optimize the prune

deploy/slim/prune/sensitivity_anal.py

@@ -110,25 +110,42 @@ def main(config, device, logger, vdl_writer):
         logger.info("metric['hmean']: {}".format(metric['hmean']))
         return metric['hmean']
 
+    run_sensitive_analysis = False
+    """
+    run_sensitive_analysis=True: 
+        Automatically compute the sensitivities of convolutions in a model. 
+        The sensitivity of a convolution is the losses of accuracy on test dataset in 
+        differenct pruned ratios. The sensitivities can be used to get a group of best 
+        ratios with some condition.
+    
+    run_sensitive_analysis=False: 
+        Set prune trim ratio to a fixed value, such as 10%. The larger the value, 
+        the more convolution weights will be cropped.
+
+    """
+
+    if run_sensitive_analysis:
         params_sensitive = pruner.sensitive(
             eval_func=eval_fn,
-        sen_file="./sen.pickle",
+            sen_file="./deploy/slim/prune/sen.pickle",
             skip_vars=[
-            "conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"
+                "conv2d_57.w_0", "conv2d_transpose_2.w_0",
+                "conv2d_transpose_3.w_0"
             ])
-
         logger.info(
             "The sensitivity analysis results of model parameters saved in sen.pickle"
         )
         # calculate pruned params's ratio
-    params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
+        params_sensitive = pruner._get_ratios_by_loss(
+            params_sensitive, loss=0.02)
         for key in params_sensitive.keys():
             logger.info("{}, {}".format(key, params_sensitive[key]))
-
-    #params_sensitive = {}
-    #for param in model.parameters():
-    #    if 'transpose' not in param.name and 'linear' not in param.name:
-    #        params_sensitive[param.name] = 0.1  
+    else:
+        params_sensitive = {}
+        for param in model.parameters():
+            if 'transpose' not in param.name and 'linear' not in param.name:
+                # set prune ratio as 10%. The larger the value, the more convolution weights will be cropped
+                params_sensitive[param.name] = 0.1
 
     plan = pruner.prune_vars(params_sensitive, [0])
 

tools/program.py

@@ -351,7 +351,7 @@ def eval(model,
          valid_dataloader,
          post_process_class,
          eval_class,
-         model_type,
+         model_type=None,
          use_srn=False,
          use_sar=False):
     model.eval()