2.我对kvasir数据集拆分为train 800张，val100张，test100张，你知乎上figture6中的4张样本刚好不在test中(以下这4张称为关键样本)
cju15czxqp3lv0835jvhgzurz.jpg -> val
cju7ebe962hr409872ovibahw.jpg -> train
cju16whaj0e7n0855q7b6cjkm.jpg -> train
ck2bxw18mmz1k0725litqq2mc.jpg -> train
4.我使用同样的config cod-sam-vit-l.yaml，使用步骤3中的model_epoch_best.pth，运行test.sh得到的dice和iou为
# ################ Results ################ (sam3-adapter)
# 0.9120600180868292: 0.9121
# 0.8430836232751606: 0.8431
# 0.0: 0.0000
# 0.0: 0.0000
# #########################################
# ========================================
# Final mIoU (sam3 Style): 0.8466
# Results saved to: /mnt/tempdata/chenjp/sam-adapter/results_sam3_style_kvasir
# ========================================
# ################ Results ################
# 0.8602661245424534: 0.8603
# 0.7395057827234268: 0.7395
# 0.0: 0.0000
# 0.0: 0.0000
# #########################################
# ========================================
# Final mIoU (sam3 Style): 0.7303
# Results saved to: /mnt/tempdata/chenjp/sam-adapter/results_sam3_style_kvasir_re_pred
# ========================================
import os
import glob
import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
# ================= 配置路径 =================
sys.path.insert(0, "/mnt/tempdata/chenjp/sam3")
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
# 数据路径
IMAGE_ROOT = '/mnt/tempdata/chenjp/sam-adapter/re-pred/images'
MASK_ROOT = '/mnt/tempdata/chenjp/sam-adapter/re-pred/masks'
MODEL_DIR = "/mnt/tempdata/chenjp/sam-adapter/sam3"
# 输出结果
OUTPUT_DIR = "/mnt/tempdata/chenjp/sam-adapter/results_sam3_style_kvasir_re_pred"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# ================= 核心：官方 sam3 的坐标转换逻辑 =================
def box_xyxy_to_cxcywh(x1, y1, x2, y2):
"""
将 [x1, y1, x2, y2] 转换为 [cx, cy, w, h]
"""
w = x2 - x1
h = y2 - y1
cx = x1 + w / 2
cy = y1 + h / 2
return [cx, cy, w, h]
def normalize_bbox(box_cxcywh, img_w, img_h):
"""
归一化 [cx, cy, w, h] 到 [0, 1]
"""
cx, cy, w, h = box_cxcywh
norm_box = [
cx / img_w,
cy / img_h,
w / img_w,
h / img_h
]
return norm_box
# ================= 工具函数 =================
def get_bbox_from_mask(mask):
"""从 GT Mask 获取 [x1, y1, x2, y2] (绝对像素)"""
y_indices, x_indices = np.where(mask > 0)
if len(y_indices) == 0: return None
x_min, x_max = np.min(x_indices), np.max(x_indices)
y_min, y_max = np.min(y_indices), np.max(y_indices)
return [x_min, y_min, x_max, y_max]
def calculate_iou(pred_mask, gt_mask):
intersection = np.logical_and(pred_mask, gt_mask).sum()
union = np.logical_or(pred_mask, gt_mask).sum()
return intersection / union if union > 0 else 0.0
def save_visualization(image_np, box_xyxy, pred_mask, gt_mask, iou, score, filename):
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# 1. 原图 + Box
axes[0].imshow(image_np)
if box_xyxy is not None:
x1, y1, x2, y2 = box_xyxy
rect = plt.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=2, edgecolor='red', facecolor='none')
axes[0].add_patch(rect)
axes[0].set_title(f"Input: Image + Box\n{box_xyxy}")
axes[0].axis('off')
# 2. 预测 Mask (黑底白字)
axes[1].imshow(pred_mask, cmap='gray', vmin=0, vmax=1)
axes[1].set_title(f"Pred Mask (Score: {score:.3f})\nIoU: {iou:.4f}")
axes[1].axis('off')
# 3. GT Mask
axes[2].imshow(gt_mask, cmap='gray', vmin=0, vmax=1)
axes[2].set_title("GT Mask")
axes[2].axis('off')
plt.tight_layout()
plt.savefig(os.path.join(OUTPUT_DIR, f"{filename}_viz.png"))
plt.close(fig)
# ================= 主流程 =================
def main():
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# 1. 加载模型
print("Loading SAM 3 model...")
model = build_sam3_image_model(
checkpoint_path=os.path.join(MODEL_DIR, "sam3.pt"),
device=device, eval_mode=True, load_from_HF=False
)
processor = Sam3Processor(model, device=device)
# 2. 准备数据
img_files = sorted(glob.glob(os.path.join(IMAGE_ROOT, "*.jpg")) + glob.glob(os.path.join(IMAGE_ROOT, "*.png")))
print(f"Testing {len(img_files)} images using sam3 Logic (Norm CXCYWH)...")
all_ious = []
for i in tqdm(range(len(img_files))):
img_path = img_files[i]
filename = os.path.basename(img_path)
# 匹配 Mask
basename = os.path.splitext(filename)[0]
mask_path = None
for p in [os.path.join(MASK_ROOT, filename), os.path.join(MASK_ROOT, basename + ".png")]:
if os.path.exists(p): mask_path = p; break
if not mask_path: continue
try:
# 读取数据
image = Image.open(img_path).convert("RGB")
w, h = image.size
image_np = np.array(image)
gt_img = Image.open(mask_path).convert('L').resize((w, h), Image.NEAREST)
gt_mask = np.array(gt_img) > 128
# 获取绝对像素 Box [x1, y1, x2, y2]
bbox_xyxy = get_bbox_from_mask(gt_mask)
if bbox_xyxy is None: continue
# -------------------------------------------------
# 关键修改：严格遵循 sam3 逻辑
# -------------------------------------------------
# 1. 像素 XYXY -> 像素 CXCYWH
box_cxcywh = box_xyxy_to_cxcywh(*bbox_xyxy)
# 2. 像素 CXCYWH -> 归一化 CXCYWH (0.0-1.0)
norm_box_cxcywh = normalize_bbox(box_cxcywh, w, h)
# 3. 设置图像
inference_state = processor.set_image(image)
# 4. 重置 Prompts (sam3 里强调了这步)
processor.reset_all_prompts(inference_state)
# 5. 添加几何提示
# 注意：sam3 里 box 参数传的是 flatten list，不需要嵌套 batch
output = processor.add_geometric_prompt(
state=inference_state,
box=norm_box_cxcywh,
label=True
)
# -------------------------------------------------
masks = output["masks"]
scores = output["scores"]
# 提取最高分 Mask
if isinstance(scores, list):
best_idx = np.argmax(scores)
score_val = scores[best_idx]
else:
best_idx = torch.argmax(scores).item()
score_val = scores[best_idx].item()
if isinstance(masks, list): pred_tensor = masks[best_idx]
else: pred_tensor = masks[best_idx]
if pred_tensor.ndim == 3: pred_tensor = pred_tensor.squeeze(0)
# 后处理 Resize 回原图 (通常 SAM 输出的是内部尺寸)
if pred_tensor.shape[0] != h or pred_tensor.shape[1] != w:
pred_resized = torch.nn.functional.interpolate(
pred_tensor.unsqueeze(0).unsqueeze(0).float(),
size=(h, w),
mode='bilinear',
align_corners=False
).squeeze()
else:
pred_resized = pred_tensor
pred_prob = pred_resized.cpu().numpy()
pred_binary = (pred_prob > 0.5)
# 计算 IoU
iou = calculate_iou(pred_binary, gt_mask)
all_ious.append(iou)
# 可视化 (Box 画图时还是用 xyxy，方便观察)
save_visualization(image_np, bbox_xyxy, pred_binary, gt_mask, iou, score_val, filename)
except Exception as e:
print(f"Error {filename}: {e}")
# import traceback
# traceback.print_exc()
continue
print("\n" + "="*40)
if all_ious:
print(f"Final mIoU (sam3 Style): {np.mean(all_ious):.4f}")
print(f"Results saved to: {OUTPUT_DIR}")
else:
print("No results.")
print("="*40)
if __name__ == "__main__":
main()
为什么我并没有复现sam3-adapter比sam3更好的轮廓分割（sam3不完整，边界模糊)？
1.我使用的是kvasir数据集，使用的是main分支SAM3-Adapter-Pytorch(2).zip代码来尝试复现的
2.我对kvasir数据集拆分为train 800张，val100张，test100张，你知乎上figture6中的4张样本刚好不在test中(以下这4张称为关键样本)
cju15czxqp3lv0835jvhgzurz.jpg -> val
cju7ebe962hr409872ovibahw.jpg -> train
cju16whaj0e7n0855q7b6cjkm.jpg -> train
ck2bxw18mmz1k0725litqq2mc.jpg -> train
3.我使用
eval_type: kvasir
sam_checkpoint: /mnt/tempdata/chenjp/sam-adapter/SAM-Adapter-PyTorch/pretrained/sam3.pt
config: /mnt/tempdata/chenjp/sam-adapter/SAM3-Adapter-Pytorch/configs/cod-sam-vit-l.yaml(只改动了train_dataset，val_dataset,test_dataset的路径)
train_dataset,val_dataset,test_dataset分别对应上面的train/val/test，运行train.sh得到的pth文件
/mnt/tempdata/chenjp/sam-adapter/SAM3-Adapter-Pytorch/save/_cod-sam-vit-l/model_epoch_best.pth
4.我使用同样的config cod-sam-vit-l.yaml，使用步骤3中的model_epoch_best.pth，运行test.sh得到的dice和iou为
就如第2点中所列的几个关键样本，我单独用第3步的model_epoch_best.pth，没有感觉到sam3-adapter比sam3更好，
使用上面步骤2的4个关键样本来跑test.sh得到的结果是 (sam3-adapter)
sam3脚本得到的结果是
问题：
1.我应该怎么做才能复现这种效果？
2.还想知道你用的的sam3的脚本在代码中吗？权重文件是pt格式的吗？
3.我需要把那4个关键样本从train/val中剔除重新生成一个model_epoch_best.pth文件来跑这几个关键样本吗？还是说我使用你readme中列的其他数据集来验证？
4.我看到咱sam3-adapter的论文文章中有对比sam3和sam3-adapter，但是table1，2，3中都有sam，sam2，但没有列sam3的指标，是为什么？是sam3的指标更好，无法印证sam3-adapter的价值，不便于展示？还是疏忽？（我猜是adapter可能在sam、sam2时确实有较大提升，但在sam3时几乎无提升，或不太明显，故而不列sam3来，只凸显sam3-adapter是sota，使论文能顺利发表？）
sam3的结果与sam3-adapter的结果比较（上面是sam3下面是sam3-adapter）
使用sam3分割的代码，test_sam3_style_kvasir.py
@tianrun-chen