Spaces:
Running
Running
File size: 6,033 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | import os
from platform import processor
import torch
from torch.utils.data import DataLoader, random_split
from transformers import BlipProcessor, BlipForConditionalGeneration
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from dataset_advanced import COCODataset
from tqdm import tqdm
from PIL import Image
from pycocoevalcap.cider.cider import Cider
from dataset_advanced import COCODatasetAdvanced
# =========================
# GENERATE CAPTION
# =========================
def generate_caption(model, processor, image, device):
inputs = processor(images=image, return_tensors="pt").to(device)
with torch.no_grad():
generated_ids = model.generate(
**inputs,
max_length=30,
num_beams=5
)
caption = processor.decode(
generated_ids[0],
skip_special_tokens=True
)
return caption
# =========================
# CIDEr EVALUATION
# =========================
def evaluate_cider(model, processor, 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, 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():
if not torch.backends.mps.is_available():
raise RuntimeError("MPS not available.")
device = torch.device("mps")
print("Using device:", device)
# =========================
# CONFIG
# =========================
EPOCHS = 5
BATCH_SIZE = 6
LR = 3e-5 # Lower LR for partial unfreezing
NUM_WORKERS = 0
FINAL_MODEL_DIR = "saved_model_phase2"
os.makedirs(FINAL_MODEL_DIR, exist_ok=True)
# =========================
# LOAD MODEL
# =========================
processor = BlipProcessor.from_pretrained(
"Salesforce/blip-image-captioning-base"
)
model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-base"
)
# 🔥 Unfreeze LAST 2 vision layers only
for name, param in model.vision_model.named_parameters():
if "encoder.layers.10" in name or "encoder.layers.11" in name:
param.requires_grad = True
else:
param.requires_grad = False
model.gradient_checkpointing_enable()
model.config.use_cache = False
model.to(device)
# =========================
# DATASET SPLIT
# =========================
MODE = "long" # change to "short" or "long"
full_dataset = COCODatasetAdvanced(
"annotations/subset_10k.jsonl",
"train2017",
processor,
mode=MODE
)
train_size = int(0.9 * len(full_dataset))
val_size = len(full_dataset) - train_size
train_dataset, val_dataset = random_split(
full_dataset,
[train_size, val_size]
)
train_loader = DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS
)
val_loader = DataLoader(
val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS
)
optimizer = AdamW(
filter(lambda p: p.requires_grad, model.parameters()),
lr=LR
)
scheduler = CosineAnnealingLR(optimizer, T_max=EPOCHS)
# =========================
# EARLY STOPPING
# =========================
best_cider = 0
patience = 3
counter = 0
# =========================
# TRAIN LOOP
# =========================
for epoch in range(EPOCHS):
model.train()
total_loss = 0
progress_bar = tqdm(train_loader, desc=f"Epoch {epoch+1}")
for batch in progress_bar:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.autocast(device_type="mps", dtype=torch.float16):
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
total_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
avg_train_loss = total_loss / len(train_loader)
print(f"Epoch {epoch+1} Train Loss: {avg_train_loss:.4f}")
# =========================
# VALIDATION LOSS
# =========================
model.eval()
val_loss = 0
with torch.no_grad():
for batch in val_loader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
val_loss += outputs.loss.item()
val_loss /= len(val_loader)
print(f"Epoch {epoch+1} Validation Loss: {val_loss:.4f}")
# =========================
# CIDEr
# =========================
cider_score = evaluate_cider(model, processor, val_dataset, device)
# =========================
# SAVE BEST CIDEr MODEL
# =========================
if cider_score > best_cider:
best_cider = cider_score
counter = 0
model.save_pretrained(FINAL_MODEL_DIR)
processor.save_pretrained(FINAL_MODEL_DIR)
print("Best CIDEr model saved.")
else:
counter += 1
if counter >= patience:
print("Early stopping triggered.")
break
scheduler.step()
print("Phase 2 training complete.")
if __name__ == "__main__":
main() |