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import random
import argparse
import os
import time
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from accelerate import Accelerator
from models.transformer import Dasheng_Encoder
from models.sed_decoder import Decoder, TSED_Wrapper
from dataset.tsed import TSED_AS
from dataset.tsed_val import TSED_Val
from utils import load_yaml_with_includes, get_lr_scheduler, ConcatDatasetBatchSampler
from utils.data_aug import frame_shift, mixup, time_mask, feature_transformation
from val import val_psds
def parse_args():
parser = argparse.ArgumentParser()
# Config settings
parser.add_argument('--config-name', type=str, default='configs/model.yml')
parser.add_argument('--ckpt', type=str, default='20000.pt')
# Training settings
parser.add_argument("--amp", type=str, default='fp16')
parser.add_argument('--epochs', type=int, default=20)
parser.add_argument('--num-workers', type=int, default=8)
parser.add_argument('--num-threads', type=int, default=1)
parser.add_argument('--eval-every-step', type=int, default=5000)
parser.add_argument('--save-every-step', type=int, default=5000)
# parser.add_argument('--dataloader', type=str, default='EACaps')
parser.add_argument("--logit-normal-indices", type=bool, default=False)
# Log and random seed
parser.add_argument('--random-seed', type=int, default=2024)
parser.add_argument('--log-step', type=int, default=100)
parser.add_argument('--log-dir', type=str, default='../logs/')
parser.add_argument('--save-dir', type=str, default='../ckpts/')
return parser.parse_args()
def setup_directories(args, params):
args.log_dir = os.path.join(args.log_dir, params['model_name']) + '/'
args.save_dir = os.path.join(args.save_dir, params['model_name']) + '/'
os.makedirs(args.log_dir, exist_ok=True)
os.makedirs(args.save_dir, exist_ok=True)
def set_device(args):
torch.set_num_threads(args.num_threads)
if torch.cuda.is_available():
args.device = 'cuda'
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cuda.matmul.allow_tf32 = True
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
args.device = 'cpu'
if __name__ == '__main__':
args = parse_args()
params = load_yaml_with_includes(args.config_name)
set_device(args)
setup_directories(args, params)
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
# use accelerator for multi-gpu training
accelerator = Accelerator(mixed_precision=args.amp,
gradient_accumulation_steps=params['opt']['accumulation_steps'],
step_scheduler_with_optimizer=False)
train_set = TSED_AS(**params['data']['train_data'])
train_loader = DataLoader(train_set, batch_size=params['opt']['batch_size'], num_workers=args.num_workers)
# val_set = TSED_Val(**params['data']['val_data'])
# val_loader = DataLoader(val_set, num_workers=0, batch_size=1, shuffle=False)
test_set = TSED_Val(**params['data']['test_data'])
test_loader = DataLoader(test_set, num_workers=0, batch_size=1, shuffle=False)
encoder = Dasheng_Encoder(**params['encoder']).to(accelerator.device)
pretrained_url = 'https://zenodo.org/records/11511780/files/dasheng_base.pt?download=1'
dump = torch.hub.load_state_dict_from_url(pretrained_url, map_location='cpu')
model_parmeters = dump['model']
# pretrained_url = 'https://zenodo.org/records/13315686/files/dasheng_audioset_mAP497.pt?download=1'
# dump = torch.hub.load_state_dict_from_url(pretrained_url, map_location='cpu')
# model_parmeters = dump
encoder.load_state_dict(model_parmeters)
decoder = Decoder(**params['decoder']).to(accelerator.device)
model = TSED_Wrapper(encoder, decoder, params['ft_blocks'], params['frozen_encoder'])
print(f"Trainable Parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M")
model.load_state_dict(torch.load(args.ckpt, map_location='cpu')['model'])
if params['frozen_encoder']:
optimizer = torch.optim.AdamW(
model.parameters(),
lr=params['opt']['learning_rate'],
weight_decay=params['opt']['weight_decay'],
betas=(params['opt']['beta1'], params['opt']['beta2']),
eps=params['opt']['adam_epsilon'])
else:
optimizer = torch.optim.AdamW(
[
{'params': model.encoder.parameters(), 'lr': 0.1 * params['opt']['learning_rate']},
{'params': model.decoder.parameters(), 'lr': params['opt']['learning_rate']}
],
weight_decay=params['opt']['weight_decay'],
betas=(params['opt']['beta1'], params['opt']['beta2']),
eps=params['opt']['adam_epsilon'])
lr_scheduler = get_lr_scheduler(optimizer, 'customized', **params['opt']['lr_scheduler'])
strong_loss_func = nn.BCEWithLogitsLoss()
model, optimizer, lr_scheduler, train_loader, test_loader = accelerator.prepare(
model, optimizer, lr_scheduler, train_loader, test_loader)
global_step = 0.0
losses = 0.0
if accelerator.is_main_process:
model_module = model.module if hasattr(model, 'module') else model
val_psds(model_module, test_loader, params, epoch='test_full', split='test',
save_path=args.log_dir + 'output/', device=accelerator.device) |