for travis

.travis.yml

@@ -4,7 +4,7 @@ python:
   - "3.7-dev"  # 3.7 development branch
 # command to install dependencies
 install:
-  - pip install -r docs/requirements.txt
+  - pip install -r requirements.txt
 
 # command to run tests
 script:

jdit/trainer/super.py

@@ -372,7 +372,7 @@ def write(self, step: int, current_epoch: int, msg_dic: dict, filename: str, hea
         pdg.to_csv(path, mode="a", encoding="utf-8", index=False, header=header)
 
     def clear_regist(self):
-        self.regist_list = dict({})
+        self.regist_dict = dict({})
 
 
 class Watcher(object):

jdit/unittest/test_loger.py

@@ -1,26 +1,29 @@
 from unittest import TestCase
 import torch
-from torch.optim import Adam, RMSprop
 from jdit.optimizer import Optimizer
 from jdit.trainer.super import Loger
-import pandas as pd
 from jdit.model import Model
-from torchvision.models import Inception3
+
+
 class TestLoger(TestCase):
     def test_regist_config(self):
         log = Loger()
-        param = [torch.ones(3, 3, requires_grad=True)] * 5
-        opt = Optimizer(param, lr=0.999, weight_decay=0.03, momentum=0.5, betas=(0.1, 0.4), opt_name="RMSprop")
-        log.regist_config(1, opt)
-        print(log.__dict__["Optimizer"])
+        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),
+                        opt_name="RMSprop")
+        log.regist_config(opt)
+        self.assertEqual(log.regist_dict["Optimizer"],
+                         {'opt_name': 'RMSprop', 'lr': 0.999, 'momentum': 0.5, 'alpha': 0.99, 'eps': 1e-08,
+                          'centered': False, 'weight_decay': 0.03, 'lr_decay': '0.92'})
         opt.do_lr_decay()
-        log.regist_config(2, opt)
-        print(log.__dict__["Optimizer"])
-        log.regist_config(3, opt)
-        print(log.__dict__["Optimizer"])
-        net_G = Model(Inception3(4))
-        log.regist_config(1, net_G)
-        # log.save_config()
-        # log.close()
-
-
+        log.regist_config(opt)
+        self.assertEqual(log.regist_dict["Optimizer"]["lr"], 0.91908)
+        log.regist_config(opt)
+        self.assertEqual(log.regist_dict["Optimizer"]["lr"], 0.91908)
+        net = Model(torch.nn.Linear(10, 1))
+        log.regist_config(net)
+        self.assertEqual(log.regist_dict, {
+            'Optimizer': {'opt_name': 'RMSprop', 'lr': 0.91908, 'momentum': 0.5, 'alpha': 0.99, 'eps': 1e-08,
+                          'centered': False, 'weight_decay': 0.03, 'lr_decay': '0.92'},
+            'Model': {'model_name': 'Linear', 'init_method': 'kaiming', 'gpus': 0, 'total_params': 11,
+                      'structure': 'Linear(in_features=10, out_features=1, bias=True)'}})

jdit/unittest/test_superTrainerParallel.py

@@ -4,6 +4,7 @@
 
 class TestSuperTrainerParallel(TestCase):
     def setUp(self):
+        pass
         self.default_params = {'data_root': r"datasets/fashion_data",
                                'gpu_ids_abs': [],
                                'depth': 4,
@@ -65,6 +66,7 @@ def test__startTrain(self):
         pass
 
     def test__distribute_task_on_devices(self):
+        pass
         candidate_params_list = [
             {'gpu_ids_abs': [],
              'depth': 1,
@@ -104,6 +106,7 @@ def test__distribute_task_on_devices(self):
         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"
@@ -119,6 +122,7 @@ def test__get_gpu_ids_abs(self):
         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]))
@@ -127,6 +131,7 @@ def test__check_overlap(self):
         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"}
@@ -143,6 +148,7 @@ 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]}
@@ -155,6 +161,7 @@ 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")