def predict_train_epoch(epoch, model, train_traj_dataloader, optimizer, device):
model.train()
desc= ' -(Train)- '
total_loss, avg_loss, avg_acc = 0, 0, 0.
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
total_loc, total_cor_loc = 0, 0
eva_metric = np.zeros((6, 1))
for i, data in enumerate(train_traj_dataloader):
bert_input, bert_label, _, time_input, time_label, user = data[0].to(device).long(), \
data[1].to(device).long().contiguous().view(-1), \
data[2].to(device).long(), \
data[3].to(device).long(), \
data[4].to(device).long().contiguous().view(-1), \
data[5].to(device).long()
# data[1].to(device).long().contiguous().view(-1), \
place_logit, time_logit = model(bert_input, time_input, user)
loss, n_loc, n_cor_loc, n_cor_time = cal_loss_performance(logit1=place_logit, logit2=time_logit, label1=bert_label, label2=time_label, Predict=True)
eva_metric = get_acc(bert_label, place_logit, eva_metric)
total_loss += loss.item()
iter_100_loss += loss.item()
avg_loss = total_loss/(i+1)
total_loc += n_loc
iter_100_loc += n_loc
total_cor_loc += n_cor_loc
iter_100_cor_loc += n_cor_loc
iter_100_cor_time += n_cor_time
avg_acc = 100.*total_cor_loc/total_loc
if i % 100 == 0:
try:
if n_loc==0 and n_cor_loc==0:
n_loc = 1
print("{} epoch: {:_>2d} | iter: {:_>4d} | loss: {:<10.7f} | avg_loss: {:<10.7f} | acc: {:<4.4f} % | avg_acc: {:<4.4f} % | lr: {:<9.7f} | time_acc: {:<4.4f} %".format(
desc, epoch, i, iter_100_loss/100., avg_loss, 100.*iter_100_cor_loc/iter_100_loc, avg_acc, optimizer._print_lr(), 100.*iter_100_cor_time/iter_100_loc))
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
except Exception as e:
print(e)
exit()
optimizer.zero_grad()
loss.backward()
optimizer.step_and_update_lr()
return avg_loss, avg_acc, eva_metric/total_loc
def predict_valid_epoch(epoch, model, valid_traj_dataloader, optimizer, device):
model.eval()
desc= ' -(Valid)- '
total_loss, avg_loss, avg_acc = 0, 0, 0.
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time= 0, 0, 0, 0
total_loc, total_cor_loc = 0, 0
eva_metric = np.zeros((6, 1))
with torch.no_grad():
for i, data in enumerate(valid_traj_dataloader):
# bert_input, bert_label, segment_label, is_Next
bert_input, bert_label, _, time_input, time_label, user = data[0].to(device).long(), \
data[1].to(device).long().contiguous().view(-1), \
data[2].to(device).long(), \
data[3].to(device).long(), \
data[4].to(device).long().contiguous().view(-1), \
data[5].to(device).long()
place_logit, time_logit = model(bert_input, time_input, user)
loss, n_loc, n_cor_loc, n_cor_time = cal_loss_performance(logit1=place_logit, logit2=time_logit, label1=bert_label, label2=time_label, Predict=True)
eva_metric = get_acc(bert_label, place_logit, eva_metric)
total_loss += loss.item()
iter_100_loss += loss.item()
avg_loss = total_loss/(i+1)
total_loc += n_loc
iter_100_loc += n_loc
total_cor_loc += n_cor_loc
iter_100_cor_loc += n_cor_loc
iter_100_cor_time += n_cor_time
avg_acc = 100.*total_cor_loc/total_loc
if i % 100 == 0:
print("{} epoch: {:_>2d} | iter: {:_>4d} | loss: {:<10.7f} | avg_loss: {:<10.7f} | acc: {:<4.4f} % | avg_acc: {:<4.4f} % | lr: {:<9.7f} | time_acc: {:<4.4f} %".format(
desc, epoch, i, iter_100_loss/100., avg_loss, 100.*iter_100_cor_loc/iter_100_loc, avg_acc, optimizer._print_lr(), 100.*iter_100_cor_time/iter_100_loc))
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
return avg_loss, avg_acc, eva_metric/total_loc
def predict_train_epoch(epoch, model, train_data, train_queue, optimizer,
device, batch_size):
# predict_train_epoch(epoch_i, model, train_data, train_queue, optimizer, device)
model.train()
desc = ' -(Train)- '
total_loss, avg_loss, avg_acc = 0, 0, 0.
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
total_loc, total_cor_loc = 0, 0
eva_metric = np.zeros((6, 1))
len_queue = len(train_queue)
len_batch = int(np.ceil((len_queue / batch_size)))
train_queue = [[
train_queue.popleft() for _ in range(min(batch_size, len(train_queue)))
] for k in range(len_batch)]
for i, batch_queue in enumerate(train_queue):
max_place = max(
[len(train_data[u][idx]['loc']) for u, idx in batch_queue])
max_history = max(
[len(train_data[u][idx]['history_loc']) for u, idx in batch_queue])
loc = torch.cat([
pad_tensor(train_data[u][idx]['loc'], max_place, 0).unsqueeze(0)
for u, idx in batch_queue
],
dim=0).to(device).long()
time = torch.cat([
pad_tensor(train_data[u][idx]['tim'], max_place, 0).unsqueeze(0)
for u, idx in batch_queue
],
dim=0).to(device).long()
loc_label = torch.cat([
pad_tensor(train_data[u][idx]['target_loc'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long().contiguous().view(-1)
time_label = torch.cat([
pad_tensor(train_data[u][idx]['target_tim'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long().contiguous().view(-1)
history_loc = torch.cat([
before_pad_tensor(train_data[u][idx]['history_loc'], max_history,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long()
history_time = torch.cat([
before_pad_tensor(train_data[u][idx]['history_tim'], max_history,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long()
# his_len_traj = torch.from_numpy(np.array([train_data[u][idx]['his_len_traj'] for u, idx in batch_queue])).to(device).long()
input_loc = torch.cat([history_loc, loc], dim=1)
input_tim = torch.cat([history_time, time], dim=1)
place_logit = model(input_loc, input_tim, max_history)
loss, n_loc, n_cor_loc, n_cor_time = cal_loss_performance(
logit1=place_logit, label1=loc_label, Predict=True)
eva_metric = get_acc(loc_label, place_logit, eva_metric)
total_loss += loss.item()
iter_100_loss += loss.item()
avg_loss = total_loss / (i + 1)
total_loc += n_loc
iter_100_loc += n_loc
total_cor_loc += n_cor_loc
iter_100_cor_loc += n_cor_loc
iter_100_cor_time += n_cor_time
avg_acc = 100. * total_cor_loc / total_loc
if i % 100 == 0:
try:
if n_loc == 0 and n_cor_loc == 0:
n_loc = 1
print(
"{} epoch: {:_>2d} | iter: {:_>4d}/{:_>4d} | loss: {:<10.7f} | avg_loss: {:<10.7f} | acc: {:<4.4f} % | avg_acc: {:<4.4f} % | lr: {:<9.7f} | time_acc: {:<4.4f} %"
.format(desc, epoch, i, len_batch, iter_100_loss / 100.,
avg_loss, 100. * iter_100_cor_loc / iter_100_loc,
avg_acc, optimizer._print_lr(),
100. * iter_100_cor_time / iter_100_loc))
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
except Exception as e:
print(e)
exit()
optimizer.zero_grad()
loss.backward()
optimizer.step_and_update_lr()
return avg_loss, avg_acc, eva_metric / total_loc
def predict_valid_epoch(epoch, model, valid_data, valid_queue, optimizer,
device, batch_size):
model.eval()
desc = ' -(Valid)- '
total_loss, avg_loss, avg_acc = 0, 0, 0.
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
total_loc, total_cor_loc = 0, 0
eva_metric = np.zeros((6, 1))
len_queue = len(valid_queue)
len_batch = int(np.ceil((len_queue / batch_size)))
valid_queue = [[
valid_queue.popleft() for _ in range(min(batch_size, len(valid_queue)))
] for k in range(len_batch)]
with torch.no_grad():
for i, batch_queue in enumerate(valid_queue):
max_place = max(
[len(valid_data[u][idx]['loc']) for u, idx in batch_queue])
max_history = max([
len(valid_data[u][idx]['history_loc'])
for u, idx in batch_queue
])
loc = torch.cat([
pad_tensor(valid_data[u][idx]['loc'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long()
time = torch.cat([
pad_tensor(valid_data[u][idx]['tim'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long()
loc_label = torch.cat(
[
pad_tensor(valid_data[u][idx]['target_loc'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long().contiguous().view(-1)
time_label = torch.cat(
[
pad_tensor(valid_data[u][idx]['target_tim'], max_place,
0).unsqueeze(0) for u, idx in batch_queue
],
dim=0).to(device).long().contiguous().view(-1)
history_loc = torch.cat([
before_pad_tensor(valid_data[u][idx]['history_loc'],
max_history, 0).unsqueeze(0)
for u, idx in batch_queue
],
dim=0).to(device).long()
history_time = torch.cat([
before_pad_tensor(valid_data[u][idx]['history_tim'],
max_history, 0).unsqueeze(0)
for u, idx in batch_queue
],
dim=0).to(device).long()
input_loc = torch.cat([history_loc, loc], dim=1)
input_tim = torch.cat([history_time, time], dim=1)
place_logit = model(input_loc, input_tim, max_history)
loss, n_loc, n_cor_loc, n_cor_time = cal_loss_performance(
logit1=place_logit, label1=loc_label, Predict=True)
eva_metric = get_acc(loc_label, place_logit, eva_metric)
total_loss += loss.item()
iter_100_loss += loss.item()
avg_loss = total_loss / (i + 1)
total_loc += n_loc
iter_100_loc += n_loc
total_cor_loc += n_cor_loc
iter_100_cor_loc += n_cor_loc
iter_100_cor_time += n_cor_time
avg_acc = 100. * total_cor_loc / total_loc
if i % 100 == 0:
print(
"{} epoch: {:_>2d} | iter: {:_>4d}/{:_>4d} | loss: {:<10.7f} | avg_loss: {:<10.7f} | acc: {:<4.4f} % | avg_acc: {:<4.4f} % | lr: {:<9.7f} | time_acc: {:<4.4f} %"
.format(desc, epoch, i, len_batch, iter_100_loss / 100.,
avg_loss, 100. * iter_100_cor_loc / iter_100_loc,
avg_acc, optimizer._print_lr(),
100. * iter_100_cor_time / iter_100_loc))
iter_100_loss, iter_100_loc, iter_100_cor_loc, iter_100_cor_time = 0, 0, 0, 0
return avg_loss, avg_acc, eva_metric / total_loc
