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train_test.py
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import torch
import numpy as np
from utils.utils import load_dataset
from torch_geometric.loader import DataLoader
from torch.optim import Adam, lr_scheduler
from tqdm import tqdm
import wandb, os, time
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
""" Training """
def train(wds_train, wds_val, model, reservoirs, args, save_dir, out_f):
""" Initializing hyperparameters. """
n_epochs, learn_r = args.n_epochs, args.lr
""" Initiating the Optimizer and Learning rate scheduler. """
optimizer = Adam(model.parameters(), lr=learn_r, weight_decay=0., eps=1e-12)
lr_decay_step, lr_decay_rate = args.decay_step, args.decay_rate
opt_scheduler = lr_scheduler.MultiStepLR(optimizer, range(lr_decay_step, lr_decay_step*1000, lr_decay_step), gamma=lr_decay_rate)
if args.model_path == None:
args.model_path = os.path.join(save_dir, "model_"+args.model+"_"+str(args.n_epochs)+"_"+str(args.I)+\
"_"+str(args.wds)+".pt")
""" Checking if training using a partially trained model. """
if args.warm_start and args.model_path != None:
model_state = torch.load(args.model_path)
model.load_state_dict(model_state["model"])
optimizer.load_state_dict(model_state["optimizer"])
n_nodes = wds_train.X.shape[1]
n_edges = wds_train.edge_attr[0].shape[0]
""" Loading dataset and creating test and validation splits and batches """
train_dataset, _ = load_dataset(wds_train, n_nodes, reservoirs)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
val_dataset, _ = load_dataset(wds_val, n_nodes, reservoirs)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=True)
""" Train-validation loop """
for epoch in tqdm(range(n_epochs)):
train_losses = []
for batch in train_loader:
batch.edge_attr[:, 9:10] = 0.
model.train()
model.zero_grad()
y_hat = model(batch, r_iter=args.r_iter, epoch=epoch)
train_loss = model.loss()
train_loss.backward()
train_losses.append(train_loss.detach().cpu().item())
clip_val = 1e-5
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_val)
grad_norm_after = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1e+6)
optimizer.step()
if args.wandb:
wandb.log({
'training/loss' : train_losses[-1],
'learning_rate' : float(opt_scheduler.optimizer.param_groups[0]['lr']),
'gradient_norm' : grad_norm,
'gradient_norm_after' : grad_norm_after,
'epoch' : epoch,
})
if args.wandb:
wandb.log({
'training/loss_mean' : np.mean(train_losses),
'epoch' : epoch
})
opt_scheduler.step()
if epoch % (n_epochs//150) == 0:
model.eval()
val_losses = []
q_loss, d_hat_loss, d_tilde_loss = [], [], []
for batch_val in val_loader:
batch_val.edge_attr[:, 9:10] = 0.
with torch.no_grad():
y_hat = model(batch_val, r_iter=args.r_iter, epoch=epoch)
val_loss = model.loss()
val_losses.append(val_loss)
q_loss.append(model.loss_q)
d_hat_loss.append(model.loss_d_hat)
d_tilde_loss.append(model.loss_d_tilde)
mean_val_losses = torch.mean(torch.stack(val_losses)).detach().cpu().item()
if args.wandb:
wandb.log({
'validation/loss_mean' : torch.mean(torch.stack(val_losses)).detach().cpu().item(),
'validation/loss_h' : torch.mean(torch.stack(q_loss)).detach().cpu().item(),
'validation/loss_f1' : torch.mean(torch.stack(d_hat_loss)).detach().cpu().item(),
'validation/loss_f2' : torch.mean(torch.stack(d_tilde_loss)).detach().cpu().item(),
'epoch' : epoch,
})
print("Epoch ", epoch, ": Train loss: ", np.round(np.mean(train_losses), 8), \
" Val loss: ", np.round(mean_val_losses, 8))
print("Epoch ", epoch, ": Train loss: ", np.round(np.mean(train_losses), 8), \
" Val loss: ", np.round(mean_val_losses, 8), file=out_f)
print("Val losses (last batch) - (d, d_hat): ", np.round(torch.mean(torch.stack(d_hat_loss)).detach().cpu().item(), 8), \
", (d, d_tilde): ", np.round(torch.mean(torch.stack(d_tilde_loss)).detach().cpu().item(), 8),
", (q, q_tilde): ", np.round(torch.mean(torch.stack(q_loss)).detach().cpu().item(), 8))
print("Val losses (last batch) - (d, d_hat): ", np.round(torch.mean(torch.stack(d_hat_loss)).detach().cpu().item(), 8), \
", (d, d_tilde): ", np.round(torch.mean(torch.stack(d_tilde_loss)).detach().cpu().item(), 8),
", (q, q_tilde): ", np.round(torch.mean(torch.stack(q_loss)).detach().cpu().item(), 8), file=out_f)
if epoch % (n_epochs//15) == 0:
""" Saving the model. """
state = {
'epoch': epoch,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
print('model path:', args.model_path)
print('model path:', args.model_path, file=out_f)
torch.save(state, args.model_path)
return state, args.model_path
""" Testing """
@torch.no_grad()
def test(wds_test, model, reservoirs, args, save_dir, out_f, e=1e-32, load_model=True):
""" Loading the trained model. """
if load_model:
if args.model_path is None:
args.model_path = os.path.join(save_dir, "model_"+args.model+"_"+str(args.n_epochs)+"_"+str(args.I)+\
"_"+str(args.wds)+".pt")
model_state = torch.load(args.model_path)
model.load_state_dict(model_state["model"])
model.eval()
""" Initializing parameters and loading dataset and batches. """
n_nodes = wds_test.X.shape[1]
n_edges = wds_test.edge_attr[0].shape[0]
test_dataset, Y_test = load_dataset(wds_test, n_nodes, reservoirs)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False)
"""" Testing and saving the results. """
test_losses = []
Y_hat, F_hat, F_tilde, D_hat, D_tilde = [], [], [], [], []
t_s = time.time()
for batch in test_loader:
batch.edge_attr[:, 9:10] = 0.
y_hat = model(batch, r_iter=args.r_iter, zeta=e)
test_loss = model.loss()
test_losses.append(test_loss.detach().cpu().item())
Y_hat.append(y_hat)
F_hat.append(model.q_hat)
F_tilde.append(model.q_tilde)
D_hat.append(model.d_hat)
D_tilde.append(model.d_tilde)
print("\nEvaluation time is: ", time.time() - t_s, ' seconds. \n')
Y_hat = torch.stack(torch.vstack(Y_hat).detach().cpu().split(n_nodes))
F_hat = torch.stack(torch.vstack(F_hat).detach().cpu().split(n_edges))
F_tilde = torch.stack(torch.vstack(F_tilde).detach().cpu().split(n_edges))
D_hat = torch.stack(torch.vstack(D_hat).detach().cpu().split(n_nodes))
D_tilde = torch.stack(torch.vstack(D_tilde).detach().cpu().split(n_nodes))
print("Test loss: ", np.round(np.mean(test_losses), 8))
print("Test loss: ", np.round(np.mean(test_losses), 8), file=out_f)
return Y_test, Y_hat, test_losses, F_hat, D_hat, F_tilde, D_tilde