Spaces:
Running
Running
File size: 5,466 Bytes
a745a5e | 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 | import os
import torch
from torch.utils.data import DataLoader, random_split
from transformers import (
VisionEncoderDecoderModel,
ViTImageProcessor,
AutoTokenizer,
GPT2Config,
GPT2LMHeadModel,
ViTModel
)
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from dataset_vit_gpt2 import COCODatasetViTGPT2
from tqdm import tqdm
from pycocoevalcap.cider.cider import Cider
from PIL import Image
# ==========================================
# GENERATE CAPTION
# ==========================================
def generate_caption(model, processor, tokenizer, image, device):
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
with torch.no_grad():
output_ids = model.generate(
pixel_values=pixel_values,
num_beams=5,
max_length=20,
length_penalty=1.0,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(output_ids[0], skip_special_tokens=True)
# ==========================================
# CIDEr EVALUATION
# ==========================================
def evaluate_cider(model, processor, tokenizer, val_dataset, device, max_samples=200):
model.eval()
cider_scorer = Cider()
ground_truth = {}
predictions = {}
for idx in tqdm(range(min(max_samples, len(val_dataset))), desc="CIDEr Eval"):
real_idx = val_dataset.indices[idx]
ann = val_dataset.dataset.annotations[real_idx]
image_path = os.path.join("train2017", ann["image"])
image = Image.open(image_path).convert("RGB")
pred_caption = generate_caption(model, processor, tokenizer, image, device)
ground_truth[idx] = ann["captions"]
predictions[idx] = [pred_caption]
score, _ = cider_scorer.compute_score(ground_truth, predictions)
print(f"CIDEr Score: {score:.4f}")
model.train()
return score
# ==========================================
# MAIN
# ==========================================
def main():
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
print("Using device:", device)
EPOCHS = 5
BATCH_SIZE = 6
LR = 3e-5
SAVE_DIR = "saved_vit_gpt2"
os.makedirs(SAVE_DIR, exist_ok=True)
# ------------------------------------------
# Build Encoder + Decoder
# ------------------------------------------
encoder = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")
decoder_config = GPT2Config.from_pretrained("gpt2")
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
decoder = GPT2LMHeadModel.from_pretrained("gpt2", config=decoder_config)
model = VisionEncoderDecoderModel(
encoder=encoder,
decoder=decoder
)
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = tokenizer.eos_token_id
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.vocab_size = model.config.decoder.vocab_size
model.to(device)
# ------------------------------------------
# DATASET
# ------------------------------------------
dataset = COCODatasetViTGPT2(
"annotations/subset_10k.jsonl",
"train2017",
processor,
tokenizer,
mode="short"
)
train_size = int(0.9 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE)
optimizer = AdamW(model.parameters(), lr=LR)
scheduler = CosineAnnealingLR(optimizer, T_max=EPOCHS)
best_cider = 0
# ==========================================
# TRAIN LOOP
# ==========================================
for epoch in range(EPOCHS):
model.train()
total_loss = 0
for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}"):
pixel_values = batch["pixel_values"].to(device)
labels = batch["labels"].to(device)
outputs = model(pixel_values=pixel_values, labels=labels)
loss = outputs.loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
optimizer.zero_grad()
total_loss += loss.item()
avg_loss = total_loss / len(train_loader)
print(f"Train Loss: {avg_loss:.4f}")
# ------------------------------------------
# CIDEr Evaluation
# ------------------------------------------
cider_score = evaluate_cider(
model,
processor,
tokenizer,
val_dataset,
device
)
# Save best model
if cider_score > best_cider:
best_cider = cider_score
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)
processor.save_pretrained(SAVE_DIR)
print("Best model saved.")
scheduler.step()
print("ViT-GPT2 Training complete.")
if __name__ == "__main__":
main() |