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inference.py
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import os
import argparse
import random
import datetime
import time
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from utils.end_to_end_encoding_utils import *
from EEG_Encoder.SelfModel import *
from encoder_modules import *
from torch.utils.tensorboard import SummaryWriter
from sklearn.decomposition import PCA
gpus = [0]
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, gpus))
result_path = 'results/'
model_idx = 'stage2_encoder_training_from_scratch_clip'
parser = argparse.ArgumentParser(description='Experiment Stimuli Recognition test with vit encoder')
parser.add_argument('--dnn', default='clip', type=str)
parser.add_argument('--epoch', default='1000', type=int)
parser.add_argument('--num_sub', default=10, type=int,
help='number of subjects used in the experiments. ')
parser.add_argument('-batch_size', '--batch-size', default=128, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--seed', default=2023, type=int,
help='seed for initializing training.')
parser.add_argument('--reproduce', type=bool, default=True)
parser.add_argument('--n_way', type=int, default=200)
parser.add_argument('--pretrained', type=bool, default=True)
parser.add_argument('--is_gan', type=bool, default=True)
class Infer():
def __init__(self, args, nsub, nseed):
super(Infer, self).__init__()
self.args = args
self.num_class = 200
self.batch_size = args.batch_size
self.batch_size_test = 400
self.batch_size_img = 500
self.n_epochs = args.epoch
self.lambda_cen = 0.003
self.alpha = 0.5
self.proj_dim = 256
self.lr = 0.0002
self.b1 = 0.5
self.b2 = 0.999
self.nSub = nsub
self.nseed = nseed
self.start_epoch = 0
self.eeg_data_path = 'Data/Things-EEG2/Preprocessed_data_250Hz/'
self.img_data_path = 'Data/Things-EEG2/DNN_feature_maps/pca_feature_maps/' + args.dnn + '/pretrained-True/'
self.test_center_path = 'Data/Things-EEG2/Image_set/'
self.pretrain = False
self.log_write = open(result_path + "log_subject%d.txt" % self.nSub, 'a+')
self.writer = SummaryWriter(log_dir=result_path+'/log/')
self.Tensor = torch.cuda.FloatTensor
self.LongTensor = torch.cuda.LongTensor
self.Enc_eeg = Enc_eeg().to(args.device)
self.Proj_eeg = Proj_eeg().to(args.device)
self.Proj_img = Proj_img().to(args.device)
self.Enc_eeg = nn.DataParallel(self.Enc_eeg, device_ids=[i for i in range(len(gpus))])
self.Proj_eeg = nn.DataParallel(self.Proj_eeg, device_ids=[i for i in range(len(gpus))])
self.Proj_img = nn.DataParallel(self.Proj_img, device_ids=[i for i in range(len(gpus))])
self.Enc_eeg.load_state_dict(torch.load('model/best_checkpoints_seed42/' + model_idx + str(self.nSub) + 'Enc_eeg_cls.pth'))
self.Proj_eeg.load_state_dict(torch.load('model/best_checkpoints_seed42/' + model_idx +str(self.nSub) + 'Proj_eeg_cls.pth'))
self.Proj_img.load_state_dict(torch.load('model/best_checkpoints_seed42/' + model_idx +str(self.nSub) + 'Proj_img_cls.pth'))
self.centers = {}
print('initial define done.')
def get_eeg_data(self):
train_data = []
train_label = torch.from_numpy(np.repeat(np.arange(1654), 10))
test_data = []
test_label = np.arange(200)
train_data = np.load(self.eeg_data_path + '/sub-' + format(self.nSub, '02') + '/preprocessed_eeg_training.npy', allow_pickle=True)
# (16540, 4, 63, 250)
train_data = train_data['preprocessed_eeg_data']
train_data = np.mean(train_data, axis=1)
# (16540, 1, 63, 250)
train_data = np.expand_dims(train_data, axis=1)
# (200, 80, 63, 250)
test_data = np.load(self.eeg_data_path + '/sub-' + format(self.nSub, '02') + '/preprocessed_eeg_test.npy', allow_pickle=True)
test_data = test_data['preprocessed_eeg_data']
test_data = np.mean(test_data, axis=1)
# (200, 1, 63, 250)
test_data = np.expand_dims(test_data, axis=1)
# train_label:[] test_label: 0~199
print('load eeg successful')
return train_data, train_label, test_data, test_label
def get_image_data(self):
test_img_feature = np.load(self.img_data_path + self.args.dnn + '_feature_maps_test.npy', allow_pickle=True)
test_img_feature = np.squeeze(test_img_feature)
print('load imag successful')
return test_img_feature
def InferenceAllSubs(self, args):
# train_eeg:(16540, 1, 63, 250); test_eeg:(200, 1, 63, 250); test_label：0~199
train_eeg_data, train_eeg_label, test_eeg, test_label = self.get_eeg_data()
train_eeg_data = torch.from_numpy(train_eeg_data)
train_img_feature = np.load(self.img_data_path + self.args.dnn + '_feature_maps_training.npy', allow_pickle=True)
train_img_feature = torch.from_numpy(np.squeeze(train_img_feature))
train_dataset = torch.utils.data.TensorDataset(train_eeg_data, train_eeg_label)
self.train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=self.args.batch_size, shuffle=False)
# (16540, 768)
test_center = self.get_image_data()
test_center = np.load(self.test_center_path + 'center_all_image_' + self.args.dnn + '.npy', allow_pickle=True)
# (200, 768)
test_center = np.squeeze(test_center,1)
#####################################200 Way################################
test_eeg = torch.from_numpy(test_eeg)
test_center = torch.from_numpy(test_center)
test_label = torch.from_numpy(test_label)
test_dataset = torch.utils.data.TensorDataset(test_eeg, test_label)
self.test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=self.batch_size_test, shuffle=False)
#####################################50 Way################################
index50 = np.squeeze(np.where(test_label < self.args.n_way, True, False))
test_center_50 = test_center[index50]
test_eeg_50 = test_eeg[index50]
test_label_50 = test_label[index50]
test_dataset_50 = torch.utils.data.TensorDataset(test_eeg_50, test_label_50)
self.test_dataloader_50 = torch.utils.data.DataLoader(dataset=test_dataset_50, batch_size=self.batch_size_test, shuffle=False)
self.Enc_eeg.eval()
self.Proj_eeg.eval()
self.Proj_img.eval()
with torch.no_grad():
eeg_feat = self.Proj_eeg(self.Enc_eeg(Variable(test_eeg.type(self.Tensor))))
proj_test_center = self.Proj_img(test_center)
norm_test_center = proj_test_center / proj_test_center.norm(dim=1, keepdim=True)
norm_test_eeg_feat = eeg_feat / eeg_feat.norm(dim=1, keepdim=True)
gt_labels, predict_labels = get_inference_label(norm_test_center, norm_test_eeg_feat, test_label)
top1_acc, top3_acc, top5_acc = run_classification_test(args, norm_test_center, eeg_feat, 0)
eeg_feat_50 = self.Proj_eeg(self.Enc_eeg(Variable(test_eeg_50.type(self.Tensor))))
proj_test_center_50 = self.Proj_img(test_center_50)
norm_eeg_feat_50 = eeg_feat_50 / eeg_feat_50.norm(dim=1, keepdim=True)
norm_test_center_50 = proj_test_center_50 / proj_test_center_50.norm(dim=1, keepdim=True)
top1_acc_50, top3_acc_50, top5_acc_50 = run_classification_test(args, norm_test_center_50, norm_eeg_feat_50, 0)
print('The test Top1_200-%.6f, Top3_200-%.6f, Top5_200-%.6f' % (top1_acc, top3_acc, top5_acc))
print('The test Top1_50-%.6f, Top3_50-%.6f, Top5_50-%.6f' % (top1_acc_50, top3_acc_50, top5_acc_50))
self.log_write.write('The test Top1_200-%.6f, Top3_200-%.6f, Top5_200-%.6f\n' % (top1_acc, top3_acc, top5_acc))
self.log_write.write('The test Top1_50-%.6f, Top3_50-%.6f, Top5_50-%.6f\n' % (top1_acc_50, top3_acc_50, top5_acc_50))
self.writer.close()
return top1_acc, top3_acc, top5_acc, top1_acc_50, top3_acc_50, top5_acc_50
def main():
args = parser.parse_args()
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
num_sub = args.num_sub
cal_num = 0
for j in range(1):
aver = []
aver3 = []
aver5 = []
aver_50 = []
aver3_50 = []
aver5_50 = []
for i in range(10):
cal_num += 1
print('Subject %d' % (i+1))
infer = Infer(args, i + 1,j+1)
starttime = datetime.datetime.now()
Acc, Acc3, Acc5, Acc_50, Acc3_50, Acc5_50 = infer.InferenceAllSubs(args)
print('THE BEST ACCURACY IS ' + str(Acc))
endtime = datetime.datetime.now()
print('subject %d duration: '%(i+1) + str(endtime - starttime))
aver.append(Acc)
aver3.append(Acc3)
aver5.append(Acc5)
aver_50.append(Acc_50)
aver3_50.append(Acc3_50)
aver5_50.append(Acc5_50)
aver.append(np.mean(aver))
aver3.append(np.mean(aver3))
aver5.append(np.mean(aver5))
aver_50.append(np.mean(aver_50))
aver3_50.append(np.mean(aver3_50))
aver5_50.append(np.mean(aver5_50))
column = np.arange(1, cal_num+1).tolist()
column.append('ave')
pd_all = pd.DataFrame(columns=column, data=[aver, aver3, aver5, aver_50, aver3_50, aver5_50])
pd_all.to_csv(result_path + 'result.csv', mode='a',)
if __name__ == "__main__":
print(time.asctime(time.localtime(time.time())))
main()
print(time.asctime(time.localtime(time.time())))