bug bash for sensitivity_pruner

examples/model_compress/models/mobilenet.py

@@ -53,7 +53,7 @@ def __init__(self, n_class,  profile='normal'):
     def forward(self, x):
         x = self.conv1(x)
         x = self.features(x)
-        x = x.mean(3).mean(2)  # global average pooling
+        x = x.mean([2, 3]) # global average pooling
 
         x = self.classifier(x)
         return x

examples/model_compress/models/mobilenet_v2.py

@@ -108,7 +108,10 @@ def __init__(self, n_class=1000, input_size=224, width_mult=1.):
 
     def forward(self, x):
         x = self.features(x)
-        x = x.mean(3).mean(2)
+        # it's same with .mean(3).mean(2), but
+        # speedup only suport the mean option
+        # whose output only have two dimensions
+        x = x.mean([2, 3])
         x = self.classifier(x)
         return x
 

src/sdk/pynni/nni/compression/torch/pruning/sensitivity_pruner.py

@@ -17,7 +17,7 @@
 MAX_PRUNE_RATIO_PER_ITER = 0.95
 
 _logger = logging.getLogger('Sensitivity_Pruner')
-
+_logger.setLevel(logging.INFO)
 
 class SensitivityPruner(Pruner):
     """
@@ -202,10 +202,10 @@ def _max_prune_ratio(self, ori_acc, threshold, sensitivities):
             prune_ratios = sorted(sensitivities[layer].keys())
             last_ratio = 0
             for ratio in prune_ratios:
+                last_ratio = ratio
                 cur_acc = sensitivities[layer][ratio]
                 if cur_acc + threshold < ori_acc:
                     break
-                last_ratio = ratio
             max_ratio[layer] = last_ratio
         return max_ratio
 
@@ -244,6 +244,7 @@ def normalize(self, ratios, target_pruned):
         # MAX_PRUNE_RATIO_PER_ITER we rescal all prune
         # ratios under this threshold
         if _Max > MAX_PRUNE_RATIO_PER_ITER:
+
             for layername in ratios:
                 ratios[layername] = ratios[layername] * \
                     MAX_PRUNE_RATIO_PER_ITER / _Max
@@ -317,6 +318,7 @@ def compress(self, eval_args=None, eval_kwargs=None,
             finetune_kwargs = {}
         if self.ori_acc is None:
             self.ori_acc = self.evaluator(*eval_args, **eval_kwargs)
+        assert isinstance(self.ori_acc, float) or isinstance(self.ori_acc, int)
         if not resume_sensitivity:
             self.sensitivities = self.analyzer.analysis(
                 val_args=eval_args, val_kwargs=eval_kwargs)
@@ -330,6 +332,7 @@ def compress(self, eval_args=None, eval_kwargs=None,
         iteration_count = 0
         if self.checkpoint_dir is not None:
             os.makedirs(self.checkpoint_dir, exist_ok=True)
+        modules_wrapper_final = None
         while cur_ratio > target_ratio:
             iteration_count += 1
             # Each round have three steps:
@@ -343,9 +346,16 @@ def compress(self, eval_args=None, eval_kwargs=None,
             # layers according to the sensitivity result
             proportion = self.sparsity_proportion_calc(
                 ori_acc, self.acc_drop_threshold, self.sensitivities)
+
             new_pruneratio = self.normalize(proportion, self.sparsity_per_iter)
             cfg_list = self.create_cfg(new_pruneratio)
+            if not cfg_list:
+                _logger.error('The threshold is too small, please set a larger threshold')
+                return self.model
             _logger.debug('Pruner Config: %s', str(cfg_list))
+            cfg_str = ['%s:%.3f'%(cfg['op_names'][0], cfg['sparsity']) for cfg in cfg_list]
+            _logger.info('Current Sparsities: %s', ','.join(cfg_str))
+
             pruner = self.Pruner(self.model, cfg_list)
             pruner.compress()
             pruned_acc = self.evaluator(*eval_args, **eval_kwargs)
@@ -367,6 +377,7 @@ def compress(self, eval_args=None, eval_kwargs=None,
                 self.analyzer.already_pruned[name] = sparsity
             # update the cur_ratio
             cur_ratio = 1 - self.current_sparsity()
+            modules_wrapper_final = pruner.get_modules_wrapper()
             del pruner
             _logger.info('Currently remained weights: %f', cur_ratio)
 
@@ -383,14 +394,19 @@ def compress(self, eval_args=None, eval_kwargs=None,
                 with open(cfg_path, 'w') as jf:
                     json.dump(cfg_list, jf)
                 self.analyzer.export(sensitivity_path)
+
             if cur_ratio > target_ratio:
                 # If this is the last prune iteration, skip the time-consuming
                 # sensitivity analysis
+
                 self.analyzer.load_state_dict(self.model.state_dict())
                 self.sensitivities = self.analyzer.analysis(
                     val_args=eval_args, val_kwargs=eval_kwargs)
 
         _logger.info('After Pruning: %.2f weights remains', cur_ratio)
+        self.modules_wrapper = modules_wrapper_final
+
+        self._wrap_model()
         return self.model
 
     def calc_mask(self, wrapper, **kwargs):

src/sdk/pynni/nni/compression/torch/utils/sensitivity_analysis.py

@@ -163,28 +163,30 @@ def analysis(self, val_args=None, val_kwargs=None, specified_layers=None):
         if val_kwargs is None:
             val_kwargs = {}
         # Get the original validation metric(accuracy/loss) before pruning
-        if self.ori_metric is None:
-            self.ori_metric = self.val_func(*val_args, **val_kwargs)
+        # Get the accuracy baseline before starting the analysis.
+        self.ori_metric = self.val_func(*val_args, **val_kwargs)
         namelist = list(self.target_layer.keys())
         if specified_layers is not None:
             # only analyze several specified conv layers
             namelist = list(filter(lambda x: x in specified_layers, namelist))
         for name in namelist:
             self.sensitivities[name] = {}
             for sparsity in self.sparsities:
+                # here the sparsity is the relative sparsity of the
+                # the remained weights
                 # Calculate the actual prune ratio based on the already pruned ratio
-                sparsity = (
+                real_sparsity = (
                     1.0 - self.already_pruned[name]) * sparsity + self.already_pruned[name]
                 # TODO In current L1/L2 Filter Pruner, the 'op_types' is still necessary
                 # I think the L1/L2 Pruner should specify the op_types automaticlly
                 # according to the op_names
-                cfg = [{'sparsity': sparsity, 'op_names': [
+                cfg = [{'sparsity': real_sparsity, 'op_names': [
                     name], 'op_types': ['Conv2d']}]
                 pruner = self.Pruner(self.model, cfg)
                 pruner.compress()
                 val_metric = self.val_func(*val_args, **val_kwargs)
                 logger.info('Layer: %s Sparsity: %.2f Validation Metric: %.4f',
-                            name, sparsity, val_metric)
+                            name, real_sparsity, val_metric)
 
                 self.sensitivities[name][sparsity] = val_metric
                 pruner._unwrap_model()