for travis

README.md

@@ -71,12 +71,14 @@ training and valid_epoch data, configures info and checkpoint were save in `log`
     processing data and configures.
 
 Although it is just an example, you still can build your own project
-easily by using jdit framework. Jdit framework can deal with \* Data
-visualization. (learning curves, images in pilot process) \* CPU, GPU or
-GPUs. (Training your model on specify devices) \* Intermediate data
-storage. (Saving training data into a csv file) \* Model checkpoint
-automatically. \* Flexible templates can be used to integrate and custom
-overrides. So, let's see what is **jdit**.
+easily by using jdit framework. Jdit framework can deal with 
+* Data visualization. (learning curves, images in pilot process) 
+* CPU, GPU or GPUs. (Training your model on specify devices) 
+* Intermediate data storage. (Saving training data into a csv file) 
+* Model checkpoint automatically. 
+* Flexible templates can be used to integrate and custom overrides. 
+
+So, let's see what is **jdit** and build your own project.
 
 ## Build your own trainer
 

jdit/unittest/test_loger.py

@@ -7,7 +7,6 @@
 
 class TestLoger(TestCase):
     def test_regist_config(self):
-        pass
         log = Loger()
         param = torch.nn.Linear(10, 1)
         opt = Optimizer(param.parameters(), lr=0.999, weight_decay=0.03, momentum=0.5, betas=(0.1, 0.4),

jdit/unittest/test_model.py

@@ -7,30 +7,25 @@
 
 class TestModel(TestCase):
     def setUp(self):
-        pass
         self.mode = Sequential(Conv2d(10, 1, 3, 1, 0))
         self.epoch = 32
 
     def test_define(self):
-        pass
         net = Model()
         assert net.model is None
         net.define(self.mode, [], "kaiming", show_structure=False)
         assert net.model is not None
 
     def test_print_network(self):
-        pass
         net = Model(self.mode, show_structure=False)
         assert net.model is not None
 
     def test_weightsInit(self):
-        pass
         net = Model()
         net.init_fc = init.kaiming_normal_
         self.mode.apply(net._weight_init)
 
     def test_loadModel(self):
-        pass
         print(self.mode)
         net = Model(self.mode, show_structure=False)
         net.check_point("tm", self.epoch, "test_model")
@@ -41,7 +36,6 @@ def test_loadModel(self):
 
 
     def test_loadPoint(self):
-        pass
         net = Model(self.mode, show_structure=False)
         net.check_point("tm", self.epoch, "test_model")
         net.load_point("tm", self.epoch, "test_model")

jdit/unittest/test_superTrainerParallel.py → jdit/unittest/test_supParallelTrainer.py

@@ -1,10 +1,8 @@
 from unittest import TestCase
 from ..parallel import SupParallelTrainer
 
-
-class TestSuperTrainerParallel(TestCase):
+class TestSupParallelTrainer(TestCase):
     def setUp(self):
-        pass
         self.default_params = {'data_root': r"datasets/fashion_data",
                                'gpu_ids_abs': [],
                                'depth': 4,
@@ -59,11 +57,14 @@ def setUp(self):
         self.trainers_list = ["Trainer_A", "Trainer_B", "Trainer_C", "Trainer_D"]
         self.pt = SupParallelTrainer(self.default_params, self.unfixed_params)
 
+    def test_build_task_trainer(self):
+        self.fail()
+
     def test_train(self):
-        pass
+        self.fail()
 
-    def test__startTrain(self):
-        pass
+    def test__start_train(self):
+        self.fail()
 
     def test__distribute_task_on_devices(self):
         pass
@@ -105,33 +106,7 @@ def test__distribute_task_on_devices(self):
         gpu_used_plan = self.pt._distribute_task_on_devices(candidate_params_list)
         self.assertEqual(real_gpu_used_plan, gpu_used_plan)
 
-    def test__get_gpu_ids_abs(self):
-        pass
-        candidate_params = [
-            {'gpu_ids_abs': [],
-             'logdir': r"log/tresnet_24d_16m_1"
-             },
-            {'gpu_ids_abs': [1, 2],
-             'logdir': r"log/tresnet_24d_16m_1"
-             },
-            {'gpu_ids_abs': [1],
-             'logdir': r"log/tresnet_24d_16m_1"
-             }
-            ]
-        gpu_ids_abs = self.pt._get_gpu_ids_abs(candidate_params)
-        self.assertEqual(gpu_ids_abs, [[], [1, 2], [1]])
-
-    def test__check_overlap(self):
-        pass
-        gpuids_tuple = self.pt._check_overlap(self.candidate_gpu_ids_abs_list)
-        self.assertEqual(((), (1, 2), (1, 2), (3, 4)), gpuids_tuple)
-        gpuids_tuple = self.pt._check_overlap(([], [], [1, 2], [3, 4], [5], [6]))
-        self.assertEqual(((), (), (1, 2), (3, 4), (5,), (6,)), gpuids_tuple)
-        gpuids_tuple = self.pt._check_overlap(([1, 2], [6, 3]))
-        self.assertEqual(((1, 2), (6, 3)), gpuids_tuple)
-
     def test__build_candidate_params(self):
-        pass
         default_params = {'gpu_ids_abs': [],
                           'depth': 4,
                           'logdir': r"log/tresnet_24d_16m_1"}
@@ -148,7 +123,6 @@ def test__build_candidate_params(self):
         self.assertEqual(candidate_params, total_params, "not equal!")
 
     def test__add_logdirs_to_unfixed_params(self):
-        pass
         unfixed_params = [
             {'depth': 1, 'gpu_ids_abs': []},
             {'depth': 2, 'gpu_ids_abs': [1, 2]}
@@ -161,7 +135,13 @@ def test__add_logdirs_to_unfixed_params(self):
         self.assertEqual(final_unfixed_params, test_final_unfixed_params_list)
 
     def test__convert_to_dirname(self):
-        pass
         self.assertEqual(self.pt._convert_to_dirname("abc"), "abc")
         self.assertEqual(self.pt._convert_to_dirname("123_abc_abc****"), "123_abc_abc")
         self.assertEqual(self.pt._convert_to_dirname("*<>,/\\:?|abc"), "smallergreater___%$-abc")
+
+    def test_finish(self):
+        self.fail()
+
+    def test_error(self):
+        self.fail()
+