File size: 8,758 Bytes
3b6a091 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
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')
# 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, shuffle=True,
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(val_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('../ckpts/TSED_AS_filter/20000.0.pt', 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, val_loader = accelerator.prepare(
model, optimizer, lr_scheduler, train_loader, val_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, val_loader, params, epoch='debug', split='val',
save_path=args.log_dir + 'output/', device=accelerator.device)
for epoch in range(args.epochs):
model.train()
for step, batch in enumerate(tqdm(train_loader)):
with accelerator.accumulate(model):
audio, cls, label, _ = batch
mel = model.forward_to_spec(audio)
# data aug
mel, label = frame_shift(mel, label, params['net_pooling'])
mel, label = time_mask(mel, label, params["net_pooling"],
mask_ratios=params['data_aug']["time_mask_ratios"])
mel, _ = feature_transformation(mel, **params['data_aug']["transform"])
strong_pred = model(mel, cls)
B, N, L = label.shape
label = label.reshape(B * N, L)
label = label.unsqueeze(1)
loss = strong_loss_func(strong_pred, label)
accelerator.backward(loss)
# clip grad up
if accelerator.sync_gradients:
if 'grad_clip' in params['opt'] and params['opt']['grad_clip'] > 0:
accelerator.clip_grad_norm_(model.parameters(),
max_norm=params['opt']['grad_clip'])
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
global_step += 1/params['opt']['accumulation_steps']
losses += loss.item()/params['opt']['accumulation_steps']
if accelerator.is_main_process:
if global_step % args.log_step == 0:
current_time = time.asctime(time.localtime(time.time()))
epoch_info = f'Epoch: [{epoch + 1}][{args.epochs}]'
batch_info = f'Global Step: {global_step}'
loss_info = f'Loss: {losses / args.log_step:.6f}'
# Extract the learning rate from the optimizer
lr = optimizer.param_groups[0]['lr']
lr_info = f'Learning Rate: {lr:.6f}'
log_message = f'{current_time}\n{epoch_info} {batch_info} {loss_info} {lr_info}\n'
with open(args.log_dir + 'log.txt', mode='a') as n:
n.write(log_message)
losses = 0.0
# check performance
if (global_step + 1) % args.eval_every_step == 0:
if accelerator.is_main_process:
model_module = model.module if hasattr(model, 'module') else model
val_psds(model_module, val_loader, params, epoch=global_step+1, split='val',
save_path=args.log_dir + 'output/', device=accelerator.device)
# save model
unwrapped_model = accelerator.unwrap_model(model)
accelerator.save({
"model": model.state_dict(),
}, args.save_dir + str(global_step+1) + '.pt')
accelerator.wait_for_everyone()
model.train()
|