Modify file structure

README.md

@@ -40,7 +40,7 @@ cd candock
 python3 train.py --label 50 --input_nc 1 --dataset_dir ./datasets/simple_test --save_dir ./checkpoints/simple_test --model_name micro_multi_scale_resnet_1d --gpu_id 0 --batchsize 64 --k_fold 5
 # if you want to use cpu to train, please input --no_cuda
 ```
-* More [options](./options.py).
+* More [options](./util/options.py).
 #### Use your own data to train
 * step1: Generate signals.npy and labels.npy in the following format.
 ```python
@@ -56,4 +56,5 @@ labels = np.array([0,0,0,0,0,1,1,1,1,1])      #0->class0    1->class1
 ```bash
 python3 simple_test.py --label 50 --input_nc 1 --model_name micro_multi_scale_resnet_1d --gpu_id 0
 # if you want to use cpu to test, please input --no_cuda
-```
\ No newline at end of file
+```
+

creatnet.py → models/creatnet.py

 from torch import nn
-from models import cnn_1d,densenet,dfcnn,lstm,mobilenet,resnet,resnet_1d,squeezenet
-from models import multi_scale_resnet,multi_scale_resnet_1d,micro_multi_scale_resnet_1d
+from . import cnn_1d,densenet,dfcnn,lstm,mobilenet,resnet,resnet_1d,squeezenet, \
+multi_scale_resnet,multi_scale_resnet_1d,micro_multi_scale_resnet_1d
+# from models import cnn_1d,densenet,dfcnn,lstm,mobilenet,resnet,resnet_1d,squeezenet
+# from models import multi_scale_resnet,multi_scale_resnet_1d,micro_multi_scale_resnet_1d
 
 def CreatNet(opt):
     name = opt.model_name

simple_test.py

@@ -3,19 +3,16 @@ import numpy as np
 import torch
 import matplotlib.pyplot as plt
 
-import util
-import transformer
-import dataloader
-from options import Options
-from creatnet import CreatNet
+from util import util,transformer,dataloader,statistics,heatmap,options
+from models import creatnet
 
 '''
 --------------------------------preload data--------------------------------
 @hypox64
 2020/04/03
 '''
-opt = Options().getparse()
-net = CreatNet(opt)
+opt = options.Options().getparse()
+net = creatnet.CreatNet(opt)
 
 #load data
 signals = np.load('./datasets/simple_test/signals.npy')

train.py

@@ -7,15 +7,10 @@ from torch import nn, optim
 import warnings
 warnings.filterwarnings("ignore")
 
-import util
-import transformer
-import dataloader
-import statistics
-import heatmap
-from creatnet import CreatNet
-from options import Options
-
-opt = Options().getparse()
+from util import util,transformer,dataloader,statistics,heatmap,options
+from models import creatnet
+
+opt = options.Options().getparse()
 torch.cuda.set_device(opt.gpu_id)
 t1 = time.time()
 
@@ -37,7 +32,7 @@ train_sequences,test_sequences = transformer.k_fold_generator(len(labels),opt.k_
 t2 = time.time()
 print('load data cost time: %.2f'% (t2-t1),'s')
 
-net=CreatNet(opt)
+net=creatnet.CreatNet(opt)
 util.writelog('network:\n'+str(net),opt,True)
 
 util.show_paramsnumber(net,opt)

dataloader.py → util/dataloader.py

@@ -5,9 +5,10 @@ import random
 import scipy.io as sio
 import numpy as np
 
-import dsp
-import transformer
-import statistics
+from . import dsp,transformer,statistics
+# import dsp
+# import transformer
+# import statistics
 
 
 def trimdata(data,num):

options.py → util/options.py

 import argparse
 import os
 import time
-import util
+from . import util
 
 class Options():
     def __init__(self):
@@ -25,7 +25,7 @@ class Options():
         self.parser.add_argument('--continue_train', action='store_true', help='if specified, continue train')
         self.parser.add_argument('--lr', type=float, default=0.001,help='learning rate') 
         self.parser.add_argument('--batchsize', type=int, default=64,help='batchsize')
-        self.parser.add_argument('--weight_mod', type=str, default='normal',help='Choose weight mode: auto | normal')
+        self.parser.add_argument('--weight_mod', type=str, default='auto',help='Choose weight mode: auto | normal')
         self.parser.add_argument('--epochs', type=int, default=20,help='end epoch')
         self.parser.add_argument('--network_save_freq', type=int, default=5,help='the freq to save network')
         self.parser.add_argument('--k_fold', type=int, default=0,help='fold_num of k-fold.if 0 or 1,no k-fold')

statistics.py → util/statistics.py

@@ -2,7 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import util
 import os
-import heatmap
+from . import heatmap
 
 def label_statistics(labels):
     #for sleep label: N3->0  N2->1  N1->2  REM->3  W->4

transformer.py → util/transformer.py

@@ -2,7 +2,8 @@ import os
 import random
 import numpy as np
 import torch
-import dsp
+from . import dsp
+# import dsp
 
 def trimdata(data,num):
     return data[:num*int(len(data)/num)]