class Trainer(BaseTrainer):
"""
Trainer class
"""
def __init__(self, model, criterion, metric_ftns, optimizer, config, device,
data_loader, valid_data_loader=None, lr_scheduler=None, len_epoch=None):
super().__init__(model, criterion, metric_ftns, optimizer, config)
self.config = config
self.device = device
self.data_loader = data_loader
if len_epoch is None:
# epoch-based training
self.len_epoch = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_epoch = len_epoch
self.valid_data_loader = valid_data_loader
self.do_validation = self.valid_data_loader is not None
self.lr_scheduler = lr_scheduler
self.log_step = int(np.sqrt(data_loader.batch_size))
self.best_valid = 0
self.train_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer)
self.valid_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer)
self.file_metrics = str(self.checkpoint_dir / 'metrics.csv')
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Integer, current training epoch.
:return: A log that contains average loss and metric in this epoch.
"""
self.model.train()
self.train_metrics.reset()
for batch_idx, (data, target) in enumerate(self.data_loader):
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output = self.model(data)
loss = self.criterion(output, target)
loss.backward()
self.optimizer.step()
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
self.train_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.train_metrics.update(met.__name__, met(output, target))
if batch_idx % self.log_step == 0:
"""self.logger.debug('Train Epoch: {} {} Loss: {:.6f}'.format(
epoch,
self._progress(batch_idx),
loss.item()))
"""
self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
if batch_idx == self.len_epoch:
break
log = self.train_metrics.result()
if self.do_validation:
val_log = self._valid_epoch(epoch)
log.update(**{'val_'+k : v for k, v in val_log.items()})
if self.lr_scheduler is not None:
self.lr_scheduler.step()
output = self.model(data)
loss = self.criterion(output, target)
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
self.writer.add_scalar('Loss', loss)
self._save_csv(epoch, log)
return log
def _valid_epoch(self, epoch):
"""
Validate after training an epoch
:param epoch: Integer, current training epoch.
:return: A log that contains information about validation
"""
self.model.eval()
self.valid_metrics.reset()
with torch.no_grad():
for batch_idx, (data, target) in enumerate(self.valid_data_loader):
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
loss = self.criterion(output, target)
self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + batch_idx, 'valid')
self.valid_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.valid_metrics.update(met.__name__, met(output, target))
self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
# add histogram of model parameters to the tensorboard
for name, p in self.model.named_parameters():
self.writer.add_histogram(name, p, bins='auto')
# we added spùe custom here
output = self.model(data)
loss = self.criterion(output, target)
self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + batch_idx, 'valid')
self.writer.add_scalar('Loss', loss)
val_log = self.valid_metrics.result()
actual_accu = val_log['accuracy']
if(actual_accu - self.best_valid > 0.0025 and self.save):
self.best_valid = actual_accu
if self.tensorboard: # is true you can use tensorboard
self._save_checkpoint(epoch, save_best=True)
filename = str(self.checkpoint_dir / 'checkpoint-best-epoch.pth')
torch.save(self.model.state_dict(), filename)
self.logger.info("Saving checkpoint: {} ...".format(filename))
return val_log
def _progress(self, batch_idx):
base = '[{}/{} ({:.0f}%)]'
if hasattr(self.data_loader, 'n_samples'):
current = batch_idx * self.data_loader.batch_size
total = self.data_loader.n_samples
else:
current = batch_idx
total = self.len_epoch
return base.format(current, total, 100.0 * current / total)
def _save_csv(self, epoch ,log):
"""
Saving checkpoints
:param epoch: current epoch number
:param log: logging information of the epoch
"""
fichier = open(self.file_metrics, "a")
if epoch == 1:
fichier.write("epoch,")
for key in log:
fichier.write(str(key) +",")
fichier.write("\n")
fichier.write(str(epoch) +",")
for key in log:
fichier.write(str(log[key]) + ",")
fichier.write("\n")
fichier.close()
class Seq2SeqSimpleTrainer(BaseTrainer):
"""
Trainer for a simple seq2seq mode.
"""
def __init__(self,
model,
criterion,
train_metric_ftns,
eval_metric_fns,
optimizer,
config,
device,
data_loader,
valid_data_loader=None,
lr_scheduler=None,
len_epoch=None,
validate_only=False):
"""
:param model: The model to train.
:param criterion: we ignore this value and overwrite it
:param train_metric_ftns: The metric function names to use for training.
:param eval_metric_fns: The metric function names to use for evaluating.
:param optimizer: The optimizer to use.
:param config: The configuration file for the run.
:param device: The device to train on.
:param data_loader: The training data loader to use.
:param valid_data_loader: The validation data loader to use.
:param lr_scheduler: scheduler for the learning rate.
:param len_epoch: The amount of examples in an epoch.
:param validate_only: use if resumed, only run validation on the last resumed checkpoint.
"""
self.vocab = model.vocab
self.pad_idx = self.vocab['<pad>']
self.criterion = criterion
super().__init__(model, self.criterion, train_metric_ftns,
eval_metric_fns, optimizer, config, device,
data_loader, valid_data_loader, lr_scheduler)
self.question_pad_length = config['data_loader']['question_pad_length']
self.qdmr_pad_length = config['data_loader']['qdmr_pad_length']
self.lexicon_pad_length = config['data_loader']['lexicon_pad_length']
if len_epoch is None:
# epoch-based training
self.len_epoch = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_epoch = len_epoch
self.do_validation = self.valid_data_loader is not None
self.log_step = int(np.sqrt(data_loader.batch_size))
self.train_metrics = MetricTracker(
'loss',
*[m.__name__ for m in self.train_metric_ftns],
writer=self.writer)
# Define evaluator.
self.evaluator = Seq2SeqSimpleTester(self.model, self.criterion,
self.eval_metric_ftns,
self.config, self.device,
self.valid_data_loader, True)
# Run validation and exit.
if validate_only:
val_log = self.evaluator.test()
log = {'val_' + k: round(v, 5) for k, v in val_log.items()}
print(log)
exit()
def _train_epoch(self, epoch):
"""
Training logic for an epoch.
:param epoch: Integer, current training epoch.
:return: A log that contains average loss and metric in this epoch.
"""
# Sets the model to training mode.
self.model.train()
self.train_metrics.reset()
convert_to_program = self.data_loader.gold_type_is_qdmr()
with tqdm(total=len(self.data_loader)) as progbar:
for batch_idx, (_, data, target,
lexicon_str) in enumerate(self.data_loader):
data, mask_data = batch_to_tensor(self.vocab, data,
self.question_pad_length,
self.device)
target, mask_target = batch_to_tensor(self.vocab, target,
self.qdmr_pad_length,
self.device)
lexicon_ids, mask_lexicon = tokenize_lexicon_str(
self.vocab, lexicon_str, self.qdmr_pad_length, self.device)
# Run the model on the batch
self.optimizer.zero_grad()
# out shape is (batch_size, seq_len, output_size)
output, mask_output = self.model(data, target, lexicon_ids)
# CEloss expects (minibatch, classes, seq_len)
# out after transpose is (batch_size, output_size, seq_len)
# output = torch.transpose(output, 1, 2)
# Calculate the loss and perform optimization step.
# TODO test properly use of masks
# output dims should be (batch_size, num_decoding_steps, num_classes)
loss = self.criterion(output, mask_output, target, mask_target)
loss.backward()
self.optimizer.step()
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
with torch.no_grad():
pred = torch.argmax(output, dim=1)
# data_str = batch_to_str(self.vocab, data, mask_data, convert_to_program=False)
# target_str = batch_to_str(self.vocab, target, mask_target, convert_to_program=convert_to_program)
# pred_str = pred_batch_to_str(self.vocab, pred, convert_to_program=convert_to_program)
# Update metrics
self.train_metrics.update('loss', loss.item())
# for met in self.metric_ftns:
# self.train_metrics.update(met.__name__, met(pred_str, target_str, data_str))
# Log progress
if batch_idx % self.log_step == 0:
self.logger.debug('Train Epoch: {} {} Loss: {:.6f}'.format(
epoch, self._progress(batch_idx), loss.item()))
# TODO set this to write the text examples or remove
# self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
if batch_idx == self.len_epoch:
break
# Update the progress bar.
progbar.update(1)
epoch_part = str(epoch) + '/' + str(self.epochs)
progbar.set_postfix(
epoch=epoch_part,
LOSS=loss.item(),
batch_size=self.data_loader.init_kwargs['batch_size'],
samples=self.data_loader.n_samples)
# Save the calculated metrics for that epoch.
log = self.train_metrics.result()
# If validation split exists, evaluate on validation set as well.
if self.do_validation:
# TODO print epoch stuff and add epoch to writer
# TODO self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + batch_idx, 'valid')
val_log = self.evaluator.test()
log.update(**{'val_' + k: round(v, 5) for k, v in val_log.items()})
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return log
def _progress(self, batch_idx):
base = '[{}/{} ({:.0f}%)]'
if hasattr(self.data_loader, 'n_samples'):
current = batch_idx * self.data_loader.batch_size
total = self.data_loader.n_samples
else:
current = batch_idx
total = self.len_epoch
return base.format(current, total, 100.0 * current / total)
class MNISTTrainer(BaseTrainer):
"""
Trainer class
"""
def __init__(self,
model,
criterion,
metric_fns,
optimizer,
config,
device,
data_loader,
valid_data_loader=None,
lr_scheduler=None,
len_epoch=None):
super().__init__(model, criterion, metric_fns, optimizer, config,
device, data_loader, valid_data_loader, lr_scheduler)
if len_epoch is None:
# epoch-based training
self.len_epoch = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_epoch = len_epoch
self.do_validation = self.valid_data_loader is not None
self.log_step = int(np.sqrt(data_loader.batch_size))
self.train_metrics = MetricTracker(
'loss',
*[m.__name__ for m in self.metric_ftns],
writer=self.writer)
# Define evaluator
self.evaluator = MNISTTester(self.model, self.criterion,
self.metric_ftns, self.config,
self.device, self.valid_data_loader, True)
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Integer, current training epoch.
:return: A log that contains average loss and metric in this epoch.
"""
self.model.train()
self.train_metrics.reset()
with tqdm(total=self.data_loader.n_samples) as progbar:
for batch_idx, (data, target) in enumerate(self.data_loader):
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output = self.model(data)
loss = self.criterion(output, target)
loss.backward()
self.optimizer.step()
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
self.train_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.train_metrics.update(met.__name__,
met(output, target))
if batch_idx % self.log_step == 0:
self.logger.debug('Train Epoch: {} {} Loss: {:.6f}'.format(
epoch, self._progress(batch_idx), loss.item()))
self.writer.add_image(
'input', make_grid(data.cpu(), nrow=8, normalize=True))
if batch_idx == self.len_epoch:
break
progbar.update(self.data_loader.init_kwargs['batch_size'])
epoch_part = str(epoch) + '/' + str(self.epochs)
progbar.set_postfix(epoch=epoch_part, NLL=loss.item())
log = self.train_metrics.result()
if self.do_validation:
val_log = self.evaluator.test()
log.update(**{'val_' + k: round(v, 5) for k, v in val_log.items()})
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return log
def _progress(self, batch_idx):
base = '[{}/{} ({:.0f}%)]'
if hasattr(self.data_loader, 'n_samples'):
current = batch_idx * self.data_loader.batch_size
total = self.data_loader.n_samples
else:
current = batch_idx
total = self.len_epoch
return base.format(current, total, 100.0 * current / total)
class Trainer():
def __init__(self,
model,
criterion,
metrics_name,
optimizer,
train_loader,
logger,
log_dir,
nb_epochs,
save_dir,
device="cuda:0",
log_step=10,
start_epoch=0,
enable_tensorboard=True,
valid_loader=None,
lr_scheduler=None,
monitor="min val_loss",
early_stop=10,
save_epoch_period=1,
resume=""):
self.model = model
self.criterion = criterion
self.metrics_name = metrics_name
self.optimizer = optimizer
self.train_loader = train_loader
self.valid_loader = valid_loader
self.len_epoch = len(self.train_loader)
self.do_validation = (self.valid_loader is not None)
self.lr_scheduler = lr_scheduler
self.log_step = log_step
self.epochs = nb_epochs
self.start_epoch = start_epoch + 1
self.logger = logger
self.device = device
self.save_period = save_epoch_period
self.writer = TensorboardWriter(log_dir, self.logger,
enable_tensorboard)
self.train_metrics = MetricTracker('loss',
*self.metrics_name,
writer=self.writer)
self.valid_metrics = MetricTracker('loss',
*self.metrics_name,
writer=self.writer)
self.checkpoint_dir = save_dir
if monitor == 'off':
self.mnt_mode = 'off'
self.mnt_best = 0
else:
self.mnt_mode, self.mnt_metric = monitor.split()
assert self.mnt_mode in ['min', 'max']
self.mnt_best = inf if self.mnt_mode == 'min' else -inf
self.early_stop = early_stop
if resume != "":
self._resume_checkpoint(resume_path=resume)
self.model.to(self.device)
def train(self):
not_improved_count = 0
for epoch in range(self.start_epoch, self.epochs + 1):
result = self._train_epoch(epoch)
log = {'epoch': epoch}
log.update(result)
self.logger.info(' {:15s}: {}'.format(str("mnt best"),
self.mnt_best))
for key, value in log.items():
self.logger.info(' {:15s}: {}'.format(str(key), value))
best = False
if self.mnt_mode != 'off':
try:
# check whether model performance improved or not, according to specified metric(mnt_metric)
improved = (self.mnt_mode == 'min' and log[self.mnt_metric] < self.mnt_best) or \
(self.mnt_mode == 'max' and log[self.mnt_metric] > self.mnt_best)
except KeyError:
self.logger.warning(
"Warning: Metric '{}' is not found. "
"Model performance monitoring is disabled.".format(
self.mnt_metric))
self.mnt_mode = 'off'
improved = False
if improved:
self.mnt_best = log[self.mnt_metric]
not_improved_count = 0
best = True
else:
not_improved_count += 1
if (not_improved_count > self.early_stop) and (self.early_stop
> 0):
self.logger.info(
"Validation performance didn\'t improve for {} epochs. "
"Training stops.".format(self.early_stop))
break
if epoch % self.save_period == 0:
self._save_checkpoint(epoch, best)
def _train_epoch(self, epoch):
self.model.train()
self.train_metrics.reset()
start_time = time.time()
for batch_idx, sample in enumerate(self.train_loader):
data = sample['image']
target = sample['mask']
data, target = data.to(self.device), target.to(self.device)
current_lr = self.lr_scheduler(self.optimizer, batch_idx, epoch)
self.optimizer.zero_grad()
output = self.model(data)
loss = self.criterion(output, target)
loss.backward()
self.optimizer.step()
self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
self.train_metrics.update('loss', loss.item())
for met_name in self.metrics_name:
self.train_metrics.update(
met_name,
getattr(metrics, met_name)(output, target))
if batch_idx % self.log_step == 0:
time_to_run = time.time() - start_time
start_time = time.time()
speed = self.log_step / time_to_run
self.logger.debug('Train Epoch: {} {} Loss: {:.6f} LR: {:.6f} Speed: {:.4f}iters/s' \
.format(epoch, self._progress(batch_idx), loss.item(), current_lr, speed))
for met_name in self.metrics_name:
self.writer.add_scalar(met_name,
self.train_metrics.avg(met_name))
self.writer.add_scalar('loss', self.train_metrics.avg('loss'))
self.writer.add_scalar("lr", current_lr)
# self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
assert batch_idx <= self.len_epoch
log = self.train_metrics.result()
if self.do_validation:
print("Start validation")
val_log, iou_classes = self._valid_epoch(epoch)
log.update(**{'val_' + k: v for k, v in val_log.items()})
for key, value in iou_classes.items():
log.update({key: value})
return log
def _valid_epoch(self, epoch):
self.model.eval()
self.valid_metrics.reset()
iou_tracker = metrics.IoU(2)
with torch.no_grad():
for batch_idx, sample in enumerate(self.valid_loader):
data = sample['image']
target = sample['mask']
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
loss = self.criterion(output, target)
self.writer.set_step(
(epoch - 1) * len(self.valid_loader) + batch_idx, 'valid')
self.valid_metrics.update('loss', loss.item())
# self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
target = target.cpu().numpy()
output = output[:, 0]
output = output.data.cpu().numpy()
pred = np.zeros_like(output)
pred[output > 0.5] = 1
pred = pred.astype(np.int64)
for i in range(len(target)):
iou_tracker.add_batch(target[i], pred[i])
iou_classes = iou_tracker.get_iou()
for key, value in iou_classes.items():
self.writer.add_scalar(key, value)
self.writer.add_scalar('val_loss', self.valid_metrics.avg('loss'))
for met_name in self.metrics_name:
self.writer.add_scalar(met_name, self.valid_metrics.avg(met_name))
# for name, p in self.model.named_parameters():
# print(name, p)
# self.writer.add_histogram(name, p.cpu().data.numpy(), bins='auto')
#
return self.valid_metrics.result(), iou_classes
def _progress(self, batch_idx):
base = '[{}/{} ({:.0f}%)]'
current = batch_idx
total = self.len_epoch
return base.format(current, total, 100.0 * current / total)
def _save_checkpoint(self, epoch, save_best=False):
"""
Saving checkpoints
:param epoch: current epoch number
:param log: logging information of the epoch
:param save_best: if True, rename the saved checkpoint to 'model_best.pth'
"""
arch = type(self.model).__name__
state = {
'arch': arch,
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'monitor_best': self.mnt_best,
# 'config': self.config
}
filename = str(self.checkpoint_dir /
'checkpoint-epoch{:06d}.pth'.format(epoch))
torch.save(state, filename)
self.delete_checkpoint()
self.logger.info("Saving checkpoint: {} ...".format(filename))
if save_best:
best_path = str(self.checkpoint_dir / 'model_best.pth')
torch.save(state, best_path)
self.logger.info("Saving current best: model_best.pth ...")
def delete_checkpoint(self):
checkpoints_file = list(
self.checkpoint_dir.glob("checkpoint-epoch*.pth"))
checkpoints_file.sort()
for checkpoint_file in checkpoints_file[:-5]:
os.remove(str(checkpoint_file.absolute()))
def _resume_checkpoint(self, resume_path):
self.logger.info("Loading checkpoint: {} ...".format(resume_path))
checkpoint = torch.load(resume_path)
self.start_epoch = checkpoint['epoch'] + 1
self.mnt_best = checkpoint['monitor_best']
self.model.load_state_dict(checkpoint['state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.logger.info(
"Checkpoint loaded. Resume training from epoch {}".format(
self.start_epoch))
def main(config, use_transformers):
logger = config.get_logger('test')
# 1
# device = torch.device('cpu')
# 2
device = torch.device('cuda:{}'.format(config.config['device_id'])
if config.config['n_gpu'] > 0 else 'cpu')
# 测试集语料
test_dataset = config.init_obj('test_dataset',
module_data_process,
device=device)
if use_transformers:
test_dataloader = config.init_obj(
'test_data_loader',
module_dataloader,
dataset=test_dataset.data_set,
collate_fn=test_dataset.bert_collate_fn_4_inference)
model = config.init_obj('model_arch', module_arch, word_embedding=None)
else:
# 原始语料，只需要dataset，不需要dataloader，拿到dataset.word_embedding，普通神经网网络才需要
dataset = config.init_obj('dataset',
module_data_process,
device=device)
test_dataloader = config.init_obj(
'test_data_loader',
module_dataloader,
dataset=test_dataset.data_set,
collate_fn=test_dataset.collate_fn_4_inference)
model = config.init_obj('model_arch',
module_arch,
word_embedding=dataset.word_embedding)
if config['n_gpu'] > 1:
device_ids = list(
map(lambda x: int(x), config.config['device_id'].split(',')))
model = torch.nn.DataParallel(model, device_ids=device_ids)
logger.info('Loading checkpoint: {} ...'.format(config.resume))
# checkpoint = torch.load(pathlib2.PureWindowsPath(config.resume))
# checkpoint = torch.load(config.resume.replace('\\', '/'))
# checkpoint = torch.load("\\saved\\text_cnn_1d\\models\\0706_122111\\checkpoint-epoch15.pth")
# checkpoint = torch.load(pathlib2.PureWindowsPath(str(config.resume)))
# checkpoint = torch.load(pathlib.PurePath(config.resume))
# checkpoint = torch.load(pathlib.PureWindowsPath(config.resume))
# checkpoint = torch.load(str(pathlib.PureWindowsPath(config.resume)))
# checkpoint = torch.load(pathlib.PureWindowsPath(os.path.join(str(config.resume))))
# checkpoint = torch.load(os.path.join(str(config.resume)))
# checkpoint = torch.load(open(os.path.join(str(config.resume)), 'rb'))
# checkpoint = torch.load(open(pathlib.joinpath(str(config.resume)), 'rb'))
checkpoint = torch.load(config.resume)
state_dict = checkpoint['state_dict']
model.load_state_dict(state_dict)
# 2
model = model.cuda()
model.eval()
metric_ftns = [getattr(module_metric, met) for met in config['metrics']]
test_metrics = MetricTracker(*[m.__name__ for m in metric_ftns])
with torch.no_grad():
for i, batch_data in enumerate(test_dataloader):
# 一个batch，128条评论
input_token_ids, _, seq_lens, class_labels, texts = batch_data
# 输出值
output = model(input_token_ids, _, seq_lens).squeeze(1)
# 真实类别
class_labels = class_labels
# bert时候，到时候再写个布尔吧，这样不再多做一点处理（6222%128=78个结尾不去算了）
if (i + 1) % 8 == 1:
output_one = output.clone()
class_labels_one = class_labels.clone()
elif (i + 1) % 8 == 2:
output_two = output.clone()
class_labels_two = class_labels.clone()
elif (i + 1) % 8 == 3:
output_three = output.clone()
class_labels_three = class_labels.clone()
elif (i + 1) % 8 == 4:
output_four = output.clone()
class_labels_four = class_labels.clone()
elif (i + 1) % 8 == 5:
output_five = output.clone()
class_labels_five = class_labels.clone()
elif (i + 1) % 8 == 6:
output_six = output.clone()
class_labels_six = class_labels.clone()
elif (i + 1) % 8 == 7:
output_seven = output.clone()
class_labels_seven = class_labels.clone()
else:
pred_tensor = torch.cat(
(output_one, output_two, output_three, output_four,
output_five, output_six, output_seven, output), 0)
label_tensor = torch.cat(
(class_labels_one, class_labels_two, class_labels_three,
class_labels_four, class_labels_five, class_labels_six,
class_labels_seven, class_labels), 0)
for met in metric_ftns:
test_metrics.update(met.__name__,
met(pred_tensor, label_tensor))
# # 普通时候
# for met in metric_ftns:
# test_metrics.update(met.__name__, met(output, class_labels))
test_log = test_metrics.result()
for k, v in test_log.items():
logger.info(' {:25s}: {}'.format(str(k), v))
print(test_log['binary_auc'])
return test_log['binary_auc']
class MNISTTester(BaseTester):
"""
Trainer for a simple seq2seq mode.
"""
def __init__(self,
model,
criterion,
metric_fns,
config,
device,
data_loader,
evaluation=True):
"""
:param model:
:param criterion: we ignore this value and overwrite it
:param metric_fns:
:param optimizer:
:param config:
:param device:
:param data_loader:
:param valid_data_loader:
:param lr_scheduler:
:param len_epoch:
"""
super().__init__(model, criterion, metric_fns, config, device,
data_loader, evaluation)
self.valid_metrics = MetricTracker(
'loss',
*[m.__name__ for m in self.metric_ftns],
writer=self.writer)
def _evaluate(self):
"""
Validate after training an epoch.
Used with gold target
:param epoch: Integer, current training epoch.
:return: A log that contains information about validation
"""
# Sets the model to evaluation mode.
self.valid_metrics.reset()
total_loss = 0.0
total_metrics = torch.zeros(len(self.metric_ftns))
for i, (data, target) in enumerate(tqdm(self.data_loader)):
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
# computing loss, metrics on test set
loss = self.criterion(output, target)
batch_size = data.shape[0]
total_loss += loss.item() * batch_size
for i, metric in enumerate(self.metric_ftns):
total_metrics[i] += metric(output, target) * batch_size
self.valid_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.valid_metrics.update(met.__name__, met(output, target))
self.writer.add_image(
'input', make_grid(data.cpu(), nrow=8, normalize=True))
# add histogram of model parameters to the tensorboard
for name, p in self.model.named_parameters():
self.writer.add_histogram(name, p, bins='auto')
return self.valid_metrics.result()
def _predict_without_target(self):
return self._evaluate()
def main(config):
# logger = config.get_logger('test')
# setup data_loader instances
data_loader = getattr(module_data, config['data_loader']['type'])(
mode="test",
data_root="/root/userfolder/Dataset/ImagesAnnotations_aug/",
fold=0,
num_workers=4,
batch_size=96)
# build model architecture
model = config.init_obj('arch', module_arch)
params = compute_params(model)
print(model)
print('the params of model is: ', params)
# logger.info(model)
# get function handles of loss and metrics
# loss_fn = getattr(module_loss, config['loss'])
loss_fn = nn.BCEWithLogitsLoss()
metric_fns = [getattr(module_metric, met) for met in config['metrics']]
resume_path = os.path.join(config['project_root'],
config['trainer']['resume_path'])
checkpoint = torch.load(resume_path, map_location=torch.device('cpu'))
# logger.info('Loading checkpoint: {} ...'.format(resume_path))
print('Loading checkpoint: {} ...'.format(resume_path))
state_dict = checkpoint['state_dict']
gpus = config['gpu_device']
if len(gpus) > 1:
model = torch.nn.DataParallel(model)
model.load_state_dict(state_dict)
# prepare model for testing
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device(
'cuda:{}'.format(gpus[0]) if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
total_loss = 0.0
total_metrics = torch.zeros(len(metric_fns))
outputs = []
targets = []
test_metrics = MetricTracker('loss',
'time',
*[m.__name__ for m in metric_fns],
writer=None)
image_shape = None
f_dir, f_name = os.path.split(resume_path)
csv_path = os.path.join(f_dir, 'prediction.csv')
f = open(csv_path, 'w')
csv_writer = csv.writer(f)
keys = ['label', 'pred']
values = []
csv_writer.writerow(keys)
with torch.no_grad():
for i, (data, target) in enumerate(tqdm(data_loader.test_loader)):
data, target = data.to(device), target.to(device).float()
# data, target = data.cuda(), target.cuda().float()
image_shape = [data.shape[2], data.shape[3]]
torch.cuda.synchronize(device)
start = time.time()
# with torch.autograd.profiler.profile(use_cuda=True) as prof:
output = model(data)
torch.cuda.synchronize(device)
end = time.time()
# print('time:',end-start)
pred = output.clone() # [batch, c]
pred_list = torch.sigmoid(pred).squeeze().tolist()
label = target.clone() # [batch]
label_list = label.squeeze().tolist()
_ = [
values.append([label_list[index], pred_list[index]])
for index in range(len(pred_list))
]
output = output.unsqueeze(dim=2).unsqueeze(dim=3)
target = target.unsqueeze(dim=2)
outputs.append(output.clone())
targets.append(target.clone())
loss = loss_fn(output.squeeze(dim=1), target)
total_loss += loss.item()
test_metrics.update('time', end - start)
# for i, metric in enumerate(metric_fns):
# total_metrics[i] += metric(output, target, apply_nonlin=True)
# print(prof)
csv_writer.writerows(values)
f.close()
outputs = torch.cat(outputs, dim=0) # [steps*batch, 1, 1, 1]
targets = torch.cat(targets, dim=0)
for met in metric_fns:
test_metrics.update(met.__name__, met(outputs, targets))
log = test_metrics.result()
print(log)
# summary(model, (1,496, 384))
time_results = compute_precise_time(model, [496, 384], 96, loss_fn, device)
print(time_results)
reset_bn_stats(model)
return
class Seq2SeqSimpleTester(BaseTester):
"""
Trainer for a simple seq2seq mode.
"""
def __init__(self, model, criterion, metric_ftns, config, device,
data_loader, evaluation=True):
"""
:param model: A model to test.
:param criterion: we ignore this value and overwrite it
:param metric_ftns: The names of the metric functions to use.
:param config: The configuration.
:param device: The device to use for the testing.
:param data_loader: The dataloader to use for loading the testing data.
"""
self.vocab = model.vocab
self.question_pad_length = config['data_loader']['question_pad_length']
self.qdmr_pad_length = config['data_loader']['qdmr_pad_length']
self.lexicon_pad_length = config['data_loader']['lexicon_pad_length']
self.pad_idx = self.vocab['<pad>']
# Overriding the criterion.
# self.criterion = CrossEntropyLoss(ignore_index=self.pad_idx)
self.criterion = criterion
super().__init__(model, self.criterion, metric_ftns, config, device, data_loader, evaluation)
self.valid_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer)
def _evaluate(self):
"""
Validate after training an epoch.
Used with gold target
:param epoch: Integer, current training epoch.
:return: A log that contains information about validation
"""
# Choose 2 random examples from the dev set and print their prediction.
batch_index1 = random.randint(0, len(self.data_loader) - 1) - 1
example_index1 = random.randint(0, self.data_loader.batch_size - 1)
batch_index2 = random.randint(0, len(self.data_loader) - 1) - 1
example_index2 = random.randint(0, self.data_loader.batch_size - 1)
questions = []
decompositions = []
targets = []
convert_to_program = self.data_loader.gold_type_is_qdmr()
# Sets the model to evaluation mode.
self.valid_metrics.reset()
with tqdm(total=len(self.data_loader)) as progbar:
for batch_idx, (_, data, target, lexicon_str) in enumerate(self.data_loader):
data, mask_data = batch_to_tensor(self.vocab, data, self.question_pad_length, self.device)
target, mask_target = batch_to_tensor(self.vocab, target, self.qdmr_pad_length, self.device)
lexicon_ids, mask_lexicon = tokenize_lexicon_str(self.vocab, lexicon_str, self.qdmr_pad_length, self.device)
start = time.time()
# Run the model on the batch and calculate the loss
output, mask_output = self.model(data, target, lexicon_ids, evaluation_mode=True)
loss = self.criterion(output, mask_output, target, mask_target)
output = torch.transpose(output, 1, 2)
pred = torch.argmax(output, dim=1)
start = time.time()
# Convert the predictions/ targets/questions from tensor of token_ids to list of strings.
# TODO do we need to convert here or can we use the originals? (for data and target)
data_str = batch_to_str(self.vocab, data, mask_data, convert_to_program=False)
target_str = batch_to_str(self.vocab, target, mask_target, convert_to_program=convert_to_program)
pred_str = pred_batch_to_str(self.vocab, pred, convert_to_program=convert_to_program)
self.valid_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.valid_metrics.update(met.__name__, met(pred_str, target_str, data_str))
# Print example for predictions.
if batch_idx == batch_index1:
questions.append(data_str[example_index1])
decompositions.append(pred_str[example_index1])
targets.append(target_str[example_index1])
if batch_idx == batch_index2:
questions.append(data_str[example_index2])
decompositions.append(pred_str[example_index2])
targets.append(target_str[example_index2])
# Update the progress bar.
progbar.update(1)
progbar.set_postfix(LOSS=loss.item(),
batch_size=self.data_loader.init_kwargs['batch_size'],
samples=self.data_loader.n_samples)
# Print example predictions.
for question, decomposition, target in zip(questions, decompositions, targets):
print('\ndecomposition example:')
print('question:\t\t', question)
print('decomposition:\t', decomposition)
print('target:\t\t\t', target)
print()
# add histogram of model parameters to the tensorboard
for name, p in self.model.named_parameters():
self.writer.add_histogram(name, p, bins='auto')
return self.valid_metrics.result()
def _predict_without_target(self):
"""
get model predictions for testing.
Used without targets
:return: A log that contains information about predictions
"""
qid_col = []
pred_col = []
question_col = []
convert_to_program = self.data_loader.gold_type_is_qdmr()
# Sets the model to evaluation mode.
self.valid_metrics.reset()
with tqdm(total=len(self.data_loader)) as progbar:
for batch_idx, (question_ids, data, target, lexicon_str) in enumerate(self.data_loader):
data, mask_data = batch_to_tensor(self.vocab, data, self.question_pad_length, self.device)
target, mask_target = batch_to_tensor(self.vocab, target, self.qdmr_pad_length, self.device)
lexicon_ids, mask_lexicon = tokenize_lexicon_str(self.vocab, lexicon_str, self.qdmr_pad_length, self.device)
start = time.time()
# Run the model on the batch and calculate the loss
output, mask_output = self.model(data, target, lexicon_ids, evaluation_mode=True)
loss = self.criterion(output, mask_output, target, mask_target)
output = torch.transpose(output, 1, 2)
pred = torch.argmax(output, dim=1)
start = time.time()
# Convert the predictions/ targets/questions from tensor of token_ids to list of strings.
# TODO do we need to convert here or can we use the originals? (for data and target)
data_str = batch_to_str(self.vocab, data, mask_data, convert_to_program=False)
target_str = batch_to_str(self.vocab, target, mask_target, convert_to_program=convert_to_program)
pred_str = pred_batch_to_str(self.vocab, pred, convert_to_program=convert_to_program)
for i, question_id in enumerate(question_ids):
self.logger.info('{}:{}'.format(question_id, data_str[i]))
qid_col.extend(question_ids)
pred_col.extend(pred_str)
question_col.extend(data_str)
self.valid_metrics.update('loss', loss.item())
for met in self.metric_ftns:
self.valid_metrics.update(met.__name__, met(pred_str, target_str, data_str))
# Update the progress bar.
progbar.update(1)
progbar.set_postfix(LOSS=loss.item(),
batch_size=self.data_loader.init_kwargs['batch_size'],
samples=self.data_loader.n_samples)
d = {'question_id': qid_col, 'question_text': question_col, 'decomposition': pred_col}
programs_df = pd.DataFrame(data=d)
programs_df.to_csv(self.predictions_file_name, index=False, encoding='utf-8')
# add histogram of model parameters to the tensorboard
for name, p in self.model.named_parameters():
self.writer.add_histogram(name, p, bins='auto')
return self.valid_metrics.result()
