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import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import seaborn as sns
'''
freeze_feature_extractor=True 시 Feature Extractor를 동결 (Pretraining)
unfreeze_feature_extractor()를 호출하면 Fine-Tuning 가능
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import seaborn as sns
from transformers import Wav2Vec2Model
class cnn(nn.Module):
def __init__(self, embed_dim=512):
super(cnn, self).__init__()
self.conv_block = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.AdaptiveAvgPool2d((4, 4))
)
self.projection = nn.Linear(32 * 4 * 4, embed_dim)
def forward(self, x):
x = self.conv_block(x)
B, C, H, W = x.shape
x = x.view(B, -1)
x = self.projection(x)
return x
class CrossAttentionLayer(nn.Module):
def __init__(self, embed_dim, num_heads):
super(CrossAttentionLayer, self).__init__()
self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)
self.layer_norm = nn.LayerNorm(embed_dim)
self.feed_forward = nn.Sequential(
nn.Linear(embed_dim, embed_dim * 4),
nn.ReLU(),
nn.Linear(embed_dim * 4, embed_dim)
)
self.attention_weights = None
def forward(self, x, cross_input):
# Cross-attention between x and cross_input
attn_output, attn_weights = self.multihead_attn(query=x, key=cross_input, value=cross_input)
self.attention_weights = attn_weights
x = self.layer_norm(x + attn_output)
feed_forward_output = self.feed_forward(x)
x = self.layer_norm(x + feed_forward_output)
return x
class CrossAttentionViT(nn.Module):
def __init__(self, embed_dim=512, num_heads=8, num_layers=6, num_classes=2):
super(CrossAttentionViT, self).__init__()
self.cross_attention_layers = nn.ModuleList([
CrossAttentionLayer(embed_dim, num_heads) for _ in range(num_layers)
])
encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.classifier = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, num_classes)
)
def forward(self, x, cross_attention_input):
self.attention_maps = []
for layer in self.cross_attention_layers:
x = layer(x, cross_attention_input)
self.attention_maps.append(layer.attention_weights)
x = x.unsqueeze(1).permute(1, 0, 2)
x = self.transformer(x)
x = x.mean(dim=0)
x = self.classifier(x)
return x
class CCV(nn.Module):
def __init__(self, embed_dim=512, num_heads=8, num_layers=6, num_classes=2):
super(CCV, self).__init__()
self.encoder = cnn(embed_dim=embed_dim)
self.decoder = CrossAttentionViT(embed_dim=embed_dim, num_heads=num_heads, num_layers=num_layers, num_classes=num_classes)
def forward(self, x, cross_attention_input=None):
x = self.encoder(x)
if cross_attention_input is None:
cross_attention_input = x
x = self.decoder(x, cross_attention_input)
# Attention Map 저장
self.attention_maps = self.decoder.attention_maps
return x
def get_attention_maps(self):
return self.attention_maps
import torch
import torch.nn as nn
from transformers import Wav2Vec2Model
class Wav2Vec2ForFakeMusic(nn.Module):
def __init__(self, num_classes=2, freeze_feature_extractor=True):
super(Wav2Vec2ForFakeMusic, self).__init__()
self.wav2vec = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base")
if freeze_feature_extractor:
for param in self.wav2vec.parameters():
param.requires_grad = False
self.classifier = nn.Sequential(
nn.Linear(self.wav2vec.config.hidden_size, 256), # 768 → 256
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, num_classes) # 256 → 2 (Binary Classification)
)
def forward(self, x):
x = x.squeeze(1)
output = self.wav2vec(x)
features = output["last_hidden_state"] # (batch_size, seq_len, feature_dim)
pooled_features = features.mean(dim=1) # ✅ Mean Pooling 적용 (batch_size, feature_dim)
logits = self.classifier(pooled_features) # (batch_size, num_classes)
return logits, pooled_features
def visualize_attention_map(attn_map, mel_spec, layer_idx):
attn_map = attn_map.mean(dim=1).squeeze().cpu().numpy() # 여러 head 평균
mel_spec = mel_spec.squeeze().cpu().numpy()
fig, axs = plt.subplots(2, 1, figsize=(10, 8))
# 1Log-Mel Spectrogram 시각화
sns.heatmap(mel_spec, cmap='inferno', ax=axs[0])
axs[0].set_title("Log-Mel Spectrogram")
axs[0].set_xlabel("Time Frames")
axs[0].set_ylabel("Mel Frequency Bins")
# Attention Map 시각화
sns.heatmap(attn_map, cmap='viridis', ax=axs[1])
axs[1].set_title(f"Attention Map (Layer {layer_idx})")
axs[1].set_xlabel("Time Frames")
axs[1].set_ylabel("Query Positions")
plt.tight_layout()
plt.show()
plt.savefig("/data/kym/AI_Music_Detection/Code/model/attention_map/crossattn.png")
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