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train.py
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train.py
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from __future__ import division
from __future__ import print_function
import math
import sys
import time
import argparse
import torch.optim as optim
from Model import MTPool
from utils import *
import torch.nn.functional as F
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
datasets = ["AtrialFibrillation", "FingerMovements", "HandMovementDirection", "Handwriting", "Heartbeat",
"Libras", "LSST", "MotorImagery", "NATOPS", "PenDigits", "SelfRegulationSCP2", "StandWalkJump"]
parser = argparse.ArgumentParser()
# dataset settings
parser.add_argument('--data_path', type=str, default="./dataset/Preprocess/",
help='the path of data.')
parser.add_argument('--dataset', type=str, default="StandWalkJump",
help='time series dataset. Options: See the datasets list')
parser.add_argument('--gnn', type=str, default="GNN",
help='GNN or GIN')
parser.add_argument('--relation', type=str, default="corr",
help='dynamic or corr or all_one')
parser.add_argument('--pooling', type=str, default="CoSimPool",
help='CoSimPool or MemPool or DiffPool or SAGPool')
# cuda settings
parser.add_argument('--no-cuda', action='store_true', default=False,
help='Disables CUDA training.')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--use_cuda', type=int, default=1, help='cpu or gpu.')
# Training parameter settings
parser.add_argument('--epochs', type=int, default=100000,
help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=1e-5,
help='Initial learning rate. default:[0.00001]')
parser.add_argument('--wd', type=float, default=1e-3,
help='Weight decay (L2 loss on parameters). default: 5e-3')
parser.add_argument('--stop_thres', type=float, default=1e-9,
help='The stop threshold for the training error. If the difference between training losses '
'between epoches are less than the threshold, the training will be stopped. Default:1e-9')
args = parser.parse_args()
if args.use_cuda==1:
args.cuda = torch.cuda.is_available()
else:
args.cuda = False
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.autograd.set_detect_anomaly(True)
print("\nParameters:")
for attr, value in sorted(args.__dict__.items()):
print("\t{}={}".format(attr.upper(), value))
print("Loading dataset", args.dataset, "...")
# Model and optimizer
model_type = "MTPool"
if model_type == "MTPool":
features, labels, idx_train, idx_val, idx_test, nclass \
= load_raw_ts(args.data_path, dataset=args.dataset)
print("Data shape:", features.size())
model = MTPool(use_cuda=args.cuda,
dataset_path=args.data_path,
dataset=args.dataset,
graph_method=args.gnn,
relation_method=args.relation,
pooling_method=args.pooling
)
# cuda
if args.cuda:
model.cuda()
features, labels, idx_train = features.cuda(), labels.cuda(), idx_train.cuda()
input = (features, labels, idx_train, idx_val, idx_test)
# init the optimizer
optimizer = optim.Adam(model.parameters(),
lr=args.lr, weight_decay=args.wd)
# training function
def train():
loss_list = [sys.maxsize]
test_best_possible, best_so_far = 0.0, sys.maxsize
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
output = model(input)
# print("output",(output[idx_train]).T)
# print("label", (labels[:len(idx_train)]).T)
# y = labels[:len(idx_train)]
# torch.save("y.pt",labels)
loss_train = F.cross_entropy(output[idx_train], torch.squeeze(labels[:len(idx_train)]))
loss_train = loss_train
loss_list.append(loss_train.item())
acc_train = accuracy(output[idx_train], labels[idx_train])
loss_train.backward()
optimizer.step()
output = model(input, test=True)
# print("output", (output.T))
# print("label", (labels[len(idx_train):]).T)
loss_val = F.cross_entropy(output, torch.squeeze(labels[len(idx_train):]))
acc_val = accuracy(output, labels[len(idx_train):])
print('Epoch: {:04d}'.format(epoch + 1),
'loss_train: {:.8f}'.format(loss_train.item()),
'acc_train: {:.4f}'.format(acc_train.item()),
'lv: {:.4f}'.format(loss_val.item()),
'av: {:.4f}'.format(acc_val.item()),
'time: {:.4f}s'.format(time.time() - t))
if acc_val.item() > test_best_possible:
test_best_possible = acc_val.item()
if best_so_far > loss_train.item():
best_so_far = loss_train.item()
test_acc = acc_val.item()
print("test_acc: " + str(test_acc))
print("best possible: " + str(test_best_possible))
# test function
def test():
output = model(input,test=True)
#print(output[idx_test])
loss_test= F.cross_entropy(output, torch.squeeze(labels[len(idx_train):]))
acc_test = accuracy(output, labels[len(idx_train):])
print(args.dataset, "Test set results:",
"loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
# Train model
t_total = time.time()
train()
print("Optimization Finished!")
print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
# Testing
test()