migrage static api in prune module (#566)

* fix prune demo

* migrage static api

* fix a bug

demo/prune/train.py

@@ -124,8 +124,7 @@ def compress(args):
     # model definition
     model = models.__dict__[args.model]()
     out = model.net(input=image, class_dim=class_dim)
-    cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
-    avg_cost = paddle.mean(x=cost)
+    avg_cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
     acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
     acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
     val_program = paddle.static.default_main_program().clone(for_test=True)
@@ -144,8 +143,8 @@ def compress(args):
 
         _logger.info("Load pretrained model from {}".format(
             args.pretrained_model))
-        paddle.fluid.io.load_vars(
-            exe, args.pretrained_model, predicate=if_exist)
+        paddle.static.load(paddle.static.default_main_program(),
+                           args.pretrained_model, exe)
 
     train_loader = paddle.io.DataLoader(
         train_dataset,
@@ -245,8 +244,10 @@ def compress(args):
         if args.save_inference:
             infer_model_path = os.path.join(args.model_path, "infer_models",
                                             str(i))
-            paddle.fluid.io.save_inference_model(infer_model_path, ["image"],
-                                                 [out], exe, pruned_val_program)
+            paddle.static.save_inference_model(
+                infer_model_path, ["image"], [out],
+                exe,
+                program=pruned_val_program)
             _logger.info("Saved inference model into [{}]".format(
                 infer_model_path))
 

paddleslim/prune/auto_pruner.py

@@ -36,7 +36,7 @@ class AutoPruner(object):
     Args:
         program(Program): The program to be pruned.
         scope(Scope): The scope to be pruned.
-        place(fluid.Place): The device place of parameters.
+        place(paddle.CUDAPlace||paddle.CPUPlace): The device place of parameters.
         params(list<str>): The names of parameters to be pruned.
         init_ratios(list<float>|float): Init ratios used to pruned parameters in `params`.
             List means ratios used for pruning each parameter in `params`.

paddleslim/prune/prune_io.py

@@ -25,17 +25,16 @@ def save_model(exe, graph, dirname):
     graph = GraphWrapper(graph) if isinstance(graph,
                                               paddle.static.Program) else graph
 
-    paddle.fluid.io.save_persistables(
-        executor=exe,
-        dirname=dirname,
-        main_program=graph.program,
-        filename=None)
+    paddle.static.save(program=graph.program, model_path=dirname)
     weights_file = dirname
     _logger.info("Save model weights into {}".format(weights_file))
     shapes = {}
-    for var in paddle.fluid.io.get_program_persistable_vars(graph.program):
-        shapes[var.name] = var.shape
+    for var in graph.program.list_vars():
+        if var.persistable:
+            shapes[var.name] = var.shape
     SHAPES_FILE = os.path.join(dirname, _SHAPES_FILE)
+    if not os.path.exists(dirname):
+        os.makedirs(dirname)
     with open(SHAPES_FILE, "w") as f:
         json.dump(shapes, f)
         _logger.info("Save shapes of weights into {}".format(SHAPES_FILE))
@@ -65,11 +64,7 @@ def load_model(exe, graph, dirname):
                 _logger.info('{} is not loaded'.format(param_name))
 
     _logger.info("Load shapes of weights from {}".format(SHAPES_FILE))
-    paddle.fluid.io.load_persistables(
-        executor=exe,
-        dirname=dirname,
-        main_program=graph.program,
-        filename=None)
+    paddle.static.load(program=graph.program, model_path=dirname, executor=exe)
     graph.update_groups_of_conv()
     graph.infer_shape()
     _logger.info("Load weights from {}".format(dirname))

paddleslim/prune/pruner.py

@@ -65,10 +65,10 @@ class Pruner():
         Args:
 
             program(paddle.static.Program): The program to be pruned.
-            scope(fluid.Scope): The scope storing paramaters to be pruned.
+            scope(paddle.static.Scope): The scope storing paramaters to be pruned.
             params(list<str>): A list of parameter names to be pruned.
             ratios(list<float>): A list of ratios to be used to pruning parameters.
-            place(fluid.Place): The device place of filter parameters. Defalut: None.
+            place(paddle.CUDAPlace||paddle.CPUPlace): The device place of filter parameters. Defalut: None.
             lazy(bool): True means setting the pruned elements to zero.
                         False means cutting down the pruned elements. Default: False.
             only_graph(bool): True means only modifying the graph.