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143710c 7b4e05b 143710c 7b4e05b 143710c 7b4e05b 143710c 7b4e05b 143710c 7b4e05b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 | """Decoder implementations."""
from __future__ import annotations
import torch
import torch.nn as nn
from src.models.base import BaseDecoder
class DecoderLSTM(BaseDecoder):
"""Image-as-first-token LSTM decoder (Show and Tell style baseline)."""
def __init__(
self,
vocab_size: int,
embed_dim: int = 256,
hidden_dim: int = 512,
num_layers: int = 1,
dropout: float = 0.0,
**kwargs,
):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.lstm = nn.LSTM(
embed_dim,
hidden_dim,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0.0,
)
# NOTE: nn.LSTM's own `dropout` arg only applies BETWEEN stacked layers,
# so it has no effect with num_layers=1 (our baseline). This separate
# output_dropout applies to the LSTM's output before the final
# projection, giving a real regularization effect even for a
# single-layer LSTM.
self.output_dropout = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_dim, vocab_size)
def forward(self, image_embed: torch.Tensor, input_seq: torch.Tensor) -> torch.Tensor:
# image_embed: (batch, embed_dim)
# input_seq: (batch, seq_len) token indices (teacher-forced)
word_embeds = self.embedding(input_seq) # (batch, seq_len, embed_dim)
image_step = image_embed.unsqueeze(1) # (batch, 1, embed_dim)
lstm_input = torch.cat([image_step, word_embeds], dim=1) # (batch, seq_len+1, embed_dim)
lstm_out, _ = self.lstm(lstm_input) # (batch, seq_len+1, hidden_dim)
lstm_out = self.output_dropout(lstm_out)
logits = self.fc(lstm_out) # (batch, seq_len+1, vocab_size)
# drop the output at the image step (index 0) -- it has no word target
return logits[:, 1:, :] # (batch, seq_len, vocab_size)
@torch.no_grad()
def generate(
self,
image_embed: torch.Tensor,
start_idx: int,
end_idx: int,
max_len: int,
) -> list[int]:
assert image_embed.size(0) == 1, "generate() supports batch_size=1"
device = image_embed.device
image_step = image_embed.unsqueeze(1)
_, hidden = self.lstm(image_step)
current_token = torch.tensor([[start_idx]], device=device)
generated: list[int] = []
for _ in range(max_len):
word_embed = self.embedding(current_token)
lstm_out, hidden = self.lstm(word_embed, hidden)
logits = self.fc(lstm_out.squeeze(1))
predicted_idx = int(logits.argmax(dim=-1).item())
if predicted_idx == end_idx:
break
generated.append(predicted_idx)
current_token = torch.tensor([[predicted_idx]], device=device)
return generated
@torch.no_grad()
def generate_beam(
self,
image_embed: torch.Tensor,
start_idx: int,
end_idx: int,
max_len: int,
beam_width: int = 3,
) -> list[int]:
"""Beam search decoding for a SINGLE image (batch=1).
Keeps the top `beam_width` candidate sequences at each step instead of
committing to a single best word (as generate()/greedy does), which
can recover from a locally-suboptimal early word choice. No retraining
required -- this only changes inference-time decoding.
"""
assert image_embed.size(0) == 1, "generate_beam() supports batch_size=1"
device = image_embed.device
image_step = image_embed.unsqueeze(1) # (1, 1, embed_dim)
_, init_hidden = self.lstm(image_step)
# each beam: (token_ids, cumulative_log_prob, hidden_state, finished)
beams = [([start_idx], 0.0, init_hidden, False)]
for _ in range(max_len):
all_candidates = []
for tokens, log_prob, hidden, finished in beams:
if finished:
all_candidates.append((tokens, log_prob, hidden, finished))
continue
last_token = torch.tensor([[tokens[-1]]], device=device)
word_embed = self.embedding(last_token)
lstm_out, new_hidden = self.lstm(word_embed, hidden)
logits = self.fc(lstm_out.squeeze(1))
log_probs = torch.log_softmax(logits, dim=-1).squeeze(0)
top_log_probs, top_indices = log_probs.topk(beam_width)
for lp, idx in zip(top_log_probs.tolist(), top_indices.tolist()):
new_tokens = tokens + [idx]
new_finished = idx == end_idx
all_candidates.append((new_tokens, log_prob + lp, new_hidden, new_finished))
all_candidates.sort(key=lambda c: c[1], reverse=True)
beams = all_candidates[:beam_width]
if all(finished for _, _, _, finished in beams):
break
best_tokens, _, _, _ = max(beams, key=lambda c: c[1])
result = best_tokens[1:]
if result and result[-1] == end_idx:
result = result[:-1]
return result
class BahdanauAttention(nn.Module):
"""Additive (Bahdanau-style) attention over spatial image regions.
At each decoder step, computes a weighted combination of the encoder's
spatial feature grid, where the weights depend on the current decoder
hidden state -- this is what lets the decoder "look at" different image
regions for different words, instead of relying on one static vector.
"""
def __init__(self, embed_dim: int, hidden_dim: int, attention_dim: int):
super().__init__()
self.encoder_att = nn.Linear(embed_dim, attention_dim)
self.decoder_att = nn.Linear(hidden_dim, attention_dim)
self.full_att = nn.Linear(attention_dim, 1)
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1)
def forward(self, encoder_out: torch.Tensor, decoder_hidden: torch.Tensor):
att1 = self.encoder_att(encoder_out)
att2 = self.decoder_att(decoder_hidden).unsqueeze(1)
att = self.full_att(self.relu(att1 + att2)).squeeze(2)
alpha = self.softmax(att)
context = (encoder_out * alpha.unsqueeze(2)).sum(dim=1)
return context, alpha
class DecoderAttentionLSTM(BaseDecoder):
"""LSTM decoder with Bahdanau-style attention over spatial image features.
Unlike DecoderLSTM (which injects one static image vector as the first
LSTM step), this decoder recomputes a fresh, weighted combination of
image regions at EVERY generation step, using an LSTMCell in an explicit
loop (required since each step's attention depends on that step's hidden
state -- unlike DecoderLSTM, the whole sequence can't be processed in one
nn.LSTM call).
"""
def __init__(
self,
vocab_size: int,
embed_dim: int = 256,
hidden_dim: int = 512,
attention_dim: int = 256,
dropout: float = 0.5,
**kwargs,
):
super().__init__()
self.embed_dim = embed_dim
self.hidden_dim = hidden_dim
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.attention = BahdanauAttention(embed_dim, hidden_dim, attention_dim)
self.lstm_cell = nn.LSTMCell(embed_dim + embed_dim, hidden_dim)
self.init_h = nn.Linear(embed_dim, hidden_dim)
self.init_c = nn.Linear(embed_dim, hidden_dim)
self.f_beta = nn.Linear(hidden_dim, embed_dim)
self.sigmoid = nn.Sigmoid()
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_dim, vocab_size)
def _init_hidden_state(self, encoder_out: torch.Tensor):
mean_encoder_out = encoder_out.mean(dim=1)
h = self.init_h(mean_encoder_out)
c = self.init_c(mean_encoder_out)
return h, c
def forward(self, image_embed: torch.Tensor, input_seq: torch.Tensor) -> torch.Tensor:
batch_size = image_embed.size(0)
seq_len = input_seq.size(1)
vocab_size = self.fc.out_features
device = image_embed.device
embeddings = self.embedding(input_seq)
h, c = self._init_hidden_state(image_embed)
outputs = torch.zeros(batch_size, seq_len, vocab_size, device=device)
for t in range(seq_len):
context, _ = self.attention(image_embed, h)
gate = self.sigmoid(self.f_beta(h))
context = gate * context
lstm_input = torch.cat([embeddings[:, t, :], context], dim=1)
h, c = self.lstm_cell(lstm_input, (h, c))
preds = self.fc(self.dropout(h))
outputs[:, t, :] = preds
return outputs
@torch.no_grad()
def generate(
self,
image_embed: torch.Tensor,
start_idx: int,
end_idx: int,
max_len: int,
) -> list[int]:
assert image_embed.size(0) == 1, "generate() supports batch_size=1"
device = image_embed.device
h, c = self._init_hidden_state(image_embed)
current_token = torch.tensor([start_idx], device=device)
generated: list[int] = []
for _ in range(max_len):
word_embed = self.embedding(current_token)
context, _ = self.attention(image_embed, h)
gate = self.sigmoid(self.f_beta(h))
context = gate * context
lstm_input = torch.cat([word_embed, context], dim=1)
h, c = self.lstm_cell(lstm_input, (h, c))
logits = self.fc(h)
predicted_idx = int(logits.argmax(dim=-1).item())
if predicted_idx == end_idx:
break
generated.append(predicted_idx)
current_token = torch.tensor([predicted_idx], device=device)
return generated
@torch.no_grad()
def generate_beam(
self,
image_embed: torch.Tensor,
start_idx: int,
end_idx: int,
max_len: int,
beam_width: int = 3,
) -> list[int]:
"""Beam search decoding for a SINGLE image (batch=1).
Same beam-search principle as DecoderLSTM.generate_beam(), adapted for
the LSTMCell + per-step attention loop: each beam carries its own
(h, c) hidden state, since attention (and therefore the next word
distribution) depends on each beam's own hidden state, not a shared one.
"""
assert image_embed.size(0) == 1, "generate_beam() supports batch_size=1"
device = image_embed.device
init_h, init_c = self._init_hidden_state(image_embed)
# each beam: (token_ids, cumulative_log_prob, h, c, finished)
beams = [([start_idx], 0.0, init_h, init_c, False)]
for _ in range(max_len):
all_candidates = []
for tokens, log_prob, h, c, finished in beams:
if finished:
all_candidates.append((tokens, log_prob, h, c, finished))
continue
last_token = torch.tensor([tokens[-1]], device=device)
word_embed = self.embedding(last_token)
context, _ = self.attention(image_embed, h)
gate = self.sigmoid(self.f_beta(h))
context = gate * context
lstm_input = torch.cat([word_embed, context], dim=1)
new_h, new_c = self.lstm_cell(lstm_input, (h, c))
logits = self.fc(new_h)
log_probs = torch.log_softmax(logits, dim=-1).squeeze(0)
top_log_probs, top_indices = log_probs.topk(beam_width)
for lp, idx in zip(top_log_probs.tolist(), top_indices.tolist()):
new_tokens = tokens + [idx]
new_finished = idx == end_idx
all_candidates.append((new_tokens, log_prob + lp, new_h, new_c, new_finished))
all_candidates.sort(key=lambda cand: cand[1], reverse=True)
beams = all_candidates[:beam_width]
if all(finished for _, _, _, _, finished in beams):
break
best_tokens, _, _, _, _ = max(beams, key=lambda cand: cand[1])
result = best_tokens[1:]
if result and result[-1] == end_idx:
result = result[:-1]
return result
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