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import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
import numpy as np
import torchaudio
import yaml
from .utils import calculate_metrics
from preprocessing.pipelines import AudioPipeline
# Architecture based on: https://github.com/minzwon/sota-music-tagging-models/blob/36aa13b7205ff156cf4dcab60fd69957da453151/training/model.py
class ResidualDancer(nn.Module):
def __init__(self,n_channels=128, n_classes=50):
super().__init__()
# Spectrogram
self.spec_bn = nn.BatchNorm2d(1)
# CNN
self.res_layers = nn.Sequential(
ResBlock(1, n_channels, stride=2),
ResBlock(n_channels, n_channels, stride=2),
ResBlock(n_channels, n_channels*2, stride=2),
ResBlock(n_channels*2, n_channels*2, stride=2),
ResBlock(n_channels*2, n_channels*2, stride=2),
ResBlock(n_channels*2, n_channels*2, stride=2),
ResBlock(n_channels*2, n_channels*4, stride=2)
)
# Dense
self.dense1 = nn.Linear(n_channels*4, n_channels*4)
self.bn = nn.BatchNorm1d(n_channels*4)
self.dense2 = nn.Linear(n_channels*4, n_classes)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.spec_bn(x)
# CNN
x = self.res_layers(x)
x = x.squeeze(2)
# Global Max Pooling
if x.size(-1) != 1:
x = nn.MaxPool1d(x.size(-1))(x)
x = x.squeeze(2)
# Dense
x = self.dense1(x)
x = self.bn(x)
x = F.relu(x)
x = self.dropout(x)
x = self.dense2(x)
x = nn.Sigmoid()(x)
return x
class ResBlock(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=2):
super().__init__()
# convolution
self.conv_1 = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_1 = nn.BatchNorm2d(output_channels)
self.conv_2 = nn.Conv2d(output_channels, output_channels, shape, padding=shape//2)
self.bn_2 = nn.BatchNorm2d(output_channels)
# residual
self.diff = False
if (stride != 1) or (input_channels != output_channels):
self.conv_3 = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_3 = nn.BatchNorm2d(output_channels)
self.diff = True
self.relu = nn.ReLU()
def forward(self, x):
# convolution
out = self.bn_2(self.conv_2(self.relu(self.bn_1(self.conv_1(x)))))
# residual
if self.diff:
x = self.bn_3(self.conv_3(x))
out = x + out
out = self.relu(out)
return out
class TrainingEnvironment(pl.LightningModule):
def __init__(self, model: nn.Module, criterion: nn.Module, learning_rate=1e-4, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model = model
self.criterion = criterion
self.learning_rate = learning_rate
def training_step(self, batch: tuple[torch.Tensor, torch.TensorType], batch_index: int) -> torch.Tensor:
features, labels = batch
outputs = self.model(features)
loss = self.criterion(outputs, labels)
batch_metrics = calculate_metrics(outputs, labels)
self.log_dict(batch_metrics)
return loss
def validation_step(self, batch:tuple[torch.Tensor, torch.TensorType], batch_index:int):
x, y = batch
preds = self.model(x)
metrics = calculate_metrics(preds, y, prefix="val_")
metrics["val_loss"] = self.criterion(preds, y)
self.log_dict(metrics)
def test_step(self, batch:tuple[torch.Tensor, torch.TensorType], batch_index:int):
x, y = batch
preds = self.model(x)
self.log_dict(calculate_metrics(preds, y, prefix="test_"))
def configure_optimizers(self):
return torch.optim.Adam(self.model.parameters(), lr=self.learning_rate)
class DancePredictor:
def __init__(
self,
weight_path:str,
labels:list[str],
expected_duration=6,
threshold=0.5,
resample_frequency=16000,
device="cpu"):
super().__init__()
self.expected_duration = expected_duration
self.threshold = threshold
self.resample_frequency = resample_frequency
self.audio_pipeline = AudioPipeline(input_freq=self.resample_frequency)
self.labels = np.array(labels)
self.device = device
self.model = self.get_model(weight_path)
def get_model(self, weight_path:str) -> nn.Module:
weights = torch.load(weight_path, map_location=self.device)["state_dict"]
model = ResidualDancer(n_classes=len(self.labels))
for key in list(weights):
weights[key.replace("model.", "")] = weights.pop(key)
model.load_state_dict(weights)
return model.to(self.device).eval()
@classmethod
def from_config(cls, config_path:str) -> "DancePredictor":
with open(config_path, "r") as f:
config = yaml.safe_load(f)
return DancePredictor(**config)
@torch.no_grad()
def __call__(self, waveform: np.ndarray, sample_rate:int) -> dict[str,float]:
min_sample_len = sample_rate * self.expected_duration
if min_sample_len > len(waveform):
raise Exception("You must record for at least 6 seconds")
if len(waveform.shape) > 1 and waveform.shape[1] > 1:
waveform = waveform.transpose(1,0)
waveform = waveform.mean(axis=0, keepdims=True)
else:
waveform = np.expand_dims(waveform, 0)
waveform = waveform[: ,:min_sample_len]
waveform = torch.from_numpy(waveform.astype("int16"))
waveform = torchaudio.functional.apply_codec(waveform,sample_rate, "wav", channels_first=True)
waveform = torchaudio.functional.resample(waveform, sample_rate,self.resample_frequency)
spectrogram = self.audio_pipeline(waveform)
spectrogram = spectrogram.unsqueeze(0).to(self.device)
results = self.model(spectrogram)
results = results.squeeze(0).detach().cpu().numpy()
result_mask = results > self.threshold
probs = results[result_mask]
dances = self.labels[result_mask]
return {dance:float(prob) for dance, prob in zip(dances, probs)}
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