sl_solver.py
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import copy
import os
import torch
from data import build_dataloader
from loss import build_loss
from model import build_model
from optimizer import build_lr_scheduler, build_optimizer
from utils import SOLVER_REGISTRY, get_logger_and_log_dir
from utils import sequence_mask
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
@SOLVER_REGISTRY.register()
class SLSolver(object):
def __init__(self, cfg):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.cfg = copy.deepcopy(cfg)
self.train_loader, self.val_loader = build_dataloader(cfg)
self.train_loader_size, self.val_loader_size = len(self.train_loader), len(self.val_loader)
self.train_dataset_size, self.val_dataset_size = len(self.train_loader.dataset), len(self.val_loader.dataset)
# BatchNorm ?
self.model = build_model(cfg).to(self.device)
self.loss_fn = build_loss(cfg)
self.optimizer = build_optimizer(cfg)(self.model.parameters(), **cfg['solver']['optimizer']['args'])
self.hyper_params = cfg['solver']['args']
self.base_on = self.hyper_params['base_on']
self.model_path = self.hyper_params['model_path']
try:
self.epoch = self.hyper_params['epoch']
except Exception:
raise 'should contain epoch in {solver.args}'
self.logger, self.log_dir = get_logger_and_log_dir(**cfg['solver']['logger'])
def accuracy(self, y_pred, y_true, valid_lens, thresholds=0.5):
# [batch_size, seq_len, num_classes]
y_pred_sigmoid = torch.nn.Sigmoid()(y_pred)
# [batch_size, seq_len]
y_pred_idx = torch.argmax(y_pred_sigmoid, dim=-1) + 1
# [batch_size, seq_len]
y_pred_is_other = (torch.amax(y_pred_sigmoid, dim=-1) > thresholds).int()
y_pred_rebuild = torch.multiply(y_pred_idx, y_pred_is_other)
y_true_idx = torch.argmax(y_true, dim=-1) + 1
y_true_is_other = torch.sum(y_true, dim=-1).int()
y_true_rebuild = torch.multiply(y_true_idx, y_true_is_other)
masked_y_true_rebuild = sequence_mask(y_true_rebuild, valid_lens, value=-1)
return torch.sum((y_pred_rebuild == masked_y_true_rebuild).int()).item()
def train_loop(self):
self.model.train()
seq_lens_sum = torch.zeros(1).to(self.device)
train_loss = torch.zeros(1).to(self.device)
correct = torch.zeros(1).to(self.device)
for batch, (X, y, valid_lens) in enumerate(self.train_loader):
X, y, valid_lens = X.to(self.device), y.to(self.device), valid_lens.to(self.device)
pred = self.model(X, valid_lens)
# [batch_size, seq_len, num_classes]
loss = self.loss_fn(pred, y, valid_lens)
train_loss += loss.sum()
if batch % 100 == 0:
loss_value, current = loss.sum().item(), batch
self.logger.info(f'train iteration: {current}/{self.train_loader_size}, train loss: {loss_value :.4f}')
self.optimizer.zero_grad()
loss.sum().backward()
self.optimizer.step()
seq_lens_sum += valid_lens.sum()
correct += self.accuracy(pred, y, valid_lens)
# correct /= self.train_dataset_size
correct /= seq_lens_sum
train_loss /= self.train_loader_size
self.logger.info(f'train accuracy: {correct.item() :.4f}, train mean loss: {train_loss.item() :.4f}')
@torch.no_grad()
def val_loop(self, t):
self.model.eval()
seq_lens_sum = torch.zeros(1).to(self.device)
val_loss = torch.zeros(1).to(self.device)
correct = torch.zeros(1).to(self.device)
for X, y, valid_lens in self.val_loader:
X, y, valid_lens = X.to(self.device), y.to(self.device), valid_lens.to(self.device)
# pred = torch.nn.Sigmoid()(self.model(X))
pred = self.model(X, valid_lens)
# [batch_size, seq_len, num_classes]
loss = self.loss_fn(pred, y, valid_lens)
val_loss += loss.sum()
seq_lens_sum += valid_lens.sum()
correct += self.accuracy(pred, y, valid_lens)
# correct /= self.val_dataset_size
correct /= seq_lens_sum
val_loss /= self.val_loader_size
self.logger.info(f"val accuracy: {correct.item() :.4f}, val mean loss: {val_loss.item() :.4f}")
def save_checkpoint(self, epoch_id):
self.model.eval()
torch.save(self.model.state_dict(), os.path.join(self.log_dir, f'ckpt_epoch_{epoch_id}.pt'))
def run(self):
if isinstance(self.base_on, str) and os.path.exists(self.base_on):
self.model.load_state_dict(torch.load(self.base_on))
self.logger.info(f'==> Load Model from {self.base_on}')
self.logger.info('==> Start Training')
print(self.model)
lr_scheduler = build_lr_scheduler(self.cfg)(self.optimizer, **self.cfg['solver']['lr_scheduler']['args'])
for t in range(self.epoch):
self.logger.info(f'==> epoch {t + 1}')
self.train_loop()
self.val_loop(t + 1)
self.save_checkpoint(t + 1)
lr_scheduler.step()
self.logger.info('==> End Training')
# def run(self):
# from torch.nn import functional
# y = functional.one_hot(torch.randint(0, 10, (8, 100)), 10)
# valid_lens = torch.randint(50, 100, (8, ))
# print(valid_lens)
# pred = functional.one_hot(torch.randint(0, 10, (8, 100)), 10)
# print(self.accuracy(pred, y, valid_lens))
def evaluate(self):
if isinstance(self.model_path, str) and os.path.exists(self.model_path):
self.model.load_state_dict(torch.load(self.model_path))
self.logger.info(f'==> Load Model from {self.model_path}')
else:
return
self.model.eval()
label_true_list = []
label_pred_list = []
for X, y, valid_lens in self.val_loader:
X, y_true, valid_lens = X.to(self.device), y.to(self.device), valid_lens.to(self.device)
# pred = torch.nn.Sigmoid()(self.model(X))
y_pred = self.model(X, valid_lens)
# [batch_size, seq_len, num_classes]
y_pred_sigmoid = torch.nn.Sigmoid()(y_pred)
# [batch_size, seq_len]
y_pred_idx = torch.argmax(y_pred_sigmoid, dim=-1) + 1
# [batch_size, seq_len]
y_pred_is_other = (torch.amax(y_pred_sigmoid, dim=-1) > 0.5).int()
y_pred_rebuild = torch.multiply(y_pred_idx, y_pred_is_other)
y_true_idx = torch.argmax(y_true, dim=-1) + 1
y_true_is_other = torch.sum(y_true, dim=-1).int()
y_true_rebuild = torch.multiply(y_true_idx, y_true_is_other)
# masked_y_true_rebuild = sequence_mask(y_true_rebuild, valid_lens, value=-1)
for idx, seq_result in enumerate(y_true_rebuild.cpu().numpy().tolist()):
label_true_list.extend(seq_result[: valid_lens.cpu().numpy()[idx]])
for idx, seq_result in enumerate(y_pred_rebuild.cpu().numpy().tolist()):
label_pred_list.extend(seq_result[: valid_lens.cpu().numpy()[idx]])
acc = accuracy_score(label_true_list, label_pred_list)
cm = confusion_matrix(label_true_list, label_pred_list)
report = classification_report(label_true_list, label_pred_list)
print(acc)
print(cm)
print(report)