Add mlp

.gitignore

@@ -138,6 +138,7 @@ checkpoints/
 /train_backup.py
 /tools/client_data
 /tools/server_data
+/trainscript.py
 *.pth
 *.edf
 *log*
\ No newline at end of file

models/core.py

@@ -42,7 +42,7 @@ class Core(object):
         self.test_flag = True
 
         if printflag:
-            util.writelog('network:\n'+str(self.net),self.opt,True)
+            #util.writelog('network:\n'+str(self.net),self.opt,True)
             show_paramsnumber(self.net,self.opt)
 
         if self.opt.pretrained != '':
@@ -85,7 +85,8 @@ class Core(object):
         self.queue = Queue(self.opt.load_thread*2)
         process_batch_num = len(sequences)//self.opt.batchsize//self.opt.load_thread
         if process_batch_num == 0:
-            print('\033[1;33m'+'Warning: too much load thread'+'\033[0m') 
+            if self.epoch == 1:
+                print('\033[1;33m'+'Warning: too much load thread'+'\033[0m') 
             self.start_process(signals,labels,sequences)
         else:
             for i in range(self.opt.load_thread):
@@ -130,8 +131,8 @@ class Core(object):
             loss.backward()
             self.optimizer.step()
        
-        self.plot_result['train'].append(epoch_loss/i)
-        plot.draw_loss(self.plot_result,self.epoch+i/(sequences.shape[0]/self.opt.batchsize),self.opt)
+        self.plot_result['train'].append(epoch_loss/(i+1))
+        plot.draw_loss(self.plot_result,self.epoch+(i+1)/(sequences.shape[0]/self.opt.batchsize),self.opt)
         # if self.opt.model_name != 'autoencoder':
         #     plot.draw_heatmap(confusion_mat,self.opt,name = 'current_train')
 
@@ -142,6 +143,7 @@ class Core(object):
         epoch_loss = 0
         confusion_mat = np.zeros((self.opt.label,self.opt.label), dtype=int)
 
+        np.random.shuffle(sequences)
         self.process_pool_init(signals, labels, sequences)
         for i in range(len(sequences)//self.opt.batchsize):
             signal,label = self.queue.get()
@@ -160,7 +162,7 @@ class Core(object):
             print('epoch:'+str(self.epoch),' macro-prec,reca,F1,err,kappa: '+str(statistics.report(confusion_mat)))
             self.plot_result['F1'].append(statistics.report(confusion_mat)[2])
         
-        self.plot_result['eval'].append(epoch_loss/i) 
+        self.plot_result['eval'].append(epoch_loss/(i+1)) 
 
         self.epoch +=1
         self.confusion_mats.append(confusion_mat)

models/creatnet.py

 from torch import nn
-from .net_1d import cnn_1d,lstm,resnet_1d,multi_scale_resnet_1d,micro_multi_scale_resnet_1d,autoencoder
+from .net_1d import cnn_1d,lstm,resnet_1d,multi_scale_resnet_1d,micro_multi_scale_resnet_1d,autoencoder,mlp
 from .net_2d import densenet,dfcnn,mobilenet,resnet,squeezenet,multi_scale_resnet
 
 
@@ -9,6 +9,9 @@ def creatnet(opt):
     #encoder
     if name =='autoencoder':
         net = autoencoder.Autoencoder(opt.input_nc, opt.feature, opt.label,opt.finesize)
+    #mlp
+    if name =='mlp':
+        net = mlp.mlp(opt.input_nc, opt.label, opt.finesize)
     #lstm
     elif name =='lstm':
         net =  lstm.lstm(opt.lstm_inputsize,opt.lstm_timestep,input_nc=opt.input_nc,num_classes=opt.label)

models/net_1d/mlp.py

+import torch
+from torch import nn
+import torch.nn.functional as F
+
+class mlp(nn.Module):
+    def __init__(self, input_nc,num_classes,datasize):
+        super(mlp, self).__init__()
+
+        self.net = nn.Sequential(
+            nn.Linear(datasize*input_nc, 128),
+            nn.Tanh(),
+            nn.Linear(128, 64),
+            nn.Tanh(),
+            nn.Linear(64, 64),
+            nn.Tanh(),
+            nn.Linear(64, num_classes),
+        )
+
+    def forward(self, x):
+        x = x.view(x.size(0),-1)
+        x = self.net(x)
+        return x
\ No newline at end of file

tools/server.py

@@ -132,4 +132,4 @@ def handlepost():
 
     return {'return':'error'}
 
-app.run("0.0.0.0", port= 4000, debug=True)
+app.run("0.0.0.0", port= 4000, debug=False)

util/dsp.py

 import scipy.signal
 import scipy.fftpack as fftpack
 import numpy as np
+import pywt
 
 def sin(f,fs,time):
     x = np.linspace(0, 2*np.pi*f*time, fs*time)
@@ -23,10 +24,32 @@ def medfilt(signal,x):
 def cleanoffset(signal):
     return signal - np.mean(signal)
 
-def bpf_fir(signal,fs,fc1,fc2,numtaps=101):
-    b=scipy.signal.firwin(numtaps, [fc1, fc2], pass_zero=False,fs=fs)
-    result = scipy.signal.lfilter(b, 1, signal)
-    return result
+def showfreq(signal,fs,fc=0):
+    """
+    return f,fft
+    """
+    if fc==0:
+        kc = int(len(signal)/2)
+    else:   
+        kc = int(len(signal)/fs*fc)
+    signal_fft = np.abs(scipy.fftpack.fft(signal))
+    f = np.linspace(0,fs/2,num=int(len(signal_fft)/2))
+    return f[:kc],signal_fft[0:int(len(signal_fft)/2)][:kc]
+
+def bpf(signal, fs, fc1, fc2, numtaps=3, mode='iir'):
+    if mode == 'iir':
+        b,a = scipy.signal.iirfilter(numtaps, [fc1,fc2], fs=fs)
+    elif mode == 'fir':
+        b = scipy.signal.firwin(numtaps, [fc1, fc2], pass_zero=False,fs=fs)
+        a = 1       
+    return scipy.signal.lfilter(b, a, signal)
+
+def wave_filter(signal,wave,level,usedcoeffs):
+    coeffs = pywt.wavedec(signal, wave, level=level)
+    for i in range(len(usedcoeffs)):
+        if usedcoeffs[i] == 0:
+            coeffs[i] = np.zeros_like(coeffs[i])
+    return pywt.waverec(coeffs, wave, mode='symmetric', axis=-1)
 
 def fft_filter(signal,fs,fc=[],type = 'bandpass'):
     '''

util/options.py

@@ -48,7 +48,7 @@ class Options():
         # ------------------------Network------------------------
         """Available Network
         1d: lstm, cnn_1d, resnet18_1d, resnet34_1d, multi_scale_resnet_1d,
-            micro_multi_scale_resnet_1d,autoencoder
+            micro_multi_scale_resnet_1d,autoencoder,mlp
         2d: mobilenet, dfcnn, multi_scale_resnet, resnet18, resnet50, resnet101,
             densenet121, densenet201, squeezenet
         """
@@ -100,7 +100,7 @@ class Options():
 
         if self.opt.model_type == 'auto':
             if self.opt.model_name in ['lstm', 'cnn_1d', 'resnet18_1d', 'resnet34_1d', 
-                'multi_scale_resnet_1d','micro_multi_scale_resnet_1d','autoencoder']:
+                'multi_scale_resnet_1d','micro_multi_scale_resnet_1d','autoencoder','mlp']:
                 self.opt.model_type = '1d'
             elif self.opt.model_name in ['dfcnn', 'multi_scale_resnet', 'resnet18', 'resnet50',
                 'resnet101','densenet121', 'densenet201', 'squeezenet', 'mobilenet']:

util/util.py

 import os
 import string
 import random
+import shutil
 
 def randomstr(num):
     return ''.join(random.sample(string.ascii_letters + string.digits, num))
@@ -38,4 +39,10 @@ def loadfile(path):
 def savefile(file,path):
     wf = open(path,'wb')
     wf.write(file)
-    wf.close()
\ No newline at end of file
+    wf.close()
+
+def copyfile(src,dst):
+    try:
+        shutil.copyfile(src, dst)
+    except Exception as e:
+        print(e)
\ No newline at end of file