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main.py
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main.py
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import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import h5py
import math
import time
import librosa
import logging
import matplotlib.pyplot as plt
from sklearn import metrics
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from utilities import (create_folder, get_filename, create_logging,
StatisticsContainer, RegressionPostProcessor)
from data_generator import MaestroDataset, Augmentor, Sampler, TestSampler, collate_fn
from models import Regress_onset_offset_frame_velocity_CRNN, Regress_pedal_CRNN
from pytorch_utils import move_data_to_device
from losses import get_loss_func
from evaluate import SegmentEvaluator
import config
def train(args):
"""Train a piano transcription system.
Args:
workspace: str, directory of your workspace
model_type: str, e.g. 'Regressonset_regressoffset_frame_velocity_CRNN'
loss_type: str, e.g. 'regress_onset_offset_frame_velocity_bce'
augmentation: str, e.g. 'none'
batch_size: int
learning_rate: float
reduce_iteration: int
resume_iteration: int
early_stop: int
device: 'cuda' | 'cpu'
mini_data: bool
"""
# Arugments & parameters
workspace = args.workspace
model_type = args.model_type
loss_type = args.loss_type
augmentation = args.augmentation
max_note_shift = args.max_note_shift
batch_size = args.batch_size
learning_rate = args.learning_rate
reduce_iteration = args.reduce_iteration
resume_iteration = args.resume_iteration
early_stop = args.early_stop
device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
mini_data = args.mini_data
filename = args.filename
sample_rate = config.sample_rate
segment_seconds = config.segment_seconds
hop_seconds = config.hop_seconds
segment_samples = int(segment_seconds * sample_rate)
frames_per_second = config.frames_per_second
classes_num = config.classes_num
num_workers = 8
# Loss function
loss_func = get_loss_func(loss_type)
# Paths
hdf5s_dir = os.path.join(workspace, 'hdf5s', 'maestro')
checkpoints_dir = os.path.join(workspace, 'checkpoints', filename,
model_type, 'loss_type={}'.format(loss_type),
'augmentation={}'.format(augmentation),
'max_note_shift={}'.format(max_note_shift),
'batch_size={}'.format(batch_size))
create_folder(checkpoints_dir)
statistics_path = os.path.join(workspace, 'statistics', filename,
model_type, 'loss_type={}'.format(loss_type),
'augmentation={}'.format(augmentation),
'max_note_shift={}'.format(max_note_shift),
'batch_size={}'.format(batch_size), 'statistics.pkl')
create_folder(os.path.dirname(statistics_path))
logs_dir = os.path.join(workspace, 'logs', filename,
model_type, 'loss_type={}'.format(loss_type),
'augmentation={}'.format(augmentation),
'max_note_shift={}'.format(max_note_shift),
'batch_size={}'.format(batch_size))
create_folder(logs_dir)
create_logging(logs_dir, filemode='w')
logging.info(args)
if 'cuda' in str(device):
logging.info('Using GPU.')
device = 'cuda'
else:
logging.info('Using CPU.')
device = 'cpu'
# Model
Model = eval(model_type)
model = Model(frames_per_second=frames_per_second, classes_num=classes_num)
if augmentation == 'none':
augmentor = None
elif augmentation == 'aug':
augmentor = Augmentor()
else:
raise Exception('Incorrect argumentation!')
# Dataset
train_dataset = MaestroDataset(hdf5s_dir=hdf5s_dir,
segment_seconds=segment_seconds, frames_per_second=frames_per_second,
max_note_shift=max_note_shift, augmentor=augmentor)
evaluate_dataset = MaestroDataset(hdf5s_dir=hdf5s_dir,
segment_seconds=segment_seconds, frames_per_second=frames_per_second,
max_note_shift=0)
# Sampler for training
train_sampler = Sampler(hdf5s_dir=hdf5s_dir, split='train',
segment_seconds=segment_seconds, hop_seconds=hop_seconds,
batch_size=batch_size, mini_data=mini_data)
# Sampler for evaluation
evaluate_train_sampler = TestSampler(hdf5s_dir=hdf5s_dir,
split='train', segment_seconds=segment_seconds, hop_seconds=hop_seconds,
batch_size=batch_size, mini_data=mini_data)
evaluate_validate_sampler = TestSampler(hdf5s_dir=hdf5s_dir,
split='validation', segment_seconds=segment_seconds, hop_seconds=hop_seconds,
batch_size=batch_size, mini_data=mini_data)
evaluate_test_sampler = TestSampler(hdf5s_dir=hdf5s_dir,
split='test', segment_seconds=segment_seconds, hop_seconds=hop_seconds,
batch_size=batch_size, mini_data=mini_data)
# Dataloader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_sampler=train_sampler, collate_fn=collate_fn,
num_workers=num_workers, pin_memory=True)
evaluate_train_loader = torch.utils.data.DataLoader(dataset=evaluate_dataset,
batch_sampler=evaluate_train_sampler, collate_fn=collate_fn,
num_workers=num_workers, pin_memory=True)
validate_loader = torch.utils.data.DataLoader(dataset=evaluate_dataset,
batch_sampler=evaluate_validate_sampler, collate_fn=collate_fn,
num_workers=num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(dataset=evaluate_dataset,
batch_sampler=evaluate_test_sampler, collate_fn=collate_fn,
num_workers=num_workers, pin_memory=True)
# Evaluator
evaluator = SegmentEvaluator(model, batch_size)
# Statistics
statistics_container = StatisticsContainer(statistics_path)
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=learning_rate,
betas=(0.9, 0.999), eps=1e-08, weight_decay=0., amsgrad=True)
# Resume training
if resume_iteration > 0:
resume_checkpoint_path = os.path.join(workspace, 'checkpoints', filename,
model_type, 'loss_type={}'.format(loss_type),
'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size),
'{}_iterations.pth'.format(resume_iteration))
logging.info('Loading checkpoint {}'.format(resume_checkpoint_path))
checkpoint = torch.load(resume_checkpoint_path)
model.load_state_dict(checkpoint['model'])
train_sampler.load_state_dict(checkpoint['sampler'])
statistics_container.load_state_dict(resume_iteration)
iteration = checkpoint['iteration']
else:
iteration = 0
# Parallel
print('GPU number: {}'.format(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
if 'cuda' in str(device):
model.to(device)
train_bgn_time = time.time()
for batch_data_dict in train_loader:
# Evaluation
if iteration % 5000 == 0:# and iteration > 0:
logging.info('------------------------------------')
logging.info('Iteration: {}'.format(iteration))
train_fin_time = time.time()
evaluate_train_statistics = evaluator.evaluate(evaluate_train_loader)
validate_statistics = evaluator.evaluate(validate_loader)
test_statistics = evaluator.evaluate(test_loader)
logging.info(' Train statistics: {}'.format(evaluate_train_statistics))
logging.info(' Validation statistics: {}'.format(validate_statistics))
logging.info(' Test statistics: {}'.format(test_statistics))
statistics_container.append(iteration, evaluate_train_statistics, data_type='train')
statistics_container.append(iteration, validate_statistics, data_type='validation')
statistics_container.append(iteration, test_statistics, data_type='test')
statistics_container.dump()
train_time = train_fin_time - train_bgn_time
validate_time = time.time() - train_fin_time
logging.info(
'Train time: {:.3f} s, validate time: {:.3f} s'
''.format(train_time, validate_time))
train_bgn_time = time.time()
# Save model
if iteration % 20000 == 0:
checkpoint = {
'iteration': iteration,
'model': model.module.state_dict(),
'sampler': train_sampler.state_dict()}
checkpoint_path = os.path.join(
checkpoints_dir, '{}_iterations.pth'.format(iteration))
torch.save(checkpoint, checkpoint_path)
logging.info('Model saved to {}'.format(checkpoint_path))
# Reduce learning rate
if iteration % reduce_iteration == 0 and iteration > 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.9
# Move data to device
for key in batch_data_dict.keys():
batch_data_dict[key] = move_data_to_device(batch_data_dict[key], device)
model.train()
batch_output_dict = model(batch_data_dict['waveform'])
loss = loss_func(model, batch_output_dict, batch_data_dict)
print(iteration, loss)
# Backward
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Stop learning
if iteration == early_stop:
break
iteration += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Example of parser. ')
subparsers = parser.add_subparsers(dest='mode')
parser_train = subparsers.add_parser('train')
parser_train.add_argument('--workspace', type=str, required=True)
parser_train.add_argument('--model_type', type=str, required=True)
parser_train.add_argument('--loss_type', type=str, required=True)
parser_train.add_argument('--augmentation', type=str, required=True, choices=['none', 'aug'])
parser_train.add_argument('--max_note_shift', type=int, required=True)
parser_train.add_argument('--batch_size', type=int, required=True)
parser_train.add_argument('--learning_rate', type=float, required=True)
parser_train.add_argument('--reduce_iteration', type=int, required=True)
parser_train.add_argument('--resume_iteration', type=int, required=True)
parser_train.add_argument('--early_stop', type=int, required=True)
parser_train.add_argument('--mini_data', action='store_true', default=False)
parser_train.add_argument('--cuda', action='store_true', default=False)
args = parser.parse_args()
args.filename = get_filename(__file__)
if args.mode == 'train':
train(args)
else:
raise Exception('Error argument!')