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DAN.py
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DAN.py
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'''
Description:
Author: voicebeer
Date: 2020-09-14 01:01:51
LastEditTime: 2021-12-28 01:55:41
'''
# standard
import argparse
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import copy
import random
import time
import math
from torch.utils.tensorboard import SummaryWriter
#
import utils
import models
# random seed
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
setup_seed(20)
# writer = SummaryWriter()
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
class DANNet():
def __init__(self, model=models.DAN(), source_loader=0, target_loader=0, batch_size=64, iteration=10000, lr=0.001, momentum=0.9, log_interval=10):
self.model = model
self.model.to(device)
self.source_loader = source_loader
self.target_loader = target_loader
self.batch_size = batch_size
self.iteration = iteration
self.lr = lr
self.momentum = momentum
self.log_interval = log_interval
def __getModel__(self):
return self.model
def train(self):
# best_model_wts = copy.deepcopy(model.state_dict())
source_iter = iter(self.source_loader)
target_iter = iter(self.target_loader)
correct = 0
for i in range(1, self.iteration+1):
self.model.train()
# LEARNING_RATE = self.lr / math.pow((1 + 10 * (i - 1) / (self.iteration)), 0.75)
LEARNING_RATE = self.lr
# if (i - 1) % 100 == 0:
# print("Learning rate: ", LEARNING_RATE)
# optimizer = torch.optim.SGD(self.model.parameters(), lr=LEARNING_RATE, momentum=self.momentum)
optimizer = torch.optim.Adam(
self.model.parameters(), lr=LEARNING_RATE)
try:
source_data, source_label = next(source_iter)
except Exception as err:
source_iter = iter(self.source_loader)
source_data, source_label = next(source_iter)
try:
target_data, _ = next(target_iter)
except Exception as err:
target_iter = iter(self.target_loader)
target_data, _ = next(target_iter)
source_data, source_label = source_data.to(
device), source_label.to(device)
target_data = target_data.to(device)
optimizer.zero_grad()
source_prediction, mmd_loss = self.model(
source_data, data_tgt=target_data)
cls_loss = F.nll_loss(F.log_softmax(
source_prediction, dim=1), source_label.squeeze())
gamma = 2 / (1 + math.exp(-10 * (i) / (iteration))) - 1
loss = cls_loss + gamma * mmd_loss
loss.backward()
optimizer.step()
# if i % log_interval == 0:
# print('Iter: {} [({:.0f}%)]\tLoss: {:.6f}\tsoft_loss: {:.6f}\tmmd_loss {:.6f}'.format(
# i, 100.*i/self.iteration, loss.item(), cls_loss.item(), mmd_loss.item()
# )
# )
if i % (log_interval * 20) == 0:
t_correct = self.test(i)
if t_correct > correct:
correct = t_correct
# print('to target max correct: ', correct.item(), "\n")
return 100. * correct / len(self.target_loader.dataset)
def test(self, iteration):
self.model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in self.target_loader:
data = data.to(device)
target = target.to(device)
preds, mmd_loss = self.model(data, data)
test_loss += F.nll_loss(F.log_softmax(preds, dim=1),
target.squeeze(), reduction='sum').item()
pred = preds.data.max(1)[1]
correct += pred.eq(target.data.squeeze()).cpu().sum()
test_loss /= len(self.target_loader.dataset)
# writer.add_scalar("Test/Test loss", test_loss, iteration)
# print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
# test_loss, correct, len(self.target_loader.dataset),
# 100. * correct / len(self.target_loader.dataset)
# ))
return correct
def cross_subject(data, label, session_id, subject_id, category_number, batch_size, iteration, lr, momentum, log_interval):
# LOSO
one_session_data, one_session_label = copy.deepcopy(
data[session_id]), copy.deepcopy(label[session_id])
train_idxs = list(range(15))
del train_idxs[subject_id]
test_idx = subject_id
target_data, target_label = one_session_data[test_idx], one_session_label[test_idx]
source_data, source_label = copy.deepcopy(
one_session_data[train_idxs]), copy.deepcopy(one_session_label[train_idxs])
del one_session_label
del one_session_data
# print(len(source_data))
source_data_comb = source_data[0]
source_label_comb = source_label[0]
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
print('Target_subject_id: {}, current_session_id: {}, acc: {}'.format(
test_idx, session_id, acc))
return acc
def cross_session(data, label, session_id, subject_id, category_number, batch_size, iteration, lr, momentum, log_interval):
# LOSO
train_idxs = list(range(3))
del train_idxs[session_id]
test_idx = session_id
target_data, target_label = copy.deepcopy(
data[test_idx][subject_id]), copy.deepcopy(label[test_idx][subject_id])
source_data, source_label = copy.deepcopy(
data[train_idxs][:, subject_id]), copy.deepcopy(label[train_idxs][:, subject_id])
source_data_comb = np.vstack((source_data[0], source_data[1]))
source_label_comb = np.vstack((source_label[0], source_label[1]))
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
print('Target_session_id: {}, current_subject_id: {}, acc: {}'.format(
test_idx, subject_id, acc))
return acc
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='DAN parameters')
parser.add_argument('--dataset', type=str, default='seed3',
help='the dataset used for DAN, "seed3" or "seed4"')
parser.add_argument('--norm_type', type=str, default='ele',
help='the normalization type used for data, "ele", "sample", "global" or "none"')
parser.add_argument('--batch_size', type=int, default=256,
help='size for one batch, integer')
parser.add_argument('--epoch', type=int, default=200,
help='training epoch, integer')
parser.add_argument('--lr', type=float, default=0.01, help='learning rate')
args = parser.parse_args()
dataset_name = args.dataset
bn = args.norm_type
# data preparation
print('Model name: DAN. Dataset name: ', dataset_name)
data, label = utils.load_data(dataset_name)
print('Normalization type: ', bn)
if bn == 'ele':
data_tmp = copy.deepcopy(data)
label_tmp = copy.deepcopy(label)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.norminy(data_tmp[i][j])
elif bn == 'sample':
data_tmp = copy.deepcopy(data)
label_tmp = copy.deepcopy(label)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.norminx(data_tmp[i][j])
elif bn == 'global':
data_tmp = copy.deepcopy(data)
label_tmp = copy.deepcopy(label)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.normalization(data_tmp[i][j])
elif bn == 'none':
data_tmp = copy.deepcopy(data)
label_tmp = copy.deepcopy(label)
else:
pass
trial_total, category_number, _ = utils.get_number_of_label_n_trial(
dataset_name)
# training settings
batch_size = args.batch_size
epoch = args.epoch
lr = args.lr
print('BS: {}, epoch: {}'.format(batch_size, epoch))
momentum = 0.9
log_interval = 10
iteration = 0
if dataset_name == 'seed3':
iteration = math.ceil(epoch*3394/batch_size)
elif dataset_name == 'seed4':
iteration = math.ceil(epoch*820/batch_size)
else:
iteration = 5000
print('Iteration: {}'.format(iteration))
# store the results
csub = []
csesn = []
# LOSO
for session_id_main in range(3):
for subject_id_main in range(15):
csub.append(cross_subject(data_tmp, label_tmp, session_id_main, subject_id_main, category_number,
batch_size, iteration, lr, momentum, log_interval))
for subject_id_main in range(15):
for session_id_main in range(3):
csesn.append(cross_session(data_tmp, label_tmp, session_id_main, subject_id_main, category_number,
batch_size, iteration, lr, momentum, log_interval))
print("Cross-session: ", csesn)
print("Cross-subject: ", csub)
print("Cross-session mean: ", np.mean(csesn), "std: ", np.std(csesn))
print("Cross-subject mean: ", np.mean(csub), "std: ", np.std(csub))