Enhence auto pruning and fix save/load graph when using py_reader

1. Enhence auto pruning. 
2. Fix save/load graph when using py_reader.
test=develop

python/paddle/fluid/contrib/slim/graph/graph_wrapper.py

@@ -477,8 +477,12 @@ class GraphWrapper(object):
         for var in self.program.list_vars():
             if var.persistable and var.name not in self.persistables:
                 self.persistables[var.name] = var
+        persistables = []
+        for var in self.persistables:
+            if 'reader' not in var and 'double_buffer' not in var:
+                persistables.append(self.persistables[var])
 
-        io.save_vars(exe.exe, path, vars=self.persistables.values())
+        io.save_vars(exe.exe, path, vars=persistables)
 
     def load_persistables(self, path, exe):
         """
@@ -491,8 +495,11 @@ class GraphWrapper(object):
         def if_exist(var):
             return os.path.exists(os.path.join(path, var.name))
 
-        io.load_vars(
-            exe.exe, path, vars=self.persistables.values(), predicate=if_exist)
+        persistables = []
+        for var in self.persistables:
+            if 'reader' not in var and 'double_buffer' not in var:
+                persistables.append(self.persistables[var])
+        io.load_vars(exe.exe, path, vars=persistables, predicate=if_exist)
 
     def update_param_shape(self, scope):
         """

python/paddle/fluid/contrib/slim/prune/auto_prune_strategy.py

@@ -39,7 +39,9 @@ class AutoPruneStrategy(PruneStrategy):
                  max_ratio=0.7,
                  metric_name='top1_acc',
                  pruned_params='conv.*_weights',
-                 retrain_epoch=0):
+                 retrain_epoch=0,
+                 uniform_range=None,
+                 init_tokens=None):
         """
         Args:
             pruner(slim.Pruner): The pruner used to prune the parameters. Default: None.
@@ -52,6 +54,8 @@ class AutoPruneStrategy(PruneStrategy):
                          It should be one of keys in out_nodes of graph wrapper. Default: 'top1_acc'
             pruned_params(str): The pattern str to match the parameter names to be pruned. Default: 'conv.*_weights'
             retrain_epoch(int): The training epochs in each seaching step. Default: 0
+            uniform_range(int): The token range in each position of tokens generated by controller. None means getting the range automatically. Default: None.
+            init_tokens(list<int>): The initial tokens. None means getting the initial tokens automatically. Default: None.
         """
         super(AutoPruneStrategy, self).__init__(pruner, start_epoch, end_epoch,
                                                 0.0, metric_name, pruned_params)
@@ -60,8 +64,9 @@ class AutoPruneStrategy(PruneStrategy):
         self._controller = controller
         self._metric_name = metric_name
         self._pruned_param_names = []
-        self._retrain_epoch = 0
-
+        self._retrain_epoch = retrain_epoch
+        self._uniform_range = uniform_range
+        self._init_tokens = init_tokens
         self._current_tokens = None
 
     def on_compression_begin(self, context):
@@ -75,9 +80,18 @@ class AutoPruneStrategy(PruneStrategy):
             if re.match(self.pruned_params, param.name()):
                 self._pruned_param_names.append(param.name())
 
-        self._current_tokens = self._get_init_tokens(context)
-        self._range_table = copy.deepcopy(self._current_tokens)
+        if self._init_tokens is not None:
+            self._current_tokens = self._init_tokens
+        else:
+            self._current_tokens = self._get_init_tokens(context)
 
+        if self._uniform_range is not None:
+            self._range_table = [round(self._uniform_range, 2) / 0.01] * len(
+                self._pruned_param_names)
+        else:
+            self._range_table = copy.deepcopy(self._current_tokens)
+        _logger.info('init tokens: {}'.format(self._current_tokens))
+        _logger.info("range_table: {}".format(self._range_table))
         constrain_func = functools.partial(
             self._constrain_func, context=context)
 
@@ -104,14 +118,20 @@ class AutoPruneStrategy(PruneStrategy):
             context.eval_graph.var(param).set_shape(param_shape_backup[param])
         flops_ratio = (1 - float(flops) / ori_flops)
         if flops_ratio >= self._min_ratio and flops_ratio <= self._max_ratio:
+            _logger.info("Success try [{}]; flops: -{}".format(tokens,
+                                                               flops_ratio))
             return True
         else:
+            _logger.info("Failed try [{}]; flops: -{}".format(tokens,
+                                                              flops_ratio))
             return False
 
     def _get_init_tokens(self, context):
         """Get initial tokens.
         """
         ratios = self._get_uniform_ratios(context)
+        _logger.info('Get init ratios: {}'.format(
+            [round(r, 2) for r in ratios]))
         return self._ratios_to_tokens(ratios)
 
     def _ratios_to_tokens(self, ratios):
@@ -171,7 +191,7 @@ class AutoPruneStrategy(PruneStrategy):
         if context.epoch_id >= self.start_epoch and context.epoch_id <= self.end_epoch and (
                 self._retrain_epoch == 0 or
             (context.epoch_id - self.start_epoch) % self._retrain_epoch == 0):
-            self._current_tokens = self._controller.next_tokens()
+            _logger.info("on_epoch_begin")
             params = self._pruned_param_names
             ratios = self._tokens_to_ratios(self._current_tokens)
 
@@ -189,7 +209,7 @@ class AutoPruneStrategy(PruneStrategy):
             context.optimize_graph.update_groups_of_conv()
             context.eval_graph.update_groups_of_conv()
             context.optimize_graph.compile(
-                mem_opt=True)  # to update the compiled program
+                mem_opt=False)  # to update the compiled program
             context.skip_training = (self._retrain_epoch == 0)
 
     def on_epoch_end(self, context):
@@ -199,10 +219,13 @@ class AutoPruneStrategy(PruneStrategy):
         """
         if context.epoch_id >= self.start_epoch and context.epoch_id < self.end_epoch and (
                 self._retrain_epoch == 0 or
-            (context.epoch_id - self.start_epoch) % self._retrain_epoch == 0):
+            (context.epoch_id - self.start_epoch + 1
+             ) % self._retrain_epoch == 0):
+            _logger.info("on_epoch_end")
             reward = context.eval_results[self._metric_name][-1]
             self._controller.update(self._current_tokens, reward)
 
+            self._current_tokens = self._controller.next_tokens()
             # restore pruned parameters
             for param_name in self._param_backup.keys():
                 param_t = context.scope.find_var(param_name).get_tensor()
@@ -218,7 +241,7 @@ class AutoPruneStrategy(PruneStrategy):
             context.optimize_graph.update_groups_of_conv()
             context.eval_graph.update_groups_of_conv()
             context.optimize_graph.compile(
-                mem_opt=True)  # to update the compiled program
+                mem_opt=False)  # to update the compiled program
 
         elif context.epoch_id == self.end_epoch:  # restore graph for final training
             # restore pruned parameters