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169 lines
6.2 KiB
169 lines
6.2 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import paddle
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import yaml
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from paddleaudio.utils import logger
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from paddleaudio.utils import Timer
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from paddlespeech.kws.exps.mdtc.collate import collate_features
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from paddlespeech.kws.models.loss import max_pooling_loss
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from paddlespeech.kws.models.mdtc import KWSModel
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--cfg_path", type=str, required=True)
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args = parser.parse_args()
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# yapf: enable
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if __name__ == '__main__':
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nranks = paddle.distributed.get_world_size()
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if paddle.distributed.get_world_size() > 1:
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paddle.distributed.init_parallel_env()
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local_rank = paddle.distributed.get_rank()
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args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
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with open(args.cfg_path, 'r') as f:
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config = yaml.safe_load(f)
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model_conf = config['model']
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data_conf = config['data']
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feat_conf = config['feature']
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training_conf = config['training']
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# Dataset
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ds_class = dynamic_import(data_conf['dataset'])
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train_ds = ds_class(
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data_dir=data_conf['data_dir'], mode='train', **feat_conf)
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dev_ds = ds_class(data_dir=data_conf['data_dir'], mode='dev', **feat_conf)
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train_sampler = paddle.io.DistributedBatchSampler(
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train_ds,
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batch_size=training_conf['batch_size'],
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shuffle=True,
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drop_last=False)
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train_loader = paddle.io.DataLoader(
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train_ds,
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batch_sampler=train_sampler,
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num_workers=training_conf['num_workers'],
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return_list=True,
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use_buffer_reader=True,
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collate_fn=collate_features, )
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# Model
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backbone_class = dynamic_import(model_conf['backbone'])
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backbone = backbone_class(**model_conf['config'])
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model = KWSModel(backbone=backbone, num_keywords=model_conf['num_keywords'])
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model = paddle.DataParallel(model)
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clip = paddle.nn.ClipGradByGlobalNorm(training_conf['grad_clip'])
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optimizer = paddle.optimizer.Adam(
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learning_rate=training_conf['learning_rate'],
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weight_decay=training_conf['weight_decay'],
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parameters=model.parameters(),
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grad_clip=clip)
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criterion = max_pooling_loss
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steps_per_epoch = len(train_sampler)
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timer = Timer(steps_per_epoch * training_conf['epochs'])
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timer.start()
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for epoch in range(1, training_conf['epochs'] + 1):
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model.train()
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avg_loss = 0
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num_corrects = 0
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num_samples = 0
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for batch_idx, batch in enumerate(train_loader):
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keys, feats, labels, lengths = batch
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logits = model(feats)
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loss, corrects, acc = criterion(logits, labels, lengths)
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loss.backward()
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optimizer.step()
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.LRScheduler):
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optimizer._learning_rate.step()
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optimizer.clear_grad()
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# Calculate loss
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avg_loss += loss.numpy()[0]
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# Calculate metrics
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num_corrects += corrects
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num_samples += feats.shape[0]
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timer.count()
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if (batch_idx + 1
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) % training_conf['log_freq'] == 0 and local_rank == 0:
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lr = optimizer.get_lr()
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avg_loss /= training_conf['log_freq']
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avg_acc = num_corrects / num_samples
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print_msg = 'Epoch={}/{}, Step={}/{}'.format(
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epoch, training_conf['epochs'], batch_idx + 1,
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steps_per_epoch)
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print_msg += ' loss={:.4f}'.format(avg_loss)
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print_msg += ' acc={:.4f}'.format(avg_acc)
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print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
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lr, timer.timing, timer.eta)
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logger.train(print_msg)
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avg_loss = 0
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num_corrects = 0
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num_samples = 0
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if epoch % training_conf[
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'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
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dev_sampler = paddle.io.BatchSampler(
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dev_ds,
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batch_size=training_conf['batch_size'],
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shuffle=False,
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drop_last=False)
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dev_loader = paddle.io.DataLoader(
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dev_ds,
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batch_sampler=dev_sampler,
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num_workers=training_conf['num_workers'],
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return_list=True,
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use_buffer_reader=True,
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collate_fn=collate_features, )
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model.eval()
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num_corrects = 0
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num_samples = 0
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with logger.processing('Evaluation on validation dataset'):
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for batch_idx, batch in enumerate(dev_loader):
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keys, feats, labels, lengths = batch
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logits = model(feats)
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loss, corrects, acc = criterion(logits, labels, lengths)
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num_corrects += corrects
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num_samples += feats.shape[0]
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eval_acc = num_corrects / num_samples
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print_msg = '[Evaluation result]'
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print_msg += ' dev_acc={:.4f}'.format(eval_acc)
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logger.eval(print_msg)
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# Save model
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save_dir = os.path.join(training_conf['checkpoint_dir'],
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'epoch_{}'.format(epoch))
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logger.info('Saving model checkpoint to {}'.format(save_dir))
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paddle.save(model.state_dict(),
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os.path.join(save_dir, 'model.pdparams'))
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paddle.save(optimizer.state_dict(),
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os.path.join(save_dir, 'model.pdopt'))
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