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01fdb75 | 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 | # Unified Evaluation Script for Medical Image Segmentation
# Loads best checkpoint and computes Dice + IoU on val set
import os
import argparse
import torch
import numpy as np
from torch.utils.data import DataLoader
import segmentation_models_pytorch as smp
from models.transunet.vit_seg_modeling import VisionTransformer as ViT_seg
from models.transunet.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg
from datasets import SegmentationDataset, get_dataset_config
from metrics import compute_dice_iou_binary, compute_dice_iou_multiclass, MetricTracker
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate Segmentation Models')
parser.add_argument('--dataset', type=str, required=True,
choices=['cvc', 'kvasir', 'refuge2', 'all'])
parser.add_argument('--model', type=str, required=True,
choices=['unet', 'transunet', 'all'])
parser.add_argument('--resolution', type=int, default=224)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--save_dir', type=str, default='checkpoints')
return parser.parse_args()
def build_unet(task, num_classes):
if task == 'binary':
return smp.Unet(encoder_name='resnet34', encoder_weights=None,
in_channels=3, classes=1)
else:
return smp.Unet(encoder_name='resnet34', encoder_weights=None,
in_channels=3, classes=num_classes)
def build_transunet(task, num_classes, resolution):
vit_config = CONFIGS_ViT_seg['R50-ViT-B_16']
grid_size = resolution // 16
vit_config.patches.grid = (grid_size, grid_size)
if task == 'binary':
vit_config.n_classes = 1
else:
vit_config.n_classes = num_classes
return ViT_seg(vit_config, img_size=resolution,
num_classes=vit_config.n_classes)
@torch.no_grad()
def evaluate(model, loader, device, task, num_classes):
model.eval()
tracker = MetricTracker()
all_per_class_dice = {}
all_per_class_iou = {}
for images, masks in loader:
images = images.to(device)
masks = masks.to(device)
logits = model(images)
if task == 'binary':
dice, iou = compute_dice_iou_binary(logits, masks)
else:
dice, iou, pcd, pci = compute_dice_iou_multiclass(
logits, masks, num_classes=num_classes)
for c in pcd:
all_per_class_dice.setdefault(c, []).append(pcd[c])
all_per_class_iou.setdefault(c, []).append(pci[c])
tracker.update(dice, iou, images.size(0))
results = {
'dice': tracker.avg_dice,
'iou': tracker.avg_iou,
}
if task == 'multiclass':
for c in all_per_class_dice:
results[f'dice_class{c}'] = np.mean(all_per_class_dice[c])
results[f'iou_class{c}'] = np.mean(all_per_class_iou[c])
return results
def eval_one(dataset_name, model_name, args, device):
cfg = get_dataset_config(dataset_name)
task = cfg['task']
num_classes = cfg['num_classes']
# Dataset: REFUGE2 uses official test set, others use val split
eval_split = 'test' if dataset_name == 'refuge2' else 'val'
val_dataset = SegmentationDataset(dataset_name, split=eval_split,
resolution=args.resolution, seed=args.seed)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size,
shuffle=False, num_workers=args.num_workers,
pin_memory=True)
# Model
ckpt_path = os.path.join(args.save_dir, f'{model_name}_{dataset_name}', 'best.pth')
if not os.path.exists(ckpt_path):
print(f" [SKIP] Checkpoint not found: {ckpt_path}")
return None
if model_name == 'unet':
model = build_unet(task, num_classes)
else:
model = build_transunet(task, num_classes, args.resolution)
ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=False)
model.load_state_dict(ckpt['model_state_dict'])
model = model.to(device)
results = evaluate(model, val_loader, device, task, num_classes)
results['epoch'] = ckpt.get('epoch', '?')
return results
def main():
args = parse_args()
device = torch.device(f'cuda:{args.gpu}')
datasets = ['cvc', 'kvasir', 'refuge2'] if args.dataset == 'all' else [args.dataset]
models = ['unet', 'transunet'] if args.model == 'all' else [args.model]
print(f"\n{'='*70}")
print(f"Medical Image Segmentation Evaluation")
print(f"{'='*70}")
all_results = []
for ds in datasets:
cfg = get_dataset_config(ds)
for md in models:
print(f"\n--- {cfg['name']} / {md.upper()} ---")
results = eval_one(ds, md, args, device)
if results is not None:
all_results.append((ds, md, results))
print(f" Dice: {results['dice']:.4f} IoU: {results['iou']:.4f} "
f"(epoch {results['epoch']})")
if cfg['task'] == 'multiclass':
for c in range(1, cfg['num_classes']):
dk = f'dice_class{c}'
ik = f'iou_class{c}'
if dk in results:
class_names = {1: 'Optic Cup', 2: 'Optic Disc'}
name = class_names.get(c, f'Class {c}')
print(f" {name}: Dice={results[dk]:.4f} IoU={results[ik]:.4f}")
# Summary table
if len(all_results) > 1:
print(f"\n{'='*70}")
print(f"{'Dataset':<15} {'Model':<12} {'Dice':<10} {'IoU':<10}")
print(f"{'-'*70}")
for ds, md, res in all_results:
cfg = get_dataset_config(ds)
print(f"{cfg['name']:<15} {md.upper():<12} {res['dice']:<10.4f} {res['iou']:<10.4f}")
print(f"{'='*70}")
if __name__ == '__main__':
main()
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