Fix all the unittest of pruning. (#346)

paddleslim/prune/group_param.py

@@ -58,7 +58,8 @@ def collect_convs(params, graph, visited={}):
         walker = conv2d_walker(
             conv_op, pruned_params=pruned_params, visited=visited)
         walker.prune(param, pruned_axis=0, pruned_idx=[0])
-        groups.append(pruned_params)
+        if len(pruned_params) > 0:
+            groups.append(pruned_params)
     visited = set()
     uniq_groups = []
     for group in groups:

tests/test_auto_prune.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import sys
-sys.path.append("../")
-import unittest
-import paddle.fluid as fluid
-from paddleslim.prune import AutoPruner
-from paddleslim.analysis import flops
-from layers import conv_bn_layer
-
-
-class TestPrune(unittest.TestCase):
-    def test_prune(self):
-        main_program = fluid.Program()
-        startup_program = fluid.Program()
-        #   X       X              O       X              O
-        # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
-        #     |            ^ |                    ^
-        #     |____________| |____________________|
-        #
-        # X: prune output channels
-        # O: prune input channels
-        with fluid.program_guard(main_program, startup_program):
-            input = fluid.data(name="image", shape=[None, 3, 16, 16])
-            conv1 = conv_bn_layer(input, 8, 3, "conv1")
-            conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
-            sum1 = conv1 + conv2
-            conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
-            conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
-            sum2 = conv4 + sum1
-            conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
-            conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
-
-        shapes = {}
-        for param in main_program.global_block().all_parameters():
-            shapes[param.name] = param.shape
-
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
-        scope = fluid.Scope()
-        exe.run(startup_program, scope=scope)
-
-        pruned_flops = 0.5
-        pruner = AutoPruner(
-            main_program,
-            scope,
-            place,
-            params=["conv4_weights"],
-            init_ratios=[0.5],
-            pruned_flops=0.5,
-            pruned_latency=None,
-            server_addr=("", 0),
-            init_temperature=100,
-            reduce_rate=0.85,
-            max_try_number=300,
-            max_client_num=10,
-            search_steps=2,
-            max_ratios=[0.9],
-            min_ratios=[0],
-            key="auto_pruner")
-
-        base_flops = flops(main_program)
-        program = pruner.prune(main_program)
-        self.assertTrue(flops(program) <= base_flops * (1 - pruned_flops))
-        pruner.reward(1)
-
-        program = pruner.prune(main_program)
-        self.assertTrue(flops(program) <= base_flops * (1 - pruned_flops))
-        pruner.reward(1)
-
-
-if __name__ == '__main__':
-    unittest.main()

tests/test_fpgm_prune.py

@@ -63,12 +63,12 @@ class TestPrune(unittest.TestCase):
             param_shape_backup=None)
 
         shapes = {
-            "conv1_weights": (4L, 3L, 3L, 3L),
-            "conv2_weights": (4L, 4L, 3L, 3L),
-            "conv3_weights": (8L, 4L, 3L, 3L),
-            "conv4_weights": (4L, 8L, 3L, 3L),
-            "conv5_weights": (8L, 4L, 3L, 3L),
-            "conv6_weights": (8L, 8L, 3L, 3L)
+            "conv1_weights": (4, 3, 3, 3),
+            "conv2_weights": (4, 4, 3, 3),
+            "conv3_weights": (8, 4, 3, 3),
+            "conv4_weights": (4, 8, 3, 3),
+            "conv5_weights": (8, 4, 3, 3),
+            "conv6_weights": (8, 8, 3, 3)
         }
 
         for param in main_program.global_block().all_parameters():

tests/test_optimal_threshold.py

@@ -64,12 +64,12 @@ class TestPrune(unittest.TestCase):
             param_shape_backup=None)
 
         shapes = {
-            "conv1_weights": (4L, 3L, 3L, 3L),
-            "conv2_weights": (4L, 4L, 3L, 3L),
-            "conv3_weights": (8L, 4L, 3L, 3L),
-            "conv4_weights": (4L, 8L, 3L, 3L),
-            "conv5_weights": (8L, 4L, 3L, 3L),
-            "conv6_weights": (8L, 8L, 3L, 3L)
+            "conv1_weights": (4, 3, 3, 3),
+            "conv2_weights": (4, 4, 3, 3),
+            "conv3_weights": (8, 4, 3, 3),
+            "conv4_weights": (4, 8, 3, 3),
+            "conv5_weights": (8, 4, 3, 3),
+            "conv6_weights": (8, 8, 3, 3)
         }
 
         for param in main_program.global_block().all_parameters():

tests/test_prune.py

@@ -62,12 +62,12 @@ class TestPrune(unittest.TestCase):
             param_shape_backup=None)
 
         shapes = {
-            "conv1_weights": (4L, 3L, 3L, 3L),
-            "conv2_weights": (4L, 4L, 3L, 3L),
-            "conv3_weights": (8L, 4L, 3L, 3L),
-            "conv4_weights": (4L, 8L, 3L, 3L),
-            "conv5_weights": (8L, 4L, 3L, 3L),
-            "conv6_weights": (8L, 8L, 3L, 3L)
+            "conv1_weights": (4, 3, 3, 3),
+            "conv2_weights": (4, 4, 3, 3),
+            "conv3_weights": (8, 4, 3, 3),
+            "conv4_weights": (4, 8, 3, 3),
+            "conv5_weights": (8, 4, 3, 3),
+            "conv6_weights": (8, 8, 3, 3)
         }
 
         for param in main_program.global_block().all_parameters():

tests/test_sensitivity.py

@@ -17,7 +17,7 @@ import unittest
 import numpy
 import paddle
 import paddle.fluid as fluid
-from paddleslim.analysis import sensitivity
+from paddleslim.prune import sensitivity
 from layers import conv_bn_layer
 
 
@@ -47,13 +47,12 @@ class TestSensitivity(unittest.TestCase):
         val_reader = paddle.fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=128)
 
-        def eval_func(program, scope):
+        def eval_func(program):
             feeder = fluid.DataFeeder(
                 feed_list=['image', 'label'], place=place, program=program)
             acc_set = []
             for data in val_reader():
                 acc_np = exe.run(program=program,
-                                 scope=scope,
                                  feed=feeder.feed(data),
                                  fetch_list=[acc_top1])
                 acc_set.append(float(acc_np[0]))
@@ -61,8 +60,7 @@ class TestSensitivity(unittest.TestCase):
             print("acc_val_mean: {}".format(acc_val_mean))
             return acc_val_mean
 
-        sensitivity(eval_program,
-                    fluid.global_scope(), place, ["conv4_weights"], eval_func,
+        sensitivity(eval_program, place, ["conv4_weights"], eval_func,
                     "./sensitivities_file")
 
 

tests/test_slim_prune.py

@@ -63,12 +63,12 @@ class TestPrune(unittest.TestCase):
             param_shape_backup=None)
 
         shapes = {
-            "conv1_weights": (4L, 3L, 3L, 3L),
-            "conv2_weights": (4L, 4L, 3L, 3L),
-            "conv3_weights": (8L, 4L, 3L, 3L),
-            "conv4_weights": (4L, 8L, 3L, 3L),
-            "conv5_weights": (8L, 4L, 3L, 3L),
-            "conv6_weights": (8L, 8L, 3L, 3L)
+            "conv1_weights": (4, 3, 3, 3),
+            "conv2_weights": (4, 4, 3, 3),
+            "conv3_weights": (8, 4, 3, 3),
+            "conv4_weights": (4, 8, 3, 3),
+            "conv5_weights": (8, 4, 3, 3),
+            "conv6_weights": (8, 8, 3, 3)
         }
 
         for param in main_program.global_block().all_parameters():