File size: 5,457 Bytes
c22b544 | 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 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
import os
import copy
from seldnet import SELDModel
from audio_dataset import AudioCapsDataset
from loss import SELDLoss
from collate import CollateFN
from rir_dataset import RIRDataset
from util import set_seed
def train(
model,
dataloader,
optimizer,
loss_fn,
collate_fn,
epoch=0,
log_f=None,
):
model.train()
total_loss = 0
total_sed_loss = 0
total_doa_loss = 0
loss_num = 0
for batch_idx, batch in enumerate(tqdm(dataloader)):
(waveforms, metas) = collate_fn(batch)
optimizer.zero_grad()
# Forward
sed_output, doa_output = model(waveforms)
# Compute loss
sed_loss, doa_loss, loss = loss_fn(sed_output, doa_output, metas)
# Backprop
loss.backward()
optimizer.step()
total_loss += loss.item()
total_sed_loss += sed_loss.item()
total_doa_loss += doa_loss.item()
loss_num += 1
if batch_idx % 10 == 0:
log_line = f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}, SED: {sed_loss.item():.4f}, DOA: {doa_loss.item():.4f}"
print(log_line)
if log_f is not None:
log_f.write(log_line + "\n")
avg_loss = total_loss / loss_num
avg_sed_loss = total_sed_loss / loss_num
avg_doa_loss = total_doa_loss / loss_num
return avg_loss, avg_sed_loss, avg_doa_loss
def validate(model, dataloader, loss_fn, collate_fn):
model.eval()
with torch.no_grad():
total_loss = 0
total_sed_loss = 0
total_doa_loss = 0
loss_num = 0
for batch_idx, batch in enumerate(tqdm(dataloader)):
(waveforms, metas) = collate_fn(batch)
# Forward
sed_output, doa_output = model(waveforms)
# Compute loss
sed_loss, doa_loss, loss = loss_fn(sed_output, doa_output, metas)
total_loss += loss.item()
total_sed_loss += sed_loss.item()
total_doa_loss += doa_loss.item()
loss_num += 1
avg_loss = total_loss / loss_num
avg_sed_loss = total_sed_loss / loss_num
avg_doa_loss = total_doa_loss / loss_num
return avg_loss, avg_sed_loss, avg_doa_loss
def main():
batch_size = 64
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed()
# Config
out_dir = "output"
config = {
"wav_dir": "../data/wav/",
"csv_path": "../data/fixed_audiocaps2.0/train.csv",
"val_csv_path": "../data/fixed_audiocaps2.0/val.csv",
"batch_size": batch_size,
"num_classes": 344,
"epochs": 50,
"path": {
"log_dir": os.path.join(out_dir, "log"),
"ckpt_dir": os.path.join(out_dir, "ckpt"),
},
"doa_weight": 50,
}
for d in config["path"].values():
os.makedirs(d, exist_ok=True)
val_config = copy.deepcopy(config)
val_config["csv_path"] = val_config["val_csv_path"]
# Train Dataset & Dataloader
train_rir_dataset = RIRDataset("train")
train_audio_dataset = AudioCapsDataset(config)
train_collate_fn = CollateFN(train_rir_dataset, device)
train_dataloader = DataLoader(
train_audio_dataset, batch_size=config["batch_size"],
shuffle=True, num_workers=16, drop_last=True, collate_fn=lambda x:x)
# Val Dataset & Dataloader
val_rir_dataset = RIRDataset("val")
val_audio_dataset = AudioCapsDataset(val_config)
val_collate_fn = CollateFN(val_rir_dataset, device)
val_dataloader = DataLoader(
val_audio_dataset, batch_size=val_config["batch_size"],
shuffle=True, num_workers=16, drop_last=True, collate_fn=lambda x:x)
# Model
model = SELDModel(num_classes=config["num_classes"]).to(device)
# Loss & Optimizer
loss_fn = SELDLoss(num_classes=config["num_classes"], doa_weight=config["doa_weight"], device=device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# Open log file
train_log_f = open(os.path.join(config["path"]["log_dir"], "train_log.txt"), "w")
val_log_f = open(os.path.join(config["path"]["log_dir"], "val_log.txt"), "w")
# Training loop
for epoch in range(config["epochs"]):
avg_loss, avg_sed_loss, avg_doa_loss = train(
model, train_dataloader, optimizer, loss_fn, train_collate_fn, epoch, train_log_f
)
val_avg_loss, val_avg_sed_loss, val_avg_doa_loss = validate(
model, val_dataloader, loss_fn, val_collate_fn
)
log_line = f"===> Epoch {epoch} Avg Loss: {avg_loss:.4f}, SED: {avg_sed_loss:.4f}, DOA: {avg_doa_loss:.4f}\n" \
+ f" Val Loss: {val_avg_loss:.4f}, SED: {val_avg_sed_loss:.4f}, DOA: {val_avg_doa_loss:.4f}"
print(log_line)
train_log_f.write(log_line + "\n")
log_line = f"Epoch {epoch} Val Loss: {val_avg_loss:.4f}, SED: {val_avg_sed_loss:.4f}, DOA: {val_avg_doa_loss:.4f}"
val_log_f.write(log_line + "\n")
# Save model
save_path = os.path.join(config["path"]["ckpt_dir"], f"model_epoch_{epoch}.pt")
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, save_path)
if __name__ == "__main__":
main()
|