Make unitest support for Paddle2.0 API (#475)

* Make unitest support for Paddle2.0 API.

* Fix demo in static mode

* Remove unused lines

* Fix demo of darts

demo/darts/train.py

@@ -152,8 +152,7 @@ def main(args):
             count_parameters_in_MB(model.parameters())))
 
         device_num = fluid.dygraph.parallel.Env().nranks
-        step_per_epoch = int(args.trainset_num /
-                             (args.batch_size * device_num))
+        step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
         learning_rate = fluid.dygraph.CosineDecay(args.learning_rate,
                                                   step_per_epoch, args.epochs)
         clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=args.grad_clip)

demo/distillation/distill.py

@@ -102,8 +102,8 @@ def compress(args):
         raise ValueError("{} is not supported.".format(args.data))
     image_shape = [int(m) for m in image_shape.split(",")]
 
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
     student_program = fluid.Program()
     s_startup = fluid.Program()
 
@@ -234,4 +234,5 @@ def main():
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     main()

demo/nas/block_sa_nas_mobilenetv2.py

@@ -203,7 +203,7 @@ def search_mobilenetv2_block(config, args, image_size):
 
 
 if __name__ == '__main__':
-
+    paddle.enable_static()
     parser = argparse.ArgumentParser(
         description='SA NAS MobileNetV2 cifar10 argparase')
     parser.add_argument(

demo/nas/parl_nas_mobilenetv2.py

@@ -191,7 +191,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
 
 
 if __name__ == '__main__':
-
+    paddle.enable_static()
     parser = argparse.ArgumentParser(
         description='RL NAS MobileNetV2 cifar10 argparase')
     parser.add_argument(

demo/nas/rl_nas_mobilenetv2.py

@@ -176,7 +176,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
 
 
 if __name__ == '__main__':
-
+    paddle.enable_static()
     parser = argparse.ArgumentParser(
         description='RL NAS MobileNetV2 cifar10 argparase')
     parser.add_argument(

demo/nas/sa_nas_mobilenetv2.py

@@ -209,9 +209,7 @@ def test_search_result(tokens, image_size, args, config):
             drop_last=False)
     elif args.data == 'imagenet':
         train_reader = paddle.fluid.io.batch(
-            imagenet_reader.train(),
-            batch_size=args.batch_size,
-            drop_last=True)
+            imagenet_reader.train(), batch_size=args.batch_size, drop_last=True)
         test_reader = paddle.fluid.io.batch(
             imagenet_reader.val(), batch_size=args.batch_size, drop_last=False)
 
@@ -250,8 +248,8 @@ def test_search_result(tokens, image_size, args, config):
 
             _logger.info(
                 'TEST: batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
-                format(batch_id, batch_reward[0], batch_reward[1],
-                       batch_reward[2]))
+                format(batch_id, batch_reward[0], batch_reward[1], batch_reward[
+                    2]))
 
         finally_reward = np.mean(np.array(reward), axis=0)
         _logger.info(
@@ -309,5 +307,5 @@ if __name__ == '__main__':
                 args.data))
 
     config = [('MobileNetV2Space')]
-
+    paddle.enable_static()
     search_mobilenetv2(config, args, image_size, is_server=args.is_server)

PaddleOCR @ 2bdaea56

-Subproject commit 56c6c3ae0e5c9ae6b9401a9446c629e513d4617f
+Subproject commit 2bdaea56566257cf73bd1cbbf834a16f4f7ac4cf

demo/prune/train.py

@@ -114,8 +114,8 @@ def compress(args):
     else:
         raise ValueError("{} is not supported.".format(args.data))
     image_shape = [int(m) for m in image_shape.split(",")]
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     # model definition
@@ -135,8 +135,7 @@ def compress(args):
     if args.pretrained_model:
 
         def if_exist(var):
-            return os.path.exists(
-                os.path.join(args.pretrained_model, var.name))
+            return os.path.exists(os.path.join(args.pretrained_model, var.name))
 
         _logger.info("Load pretrained model from {}".format(
             args.pretrained_model))
@@ -171,10 +170,9 @@ def compress(args):
             acc_top5_ns.append(np.mean(acc_top5_n))
             batch_id += 1
 
-        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".
-                     format(epoch,
-                            np.mean(np.array(acc_top1_ns)),
-                            np.mean(np.array(acc_top5_ns))))
+        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
+            epoch,
+            np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
 
     def train(epoch, program):
 
@@ -241,6 +239,7 @@ def compress(args):
 
 
 def main():
+    paddle.enable_static()
     args = parser.parse_args()
     print_arguments(args)
     compress(args)

demo/quant/pact_quant_aware/train.py

@@ -353,6 +353,7 @@ def compress(args):
 
 
 def main():
+    paddle.enable_static()
     args = parser.parse_args()
     print_arguments(args)
     compress(args)

demo/quant/quant_aware/train.py

@@ -116,8 +116,8 @@ def compress(args):
         raise ValueError("{} is not supported.".format(args.data))
 
     image_shape = [int(m) for m in image_shape.split(",")]
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     # model definition
@@ -154,8 +154,7 @@ def compress(args):
     if args.pretrained_model:
 
         def if_exist(var):
-            return os.path.exists(
-                os.path.join(args.pretrained_model, var.name))
+            return os.path.exists(os.path.join(args.pretrained_model, var.name))
 
         fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
 
@@ -188,10 +187,9 @@ def compress(args):
             acc_top5_ns.append(np.mean(acc_top5_n))
             batch_id += 1
 
-        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".
-                     format(epoch,
-                            np.mean(np.array(acc_top1_ns)),
-                            np.mean(np.array(acc_top5_ns))))
+        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
+            epoch,
+            np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
         return np.mean(np.array(acc_top1_ns))
 
     def train(epoch, compiled_train_prog):
@@ -290,6 +288,7 @@ def compress(args):
 
 
 def main():
+    paddle.enable_static()
     args = parser.parse_args()
     print_arguments(args)
     compress(args)

demo/quant/quant_embedding/infer.py

@@ -51,8 +51,7 @@ def parse_args():
         '--use_cuda', type=int, default='0', help='whether use cuda')
     parser.add_argument(
         '--batch_size', type=int, default='5', help='batch_size')
-    parser.add_argument(
-        '--emb_size', type=int, default='64', help='batch_size')
+    parser.add_argument('--emb_size', type=int, default='64', help='batch_size')
     parser.add_argument(
         '--emb_quant',
         type=bool,
@@ -76,9 +75,7 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
                 copy_program = main_program.clone()
                 model_path = model_dir + "/pass-" + str(epoch)
                 fluid.io.load_params(
-                    executor=exe,
-                    dirname=model_path,
-                    main_program=copy_program)
+                    executor=exe, dirname=model_path, main_program=copy_program)
                 if args.emb_quant:
                     config = {
                         'quantize_op_types': 'lookup_table',
@@ -208,6 +205,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
 
 
 if __name__ == "__main__":
+    paddle.enable_static()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

demo/quant/quant_embedding/train.py

@@ -194,8 +194,8 @@ def train(args):
         os.mkdir(args.model_output_dir)
 
     filelist = GetFileList(args.train_data_dir)
-    word2vec_reader = reader.Word2VecReader(
-        args.dict_path, args.train_data_dir, filelist, 0, 1)
+    word2vec_reader = reader.Word2VecReader(args.dict_path, args.train_data_dir,
+                                            filelist, 0, 1)
 
     logger.info("dict_size: {}".format(word2vec_reader.dict_size))
     np_power = np.power(np.array(word2vec_reader.id_frequencys), 0.75)
@@ -224,5 +224,6 @@ def train(args):
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     args = parse_args()
     train(args)

demo/quant/quant_post/eval.py

@@ -87,6 +87,7 @@ def eval(args):
 
 
 def main():
+    paddle.enable_static()
     args = parser.parse_args()
     print_arguments(args)
     eval(args)

demo/quant/quant_post/export_model.py

@@ -48,8 +48,8 @@ def export_model(args):
     image_shape = [int(m) for m in image_shape.split(",")]
     image = fluid.data(
         name='image', shape=[None] + image_shape, dtype='float32')
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
     # model definition
     model = models.__dict__[args.model]()
     out = model.net(input=image, class_dim=class_dim)
@@ -61,8 +61,7 @@ def export_model(args):
     if args.pretrained_model:
 
         def if_exist(var):
-            return os.path.exists(
-                os.path.join(args.pretrained_model, var.name))
+            return os.path.exists(os.path.join(args.pretrained_model, var.name))
 
         fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
     else:
@@ -85,4 +84,5 @@ def main():
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     main()

demo/quant/quant_post/quant_post.py

@@ -57,4 +57,5 @@ def main():
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     main()

tests/static_case.py

+import unittest
+import paddle
+
+
+class StaticCase(unittest.TestCase):
+    def __init__(self, name):
+        super(StaticCase, self).__init__()
+        paddle.enable_static()
+
+    def runTest(self):
+        pass

tests/test_autoprune.py

@@ -17,10 +17,11 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
 from paddleslim.prune import AutoPruner
+from static_case import StaticCase
 from layers import conv_bn_layer
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_client_connect.py

@@ -17,6 +17,7 @@ import os
 import time
 import signal
 import unittest
+from static_case import StaticCase
 import paddle.fluid as fluid
 from paddleslim.nas import SANAS
 from paddleslim.common.controller_client import ControllerClient
@@ -44,7 +45,7 @@ def start_server(configs, port):
     return server_sanas
 
 
-class TestClientConnect(unittest.TestCase):
+class TestClientConnect(StaticCase):
     def setUp(self):
         self.configs = [('MobileNetV2BlockSpace', {'block_mask': [0]})]
         self.port = np.random.randint(8337, 8773)
@@ -58,7 +59,7 @@ class TestClientConnect(unittest.TestCase):
         start_server(self.configs, self.port)
 
 
-class TestClientConnectCase1(unittest.TestCase):
+class TestClientConnectCase1(StaticCase):
     def setUp(self):
         self.configs = [('MobileNetV2BlockSpace', {'block_mask': [0]})]
         self.port = np.random.randint(8337, 8773)
@@ -74,7 +75,7 @@ class TestClientConnectCase1(unittest.TestCase):
         os.kill(os.getpid(), 0)
 
 
-class TestClientConnectCase2(unittest.TestCase):
+class TestClientConnectCase2(StaticCase):
     def setUp(self):
         self.port = np.random.randint(8337, 8773)
         self.addr = socket.gethostbyname(socket.gethostname())

tests/test_darts.py

@@ -17,11 +17,12 @@ import paddle
 import unittest
 import paddle.fluid as fluid
 import numpy as np
+from static_case import StaticCase
 from paddleslim.nas.darts import DARTSearch
 from layers import conv_bn_layer
 
 
-class TestDARTS(unittest.TestCase):
+class TestDARTS(StaticCase):
     def test_darts(self):
         class SuperNet(fluid.dygraph.Layer):
             def __init__(self):

tests/test_deep_mutual_learning.py

@@ -17,6 +17,7 @@ import unittest
 import logging
 import numpy as np
 import paddle
+from static_case import StaticCase
 import paddle.fluid as fluid
 import paddle.dataset.mnist as reader
 from paddle.fluid.dygraph.base import to_variable
@@ -49,7 +50,7 @@ class Model(fluid.dygraph.Layer):
         return y
 
 
-class TestDML(unittest.TestCase):
+class TestDML(StaticCase):
     def test_dml(self):
         place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
         ) else fluid.CPUPlace()

tests/test_earlystop.py

@@ -17,11 +17,12 @@ import unittest
 import paddle
 from paddleslim.nas import SANAS
 from paddleslim.nas.early_stop import MedianStop
+from static_case import StaticCase
 steps = 5
 epochs = 5
 
 
-class TestMedianStop(unittest.TestCase):
+class TestMedianStop(StaticCase):
     def test_median_stop(self):
         config = [('MobileNetV2Space')]
         sanas = SANAS(config, server_addr=("", 8732), save_checkpoint=None)

tests/test_flops.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.analysis import flops
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_fpgm_prune.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_fsp_loss.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.dist import merge, fsp_loss
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestFSPLoss(unittest.TestCase):
+class TestFSPLoss(StaticCase):
     def test_fsp_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()

tests/test_group_param.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from layers import conv_bn_layer
 from paddleslim.prune import collect_convs
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_l2_loss.py

@@ -14,17 +14,19 @@
 import sys
 sys.path.append("../")
 import unittest
+import paddle
 import paddle.fluid as fluid
+from static_case import StaticCase
 from paddleslim.dist import merge, l2_loss
 from layers import conv_bn_layer
 
 
-class TestL2Loss(unittest.TestCase):
+class TestL2Loss(StaticCase):
     def test_l2_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()
         with fluid.program_guard(student_main, student_startup):
-            input = fluid.data(name="image", shape=[None, 3, 224, 224])
+            input = paddle.data(name="image", shape=[None, 3, 224, 224])
             conv1 = conv_bn_layer(input, 8, 3, "conv1")
             conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
             student_predict = conv1 + conv2
@@ -32,7 +34,7 @@ class TestL2Loss(unittest.TestCase):
         teacher_main = fluid.Program()
         teacher_startup = fluid.Program()
         with fluid.program_guard(teacher_main, teacher_startup):
-            input = fluid.data(name="image", shape=[None, 3, 224, 224])
+            input = paddle.data(name="image", shape=[None, 3, 224, 224])
             conv1 = conv_bn_layer(input, 8, 3, "conv1")
             conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
             sum1 = conv1 + conv2

tests/test_loss.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.dist import merge, loss
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestLoss(unittest.TestCase):
+class TestLoss(StaticCase):
     def test_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()

tests/test_merge.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.dist import merge
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestMerge(unittest.TestCase):
+class TestMerge(StaticCase):
     def test_merge(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()

tests/test_model_size.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.analysis import model_size
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_ofa.py

@@ -17,6 +17,7 @@ sys.path.append("../")
 import numpy as np
 import unittest
 import paddle
+from static_case import StaticCase
 import paddle.fluid as fluid
 import paddle.fluid.dygraph.nn as nn
 from paddle.nn import ReLU
@@ -119,7 +120,7 @@ class ModelLinear(fluid.dygraph.Layer):
         return inputs
 
 
-class TestOFA(unittest.TestCase):
+class TestOFA(StaticCase):
     def setUp(self):
         fluid.enable_dygraph()
         self.init_model_and_data()

tests/test_optimal_threshold.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_prune.py

@@ -14,12 +14,14 @@
 import sys
 sys.path.append("../")
 import unittest
+from static_case import StaticCase
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
+from static_case import StaticCase
 from layers import conv_bn_layer
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_prune_walker.py

@@ -18,12 +18,13 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
+from static_case import StaticCase
 from layers import conv_bn_layer
 import random
 from paddleslim.core import GraphWrapper
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_pruned_model_save_load.py

@@ -17,11 +17,12 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner, save_model, load_model
 from layers import conv_bn_layer
+from static_case import StaticCase
 import numpy as np
 import numpy
 
 
-class TestSaveAndLoad(unittest.TestCase):
+class TestSaveAndLoad(StaticCase):
     def test_prune(self):
         train_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_quant_aware.py

@@ -17,6 +17,7 @@ import unittest
 import paddle
 import paddle.fluid as fluid
 from paddleslim.quant import quant_aware, convert
+from static_case import StaticCase
 sys.path.append("../demo")
 from models import MobileNet
 from layers import conv_bn_layer
@@ -26,7 +27,7 @@ from paddle.fluid import core
 import numpy as np
 
 
-class TestQuantAwareCase1(unittest.TestCase):
+class TestQuantAwareCase1(StaticCase):
     def get_model(self):
         image = fluid.layers.data(
             name='image', shape=[1, 28, 28], dtype='float32')
@@ -88,7 +89,7 @@ class TestQuantAwareCase1(unittest.TestCase):
         self.assertTrue(convert_quant_op_nums_1 - 2 == convert_quant_op_nums_2)
 
 
-class TestQuantAwareCase2(unittest.TestCase):
+class TestQuantAwareCase2(StaticCase):
     def test_accuracy(self):
         image = fluid.layers.data(
             name='image', shape=[1, 28, 28], dtype='float32')
@@ -140,9 +141,8 @@ class TestQuantAwareCase2(unittest.TestCase):
                     fetch_list=[avg_cost, acc_top1, acc_top5])
                 iter += 1
                 if iter % 100 == 0:
-                    print(
-                        'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
-                        format(iter, cost, top1, top5))
+                    print('eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
+                          format(iter, cost, top1, top5))
                 result[0].append(cost)
                 result[1].append(top1)
                 result[2].append(top5)
@@ -158,8 +158,7 @@ class TestQuantAwareCase2(unittest.TestCase):
             'activation_quantize_type': 'moving_average_abs_max',
             'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
         }
-        quant_train_prog = quant_aware(
-            main_prog, place, config, for_test=False)
+        quant_train_prog = quant_aware(main_prog, place, config, for_test=False)
         quant_eval_prog = quant_aware(val_prog, place, config, for_test=True)
         train(quant_train_prog)
         quant_eval_prog, int8_prog = convert(

tests/test_quant_aware_user_defined.py

@@ -17,6 +17,7 @@ import unittest
 import paddle
 import paddle.fluid as fluid
 from paddleslim.quant import quant_aware, convert
+from static_case import StaticCase
 sys.path.append("../demo")
 from models import MobileNet
 from layers import conv_bn_layer
@@ -37,8 +38,7 @@ def pact(x, name=None):
         initializer=fluid.initializer.ConstantInitializer(value=init_thres),
         regularizer=fluid.regularizer.L2Decay(0.0001),
         learning_rate=1)
-    u_param = helper.create_parameter(
-        attr=u_param_attr, shape=[1], dtype=dtype)
+    u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
     x = fluid.layers.elementwise_sub(
         x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
     x = fluid.layers.elementwise_add(
@@ -51,7 +51,7 @@ def get_optimizer():
     return fluid.optimizer.MomentumOptimizer(0.0001, 0.9)
 
 
-class TestQuantAwareCase1(unittest.TestCase):
+class TestQuantAwareCase1(StaticCase):
     def get_model(self):
         image = fluid.layers.data(
             name='image', shape=[1, 28, 28], dtype='float32')
@@ -116,9 +116,8 @@ class TestQuantAwareCase1(unittest.TestCase):
                     fetch_list=[avg_cost, acc_top1, acc_top5])
                 iter += 1
                 if iter % 100 == 0:
-                    print(
-                        'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
-                        format(iter, cost, top1, top5))
+                    print('eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
+                          format(iter, cost, top1, top5))
                 result[0].append(cost)
                 result[1].append(top1)
                 result[2].append(top5)

tests/test_quant_embedding.py

@@ -17,8 +17,10 @@ import paddle.fluid as fluid
 import paddleslim.quant as quant
 import unittest
 
+from static_case import StaticCase
 
-class TestQuantEmbedding(unittest.TestCase):
+
+class TestQuantEmbedding(StaticCase):
     def test_quant_embedding(self):
         train_program = fluid.Program()
         with fluid.program_guard(train_program):

tests/test_quant_post.py

@@ -17,6 +17,7 @@ import unittest
 import paddle
 import paddle.fluid as fluid
 from paddleslim.quant import quant_post_static
+from static_case import StaticCase
 sys.path.append("../demo")
 from models import MobileNet
 from layers import conv_bn_layer
@@ -26,7 +27,7 @@ from paddle.fluid import core
 import numpy as np
 
 
-class TestQuantAwareCase1(unittest.TestCase):
+class TestQuantAwareCase1(StaticCase):
     def test_accuracy(self):
         image = fluid.layers.data(
             name='image', shape=[1, 28, 28], dtype='float32')
@@ -77,9 +78,8 @@ class TestQuantAwareCase1(unittest.TestCase):
                                            fetch_list=outputs)
                 iter += 1
                 if iter % 100 == 0:
-                    print(
-                        'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
-                        format(iter, cost, top1, top5))
+                    print('eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
+                          format(iter, cost, top1, top5))
                 result[0].append(cost)
                 result[1].append(top1)
                 result[2].append(top5)

tests/test_quant_post_only_weight.py

@@ -17,6 +17,7 @@ import unittest
 import paddle
 import paddle.fluid as fluid
 from paddleslim.quant import quant_post_dynamic
+from static_case import StaticCase
 sys.path.append("../demo")
 from models import MobileNet
 from layers import conv_bn_layer
@@ -26,7 +27,7 @@ from paddle.fluid import core
 import numpy as np
 
 
-class TestQuantPostOnlyWeightCase1(unittest.TestCase):
+class TestQuantPostOnlyWeightCase1(StaticCase):
     def test_accuracy(self):
         image = fluid.layers.data(
             name='image', shape=[1, 28, 28], dtype='float32')

tests/test_rl_nas.py

@@ -17,6 +17,7 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.nas import RLNAS
 from paddleslim.analysis import flops
+from static_case import StaticCase
 import numpy as np
 
 
@@ -31,7 +32,7 @@ def compute_op_num(program):
     return params, ch_list
 
 
-class TestRLNAS(unittest.TestCase):
+class TestRLNAS(StaticCase):
     def setUp(self):
         self.init_test_case()
         port = np.random.randint(8337, 8773)

tests/test_sa_nas.py

@@ -17,6 +17,7 @@ import os
 import sys
 import unittest
 import paddle.fluid as fluid
+from static_case import StaticCase
 from paddleslim.nas import SANAS
 from paddleslim.analysis import flops
 import numpy as np
@@ -33,7 +34,7 @@ def compute_op_num(program):
     return params, ch_list
 
 
-class TestSANAS(unittest.TestCase):
+class TestSANAS(StaticCase):
     def setUp(self):
         self.init_test_case()
         port = np.random.randint(8337, 8773)

tests/test_sensitivity.py

@@ -17,11 +17,12 @@ import unittest
 import numpy
 import paddle
 import paddle.fluid as fluid
+from static_case import StaticCase
 from paddleslim.prune import sensitivity, merge_sensitive, load_sensitivities
 from layers import conv_bn_layer
 
 
-class TestSensitivity(unittest.TestCase):
+class TestSensitivity(StaticCase):
     def test_sensitivity(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_slim_prune.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.prune import Pruner
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestPrune(unittest.TestCase):
+class TestPrune(StaticCase):
     def test_prune(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()

tests/test_soft_label_loss.py

@@ -17,9 +17,10 @@ import unittest
 import paddle.fluid as fluid
 from paddleslim.dist import merge, soft_label_loss
 from layers import conv_bn_layer
+from static_case import StaticCase
 
 
-class TestSoftLabelLoss(unittest.TestCase):
+class TestSoftLabelLoss(StaticCase):
     def test_soft_label_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()