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train.py
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train.py
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import numpy as np
import matplotlib.pyplot as plt
import torch, math
import LoadData
import argparse
from torch.utils.data import DataLoader
from pytorchtools import EarlyStopping
from sklearn.metrics import mean_squared_error
from T_Model import *
import utils
from sklearn.preprocessing import MinMaxScaler
import matplotlib
# matplotlib.use('Agg')
def test(model, data_loader,dev):
test_output = []
labels_output = []
data_output = []
running_loss_test=0
with torch.no_grad():
for data, labels in data_loader:
model.eval()
data, labels = data.float().to(dev), labels.float().to(dev)
outputs = model(data)
outputs = torch.squeeze(outputs)
loss = criterion(outputs, labels)
running_loss_test = running_loss_test + loss.item()
data = data.cpu().detach().numpy()
test_output.extend(outputs.cpu().detach().numpy())
labels = torch.squeeze(labels).cpu().detach().numpy()
labels_output.extend(labels)
data_output.extend(data)
print("Test loss: {:.2f}".format(running_loss_test))
return test_output, labels_output, data_output
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GaitMotion Dataset')
parser.add_argument('--seed', type=int, default=2023, help="random seed")
parser.add_argument('--seq_length', type=int, default=800, help="step segmentation length")
parser.add_argument('--batch_size', type=int, default=64, help="batch size")
parser.add_argument('--num_epochs', type=int, default=200, help="number of epochs")
parser.add_argument('--lr', type=float, default= 0.00005, help="learning rate")
parser.add_argument('--input_size', type=int, default=6, help="input size")
parser.add_argument('--hidden_size', type=int, default=3, help="hidden size")
parser.add_argument('--num_layers', type=int, default=3, help="number of layers")
parser.add_argument('--num_classes', type=int, default=1, help="prediction output size")
parser.add_argument('--patience', type=int, default=10, help="patience for early stop")
parser.add_argument('--rate', type=int, default=1000, help="sampling rate")
parser.add_argument('--subID', type=str, default='5', help="test participant ID")
parser.add_argument('--seq_buffer', type=int, default=800, help="buffer length for step sequence")
parser.add_argument('--type', nargs='+', help="gait patterns for training",choices= ["Normal", "Shuffle", "Stroke"], default=["Normal", "Shuffle", "Stroke"])
args = parser.parse_args()
SEED = args.seed
torch.manual_seed(SEED)
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
transform = MinMaxScaler()
files, test_files, val_files = utils.prepare_files(args)
test_dataset = LoadData.GaitDataset(filenames=test_files, batch_size=args.batch_size, seq_length=args.seq_length, seq_buffer=args.seq_buffer,
transform=transform, rate=args.rate, testing=1, mtype =args.type, testing_with_discard=0)
val_dataset = LoadData.GaitDataset(filenames=val_files, batch_size=args.batch_size, seq_length=args.seq_length, seq_buffer=args.seq_buffer,
transform=transform, rate=args.rate, testing=0, mtype =args.type)
train_dataset = LoadData.GaitDataset(filenames=files, batch_size=args.batch_size, seq_length=args.seq_length, seq_buffer=args.seq_buffer,
transform=transform, rate=args.rate, mtype =args.type)
print("test size: "+str(len(test_dataset)))
print("train size: "+str(len(train_dataset)))
print("validation size: "+str(len(val_dataset)))
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) # batch_size=None use False to debug
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False)
val_dataloader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False)
model = CNNNet(num_classes=args.num_classes, output_len=args.seq_length, output_size = args.input_size)
model.to(dev)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
early_stopping = EarlyStopping(patience=args.patience, verbose=True)
train_loss = []
val_loss = []
val_acc = []
test_acc = []
for epoch in range(args.num_epochs):
running_loss_train = 0.0
running_loss_val = 0.0
correct_val = 0
correct_test = 0
total_test = 0
for data, labels in train_dataloader:
model.train()
data, labels = data.float().to(dev), torch.squeeze(labels).float().to(dev)
outputs = model(data)
outputs = torch.squeeze(outputs)
optimizer.zero_grad()
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss_train += loss.item()
with torch.no_grad():
for data, labels in val_dataloader:
model.eval()
data, labels = data.float().to(dev), torch.squeeze(labels).float().to(dev)
outputs = model(data)
outputs = torch.squeeze(outputs)
loss = criterion(outputs, labels)
running_loss_val += loss.item()
early_stopping((running_loss_val), model)
if early_stopping.early_stop:
print("Early stopping at epoch", epoch)
break
val_loss.append(running_loss_val / len(val_dataloader))
train_loss.append(running_loss_train / len(train_dataloader))
print("Epoch {}, Train Loss: {:.4f}, Val Loss: {:.4f}"
.format(epoch, running_loss_train / len(train_dataloader),
running_loss_val / len(val_dataloader)))
test_output, labels_output, data_output = test(model, test_dataloader, dev)
plt.plot(train_loss), plt.xlabel("training loss"), plt.savefig(r'./outputs/seq_length='+str(args.seq_length) + '/'+ args.subID+'training_loss.png'), plt.show(),plt.close()
plt.plot(val_loss), plt.xlabel("validation loss"), plt.savefig(r'./outputs/seq_length='+str(args.seq_length) + '/'+args.subID+'val_loss.png'), plt.show(),plt.close()
torch.save(model.state_dict(), r'./outputs/seq_length='+str(args.seq_length)+'/model_scripted.pt')
test_output = np.asarray(test_output)
data_output = np.asarray(data_output)
labels_output = np.asarray(labels_output)
RMSE = math.sqrt(mean_squared_error(test_output, labels_output))
print("Root Mean Square: ", RMSE)
r2_score = 1 - mean_squared_error(test_output, labels_output) / np.var(test_output)
print("R Squared Error: ", r2_score)
plt.plot(test_output, label='predict label'), plt.plot(labels_output, label='true label'),
plt.legend(), plt.savefig(r'./outputs/seq_length='+str(args.seq_length) + '/'+args.subID+'test.png'), plt.show(),plt.close()
test_output = np.c_[test_output, labels_output]
# the column in the csv is ['predict','groundtruth']
np.savetxt(r'./outputs/seq_length='+str(args.seq_length) + '/'+ args.subID+'test.csv', test_output, delimiter=',')
test_dataset.subject_dict.to_csv(r'./outputs/seq_length='+str(args.seq_length)+'/'+args.subID+'sub_info.csv',index=True)