if __name__ == '__main__':
batch_size = 2
test_batch = 2
learning_rate = 0.001
epoch_num = 100
dataset_test = MyDataset('../Data/lmdbData_test', padding='zero')
dataset = MyDataset('../Data/lmdbData_train',padding='zero')
train_loader = DataLoader(dataset = dataset,batch_size=batch_size,shuffle=True)
model = HAR_PointGNN(r = 0.0005, T=3, state_dim=8,frame_num=60)
model.to(device)
if os.path.exists('./models/HAR_PointGNN.pkl'):
model.load_state_dict(torch.load('./models/HAR_PointGNN.pkl',map_location = device))
print("load model sucessfully")
adam = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(adam, step_size=1, gamma=0.93)
crossloss = nn.CrossEntropyLoss()
for epoch in range(6,epoch_num+1):
test_acc(model,dataset_test,test_batch)
model.train()
epoch_loss = 0
if __name__ == '__main__':
batch_size = 3
test_batch = 3
learning_rate = 0.0001
epoch_num = 200
dataset_test = PMDataset(dir=LMDB_DATA, map_size=10, train=False)
dataset = PMDataset(dir=LMDB_DATA, map_size=10, train=True)
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
model = HAR_PointGNN(r=-1, T=3, state_dim=11, frame_num=20, output_dim=6)
# summary(model,(1,60,250,11))
model = nn.DataParallel(model)
if os.path.exists('./models/PatientHAR_PointGNN.pkl'):
model.load_state_dict(torch.load('./models/PatientHAR_PointGNN.pkl'))
print("load model sucessfully")
model.cuda()
adam = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(adam, step_size=1, gamma=0.8)
crossloss = nn.CrossEntropyLoss()
for epoch in range(1, epoch_num + 1):
test_acc(model, dataset_test, test_batch)
learning_rate = 0.001
epoch_num = 2000
dataset_test = Falldataset(root_falls="../mmfalldata/DS2_falls",
root_normal="../mmfalldata/DS2_normal",
raw_data=False)
dataset = Falldataset(root_falls="../mmfalldata/DS1_4falls",
root_normal="../mmfalldata/DS1_4normal",
raw_data=False)
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
model = HAR_PointGNN(r=-1, T=3, state_dim=4, frame_num=10, output_dim=2)
# summary(model,(1,60,250,11))
model.to(device)
if os.path.exists('./models/mmFall_PointGNN.pkl'):
model.load_state_dict(
torch.load('./models/mmFall_PointGNN.pkl', map_location=device))
print("load model sucessfully")
adam = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(adam, step_size=1, gamma=0.98)
crossloss = nn.CrossEntropyLoss()
for epoch in range(1, epoch_num + 1):
# if epoch % 20:
# test_acc(model,dataset_test,test_batch)
batch_size = 30 #40 6
test_batch = 50 # 80 12
learning_rate = 0.005
epoch_num = 200
dataset_test = GestureDataset(train=False, with_state=True)
dataset = GestureDataset(train=True, with_state=True)
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
model = HAR_PointGNN(r=-1,
T=3,
state_dim=5,
frame_num=20,
output_dim=4,
extend=128,
lstm_hidden=16)
# summary(model,(1,60,250,11))
model = nn.DataParallel(model)
if os.path.exists('./models/gesture_PointGNN.pkl'):
model.load_state_dict(torch.load('./models/gesture_PointGNN.pkl'))
print("load model sucessfully")
model.cuda()
adam = torch.optim.Adam(model.parameters(),
lr=learning_rate,
betas=(0.9, 0.99))
if __name__ == '__main__':
batch_size = 10
test_batch = 15
learning_rate = 0.01
seed = 2021
epoch_num = 200
dataset_test = GestureDataset(train=False, with_state=True)
dataset = GestureDataset(train=True, with_state=True)
train_loader = DataLoader(dataset = dataset,batch_size=batch_size,shuffle=True)
model = HAR_PointGNN(r = -1, T=3, state_dim=5,frame_num=20, output_dim=6,extend=128, lstm_hidden = 32)
# summary(model,(1,60,250,11))
model.to(device)
if os.path.exists('./models/gesture_PointGNN.pkl'):
model.load_state_dict(torch.load('./models/gesture_PointGNN.pkl',map_location = device))
print("load model sucessfully")
adam = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(adam, step_size=1, gamma=0.85)
crossloss = nn.CrossEntropyLoss()
for epoch in range(1, epoch_num+1):
test_acc(model,dataset_test,test_batch)
model.train()
epoch_loss = 0