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from torch import nn
import clip
from transformers import GPT2Model
class AttentionDecoder(nn.Module):
def __init__(self, hidden_size, vocab_size, num_layers=1, dropout=0.3):
super().__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.attention = nn.Linear(hidden_size * 2, 1)
self.gru = nn.GRU(
input_size=hidden_size * 2,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0
)
self.ln_gru = nn.LayerNorm(hidden_size)
self.output = nn.Linear(hidden_size, vocab_size)
def forward(self, input_ids, context, hidden):
if input_ids.dim() == 1:
input_ids = input_ids.unsqueeze(1)
embeddings = self.embedding(input_ids).float()
context_expanded = context.unsqueeze(1).expand(-1, embeddings.size(1), -1)
combined = torch.cat([embeddings, context_expanded], dim=-1)
attn_weights = torch.softmax(self.attention(combined), dim=1)
attended_context = (context_expanded * attn_weights).sum(dim=1, keepdim=True)
gru_input = torch.cat([embeddings, attended_context.expand(-1, embeddings.size(1), -1)], dim=-1)
gru_output, hidden = self.gru(gru_input, hidden)
gru_output = self.ln_gru(gru_output)
return self.output(gru_output), hidden
class VQAModel(nn.Module):
def __init__(
self,
vocab_size=3600,
question_max_len=16,
answer_max_len=10,
hidden_size=512,
num_layers=2,
dropout=0.3,
device='cuda',
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
unk_token_id=3
):
super().__init__()
self.device = device
self.question_max_len = question_max_len
self.answer_max_len = answer_max_len
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.fine_tuning_mode = False
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.unk_token_id = unk_token_id
self.clip_model, self.clip_preprocess = clip.load("ViT-B/16", device=device)
for p in self.clip_model.parameters():
p.requires_grad = False
self.gpt2_model = GPT2Model.from_pretrained("distilgpt2")
self.gpt2_model.to(device)
for p in self.gpt2_model.parameters():
p.requires_grad = False
self.img_proj = nn.Linear(512, hidden_size)
self.q_proj = nn.Linear(768, hidden_size)
self.gate_layer = nn.Linear(hidden_size*2, hidden_size)
self.fusion = nn.Sequential(
nn.Linear(hidden_size*3, hidden_size),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size, hidden_size)
)
self.decoder = AttentionDecoder(hidden_size, vocab_size, num_layers, dropout)
def unfreeze_clip_layers(self, num_layers=2):
self.clip_model.train()
self.clip_model.visual.float()
total_blocks = len(self.clip_model.visual.transformer.resblocks)
for i, block in enumerate(self.clip_model.visual.transformer.resblocks):
if i >= total_blocks - num_layers:
for p in block.parameters():
p.requires_grad = True
if hasattr(self.clip_model.visual, "proj") and self.clip_model.visual.proj is not None:
if isinstance(self.clip_model.visual.proj, torch.nn.Parameter):
self.clip_model.visual.proj.requires_grad = True
else:
for p in self.clip_model.visual.proj.parameters():
p.requires_grad = True
if hasattr(self.clip_model.visual, "ln_post"):
for p in self.clip_model.visual.ln_post.parameters():
p.requires_grad = True
self.fine_tuning_mode = True
print(f"Unfrozen last {num_layers} CLIP layers")
def unfreeze_gpt2_layers(self, num_layers=1):
self.gpt2_model.train()
total_layers = len(self.gpt2_model.h)
for i, layer in enumerate(self.gpt2_model.h):
if i >= total_layers - num_layers:
for p in layer.parameters():
p.requires_grad = True
p.data = p.data.float()
for p in self.gpt2_model.ln_f.parameters():
p.requires_grad = True
p.data = p.data.float()
self.fine_tuning_mode = True
print(f"Unfrozen last {num_layers} GPT-2 layers")
def encode_image(self, images):
if self.fine_tuning_mode:
images = images.float()
img_features = self.clip_model.encode_image(images)
else:
with torch.no_grad():
img_features = self.clip_model.encode_image(images)
img_features = img_features / img_features.norm(dim=-1, keepdim=True)
return img_features.float()
def encode_question(self, input_ids, attention_mask):
if self.fine_tuning_mode:
outputs = self.gpt2_model(input_ids=input_ids, attention_mask=attention_mask)
else:
with torch.no_grad():
outputs = self.gpt2_model(input_ids=input_ids, attention_mask=attention_mask)
last_hidden = outputs.last_hidden_state
mask = attention_mask.unsqueeze(-1).to(last_hidden.dtype)
masked = last_hidden * mask
sum_hidden = masked.sum(dim=1)
lengths = mask.sum(dim=1).clamp(min=1e-6)
text_features = sum_hidden / lengths
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
return text_features.float()
def fuse_features(self, img_features, q_features):
x = torch.cat([img_features, q_features], dim=-1)
gate = torch.sigmoid(self.gate_layer(x))
fused = gate * img_features + (1-gate) * q_features
fused = self.fusion(torch.cat([fused, x], dim=-1))
return fused
def forward(self, images, questions, answer_input_ids=None):
img_features = self.encode_image(images)
img_features = self.img_proj(img_features).float()
q_features = self.encode_question(questions["input_ids"], questions["attention_mask"])
q_features = self.q_proj(q_features).float()
batch_size = img_features.size(0)
context = self.fuse_features(img_features, q_features)
hidden = torch.zeros(self.num_layers, batch_size, self.hidden_size,
device=self.device, dtype=torch.float)
if answer_input_ids is not None:
logits, _ = self.decoder(answer_input_ids, context, hidden)
return logits
else:
generated = torch.full((batch_size, self.answer_max_len), self.pad_token_id,
dtype=torch.long, device=self.device)
generated[:, 0] = self.bos_token_id
for t in range(1, self.answer_max_len):
current_input = generated[:, t-1]
logits, hidden = self.decoder(current_input, context, hidden)
next_tokens = logits.squeeze(1).argmax(dim=-1)
generated[:, t] = next_tokens
if (next_tokens == self.eos_token_id).all():
break
return generated
def generate_with_beam_search(self, images, questions, beam_width=5):
batch_size = images.size(0)
all_results = []
for b in range(batch_size):
img = images[b:b+1]
q_ids = questions["input_ids"][b:b+1]
q_mask = questions["attention_mask"][b:b+1]
img_features = self.encode_image(img)
img_features = self.img_proj(img_features).float()
q_features = self.encode_question(q_ids, q_mask)
q_features = self.q_proj(q_features).float()
context = self.fuse_features(img_features, q_features)
initial_hidden = torch.zeros(self.num_layers, 1, self.hidden_size,
device=self.device, dtype=torch.float)
beams = [(
torch.full((1, 1), self.bos_token_id, dtype=torch.long, device=self.device),
0.0,
initial_hidden
)]
completed_beams = []
for t in range(1, self.answer_max_len):
candidates = []
for seq, score, hidden in beams:
if seq[0, -1].item() == self.eos_token_id:
completed_beams.append((seq, score))
continue
current_input = seq[:, -1]
logits, new_hidden = self.decoder(current_input, context, hidden)
log_probs = torch.log_softmax(logits.squeeze(1), dim=-1)
top_log_probs, top_indices = torch.topk(log_probs[0], beam_width)
for i in range(beam_width):
next_token = top_indices[i].unsqueeze(0).unsqueeze(0)
new_seq = torch.cat([seq, next_token], dim=1)
new_score = score + top_log_probs[i].item()
candidates.append((new_seq, new_score, new_hidden))
if len(candidates) == 0:
break
beams = sorted(candidates, key=lambda x: x[1], reverse=True)[:beam_width]
all_beams = completed_beams + [(seq, score) for seq, score, _ in beams]
if len(all_beams) == 0:
result = torch.full((1, self.answer_max_len), self.pad_token_id,
dtype=torch.long, device=self.device)
else:
best_beam = max(all_beams, key=lambda x: x[1] / (x[0].size(1) ** 0.7))
result = torch.full((1, self.answer_max_len), self.pad_token_id,
dtype=torch.long, device=self.device)
seq_len = min(best_beam[0].size(1), self.answer_max_len)
result[:, :seq_len] = best_beam[0][:, :seq_len]
all_results.append(result)
return torch.cat(all_results, dim=0)
if __name__ == "__main__":
device = "cuda"
model = VQAModel(device=device).to(device)
model.eval()
fake_image = torch.randn(1, 3, 224, 224).to(device)
fake_question_ids = torch.tensor([[1, 10, 20, 30, 2, 0, 0]]).to(device)
fake_question_mask = torch.tensor([[1, 1, 1, 1, 1, 0, 0]]).to(device)
question_batch = {
"input_ids": fake_question_ids,
"attention_mask": fake_question_mask
}
output = model(fake_image, question_batch)
print(output) |