: ',torch.__file__)
+
+
+      inputs = torch.arange(batch_size * image_size * image_size * in_channels,dtype=torch.float).view((batch_size,in_channels,image_size,image_size))
+
+      layer = torch.nn.Conv2d(in_channels,out_channels,(kernel_size,kernel_size),stride=1)
+      outputs = layer(inputs)
+
+      total_flop = kernel_size * kernel_size * in_channels * out_channels * outputs.shape[-1] * outputs.shape[-2] * batch_size
+
+      runs = 5
+      tot_time = 0.
+      tt = time.time()
+      for _ in range(runs):
+         outputs = layer(inputs)
+         tot_time += time.time() - tt
+         tt = time.time()
+
+      ave_time = tot_time / runs
+
+      print('total_flop = ',total_flop,'ave_time = ',ave_time)
+
+      ave_flops = total_flop / ave_time
+      runtime = time.time() - start
+      print('runtime=',runtime,'ave_flops=',ave_flops)
+
+      return ave_flops
+   except Exception as e:
+      import traceback
+      print('received exception: ',str(e))
+      print(traceback.print_exc())
+      print('runtime=',time.time() - start)
+      return 0.
+
+
+if __name__ == '__main__':
+   point = {
+      'batch_size': 10,
+      'image_size': 512,
+      'in_channels': 3,
+      'out_channels': 64,
+      'kernel_size': 4,
+      'omp_num_threads':64,
+   }
+
+   print('flops for this setting =',run(point))
+