김민석

B2I

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#folder
experiments/ecg/dataset/preprocessed/ano0
experiments/ecg/output/beatgan/ecg/model/
/workspace/2Dtest/1234/experiments/ecg/plotUtil.py:213: fixed
import os
import numpy as np
import torch
from torch.utils.data import DataLoader,TensorDataset
from sklearn.model_selection import train_test_split
np.random.seed(42)
def normalize(seq):
'''
normalize to [-1,1]
:param seq:
:return:
'''
return 2*(seq-np.min(seq))/(np.max(seq)-np.min(seq))-1
def load_data(opt):
train_dataset=None
test_dataset=None
val_dataset=None
test_N_dataset=None
test_S_dataset = None
test_V_dataset = None
test_F_dataset = None
test_Q_dataset = None
if opt.dataset=="ecg":
'''N_samples=np.load(os.path.join(opt.dataroot, "N_samples.npy")) #NxCxL
S_samples=np.load(os.path.join(opt.dataroot, "S_samples.npy"))
V_samples = np.load(os.path.join(opt.dataroot, "V_samples.npy"))
F_samples = np.load(os.path.join(opt.dataroot, "F_samples.npy"))
Q_samples = np.load(os.path.join(opt.dataroot, "Q_samples.npy"))
'''
N_samples = np.load(os.path.join(opt.dataroot,'n_spectrogram.npy'))
S_samples = np.load(os.path.join(opt.dataroot,'s_spectrogram.npy'))
V_samples = np.load(os.path.join(opt.dataroot,'v_spectrogram.npy'))
F_samples = np.load(os.path.join(opt.dataroot,'f_spectrogram.npy'))
Q_samples = np.load(os.path.join(opt.dataroot,'q_spectrogram.npy'))
# normalize all
'''for i in range(N_samples.shape[0]):
for j in range(opt.nc):
N_samples[i][j]=normalize(N_samples[i][j][:])
N_samples=N_samples[:,:opt.nc,:]
for i in range(S_samples.shape[0]):
for j in range(opt.nc):
S_samples[i][j] = normalize(S_samples[i][j][:])
S_samples = S_samples[:, :opt.nc, :]
for i in range(V_samples.shape[0]):
for j in range(opt.nc):
V_samples[i][j] = normalize(V_samples[i][j][:])
V_samples = V_samples[:, :opt.nc, :]
for i in range(F_samples.shape[0]):
for j in range(opt.nc):
F_samples[i][j] = normalize(F_samples[i][j][:])
F_samples = F_samples[:, :opt.nc, :]
for i in range(Q_samples.shape[0]):
for j in range(opt.nc):
Q_samples[i][j] = normalize(Q_samples[i][j][:])
Q_samples = Q_samples[:, :opt.nc, :]'''
for i in range(N_samples.shape[0]):
N_samples[i] = normalize(N_samples[i])
for i in range(S_samples.shape[0]):
S_samples[i] = normalize(S_samples[i])
for i in range(V_samples.shape[0]):
V_samples[i] = normalize(V_samples[i])
for i in range(F_samples.shape[0]):
F_samples[i] = normalize(F_samples[i])
for i in range(Q_samples.shape[0]):
Q_samples[i] = normalize(Q_samples[i])
# train / test
test_N,test_N_y, train_N,train_N_y = getFloderK(N_samples,opt.folder,0)
# test_S,test_S_y, train_S,train_S_y = getFloderK(S_samples, opt.folder,1)
# test_V,test_V_y, train_V,train_V_y = getFloderK(V_samples, opt.folder,1)
# test_F,test_F_y, train_F,train_F_y = getFloderK(F_samples, opt.folder,1)
# test_Q,test_Q_y, train_Q,train_Q_y = getFloderK(Q_samples, opt.folder,1)
test_S,test_S_y=S_samples, np.ones((S_samples.shape[0], 1))
test_V, test_V_y = V_samples, np.ones((V_samples.shape[0], 1))
test_F, test_F_y = F_samples, np.ones((F_samples.shape[0], 1))
test_Q, test_Q_y = Q_samples, np.ones((Q_samples.shape[0], 1))
# train / val
train_N, val_N, train_N_y, val_N_y = getPercent(train_N, train_N_y, 0.1, 0)
test_S, val_S, test_S_y, val_S_y = getPercent(test_S, test_S_y, 0.1, 0)
test_V, val_V, test_V_y, val_V_y = getPercent(test_V, test_V_y, 0.1, 0)
test_F, val_F, test_F_y, val_F_y = getPercent(test_F, test_F_y, 0.1, 0)
test_Q, val_Q, test_Q_y, val_Q_y = getPercent(test_Q, test_Q_y, 0.1, 0)
val_data=np.concatenate([val_N,val_S,val_V,val_F,val_Q])
val_y=np.concatenate([val_N_y,val_S_y,val_V_y,val_F_y,val_Q_y])
print("train data size:{}".format(train_N.shape))
print("val data size:{}".format(val_data.shape))
print("test N data size:{}".format(test_N.shape))
print("test S data size:{}".format(test_S.shape))
print("test V data size:{}".format(test_V.shape))
print("test F data size:{}".format(test_F.shape))
print("test Q data size:{}".format(test_Q.shape))
if not opt.istest and opt.n_aug>0:
train_N,train_N_y=data_aug(train_N,train_N_y,times=opt.n_aug)
print("after aug, train data size:{}".format(train_N.shape))
train_dataset = TensorDataset(torch.Tensor(train_N),torch.Tensor(train_N_y))
val_dataset= TensorDataset(torch.Tensor(val_data), torch.Tensor(val_y))
test_N_dataset = TensorDataset(torch.Tensor(test_N), torch.Tensor(test_N_y))
test_S_dataset = TensorDataset(torch.Tensor(test_S), torch.Tensor(test_S_y))
test_V_dataset = TensorDataset(torch.Tensor(test_V), torch.Tensor(test_V_y))
test_F_dataset = TensorDataset(torch.Tensor(test_F), torch.Tensor(test_F_y))
test_Q_dataset = TensorDataset(torch.Tensor(test_Q), torch.Tensor(test_Q_y))
# assert (train_dataset is not None and test_dataset is not None and val_dataset is not None)
dataloader = {"train": DataLoader(
dataset=train_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=True),
"val": DataLoader(
dataset=val_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_N":DataLoader(
dataset=test_N_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_S": DataLoader(
dataset=test_S_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_V": DataLoader(
dataset=test_V_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_F": DataLoader(
dataset=test_F_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_Q": DataLoader(
dataset=test_Q_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
}
return dataloader
def getFloderK(data,folder,label):
normal_cnt = data.shape[0]
folder_num = int(normal_cnt / 5)
folder_idx = folder * folder_num
folder_data = data[folder_idx:folder_idx + folder_num]
remain_data = np.concatenate([data[:folder_idx], data[folder_idx + folder_num:]])
if label==0:
folder_data_y = np.zeros((folder_data.shape[0], 1))
remain_data_y=np.zeros((remain_data.shape[0], 1))
elif label==1:
folder_data_y = np.ones((folder_data.shape[0], 1))
remain_data_y = np.ones((remain_data.shape[0], 1))
else:
raise Exception("label should be 0 or 1, get:{}".format(label))
return folder_data,folder_data_y,remain_data,remain_data_y
def getPercent(data_x,data_y,percent,seed):
train_x, test_x, train_y, test_y = train_test_split(data_x, data_y,test_size=percent,random_state=seed)
return train_x, test_x, train_y, test_y
def get_full_data(dataloader):
full_data_x=[]
full_data_y=[]
for batch_data in dataloader:
batch_x,batch_y=batch_data[0],batch_data[1]
batch_x=batch_x.numpy()
batch_y=batch_y.numpy()
# print(batch_x.shape)
# assert False
for i in range(batch_x.shape[0]):
full_data_x.append(batch_x[i,0,:])
full_data_y.append(batch_y[i])
full_data_x=np.array(full_data_x)
full_data_y=np.array(full_data_y)
assert full_data_x.shape[0]==full_data_y.shape[0]
print("full data size:{}".format(full_data_x.shape))
return full_data_x,full_data_y
def data_aug(train_x,train_y,times=2):
res_train_x=[]
res_train_y=[]
for idx in range(train_x.shape[0]):
x=train_x[idx]
y=train_y[idx]
res_train_x.append(x)
res_train_y.append(y)
for i in range(times):
x_aug=aug_ts(x)
res_train_x.append(x_aug)
res_train_y.append(y)
res_train_x=np.array(res_train_x)
res_train_y=np.array(res_train_y)
return res_train_x,res_train_y
def aug_ts(x):
left_ticks_index = np.arange(0, 140)
right_ticks_index = np.arange(140, 319)
np.random.shuffle(left_ticks_index)
np.random.shuffle(right_ticks_index)
left_up_ticks = left_ticks_index[:7]
right_up_ticks = right_ticks_index[:7]
left_down_ticks = left_ticks_index[7:14]
right_down_ticks = right_ticks_index[7:14]
x_1 = np.zeros_like(x)
j = 0
for i in range(x.shape[1]):
if i in left_down_ticks or i in right_down_ticks:
continue
elif i in left_up_ticks or i in right_up_ticks:
x_1[:, j] =x[:,i]
j += 1
x_1[:, j] = (x[:, i] + x[:, i + 1]) / 2
j += 1
else:
x_1[:, j] = x[:, i]
j += 1
return x_1
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Execution change.py and change2.py file for change __samples.npy to __spectogram.npy.
The shape is changed n*2*320 to n*128*128 to n*1*128*128
import librosa
import numpy as np
import matplotlib.pyplot as plt
import librosa, librosa.display
import cv2
n_data = np.load('N_samples.npy')
s_data = np.load('S_samples.npy')
v_data = np.load('V_samples.npy')
f_data = np.load('F_samples.npy')
q_data = np.load('Q_samples.npy')
n_fft_n= 256
win_length_n=64
hp_length_n=2
sr = 360
data =n_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'n_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =s_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 's_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =v_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'v_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =f_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'f_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =q_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'q_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
import numpy as np
N_samples = np.load('n_spectrogram.npy')
S_samples = np.load('s_spectrogram.npy')
V_samples = np.load('v_spectrogram.npy')
F_samples = np.load('f_spectrogram.npy')
Q_samples = np.load('q_spectrogram.npy')
##########
S_samples = S_samples.reshape(S_samples.shape[0],1,S_samples.shape[1],S_samples.shape[2])
V_samples = V_samples.reshape(V_samples.shape[0],1,V_samples.shape[1],V_samples.shape[2])
F_samples = F_samples.reshape(F_samples.shape[0],1,F_samples.shape[1],F_samples.shape[2])
Q_samples = Q_samples.reshape(Q_samples.shape[0],1,Q_samples.shape[1],Q_samples.shape[2])
N_samples = N_samples.reshape(N_samples.shape[0],1,N_samples.shape[1],N_samples.shape[2])
np.save('q_spectrogram', Q_samples)
np.save('v_spectrogram', V_samples)
np.save('s_spectrogram', S_samples)
np.save('f_spectrogram', F_samples)
np.save('n_spectrogram', N_samples)
import os
import numpy as np
import torch
from torch.utils.data import DataLoader,TensorDataset
from model import BeatGAN
from options import Options
import matplotlib.pyplot as plt
import matplotlib
plt.rcParams["font.family"] = "Times New Roman"
matplotlib.rcParams.update({'font.size': 38})
from plotUtil import save_ts_heatmap
from data import normalize
device = torch.device("cpu")
SAVE_DIR="output/demo/"
def load_case(normal=True):
if normal:
test_samples = np.load(os.path.join("dataset/demo/", "normal_samples.npy"))
else:
test_samples = np.load(os.path.join("dataset/demo/", "abnormal_samples.npy"))
for i in range(test_samples.shape[0]):
for j in range(1):
test_samples[i][j] = normalize(test_samples[i][j][:])
test_samples = test_samples[:, :1, :]
print(test_samples.shape)
if not normal :
test_y=np.ones([test_samples.shape[0],1])
else:
test_y = np.zeros([test_samples.shape[0], 1])
test_dataset = TensorDataset(torch.Tensor(test_samples), torch.Tensor(test_y))
return DataLoader(dataset=test_dataset, # torch TensorDataset format
batch_size=64,
shuffle=False,
num_workers=0,
drop_last=False)
normal_dataloader=load_case(normal=True)
abnormal_dataloader=load_case(normal=False)
opt = Options()
opt.nc=1
opt.nz=50
opt.isize=320
opt.ndf=32
opt.ngf=32
opt.batchsize=64
opt.ngpu=1
opt.istest=True
opt.lr=0.001
opt.beta1=0.5
opt.niter=None
opt.dataset=None
opt.model = None
opt.outf=None
model=BeatGAN(opt,None,device)
model.G.load_state_dict(torch.load('model/beatgan_folder_0_G.pkl',map_location='cpu'))
model.D.load_state_dict(torch.load('model/beatgan_folder_0_D.pkl',map_location='cpu'))
model.G.eval()
model.D.eval()
with torch.no_grad():
abnormal_input=[]
abnormal_output=[]
normal_input=[]
normal_output=[]
for i, data in enumerate(abnormal_dataloader, 0):
test_x=data[0]
fake_x, _ = model.G(test_x)
batch_input = test_x.cpu().numpy()
batch_output = fake_x.cpu().numpy()
abnormal_input.append(batch_input)
abnormal_output.append(batch_output)
abnormal_input=np.concatenate(abnormal_input)
abnormal_output=np.concatenate(abnormal_output)
for i, data in enumerate(normal_dataloader, 0):
test_x=data[0]
fake_x, _ = model.G(test_x)
batch_input = test_x.cpu().numpy()
batch_output = fake_x.cpu().numpy()
normal_input.append(batch_input)
normal_output.append(batch_output)
normal_input=np.concatenate(normal_input)
normal_output=np.concatenate(normal_output)
# print(normal_input.shape)
# print(np.reshape((normal_input-normal_output)**2,(normal_input.shape[0],-1)).shape)
normal_heat= np.reshape((normal_input-normal_output)**2,(normal_input.shape[0],-1))
abnormal_heat = np.reshape((abnormal_input - abnormal_output)**2 , (abnormal_input.shape[0], -1))
# print(normal_heat.shape)
# assert False
max_val = max(np.max(normal_heat), np.max(abnormal_heat))
min_val = min(np.min(normal_heat), np.min(abnormal_heat))
normal_heat_norm = (normal_heat - min_val) / (max_val - min_val)
abnormal_heat_norm = (abnormal_heat - min_val) / (max_val - min_val)
# for fig
dataset=["normal","abnormal"]
for d in dataset:
if not os.path.exists(os.path.join(SAVE_DIR , d)):
os.makedirs(os.path.join(SAVE_DIR , d))
if d=="normal":
data_input=normal_input
data_output=normal_output
data_heat=normal_heat_norm
else:
data_input = abnormal_input
data_output = abnormal_output
data_heat = abnormal_heat_norm
for i in range(50):
input_sig=data_input[i]
output_sig=data_output[i]
heat=data_heat[i]
# print(input_sig.shape)
# print(output_sig.shape)
# print(heat.shape)
# assert False
x_points = np.arange(input_sig.shape[1])
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(6, 6), gridspec_kw={'height_ratios': [7, 1],
})
sig_in = input_sig[0, :]
sig_out = output_sig[0, :]
ax[0].plot(x_points, sig_in, 'k-', linewidth=2.5, label="ori")
ax[0].plot(x_points, sig_out, 'k--', linewidth=2.5, label="gen")
ax[0].set_yticks([])
# leg=ax[0].legend(loc="upper right",bbox_to_anchor=(1.06, 1.06))
# leg.get_frame().set_alpha(0.0)
heat_norm = np.reshape(heat, (1, -1))
# heat_norm=np.zeros((1,320))
# if d=="normal":
# heat_norm[0,100:120]=0.0003
# else:
# heat_norm[0,100:120]=0.9997
ax[1].imshow(heat_norm, cmap="jet", aspect="auto",vmin = 0,vmax = 0.2)
ax[1].set_yticks([])
# ax[1].set_xlim((0,len(x_points)))
# fig.subplots_adjust(hspace=0.01)
fig.tight_layout()
# fig.show()
# return
fig.savefig(os.path.join(SAVE_DIR+d,str(i)+"_output.png"))
fig2, ax2 = plt.subplots(1, 1)
ax2.plot(x_points, sig_in, 'k-', linewidth=2.5, label="input signal")
fig2.savefig(os.path.join(SAVE_DIR + d, str(i) + "_input.png"))
plt.clf()
print("output files are in:{}".format(SAVE_DIR))
\ No newline at end of file
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import torch
from options import Options
from data import load_data
# from dcgan import DCGAN as myModel
device = torch.device("cuda:0" if
torch.cuda.is_available() else "cpu")
print('device: ',device)
opt = Options().parse()
print(opt)
dataloader=load_data(opt)
print("load data success!!!")
if opt.model == "beatgan":
from model import BeatGAN as MyModel
else:
raise Exception("no this model :{}".format(opt.model))
model=MyModel(opt,dataloader,device)
print('\nmodel_device:',model.device,'\n')
if not opt.istest:
print("################ Train ##################")
model.train()
else:
print("################ Eval ##################")
model.load()
model.test_type()
# model.test_time()
# model.plotTestFig()
# print("threshold:{}\tf1-score:{}\tauc:{}".format( th, f1, auc))
import time,os,sys
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from network import Encoder,Decoder,AD_MODEL,weights_init,print_network
dirname=os.path.dirname
sys.path.insert(0,dirname(dirname(os.path.abspath(__file__))))
from metric import evaluate
##
class Discriminator(nn.Module):
def __init__(self, opt):
super(Discriminator, self).__init__()
model = Encoder(opt.ngpu,opt,1)
layers = list(model.main.children())
self.features = nn.Sequential(*layers[:-1])
self.classifier = nn.Sequential(layers[-1])
self.classifier.add_module('Sigmoid', nn.Sigmoid())
def forward(self, x):
features = self.features(x)
features = features
classifier = self.classifier(features)
classifier = classifier.view(-1, 1).squeeze(1)
return classifier, features
##
class Generator(nn.Module):
def __init__(self, opt):
super(Generator, self).__init__()
self.encoder1 = Encoder(opt.ngpu,opt,opt.nz)
self.decoder = Decoder(opt.ngpu,opt)
def forward(self, x):
latent_i = self.encoder1(x)
gen_x = self.decoder(latent_i)
return gen_x, latent_i
class BeatGAN(AD_MODEL):
def __init__(self, opt, dataloader, device):
super(BeatGAN, self).__init__(opt, dataloader, device)
self.dataloader = dataloader
self.device = device
self.opt=opt
self.batchsize = opt.batchsize
self.nz = opt.nz
self.niter = opt.niter
self.G = Generator( opt).to(device)
self.G.apply(weights_init)
if not self.opt.istest:
print_network(self.G)
self.D = Discriminator(opt).to(device)
self.D.apply(weights_init)
if not self.opt.istest:
print_network(self.D)
self.bce_criterion = nn.BCELoss().cuda()
self.mse_criterion=nn.MSELoss().cuda()
self.optimizerD = optim.Adam(self.D.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizerG = optim.Adam(self.G.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.total_steps = 0
self.cur_epoch=0
self.input = torch.empty(size=(self.opt.batchsize, self.opt.nc, self.opt.isize), dtype=torch.float32, device=self.device)
self.label = torch.empty(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.gt = torch.empty(size=(opt.batchsize,), dtype=torch.long, device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, self.opt.nc, self.opt.isize), dtype=torch.float32, device=self.device)
self.real_label = 1
self.fake_label= 0
self.out_d_real = None
self.feat_real = None
self.fake = None
self.latent_i = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_real = None
self.err_d_fake = None
self.err_d = None
self.out_g = None
self.err_g_adv = None
self.err_g_rec = None
self.err_g = None
def train(self):
self.train_hist = {}
self.train_hist['D_loss'] = []
self.train_hist['G_loss'] = []
self.train_hist['per_epoch_time'] = []
self.train_hist['total_time'] = []
print("Train model.")
start_time = time.time()
best_auc=0
best_auc_epoch=0
with open(os.path.join(self.outf, self.model, self.dataset, "val_info.txt"), "w") as f:
for epoch in range(self.niter):
self.cur_epoch+=1
self.train_epoch()
auc,th,f1=self.validate()
if auc > best_auc:
best_auc = auc
best_auc_epoch=self.cur_epoch
self.save_weight_GD()
f.write("[{}] auc:{:.4f} \t best_auc:{:.4f} in epoch[{}]\n".format(self.cur_epoch,auc,best_auc,best_auc_epoch ))
print("[{}] auc:{:.4f} th:{:.4f} f1:{:.4f} \t best_auc:{:.4f} in epoch[{}]\n".format(self.cur_epoch,auc,th,f1,best_auc,best_auc_epoch ))
self.train_hist['total_time'].append(time.time() - start_time)
print("Avg one epoch time: %.2f, total %d epochs time: %.2f" % (np.mean(self.train_hist['per_epoch_time']),
self.niter,
self.train_hist['total_time'][0]))
self.save(self.train_hist)
self.save_loss(self.train_hist)
def train_epoch(self):
epoch_start_time = time.time()
self.G.train()
self.D.train()
epoch_iter = 0
for data in self.dataloader["train"]:
self.total_steps += self.opt.batchsize
epoch_iter += 1
self.set_input(data)
self.optimize()
errors = self.get_errors()
self.train_hist['D_loss'].append(errors["err_d"])
self.train_hist['G_loss'].append(errors["err_g"])
if (epoch_iter % self.opt.print_freq) == 0:
print("Epoch: [%d] [%4d/%4d] D_loss(R/F): %.6f/%.6f, G_loss: %.6f" %
((self.cur_epoch), (epoch_iter), self.dataloader["train"].dataset.__len__() // self.batchsize,
errors["err_d_real"], errors["err_d_fake"], errors["err_g"]))
# print("err_adv:{} ,err_rec:{} ,err_enc:{}".format(errors["err_g_adv"],errors["err_g_rec"],errors["err_g_enc"]))
self.train_hist['per_epoch_time'].append(time.time() - epoch_start_time)
with torch.no_grad():
real_input,fake_output = self.get_generated_x()
self.visualize_pair_results(self.cur_epoch,
real_input,
fake_output,
is_train=True)
def set_input(self, input):
#self.input.data.resize_(input[0].size()).copy_(input[0])
with torch.no_grad():
self.input.resize_(input[0].size()).copy_(input[0])
#self.gt.data.resize_(input[1].size()).copy_(input[1])
with torch.no_grad():
self.gt.resize_(input[1].size()).copy_(input[1])
# fixed input for view
if self.total_steps == self.opt.batchsize:
#self.fixed_input.data.resize_(input[0].size()).copy_(input[0])
with torch.no_grad():
self.fixed_input.resize_(input[0].size()).copy_(input[0])
##
def optimize(self):
self.update_netd()
self.update_netg()
# If D loss too low, then re-initialize netD
if self.err_d.item() < 5e-6:
self.reinitialize_netd()
def update_netd(self):
##
self.D.zero_grad()
# --
# Train with real
self.label.data.resize_(self.opt.batchsize).fill_(self.real_label)
self.out_d_real, self.feat_real = self.D(self.input)
# --
# Train with fake
self.label.data.resize_(self.opt.batchsize).fill_(self.fake_label)
self.fake, self.latent_i = self.G(self.input)
self.out_d_fake, self.feat_fake = self.D(self.fake)
# --
#to~~~ delete if some problem will happen
#print('self.out_d_real: ',type(self.out_d_real))
#print('self.device: ', self.device)
#print('self.batchsize: ', self.batchsize)
#print('self.real_label: ',self.real_label)
#print(self.real_label.device)
#print('torch.full: ', torch.full((self.batchsize,), self.real_label, device=self.device).type(torch.FloatTensor))
#below the code, if .cuda() isn't exist, torch.full.device is cpu
#print('torch.full is go to cpu?: ', torch.full((self.batchsize,), self.real_label, device=self.device).type(torch.FloatTensor).cuda().device)
self.err_d_real = self.bce_criterion(self.out_d_real, torch.full((self.batchsize,), self.real_label, device=self.device).type(torch.FloatTensor).cuda())
self.err_d_fake = self.bce_criterion(self.out_d_fake, torch.full((self.batchsize,), self.fake_label, device=self.device).type(torch.FloatTensor).cuda())
self.err_d=self.err_d_real+self.err_d_fake
self.err_d.backward()
self.optimizerD.step()
##
def reinitialize_netd(self):
""" Initialize the weights of netD
"""
self.D.apply(weights_init)
print('Reloading d net')
##
def update_netg(self):
self.G.zero_grad()
self.label.data.resize_(self.opt.batchsize).fill_(self.real_label)
self.fake, self.latent_i = self.G(self.input)
self.out_g, self.feat_fake = self.D(self.fake)
_, self.feat_real = self.D(self.input)
# self.err_g_adv = self.bce_criterion(self.out_g, self.label) # loss for ce
self.err_g_adv=self.mse_criterion(self.feat_fake,self.feat_real) # loss for feature matching
self.err_g_rec = self.mse_criterion(self.fake, self.input) # constrain x' to look like x
self.err_g = self.err_g_rec + self.err_g_adv * self.opt.w_adv
self.err_g.backward()
self.optimizerG.step()
##
def get_errors(self):
errors = {'err_d':self.err_d.item(),
'err_g': self.err_g.item(),
'err_d_real': self.err_d_real.item(),
'err_d_fake': self.err_d_fake.item(),
'err_g_adv': self.err_g_adv.item(),
'err_g_rec': self.err_g_rec.item(),
}
return errors
##
def get_generated_x(self):
fake = self.G(self.fixed_input)[0]
return self.fixed_input.cpu().data.numpy(),fake.cpu().data.numpy()
##
def validate(self):
'''
validate by auc value
:return: auc
'''
y_,y_pred=self.predict(self.dataloader["val"])
rocprc,rocauc,best_th,best_f1=evaluate(y_,y_pred)
return rocauc,best_th,best_f1
def predict(self,dataloader_,scale=True):
with torch.no_grad():
self.an_scores = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.float32, device=self.device)
self.gt_labels = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.long, device=self.device)
self.latent_i = torch.zeros(size=(len(dataloader_.dataset), self.opt.nz), dtype=torch.float32, device=self.device)
self.dis_feat = torch.zeros(size=(len(dataloader_.dataset), self.opt.ndf*16*10), dtype=torch.float32,
device=self.device)
for i, data in enumerate(dataloader_, 0):
self.set_input(data)
self.fake, latent_i = self.G(self.input)
# error = torch.mean(torch.pow((d_feat.view(self.input.shape[0],-1)-d_gen_feat.view(self.input.shape[0],-1)), 2), dim=1)
#
error = torch.mean(
torch.pow((self.input.view(self.input.shape[0], -1) - self.fake.view(self.fake.shape[0], -1)), 2),
dim=1)
self.an_scores[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = error.reshape(error.size(0))
self.gt_labels[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = self.gt.reshape(error.size(0))
self.latent_i [i*self.opt.batchsize : i*self.opt.batchsize+error.size(0), :] = latent_i.reshape(error.size(0), self.opt.nz)
# Scale error vector between [0, 1]
if scale:
self.an_scores = (self.an_scores - torch.min(self.an_scores)) / (torch.max(self.an_scores) - torch.min(self.an_scores))
y_=self.gt_labels.cpu().numpy()
y_pred=self.an_scores.cpu().numpy()
return y_,y_pred
def predict_for_right(self,dataloader_,min_score,max_score,threshold,save_dir):
'''
:param dataloader:
:param min_score:
:param max_score:
:param threshold:
:param save_dir:
:return:
'''
assert save_dir is not None
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self.G.eval()
self.D.eval()
with torch.no_grad():
# Create big error tensor for the test set.
test_pair=[]
self.an_scores = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.float32, device=self.device)
for i, data in enumerate(dataloader_, 0):
self.set_input(data)
self.fake, latent_i = self.G(self.input)
error = torch.mean(
torch.pow((self.input.view(self.input.shape[0], -1) - self.fake.view(self.fake.shape[0], -1)), 2),
dim=1)
self.an_scores[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = error.reshape(error.size(0))
# # Save test images.
batch_input = self.input.cpu().numpy()
batch_output = self.fake.cpu().numpy()
ano_score=error.cpu().numpy()
assert batch_output.shape[0]==batch_input.shape[0]==ano_score.shape[0]
for idx in range(batch_input.shape[0]):
if len(test_pair)>=100:
break
normal_score=(ano_score[idx]-min_score)/(max_score-min_score)
if normal_score>=threshold:
test_pair.append((batch_input[idx],batch_output[idx]))
# print(len(test_pair))
self.saveTestPair(test_pair,save_dir)
def test_type(self):
self.G.eval()
self.D.eval()
res_th=self.opt.threshold
save_dir = os.path.join(self.outf, self.model, self.dataset, "test", str(self.opt.folder))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
y_N, y_pred_N=self.predict(self.dataloader["test_N"],scale=False)
y_S, y_pred_S = self.predict(self.dataloader["test_S"],scale=False)
y_V, y_pred_V = self.predict(self.dataloader["test_V"],scale=False)
y_F, y_pred_F = self.predict(self.dataloader["test_F"],scale=False)
y_Q, y_pred_Q = self.predict(self.dataloader["test_Q"],scale=False)
over_all=np.concatenate([y_pred_N,y_pred_S,y_pred_V,y_pred_F,y_pred_Q])
over_all_gt=np.concatenate([y_N,y_S,y_V,y_F,y_Q])
min_score,max_score=np.min(over_all),np.max(over_all)
A_res={
"S":y_pred_S,
"V":y_pred_V,
"F":y_pred_F,
"Q":y_pred_Q
}
self.analysisRes(y_pred_N,A_res,min_score,max_score,res_th,save_dir)
#save fig for Interpretable
# self.predictForRight(self.dataloader["test_N"], save_dir=os.path.join(save_dir, "N"))
self.predict_for_right(self.dataloader["test_S"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "S"))
self.predict_for_right(self.dataloader["test_V"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "V"))
self.predict_for_right(self.dataloader["test_F"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "F"))
self.predict_for_right(self.dataloader["test_Q"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "Q"))
aucprc,aucroc,best_th,best_f1=evaluate(over_all_gt,(over_all-min_score)/(max_score-min_score))
print("#############################")
print("######## Result ###########")
print("ap:{}".format(aucprc))
print("auc:{}".format(aucroc))
print("best th:{} --> best f1:{}".format(best_th,best_f1))
with open(os.path.join(save_dir,"res-record.txt"),'w') as f:
f.write("auc_prc:{}\n".format(aucprc))
f.write("auc_roc:{}\n".format(aucroc))
f.write("best th:{} --> best f1:{}".format(best_th, best_f1))
def test_time(self):
self.G.eval()
self.D.eval()
size=self.dataloader["test_N"].dataset.__len__()
start=time.time()
for i, (data_x,data_y) in enumerate(self.dataloader["test_N"], 0):
input_x=data_x
for j in range(input_x.shape[0]):
input_x_=input_x[j].view(1,input_x.shape[1],input_x.shape[2]).to(self.device)
gen_x,_ = self.G(input_x_)
error = torch.mean(
torch.pow((input_x_.view(input_x_.shape[0], -1) - gen_x.view(gen_x.shape[0], -1)), 2),
dim=1)
end=time.time()
print((end-start)/size)
No preview for this file type
import time,os,sys
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from network import Encoder,Decoder,AD_MODEL,weights_init,print_network
dirname=os.path.dirname
sys.path.insert(0,dirname(dirname(os.path.abspath(__file__))))
from metric import evaluate
##
class Discriminator(nn.Module):
def __init__(self, opt):
super(Discriminator, self).__init__()
model = Encoder(opt.ngpu,opt,1)
layers = list(model.main.children())
self.features = nn.Sequential(*layers[:-1])
self.classifier = nn.Sequential(layers[-1])
self.classifier.add_module('Sigmoid', nn.Sigmoid())
def forward(self, x):
features = self.features(x)
features = features
classifier = self.classifier(features)
classifier = classifier.view(-1, 1).squeeze(1)
return classifier, features
##
class Generator(nn.Module):
def __init__(self, opt):
super(Generator, self).__init__()
self.encoder1 = Encoder(opt.ngpu,opt,opt.nz)
self.decoder = Decoder(opt.ngpu,opt)
def forward(self, x):
latent_i = self.encoder1(x)
gen_x = self.decoder(latent_i)
return gen_x, latent_i
class BeatGAN(AD_MODEL):
def __init__(self, opt, dataloader, device):
super(BeatGAN, self).__init__(opt, dataloader, device)
self.dataloader = dataloader
self.device = device
self.opt=opt
self.batchsize = opt.batchsize
self.nz = opt.nz
self.niter = opt.niter
self.G = Generator( opt).to(device)
self.G.apply(weights_init)
if not self.opt.istest:
print_network(self.G)
self.D = Discriminator(opt).to(device)
self.D.apply(weights_init)
if not self.opt.istest:
print_network(self.D)
self.bce_criterion = nn.BCELoss()
self.mse_criterion=nn.MSELoss()
self.optimizerD = optim.Adam(self.D.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizerG = optim.Adam(self.G.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.total_steps = 0
self.cur_epoch=0
self.input = torch.empty(size=(self.opt.batchsize, self.opt.nc, self.opt.isize), dtype=torch.float32, device=self.device)
self.label = torch.empty(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.gt = torch.empty(size=(opt.batchsize,), dtype=torch.long, device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, self.opt.nc, self.opt.isize), dtype=torch.float32, device=self.device)
self.real_label = 1
self.fake_label= 0
self.out_d_real = None
self.feat_real = None
self.fake = None
self.latent_i = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_real = None
self.err_d_fake = None
self.err_d = None
self.out_g = None
self.err_g_adv = None
self.err_g_rec = None
self.err_g = None
def train(self):
self.train_hist = {}
self.train_hist['D_loss'] = []
self.train_hist['G_loss'] = []
self.train_hist['per_epoch_time'] = []
self.train_hist['total_time'] = []
print("Train model.")
start_time = time.time()
best_auc=0
best_auc_epoch=0
with open(os.path.join(self.outf, self.model, self.dataset, "val_info.txt"), "w") as f:
for epoch in range(self.niter):
self.cur_epoch+=1
self.train_epoch()
print('check2')
auc,th,f1=self.validate()
if auc > best_auc:
best_auc = auc
best_auc_epoch=self.cur_epoch
self.save_weight_GD()
f.write("[{}] auc:{:.4f} \t best_auc:{:.4f} in epoch[{}]\n".format(self.cur_epoch,auc,best_auc,best_auc_epoch ))
print("[{}] auc:{:.4f} th:{:.4f} f1:{:.4f} \t best_auc:{:.4f} in epoch[{}]\n".format(self.cur_epoch,auc,th,f1,best_auc,best_auc_epoch ))
self.train_hist['total_time'].append(time.time() - start_time)
print("Avg one epoch time: %.2f, total %d epochs time: %.2f" % (np.mean(self.train_hist['per_epoch_time']),
self.niter,
self.train_hist['total_time'][0]))
self.save(self.train_hist)
self.save_loss(self.train_hist)
print('check error point')
def train_epoch(self):
epoch_start_time = time.time()
self.G.train()
self.D.train()
epoch_iter = 0
for data in self.dataloader["train"]:
self.total_steps += self.opt.batchsize
epoch_iter += 1
self.set_input(data)
self.optimize()
errors = self.get_errors()
self.train_hist['D_loss'].append(errors["err_d"])
self.train_hist['G_loss'].append(errors["err_g"])
if (epoch_iter % self.opt.print_freq) == 0:
print("Epoch: [%d] [%4d/%4d] D_loss(R/F): %.6f/%.6f, G_loss: %.6f" %
((self.cur_epoch), (epoch_iter), self.dataloader["train"].dataset.__len__() // self.batchsize,
errors["err_d_real"], errors["err_d_fake"], errors["err_g"]))
# print("err_adv:{} ,err_rec:{} ,err_enc:{}".format(errors["err_g_adv"],errors["err_g_rec"],errors["err_g_enc"]))
self.train_hist['per_epoch_time'].append(time.time() - epoch_start_time)
with torch.no_grad():
real_input,fake_output = self.get_generated_x()
self.visualize_pair_results(self.cur_epoch,
real_input,
fake_output,
is_train=True)
def set_input(self, input):
#self.input.data.resize_(input[0].size()).copy_(input[0])
with torch.no_grad():
self.input.resize_(input[0].size()).copy_(input[0])
#self.gt.data.resize_(input[1].size()).copy_(input[1])
with torch.no_grad():
self.gt.resize_(input[1].size()).copy_(input[1])
# fixed input for view
if self.total_steps == self.opt.batchsize:
#self.fixed_input.data.resize_(input[0].size()).copy_(input[0])
with torch.no_grad():
self.fixed_input.resize_(input[0].size()).copy_(input[0])
##
def optimize(self):
self.update_netd()
self.update_netg()
# If D loss too low, then re-initialize netD
if self.err_d.item() < 5e-6:
self.reinitialize_netd()
def update_netd(self):
##
self.D.zero_grad()
# --
# Train with real
self.label.data.resize_(self.opt.batchsize).fill_(self.real_label)
self.out_d_real, self.feat_real = self.D(self.input)
# --
# Train with fake
self.label.data.resize_(self.opt.batchsize).fill_(self.fake_label)
self.fake, self.latent_i = self.G(self.input)
self.out_d_fake, self.feat_fake = self.D(self.fake)
# --
self.err_d_real = self.bce_criterion(self.out_d_real.type(torch.float), torch.full((self.batchsize,), self.real_label, device=self.device).type(torch.float))
self.err_d_fake = self.bce_criterion(self.out_d_fake.type(torch.float), torch.full((self.batchsize,), self.fake_label, device=self.device).type(torch.float))
self.err_d=self.err_d_real+self.err_d_fake
self.err_d.backward()
self.optimizerD.step()
##
def reinitialize_netd(self):
""" Initialize the weights of netD
"""
self.D.apply(weights_init)
print('Reloading d net')
##
def update_netg(self):
self.G.zero_grad()
self.label.data.resize_(self.opt.batchsize).fill_(self.real_label)
self.fake, self.latent_i = self.G(self.input)
self.out_g, self.feat_fake = self.D(self.fake)
_, self.feat_real = self.D(self.input)
# self.err_g_adv = self.bce_criterion(self.out_g, self.label) # loss for ce
self.err_g_adv=self.mse_criterion(self.feat_fake,self.feat_real) # loss for feature matching
self.err_g_rec = self.mse_criterion(self.fake, self.input) # constrain x' to look like x
self.err_g = self.err_g_rec + self.err_g_adv * self.opt.w_adv
self.err_g.backward()
self.optimizerG.step()
##
def get_errors(self):
errors = {'err_d':self.err_d.item(),
'err_g': self.err_g.item(),
'err_d_real': self.err_d_real.item(),
'err_d_fake': self.err_d_fake.item(),
'err_g_adv': self.err_g_adv.item(),
'err_g_rec': self.err_g_rec.item(),
}
return errors
##
def get_generated_x(self):
fake = self.G(self.fixed_input)[0]
return self.fixed_input.cpu().data.numpy(),fake.cpu().data.numpy()
##
def validate(self):
'''
validate by auc value
:return: auc
'''
y_,y_pred=self.predict(self.dataloader["val"])
rocprc,rocauc,best_th,best_f1=evaluate(y_,y_pred)
return rocauc,best_th,best_f1
def predict(self,dataloader_,scale=True):
with torch.no_grad():
self.an_scores = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.float32, device=self.device)
self.gt_labels = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.long, device=self.device)
self.latent_i = torch.zeros(size=(len(dataloader_.dataset), self.opt.nz), dtype=torch.float32, device=self.device)
self.dis_feat = torch.zeros(size=(len(dataloader_.dataset), self.opt.ndf*16*10), dtype=torch.float32,
device=self.device)
for i, data in enumerate(dataloader_, 0):
self.set_input(data)
self.fake, latent_i = self.G(self.input)
# error = torch.mean(torch.pow((d_feat.view(self.input.shape[0],-1)-d_gen_feat.view(self.input.shape[0],-1)), 2), dim=1)
#
error = torch.mean(
torch.pow((self.input.view(self.input.shape[0], -1) - self.fake.view(self.fake.shape[0], -1)), 2),
dim=1)
self.an_scores[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = error.reshape(error.size(0))
self.gt_labels[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = self.gt.reshape(error.size(0))
self.latent_i [i*self.opt.batchsize : i*self.opt.batchsize+error.size(0), :] = latent_i.reshape(error.size(0), self.opt.nz)
# Scale error vector between [0, 1]
if scale:
self.an_scores = (self.an_scores - torch.min(self.an_scores)) / (torch.max(self.an_scores) - torch.min(self.an_scores))
y_=self.gt_labels.cpu().numpy()
y_pred=self.an_scores.cpu().numpy()
return y_,y_pred
def predict_for_right(self,dataloader_,min_score,max_score,threshold,save_dir):
'''
:param dataloader:
:param min_score:
:param max_score:
:param threshold:
:param save_dir:
:return:
'''
assert save_dir is not None
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self.G.eval()
self.D.eval()
with torch.no_grad():
# Create big error tensor for the test set.
test_pair=[]
self.an_scores = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.float32, device=self.device)
for i, data in enumerate(dataloader_, 0):
self.set_input(data)
self.fake, latent_i = self.G(self.input)
error = torch.mean(
torch.pow((self.input.view(self.input.shape[0], -1) - self.fake.view(self.fake.shape[0], -1)), 2),
dim=1)
self.an_scores[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = error.reshape(error.size(0))
# # Save test images.
batch_input = self.input.cpu().numpy()
batch_output = self.fake.cpu().numpy()
ano_score=error.cpu().numpy()
assert batch_output.shape[0]==batch_input.shape[0]==ano_score.shape[0]
for idx in range(batch_input.shape[0]):
if len(test_pair)>=100:
break
normal_score=(ano_score[idx]-min_score)/(max_score-min_score)
if normal_score>=threshold:
test_pair.append((batch_input[idx],batch_output[idx]))
# print(len(test_pair))
self.saveTestPair(test_pair,save_dir)
def test_type(self):
self.G.eval()
self.D.eval()
res_th=self.opt.threshold
save_dir = os.path.join(self.outf, self.model, self.dataset, "test", str(self.opt.folder))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
y_N, y_pred_N=self.predict(self.dataloader["test_N"],scale=False)
y_S, y_pred_S = self.predict(self.dataloader["test_S"],scale=False)
y_V, y_pred_V = self.predict(self.dataloader["test_V"],scale=False)
y_F, y_pred_F = self.predict(self.dataloader["test_F"],scale=False)
y_Q, y_pred_Q = self.predict(self.dataloader["test_Q"],scale=False)
over_all=np.concatenate([y_pred_N,y_pred_S,y_pred_V,y_pred_F,y_pred_Q])
over_all_gt=np.concatenate([y_N,y_S,y_V,y_F,y_Q])
min_score,max_score=np.min(over_all),np.max(over_all)
A_res={
"S":y_pred_S,
"V":y_pred_V,
"F":y_pred_F,
"Q":y_pred_Q
}
self.analysisRes(y_pred_N,A_res,min_score,max_score,res_th,save_dir)
#save fig for Interpretable
# self.predictForRight(self.dataloader["test_N"], save_dir=os.path.join(save_dir, "N"))
self.predict_for_right(self.dataloader["test_S"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "S"))
self.predict_for_right(self.dataloader["test_V"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "V"))
self.predict_for_right(self.dataloader["test_F"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "F"))
self.predict_for_right(self.dataloader["test_Q"], min_score,max_score,res_th,save_dir=os.path.join(save_dir, "Q"))
aucprc,aucroc,best_th,best_f1=evaluate(over_all_gt,(over_all-min_score)/(max_score-min_score))
print("#############################")
print("######## Result ###########")
print("ap:{}".format(aucprc))
print("auc:{}".format(aucroc))
print("best th:{} --> best f1:{}".format(best_th,best_f1))
with open(os.path.join(save_dir,"res-record.txt"),'w') as f:
f.write("auc_prc:{}\n".format(aucprc))
f.write("auc_roc:{}\n".format(aucroc))
f.write("best th:{} --> best f1:{}".format(best_th, best_f1))
def test_time(self):
self.G.eval()
self.D.eval()
size=self.dataloader["test_N"].dataset.__len__()
start=time.time()
for i, (data_x,data_y) in enumerate(self.dataloader["test_N"], 0):
input_x=data_x
for j in range(input_x.shape[0]):
input_x_=input_x[j].view(1,input_x.shape[1],input_x.shape[2]).to(self.device)
gen_x,_ = self.G(input_x_)
error = torch.mean(
torch.pow((input_x_.view(input_x_.shape[0], -1) - gen_x.view(gen_x.shape[0], -1)), 2),
dim=1)
end=time.time()
print((end-start)/size)
import os,pickle
import numpy as np
import torch
import torch.nn as nn
from plotUtil import plot_dist,save_pair_fig,save_plot_sample,print_network,save_plot_pair_sample,loss_plot
def weights_init(mod):
"""
Custom weights initialization called on netG, netD and netE
:param m:
:return:
"""
classname = mod.__class__.__name__
if classname.find('Conv') != -1:
# mod.weight.data.normal_(0.0, 0.02)
nn.init.xavier_normal_(mod.weight.data)
# nn.init.kaiming_uniform_(mod.weight.data)
elif classname.find('BatchNorm') != -1:
mod.weight.data.normal_(1.0, 0.02)
mod.bias.data.fill_(0)
elif classname.find('Linear') !=-1 :
torch.nn.init.xavier_uniform(mod.weight)
mod.bias.data.fill_(0.01)
class Encoder(nn.Module):
def __init__(self, ngpu,opt,out_z):
super(Encoder, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is (nc) x 320
#nn.Conv1d(opt.nc,opt.ndf,4,2,1,bias=False),
nn.Conv2d(opt.nc,opt.ndf,4,2,1,bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 160
#nn.Conv1d(opt.ndf, opt.ndf * 2, 4, 2, 1, bias=False),
#nn.BatchNorm1d(opt.ndf * 2),
nn.Conv2d(opt.ndf, opt.ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 80
#nn.Conv1d(opt.ndf * 2, opt.ndf * 4, 4, 2, 1, bias=False),
#nn.BatchNorm1d(opt.ndf * 4),
nn.Conv2d(opt.ndf * 2, opt.ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(opt.ndf * 4, opt.ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(opt.ndf * 8, opt.ndf * 16, 4, 1, 1, bias=False),
nn.BatchNorm2d(opt.ndf * 16),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*16) x 10
#nn.Conv1d(opt.ndf * 16, out_z, 10, 1, 0, bias=False)
nn.Conv2d(opt.ndf * 16, out_z, 7, 1, 0, bias=False),
# state size. (nz) x 1
)
def forward(self, input):
if input.is_cuda and self.ngpu > 1:
output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
else:
output = self.main(input)
return output
##
class Decoder(nn.Module):
def __init__(self, ngpu,opt):
super(Decoder, self).__init__()
self.ngpu = ngpu
self.main=nn.Sequential(
nn.ConvTranspose2d(opt.nz,opt.ngf*16,7,1,0,bias=False),
nn.BatchNorm2d(opt.ngf*16),
nn.ReLU(True),
nn.ConvTranspose2d(opt.ngf * 16, opt.ngf * 8, 4, 1, 1, bias=False),
nn.BatchNorm2d(opt.ngf * 8),
nn.ReLU(True),
nn.ConvTranspose2d(opt.ngf * 8, opt.ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ngf * 4),
nn.ReLU(True),
# state size. (ngf*2) x 40
#nn.ConvTranspose1d(opt.ngf * 4, opt.ngf*2, 4, 2, 1, bias=False),
#nn.BatchNorm1d(opt.ngf*2),
nn.ConvTranspose2d(opt.ngf * 4, opt.ngf*2, 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ngf*2),
nn.ReLU(True),
# state size. (ngf) x 80
#nn.ConvTranspose1d(opt.ngf * 2, opt.ngf , 4, 2, 1, bias=False),
#nn.BatchNorm1d(opt.ngf ),
nn.ConvTranspose2d(opt.ngf * 2, opt.ngf , 4, 2, 1, bias=False),
nn.BatchNorm2d(opt.ngf ),
nn.ReLU(True),
# state size. (ngf) x 160
#nn.ConvTranspose1d(opt.ngf , opt.nc, 4, 2, 1, bias=False),
nn.ConvTranspose2d(opt.ngf , opt.nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 320
)
def forward(self, input):
if input.is_cuda and self.ngpu > 1:
output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
else:
output = self.main(input)
return output
class AD_MODEL(object):
def __init__(self,opt,dataloader,device):
self.G=None
self.D=None
self.opt=opt
self.niter=opt.niter
self.dataset=opt.dataset
self.model = opt.model
self.outf=opt.outf
def train(self):
raise NotImplementedError
def visualize_results(self, epoch,samples,is_train=True):
if is_train:
sub_folder="train"
else:
sub_folder="test"
save_dir=os.path.join(self.outf,self.model,self.dataset,sub_folder)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_plot_sample(samples, epoch, self.dataset, num_epochs=self.niter,
impath=os.path.join(save_dir,'epoch%03d' % epoch + '.png'))
def visualize_pair_results(self,epoch,samples1,samples2,is_train=True):
if is_train:
sub_folder="train"
else:
sub_folder="test"
save_dir=os.path.join(self.outf,self.model,self.dataset,sub_folder)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_plot_pair_sample(samples1, samples2, epoch, self.dataset, num_epochs=self.niter, impath=os.path.join(save_dir,'epoch%03d' % epoch + '.png'))
def save(self,train_hist):
save_dir = os.path.join(self.outf, self.model, self.dataset,"model")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(os.path.join(save_dir, self.model + '_history.pkl'), 'wb') as f:
pickle.dump(train_hist, f)
def save_weight_GD(self):
save_dir = os.path.join(self.outf, self.model, self.dataset, "model")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(self.G.state_dict(), os.path.join(save_dir, self.model+"_folder_"+str(self.opt.folder) + '_G.pkl'))
torch.save(self.D.state_dict(), os.path.join(save_dir, self.model+"_folder_"+str(self.opt.folder) + '_D.pkl'))
def load(self):
save_dir = os.path.join(self.outf, self.model, self.dataset,"model")
self.G.load_state_dict(torch.load(os.path.join(save_dir, self.model+"_folder_"+str(self.opt.folder) + '_G.pkl')))
self.D.load_state_dict(torch.load(os.path.join(save_dir, self.model+"_folder_"+str(self.opt.folder) + '_D.pkl')))
def save_loss(self,train_hist):
loss_plot(train_hist, os.path.join(self.outf, self.model, self.dataset), self.model)
def saveTestPair(self,pair,save_dir):
'''
:param pair: list of (input,output)
:param save_dir:
:return:
'''
assert save_dir is not None
for idx,p in enumerate(pair):
input=p[0]
output=p[1]
save_pair_fig(input,output,os.path.join(save_dir,str(idx)+".png"))
def analysisRes(self,N_res,A_res,min_score,max_score,threshold,save_dir):
'''
:param N_res: list of normal score
:param A_res: dict{ "S": list of S score, "V":...}
:param min_score:
:param max_score:
:return:
'''
print("############ Analysis #############")
print("############ Threshold:{} #############".format(threshold))
all_abnormal_score=[]
all_normal_score=np.array([])
for a_type in A_res:
a_score=A_res[a_type]
print("********* Type:{} *************".format(a_type))
normal_score=normal(N_res, min_score, max_score)
abnormal_score=normal(a_score, min_score, max_score)
all_abnormal_score=np.concatenate((all_abnormal_score,np.array(abnormal_score)))
all_normal_score=normal_score
plot_dist(normal_score,abnormal_score , str(self.opt.folder)+"_"+"N", a_type,
save_dir)
TP=np.count_nonzero(abnormal_score >= threshold)
FP=np.count_nonzero(normal_score >= threshold)
TN=np.count_nonzero(normal_score < threshold)
FN=np.count_nonzero(abnormal_score<threshold)
print("TP:{}".format(TP))
print("FP:{}".format(FP))
print("TN:{}".format(TN))
print("FN:{}".format(FN))
print("Accuracy:{}".format((TP + TN) * 1.0 / (TP + TN + FP + FN)))
print("Precision/ppv:{}".format(TP * 1.0 / (TP + FP)))
print("sensitivity/Recall:{}".format(TP * 1.0 / (TP + FN)))
print("specificity:{}".format(TN * 1.0 / (TN + FP)))
print("F1:{}".format(2.0 * TP / (2 * TP + FP + FN)))
# all_abnormal_score=np.reshape(np.array(all_abnormal_score),(-1))
# print(all_abnormal_score.shape)
plot_dist(all_normal_score, all_abnormal_score, str(self.opt.folder)+"_"+"N", "A",
save_dir)
def normal(array,min_val,max_val):
return (array-min_val)/(max_val-min_val)
import argparse
import os
import torch
class Options():
"""Options class
Returns:
[argparse]: argparse containing train and test options
"""
def __init__(self):
##
#
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
##
# Base
self.parser.add_argument('--dataset', default='ecg', help='ecg dataset')
self.parser.add_argument('--dataroot', default='', help='path to dataset')
self.parser.add_argument('--batchsize', type=int, default=64, help='input batch size')
self.parser.add_argument('--workers', type=int, help='number of data loading workers', default=1)
self.parser.add_argument('--isize', type=int, default=320, help='input sequence size.')
self.parser.add_argument('--nc', type=int, default=1, help='input sequence channels')
self.parser.add_argument('--nz', type=int, default=50, help='size of the latent z vector')
self.parser.add_argument('--ngf', type=int, default=32)
self.parser.add_argument('--ndf', type=int, default=32)
self.parser.add_argument('--device', type=str, default='gpu', help='Device: gpu | cpu')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
self.parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
self.parser.add_argument('--model', type=str, default='beatgan', help='choose model')
self.parser.add_argument('--outf', default='./output', help='output folder')
##
# Train
self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
self.parser.add_argument('--niter', type=int, default=100, help='number of epochs to train for')
self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
self.parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
self.parser.add_argument('--w_adv', type=float, default=1, help='parameter')
self.parser.add_argument('--folder', type=int, default=0, help='folder index 0-4')
self.parser.add_argument('--n_aug', type=int, default=0, help='aug data times')
## Test
self.parser.add_argument('--istest',action='store_true',help='train model or test model')
self.parser.add_argument('--threshold', type=float, default=0.05, help='threshold score for anomaly')
self.opt = None
def parse(self):
""" Parse Arguments.
"""
self.opt = self.parser.parse_args()
str_ids = self.opt.gpu_ids.split(',')
self.opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
self.opt.gpu_ids.append(id)
# set gpu ids
if self.opt.device == 'gpu':
torch.cuda.set_device(self.opt.gpu_ids[0])
args = vars(self.opt)
# print('------------ Options -------------')
# for k, v in sorted(args.items()):
# print('%s: %s' % (str(k), str(v)))
# print('-------------- End ----------------')
# save to the disk
self.opt.name = "%s/%s" % (self.opt.model, self.opt.dataset)
expr_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
test_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
if not os.path.isdir(expr_dir):
os.makedirs(expr_dir)
if not os.path.isdir(test_dir):
os.makedirs(test_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
return self.opt
\ No newline at end of file
auc_prc:0.906462439213977
auc_roc:0.9448355313283711
best th:0.17799395322799683 --> best f1:0.8272402145837473
\ No newline at end of file
------------ Options -------------
batchsize: 64
beta1: 0.5
dataroot: ./dataset/preprocessed/ano0/
dataset: ecg
device: gpu
folder: 0
gpu_ids: [0]
isize: 320
istest: True
lr: 0.0001
model: beatgan
n_aug: 0
name: beatgan/ecg
nc: 1
ndf: 32
ngf: 32
ngpu: 1
niter: 100
nz: 50
outf: ./output
print_freq: 100
threshold: 0.02
w_adv: 1
workers: 1
-------------- End ----------------
[1] auc:0.8544 best_auc:0.8544 in epoch[1]
[2] auc:0.8783 best_auc:0.8783 in epoch[2]
[3] auc:0.8993 best_auc:0.8993 in epoch[3]
[4] auc:0.9035 best_auc:0.9035 in epoch[4]
[5] auc:0.9211 best_auc:0.9211 in epoch[5]
[6] auc:0.9255 best_auc:0.9255 in epoch[6]
[7] auc:0.9315 best_auc:0.9315 in epoch[7]
[8] auc:0.9364 best_auc:0.9364 in epoch[8]
[9] auc:0.9355 best_auc:0.9364 in epoch[8]
[10] auc:0.9363 best_auc:0.9364 in epoch[8]
[11] auc:0.9403 best_auc:0.9403 in epoch[11]
[12] auc:0.9391 best_auc:0.9403 in epoch[11]
[13] auc:0.9393 best_auc:0.9403 in epoch[11]
[14] auc:0.9403 best_auc:0.9403 in epoch[11]
[15] auc:0.9441 best_auc:0.9441 in epoch[15]
[16] auc:0.9438 best_auc:0.9441 in epoch[15]
[17] auc:0.9407 best_auc:0.9441 in epoch[15]
[18] auc:0.9427 best_auc:0.9441 in epoch[15]
[19] auc:0.9437 best_auc:0.9441 in epoch[15]
[20] auc:0.9418 best_auc:0.9441 in epoch[15]
[21] auc:0.9460 best_auc:0.9460 in epoch[21]
[22] auc:0.9422 best_auc:0.9460 in epoch[21]
[23] auc:0.9453 best_auc:0.9460 in epoch[21]
[24] auc:0.9448 best_auc:0.9460 in epoch[21]
[25] auc:0.9450 best_auc:0.9460 in epoch[21]
[26] auc:0.9475 best_auc:0.9475 in epoch[26]
[27] auc:0.9458 best_auc:0.9475 in epoch[26]
[28] auc:0.9450 best_auc:0.9475 in epoch[26]
[29] auc:0.9471 best_auc:0.9475 in epoch[26]
[30] auc:0.9442 best_auc:0.9475 in epoch[26]
[31] auc:0.9458 best_auc:0.9475 in epoch[26]
[32] auc:0.9437 best_auc:0.9475 in epoch[26]
[33] auc:0.9465 best_auc:0.9475 in epoch[26]
[34] auc:0.9477 best_auc:0.9477 in epoch[34]
[35] auc:0.9460 best_auc:0.9477 in epoch[34]
[36] auc:0.9464 best_auc:0.9477 in epoch[34]
[37] auc:0.9466 best_auc:0.9477 in epoch[34]
[38] auc:0.9422 best_auc:0.9477 in epoch[34]
[39] auc:0.9463 best_auc:0.9477 in epoch[34]
[40] auc:0.9451 best_auc:0.9477 in epoch[34]
[41] auc:0.9439 best_auc:0.9477 in epoch[34]
[42] auc:0.9452 best_auc:0.9477 in epoch[34]
[43] auc:0.9459 best_auc:0.9477 in epoch[34]
[44] auc:0.9439 best_auc:0.9477 in epoch[34]
[45] auc:0.9459 best_auc:0.9477 in epoch[34]
[46] auc:0.9450 best_auc:0.9477 in epoch[34]
[47] auc:0.9447 best_auc:0.9477 in epoch[34]
[48] auc:0.9453 best_auc:0.9477 in epoch[34]
[49] auc:0.9462 best_auc:0.9477 in epoch[34]
[50] auc:0.9436 best_auc:0.9477 in epoch[34]
[51] auc:0.9443 best_auc:0.9477 in epoch[34]
[52] auc:0.9446 best_auc:0.9477 in epoch[34]
[53] auc:0.9421 best_auc:0.9477 in epoch[34]
[54] auc:0.9444 best_auc:0.9477 in epoch[34]
[55] auc:0.9432 best_auc:0.9477 in epoch[34]
[56] auc:0.9433 best_auc:0.9477 in epoch[34]
[57] auc:0.9443 best_auc:0.9477 in epoch[34]
[58] auc:0.9440 best_auc:0.9477 in epoch[34]
[59] auc:0.9452 best_auc:0.9477 in epoch[34]
[60] auc:0.9441 best_auc:0.9477 in epoch[34]
[61] auc:0.9435 best_auc:0.9477 in epoch[34]
[62] auc:0.9434 best_auc:0.9477 in epoch[34]
[63] auc:0.9429 best_auc:0.9477 in epoch[34]
[64] auc:0.9441 best_auc:0.9477 in epoch[34]
[65] auc:0.9452 best_auc:0.9477 in epoch[34]
[66] auc:0.9459 best_auc:0.9477 in epoch[34]
[67] auc:0.9435 best_auc:0.9477 in epoch[34]
[68] auc:0.9446 best_auc:0.9477 in epoch[34]
[69] auc:0.9447 best_auc:0.9477 in epoch[34]
[70] auc:0.9444 best_auc:0.9477 in epoch[34]
[71] auc:0.9449 best_auc:0.9477 in epoch[34]
[72] auc:0.9437 best_auc:0.9477 in epoch[34]
[73] auc:0.9415 best_auc:0.9477 in epoch[34]
[74] auc:0.9447 best_auc:0.9477 in epoch[34]
[75] auc:0.9448 best_auc:0.9477 in epoch[34]
[76] auc:0.9437 best_auc:0.9477 in epoch[34]
[77] auc:0.9422 best_auc:0.9477 in epoch[34]
[78] auc:0.9438 best_auc:0.9477 in epoch[34]
[79] auc:0.9419 best_auc:0.9477 in epoch[34]
[80] auc:0.9420 best_auc:0.9477 in epoch[34]
[81] auc:0.9424 best_auc:0.9477 in epoch[34]
[82] auc:0.9423 best_auc:0.9477 in epoch[34]
[83] auc:0.9425 best_auc:0.9477 in epoch[34]
[84] auc:0.9432 best_auc:0.9477 in epoch[34]
[85] auc:0.9443 best_auc:0.9477 in epoch[34]
[86] auc:0.9426 best_auc:0.9477 in epoch[34]
[87] auc:0.9428 best_auc:0.9477 in epoch[34]
[88] auc:0.9430 best_auc:0.9477 in epoch[34]
[89] auc:0.9431 best_auc:0.9477 in epoch[34]
[90] auc:0.9420 best_auc:0.9477 in epoch[34]
[91] auc:0.9424 best_auc:0.9477 in epoch[34]
[92] auc:0.9432 best_auc:0.9477 in epoch[34]
[93] auc:0.9434 best_auc:0.9477 in epoch[34]
[94] auc:0.9414 best_auc:0.9477 in epoch[34]
[95] auc:0.9425 best_auc:0.9477 in epoch[34]
[96] auc:0.9422 best_auc:0.9477 in epoch[34]
[97] auc:0.9434 best_auc:0.9477 in epoch[34]
[98] auc:0.9420 best_auc:0.9477 in epoch[34]
[99] auc:0.9419 best_auc:0.9477 in epoch[34]
[100] auc:0.9427 best_auc:0.9477 in epoch[34]
import os
import numpy as np
from sklearn.manifold import TSNE
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("Agg")
matplotlib.rcParams.update({'font.size': 16})
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
# sns.set_style('darkgrid')
# sns.set_palette('muted')
# sns.set_context("notebook", font_scale=1.5,
# rc={"lines.linewidth": 2.5})
def print_network(net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print(net)
print('Total number of parameters: %d' % num_params)
def save_plot_sample(samples, idx, identifier, n_samples=6, num_epochs=None,impath=None ,ncol=2):
assert n_samples <= samples.shape[0]
assert n_samples % ncol == 0
sample_length = samples.shape[2]
if not num_epochs is None:
col = hsv_to_rgb((1, 1.0*(idx)/num_epochs, 0.8))
else:
col = 'grey'
x_points = np.arange(sample_length)
nrow = int(n_samples/ncol)
fig, axarr = plt.subplots(nrow, ncol, sharex=True, figsize=(6, 6))
if identifier=="ecg":
for m in range(nrow):
for n in range(ncol):
sample = samples[n * nrow + m, 0, :]
axarr[m, n].plot(x_points, sample, color=col)
axarr[m, n].set_ylim(-1, 1)
else:
raise Exception("data type error:{}".format(identifier))
for n in range(ncol):
axarr[-1, n].xaxis.set_ticks(range(0, sample_length, int(sample_length/4)))
fig.suptitle(idx)
fig.subplots_adjust(hspace = 0.15)
assert impath is not None
fig.savefig(impath)
plt.clf()
plt.close()
return
def save_plot_pair_sample(samples1,samples2, idx, identifier, n_samples=6, num_epochs=None,impath=None ,ncol=2):
assert n_samples <= samples1.shape[0]
assert n_samples % ncol == 0
sample_length = samples1.shape[2] # N,C,L
if not num_epochs is None:
col = hsv_to_rgb((1, 1.0*(idx)/num_epochs, 0.8))
else:
col = 'grey'
x_points = np.arange(sample_length)
nrow = int(n_samples/ncol)
fig, axarr = plt.subplots(nrow, ncol, sharex=True, figsize=(6, 6))
if identifier=="ecg":
for m in range(nrow):
sample1=samples1[m,0,:]
sample2=samples2[m,0,:]
axarr[m,0].plot(x_points,sample1,color=col)
axarr[m, 1].plot(x_points, sample2, color=col)
axarr[m, 0].set_ylim(-1, 1)
axarr[m, 1].set_ylim(-1, 1)
else:
raise Exception("data type error:{}".format(identifier))
for n in range(ncol):
axarr[-1, n].xaxis.set_ticks(range(0, sample_length, int(sample_length/4)))
fig.suptitle(idx)
fig.subplots_adjust(hspace = 0.15)
assert impath is not None
fig.savefig(impath)
plt.clf()
plt.close()
return
def plot_tsne(X,y,dim=2):
tsne = TSNE(n_components=dim, verbose=1, perplexity=40, n_iter=1000)
x_proj = tsne.fit_transform(X)
# We choose a color palette with seaborn.
palette = np.array(sns.color_palette("hls", 10))
# We create a scatter plot.
f=plt.figure()
if dim==2:
ax = f.add_subplot(111)
ax.scatter(x_proj[:, 0], x_proj[:, 1], lw=0, s=40,c=palette[y.astype(np.int)])
ax.grid(True)
for axi in (ax.xaxis, ax.yaxis):
for tic in axi.get_major_ticks():
tic.tick1On = tic.tick2On = False
tic.label1On = tic.label2On = False
elif dim==3:
ax = Axes3D(f)
ax.grid(True)
ax.scatter(x_proj[:, 0], x_proj[:, 1],x_proj[:,2] ,lw=0, s=40,c=palette[y.astype(np.int)])
for axi in (ax.xaxis, ax.yaxis,ax.zaxis):
for tic in axi.get_major_ticks():
tic.tick1On = tic.tick2On = False
tic.label1On = tic.label2On = False
f.savefig("sne.png")
def plot_dist(X1,X2,label1,label2,save_dir):
assert save_dir is not None
f=plt.figure()
ax=f.add_subplot(111)
# bins = np.linspace(0, 1, 50)
# _,bins=ax.hist(X1,bins=50)
# print(bins)
#
# if logscale:
# bins = np.logspace(np.log10(bins[0]), np.log10(bins[1]), len(bins))
_, bins, _ = ax.hist(X1, bins=50,range=[0,1],density=True,alpha=0.3,color='r', label=label1)
_ = ax.hist(X2, bins=bins, alpha=0.3,density=True,color='b',label=label2)
# ax.set_yticks([])
ax.legend()
f.savefig(os.path.join(save_dir, "dist"+label1+label2+".png"))
#log scale figure
f_log=plt.figure()
ax_log=f_log.add_subplot(111)
log_bins=np.logspace(np.log10(0.01),np.log10(bins[-1]),len(bins))
_=ax_log.hist(X1, bins=log_bins, range=[0,1],alpha=0.3,density=True,color='r',label=label1)
_ = ax_log.hist(X2, bins=log_bins,density=True, alpha=0.3, color='b', label=label2)
# ax_log.set_yticks([])
ax_log.legend()
ax_log.set_xscale('log')
ax_log.set_xticks([round(x,2) for x in log_bins[::5]])
ax_log.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax_log.set_xticklabels([round(x,2) for x in log_bins[::5]], rotation=45)
f_log.savefig(os.path.join(save_dir,"logdist"+label1+label2+".png"))
def save_pair_fig(input,output,save_path):
'''
save pair signal (current for first channel)
:param input: input signal NxL
:param output: output signal
:param save_path:
:return:
'''
save_ts_heatmap(input,output,save_path)
# x_points = np.arange(input.shape[1])
# fig, ax = plt.subplots(1, 2,figsize=(6, 6))
# sig_in = input[ 0, :]
# sig_out=output[0,:]
# ax[0].plot(x_points, sig_in)
# ax[1].plot(x_points,sig_out)
# fig.savefig(save_path)
# plt.clf()
# plt.close()
def save_ts_heatmap(input,output,save_path):
x_points = np.arange(input.shape[1])
fig, ax = plt.subplots(2, 1, sharex=True,figsize=(6, 6),gridspec_kw = {'height_ratios':[6,1]})
sig_in = input[0, :]
sig_out = output[0, :]
ax[0].plot(x_points, sig_in,'k-',linewidth=2.5,label="input signal")
ax[0].plot(x_points,sig_out,'k--',linewidth=2.5,label="output signal")
ax[0].set_yticks([])
ax[0].legend(loc="upper right")
heat=(sig_out-sig_in)**2
heat_norm=(heat-np.min(heat))/(np.max(heat)-np.min(heat))
heat_norm=np.reshape(heat_norm,(1,-1))
ax[1].imshow(heat_norm, cmap="jet", aspect="auto")
ax[1].set_yticks([])
#fig.tight_layout()
# fig.show()
# return
fig.savefig(save_path)
plt.clf()
plt.close()
def loss_plot(hist, path = 'Train_hist.png', model_name = ''):
x = range(len(hist['D_loss']))
y1 = hist['D_loss']
y2 = hist['G_loss']
fig = plt.figure()
ax1=fig.add_subplot(111)
ax1.plot(x, y1,'r',label="D_loss")
ax1.set_ylabel('D_loss')
ax2 = ax1.twinx() # this is the important function
ax2.plot(x, y2, 'b',label="G_loss")
ax2.set_ylabel('G_loss')
ax2.set_xlabel('Iter')
fig.legend(loc='upper left')
ax1.grid(False)
ax2.grid(False)
# fig.tight_layout()
path = os.path.join(path, model_name + '_loss.png')
fig.savefig(path)
# fig.show()
if __name__ == '__main__':
import numpy as np
foo = np.random.normal(loc=1, size=100) # a normal distribution
bar = np.random.normal(loc=-1, size=10000) # a normal distribution
max_val=max(np.max(foo),np.max(bar))
min_val=min(np.min(foo),np.min(bar))
foo=(foo-min_val)/(max_val-min_val)
bar=(bar-min_val)/(max_val-min_val)
plot_dist(foo,bar,"1","-1")
import os
import numpy as np
import scipy.io as sio
# import cv2
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from biosppy.signals import ecg
from tqdm import tqdm
ANO_RATIO=0 # add ANO_RATIO(e.g. 0.1%) anomalous to training data
LEFT=140
RIGHT=180
DATA_DIR="./dataset/source/" # source mit-bih data
SAVE_DIR="./dataset/preprocessed/ano0/"
PATIENTS=[100,101,103,105,106,108,109,
111,112,113,114,115,116,117,118,119,
121,122,123,124,
200,201,202,203,205,207,208,209,
210,212,213,214,215,219,
220,221,222,223,228,230,231,232,233,234] #remove 102 104 207 217
'''
As recommended by the AAMI, the records with paced beats(/) were not considered,
namely 102, 104, 107, and 217.
'''
N={"N","L","R"}
S={"a", "J", "A", "S", "j", "e"}
V={"V","E"}
F={"F"}
Q={"/", "f", "Q"}
BEAT=N.union(S,V,F,Q)
ABNORMAL_BEAT=S.union(V,F,Q)
def bisearch(key, array):
'''
search value which is most closed to key
:param key:
:param array:
:return:
'''
lo = 0
hi = len(array)-1
while lo <= hi:
mid = lo + int((hi - lo) / 2)
if key <array[mid]:
hi = mid - 1
elif key>array[mid] :
lo = mid + 1
else:
return array[mid]
if hi<0:
return array[0]
if lo>=len(array):
return array[-1]
return array[hi] if (key-array[hi])<(array[lo]-key) else array[lo]
def processPatient(patient):
normal_samples = []
abnormal_samples = []
samples=[]
record = sio.loadmat(os.path.join(DATA_DIR, str(patient) + ".mat"))
annotation = sio.loadmat(os.path.join(DATA_DIR, str(patient) + "ann.mat"))
if patient==114: # patient 114 record's mlii in lead B
sig=record["signal"][:,1]
sig2=record["signal"][:,0]
else: # others' in lead A
sig = record["signal"][:,0]
sig2=record["signal"][:, 1]
assert len(sig)==len(sig2)
sig_out=ecg.ecg(signal=sig, sampling_rate=360., show=False)
sig=sig_out["filtered"]
sig2=ecg.ecg(signal=sig2, sampling_rate=360., show=False)["filtered"]
r_peaks=sig_out["rpeaks"]
ts = record["tm"]
ann_types = annotation["type"]
ann_signal_idx = annotation["ann"]
for ann_idx, ann_type in enumerate(ann_types):
if ann_type in BEAT:
sig_idx=ann_signal_idx[ann_idx][0]
if sig_idx-LEFT>=0 and sig_idx+RIGHT<len(sig):
if ann_type in N:
closed_rpeak_idx=bisearch(sig_idx,r_peaks)
if abs(closed_rpeak_idx-sig_idx)<10:
# normal_samples.append((sig[sig_idx-LEFT:sig_idx+RIGHT],ann_type))
samples.append(([sig[sig_idx-LEFT:sig_idx+RIGHT],sig2[sig_idx-LEFT:sig_idx+RIGHT]],'N',ann_type))
else:
# abnormal_samples.append((sig[sig_idx-LEFT:sig_idx+RIGHT],ann_type))
AAMI_label=""
if ann_type in S:
AAMI_label = "S"
elif ann_type in V:
AAMI_label = "V"
elif ann_type in F:
AAMI_label = "F"
elif ann_type in Q:
AAMI_label="Q"
else:
raise Exception("annonation type error")
assert AAMI_label!=""
samples.append(([sig[sig_idx - LEFT:sig_idx + RIGHT],sig2[sig_idx-LEFT:sig_idx+RIGHT]], AAMI_label, ann_type))
return np.array(samples)
def process():
Full_samples=[]
N_samples=[]
V_samples=[]
S_samples=[]
F_samples=[]
Q_samples=[]
print("Read from records")
for patient in tqdm(PATIENTS):
samples=processPatient(patient)
for sample in samples:
Full_samples.append(sample)
if sample[1]=="N":
N_samples.append(sample[0])
elif sample[1]=="S":
S_samples.append(sample[0])
elif sample[1]=="V":
V_samples.append(sample[0])
elif sample[1]=="F":
F_samples.append(sample[0])
elif sample[1]=="Q":
Q_samples.append(sample[0])
else:
raise Exception("sample AAMI type error, input type: {}".format(sample[1]))
Full_samples=np.array(Full_samples)
N_samples=np.array(N_samples)
V_samples=np.array(V_samples)
S_samples=np.array(S_samples)
F_samples=np.array(F_samples)
Q_samples=np.array(Q_samples)
np.random.shuffle(N_samples)
np.random.shuffle(V_samples)
np.random.shuffle(S_samples)
np.random.shuffle(F_samples)
np.random.shuffle(Q_samples)
if ANO_RATIO >0:
print("before shapes")
print("N \t:{}".format(N_samples.shape))
print("V \t:{}".format(V_samples.shape))
print("S \t:{}".format(S_samples.shape))
print("F \t:{}".format(F_samples.shape))
print("Q \t:{}".format(Q_samples.shape))
N_size=N_samples.shape[0]
V_size=V_samples.shape[0]
S_size=S_samples.shape[0]
F_size=F_samples.shape[0]
Q_size=Q_samples.shape[0]
ANO_SIZE=V_size+S_size+F_size+Q_size
ano_size=int(ANO_RATIO/(1+ANO_RATIO)*N_size)
V_ano_size=int(ano_size*V_size/ANO_SIZE)
S_ano_size = int(ano_size * S_size / ANO_SIZE)
F_ano_size= int(ano_size * F_size / ANO_SIZE)
Q_ano_size = int(ano_size * Q_size / ANO_SIZE)
if Q_ano_size==0:
Q_ano_size=1
N_samples=np.concatenate([N_samples,V_samples[-V_ano_size:]])
V_samples=V_samples[:-V_ano_size]
N_samples = np.concatenate([N_samples, S_samples[-S_ano_size:]])
S_samples = S_samples[:-S_ano_size]
N_samples = np.concatenate([N_samples, F_samples[-F_ano_size:]])
F_samples = F_samples[:-F_ano_size]
N_samples = np.concatenate([N_samples, Q_samples[-Q_ano_size:]])
Q_samples = Q_samples[:-Q_ano_size]
print("#########")
print("N \t:{}".format(N_samples.shape))
print("V \t:{}".format(V_samples.shape))
print("S \t:{}".format(S_samples.shape))
print("F \t:{}".format(F_samples.shape))
print("Q \t:{}".format(Q_samples.shape))
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
np.save(os.path.join(SAVE_DIR, "full_samples.npy"), Full_samples)
np.save(os.path.join(SAVE_DIR, "N_samples.npy"), N_samples)
np.save(os.path.join(SAVE_DIR, "V_samples.npy"), V_samples)
np.save(os.path.join(SAVE_DIR, "S_samples.npy"), S_samples)
np.save(os.path.join(SAVE_DIR, "F_samples.npy"), F_samples)
np.save(os.path.join(SAVE_DIR, "Q_samples.npy"), Q_samples)
print("Finshed !!!")
if __name__ == '__main__':
process()
import os
import numpy as np
from sklearn.metrics import roc_curve, auc, average_precision_score, f1_score,classification_report,confusion_matrix
from scipy.optimize import brentq
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
def evaluate(labels, scores,res_th=None, saveto=None):
'''
metric for auc/ap
:param labels:
:param scores:
:param res_th:
:param saveto:
:return:
'''
fpr = dict()
tpr = dict()
roc_auc = dict()
# True/False Positive Rates.
fpr, tpr, ths = roc_curve(labels, scores)
roc_auc = auc(fpr, tpr)
# Equal Error Rate
eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
if saveto:
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', lw=lw, label='(AUC = %0.2f, EER = %0.2f)' % (roc_auc, eer))
plt.plot([eer], [1-eer], marker='o', markersize=5, color="navy")
plt.plot([0, 1], [1, 0], color='navy', lw=1, linestyle=':')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.savefig(os.path.join(saveto, "ROC.pdf"))
plt.close()
# best f1
best_f1 = 0
best_threshold = 0
for threshold in ths:
tmp_scores = scores.copy()
tmp_scores[tmp_scores >= threshold] = 1
tmp_scores[tmp_scores < threshold] = 0
cur_f1 = f1_score(labels, tmp_scores)
if cur_f1 > best_f1:
best_f1 = cur_f1
best_threshold = threshold
#threshold f1
if res_th is not None and saveto is not None:
tmp_scores = scores.copy()
tmp_scores[tmp_scores >= res_th] = 1
tmp_scores[tmp_scores < res_th] = 0
print(classification_report(labels,tmp_scores))
print(confusion_matrix(labels,tmp_scores))
auc_prc=average_precision_score(labels,scores)
return auc_prc,roc_auc,best_threshold,best_f1
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-37.733500 -22.836800 -22.218800 8.504300
-37.786000 -23.403700 -21.260300 7.498800
-37.544100 -23.847200 -20.380000 6.305360
-37.151000 -24.213000 -19.835100 4.895550
-36.869600 -24.018000 -19.328700 3.205630
-36.810600 -24.022200 -18.198600 2.114290
-37.011000 -24.387700 -17.716800 0.403600
-36.817600 -24.838100 -17.248500 -1.114130
-36.540100 -24.859000 -16.744300 -2.704720
-36.142500 -24.955600 -15.864600 -3.805170
-35.892700 -24.953700 -14.865400 -4.748300
-35.748900 -25.124200 -14.422200 -6.222440
-35.337300 -25.299200 -14.090700 -7.920750
-35.253900 -25.536600 -13.642400 -9.707040
-35.117500 -25.807100 -12.131900 -10.017700
-34.916200 -26.032700 -10.704400 -10.392100
-34.419900 -26.147400 -10.653100 -12.667700
-33.735900 -26.104300 -9.743190 -13.787200
-34.530800 -27.430800 -8.654470 -14.333200
-33.777000 -27.127400 -8.089600 -15.770500
-33.092100 -27.135700 -7.216910 -16.984200
-32.780900 -27.454800 -6.539800 -17.998000
-32.629900 -28.218100 -5.807190 -18.782700
-32.370800 -28.944500 -5.705980 -20.090300
-31.883600 -29.120000 -5.526330 -20.955500
-30.537000 -28.417700 -4.887790 -21.794900
-30.733500 -29.486600 -4.690710 -22.969800
-30.980900 -30.650500 -5.048980 -24.630900
-31.120700 -31.521000 -4.366330 -25.225400
-30.349300 -31.298700 -3.691870 -25.595300
-29.745800 -31.453700 -3.686600 -26.979700
-29.161100 -31.708600 -2.336180 -26.886900
-28.528900 -31.855300 -1.127990 -27.200500
-27.828000 -31.823800 -0.360050 -27.826700
-27.270700 -31.980400 0.300805 -28.318300
-27.322100 -32.554200 0.979346 -28.516800
-27.783000 -33.477200 1.615570 -28.713900
-26.526400 -32.671200 2.176050 -28.844700
-25.987100 -32.720200 3.115800 -28.796900
-25.206200 -32.582900 4.079080 -28.654600
-25.178100 -33.240300 5.114870 -28.595000
-25.690000 -34.157300 5.682890 -28.838900
-25.284500 -34.256900 7.108960 -28.668200
-24.610000 -34.194000 7.966780 -28.684900
-23.601200 -33.592000 9.126510 -28.658300
-23.727600 -33.964700 10.297700 -28.554300
-23.559900 -34.104400 11.083200 -28.034900
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
import argparse
from preprocess import load_data
from model import BeatGAN_MOCAP
device = torch.device("cuda:0" if
torch.cuda.is_available() else "cpu")
if not os.path.exists("output"):
os.mkdir("output")
dataloader=load_data()
print("load data success!!!")
model=BeatGAN_MOCAP(dataloader,device)
parser = argparse.ArgumentParser()
parser.add_argument('-m', type=str, default='train',help="mode: /train/eval ")
args = parser.parse_args()
if args.m=="train":
model.train()
elif args.m=="eval":
model.test_score_dist() #for score distribution. Fig 4
elif args.m=="pic":
model.test_for_pic() # for case study on walk/run/jump. part of Fig 3
else:
raise Exception("args error m:{}".format(args.m))
import os,pickle,time,sys
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.image as mpimg
import matplotlib
matplotlib.use("Agg")
matplotlib.rcParams.update({'font.size': 25})
plt.rcParams["font.family"] = "Times New Roman"
dirname=os.path.dirname
sys.path.insert(0,dirname(dirname(os.path.abspath(__file__))))
from metric import evaluate
def weights_init(mod):
"""
Custom weights initialization called on netG, netD and netE
:param m:
:return:
"""
classname = mod.__class__.__name__
if classname.find('Conv') != -1:
# mod.weight.data.normal_(0.0, 0.02)
nn.init.xavier_normal_(mod.weight.data)
# nn.init.kaiming_uniform_(mod.weight.data)
elif classname.find('BatchNorm') != -1:
mod.weight.data.normal_(1.0, 0.02)
mod.bias.data.fill_(0)
elif classname.find('Linear') !=-1 :
torch.nn.init.xavier_uniform(mod.weight)
mod.bias.data.fill_(0.01)
class Generator(nn.Module):
def __init__(self, nc):
super(Generator, self).__init__()
self.encoder = nn.Sequential(
# input is (nc) x 64
nn.Linear(nc * 64, 256),
nn.Tanh(),
nn.Linear(256, 128),
nn.Tanh(),
nn.Linear(128, 32),
nn.Tanh(),
nn.Linear(32, 10),
)
self.decoder = nn.Sequential(
nn.Linear(10, 32),
nn.Tanh(),
nn.Linear(32, 128),
nn.Tanh(),
nn.Linear(128, 256),
nn.Tanh(),
nn.Linear(256, nc * 64),
nn.Tanh(),
)
def forward(self, input):
input=input.view(input.shape[0],-1)
z = self.encoder(input)
output = self.decoder(z)
output=output.view(output.shape[0],4,-1)
return output
class Discriminator(nn.Module):
def __init__(self, nc):
super(Discriminator, self).__init__()
self.features = nn.Sequential(
# input is (nc) x 64
nn.Linear(nc * 64, 256),
nn.Tanh(),
nn.Linear(256, 128),
nn.Tanh(),
nn.Linear(128, 32),
nn.Tanh(),
)
self.classifier=nn.Sequential(
nn.Linear(32, 1),
nn.Sigmoid()
)
def forward(self, input):
input=input.view(input.shape[0],-1)
features = self.features(input)
# features = self.feat(features.view(features.shape[0],-1))
# features=features.view(out_features.shape[0],-1)
classifier = self.classifier(features)
classifier = classifier.view(-1, 1).squeeze(1)
return classifier, features
class BeatGAN_MOCAP:
def __init__(self, dataloader, device):
self.dataloader = dataloader
self.device = device
self.batchsize = 64
self.nz = 10
self.niter = 300
self.nc=4
self.lr=0.0001
self.beta1=0.5
self.G = Generator( self.nc).to(device)
self.G.apply(weights_init)
self.D = Discriminator(self.nc).to(device)
self.D.apply(weights_init)
# Loss Functions
self.bce_criterion = nn.BCELoss()
self.l1_criterion=nn.L1Loss()
self.mse_criterion=nn.MSELoss()
self.optimizerD = optim.Adam(self.D.parameters(), lr=self.lr, betas=(self.beta1, 0.999))
self.optimizerG = optim.Adam(self.G.parameters(), lr=self.lr, betas=(self.beta1, 0.999))
self.total_steps = 0
self.cur_epoch = 0
self.real_label = 1
self.fake_label = 0
# -- Discriminator attributes.
self.err_d_real = None
self.err_d_fake = None
# -- Generator attributes.
self.err_g = None
def train(self):
self.train_hist = {}
self.train_hist['D_loss'] = []
self.train_hist['G_loss'] = []
self.train_hist['per_epoch_time'] = []
self.train_hist['total_time'] = []
print(" Train model ")
best_auc=0
best_auc_epoch=0
for epoch in range(self.niter):
self.cur_epoch+=1
self.train_epoch()
auc,th,f1=self.validate()
if auc > best_auc:
best_auc = auc
best_auc_epoch=self.cur_epoch
self.save_weight_GD()
print("[{}] auc:{:.4f} th:{:.4f} f1:{:.4f} \t best_auc:{:.4f} in epoch[{}]\n".format(self.cur_epoch,auc,th,f1,best_auc,best_auc_epoch ))
def train_epoch(self):
self.G.train()
self.D.train()
for train_x,train_y in self.dataloader["train"]:
train_x=train_x.to(self.device)
train_y=train_y.to(self.device)
for i in range(1):
self.D.zero_grad()
batch_size=train_x.shape[0]
self.y_real_, self.y_fake_ = torch.ones(batch_size).to(self.device), torch.zeros(batch_size).to(
self.device)
# Train with real
out_d_real, _ = self.D(train_x)
# Train with fake
fake = self.G(train_x)
out_d_fake, _ = self.D(fake)
# --
self.err_d_real = self.bce_criterion(out_d_real,self.y_real_)
self.err_d_fake = self.bce_criterion(out_d_fake,self.y_fake_)
self.err_d = self.err_d_real + self.err_d_fake
self.err_d.backward(retain_graph=True)
self.optimizerD.step()
self.G.zero_grad()
_, feat_fake = self.D(fake)
_, feat_real = self.D(train_x)
self.err_g_adv = self.mse_criterion(feat_fake, feat_real) # loss for feature matching
self.err_g_rec = self.mse_criterion(fake, train_x) # constrain x' to look like x
self.err_g = self.err_g_rec+0.01*self.err_g_adv
self.err_g.backward()
self.optimizerG.step()
# print(self.err_g.item())
print("d_loss(R/F):{}/{}, g_loss:A/R{}/{}".format(self.err_d_real.item(),self.err_d_fake.item(),self.err_g_adv.item(),self.err_g_rec))
if self.cur_epoch %50==0:
fig, axarr = plt.subplots(2, 2, sharex=True, figsize=(6, 6))
fake_ts=self.G(train_x).detach().cpu().numpy()
input_ts=train_x.cpu().numpy()
for r in range(2):
axarr[r, 0].plot(np.transpose(input_ts[r],(1,0)))
axarr[r, 0].set_ylim(-1, 1)
for r in range(2):
axarr[r, 1].plot(np.transpose(fake_ts[r],(1,0)))
axarr[r, 1].set_ylim(-1, 1)
# fig.savefig("./output/train/"+str(self.cur_epoch)+".png")
def validate(self):
y_, y_pred = self.predict(self.dataloader["test"])
rocprc, rocauc, best_th, best_f1 = evaluate(y_, y_pred)
return rocauc, best_th, best_f1
def predict(self,dataloader_,scale=True):
with torch.no_grad():
# Create big error tensor for the test set.
test_pair=[]
self.an_scores = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.float32, device=self.device)
self.gt_labels = torch.zeros(size=(len(dataloader_.dataset),), dtype=torch.long, device=self.device)
# self.dis_feat = torch.zeros(size=(len(dataloader_.dataset), self.opt.ndf*16*10), dtype=torch.float32,
# device=self.device)
for i, data in enumerate(dataloader_, 0):
test_x=data[0].to(self.device)
test_y=data[1].to(self.device)
fake = self.G(test_x)
error = torch.mean(
torch.pow((test_x.view(test_x.shape[0], -1) - fake.view(fake.shape[0], -1)), 2),
dim=1)
self.an_scores[i*self.batchsize : i*self.batchsize+error.size(0)] = error.reshape(error.size(0))
self.gt_labels[i*self.batchsize : i*self.batchsize+error.size(0)] = test_y.reshape(error.size(0))
# self.dis_feat[i * self.opt.batchsize: i * self.opt.batchsize + error.size(0), :] = d_feat.reshape(
# error.size(0), self.opt.ndf*16*10)
# Scale error vector between [0, 1]
if scale:
self.an_scores = (self.an_scores - torch.min(self.an_scores)) / (torch.max(self.an_scores) - torch.min(self.an_scores))
y_=self.gt_labels.cpu().numpy()
y_pred=self.an_scores.cpu().numpy()
# print(y_pred)
return y_,y_pred
def save_weight_GD(self):
# save_dir=os.path.join("./output/","model")
save_dir="model"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(self.G.state_dict(),
os.path.join(save_dir, 'G.pkl'))
torch.save(self.D.state_dict(),
os.path.join(save_dir,'D.pkl'))
def load(self):
save_dir = "model"
self.G.load_state_dict(
torch.load(os.path.join(save_dir, 'G.pkl')))
self.D.load_state_dict(
torch.load(os.path.join(save_dir,'D.pkl')))
def test_score_dist(self):
self.load()
self.G.eval()
self.D.eval()
y_, y_pred = self.predict(self.dataloader["test"])
f = plt.figure()
ax = f.add_subplot(111)
X1=[]
X2=[]
for gt,sc in zip(y_,y_pred):
if gt==0:
X1.append(sc)
else:
X2.append(sc)
_, bins, _ = ax.hist(X1, bins=55, range=[0, 1], density=True, alpha=0.3, color='r', label="walk")
_ = ax.hist(X2, bins=bins, alpha=0.3, density=True, color='b', label="others")
ax.set_yticks([])
ax.set_xticks(np.arange(0,1.2, 0.2))
ax.legend()
f.savefig(os.path.join("./output", "dist.pdf"))
auc_prc, roc_auc, best_threshold, best_f1=evaluate(y_, y_pred)
print("ap:{}".format(auc_prc))
print("auc:{}".format(roc_auc))
print("best threshold:{} ==> F1:{}".format(best_threshold,best_f1))
def test_for_exp(self):
self.load()
self.G.eval()
self.D.eval()
test_0_cnt=0
test_1_cnt=1
legends=["lhumerus","rhumerus" , "lfemur", "rfemur"]
with torch.no_grad():
for i, data in enumerate(self.dataloader["test"], 0):
data_x,data_y=data[0],data[1]
gen_x=self.G(data_x.to(self.device))
for i in range(data_x.shape[0]):
input_x,input_y=data_x[i],data_y[i]
if input_y.item() ==1:
test_1_cnt+=1
if test_1_cnt>20:
return
fig, axarr = plt.subplots(1,2, figsize=(6, 6))
fake_ts = gen_x[i].cpu().numpy()
input_ts = input_x.cpu().numpy()
ax0=axarr[0].plot(np.transpose(input_ts, (1, 0)))
ax1=axarr[ 1].plot(np.transpose(fake_ts, (1, 0)))
fig.legend(iter(ax0), legends)
# ax1.legend(iter(ax1), legends)
fig.savefig("./output/test1/" + str(test_1_cnt) + ".png")
else:
test_0_cnt += 1
if test_0_cnt > 20:
return
fig, axarr = plt.subplots(1, 2, figsize=(6, 6))
fake_ts = gen_x[i].cpu().numpy()
input_ts = input_x.cpu().numpy()
ax0=axarr[0].plot(np.transpose(input_ts, (1, 0)))
ax1=axarr[1].plot(np.transpose(fake_ts, (1, 0)))
fig.legend(iter(ax0), legends)
fig.savefig("./output/test0/" + str(test_1_cnt) + ".png")
def test_for_pic(self):
from preprocess import load_for_pic
self.load()
self.G.eval()
self.D.eval()
walk_ts,run_ts=load_for_pic(ts_type="run")
print(walk_ts.shape)
input_walk=[]
output_walk=[]
walk_heat=[]
input_run = []
output_run = []
run_heat = []
legends = ["lhumerus", "rhumerus", "lfemur", "rfemur"]
with torch.no_grad():
#get res from walk
for i in range(int(walk_ts.shape[1]/64)):
ts=walk_ts[:,i*64:i*64+64]
assert ts.shape[0]==4
fake_ts=self.G(torch.from_numpy(np.array([ts])).float())
fake_ts=fake_ts.numpy()[0]
# heat_score=np.sqrt(np.sum((ts-fake_ts)**2,axis=0))
heat_score=np.max((ts-fake_ts)**2,axis=0)
input_walk.append(ts)
output_walk.append(fake_ts)
walk_heat.append(heat_score)
input_walk=np.concatenate(input_walk, axis=1)
output_walk=np.concatenate(output_walk, axis=1)
walk_heat=np.concatenate(walk_heat)
assert input_walk.shape[0]==4
# get res from run
for i in range(int(run_ts.shape[1] / 64)):
ts = run_ts[:,i * 64:i * 64 + 64]
assert ts.shape[0] == 4
fake_ts = self.G(torch.from_numpy(np.array([ts])).float().to(self.device))
fake_ts = fake_ts.numpy()[0]
heat_score = np.sqrt(np.sum((ts - fake_ts) ** 2, axis=0))
input_run.append(ts)
output_run.append(fake_ts)
run_heat.append(heat_score)
input_run = np.concatenate(input_run, axis=1)
output_run = np.concatenate(output_run, axis=1)
run_heat = np.concatenate(run_heat)
assert input_run.shape[0] == 4
SIZE = 1
for i in range(len(walk_heat / SIZE)):
walk_heat[SIZE * i:SIZE * i + SIZE] = np.sum(walk_heat[SIZE * i:SIZE * i + SIZE]) / SIZE
for i in range(len(run_heat / SIZE)):
run_heat[SIZE * i:SIZE * i + SIZE] = np.sum(run_heat[SIZE * i:SIZE * i + SIZE]) / SIZE
max_val=max(np.max(walk_heat),np.max(run_heat))
min_val=min(np.min(walk_heat),np.min(run_heat))
walk_heat_norm = np.reshape((walk_heat-min_val)/(max_val-min_val), (1, -1))
run_heat_norm=np.reshape((run_heat-min_val)/(max_val-min_val),(1,-1))
# for fig
walk_points = np.arange(input_walk.shape[1])
run_points = np.arange(input_run.shape[1])
fig = plt.figure(figsize=(14,3 ))
outer = gridspec.GridSpec(2, 3 ,wspace=0.2,height_ratios=[7, 1], width_ratios=[18,3,2])
for i in range(6):
if i == 0:
inner = gridspec.GridSpecFromSubplotSpec(1, 1,
subplot_spec=outer[i])
ax = plt.Subplot(fig, inner[0])
ax0 = ax.plot(walk_points, np.transpose(input_walk))
ax.set_xlim((0, 210+128))
ax.set_yticks([])
ax.set_xticks([])
ax.margins(0)
fig.add_subplot(ax)
elif i == 1:
inner = gridspec.GridSpecFromSubplotSpec(2, 1,
subplot_spec=outer[i], hspace=0.1)
ax_inner_0 = plt.Subplot(fig, inner[0])
start=210
ax_inner_0.plot(run_points, np.transpose(input_walk)[start:start+run_points.shape[0],:])
ax_inner_0.set_yticks([])
ax_inner_0.set_xticks([])
ax_inner_0.margins(0)
fig.add_subplot(ax_inner_0)
ax_inner_1 = plt.Subplot(fig, inner[1])
ax_inner_1.plot(run_points, np.transpose(input_run))
ax_inner_1.set_yticks([])
ax_inner_1.set_xticks([])
ax_inner_1.margins(0)
fig.add_subplot(ax_inner_1)
elif i == 2:
inner = gridspec.GridSpecFromSubplotSpec(2, 1,
subplot_spec=outer[i], hspace=0.1)
ax_inner_0 = plt.Subplot(fig, inner[0])
fig_walk= mpimg.imread("model/fig_walk.png")
ax_inner_0.imshow(fig_walk,aspect="auto")
ax_inner_0.set_yticks([])
ax_inner_0.set_xticks([])
fig.add_subplot(ax_inner_0)
ax_inner_1 = plt.Subplot(fig, inner[1])
fig_run=mpimg.imread("model/fig_run.png")
ax_inner_1.imshow(fig_run,aspect="auto")
ax_inner_1.set_yticks([])
ax_inner_1.set_xticks([])
fig.add_subplot(ax_inner_1)
elif i == 3:
inner = gridspec.GridSpecFromSubplotSpec(1, 1,
subplot_spec=outer[i])
ax = plt.Subplot(fig, inner[0])
ax.imshow(walk_heat_norm, cmap="jet", aspect="auto",vmin = 0,vmax = 0.6)
ax.set_yticks([])
# ax.set_xticks([])
fig.add_subplot(ax)
elif i == 4:
inner = gridspec.GridSpecFromSubplotSpec(1, 1,
subplot_spec=outer[i])
ax = plt.Subplot(fig, inner[0])
ax.imshow(run_heat_norm, cmap="jet", aspect="auto",vmin = 0,vmax = 0.6)
ax.set_yticks([])
fig.add_subplot(ax)
# fig.legend(iter(ax0), legends,loc="upper left")
leg=fig.legend(iter(ax0),legends,bbox_to_anchor=(0.12, 0.985), loc='upper left', ncol=4,prop={'size': 12})
leg.get_frame().set_alpha(0)
# fig.tight_layout()
# fig.show()
# return
fig.savefig("./output/pic_run.pdf")
plt.clf()
plt.close()
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import os
import numpy as np
import torch
from torch.utils.data import DataLoader,TensorDataset
DATA_DIR="./dataset"
WALK=["35_01","35_02","35_03","35_04","35_05","35_06","35_07","35_08","35_09","35_10",
"35_11","35_12","35_13","35_14","35_15","35_16"]
RUN=["35_17","35_18","35_19","35_20","35_21","35_22","35_23","35_24","35_25","35_26"]
WINDOW=64
STRIDE_TRAIN=5
STRIDE_TEST=20
def normalize(seq):
'''
normalize to [-1,1]
:param seq:
:return:
'''
return 2*(seq-np.min(seq))/(np.max(seq)-np.min(seq))-1
def read_mocap_file(file_path):
timeseries=[]
with open(file_path,"r") as f:
for line in f.readlines():
x=line.strip().split(" ")
timeseries.append([float(xx) for xx in x])
timeseries=np.array(timeseries)
for i in range(timeseries.shape[1]):
timeseries[:,i]=normalize(timeseries[:,i])
return timeseries
def stat_data():
cnt=0
for walk in WALK:
path=os.path.join(DATA_DIR, "walk", walk + ".amc.4d")
ts=read_mocap_file(path)
# print(ts.shape)
cnt+=ts.shape[0]
print (cnt)
for run in RUN:
path = os.path.join(DATA_DIR, "run", run + ".amc.4d")
ts = read_mocap_file(path)
# print(ts.shape)
cnt += ts.shape[0]
print(cnt)
path = os.path.join(DATA_DIR, "other", "49_02.amc.4d")
ts = read_mocap_file(path)
cnt += ts.shape[0]
print(cnt)
def get_from_one(file_path,train=True):
ts=read_mocap_file(file_path)
ts_length=ts.shape[0]
samples=[]
stride=STRIDE_TRAIN if train else STRIDE_TEST
for start in np.arange(0,ts_length,stride):
if start+WINDOW>=ts_length:
break
samples.append(ts[start:start+WINDOW,:])
# print(len(samples))
# print(ts_length)
# print(WINDOW)
# print(stride)
assert len(samples)== np.ceil(((ts_length-WINDOW)/stride))
return np.array(samples)
def load_data():
batchsize=64
train_x=None
for walk in WALK[:-2]:
ts=get_from_one(os.path.join(DATA_DIR,"walk",walk+".amc.4d"),train=True)
if train_x is None:
train_x=ts
else:
train_x=np.concatenate([train_x,ts])
train_y=np.zeros([train_x.shape[0],1])
test_x=None
normal_test_cnt=0
for walk in WALK[-2:]:
ts = get_from_one(os.path.join(DATA_DIR, "walk", walk + ".amc.4d"), train=True)
if test_x is None:
test_x=ts
else:
test_x = np.concatenate([test_x, ts])
normal_test_cnt+=ts.shape[0]
for run in RUN[:]:
ts = get_from_one(os.path.join(DATA_DIR, "run", run + ".amc.4d"), train=True)
test_x = np.concatenate([test_x, ts])
# add jump test data for experiment
ts = get_from_one(os.path.join(DATA_DIR,"other","49_02.amc.4d"),train=True)
test_x = np.concatenate([test_x, ts])
test_y=np.ones([test_x.shape[0],1])
test_y[:normal_test_cnt,:]=0
train_x=np.transpose(train_x,(0,2,1))
test_x=np.transpose(test_x, (0, 2, 1))
print(train_x.shape)
print(test_x.shape)
# print(normal_test_cnt)
# print(test_y)
train_dataset = TensorDataset(torch.Tensor(train_x), torch.Tensor(train_y))
test_dataset = TensorDataset(torch.Tensor(test_x), torch.Tensor(test_y))
dataloader = {"train": DataLoader(
dataset=train_dataset, # torch TensorDataset format
batch_size=batchsize, # mini batch size
shuffle=True,
num_workers=0,
drop_last=False),
"test": DataLoader(
dataset=test_dataset, # torch TensorDataset format
batch_size=batchsize, # mini batch size
shuffle=True,
num_workers=0,
drop_last=False),
}
return dataloader
def load_for_pic(ts_type="run"):
walk_ts = read_mocap_file(os.path.join(DATA_DIR, "walk", WALK[-1] + ".amc.4d"))
walk_ts = np.transpose(walk_ts)
if ts_type=="run":
run_ts = read_mocap_file(os.path.join(DATA_DIR, "run", RUN[1] + ".amc.4d"))
run_ts = np.transpose(run_ts)
ret_ts=run_ts
elif ts_type=="jump":
jump_ts=read_mocap_file(os.path.join(DATA_DIR, "other", "49_02.amc.4d"))
jump_ts = np.transpose(jump_ts)
ret_ts=jump_ts[:,600:750] #jump
# ret_ts=jump_ts[:1500,1650] #hop
else:
raise Exception("ts type error!!!")
return walk_ts,ret_ts
if __name__ == '__main__':
# get_from_one(os.path.join(DATA_DIR,"run",RUN[0]+".amc.4d"))
# load_data()
stat_data()
# ts1,ts2=load_for_pic(ts_type="jump")
# print(ts1.shape)
# print(ts2.shape)
<changed>
model.py
1) Error appear
Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
-> class BeatGan
-> self.bce_criterion = nn.BCELoss().cuda()
self.mse_criterion = nn.MSELoss().cuda()
-> def update_netd
->
self.err_d_real = self.bce_criterion(self.out_d_real, torch.full((self.batchsize,), self.real_label, device=self.device).type(torch.FloatTensor).cuda())
self.err_d_fake = self.bce_criterion(self.out_d_fake, torch.full((self.batchsize,), self.fake_label, device=self.device).type(torch.FloatTensor).cuda())
data.py
1) if you use 2D signal data, the data shape must (num_of_data, 1, width, height)
2) I changed "np.load()" parts for utilize.
3) normalize part is changed.
network.py
1) Conv2D(input channel, output channel, kernel_size, stride, padding)
output size = output channel * (input_size-kernel_size+2*padding)/stride+1
We use 128*128 data so we set "Encoder"'s Conv2d's parameters like that.
The result is 50*1*1
2) Decoder is inverse of Encoder
option.py
1) change batchsize from 64 to 32, because Memory allocate issue was happened.
etc)
1) 2D-data must have the shape (n,1,w,h)
1D-data is (n,1,w)
1-1) So, you should go to experiments/ecg/dataset/preprocessed.
Push change.py and change2.py to ano0.
Diretory ano0 should have _ _samples.npy file.
#!/bin/bash
cd experiments/ecg
test=1 # 0 means train the model, 1 means evaluate the model
threshold=0.02
fold_cnt=1
dataroot="./dataset/preprocessed/ano0/"
model="beatgan"
w_adv=1
niter=100
lr=0.0001
n_aug=0
outf="./output"
for (( i=0; i<$fold_cnt; i+=1))
do
echo "#################################"
echo "######## Folder $i ############"
if [ $test = 0 ]; then
python -u main.py \
--dataroot $dataroot \
--model $model \
--niter $niter \
--lr $lr \
--outf $outf \
--folder $i
else
python -u main.py \
--dataroot $dataroot \
--model $model \
--niter $niter \
--lr $lr \
--outf $outf \
--folder $i \
--outf $outf \
--istest \
--threshold $threshold
fi
done
#!/bin/bash
cd experiments/ecg
test=1 # 0 means train the model, 1 means evaluate the model
threshold=0.02
fold_cnt=1
dataroot="./dataset/preprocessed/ano0/"
model="beatgan"
w_adv=1
niter=5
lr=0.0001
n_aug=0
outf="./output"
for (( i=0; i<$fold_cnt; i+=1))
do
echo "#################################"
echo "######## Folder $i ############"
if [ $test = 0 ]; then
python -u main.py \
--dataroot $dataroot \
--model $model \
--niter $niter \
--lr $lr \
--outf $outf \
--folder $i
else
python -u main.py \
--dataroot $dataroot \
--model $model \
--niter $niter \
--lr $lr \
--outf $outf \
--folder $i \
--outf $outf \
--istest \
--threshold $threshold
fi
done
cd experiments/ecg
python demo.py
\ No newline at end of file
#!/bin/bash
cd experiments/mocap
# python main.py # for train
python main.py -m eval # for evaluation