# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import ast import os import paddle from model import SoundClassifier from paddlespeech.cls.datasets import ESC50 from paddlespeech.cls.features import LogMelSpectrogram from paddlespeech.cls.models.panns import cnn14 from paddlespeech.cls.utils import logger from paddlespeech.cls.utils import Timer # yapf: disable parser = argparse.ArgumentParser(__doc__) parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.") parser.add_argument("--epochs", type=int, default=50, help="Number of epoches for fine-tuning.") parser.add_argument("--gpu_feat", type=ast.literal_eval, default=False, help="Use gpu to extract feature.") parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.") parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.") parser.add_argument("--num_workers", type=int, default=0, help="Number of workers in dataloader.") parser.add_argument("--checkpoint_dir", type=str, default='./checkpoint', help="Directory to save model checkpoints.") parser.add_argument("--save_freq", type=int, default=10, help="Save checkpoint every n epoch.") parser.add_argument("--log_freq", type=int, default=10, help="Log the training infomation every n steps.") args = parser.parse_args() # yapf: enable if __name__ == "__main__": paddle.set_device(args.device) nranks = paddle.distributed.get_world_size() if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() local_rank = paddle.distributed.get_rank() backbone = cnn14(pretrained=True, extract_embedding=True) model = SoundClassifier(backbone, num_class=len(ESC50.label_list)) model = paddle.DataParallel(model) optimizer = paddle.optimizer.Adam( learning_rate=args.learning_rate, parameters=model.parameters()) criterion = paddle.nn.loss.CrossEntropyLoss() if args.gpu_feat: train_ds = ESC50(mode='train') dev_ds = ESC50(mode='dev') feature_extractor = LogMelSpectrogram(sr=16000, hop_length=320) else: train_ds = ESC50(mode='train', feat_type='melspectrogram') dev_ds = ESC50(mode='dev', feat_type='melspectrogram') train_sampler = paddle.io.DistributedBatchSampler( train_ds, batch_size=args.batch_size, shuffle=True, drop_last=False) train_loader = paddle.io.DataLoader( train_ds, batch_sampler=train_sampler, num_workers=args.num_workers, return_list=True, use_buffer_reader=True, ) steps_per_epoch = len(train_sampler) timer = Timer(steps_per_epoch * args.epochs) timer.start() for epoch in range(1, args.epochs + 1): model.train() avg_loss = 0 num_corrects = 0 num_samples = 0 for batch_idx, batch in enumerate(train_loader): if args.gpu_feat: waveforms, labels = batch feats = feature_extractor( waveforms ) # Need a padding when lengths of waveforms differ in a batch. feats = paddle.transpose(feats, [0, 2, 1]) # To [N, length, n_mels] else: feats, labels = batch logits = model(feats) loss = criterion(logits, labels) loss.backward() optimizer.step() if isinstance(optimizer._learning_rate, paddle.optimizer.lr.LRScheduler): optimizer._learning_rate.step() optimizer.clear_grad() # Calculate loss avg_loss += loss.numpy()[0] # Calculate metrics preds = paddle.argmax(logits, axis=1) num_corrects += (preds == labels).numpy().sum() num_samples += feats.shape[0] timer.count() if (batch_idx + 1) % args.log_freq == 0 and local_rank == 0: lr = optimizer.get_lr() avg_loss /= args.log_freq avg_acc = num_corrects / num_samples print_msg = 'Epoch={}/{}, Step={}/{}'.format( epoch, args.epochs, batch_idx + 1, steps_per_epoch) print_msg += ' loss={:.4f}'.format(avg_loss) print_msg += ' acc={:.4f}'.format(avg_acc) print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format( lr, timer.timing, timer.eta) logger.train(print_msg) avg_loss = 0 num_corrects = 0 num_samples = 0 if epoch % args.save_freq == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0: dev_sampler = paddle.io.BatchSampler( dev_ds, batch_size=args.batch_size, shuffle=False, drop_last=False) dev_loader = paddle.io.DataLoader( dev_ds, batch_sampler=dev_sampler, num_workers=args.num_workers, return_list=True, ) model.eval() num_corrects = 0 num_samples = 0 with logger.processing('Evaluation on validation dataset'): for batch_idx, batch in enumerate(dev_loader): if args.gpu_feat: waveforms, labels = batch feats = feature_extractor(waveforms) feats = paddle.transpose(feats, [0, 2, 1]) else: feats, labels = batch logits = model(feats) preds = paddle.argmax(logits, axis=1) num_corrects += (preds == labels).numpy().sum() num_samples += feats.shape[0] print_msg = '[Evaluation result]' print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples) logger.eval(print_msg) # Save model save_dir = os.path.join(args.checkpoint_dir, 'epoch_{}'.format(epoch)) logger.info('Saving model checkpoint to {}'.format(save_dir)) paddle.save(model.state_dict(), os.path.join(save_dir, 'model.pdparams')) paddle.save(optimizer.state_dict(), os.path.join(save_dir, 'model.pdopt'))