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import logging
import os
import hydra
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.layers.torch import Rearrange
from torch.utils.data import DataLoader, Dataset
from .utils import Accuracy
logger = logging.getLogger(__name__)
def save_ckpt(model, path, model_class):
ckpt = {
"state_dict": model.state_dict(),
"padding_token": model.padding_token,
"model_class": model_class,
}
torch.save(ckpt, path)
def load_ckpt(path):
ckpt = torch.load(path)
ckpt["model_class"]["_target_"] = "emotion_models.duration_predictor.CnnPredictor"
model = hydra.utils.instantiate(ckpt["model_class"])
model.load_state_dict(ckpt["state_dict"])
model.padding_token = ckpt["padding_token"]
model = model.cpu()
model.eval()
return model
class Collator:
def __init__(self, padding_idx):
self.padding_idx = padding_idx
def __call__(self, batch):
x = [item[0] for item in batch]
lengths = [len(item) for item in x]
x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True, padding_value=self.padding_idx)
y = [item[1] for item in batch]
y = torch.nn.utils.rnn.pad_sequence(y, batch_first=True, padding_value=self.padding_idx)
mask = (x != self.padding_idx)
return x, y, mask, lengths
class Predictor(nn.Module):
def __init__(self, n_tokens, emb_dim):
super(Predictor, self).__init__()
self.n_tokens = n_tokens
self.emb_dim = emb_dim
self.padding_token = n_tokens
# add 1 extra embedding for padding token, set the padding index to be the last token
# (tokens from the clustering start at index 0)
self.emb = nn.Embedding(n_tokens + 1, emb_dim, padding_idx=self.padding_token)
def inflate_input(self, batch):
""" get a sequence of tokens, predict their durations
and inflate them accordingly """
batch_durs = self.forward(batch)
batch_durs = torch.exp(batch_durs) - 1
batch_durs = batch_durs.round()
output = []
for seq, durs in zip(batch, batch_durs):
inflated_seq = []
for token, n in zip(seq, durs):
if token == self.padding_token:
break
n = int(n.item())
token = int(token.item())
inflated_seq.extend([token for _ in range(n)])
output.append(inflated_seq)
output = torch.LongTensor(output)
return output
class CnnPredictor(Predictor):
def __init__(self, n_tokens, emb_dim, channels, kernel, output_dim, dropout, n_layers):
super(CnnPredictor, self).__init__(n_tokens=n_tokens, emb_dim=emb_dim)
layers = [
Rearrange("b t c -> b c t"),
nn.Conv1d(emb_dim, channels, kernel_size=kernel, padding=(kernel - 1) // 2),
Rearrange("b c t -> b t c"),
nn.ReLU(),
nn.LayerNorm(channels),
nn.Dropout(dropout),
]
for _ in range(n_layers-1):
layers += [
Rearrange("b t c -> b c t"),
nn.Conv1d(channels, channels, kernel_size=kernel, padding=(kernel - 1) // 2),
Rearrange("b c t -> b t c"),
nn.ReLU(),
nn.LayerNorm(channels),
nn.Dropout(dropout),
]
self.conv_layer = nn.Sequential(*layers)
self.proj = nn.Linear(channels, output_dim)
def forward(self, x):
x = self.emb(x)
x = self.conv_layer(x)
x = self.proj(x)
x = x.squeeze(-1)
return x
def l2_log_loss(input, target):
return F.mse_loss(
input=input.float(),
target=torch.log(target.float() + 1),
reduce=False
)
class DurationDataset(Dataset):
def __init__(self, tsv_path, km_path, substring=""):
lines = open(tsv_path, "r").readlines()
self.root, self.tsv = lines[0], lines[1:]
self.km = open(km_path, "r").readlines()
logger.info(f"loaded {len(self.km)} files")
if substring != "":
tsv, km = [], []
for tsv_line, km_line in zip(self.tsv, self.km):
if substring.lower() in tsv_line.lower():
tsv.append(tsv_line)
km.append(km_line)
self.tsv, self.km = tsv, km
logger.info(f"after filtering: {len(self.km)} files")
def __len__(self):
return len(self.km)
def __getitem__(self, i):
x = self.km[i]
x = x.split(" ")
x = list(map(int, x))
y = []
xd = []
count = 1
for x1, x2 in zip(x[:-1], x[1:]):
if x1 == x2:
count += 1
continue
else:
y.append(count)
xd.append(x1)
count = 1
xd = torch.LongTensor(xd)
y = torch.LongTensor(y)
return xd, y
def train(cfg):
device = "cuda:0"
model = hydra.utils.instantiate(cfg[cfg.model]).to(device)
optimizer = hydra.utils.instantiate(cfg.optimizer, model.parameters())
# add 1 extra embedding for padding token, set the padding index to be the last token
# (tokens from the clustering start at index 0)
collate_fn = Collator(padding_idx=model.padding_token)
logger.info(f"data: {cfg.train_tsv}")
train_ds = DurationDataset(cfg.train_tsv, cfg.train_km, substring=cfg.substring)
valid_ds = DurationDataset(cfg.valid_tsv, cfg.valid_km, substring=cfg.substring)
train_dl = DataLoader(train_ds, batch_size=32, shuffle=True, collate_fn=collate_fn)
valid_dl = DataLoader(valid_ds, batch_size=32, shuffle=False, collate_fn=collate_fn)
best_loss = float("inf")
for epoch in range(cfg.epochs):
train_loss, train_loss_scaled = train_epoch(model, train_dl, l2_log_loss, optimizer, device)
valid_loss, valid_loss_scaled, *acc = valid_epoch(model, valid_dl, l2_log_loss, device)
acc0, acc1, acc2, acc3 = acc
if valid_loss_scaled < best_loss:
path = f"{os.getcwd()}/{cfg.substring}.ckpt"
save_ckpt(model, path, cfg[cfg.model])
best_loss = valid_loss_scaled
logger.info(f"saved checkpoint: {path}")
logger.info(f"[epoch {epoch}] train loss: {train_loss:.3f}, train scaled: {train_loss_scaled:.3f}")
logger.info(f"[epoch {epoch}] valid loss: {valid_loss:.3f}, valid scaled: {valid_loss_scaled:.3f}")
logger.info(f"acc: {acc0,acc1,acc2,acc3}")
def train_epoch(model, loader, criterion, optimizer, device):
model.train()
epoch_loss = 0
epoch_loss_scaled = 0
for x, y, mask, _ in loader:
x, y, mask = x.to(device), y.to(device), mask.to(device)
yhat = model(x)
loss = criterion(yhat, y) * mask
loss = torch.mean(loss)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
epoch_loss += loss.item()
# get normal scale loss
yhat_scaled = torch.exp(yhat) - 1
yhat_scaled = torch.round(yhat_scaled)
scaled_loss = torch.mean(torch.abs(yhat_scaled - y) * mask)
epoch_loss_scaled += scaled_loss.item()
return epoch_loss / len(loader), epoch_loss_scaled / len(loader)
def valid_epoch(model, loader, criterion, device):
model.eval()
epoch_loss = 0
epoch_loss_scaled = 0
acc = Accuracy()
for x, y, mask, _ in loader:
x, y, mask = x.to(device), y.to(device), mask.to(device)
yhat = model(x)
loss = criterion(yhat, y) * mask
loss = torch.mean(loss)
epoch_loss += loss.item()
# get normal scale loss
yhat_scaled = torch.exp(yhat) - 1
yhat_scaled = torch.round(yhat_scaled)
scaled_loss = torch.sum(torch.abs(yhat_scaled - y) * mask) / mask.sum()
acc.update(yhat_scaled[mask].view(-1).float(), y[mask].view(-1).float())
epoch_loss_scaled += scaled_loss.item()
logger.info(f"example y: {y[0, :10].tolist()}")
logger.info(f"example yhat: {yhat_scaled[0, :10].tolist()}")
acc0 = acc.acc(tol=0)
acc1 = acc.acc(tol=1)
acc2 = acc.acc(tol=2)
acc3 = acc.acc(tol=3)
logger.info(f"accs: {acc0,acc1,acc2,acc3}")
return epoch_loss / len(loader), epoch_loss_scaled / len(loader), acc0, acc1, acc2, acc3
@hydra.main(config_path=".", config_name="duration_predictor.yaml")
def main(cfg):
logger.info(f"{cfg}")
train(cfg)
if __name__ == "__main__":
main()
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