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main.py
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import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
from tqdm import tqdm
from utils import load_data, load_adj, split_data
from models.graphcnn import GraphCNN
torch.backends.cudnn.enabled = False
def train(model, device, train_loader, optimizer, epoch):
model.train()
pbar = tqdm(train_loader, unit='batch')
loss_all = 0
num_all = 0
for step, (batch_feature, target) in enumerate(pbar):
batch_feature, target = batch_feature.to(device), target.to(device)
output = model(batch_feature)
criterion = nn.MSELoss(reduction='sum')
# compute loss
loss = criterion(output, target)
# backprop
if optimizer is not None:
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_all += loss.detach().cpu().numpy()
num_all += batch_feature.shape[0]
# report
pbar.set_description('epoch: %d' % epoch)
train_loss = loss_all / num_all
print("loss training: %f" % train_loss)
return train_loss
def evaluate(model, device, loader):
model.eval()
mae_all = 0
cor_all = np.zeros((2, 2))
num_all = 0
with torch.no_grad():
for batch_feature, target in loader:
batch_feature, target = batch_feature.to(device), target.to(device)
output = model(batch_feature)
mae_all += nn.L1Loss(reduction='sum')(output, target).detach().cpu().item()
for i in range(output.shape[0]):
cor_all += np.corrcoef(output[i].detach().cpu().numpy(), target[i].detach().cpu().numpy())
num_all += batch_feature.shape[0]
mae = mae_all / num_all
cor = cor_all[0, 1] / num_all
return mae, cor
def main():
# Parameters settings
parser = argparse.ArgumentParser(description='PyTorch implementation of graph emotion decoding (GED)')
parser.add_argument('--subject_id', type=int, default=1,
help='identifier of subject (default: 1)')
parser.add_argument('--num_sessions', type=int, default=5,
help='number of sessions (default: 5)')
parser.add_argument('--category_file', type=str, default="category", choices=["category", "categcontinuous"],
help='type of emotion category scores: binary (category) or continuous (categcontinuous)')
parser.add_argument('--device', type=int, default=0,
help='which gpu to use if any (default: 0)')
parser.add_argument('--batch_size', type=int, default=16,
help='input batch size for training (default: 16)')
parser.add_argument('--epochs', type=int, default=300,
help='maximum number of training epochs (default: 300)')
parser.add_argument('--lr', type=float, default=0.01,
help='initial learning rate (default: 0.01)')
parser.add_argument('--seed', type=int, default=0,
help='random seed for splitting the dataset into 10 folds (default: 0)')
parser.add_argument('--fold_idx', type=int, default=0,
help='fold index in 10-fold validation (should be less then 10)')
parser.add_argument('--num_parts', type=int, default=4,
help='number of equal parts along each (x or y or z) axis (n, default: 4)')
parser.add_argument('--num_activations', type=int, default=150,
help='number of active brain areas for each stimulus (l, default: 150)')
parser.add_argument('--num_interactions', type=int, default=50,
help='number of connected/interactive brain areas for each emotion (m, default: 50)')
parser.add_argument('--num_layers', type=int, default=5,
help='number of layers INCLUDING the input one (default: 5)')
parser.add_argument('--num_mlp_layers', type=int, default=2,
help='number of layers for MLP EXCLUDING the input one (default: 2)')
parser.add_argument('--hidden_dim', type=int, default=64,
help='number of hidden units (default: 64)')
parser.add_argument('--final_dropout', type=float, default=0.5,
help='dropout ratio after the final layer (default: 0.5)')
parser.add_argument('--neighbor_pooling_type', type=str, default="average", choices=["sum", "average"],
help='pooling for neighboring nodes: sum or average')
args = parser.parse_args()
# set up gpu device
device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu")
sj = load_data(args.category_file, args.subject_id, args.num_sessions, args.num_parts)
# 10-fold cross validation. Conduct an experiment on the fold specified by args.fold_idx.
train_idx, test_idx = split_data(sj.features.shape[0], args.seed, args.fold_idx)
train_data = Data.TensorDataset(sj.features[train_idx], sj.labels[train_idx])
test_data = Data.TensorDataset(sj.features[test_idx], sj.labels[test_idx])
num_classes = sj.labels.shape[1]
num_nodes = sj.labels.shape[1] + sj.area_avgs.shape[1]
# construct the emotion-brain bipartite graph
edges = load_adj(sj.labels[train_idx], sj.area_avgs[train_idx], args.num_activations, args.num_interactions)
edge_mat = torch.LongTensor(edges).transpose(0, 1)
model = GraphCNN(args.num_layers, args.num_mlp_layers, train_data[0][0].shape[1], args.hidden_dim, num_classes,
args.final_dropout, num_nodes, edge_mat, args.neighbor_pooling_type, device).to(device)
train_loader = Data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True)
test_loader = Data.DataLoader(test_data, batch_size=args.batch_size, shuffle=True)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5)
for epoch in range(1, args.epochs + 1):
train_loss = train(model, device, train_loader, optimizer, epoch)
mae_train, cor_train = evaluate(model, device, train_loader)
mae_test, cor_test = evaluate(model, device, test_loader)
print("MAE train: %f, test: %f" % (mae_train, mae_test))
print("correlation train: %f, test: %f" % (cor_train, cor_test))
# with open(filename, 'a') as f:
# f.write("%f %f %f %f %f" % (train_loss, mae_train, mae_test, cor_train, cor_test))
# f.write("\n")
scheduler.step()
print("")
if __name__ == '__main__':
main()