def main():
import torch
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.autograd import Variable
from trainer import fit
import numpy as np
cuda = torch.cuda.is_available()
# Training settings
parser = argparse.ArgumentParser(
description='cross subject domain adaptation')
parser.add_argument('--batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=100,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model',
action='store_true',
default=True,
help='For Saving the current Model')
# Writer will output to ./runs/ directory by default
fold_idx = 4
gamma = 1.0
margin = 1.0
DAsetting = False
args = parser.parse_args()
args.seed = 0
args.use_tensorboard = True
args.save_model = True
n_epochs = 100
folder_name = 'exp7_deep100'
comment = 'w/bn fold_' + str(fold_idx) + '_g_' + str(gamma) + '_m_' + str(
margin)
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
device = torch.device("cuda" if use_cuda else "cpu")
#kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
from datetime import datetime
import os
loging = False
x_data, y_data = load_smt()
#get subject number
y_subj = np.zeros([108, 200])
for i in range(108):
y_subj[i, :] = i * 2
y_subj = y_data.reshape(108, 200) + y_subj
y_subj = y_subj.reshape(21600)
#y_subj = np.concatenate([y_data,y_subj],axis=1)
# For classification data
valtype = 'subj'
# if x_data.shape[2] != 60:
# x_data = x_data[:,:,2:,:]
# plt.imshow(x_data[1000,0,:,:])
# #subj - 0-27 train
# train_subj1 = np.r_[0:27]
# train_subj2 = np.r_[0:27]+54
#
# test_subj = np.r_[27:54,54+27:108]
#chidx = np.r_[7:11, 12:15, 17:21, 32:41] #오연조건
# chidx = np.r_[2:56, 60:62]
# x_data = x_data[:,:,chidx,:]
# For Domain adaptation setting
if DAsetting:
train_subj1 = np.r_[27:54]
train_subj2 = np.r_[27:54] + 54
test_subj = np.r_[0:27, 54 + 0:54 + 27]
trial_s = (0, 200)
trial_t = (0, 200)
trial_val = (0, 200)
dataset_train1 = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj1,
trial=trial_s)
dataset_train2 = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj2,
trial=trial_t)
dataset_train = dataset_train1.__add__(dataset_train2)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
triplet_dataset_train1 = TripletGiga(x=x_data,
y=y_subj,
valtype=valtype,
istrain=True,
subj=train_subj1,
trial=trial_s)
triplet_dataset_train2 = TripletGiga(x=x_data,
y=y_subj,
valtype=valtype,
istrain=True,
subj=train_subj2,
trial=trial_t)
triplet_dataset_train = triplet_dataset_train1.__add__(
triplet_dataset_train2)
triplet_dataset_test = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
else: #DG setting
test_subj = np.r_[fold_idx * 9:fold_idx * 9 + 9,
fold_idx * 9 + 54:fold_idx * 9 + 9 + 54]
print('test subj:' + str(test_subj))
train_subj = np.setxor1d(np.r_[0:108], test_subj)
trial_train = (0, 200)
trial_val = (0, 200)
dataset_train = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj,
trial=trial_train)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
triplet_dataset_train = TripletGiga2(x=x_data,
y=y_subj,
valtype=valtype,
istrain=True,
subj=train_subj,
trial=trial_train)
triplet_dataset_test = TripletGiga2(x=x_data,
y=y_subj,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
train_loader = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset_test,
batch_size=args.batch_size,
shuffle=False)
triplet_train_loader = torch.utils.data.DataLoader(
triplet_dataset_train, batch_size=args.batch_size, shuffle=True)
triplet_test_loader = torch.utils.data.DataLoader(
triplet_dataset_test, batch_size=args.batch_size, shuffle=False)
###################################################################################################################
# make model for metric learning
from networks import basenet, Deep4Net, EmbeddingDeep4CNN, EmbeddingDeep4CNN_bn, TripletNet, FineShallowCNN, EmbeddingDeepCNN, QuintupletNet, EmbeddingShallowCNN
from losses import TripletLoss_dev2, TripLoss
embedding_net = Deep4Net()
print(embedding_net)
model = TripletNet(embedding_net)
#exp3-1 fc레이어 한층더
# model.fc = nn.Sequential(
# nn.Linear(model.num_hidden,128),
# nn.ReLU(),
# nn.Dropout(),
# nn.Linear(128,2)
# )
if cuda:
model.cuda()
loss_fn = TripletLoss_dev2(margin, gamma).cuda()
log_interval = 10
# ##########################################################
# optimizer = optim.Adam(model.parameters(), lr=0.01)
# scheduler = lr_scheduler.StepLR(optimizer, 10, gamma=1, last_epoch=-1)
# exp1 : 62ch 0~5fold까지 셋팅
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=0.5, last_epoch=-1)
#exp2 : 운동영역주변 20ch, train성능이 fit하지 않는 현상이 g=0.7,1.0 양족에서 모두 나타나서, 기존의 러닝레이트보다 강하게 줘보고 실험코자함
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=1.0, last_epoch=-1)
# #
#exp4, exp5
optimizer = optim.SGD(model.parameters(), lr=0.005 / gamma, momentum=0.9)
scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=0.8,
last_epoch=-1) #너무 빨리 떨구면 언더피팅하는듯
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=0.8, last_epoch=-1) #너무 빨리 떨구면 언더피팅하는듯
# exp5
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# scheduler = lr_scheduler.StepLR(optimizer, 10, gamma=0.5, last_epoch=-1)
#model for validation
evalmodel = nn.Sequential(model.embedding_net, model.fc,
nn.LogSoftmax(dim=1)).to(device)
print('____________DANet____________')
print(model)
#save someting
if (args.save_model):
model_save_path = 'model/' + folder_name + '/' + comment + '/'
if not os.path.isdir(model_save_path):
os.makedirs(model_save_path)
if loging:
fname = model_save_path + datetime.today().strftime(
"%m_%d_%H_%M") + ".txt"
f = open(fname, 'w')
if args.use_tensorboard:
writer = SummaryWriter(comment=comment)
# load_model_path = 'C:\\Users\dk\PycharmProjects\giga_cnn\model\deep100_negsubj\\fold_0_g_0.7\danet_0.7_49.pt'
#'C:\\Users\dk\PycharmProjects\giga_cnn\구모델\\clf_83_8.pt'#'clf_29.pt' #'triplet_mg26.pt'#'clf_triplet2_5.pt' #'triplet_31.pt'
load_model_path = 'C:\\Users\dk\PycharmProjects\giga_cnn\model\exp6_basenet\\fold_0_g_0.6\danet_0.6_86.pt'
load_model_path = None
if load_model_path is not None:
model.load_state_dict(torch.load(load_model_path))
for epochidx in range(n_epochs):
fit(triplet_train_loader, triplet_test_loader, model, loss_fn,
optimizer, scheduler, epochidx, n_epochs, cuda, log_interval)
print(epochidx)
train_loss, train_score = eval(args, evalmodel, device, train_loader)
eval_loss, eval_score = eval(args, evalmodel, device, test_loader)
if args.use_tensorboard:
writer.add_scalar('Train/Loss',
np.mean(train_loss) / 100, epochidx)
writer.add_scalar('Train/Acc',
np.mean(train_score) / 100, epochidx)
writer.add_scalar('Eval/Loss', np.mean(eval_loss) / 100, epochidx)
writer.add_scalar('Eval/Acc', np.mean(eval_score) / 100, epochidx)
writer.close()
if args.save_model:
torch.save(
model.state_dict(), model_save_path + 'danet_' + str(gamma) +
'_' + str(epochidx) + '.pt')
def main():
import torch
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.autograd import Variable
from trainer import fit
import numpy as np
cuda = torch.cuda.is_available()
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=100,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model',
action='store_true',
default=True,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
from datetime import datetime
import os
loging = False
ismultitask = False
loso = False
if (args.save_model):
model_save_path = 'model/triplet/'
if not os.path.isdir(model_save_path):
os.makedirs(model_save_path)
if loging:
fname = model_save_path + datetime.today().strftime(
"%m_%d_%H_%M") + ".txt"
f = open(fname, 'w')
x_data, y_data = load_smt()
y_subj = np.zeros([108, 200])
for i in range(108):
y_subj[i, :] = i * 2
y_subj = y_data.reshape(108, 200) + y_subj
y_subj = y_subj.reshape(21600)
# nonbciilli = np.s_[0,1,2,4,5,8,16,17,18,20,21,27,28,29,30,32,35,36,38,42,43,44,51]
valtype = 'sess'
if valtype == 'loso':
for subj in range(0, 54):
model = Deep4CNN(ismult=ismultitask).to(device)
#model.load_state_dict(torch.load(model_save_path+ "J_" + str(subj) + 'basecnn.pt'))
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
optimizer_fine = optim.SGD(model.parameters(),
lr=0.005,
momentum=args.momentum)
dataset_train = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
sess=1,
subj=subj)
train_loader = torch.utils.data.DataLoader(
dataset_train,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
sess=2,
subj=subj)
test_loader = torch.utils.data.DataLoader(
dataset_test,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
# dataset_fine = GigaDataset_LOSO(x=x_data, y=y_data, fine=True, istrain=True, sess=2, subj=subj)
# fine_loader = torch.utils.data.DataLoader(dataset_fine, batch_size=args.batch_size, shuffle=True, **kwargs)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
print("joint-train")
#LOSO joint training
j_loss, j_score = eval(args, model, device, test_loader)
if epoch > 30:
if (args.save_model):
torch.save(
model.state_dict(), model_save_path + "model_" +
str(subj) + "_" + str(epoch) + '.pt')
# #fine tuning
# for epoch in range(1, 10):
# train_mt(args, model, device, fine_loader, optimizer_fine, epoch)
#
# print("fine-tuning")
# f_loss, f_score = eval(args, model, device, test_loader)
if (args.save_model):
torch.save(model.state_dict(),
model_save_path + "F_" + str(subj) + 'basecnn.pt')
if loging:
f = open(fname, 'a')
f.write(
str(subj) + " " + "jl : " + str(j_loss) + " " +
str(j_score) + '\n')
f.close()
elif valtype == 'sess':
from networks import EmbeddingDeep4CNN, TripletNet, FineShallowCNN, EmbeddingDeepCNN, QuintupletNet
from losses import TripletLoss_dev2
# make model for metric learning
margin = 1
embedding_net = EmbeddingDeep4CNN()
# clf_net = nn.Sequential(EmbeddingDeep4CNN(),nn.Linear(1000,2),nn.Dropout(p=1),nn.LogSoftmax(dim=1))
print(embedding_net)
model = TripletNet(embedding_net)
if cuda:
model.cuda()
loss_fn = TripletLoss_dev2(margin).cuda()
n_epochs = 1
log_interval = 10
load_model_path = None #'triplet_mg26.pt'#'clf_triplet2_5.pt' #'triplet_31.pt'
model.fc = nn.Sequential(nn.Linear(1000, 2), nn.Dropout(p=0.5))
if load_model_path is not None:
model.load_state_dict(torch.load(load_model_path))
# For classification
dataset_train = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
sess=1)
train_loader = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
sess=2,
subj=-1)
test_loader = torch.utils.data.DataLoader(dataset_test,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
#make model for classification
newmodel = nn.Sequential(model.embedding_net, nn.Linear(1000, 2),
nn.LogSoftmax(dim=1)).to(device)
print(newmodel)
newmodel.to(device)
optimizer = optim.SGD(newmodel.parameters(), lr=0.01, momentum=0.9)
# optimizer = optim.Adam(newmodel.parameters())
for epoch in range(0):
train(args, newmodel, device, train_loader, optimizer, epoch)
j_loss, j_score = eval(args, newmodel, device, test_loader)
# For embedding
triplet_dataset_train = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
sess=1)
triplet_train_loader = torch.utils.data.DataLoader(
triplet_dataset_train,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
triplet_dataset_test = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
sess=2,
subj=-1)
triplet_test_loader = torch.utils.data.DataLoader(
triplet_dataset_test,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = lr_scheduler.StepLR(optimizer, 8, gamma=1, last_epoch=-1)
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSplit, cross_val_score
lda = LinearDiscriminantAnalysis()
Testmodel = nn.Sequential(model.embedding_net, model.fc,
nn.LogSoftmax(dim=1)).to(device)
print(Testmodel)
for temp in range(1, 30): #10epoch마다 세이브
fit(triplet_train_loader, triplet_test_loader, model, loss_fn,
optimizer, scheduler, n_epochs, cuda, log_interval)
j_loss, j_score = eval(args, Testmodel, device, test_loader)
torch.save(model.state_dict(), 'clf_' + str(temp) + '.pt')
Testmodel = nn.Sequential(model.embedding_net, model.fc,
nn.LogSoftmax(dim=1)).to(device)
print(Testmodel)
j_loss, j_score = eval(args, Testmodel, device, test_loader)
dataset_train_subj = GigaDataset(x=x_data,
y=y_subj,
valtype=valtype,
istrain=True,
sess=1)
train_loader_subj = torch.utils.data.DataLoader(
dataset_train_subj,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
dataset_test_subj = GigaDataset(x=x_data,
y=y_subj,
valtype=valtype,
istrain=False,
sess=2,
subj=-1)
test_loader_subj = torch.utils.data.DataLoader(
dataset_test_subj,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
train_embeddings_tl, train_labels_tl = extract_embeddings(
train_loader_subj, model.embedding_net, 1000)
val_embeddings_tl, val_labels_tl = extract_embeddings(
test_loader_subj, model.embedding_net, 1000)
train_labels_tl_subj = train_labels_tl - train_labels_tl % 2
# from torchvision import datasets, models, transforms
# temp = model.embedding_net.children()
# newmodel = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
# for param in model.embedding_net.parameters():
# param.requires_grad = True
#newembedding_net = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
#
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, perplexity=30)
#features = np.concatenate([train_embeddings_tl,val_embeddings_tl])
#val_labels_tl = val_labels_tl+2
#labels = np.concatenate([train_labels_tl,val_labels_tl])
train_tsne = tsne.fit_transform(train_embeddings_tl)
plot_embeddings(train_tsne, train_labels_tl % 2)
val_tsne = tsne.fit_transform(val_embeddings_tl)
plot_embeddings(val_tsne, val_labels_tl % 2)
for param in model.embedding_net.parameters():
param.requires_grad = True
#embedding_net2 = EmbeddingDeep4CNN()
newmodel = nn.Sequential(
model.embedding_net,
nn.Linear(1000, 2),
nn.Dropout(p=0.5),
nn.LogSoftmax(dim=1),
).to(device)
print(newmodel)
#newmodel.fc_lr = nn.Linear(1000,2)
newmodel.to(device)
optimizer = optim.SGD(newmodel.parameters(), lr=0.01, momentum=0.9)
#optimizer = optim.Adam(newmodel.parameters())
for epoch in range(1, 100):
train(args, newmodel, device, train_loader, optimizer, epoch)
j_loss, j_score = eval(args, newmodel, device, test_loader)
if args.save_model:
torch.save(newmodel.state_dict(), 'clf_83_8.pt')
newmodel.load_state_dict(torch.load('clf_83_8.pt'))
def main():
import torch
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.autograd import Variable
from trainer import fit
import numpy as np
cuda = torch.cuda.is_available()
# Training settings
parser = argparse.ArgumentParser(
description='cross subject domain adaptation')
parser.add_argument('--batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=100,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model',
action='store_true',
default=True,
help='For Saving the current Model')
# Writer will output to ./runs/ directory by default
writer = SummaryWriter()
args = parser.parse_args()
args.seed = 0
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if use_cuda else "cpu")
#kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
from datetime import datetime
import os
loging = False
if (args.save_model):
model_save_path = 'model/DANet(Deep4Net100)_test/'
if not os.path.isdir(model_save_path):
os.makedirs(model_save_path)
if loging:
fname = model_save_path + datetime.today().strftime(
"%m_%d_%H_%M") + ".txt"
f = open(fname, 'w')
x_data, y_data = load_smt()
#get subject number
y_subj = np.zeros([108, 200])
for i in range(108):
y_subj[i, :] = i * 2
y_subj = y_data.reshape(108, 200) + y_subj
y_subj = y_subj.reshape(21600)
# nonbciilli = np.s_[0,1,2,4,5,8,16,17,18,20,21,27,28,29,30,32,35,36,38,42,43,44,51]
valtype = 'sess'
if valtype == 'loso':
pass
elif valtype == 'sess':
from networks import EmbeddingDeep4CNN, TripletNet, FineShallowCNN, EmbeddingDeepCNN, QuintupletNet, EmbeddingShallowCNN
from losses import TripletLoss_dev2
# make model for metric learning
margin = 1.0
gamma = 0.7
embedding_net = EmbeddingDeep4CNN()
print(embedding_net)
model = TripletNet(embedding_net)
#model.fc = nn.Linear(embedding_net.num_hidden,2)
if cuda:
model.cuda()
loss_fn = TripletLoss_dev2(margin, gamma).cuda()
n_epochs = 1
log_interval = 10
#load_model_path = model_save_path+'dgnet1.027.pt' #'C:\\Users\dk\PycharmProjects\giga_cnn\구모델\\clf_83_8.pt'#'clf_29.pt' #'triplet_mg26.pt'#'clf_triplet2_5.pt' #'triplet_31.pt'
load_model_path = None
if load_model_path is not None:
model.load_state_dict(torch.load(load_model_path))
# For classification data
dataset_train = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
sess=1)
train_loader = torch.utils.data.DataLoader(
dataset_train,
batch_size=args.batch_size,
shuffle=True,
)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
sess=2,
subj=-1)
test_loader = torch.utils.data.DataLoader(
dataset_test,
batch_size=args.batch_size,
shuffle=False,
)
# For Domain adaptation
triplet_dataset_train = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
sess=1)
triplet_train_loader = torch.utils.data.DataLoader(
triplet_dataset_train, batch_size=args.batch_size, shuffle=True)
triplet_dataset_test = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
sess=2,
subj=-1)
triplet_test_loader = torch.utils.data.DataLoader(
triplet_dataset_test, batch_size=args.batch_size, shuffle=False)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = lr_scheduler.StepLR(optimizer,
10,
gamma=0.5,
last_epoch=-1)
#model for validation
evalmodel = nn.Sequential(model.embedding_net, model.fc,
nn.LogSoftmax(dim=1)).to(device)
print('____________DANet____________')
print(model)
#
# from torch.utils.tensorboard import SummaryWriter
# writer = SummaryWriter()
# images, labels = next(iter(train_loader))
# import torchvision
# #grid = torchvision.utils.make_grid(images)
# writer.add_images('images',images)
#
# writer.add_embedding(metadata=train_embeddings_tl)
# # writer.add_embedding(metadata = val_embeddings_tl)
# writer.close()
for temp in range(1, 50):
fit(triplet_train_loader, triplet_test_loader, model, loss_fn,
optimizer, scheduler, n_epochs, cuda, log_interval)
train_loss, j_score = eval(args, evalmodel, device, train_loader)
eval_loss, j_score = eval(args, evalmodel, device, test_loader)
writer.add_scalar('Train/Loss', np.mean(train_loss) / 100, temp)
writer.add_scalar('Eval/Loss', np.mean(eval_loss) / 100, temp)
writer.close()
np.mean(train_loss)
torch.save(
model.state_dict(), model_save_path + 'dgnet_' + str(gamma) +
'_' + str(temp) + '.pt')
#for visualization
dataset_train_subj = GigaDataset(x=x_data,
y=y_subj,
valtype='subj',
istrain=True,
sess=1,
subj=np.r_[0:10])
train_loader_subj = torch.utils.data.DataLoader(
dataset_train_subj,
batch_size=args.batch_size,
shuffle=True,
)
dataset_test_subj = GigaDataset(x=x_data,
y=y_subj,
valtype='sess',
istrain=False,
sess=2,
subj=-1)
test_loader_subj = torch.utils.data.DataLoader(
dataset_test_subj,
batch_size=args.batch_size,
shuffle=False,
)
# train_embeddings_tl, train_labels_tl = extract_features(train_loader_subj.dataset, model.embedding_net.convnet,0,100)
# val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader_subj, model.embedding_net, 1000)
train_embeddings_tl, train_labels_tl = extract_embeddings(
train_loader_subj, model.embedding_net,
model.embedding_net.num_hidden)
val_embeddings_tl, val_labels_tl = extract_embeddings(
test_loader_subj, model.embedding_net,
model.embedding_net.num_hidden)
#
# train_embeddings_tl, train_labels_tl = extract_embeddings(train_loader_subj, model.embedding_net.convnet[0], 1000)
# val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader_subj, model.embedding_net, 1000)
# = train_labels_tl-train_labels_tl%2
# from torchvision import datasets, models, transforms
# temp = model.embedding_net.children()
# newmodel = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
# for param in model.embedding_net.parameters():
# param.requires_grad = True
#newembedding_net = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
#
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, perplexity=30)
#features = np.concatenate([train_embeddings_tl,val_embeddings_tl])
#val_labels_tl = val_labels_tl+2
#labels = np.concatenate([train_labels_tl,val_labels_tl])
train_tsne = tsne.fit_transform(train_embeddings_tl)
plot_features(train_tsne, train_labels_tl)
plot_features(train_tsne, train_labels_tl % 2)
for i in range(0, 10):
plot_features(train_tsne[i * 200:(i + 1) * 200],
train_labels_tl[i * 200:(i + 1) * 200])
plot_features3d(train_tsne, train_labels_tl)
val_tsne = tsne.fit_transform(val_embeddings_tl)
plot_features(val_tsne, val_labels_tl - 108)
plot_features(val_tsne, val_labels_tl % 2)
plot_features3d(val_tsne, val_labels_tl % 2)
plot_embeddings(val_tsne,
(val_labels_tl - 108) - (val_labels_tl - 108) % 2)
for param in model.embedding_net.parameters():
param.requires_grad = True
#embedding_net2 = EmbeddingDeep4CNN()
newmodel = nn.Sequential(
model.embedding_net,
nn.Linear(1000, 2),
nn.Dropout(p=0.5),
nn.LogSoftmax(dim=1),
).to(device)
print(newmodel)
#newmodel.fc_lr = nn.Linear(1000,2)
newmodel.to(device)
optimizer = optim.SGD(newmodel.parameters(), lr=0.001, momentum=0.9)
#optimizer = optim.Adam(newmodel.parameters())
for epoch in range(1, 100):
train(args, newmodel, device, train_loader, optimizer, epoch)
j_loss, j_score = eval(args, newmodel, device, test_loader)
if args.save_model:
torch.save(newmodel.state_dict(), 'clf_83_8.pt')
newmodel.load_state_dict(torch.load(load_model_path))
def main():
import torch
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.autograd import Variable
from trainer import fit
import numpy as np
cuda = torch.cuda.is_available()
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=100, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=100, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=True,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
from datetime import datetime
import os
loging = False
ismultitask = False
loso = False
if (args.save_model):
model_save_path = 'model/triplet/'
if not os.path.isdir(model_save_path):
os.makedirs(model_save_path)
if loging:
fname = model_save_path + datetime.today().strftime("%m_%d_%H_%M") + ".txt"
f = open(fname, 'w')
x_data, y_data = load_smt()
y_subj = np.zeros([108, 200])
for i in range(108):
y_subj[i, :] = i * 2
y_subj = y_data.reshape(108, 200) + y_subj
y_subj = y_subj.reshape(21600)
# nonbciilli = np.s_[0,1,2,4,5,8,16,17,18,20,21,27,28,29,30,32,35,36,38,42,43,44,51]
valtype = 'sess'
if valtype == 'loso':
pass
elif valtype == 'sess':
from networks import EmbeddingDeep4CNN, TripletNet, FineShallowCNN, EmbeddingDeepCNN, QuintupletNet, EmbeddingShallowCNN
from losses import TripletLoss_dev2
# make model for metric learning
margin = 1
embedding_net = EmbeddingShallowCNN()
# clf_net = nn.Sequential(EmbeddingDeep4CNN(),nn.Linear(1000,2),nn.Dropout(p=1),nn.LogSoftmax(dim=1))
print(embedding_net)
model = TripletNet(embedding_net)
if cuda:
model.cuda()
loss_fn = TripletLoss_dev2(margin).cuda()
n_epochs =1
log_interval = 10
load_model_path = None#'C:\\Users\dk\PycharmProjects\giga_cnn\구모델\\clf_83_8.pt'#'clf_29.pt' #'triplet_mg26.pt'#'clf_triplet2_5.pt' #'triplet_31.pt'
model.fc = nn.Sequential(nn.Linear(1000, 2), nn.Dropout(p=0.5))
if load_model_path is not None:
embedding_net.load_state_dict(torch.load(load_model_path))
# For classification
dataset_train = GigaDataset(x=x_data, y=y_data, valtype=valtype, istrain=True, sess=1)
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=True,
**kwargs)
dataset_test = GigaDataset(x=x_data, y=y_data, valtype=valtype, istrain=False, sess=2, subj=-1)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=args.batch_size, shuffle=False,
**kwargs)
#make model for classification
newmodel = nn.Sequential(model.embedding_net,
nn.Linear(1000, 2),
nn.Dropout(p=0.5),
nn.LogSoftmax(dim=1)
).to(device)
print(newmodel)
newmodel.to(device)
optimizer = optim.SGD(newmodel.parameters(), lr=0.001, momentum=0.9)
# optimizer = optim.Adam(newmodel.parameters())
for epoch in range(0):
train(args, newmodel, device, train_loader, optimizer, epoch)
j_loss, j_score = eval(args, newmodel, device, test_loader)
# For embedding
triplet_dataset_train = TripletGiga(x=x_data, y=y_data,valtype=valtype, istrain=True, sess=1)
triplet_train_loader = torch.utils.data.DataLoader(triplet_dataset_train, batch_size=args.batch_size, shuffle=True, **kwargs)
triplet_dataset_test = TripletGiga(x=x_data, y=y_data,valtype=valtype, istrain=False, sess=2, subj=-1)
triplet_test_loader = torch.utils.data.DataLoader(triplet_dataset_test, batch_size=args.batch_size, shuffle=False, **kwargs)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = lr_scheduler.StepLR(optimizer, 8, gamma=1, last_epoch=-1)
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSplit, cross_val_score
lda = LinearDiscriminantAnalysis()
Testmodel = nn.Sequential(model.embedding_net,
model.fc,
nn.LogSoftmax(dim=1)).to(device)
# tempEmbeddingNet = nn.Sequential(model.embedding_net,
# nn.Linear(1000,1000),
# nn.Sigmoid())
# model = TripletNet(embedding_net)
print(model)
for temp in range(1, 30): # 10epoch마다 세이브
fit(triplet_train_loader, triplet_test_loader, model, loss_fn, optimizer, scheduler, n_epochs, cuda,
log_interval)
j_loss, j_score = eval(args, Testmodel, device, train_loader)
j_loss, j_score = eval(args, Testmodel, device, test_loader)
torch.save(model.state_dict(), 'clf_' + str(temp) + '.pt')
torch.save(model.state_dict(), 'shallowDG_150epoch_82acc' + str(temp) + '.pt')
#for visualization
dataset_train_subj = GigaDataset(x=x_data, y=y_subj, valtype=valtype, istrain=True, sess=1)
train_loader_subj = torch.utils.data.DataLoader(dataset_train_subj, batch_size=args.batch_size, shuffle=True,
**kwargs)
dataset_test_subj = GigaDataset(x=x_data, y=y_subj, valtype=valtype, istrain=False, sess=2, subj=-1)
test_loader_subj = torch.utils.data.DataLoader(dataset_test_subj, batch_size=args.batch_size, shuffle=False,
**kwargs)
# train_embeddings_tl, train_labels_tl = extract_features(train_loader_subj.dataset, model.embedding_net.convnet,0,100)
# val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader_subj, model.embedding_net, 1000)
train_embeddings_tl, train_labels_tl = extract_embeddings(train_loader_subj, model.embedding_net,1000)
val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader_subj, model.embedding_net,1000)
#
# train_embeddings_tl, train_labels_tl = extract_embeddings(train_loader_subj, model.embedding_net.convnet[0], 1000)
# val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader_subj, model.embedding_net, 1000)
# = train_labels_tl-train_labels_tl%2
# from torchvision import datasets, models, transforms
# temp = model.embedding_net.children()
# newmodel = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
# for param in model.embedding_net.parameters():
# param.requires_grad = True
#newembedding_net = torch.nn.Sequential(*(list(model.embedding_net.children())[:]))
#
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2,perplexity=30)
#features = np.concatenate([train_embeddings_tl,val_embeddings_tl])
#val_labels_tl = val_labels_tl+2
#labels = np.concatenate([train_labels_tl,val_labels_tl])
train_tsne = tsne.fit_transform(train_embeddings_tl[0:2000])
# plot_embeddings(train_tsne,train_labels_tl[0:1000])
plot_features(train_tsne,train_labels_tl[0:2000])
plot_features3d(train_tsne,train_labels_tl[0:1000]%2)
val_tsne = tsne.fit_transform(val_embeddings_tl)
plot_embeddings(val_tsne, (val_labels_tl-108)-(val_labels_tl-108)%2)
for param in model.embedding_net.parameters():
param.requires_grad = True
#embedding_net2 = EmbeddingDeep4CNN()
newmodel = nn.Sequential(model.embedding_net,
nn.Linear(1000, 2),
nn.Dropout(p=0.5),
nn.LogSoftmax(dim=1),
).to(device)
print(newmodel)
#newmodel.fc_lr = nn.Linear(1000,2)
newmodel.to(device)
optimizer = optim.SGD(newmodel.parameters(), lr=0.001, momentum=0.9)
#optimizer = optim.Adam(newmodel.parameters())
for epoch in range(1, 100):
train(args, newmodel, device, train_loader, optimizer, epoch)
j_loss, j_score = eval(args, newmodel, device, test_loader)
if args.save_model:
torch.save(newmodel.state_dict(),'clf_83_8.pt')
newmodel.load_state_dict(torch.load(load_model_path))
# Visualize feature maps
activation = {}
def get_activation(name):
def hook(model, input, output):
activation[name] = output.detach()
return hook
handle = model.embedding_net.convnet[0].register_forward_hook(get_activation('fc'))
handle.remove()
model.embedding_net.convnet[0]._forward_hooks.clear()
train_loader.dataset
with torch.no_grad():
model.eval()
# num_ftrs = model.embedding_net.fc.out_features
embeddings = np.zeros((len(train_loader.dataset), num_ftrs))
labels = np.zeros(len(train_loader.dataset))
k = 0
for images, target in train_loader:
if cuda:
images = images.cuda()
embeddings[k:k + len(images)] = model.get_embedding(images).data.cpu().numpy()
labels[k:k + len(images)] = target.numpy()
k += len(images)
features = SaveFeatures(model.embedding_net.convnet[0])
temp = features.features.data.cpu().numpy()
del features.features
torch.cuda.empty_cache()
for images, target in train_loader:
if cuda:
images = images.cuda()
output = model.embedding_net(images)
activation = []
def get_activation():
def hook(model, input, output):
activation.append(output)
print(output)
return hook
act = activation['conv1'].squeeze()
fig, axarr = plt.subplots(act.size(0))
for idx in range(act.size(0)):
axarr[idx].imshow(act[idx])
actmap = []
def printnorm(self, input, output):
# input is a tuple of packed inputs
# output is a Tensor. output.data is the Tensor we are interested
print('Inside ' + self.__class__.__name__ + ' forward')
print('')
print('input: ', type(input))
print('input[0]: ', type(input[0]))
print('output: ', type(output))
print('')
print('input size:', input[0].size())
print('output size:', output.data.size())
print('output norm:', output.data.norm())
return output.data
model.embedding_net.convnet[0].register_forward_hook(printnorm)
out = model(input)
fig, axarr = plt.subplots(10)
for idx in range(10):
axarr[idx].imshow(temp[idx,1,:,:])
def main():
import torch
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.autograd import Variable
from trainer import fit
import numpy as np
cuda = torch.cuda.is_available()
# Training settings
parser = argparse.ArgumentParser(
description='cross subject domain adaptation')
parser.add_argument('--batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=100,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model',
action='store_true',
default=True,
help='For Saving the current Model')
# Writer will output to ./runs/ directory by default
fold_idx = 5
gamma = 1
DAsetting = False
args = parser.parse_args()
args.seed = 0
args.use_tensorboard = True
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if use_cuda else "cpu")
#kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
from datetime import datetime
import os
loging = False
x_data, y_data = load_smt()
#get subject number
y_subj = np.zeros([108, 200])
for i in range(108):
y_subj[i, :] = i * 2
y_subj = y_data.reshape(108, 200) + y_subj
y_subj = y_subj.reshape(21600)
# For classification data
valtype = 'subj'
# #subj - 0-27 train
# train_subj1 = np.r_[0:27]
# train_subj2 = np.r_[0:27]+54
#
# test_subj = np.r_[27:54,54+27:108]
# For Domain adaptation setting
if DAsetting:
train_subj1 = np.r_[27:54]
train_subj2 = np.r_[27:54] + 54
test_subj = np.r_[0:27, 54 + 0:54 + 27]
trial_s = (0, 200)
trial_t = (0, 200)
trial_val = (0, 200)
dataset_train1 = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj1,
trial=trial_s)
dataset_train2 = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj2,
trial=trial_t)
dataset_train = dataset_train1.__add__(dataset_train2)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
triplet_dataset_train1 = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj1,
trial=trial_s)
triplet_dataset_train2 = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj2,
trial=trial_t)
triplet_dataset_train = triplet_dataset_train1.__add__(
triplet_dataset_train2)
triplet_dataset_test = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
else: #DG setting
test_subj = np.r_[fold_idx * 9:fold_idx * 9 + 9,
fold_idx + 54:fold_idx + 9 + 54]
train_subj = np.setxor1d(np.r_[0:108], test_subj)
trial_train = (0, 200)
trial_val = (0, 200)
dataset_train = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj,
trial=trial_train)
dataset_test = GigaDataset(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
triplet_dataset_train = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=True,
subj=train_subj,
trial=trial_train)
triplet_dataset_test = TripletGiga(x=x_data,
y=y_data,
valtype=valtype,
istrain=False,
subj=test_subj,
trial=trial_val)
train_loader = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset_test,
batch_size=args.batch_size,
shuffle=False)
triplet_train_loader = torch.utils.data.DataLoader(
triplet_dataset_train, batch_size=args.batch_size, shuffle=True)
triplet_test_loader = torch.utils.data.DataLoader(
triplet_dataset_test, batch_size=args.batch_size, shuffle=False)
###################################################################################################################
# make model for metric learning
from networks import Deep4Net, EmbeddingDeep4CNN, EmbeddingDeep4CNN_bn, TripletNet, FineShallowCNN, EmbeddingDeepCNN, QuintupletNet, EmbeddingShallowCNN
from losses import TripletLoss_dev2
margin = 1.0
embedding_net = Deep4Net()
print(embedding_net)
model = TripletNet(embedding_net)
if cuda:
model.cuda()
loss_fn = TripletLoss_dev2(margin, gamma).cuda()
n_epochs = 1
log_interval = 10
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=0.5, last_epoch=-1)
optimizer = optim.Adam(model.parameters(), lr=0.01)
scheduler = lr_scheduler.StepLR(optimizer, 10, gamma=1, last_epoch=-1)
#model for validation
evalmodel = nn.Sequential(model.embedding_net, model.fc,
nn.LogSoftmax(dim=1)).to(device)
print('____________DANet____________')
print(model)
comment = 'fold_' + str(fold_idx) + '_g_' + str(gamma)
#save someting
if (args.save_model):
model_save_path = 'model/Deep4Net_(test0604)/' + comment + '/'
if not os.path.isdir(model_save_path):
os.makedirs(model_save_path)
if loging:
fname = model_save_path + datetime.today().strftime(
"%m_%d_%H_%M") + ".txt"
f = open(fname, 'w')
if args.use_tensorboard:
writer = SummaryWriter(comment=comment)
# load_model_path = model_save_path+'danet_0.7_49.pt' #'C:\\Users\dk\PycharmProjects\giga_cnn\구모델\\clf_83_8.pt'#'clf_29.pt' #'triplet_mg26.pt'#'clf_triplet2_5.pt' #'triplet_31.pt'
load_model_path = None
if load_model_path is not None:
model.load_state_dict(torch.load(load_model_path))
for temp in range(1, 50):
fit(triplet_train_loader, triplet_test_loader, model, loss_fn,
optimizer, scheduler, n_epochs, cuda, log_interval)
print(temp)
train_loss, train_score = eval(args, evalmodel, device, train_loader)
eval_loss, eval_score = eval(args, evalmodel, device, test_loader)
if args.use_tensorboard:
writer.add_scalar('Train/Loss', np.mean(train_loss) / 100, temp)
writer.add_scalar('Train/Acc', np.mean(train_score) / 100, temp)
writer.add_scalar('Eval/Loss', np.mean(eval_loss) / 100, temp)
writer.add_scalar('Eval/Acc', np.mean(eval_score) / 100, temp)
writer.close()
torch.save(
model.state_dict(),
model_save_path + 'danet_' + str(gamma) + '_' + str(temp) + '.pt')