File size: 5,538 Bytes
a6fae72 | 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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = [
"FeatureAttention",
"TimeAttention",
"PositionEncodeLearnable",
"ConvGRUTransformerHLV10"
]
class FeatureAttention(nn.Module):
def __init__(self, feature_dim):
super().__init__()
self.attn = nn.Linear(feature_dim, feature_dim)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
# x: [batch, seq_len, features]
weights = self.softmax(self.attn(x)) # [batch, seq_len, features]
out = x * weights
return out.sum(dim=-1) # [batch, seq_len]
class TimeAttention(nn.Module):
def __init__(self, seq_len):
super().__init__()
self.attn = nn.Linear(seq_len, seq_len)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
# x: [batch, seq_len, features]
x_t = x.transpose(1, 2) # [batch, features, seq_len]
# ต้อง reshape ก่อนเข้า linear layer
batch_size, features, seq_len = x_t.shape
x_flat = x_t.reshape(batch_size * features, seq_len) # [batch*features, seq_len]
weights = self.softmax(self.attn(x_flat)) # [batch*features, seq_len]
weights = weights.reshape(batch_size, features, seq_len) # [batch, features, seq_len]
out = x_t * weights
return out.sum(dim=-1) # [batch, features]
class PositionEncodeLearnable(nn.Module):
def __init__(self, seq_len, d_model):
super().__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, seq_len, d_model))
def forward(self, x):
return x + self.pos_embedding
class ConvGRUTransformerHLV10(nn.Module):
def __init__(self,
input_dim,
conv_channels=32,
gru_hidden=64,
nhead=4,
num_encoder_layers=1,
dim_feedforward=128,
seq_len=3,
kernel_size=3,
dropout=0.1,
output_steps=1
):
super().__init__()
self.seq_len = seq_len
self.kernel_size = kernel_size
self.output_steps = output_steps
# --- Attention Modules ---
self.feature_attn = FeatureAttention(input_dim)
# --- Conv1D Layer (Causal) ---
self.conv1 = nn.Conv1d(
in_channels=input_dim,
out_channels=conv_channels,
kernel_size=self.kernel_size,
dilation=1,
padding=0
)
self.conv_bn = nn.BatchNorm1d(conv_channels)
# --- GRU Layer ---
self.gru = nn.GRU(
input_size=conv_channels,
hidden_size=gru_hidden,
batch_first=True
)
self.time_attn = TimeAttention(seq_len)
# --- Learnable Positional Encoding ---
self.pos_encoder = PositionEncodeLearnable(seq_len, gru_hidden)
# --- Transformer Encoder ---
encoder_layer = nn.TransformerEncoderLayer(
d_model=gru_hidden,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
batch_first=True,
activation="gelu"
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
# --- Output Head ---
self.fc_high = nn.Linear(gru_hidden, output_steps)
self.fc_low = nn.Linear(gru_hidden, output_steps)
def forward(self, x):
# x: (batch, seq_len, features)
batch_size, seq_len, features = x.size()
# --- Feature-wise Attention ก่อน Conv ---
feat_attn = self.feature_attn(x) # (batch, seq_len)
x = x * feat_attn.unsqueeze(-1) # apply attention weight
# --- Conv1D ---
x_conv = x.permute(0, 2, 1) # (batch, features, seq_len)
pad = self.kernel_size - 1
x_conv = F.pad(x_conv, (pad, 0))
x_conv = self.conv1(x_conv)
x_conv = self.conv_bn(x_conv)
x_conv = torch.tanh(x_conv)
x_conv = x_conv[:, :, -seq_len:]
x_conv = x_conv.permute(0, 2, 1) # (batch, seq_len, conv_channels)
# --- GRU ---
gru_out, _ = self.gru(x_conv)
if gru_out.size(1) != self.seq_len:
current_seq_len = gru_out.size(1)
if current_seq_len < self.seq_len:
pad_size = self.seq_len - current_seq_len
gru_out = F.pad(gru_out, (0, 0, 0, pad_size))
else:
gru_out = gru_out[:, :self.seq_len, :]
# --- Time-wise Attention หลัง GRU ---
time_attn_out = self.time_attn(gru_out) # (batch, gru_hidden)
x_attn = time_attn_out.unsqueeze(1).repeat(1, self.seq_len, 1)
# --- Positional Encoding ---
x_attn = self.pos_encoder(x_attn)
# --- Transformer Encoder ---
trans_out = self.transformer(x_attn)
# --- Output ---
last_steps = trans_out[:, -self.output_steps:, :]
high_out = self.fc_high(last_steps)
low_out = self.fc_low(last_steps)
return high_out.squeeze(-1), low_out.squeeze(-1)
|