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59ba849 | 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 | import torch
import torch.nn as nn
import gradio as gr
from PIL import Image
import torchvision.transforms as transforms
from torchvision import models
# -------------------------
# Model definitions (must match training)
# -------------------------
PAD_TOKEN = "<pad>"
UNK_TOKEN = "<unk>"
class Encoder(nn.Module):
def __init__(self, in_dim=2048, hidden_size=512):
super().__init__()
self.fc = nn.Linear(in_dim, hidden_size)
self.relu = nn.ReLU()
def forward(self, feat):
return self.relu(self.fc(feat))
class Decoder(nn.Module):
def __init__(self, vocab_size, pad_id, embed_dim=256, hidden_size=512, dropout=0.1):
super().__init__()
self.embed = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_id)
self.lstm = nn.LSTM(embed_dim, hidden_size, num_layers=1, batch_first=True)
self.dropout = nn.Dropout(dropout)
self.fc_out = nn.Linear(hidden_size, vocab_size)
class Img2Caption(nn.Module):
def __init__(self, vocab_size, pad_id, hidden_size=512, embed_dim=256):
super().__init__()
self.encoder = Encoder(in_dim=2048, hidden_size=hidden_size)
self.decoder = Decoder(vocab_size=vocab_size, pad_id=pad_id, embed_dim=embed_dim, hidden_size=hidden_size)
# -------------------------
# Load checkpoint
# -------------------------
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
CKPT_PATH = "img_caption_seq2seq.pth"
ckpt = torch.load(CKPT_PATH, map_location=DEVICE)
word2idx = ckpt["word2idx"]
idx2word = ckpt["idx2word"]
max_len = ckpt.get("max_len", 30)
pad_id = word2idx[PAD_TOKEN]
start_id = word2idx["<start>"]
end_id = word2idx["<end>"]
model = Img2Caption(vocab_size=len(word2idx), pad_id=pad_id).to(DEVICE)
model.load_state_dict(ckpt["model_state"])
model.eval()
# -------------------------
# ResNet50 feature extractor (on-the-fly)
# -------------------------
resnet = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
resnet = nn.Sequential(*list(resnet.children())[:-1]).to(DEVICE)
resnet.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
def decode_tokens(token_ids):
words = []
for tid in token_ids:
w = idx2word.get(int(tid), UNK_TOKEN)
if w == "<end>":
break
if w not in ["<start>", "<pad>"]:
words.append(w)
return " ".join(words)
@torch.no_grad()
def greedy_caption(feat_vec, max_words=30):
feat = torch.tensor(feat_vec, dtype=torch.float32).unsqueeze(0).to(DEVICE) # [1,2048]
h0 = model.encoder(feat) # [1,hidden]
last = start_id
out_tokens = []
h = h0.unsqueeze(0) # [1,1,hidden]
c = torch.zeros_like(h)
for _ in range(max_words):
cur = torch.tensor([[last]], dtype=torch.long).to(DEVICE)
emb = model.decoder.embed(cur) # [1,1,E]
lstm_out, (h, c) = model.decoder.lstm(emb, (h, c)) # [1,1,H]
logits = model.decoder.fc_out(lstm_out.squeeze(1)) # [1,V]
nxt = int(torch.argmax(logits, dim=-1).item())
if nxt == end_id:
break
out_tokens.append(nxt)
last = nxt
return decode_tokens(out_tokens)
@torch.no_grad()
def beam_caption(feat_vec, beam_size=3, max_words=30):
feat = torch.tensor(feat_vec, dtype=torch.float32).unsqueeze(0).to(DEVICE)
h0 = model.encoder(feat)
h = h0.unsqueeze(0)
c = torch.zeros_like(h)
beams = [([], 0.0, h, c, start_id)] # (tokens, score, h, c, last)
for _ in range(max_words):
new_beams = []
for tokens, score, h_i, c_i, last in beams:
if last == end_id:
new_beams.append((tokens, score, h_i, c_i, last))
continue
cur = torch.tensor([[last]], dtype=torch.long).to(DEVICE)
emb = model.decoder.embed(cur)
lstm_out, (h_new, c_new) = model.decoder.lstm(emb, (h_i, c_i))
logits = model.decoder.fc_out(lstm_out.squeeze(1))
log_probs = torch.log_softmax(logits, dim=-1).squeeze(0)
topk = torch.topk(log_probs, beam_size)
for lp, idx in zip(topk.values.tolist(), topk.indices.tolist()):
new_beams.append((tokens + [idx], score + lp, h_new, c_new, idx))
new_beams.sort(key=lambda x: x[1], reverse=True)
beams = new_beams[:beam_size]
if all(b[4] == end_id for b in beams):
break
best = beams[0][0]
if len(best) and best[-1] == end_id:
best = best[:-1]
return decode_tokens(best)
@torch.no_grad()
def caption_image(img: Image.Image, decoding="Beam Search"):
img = img.convert("RGB")
x = transform(img).unsqueeze(0).to(DEVICE)
feat = resnet(x).view(1, -1).squeeze(0).cpu().numpy() # [2048]
if decoding == "Greedy":
return greedy_caption(feat, max_words=30)
return beam_caption(feat, beam_size=3, max_words=30)
demo = gr.Interface(
fn=caption_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Radio(["Beam Search", "Greedy"], value="Beam Search", label="Decoding")
],
outputs=gr.Textbox(label="Generated Caption"),
title="Seq2Seq Image Captioning (Flickr30k)",
description="Upload an image and generate a caption using a ResNet50 + LSTM Seq2Seq model."
)
if __name__ == "__main__":
demo.launch()
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