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Copy pathutils.py
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174 lines (149 loc) · 7.79 KB
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import numpy as np
import torch
from torch.optim import Adam
from tqdm import tqdm
import os
def train_stop(model, train_loader, valid_loaders, log_dir, lr, epoch, valid_step_interval, device, citys, patience=10):
log_file_train = os.path.join(log_dir, f"log_train.txt")
with open(log_file_train, "w") as f: # open for writing to clear the file
pass
optimizer = model.configure_optimizers(weight_decay=0.1, learning_rate=lr)
p1 = int(0.4 * epoch)
p2 = int(0.6 * epoch)
p3 = int(0.9 * epoch)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[p1, p2, p3], gamma=0.4
)
best_valid_loss_dict = {}
patience_counter_dict = {}
for city in citys:
best_valid_loss_dict[f'{city}'] = 1e10
patience_counter_dict[f'{city}'] = 0
log_file_val = os.path.join(log_dir, f"log_val_{city}.txt")
with open(log_file_val, "w") as f: # open for writing to clear the file
pass
for epoch_no in range(epoch):
avg_loss = 0
model.train()
with tqdm(train_loader, mininterval=5.0, maxinterval=50.0) as it:
for batch_no, train_batch in enumerate(it, start=1):
optimizer.zero_grad()
x_train = train_batch[0]
y_train = train_batch[1]
ts = train_batch[2]
train_city = train_batch[3][0]
vocab = np.load(f'./location_feature/vocab_{train_city}.npy')
vocab = np.pad(vocab, ((2, 0), (0, 0)), mode='constant', constant_values=0)
vocab = torch.from_numpy(vocab)
vocab = vocab.to(torch.float32)
output = model(x_train, ts, y_train, vocab, device)
loss = output['loss']
loss.backward()
avg_loss += loss.item()
loss_avg = avg_loss / batch_no
optimizer.step()
with open(log_file_train, "a") as f:
f.write(f"{epoch_no}\t{batch_no}\t train \t{loss_avg:.6f}\n")
it.set_postfix(
ordered_dict={
"avg_epoch_loss": loss_avg,
"epoch": epoch_no,
},
refresh=False,
)
if valid_loaders is not None and (batch_no + 1) % valid_step_interval == 0:
model.eval()
with torch.no_grad():
for valid_loader in valid_loaders:
avg_loss_valid = 0
with tqdm(valid_loader, mininterval=5.0, maxinterval=50.0) as it:
for batch_no_val, valid_batch in enumerate(it, start=1):
x_val = valid_batch[0]
y_val = valid_batch[1]
ts = valid_batch[2]
val_city = valid_batch[3][0]
vocab = np.load(f'./location_feature/vocab_{val_city}.npy')
vocab = np.pad(vocab, ((2, 0), (0, 0)), mode='constant', constant_values=0)
vocab = torch.from_numpy(vocab)
vocab = vocab.to(torch.float32)
output = model(x_val, ts, y_val, vocab, device)
loss = output['loss']
avg_loss_valid += loss.item()
loss_avg_valid = avg_loss_valid / batch_no_val
it.set_postfix(
ordered_dict={
"valid_avg_loss": loss_avg_valid,
"epoch": epoch_no,
},
refresh=False,
)
log_file_val = os.path.join(log_dir, f"log_val_{val_city}.txt")
with open(log_file_val, "a") as f:
f.write(f"{epoch_no}\t{batch_no}\t val \t{loss_avg_valid:.6f}\n")
if best_valid_loss_dict[f'{val_city}'] > avg_loss_valid:
output_path = log_dir + f"/model_{val_city}.pth"
torch.save(model.state_dict(), output_path)
best_valid_loss_dict[f'{val_city}'] = avg_loss_valid
patience_counter_dict[f'{val_city}'] = 0 # Reset patience counter
print(
"\n best loss is updated to ",
avg_loss_valid / batch_no_val,
"at",
epoch_no, val_city
)
else:
patience_counter_dict[f'{val_city}'] += 1
# if patience_counter_dict[f'{val_city}'] >= patience:
if all(value > patience for value in patience_counter_dict.values()):
print(f"\n Early stopping triggered for {val_city} at epoch {epoch_no}")
return # Stop training
lr_scheduler.step()
def evaluate(model,test_loader,log_dir,B,city,device):
log_file_test = os.path.join(log_dir, f"log_{city}_test.txt")
with open(log_file_test, "w") as f: # open for writing to clear the file
pass
model.load_state_dict(torch.load(log_dir + f"/model_{city[0]}.pth"))
model.eval()
acc1 = 0
acc3 = 0
acc5 = 0
size = 0
val_loss_accum = 0.0
batch = 0
with tqdm(test_loader, mininterval=5.0, maxinterval=50.0) as it:
for batch_no, test_batch in enumerate(it, start=1):
batch = batch_no+1
x_test = test_batch[0]
y_test = test_batch[1]
ts = test_batch[2]
test_city = test_batch[3][0]
vocab = np.load(f'./location_feature/vocab_{test_city}.npy')
vocab = np.pad(vocab, ((2,0), (0, 0)), mode='constant', constant_values=0)
vocab = torch.from_numpy(vocab)
vocab = vocab.to(torch.float32)
output = model(x_test,ts,y_test,vocab,device)
loss = output['loss']
val_loss_accum += loss.detach()
pred = output['logits']#[B T vocab_size]
pred[:,:,0] = float('-inf')
y_test = y_test.to(device)
for b in range(B):
_, pred_indices = torch.topk(pred[b], 100)
valid_mask = y_test[b] > 0
valid_y_val = y_test[b][valid_mask]
valid_pred_indices = pred_indices[valid_mask]
valid_y_val_expanded = valid_y_val.unsqueeze(1)
l= valid_y_val_expanded.size(0)
size +=l
a1 = torch.sum(valid_pred_indices[:, 0:1] == valid_y_val_expanded).item()
a3 = torch.sum(valid_pred_indices[:, 0:3] == valid_y_val_expanded).item()
a5 = torch.sum(valid_pred_indices[:,0:5] == valid_y_val_expanded).item()
acc1 += a1
acc3 += a3
acc5 += a5
val_loss_accum=val_loss_accum/ batch
acc1 = acc1/size
acc3 = acc3/size
acc5 = acc5/size
with open(log_file_test, "a") as f:
f.write(f"{val_loss_accum}\t{acc1:.6f}\t{acc3:.6f}\t{acc5:.6f}\t{size}\n")