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# evaluate_batched.py
import os
import json
from tqdm import tqdm
import torch
from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction
from src.utils import count_encoder_decoder_params, load_experiment
from src.inference import load_image, generate_caption
from data.transforms import build_coco_transform
try:
from pycocoevalcap.cider.cider import Cider
from pycocoevalcap.rouge.rouge import Rouge
HAS_COCOEVAL = True
except ImportError:
print("WARNING: pycocoevalcap not installed → CIDEr/ROUGE disabled.")
HAS_COCOEVAL = False
# Batched Evaluation (non-breaking addition)
@torch.no_grad()
def evaluate_batched(
model,
tokenizer,
preprocess,
image_size,
data_dir="data/processed",
save_dir="checkpoints",
device="cuda",
batch_size=16,
num_beams=1,
):
"""
Batched version of evaluate().
"""
from src.inference import load_images_batch, generate_captions_batch
captions_path = os.path.join(data_dir, "captions.json")
splits_path = os.path.join(data_dir, "splits.json")
captions = json.load(open(captions_path))
splits = json.load(open(splits_path))
val_ids = splits["val"]
preds = []
refs_tokenized = []
refs_strings = []
print(f"Running *batched* evaluation on {len(val_ids)} images… (batch={batch_size})\n")
# Loop in batches
for start in tqdm(range(0, len(val_ids), batch_size), desc="Evaluating (batched)"):
end = min(start + batch_size, len(val_ids))
batch_ids = val_ids[start:end]
# Image paths
img_paths = [
os.path.join(data_dir, "images", f"{int(i):012d}.jpg")
for i in batch_ids
]
# Load batch into tensor
img_batch = load_images_batch(img_paths, preprocess, image_size).to(device)
# Generate predictions for batch
batch_preds = generate_captions_batch(
model,
tokenizer,
img_batch,
device=device,
num_beams=num_beams,
max_new_tokens=32
)
# Collect references
for i, img_id in enumerate(batch_ids):
gt_caps = captions[str(img_id)]["captions"]
refs_strings.append(gt_caps)
refs_tokenized.append([c.split() for c in gt_caps])
preds.append(batch_preds[i])
# Print sample predictions (20 samples, same as evaluate())
print("\nSample Predictions:\n")
num_examples = 20
for i in range(min(num_examples, len(preds))):
img_id = val_ids[i]
print(f"Image ID: {img_id}")
print(f"Prediction: {preds[i]}")
print("Ground Truths:")
for ref in refs_strings[i]:
print(f" - {ref}")
print("-" * 60)
# Compute BLEU
smoothie = SmoothingFunction().method3
bleu1 = corpus_bleu(
refs_tokenized, [p.split() for p in preds],
weights=(1, 0, 0, 0),
smoothing_function=smoothie
)
bleu4 = corpus_bleu(
refs_tokenized, [p.split() for p in preds],
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=smoothie
)
scores = {"BLEU-1": bleu1, "BLEU-4": bleu4}
# CIDEr / ROUGE
if HAS_COCOEVAL:
cider_refs = {}
cider_preds = {}
for i in range(len(preds)):
img_id = val_ids[i]
cid = str(int(img_id))
cider_refs[cid] = captions[cid]["captions"]
cider_preds[cid] = [preds[i]]
cider = Cider()
cider_score, _ = cider.compute_score(cider_refs, cider_preds)
scores["CIDEr"] = cider_score
rouge = Rouge()
rouge_score, _ = rouge.compute_score(cider_refs, cider_preds)
scores["ROUGE-L"] = rouge_score
# Save results
samples_full = []
for i in range(len(preds)):
img_id = val_ids[i]
samples_full.append({
"id": int(img_id),
"prediction": preds[i],
"references": refs_strings[i],
"image": f"{int(img_id):012d}.jpg",
})
samples_preview = samples_full[:20]
param_info = count_encoder_decoder_params(model)
out_path = os.path.join(save_dir, "eval_results.json")
with open(out_path, "w") as f:
json.dump({
"scores": scores,
"derived_params": param_info,
"samples_preview": samples_preview,
"samples_full": samples_full
}, f, indent=2)
# Print final scores
print("\nEvaluation Scores:")
for k, v in scores.items():
print(f"{k}: {v:.4f}")
print(f"\nSaved batched results to: {out_path}")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=str, required=True,
help="Path to checkpoint directory")
parser.add_argument("--data_dir", type=str, default="data/processed")
parser.add_argument("--batch_size", type=int, default=16,
help="Batch size for batched evaluation")
parser.add_argument("--num_beams", type=int, default=1,
help="For beam search")
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}")
# Load model + tokenizer
model, tokenizer, meta, config = load_experiment(args.checkpoint, device=device)
image_size = config["model"].get("image_size", 224)
preprocess = build_coco_transform(image_size=image_size)
# Run batched evaluation
evaluate_batched(
model=model,
tokenizer=tokenizer,
preprocess=preprocess,
image_size=image_size,
data_dir=args.data_dir,
save_dir=args.checkpoint,
device=device,
batch_size=args.batch_size,
num_beams=args.num_beams
)
# Usage:
# python evaluate_batched.py --checkpoint checkpoints/vision_t5/20251117_171912 --batch_size 16
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