Spaces:
Running
Running
File size: 4,630 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 | import os
import torch
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader, random_split
from transformers import BlipForConditionalGeneration, BlipProcessor
from tqdm import tqdm
from src.data.coco_advanced_dataset import COCODatasetAdvanced
from src.evaluation.cider_eval import evaluate_cider
def main() -> None:
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 "mixed" as needed
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 = 0.0
patience = 3
counter = 0
# =========================
# TRAIN LOOP
# =========================
for epoch in range(EPOCHS):
model.train()
total_loss = 0.0
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 = 0.0
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()
|