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b891e61 | 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 | """Evaluate trained CLIPSeg model and generate prediction masks + visuals."""
import json
import time
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import torch
import yaml
from PIL import Image
from torch.utils.data import DataLoader
from tqdm import tqdm
from src.data.dataset import DrywallSegDataset, collate_fn
from src.model.clipseg_wrapper import load_model_and_processor
from src.train import compute_metrics, get_device
PROJECT_ROOT = Path(__file__).resolve().parents[1]
def evaluate(config_path: str | None = None):
config_path = config_path or str(PROJECT_ROOT / "configs" / "train_config.yaml")
with open(config_path) as f:
config = yaml.safe_load(f)
device = get_device()
threshold = config["evaluation"]["threshold"]
# Load model with best checkpoint
model, processor = load_model_and_processor(config["model"]["name"], config["model"]["freeze_backbone"])
ckpt_path = PROJECT_ROOT / "outputs" / "checkpoints" / "best_model.pt"
model.load_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True))
model = model.to(device)
model.eval()
# Model size
model_size_mb = sum(p.numel() * p.element_size() for p in model.parameters()) / (1024 * 1024)
# Test data
splits_dir = PROJECT_ROOT / "data" / "splits"
test_ds = DrywallSegDataset(str(splits_dir / "test.json"), processor, config["data"]["image_size"])
test_loader = DataLoader(test_ds, batch_size=config["training"]["batch_size"], shuffle=False,
collate_fn=collate_fn, num_workers=0)
# Run evaluation
masks_dir = PROJECT_ROOT / "outputs" / "masks"
masks_dir.mkdir(parents=True, exist_ok=True)
all_metrics = {"taping": {"miou": [], "dice": []}, "cracks": {"miou": [], "dice": []}}
inference_times = []
visual_examples = [] # Collect for visualization
total_samples = 0
with torch.no_grad():
for batch in tqdm(test_loader, desc="Evaluating"):
pixel_values = batch["pixel_values"].to(device)
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"].to(device)
t0 = time.time()
outputs = model(pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask)
inference_times.append((time.time() - t0) / pixel_values.size(0))
logits = outputs.logits
metrics = compute_metrics(logits, labels, threshold)
preds = (torch.sigmoid(logits) > threshold).cpu().numpy().astype(np.uint8)
for i in range(pixel_values.size(0)):
ds_name = batch["dataset"][i]
all_metrics[ds_name]["miou"].append(metrics["miou"])
all_metrics[ds_name]["dice"].append(metrics["dice"])
# Save prediction mask at original resolution
orig_w, orig_h = batch["orig_width"][i], batch["orig_height"][i]
pred_mask = Image.fromarray(preds[i] * 255, mode="L")
pred_mask = pred_mask.resize((orig_w, orig_h), Image.NEAREST)
prompt_slug = batch["prompt"][i].replace(" ", "_")
img_stem = Path(batch["image_path"][i]).stem
mask_filename = f"{img_stem}__{prompt_slug}.png"
pred_mask.save(masks_dir / mask_filename)
total_samples += 1
# Collect visual examples
if len(visual_examples) < config["evaluation"]["num_visual_examples"]:
visual_examples.append({
"image_path": batch["image_path"][i],
"mask_path": batch["mask_path"][i],
"pred_mask": preds[i],
"prompt": batch["prompt"][i],
"dataset": ds_name,
})
# Aggregate metrics
results = {"per_class": {}, "overall": {}}
all_miou, all_dice = [], []
for ds_name in ["taping", "cracks"]:
m = all_metrics[ds_name]
if m["miou"]:
results["per_class"][ds_name] = {
"miou": round(float(np.mean(m["miou"])), 4),
"dice": round(float(np.mean(m["dice"])), 4),
"samples": len(m["miou"]),
}
all_miou.extend(m["miou"])
all_dice.extend(m["dice"])
results["overall"] = {
"miou": round(float(np.mean(all_miou)), 4) if all_miou else 0,
"dice": round(float(np.mean(all_dice)), 4) if all_dice else 0,
"total_samples": total_samples,
}
results["runtime"] = {
"avg_inference_ms": round(float(np.mean(inference_times)) * 1000, 1),
"model_size_mb": round(model_size_mb, 1),
}
# Save results
log_dir = PROJECT_ROOT / "outputs" / "logs"
log_dir.mkdir(parents=True, exist_ok=True)
with open(log_dir / "test_results.json", "w") as f:
json.dump(results, f, indent=2)
print(f"\n{'='*60}")
print(f"Test Results")
print(f"{'='*60}")
for ds_name, m in results["per_class"].items():
print(f" {ds_name:>10s}: mIoU={m['miou']:.4f} Dice={m['dice']:.4f} (n={m['samples']})")
print(f" {'overall':>10s}: mIoU={results['overall']['miou']:.4f} Dice={results['overall']['dice']:.4f}")
print(f" Avg inference: {results['runtime']['avg_inference_ms']:.1f} ms/image")
print(f" Model size: {results['runtime']['model_size_mb']:.1f} MB")
# Generate visual comparison figures
_generate_visuals(visual_examples, PROJECT_ROOT / "reports" / "figures")
return results
def _generate_visuals(examples: list[dict], output_dir: Path):
"""Generate original | GT | prediction comparison figures."""
output_dir.mkdir(parents=True, exist_ok=True)
if not examples:
return
fig, axes = plt.subplots(len(examples), 3, figsize=(12, 4 * len(examples)))
if len(examples) == 1:
axes = [axes]
for i, ex in enumerate(examples):
img = Image.open(ex["image_path"]).convert("RGB")
gt = Image.open(ex["mask_path"]).convert("L")
pred = Image.fromarray(ex["pred_mask"] * 255, mode="L")
axes[i][0].imshow(img)
axes[i][0].set_title(f"Original ({ex['dataset']})")
axes[i][0].axis("off")
axes[i][1].imshow(gt, cmap="gray", vmin=0, vmax=255)
axes[i][1].set_title("Ground Truth")
axes[i][1].axis("off")
axes[i][2].imshow(pred, cmap="gray", vmin=0, vmax=255)
axes[i][2].set_title(f"Prediction: \"{ex['prompt']}\"")
axes[i][2].axis("off")
plt.tight_layout()
plt.savefig(output_dir / "visual_comparison.png", dpi=150, bbox_inches="tight")
plt.close()
print(f"Saved visual comparison to {output_dir / 'visual_comparison.png'}")
if __name__ == "__main__":
evaluate()
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