def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
json_file = '../configs/example.json'
# config = process_config(args.config)
config = process_config(json_file)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
sess = tf.compat.v1.Session()
# create your data generator
data = DataGenerator(config)
data.generate_data()
# create an instance of the model you want
model = ExampleModel(config)
# create tensorboard logger
logger = Logger(sess, config)
# create trainer and pass all the previous components to it
trainer = ExampleTrainer(sess, model, data, config, logger)
#load model if exists
model.load(sess)
# here you train your model
trainer.train()
# here you evaluate your model
evaluator = Evaluator(trainer.sess, trainer.model, data, config, logger)
evaluator.evaluate()
evaluator.analysis_results()
def main():
# capture the config path from the run arguments
# then process the json configuration file
config = fetch_args()
# create the experiments dirs
create_dirs([config.tensorboard_log_dir, config.checkpoint_dir])
print('Create the data generator.')
train_data_loader = DataLoader(config, 'train')
valid_data_loader = DataLoader(config, 'valid')
print('Create the model.')
model = Model(config)
if config.pretrained_model_checkpoint is not None:
model.load(config.pretrained_model_checkpoint)
if config.evaluate:
print('Predicting on test set.')
test_data_loader = DataLoader(config, 'test')
evaluator = Evaluator(model.model, test_data_loader, config)
evaluator.evaluate()
exit(0)
print('Create the trainer')
trainer = Trainer(model.model, train_data_loader, valid_data_loader, config)
print('Start training the model.')
trainer.train()
def evaluate(models,
data_set_path,
log_save_path,
measurement,
test_times=1):
data_set_info_file = open(f"{data_set_path}\\data_set_info.json", 'r')
data_set_info = json.load(data_set_info_file)
if not os.path.exists(f"{log_save_path}\\{data_set_info['name']}"):
os.mkdir(f"{log_save_path}\\{data_set_info['name']}")
shutil.copy(f"{data_set_path}\\data_set_info.json", f"{log_save_path}\\{data_set_info['name']}")
stream_list = [path for path in os.listdir(data_set_path) if os.path.isdir(f"{data_set_path}\\{path}")]
stream_list.sort(key=lambda s: int(s.split('_')[1])) # stream_number
records_list = []
for stream in stream_list:
log_stream_path = f"{log_save_path}\\{data_set_info['name']}\\{stream}"
if not os.path.exists(log_stream_path):
os.mkdir(log_stream_path)
if not os.path.exists(f"{log_stream_path}\\record"):
os.mkdir(f"{log_stream_path}\\record")
if not os.path.exists(f"{log_stream_path}\\figure"):
os.mkdir(f"{log_stream_path}\\figure")
data = pd.read_csv(f"{data_set_path}\\{stream}\\data.csv", header=None)
with open(f"{data_set_path}\\{stream}\\data_info.json", 'r') as data_info_file:
data_info = json.load(data_info_file)
X = np.array(data.iloc[:, 1:])
y = np.array(data.iloc[:, 0])
perf_records = {}
for model in models:
for _ in range(test_times):
test_model = copy.deepcopy(models[model])
test_model.budget = data_info['budget']
evaluator = Evaluator(measurement=measurement,
pretrain_size=1,
batch_size=1,
budget=data_info['budget'])
if model not in perf_records.keys():
perf_records[model] = evaluator.evaluate(X, y, model=test_model)
else:
perf_records[model] += evaluator.evaluate(X, y, model=test_model)
perf_records[model] = perf_records[model] / test_times
perf_records = pd.DataFrame(perf_records)
records_list.append(perf_records)
perf_records.to_csv(f"{log_stream_path}\\record\\{measurement}.csv", index=None)
plot_lines(perf_records, f"{log_stream_path}\\figure\\{measurement}", "pdf", 15, 'time', measurement)
report_file = open(f"{log_save_path}\\report.md", 'a')
report_file.write(f"# {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())}\n")
report_file.write(f"{data_set_info}\n\n")
report_file.write(f"{measurement}\n\n")
table = get_report(data_set_info, records_list, file_type="md")
report_file.write(f"{table}\n")
report_file.close()
def main(config: DictConfig) -> None:
print(OmegaConf.to_yaml(config))
torch.manual_seed(config.eval.seed)
torch.cuda.manual_seed_all(config.eval.seed)
np.random.seed(config.eval.seed)
random.seed(config.eval.seed)
use_cuda = config.eval.cuda and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
char2id, id2char = load_label(config.eval.label_path, config.eval.blank_id)
audio_paths, transcripts, _, _ = load_dataset(config.eval.dataset_path,
config.eval.mode)
test_dataset = SpectrogramDataset(
config.eval.audio_path,
audio_paths,
transcripts,
config.audio.sampling_rate,
config.audio.n_mel,
config.audio.frame_length,
config.audio.frame_stride,
config.audio.extension,
config.train.sos_id,
config.train.eos_id,
)
test_loader = AudioDataLoader(
test_dataset,
batch_size=config.eval.batch_size,
num_workers=config.eval.num_workers,
)
model = load_test_model(config, device)
print('Start Test !!!')
evaluator = Evaluator(config, device, test_loader, id2char)
evaluator.evaluate(model)
class Trainer(object):
"""Trainer Class"""
def __init__(self, optimizer, criterion, batch_size, device):
self.optimizer = optimizer
self.criterion = criterion
self.batch_size = batch_size
self.device = device
self.evaluator = Evaluator(criterion=self.criterion)
def _train_batch(self, model, iterator, iteratorQuery, teacher_ratio,
clip):
model.train()
epoch_loss = 0
for _, batch in enumerate(zip(iterator, iteratorQuery)):
batch_ques, batch_query = batch
src_ques, src_len_ques = batch_ques.src
src_query, src_len_query = batch_query.src
trg = batch_query.trg
self.optimizer.zero_grad()
input_trg = trg if model.name == RNN_NAME else trg[:, :-1]
output = model(src_ques, src_len_ques, src_query, src_len_query,
input_trg, teacher_ratio)
trg = trg.t() if model.name == RNN_NAME else trg[:, 1:]
output = output.contiguous().view(-1, output.shape[-1])
trg = trg.contiguous().view(-1)
# output: (batch_size * trg_len) x output_dim
# trg: (batch_size * trg_len)
loss = self.criterion(output, trg)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
self.optimizer.step()
epoch_loss += loss.item()
length = len(iterator)
return epoch_loss / len(iterator)
def _get_iterators(self, train_data, valid_data, model_name):
return BucketIterator.splits((train_data, valid_data),
repeat=False,
batch_size=self.batch_size,
sort_within_batch=False,
sort_key=lambda x: len(x.src),
device=self.device)
def _epoch_time(self, start_time, end_time):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
def _log_epoch(self, train_loss, valid_loss, epoch, start_time, end_time):
minutes, seconds = self._epoch_time(start_time, end_time)
print(f'Epoch: {epoch+1:02} | Time: {minutes}m {seconds}s')
print(
f'\tTrain Loss: {train_loss:.3f} | Train PPL: {np.exp(train_loss):7.3f}'
)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {np.exp(valid_loss):7.3f}'
)
def _train_epoches(self, model, train_data, train_data_query, valid_data,
valid_data_query, num_of_epochs, teacher_ratio, clip):
best_valid_loss = float('inf')
# pylint: disable=unbalanced-tuple-unpacking
train_iterator, valid_iterator = self._get_iterators(
train_data, valid_data, model.name)
train_iterator_query, valid_iterator_query = self._get_iterators(
train_data_query, valid_data_query, model.name)
for epoch in range(num_of_epochs):
start_time = time.time()
train_loss = self._train_batch(model, train_iterator,
train_iterator_query, teacher_ratio,
clip)
valid_loss = self.evaluator.evaluate(model, valid_iterator,
valid_iterator_query,
teacher_ratio)
end_time = time.time()
self._log_epoch(train_loss, valid_loss, epoch, start_time,
end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
Chechpoint.save(model)
def train(self,
model,
train_data,
train_data_query,
valid_data,
valid_data_query,
num_of_epochs=20,
teacher_ratio=1.0,
clip=1):
"""Train model"""
self._train_epoches(model, train_data, train_data_query, valid_data,
valid_data_query, num_of_epochs, teacher_ratio,
clip)
class Experiment():
dataset = None
dataset_switch = None
model_manager = None
switch_manager = None
evaluator = None
force = None
def __init__(self, dataset_id, dataset_switch_id, force):
self._init_dir()
self.dataset = DataSet(dataset_id = dataset_id, sframe = True)
self.dataset_switch = DataSet(dataset_id = dataset_switch_id, sframe = False)
self.model_manager = ModelManager()
self.switch_manager = SwitchManager()
self.evaluator = Evaluator()
self.force = force
def _init_dir(self):
import os
from settings import DIR
for d in DIR:
if not os.path.exists(d):
os.makedirs(d)
def _train_rec_models(self):
self.model_manager.train_models(dataset = self.dataset)
def _test_rec_models(self):
self.model_manager.test_models(dataset = self.dataset)
def _evaluate_rec_models(self):
self.model_manager.evaluate_models(dataset = self.dataset)
def _create_datasets_switch(self):
self.dataset_switch.prepare_switch_dataset(dataset = self.dataset, model_manager = self.model_manager,
force = self.force)
def _train_switch(self):
self.switch_manager.train_models(dataset_switch = self.dataset_switch, force = self.force)
def _test_switch(self):
self.switch_manager.rating_prediction_switches(dataset = self.dataset, dataset_switch = self.dataset_switch,
model_manager = self.model_manager, force = self.force)
def _evaluate(self):
self.evaluator.evaluate(dataset = self.dataset, dataset_switch = self.dataset_switch,
model_manager = self.model_manager, switch_manager = self.switch_manager,
force = True)
def run(self):
self._train_rec_models()
self._test_rec_models()
self._evaluate_rec_models()
self._create_datasets_switch()
self._train_switch()
self._test_switch()
self._evaluate()
lang = Lang()
trans = U.tokenizeTranscripts('train')
lang.init_lang(trans)
output_size = lang.num_items
dev_dataset = SpeechDataset(lang, 'dev')
dev_dataloader = SpeechDataLoader(dev_dataset, batch_size=batch_size)
num_layers = 3
hidden_size = 256
input_size = 40
key_size = 128
value_size = 128
bidirectional = True
p = 3
embedding_size = 128
encoder = EncoderRNN(input_size, hidden_size, key_size, value_size, num_layers, bidirectional, p)
decoder = DecoderRNN(output_size, embedding_size, hidden_size, key_size, value_size, num_layers)
teacher_forcing_ratio = 1.0
las = LAS(encoder, decoder, teacher_forcing_ratio)
criterion = nn.CrossEntropyLoss(size_average=False, ignore_index=C.PAD_TOKEN_IDX)
evaluator = Evaluator(criterion)
evaluator.evaluate(las, dev_dataloader)
max_len,
hidden_size,
bidirectional=bidirectional,
variable_lengths=True)
dual_encoder = DualEncoder(context_encoder, response_encoder)
if torch.cuda.is_available():
dual_encoder.cuda()
for param in dual_encoder.parameters():
param.data.uniform_(-0.08, 0.08)
# train
t = SupervisedTrainer(loss_func=loss_func,
batch_size=1,
checkpoint_every=30,
print_every=100,
expt_dir=opt.expt_dir)
t.train(dual_encoder,
train,
batch_size=1,
num_epochs=20,
dev_data=dev,
optimizer=optimizer,
resume=opt.resume)
evaluator = Evaluator(batch_size=1)
l, precision, recall = evaluator.evaluate(dual_encoder, dev)
print("Precision: {}, Recall: {}".format(precision, recall))
class SupervisedTrainer(object):
def __init__(self,
export_dir='experiment',
loss=NLLLoss(),
batch_size=64,
random_seed=None,
checkpoint_every=100,
print_every=100):
self._trainer = "Simple Trainer"
self.random_seed = random_seed
if random_seed is not None:
random.seed(random_seed)
torch.manual_seed(random_seed)
self.loss = loss
self.evaluator = Evaluator(loss=self.loss, batch_size=batch_size)
self.optimizer = None
self.checkpoint_every = checkpoint_every
self.print_every = print_every
if not os.path.isabs(export_dir):
export_dir = os.path.join(os.getcwd(), export_dir)
self.export_dir = export_dir
if not os.path.exists(self.export_dir):
os.makedirs(self.export_dir)
self.batch_size = batch_size
self.logger = logging.getLogger(__name__)
def _train_batch(self,
input_variable: torch.Tensor,
input_lengths,
target_variable,
model,
teacher_model=None,
teacher_forcing_ratio=.5):
loss = self.loss
loss.reset()
input_batch_size = input_variable.size(0)
input_seq_length = input_variable.size(1)
target_batch_size = target_variable.size(0)
target_seq_length = target_variable.size(1)
if teacher_model is not None:
tgt_arr = np.array([[model.sos_id]] * batch_size, 'i')
tgt_var = torch.autograd.Variable(
torch.LongTensor(tgt_arr).type(torch.LongTensor))
transformer_output = model(input_variable, tgt_var)
# transformer_output [batch_size,1,tgt_vocab_size]
teacher_output, teacher_hidden, other = teacher_model(
target_variable, target_variable.size(1))
else:
input_var = input_variable.view(input_seq_length, input_batch_size,
1)
target_var = target_variable.view(target_seq_length,
target_batch_size, 1)
aacc = input_lengths.size()
decoder_output, other = model(input_var, target_var, input_lengths)
for step, step_output in enumerate(decoder_output):
if teacher_model is not None:
# step_output =
tgt = target_variable[:, step + 1].contiguous().view(-1)
#f**k = teacher_output[-step].permute(2,1,0)
loss.eval_batch(step_output, tgt, teacher_output[-step])
else:
liner_func = nn.Linear(128, 8)
dec_out = liner_func(step_output)
dec_out: torch.Tensor = F.log_softmax(dec_out, dim=1)
loss.eval_batch(dec_out.view(input_batch_size, -1),
target_variable[:, step + 1])
model.zero_grad()
loss.backward()
self.optimizer.step()
return loss.get_loss()
def _train_epoches(self,
data,
model,
teacher_model,
n_epochs,
start_epoch,
start_step,
dev_data,
teacher_forcing_ratio=0):
log = self.logger
print_loss_total = 0
epoch_loss_total = 0
device = None if torch.cuda.is_available() else -1
batch_iterator = torchtext.data.BucketIterator(
dataset=data,
batch_size=self.batch_size,
sort=False,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
device=device,
repeat=False)
step_per_epoch = len(batch_iterator)
total_steps = step_per_epoch * n_epochs
step = start_step
step_elapsed = 0
for epoch in range(start_epoch, n_epochs + 1):
log.debug("Epoch: %d, Step: %d" % (epoch, step))
batch_generator = batch_iterator.__iter__()
for _ in range((epoch - 1) * step_per_epoch, step):
next(batch_generator)
model.train(True)
for batch in batch_generator:
step += 1
step_elapsed += 1
input_var, input_length = getattr(batch, 'src')
target_var = getattr(batch, 'tgt')
loss = self._train_batch(input_variable=input_var,
input_lengths=input_length,
target_variable=target_var,
model=model,
teacher_model=teacher_model)
print_loss_total += loss
epoch_loss_total += loss
if step % self.print_every == 0 and step_elapsed > self.print_every:
print_loss_avg = print_loss_total / self.print_every
print_loss_total = 0
log_msg = 'Progress: %d%%, Train %s: %.4f' % (
step / total_steps * 100, self.loss.name,
print_loss_avg)
log.info(log_msg)
# Checkpoint
if step % self.checkpoint_every == 0 or step == total_steps:
Checkpoint(model=model,
optimizer=self.optimizer,
epoch=epoch,
step=step,
input_vocab=data.fields['src'].vocab,
output_vocab=data.fields['tgt'].vocab).save(
self.export_dir)
if step_elapsed == 0:
continue
epoch_loss_avg = epoch_loss_total / \
min(step_per_epoch, step - start_step)
epoch_loss_total = 0
log_msg = "Finished epoch %d: Train %s: %.4f" % (
epoch, self.loss.name, epoch_loss_avg)
if dev_data is not None:
dev_loss, accuracy = self.evaluator.evaluate(model, dev_data)
self.optimizer.update(dev_loss, epoch)
log_msg += ", Dev %s: %.4f, Accuracy: %.4f" % (
self.loss.name, dev_loss, accuracy)
model.train(mode=True)
else:
self.optimizer.update(epoch_loss_avg, epoch)
log.info(log_msg)
def train(self,
model,
data,
teacher_model=None,
num_epochs=5,
resume=False,
dev_data=None,
optimizer=None,
teacher_forcing_ratio=0):
if resume:
latest_checkpoint_path = Checkpoint.get_latest_checkpoint(
self.expt_dir)
resume_checkpoint = Checkpoint.load(latest_checkpoint_path)
model = resume_checkpoint.model
self.optimizer = resume_checkpoint.optimizer
resume_optim = self.optimizer.optimizer
defaults = resume_optim.param_groups[0]
defaults.pop('param', None)
defaults.pop('initial_lr', None)
self.optimizer.optimizer = resume_optim.__class__(
model.parameters(), **defaults)
start_epoch = resume_checkpoint.epoch
step = resume_checkpoint.step
else:
start_epoch = 1
step = 0
if optimizer is None:
optimizer = Optimizer(optim.Adam(model.parameters()),
max_grad_norm=5)
self.optimizer = optimizer
self.logger.info("Optimizer: %s, Scheduler: %s" %
(self.optimizer.optimizer, self.optimizer.scheduler))
self._train_epoches(data,
model,
teacher_model,
num_epochs,
start_epoch,
step,
dev_data=dev_data,
teacher_forcing_ratio=teacher_forcing_ratio)
return model
import os
from options.test_options import TestOptions
from evaluator import train_classifier
from evaluator.evaluator import EvaluatorDataset
from evaluator.evaluator import Evaluator
from evaluator.dataset import ClassifierDataset
from torch.utils.data import DataLoader
if __name__=="__main__":
opt = TestOptions().parse()
classifier_path = 'evaluator/checkpoints/latest_{}_resnet.pth'.format(opt.evaluate_mode)
if not os.path.exists(classifier_path):
train_classifier(mode=opt.evaluate_mode)
training_data = ClassifierDataset(opt.evaluate_mode)
dataset = EvaluatorDataset(opt)
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=4)
evaluator = Evaluator(opt, num_classes=training_data.num_classes, text2label=training_data.text2label)
for data in dataloader:
try:
evaluator.evaluate(data)
evaluator.record_current_results()
except:
print('!!!error!!! pass current data')
evaluator.compute_final_results()
class Trainer:
def __init__(self, criterion):
super(Trainer, self).__init__()
self.criterion = criterion
self.evaluator = Evaluator(criterion)
def _train_batch(self, model, input_variables, input_lengths,
target_variables):
decoder_outputs, ret_dict = model(input_variables, input_lengths,
target_variables)
acc_loss = self.criterion(decoder_outputs.contiguous(),
target_variables[1:, :].contiguous())
acc_loss = acc_loss.view(
target_variables.size(0) - 1, target_variables.size(1))
acc_loss = acc_loss.sum(0).mean()
self.optimizer.zero_grad()
acc_loss.backward()
params = itertools.chain.from_iterable(
[group['params'] for group in self.optimizer.param_groups])
torch.nn.utils.clip_grad_norm_(params, max_norm=self.max_grad_norm)
self.optimizer.step()
return acc_loss.data.item()
def train(self, train_dataloader, dev_dataloader, model, lr, num_epochs):
self.max_grad_norm = 5
self.optimizer = torch.optim.Adam(model.parameters(), lr=lr)
for epoch in range(9, num_epochs):
model.train(True)
epoch_loss = 0
num_batches = len(train_dataloader.dataloader)
for (batch_idx, data) in enumerate(train_dataloader.dataloader):
input_variables, input_lengths, target_variables, target_lengths = data
input_variables = U.var(
torch.from_numpy(input_variables).float())
target_variables = U.var(
torch.from_numpy(target_variables).long())
input_variables = input_variables.transpose(0, 1)
target_variables = target_variables.transpose(0, 1)
batch_loss = self._train_batch(model, input_variables,
input_lengths, target_variables)
epoch_loss += batch_loss
if batch_idx % 50 == 0:
print("batch %d avg_loss %f" % (batch_idx, epoch_loss /
(batch_idx + 1)))
print("epoch %d train_epoch_loss %f" %
(epoch, epoch_loss / num_batches))
if epoch % 1 == 0:
U.checkpoint(epoch, model)
val_epoch_loss = self.evaluator.evaluate(model, dev_dataloader)
print("epoch %d val_epoch_loss %f" % (epoch, val_epoch_loss))
