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import numpy as np
import torch
import torch.nn as nn
import torchaudio
import os
import random
from attention_modules import BertConfig, BertEncoder, BertPooler
class Conv_1d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=1, pooling=2):
super(Conv_1d, self).__init__()
self.conv = nn.Conv1d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn = nn.BatchNorm1d(output_channels)
self.relu = nn.ReLU()
self.mp = nn.MaxPool1d(pooling)
def forward(self, x):
out = self.mp(self.relu(self.bn(self.conv(x))))
return out
class Conv_2d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=1, pooling=2):
super(Conv_2d, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn = nn.BatchNorm2d(output_channels)
self.relu = nn.ReLU()
self.mp = nn.MaxPool2d(pooling)
def forward(self, x):
out = self.mp(self.relu(self.bn(self.conv(x))))
return out
class Res_2d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=2):
super(Res_2d, self).__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 CNNSA(nn.Module):
'''
Won et al. 2019
Toward interpretable music tagging with self-attention.
Feature extraction with CNN + temporal summary with Transformer encoder.
'''
def __init__(self,
n_channels=128,
sample_rate=16000,
n_fft=512,
f_min=0.0,
f_max=8000.0,
n_mels=128,
n_class=50):
super(CNNSA, self).__init__()
# Spectrogram
self.spec = torchaudio.transforms.MelSpectrogram(sample_rate=sample_rate,
n_fft=n_fft,
f_min=f_min,
f_max=f_max,
n_mels=n_mels)
self.to_db = torchaudio.transforms.AmplitudeToDB()
self.spec_bn = nn.BatchNorm2d(1)
# CNN
self.layer1 = Res_2d(1, n_channels, stride=2)
self.layer2 = Res_2d(n_channels, n_channels, stride=2)
self.layer3 = Res_2d(n_channels, n_channels*2, stride=2)
self.layer4 = Res_2d(n_channels*2, n_channels*2, stride=(2, 1))
self.layer5 = Res_2d(n_channels*2, n_channels*2, stride=(2, 1))
self.layer6 = Res_2d(n_channels*2, n_channels*2, stride=(2, 1))
self.layer7 = Res_2d(n_channels*2, n_channels*2, stride=(2, 1))
# Transformer encoder
bert_config = BertConfig(vocab_size=256,
hidden_size=256,
num_hidden_layers=2,
num_attention_heads=8,
intermediate_size=1024,
hidden_act="gelu",
hidden_dropout_prob=0.4,
max_position_embeddings=700,
attention_probs_dropout_prob=0.5)
self.encoder = BertEncoder(bert_config)
self.pooler = BertPooler(bert_config)
self.vec_cls = self.get_cls(256)
# Dense
self.dropout = nn.Dropout(0.5)
self.dense = nn.Linear(256, n_class)
def get_cls(self, channel):
np.random.seed(0)
single_cls = torch.Tensor(np.random.random((1, channel)))
vec_cls = torch.cat([single_cls for _ in range(64)], dim=0)
vec_cls = vec_cls.unsqueeze(1)
return vec_cls
def append_cls(self, x):
batch, _, _ = x.size()
part_vec_cls = self.vec_cls[:batch].clone()
part_vec_cls = part_vec_cls.to(x.device)
return torch.cat([part_vec_cls, x], dim=1)
def get_spec(self, ids, audio_length=15*16000, allow_random=False):
wav_list = list()
for id in ids:
audio_path = os.path.join("/import/c4dm-datasets/Music4All/music4all/audios", id + '.mp3')
(wav, sample_rate) = torchaudio.backend.sox_io_backend.load(audio_path)
# to mono
mono_wav = torch.mean(wav, dim=0)
# cut length
if allow_random:
random_index = random.randint(0, len(mono_wav) - audio_length - 1)
else:
random_index = 0
mono_wav_cut = mono_wav[random_index: random_index + audio_length]
wav_list.append(mono_wav_cut)
# merge wav to (bs, length)
data = torch.stack(wav_list, dim=0)
# to spectrogram
spectrogram = self.spec(data.cuda())
return spectrogram
def forward(self, ids):
# Spectrogram
# for batch
spec = self.get_spec(ids)
spec_db = self.to_db(spec)
x = spec_db.unsqueeze(1) # add channel dim
x = self.spec_bn(x)
# CNN
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
x = x.squeeze(2)
# Get [CLS] token
x = x.permute(0, 2, 1)
x = self.append_cls(x)
# Transformer encoder
x = self.encoder(x)
x = x[-1] # last layer
# x = self.pooler(x)
#
# # Dense
# x = self.dropout(x)
# x = self.dense(x)
# x = nn.Sigmoid()(x)
return x # return the last layer. Shape: (length, 256)
# test code
# model = CNNSA()
# model.load_state_dict(torch.load("best_model.pth"))
# id = ["wlIcjSZkgW0cgWrm", "wlIcjSZkgW0cgWrm"]
# output = model(id) |