Merge branch 'develop' into 'develop'

Fix pruning of concat operator.

See merge request !63

paddleslim/prune/pruner.py

@@ -130,8 +130,16 @@ class Pruner():
                         param.name() not in param_backup):
                     param_backup[param.name()] = copy.deepcopy(
                         np.array(param_t))
-                pruned_param = self._prune_tensor(
-                    np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
+                try:
+                    pruned_param = self._prune_tensor(
+                        np.array(param_t),
+                        pruned_idx,
+                        pruned_axis=0,
+                        lazy=lazy)
+                except IndexError as e:
+                    _logger.error("Pruning {}, but get [{}]".format(param.name(
+                    ), e))
+
                 param_t.set(pruned_param, place)
                 ori_shape = param.shape()
                 if param_shape_backup is not None and (
@@ -171,7 +179,6 @@ class Pruner():
         """
         if params[0].name() in self.pruned_list[pruned_axis]:
             return
-
         if only_graph:
             pruned_num = len(pruned_idx)
             for param in params:
@@ -210,40 +217,55 @@ class Pruner():
                 ), ori_shape, new_shape))
                 self.pruned_list[pruned_axis].append(param.name())
 
-    def _forward_search_related_op(self, graph, param):
+    def _forward_search_related_op(self, graph, node):
         """
         Forward search operators that will be affected by pruning of param.
         Args:
             graph(GraphWrapper): The graph to be searched.
-            param(VarWrapper): The current pruned parameter.
+            node(VarWrapper|OpWrapper): The current pruned parameter or operator.
         Returns:
             list<OpWrapper>: A list of operators.
         """
-        assert isinstance(param, VarWrapper)
         visited = {}
         for op in graph.ops():
             visited[op.idx()] = False
         stack = []
-        for op in graph.ops():
-            if (not op.is_bwd_op()) and (param in op.all_inputs()):
-                stack.append(op)
         visit_path = []
+        if isinstance(node, VarWrapper):
+            for op in graph.ops():
+                if (not op.is_bwd_op()) and (node in op.all_inputs()):
+                    next_ops = self._get_next_unvisited_op(graph, visited, op)
+                    #                visit_path.append(op)
+                    visited[op.idx()] = True
+                    for next_op in next_ops:
+                        if visited[next_op.idx()] == False:
+                            stack.append(next_op)
+                            visit_path.append(next_op)
+                            visited[next_op.idx()] = True
+        elif isinstance(node, OpWrapper):
+            next_ops = self._get_next_unvisited_op(graph, visited, node)
+            for next_op in next_ops:
+                if visited[next_op.idx()] == False:
+                    stack.append(next_op)
+                    visit_path.append(next_op)
+                    visited[next_op.idx()] = True
         while len(stack) > 0:
-            top_op = stack[len(stack) - 1]
-            if visited[top_op.idx()] == False:
-                visit_path.append(top_op)
-                visited[top_op.idx()] = True
+            #top_op = stack[len(stack) - 1]
+            top_op = stack.pop(0)
             next_ops = None
-            if top_op.type() == "conv2d" and param not in top_op.all_inputs():
+            if top_op.type() in ["conv2d", "deformable_conv"]:
                 next_ops = None
-            elif top_op.type() == "mul":
+            elif top_op.type() in ["mul", "concat"]:
                 next_ops = None
             else:
                 next_ops = self._get_next_unvisited_op(graph, visited, top_op)
-            if next_ops == None:
-                stack.pop()
-            else:
-                stack += next_ops
+            if next_ops != None:
+                for op in next_ops:
+                    if visited[op.idx()] == False:
+                        stack.append(op)
+                        visit_path.append(op)
+                        visited[op.idx()] = True
+
         return visit_path
 
     def _get_next_unvisited_op(self, graph, visited, top_op):
@@ -261,7 +283,7 @@ class Pruner():
         for op in graph.next_ops(top_op):
             if (visited[op.idx()] == False) and (not op.is_bwd_op()):
                 next_ops.append(op)
-        return next_ops if len(next_ops) > 0 else None
+        return next_ops
 
     def _get_accumulator(self, graph, param):
         """
@@ -317,7 +339,8 @@ class Pruner():
         if param.name() in self.pruned_list[0]:
             return
         related_ops = self._forward_search_related_op(graph, param)
-
+        for op in related_ops:
+            _logger.debug("relate op: {};".format(op))
         if ratio is None:
             assert pruned_idxs is not None
             self._prune_parameter_by_idx(
@@ -339,17 +362,20 @@ class Pruner():
                 only_graph=only_graph,
                 param_backup=param_backup,
                 param_shape_backup=param_shape_backup)
-        corrected_idxs = pruned_idxs[:]
+        self._prune_ops(related_ops, pruned_idxs, graph, scope, place, lazy,
+                        only_graph, param_backup, param_shape_backup)
 
-        for idx, op in enumerate(related_ops):
-            if op.type() == "conv2d" and (param not in op.all_inputs()):
+    def _prune_ops(self, ops, pruned_idxs, graph, scope, place, lazy,
+                   only_graph, param_backup, param_shape_backup):
+        for idx, op in enumerate(ops):
+            if op.type() in ["conv2d", "deformable_conv"]:
                 for in_var in op.all_inputs():
                     if graph.is_parameter(in_var):
                         conv_param = in_var
                         self._prune_parameter_by_idx(
                             scope, [conv_param] + self._get_accumulator(
                                 graph, conv_param),
-                            corrected_idxs,
+                            pruned_idxs,
                             pruned_axis=1,
                             place=place,
                             lazy=lazy,
@@ -363,7 +389,7 @@ class Pruner():
                         self._prune_parameter_by_idx(
                             scope, [conv_param] + self._get_accumulator(
                                 graph, conv_param),
-                            corrected_idxs,
+                            pruned_idxs,
                             pruned_axis=0,
                             place=place,
                             lazy=lazy,
@@ -397,7 +423,7 @@ class Pruner():
                 idx = []
                 feature_map_size = fc_input.shape()[2] * fc_input.shape()[3]
                 range_idx = np.array(range(feature_map_size))
-                for i in corrected_idxs:
+                for i in pruned_idxs:
                     idx += list(range_idx + i * feature_map_size)
                 corrected_idxs = idx
                 self._prune_parameter_by_idx(
@@ -412,23 +438,37 @@ class Pruner():
 
             elif op.type() == "concat":
                 concat_inputs = op.all_inputs()
-                last_op = related_ops[idx - 1]
-                for out_var in last_op.all_outputs():
-                    if out_var in concat_inputs:
-                        concat_idx = concat_inputs.index(out_var)
+                last_op = ops[idx - 1]
+                concat_idx = None
+                for last_op in reversed(ops):
+                    for out_var in last_op.all_outputs():
+                        if out_var in concat_inputs:
+                            concat_idx = concat_inputs.index(out_var)
+                            break
+                    if concat_idx is not None:
+                        break
                 offset = 0
                 for ci in range(concat_idx):
                     offset += concat_inputs[ci].shape()[1]
                 corrected_idxs = [x + offset for x in pruned_idxs]
+                related_ops = self._forward_search_related_op(graph, op)
+
+                for op in related_ops:
+                    _logger.debug("concat relate op: {};".format(op))
+
+                self._prune_ops(related_ops, corrected_idxs, graph, scope,
+                                place, lazy, only_graph, param_backup,
+                                param_shape_backup)
             elif op.type() == "batch_norm":
                 bn_inputs = op.all_inputs()
-                mean = bn_inputs[2]
+                in_num = len(bn_inputs)
+                beta = bn_inputs[0]
+                mean = bn_inputs[1]
+                alpha = bn_inputs[2]
                 variance = bn_inputs[3]
-                alpha = bn_inputs[0]
-                beta = bn_inputs[1]
                 self._prune_parameter_by_idx(
                     scope, [mean] + self._get_accumulator(graph, mean),
-                    corrected_idxs,
+                    pruned_idxs,
                     pruned_axis=0,
                     place=place,
                     lazy=lazy,
@@ -437,7 +477,7 @@ class Pruner():
                     param_shape_backup=param_shape_backup)
                 self._prune_parameter_by_idx(
                     scope, [variance] + self._get_accumulator(graph, variance),
-                    corrected_idxs,
+                    pruned_idxs,
                     pruned_axis=0,
                     place=place,
                     lazy=lazy,
@@ -446,7 +486,7 @@ class Pruner():
                     param_shape_backup=param_shape_backup)
                 self._prune_parameter_by_idx(
                     scope, [alpha] + self._get_accumulator(graph, alpha),
-                    corrected_idxs,
+                    pruned_idxs,
                     pruned_axis=0,
                     place=place,
                     lazy=lazy,
@@ -455,7 +495,7 @@ class Pruner():
                     param_shape_backup=param_shape_backup)
                 self._prune_parameter_by_idx(
                     scope, [beta] + self._get_accumulator(graph, beta),
-                    corrected_idxs,
+                    pruned_idxs,
                     pruned_axis=0,
                     place=place,
                     lazy=lazy,
@@ -491,6 +531,7 @@ class Pruner():
         self.pruned_list = [[], []]
         for param, ratio in zip(params, ratios):
             assert isinstance(param, str) or isinstance(param, unicode)
+            _logger.info("pruning param: {}".format(param))
             param = graph.var(param)
             self._forward_pruning_ralated_params(
                 graph,
@@ -504,9 +545,10 @@ class Pruner():
                 param_shape_backup=param_shape_backup)
             ops = param.outputs()
             for op in ops:
-                if op.type() == 'conv2d':
+                if op.type() in ['conv2d', 'deformable_conv']:
                     brother_ops = self._search_brother_ops(graph, op)
                     for broher in brother_ops:
+                        _logger.debug("pruning brother: {}".format(broher))
                         for p in graph.get_param_by_op(broher):
                             self._forward_pruning_ralated_params(
                                 graph,
@@ -534,8 +576,11 @@ class Pruner():
         stack = []
         brothers = []
         for op in graph.next_ops(op_node):
-            if ("conv2d" not in op.type()) and (op.type() != 'fc') and (
-                    not op.is_bwd_op()) and (not op.is_opt_op()):
+            if ("conv2d" not in op.type()) and (
+                    "concat" not in op.type()) and (
+                        "deformable_conv" not in op.type()) and (
+                            op.type() != 'fc') and (
+                                not op.is_bwd_op()) and (not op.is_opt_op()):
                 stack.append(op)
                 visited.append(op.idx())
         while len(stack) > 0:
@@ -546,6 +591,7 @@ class Pruner():
                     _logger.debug("----------go back from {} to {}----------".
                                   format(top_op, parent))
                     if (('conv2d' in parent.type()) or
+                        ("deformable_conv" in parent.type()) or
                         (parent.type() == 'fc')):
                         brothers.append(parent)
                     else:
@@ -553,10 +599,13 @@ class Pruner():
                     visited.append(parent.idx())
 
             for child in graph.next_ops(top_op):
-                if ('conv2d' not in child.type()
-                    ) and (child.type() != 'fc') and (
-                        child.idx() not in visited) and (
-                            not child.is_bwd_op()) and (not child.is_opt_op()):
+                if ('conv2d' not in child.type()) and (
+                        "concat" not in child.type()) and (
+                            'deformable_conv' not in child.type()) and (
+                                child.type() != 'fc') and (
+                                    child.idx() not in visited) and (
+                                        not child.is_bwd_op()) and (
+                                            not child.is_opt_op()):
                     stack.append(child)
                     visited.append(child.idx())
         _logger.debug("brothers: {}".format(brothers))