{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":54858,"offset":54848,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u4f60\u7ed9\u7684\u4ee3\u7801\u4e3b\u8981\u662f\u7528cv2\u8bfb\u53d6\u56fe\u50cf\u7684\uff0c\u540c\u65f6\u4f60\u60f3\u201c\u53c2\u7167\u4e0b\u9762\u4ee3\u7801\u4e2d\u8bfb\u53d6npy\u7684\u65b9\u5f0f\u201d\uff0c\u770b\u8d77\u6765\u662f\u60f3\u628a\u56fe\u50cf\u8bfb\u53d6\u4e0e\u9884\u5904\u7406\u65b9\u5f0f\u548c`quick_test.py`\u91cc\u5bf9npy\u8bfb\u53d6\u53ca\u5904\u7406\u7684\u517c\u5bb9\u6027\u7ed3\u5408\u8d77\u6765\u3002\n\n\u5728\u4f60\u73b0\u6709\u4ee3\u7801\u4e2d\uff0c\u8bfb\u53d6\u6df1\u5ea6\u56fe\u3001\u4f4e\u5206\u8fa8\u7387\u56fe\u4e3a`cv2.imread(..., cv2.IMREAD_UNCHANGED)`\uff0cRGB\u56fe\u4e3a\u5f69\u8272\u8bfb\u53d6\uff08\u7136\u540e\u8f6cRGB\uff09\uff0c\u7136\u540e\u5bf9\u6df1\u5ea6\u56fe\u8fdb\u884c\u5f52\u4e00\u5316\uff08`normalize_img`\uff09\uff0c\u5bf9RGB\u505a\u5f52\u4e00\u5316\u540e\u8f6cCHW\uff0c`mod_crop`\u88c1\u526a\u540e\u8f6ctensor\uff0c\u9001\u5165\u6a21\u578b\u3002  \n\u800c`quick_test.py`\u91cc\u662f\u76f4\u63a5\u7528`np.load`\u52a0\u8f7dnpy\u6570\u7ec4\uff0c\u7136\u540e\u7c7b\u4f3c\u5f52\u4e00\u5316\u548c\u88c1\u8fb9\u3002\n\n---\n\n### \u4f60\u8981\u201c\u53c2\u7167\u8bfb\u53d6npy\u7684\u65b9\u5f0f\u201d\uff0c\u6211\u7406\u89e3\u7684\u4e3b\u8981\u6539\u52a8\u70b9\u662f\uff1a\n\n- \u4f60\u8981\u8bfb\u53d6\u7684`depth`\u3001`depth_x4`\u3001`rgb`\u90fd\u662f\u7528npy\u683c\u5f0f\u5b58\u5728\u7684\u3002\n- \u4f60\u8981\u5bf9npy\u6570\u7ec4\u8fdb\u884c\u540c\u6837\u7684\u5f52\u4e00\u5316\u3001\u88c1\u526a\u53catensor\u8f6c\u5316\u5904\u7406\u3002\n- \u540e\u7eed\u6a21\u578b\u8f93\u5165\u548cRMSE\u8ba1\u7b97\u903b\u8f91\u4fdd\u6301\u4e0d\u53d8\u3002\n\n---\n\n### \u57fa\u4e8e\u4f60\u7ed9\u7684\u4ee3\u7801\uff0c\u6211\u7ed9\u4f60\u4e00\u4efd\u4fee\u6539\u65b9\u6848\uff1a\n\n```python\nimport os\nimport glob\nimport torch\nimport numpy as np\nfrom ahmf import AHMF\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\ndef mod_crop(*imgs, modulo):\n    def crop(img):\n        if img.ndim == 2:\n            h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        elif img.ndim == 3:\n            c, h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[:, crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        else:\n            raise ValueError(f\"Unsupported image dimension: {img.ndim}\")\n    out = [crop(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef normalize_img(img):\n    # \u4fdd\u6301\u539f\u6570\u636e\u7c7b\u578b\uff0c\u5e76\u5f52\u4e00\u5316\uff08\u548cquick_test\u7c7b\u4f3c\uff09\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        # float\u7c7b\u578b\u6216\u8005\u5176\u5b83\n        max_val = float(np.max(img)) if np.max(img) > 1.0 else 1.0\n        return img.astype(np.float32) / max_val, max_val\n\ndef calc_rmse(pred, target):\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n\ndef main():\n    scale = 4    \n    model = AHMF(scale=scale, act='PReLU', in_channels=1, guidance_channels=3)\n    ckpt_path = f'/home/jianruobing/code/AHMF-main/AHMF-test/model_x{scale}.pth'\n    checkpoint = torch.load(ckpt_path, map_location='cpu')\n    model.load_state_dict(checkpoint['state_dict'])\n    model = model.cuda().eval()\n    \n    input_dir = '/home/jianruobing/code/AHMF-main/imgs'\n    output_dir = './output'\n    os.makedirs(output_dir, exist_ok=True)\n\n    # \u67e5\u627enpy\u6587\u4ef6\n    all_files = []\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth_x4.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_rgb.npy')))\n\n    # \u6309\u524d\u7f00\u5206\u7ec4\n    groups = {}\n    for fpath in all_files:\n        fname = os.path.basename(fpath)\n        if '_depth_x4' in fname:\n            prefix = fname.split('_depth_x4')[0]\n            key = 'depth_x4'\n        elif '_depth' in fname:\n            prefix = fname.split('_depth')[0]\n            key = 'depth'\n        elif '_rgb' in fname:\n            prefix = fname.split('_rgb')[0]\n            key = 'rgb'\n        else:\n            continue\n        groups.setdefault(prefix, {})[key] = fpath\n\n    print(f'Found {len(groups)} image groups.')\n\n    for prefix, files in groups.items():\n        if not {'depth', 'depth_x4', 'rgb'}.issubset(files.keys()):\n            print(f'Warning: missing files for {prefix}, skipped.')\n            continue\n\n        dep = np.load(files['depth'])\n        lr = np.load(files['depth_x4'])\n        rgb = np.load(files['rgb'])\n\n        print(f\"{prefix}: Input dep dtype = {dep.dtype}, shape = {dep.shape}\")\n        print(f\"{prefix}: Input rgb dtype = {rgb.dtype}, shape = {rgb.shape}\")\n        print(f\"{prefix}: Input lr dtype = {lr.dtype}, shape = {lr.shape}\")\n\n        # \u5f52\u4e00\u5316\n        dep_norm, dep_max = normalize_img(dep)\n        lr_norm, lr_max = normalize_img(lr)\n        # rgb\u5047\u8bbeshape H, W, C\uff0c\u8f6cCHW\u5e76\u5f52\u4e00\u5316\uff080~1\uff09\n        rgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n        # mod_crop\uff0c\u6df1\u5ea6\u548crgb\u90fd\u62691\u7ef4\uff08channel\uff09\uff0crgb\u8f6cCHW\u5728\u524d\u9762\u5b8c\u6210\u4e86\n        dep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\n        rgb_norm = mod_crop(rgb_norm, modulo=32)\n        lr_norm = mod_crop(lr_norm, modulo=32)\n\n        # lr\u4e0a\u91c7\u6837\u7528cv2\u4e00\u6837\u53cc\u7ebf\u6027\u63d2\u503c\n        h, w = lr_norm.shape[-2], lr_norm.shape[-1]  # \u5bf9\u5e94\u6df1\u5ea6h,w\n        import cv2\n        lr_up = cv2.resize(lr_norm, (w * scale, h * scale), interpolation=cv2.INTER_LINEAR)\n\n        # \u786e\u4fddshape\u7ef4\u5ea6\u5bf9\u5e94\n        if lr_norm.ndim == 2:\n            lr_norm = np.expand_dims(lr_norm, 0)\n        if lr_up.ndim == 2:\n            lr_up = np.expand_dims(lr_up, 0)\n\n        # \u8f6ctensor\u5e76\u589e\u52a0batch\u7ef4\n        dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm, rgb_norm, lr_norm, lr_up)\n        dep_t = dep_t.unsqueeze(0).cuda()\n        rgb_t = rgb_t.unsqueeze(0).cuda()\n        lr_t = lr_t.unsqueeze(0).cuda()\n        lr_up_t = lr_up_t.unsqueeze(0).cuda()\n\n        inputs = {\n            'img_gt': dep_t,\n            'img_rgb': rgb_t,\n            'img_lr': lr_t,\n            'lr_up': lr_up_t,\n        }\n\n        with torch.no_grad():\n            out = model(inputs)['img_out']\n\n        # \u53cd\u5f52\u4e00\u5316\u8fd8\u539f\n        out_img = out.squeeze().cpu().numpy()\n        out_img = np.clip(out_img, 0, 1) * dep_max\n\n        # \u8f6c\u56de\u6574\u6570\u683c\u5f0f\u505aRMSE\u8ba1\u7b97\n        if dep.dtype == np.uint8:\n            out_img_int = np.round(out_img).astype(np.uint8)\n            dep_int = dep\n        elif dep.dtype == np.uint16:\n            out_img_int = np.round(out_img).astype(np.uint16)\n            dep_int = dep\n        else:\n            # float\u6216\u8005\u5176\u5b83\u7c7b\u578b\uff08\u5982int32\uff09\uff0c\u4fdd\u6301float32\uff0cRMSE\u65f6\u4f1a\u81ea\u52a8\u8f6c\u6362\n            out_img_int = out_img.astype(np.float32)\n            dep_int = dep.astype(np.float32)\n\n        rmse_val = calc_rmse(out_img_int, dep_int)\n        print(f'{prefix}: RMSE = {rmse_val:.4f}')\n\n        # \u4fdd\u5b58\u7ed3\u679c\u56fe\u4e3apng\uff0c\u9700\u5148\u8f6cuint8\u6216uint16\n        save_path = os.path.join(output_dir, f'{prefix}_sr.png')\n        import cv2\n        cv2.imwrite(save_path, out_img_int)\n        print(f'Saved SR image to {save_path}')\n\nif __name__ == '__main__':\n    main()\n```\n\n---\n\n### \u8bf4\u660e\n\n- \u8bfb\u53d6`npy`\u65b9\u5f0f\u7528\u7684\u662f`np.load`\uff0c\u5bf9\u5e94\u4f60\u7ed9\u7684`quick_test.py`\u8bfb\u53d6\u65b9\u5f0f\u3002\n- \u5f52\u4e00\u5316\u65b9\u5f0f\u548c`quick_test.py`\u7684\u5904\u7406\u4fdd\u6301\u543b\u5408\uff08\u5f52\u4e00\u5316\u5230[0,1]\uff0c\u4fdd\u7559\u6700\u5927\u503c\uff09\u3002\n- rgb\u4eceHWC\u8f6c\u6362\u4e3aCHW\uff1a`np.transpose(rgb, (2,0,1))`\uff0c\u5e76\u9664255\u5f52\u4e00\u5316\u3002\n- `mod_crop`\u6539\u9020\u6210\u63a5\u53d7CHW\u683c\u5f0f\u56fe\u50cf\u3002\n- \u53cc\u7ebf\u6027\u63d2\u503c\u7528cv2\u7684`cv2.resize`\u5b9e\u73b0\uff0c\u4e0e\u4e4b\u524d\u51fd\u6570\u4e00\u81f4\u3002\n- \u8f6cTensor\u90e8\u5206\u7528\u4f60\u793a\u4f8b\u91cc\u7684`np_to_tensor`\u3002\n- RMSE\u8ba1\u7b97\u8c03\u7528\u4f60\u7684`calc_rmse`\u3002\n- \u8f93\u51fa\u540e\u6062\u590d\u5230\u539f\u59cb\u50cf\u7d20\u503c\u8303\u56f4\uff0c\u8f6c\u4e3a\u6574\u6570\u7c7b\u578b\u4fdd\u5b58PNG\u3002\n\n\u8fd9\u6837\u4fee\u6539\u540e\uff0c\u4f60\u7684\u8f93\u5165\u6570\u636e\u7ed3\u6784\u548c\u9884\u5904\u7406\u6d41\u7a0b\u57fa\u672c\u4e0e`quick_test.py`\u4f3c\u7684\uff0c\u65b9\u4fbf\u5bf9\u63a5\u3002\n\n\u5982\u679c\u4f60\u8fd8\u6709\u5176\u5b83\u7ec6\u8282\u9700\u8981\u540c\u6b65\uff0c\u53ef\u4ee5\u544a\u8bc9\u6211\uff01","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":16538,"n_turns_in_conversation":16,"n_words":1524,"source_row_offset":54848,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"71dfcfea5145fbd111425f46","turn_index":6,"user_text":"\u8bf7\u53c2\u7167\u4e0b\u8ff0\u4ee3\u7801\u4e2d\u8bfb\u53d6npy\u6587\u4ef6\u7684\u65b9\u5f0f\uff1a\n# -*- coding: utf-8 -*-\n\"\"\"\n@Author  :   zhwzhong\n@License :   (C) Copyright 2013-2018, hit\n@Contact :   <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>\n@Software:   PyCharm\n@File    :   quick_test.py\n@Time    :   2021/8/31 20:32\n@Desc    :\n\"\"\"\nimport glob\nimport math\nimport tqdm\nimport numpy as np\nimport torch\n\nimport utility\nfrom option import args\nfrom data import get_dataloader\nfrom importlib import import_module\n\nargs.scale = 8\nargs.down_type = 'nearest'\n\ndevice = torch.device('cpu' if args.cpu else 'cuda')\nmodule = import_module('models.' + args.model_name.lower())\nmodel = module.make_model(args).to(device)\n\nmodel = torch.nn.parallel.DataParallel(model, device_ids=list(range(args.num_gpus)))\nprint(utility.get_parameter_number(model))\ndevice_id = torch.cuda.current_device()\n\n\nload_name = './pre_trained/net_{}_x{}.pth'.format(args.down_type, args.scale)\nprint(load_name)\ncheckpoint = torch.load(load_name, map_location=lambda storage, loc: storage.cuda(device_id))\nmodel.load_state_dict(checkpoint['state'])\nmodel.eval()\n\ntest_set = ['Middlebury', 'Lu', 'test', 'Sintel'] # , 'Middlebury', 'test', 'Sintel'\nfor test_name in test_set:\n    sum_rmse = []\n    for gt_name in tqdm.tqdm(sorted(glob.glob('./test_data/{}/gt/*.npy'.format(test_name)))):\n        gt_img = np.load(gt_name)\n        rgb_img = np.load(gt_name.replace('gt', 'rgb'))\n\n        # Following DKN, we use bicubic in PIL to degrade GT image (for bicubic), and crop the border\n        # before calculate the RMSE values. (reference: https://github.com/cvlab-yonsei/dkn/issues/1)\n        module = max(int(math.pow(2, 1 + args.num_pyramid)), args.scale)\n        tmp_gt = utility.mod_crop(gt_img, modulo=module)\n        if args.down_type == 'nearest':\n            tmp_gt = (tmp_gt - np.min(tmp_gt)) / (np.max(tmp_gt) - np.min(tmp_gt))\n            lr_img = utility.get_lowers(tmp_gt, factor=args.scale, mode=args.down_direction)\n        else:\n            tmp_gt = (tmp_gt - np.min(tmp_gt)) / (np.max(tmp_gt) - np.min(tmp_gt))\n            lr_img = utility.get_lowers(tmp_gt, factor=args.scale, mode='bicubic')\n        lr_up = utility.get_lowers(lr_img, factor=1 / args.scale, mode='bicubic')\n        lr_img, gt_img = np.expand_dims(lr_img, 0), np.expand_dims(gt_img, 0)\n        lr_up = np.expand_dims(lr_up, 0)\n        if args.guide_channels == 1:\n            rgb_img = np.expand_dims(utility.rgb2gray(rgb_img), 2)\n\n        rgb_img = np.float32(np.transpose(rgb_img, axes=(2, 0, 1))) / 255.\n\n        gt_img, rgb_img = utility.mod_crop(gt_img, modulo=module), utility.mod_crop(rgb_img, modulo=module)\n\n        lr_img, lr_up, gt_img, rgb_img = utility.np_to_tensor(lr_img, lr_up, gt_img, rgb_img)\n\n        lr_img, lr_up, gt_img, rgb_img = lr_img.unsqueeze(0), lr_up.unsqueeze(0), gt_img.unsqueeze(0), rgb_img.unsqueeze(0)\n        lr_img, lr_up, gt_img, rgb_img = lr_img.to(device), lr_up.to(device), gt_img.to(device), rgb_img.to(device)\n\n        out = model(lr=lr_img.contiguous(), rgb=rgb_img.contiguous(), lr_up=lr_up.contiguous())[-1]\n\n\n        if test_name == 'test':\n            mul_ratio = 100\n        elif test_name == 'Sintel':\n            mul_ratio = 255\n        else:\n            mul_ratio = 1\n\n        rmse, _ = utility.root_mean_sqrt_error(im_pred=out.contiguous(), im_true=gt_img.contiguous(), border=6, mul_ratio=mul_ratio, is_train=False)\n        sum_rmse.append(rmse)\n\n    print('{}: {:.2f}'.format(test_name, np.mean(sum_rmse)))\n\u4fee\u6539\u4e0b\u8ff0\u4ee3\u7801\uff1a\nimport os\nimport cv2\nimport glob\nimport torch\nimport numpy as np\nfrom ahmf import AHMF\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\ndef mod_crop(*imgs, modulo):\n    def crop(img):\n        if img.ndim == 2:\n            h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        elif img.ndim == 3:\n            c, h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[:, crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        else:\n            raise ValueError(f\"Unsupported image dimension: {img.ndim}\")\n    out = [crop(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef quantize(img, rgb_range):\n    pixel_range = 255 / rgb_range\n    return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range)\n\ndef imresize(img, scale_factor):\n    if img.ndim == 2:\n        h, w = img.shape\n        new_h, new_w = int(h * scale_factor), int(w * scale_factor)\n        resized = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)\n        return resized\n    elif img.ndim == 3:\n        c, h, w = img.shape\n        new_h, new_w = int(h * scale_factor), int(w * scale_factor)\n        img_hw_c = img.transpose(1, 2, 0)\n        resized = cv2.resize(img_hw_c, (new_w, new_h), interpolation=cv2.INTER_LINEAR)\n        return resized.transpose(2, 0, 1)\n    else:\n        raise ValueError(\"Input array must be 2D or 3D\")\n\ndef normalize_img(img):\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        return img.astype(np.float32), 255\n\ndef calc_rmse(pred, target):\n    \"\"\"\u8ba1\u7b97RMSE\uff0c\u8f93\u5165\u5747\u4e3anumpy\u6574\u6570\u6570\u7ec4\"\"\"\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n\ndef main():\n    scale = 4    \n    model = AHMF(scale=scale, act='PReLU', in_channels=1, guidance_channels=3)\n    ckpt_path = f'/home/jianruobing/code/AHMF-main/AHMF-test/model_x{scale}.pth'\n    checkpoint = torch.load(ckpt_path, map_location='cpu')\n    model.load_state_dict(checkpoint['state_dict'])\n    model = model.cuda().eval()\n    \n    input_dir = '/home/jianruobing/code/AHMF-main/imgs'\n    output_dir = './output'\n    os.makedirs(output_dir, exist_ok=True)\n\n    exts = ['png', 'jpg', 'jpeg']\n    all_files = []\n    for ext in exts:\n        all_files.extend(glob.glob(os.path.join(input_dir, f'*_depth.{ext}')))\n        all_files.extend(glob.glob(os.path.join(input_dir, f'*_depth_x4.{ext}')))\n        all_files.extend(glob.glob(os.path.join(input_dir, f'*_rgb.{ext}')))\n\n    groups = {}\n    for fpath in all_files:\n        fname = os.path.basename(fpath)\n        if '_depth_x4' in fname:\n            prefix = fname.split('_depth_x4')[0]\n            key = 'depth_x4'\n        elif '_depth' in fname:\n            prefix = fname.split('_depth')[0]\n            key = 'depth'\n        elif '_rgb' in fname:\n            prefix = fname.split('_rgb')[0]\n            key = 'rgb'\n        else:\n            continue\n        groups.setdefault(prefix, {})[key] = fpath\n\n    print(f'Found {len(groups)} image groups.')\n\n    for prefix, files in groups.items():\n        if not {'depth', 'depth_x4', 'rgb'}.issubset(files.keys()):\n            print(f'Warning: missing files for {prefix}, skipped.')\n            continue\n\n        dep = cv2.imread(files['depth'], cv2.IMREAD_UNCHANGED)\n        lr = cv2.imread(files['depth_x4'], cv2.IMREAD_UNCHANGED)\n        rgb = cv2.imread(files['rgb'], cv2.IMREAD_COLOR)\n\n        print(f\"{prefix}: Input dep dtype = {dep.dtype}, shape = {dep.shape}\")\n        print(f\"{prefix}: Input rgb dtype = {rgb.dtype}, shape = {rgb.shape}\")\n        print(f\"{prefix}: Input lr dtype = {lr.dtype}, shape = {lr.shape}\")\n\n        if dep is None or lr is None or rgb is None:\n            print(f'Failed loading images for {prefix}')\n            continue\n        \n        rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)\n\n        # \u5f52\u4e00\u5316\u8f93\u5165\u56fe\u50cf\uff0c\u83b7\u53d6\u6700\u5927\u503c\uff08255\u621665535\uff09\n        dep_norm, dep_max = normalize_img(dep)\n        lr_norm, lr_max = normalize_img(lr)\n        rgb_norm = rgb.astype(np.float32) / 255.0\n\n        dep_norm, rgb_norm = mod_crop(np.expand_dims(dep_norm, 0), rgb_norm.transpose(2, 0, 1), modulo=32)\n        lr_norm = mod_crop(lr_norm, modulo=32)\n\n        # \u4e0a\u91c7\u6837 lr\n        lr_up = imresize(lr_norm, scale)\n\n        if lr_norm.ndim == 2:\n            lr_norm = np.expand_dims(lr_norm, 0)\n        if lr_up.ndim == 2:\n            lr_up = np.expand_dims(lr_up, 0)\n\n        dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm, rgb_norm, lr_norm, lr_up)\n        dep_t = dep_t.unsqueeze(0).cuda()\n        rgb_t = rgb_t.unsqueeze(0).cuda()\n        lr_t = lr_t.unsqueeze(0).cuda()\n        lr_up_t = lr_up_t.unsqueeze(0).cuda()\n        \n        inputs = {\n            'img_gt': dep_t,\n            'img_rgb': rgb_t,\n            'img_lr': lr_t,\n            'lr_up': lr_up_t,\n        }\n\n        with torch.no_grad():\n            out = model(inputs)['img_out']\n\n        # \u53cd\u5f52\u4e00\u5316\u5230\u539f\u59cb\u50cf\u7d20\u8303\u56f4\n        out_img = out.squeeze().cpu().numpy()\n        out_img = np.clip(out_img, 0, 1) * dep_max\n\n        # \u8f6c\u4e3a\u5bf9\u5e94\u6574\u6570\u683c\u5f0f\n        if dep.dtype == np.uint8:\n            out_img_int = np.round(out_img).astype(np.uint8)\n            dep_int = dep\n        elif dep.dtype == np.uint16:\n            out_img_int = np.round(out_img).astype(np.uint16)\n            dep_int = dep\n        else:\n            out_img_int = out_img.astype(np.float32)\n            dep_int = dep.astype(np.float32)\n\n        # \u8ba1\u7b97RMSE\uff0c\u57fa\u4e8e\u6574\u578b\u56fe\u50cf\n        rmse_val = calc_rmse(out_img_int, dep_int)\n        print(f'{prefix}: RMSE = {rmse_val:.4f}')\n\n        save_path = os.path.join(output_dir, f'{prefix}_sr.png')\n        # \u4fdd\u5b58\u65f6\u5f3a\u5236\u8f6c\u6362\u4e3a\u517c\u5bb9\u683c\u5f0f\n        cv2.imwrite(save_path, out_img_int)\n        print(f'Saved SR image to {save_path}')\n\nif __name__ == '__main__':\n    main()"},{"assistant_initiates":false,"asst_text":"\u4f60\u7684\u62a5\u9519\u4fe1\u606f\uff1a\n\n```\nRuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 48 for tensor number 1 in the list.\n```\n\n\u53d1\u751f\u5728\u6a21\u578b\u4e2d\u5bf9depth\u548cguidance\uff08rgb\uff09\u4e24\u4e2a\u5f20\u91cf\u5728`dim=1`\u4ee5\u5916\u7684\u5176\u4ed6\u7ef4\u5ea6\u62fc\u63a5\u65f6\u62a5\u9519\uff0c\u610f\u601d\u662f\u4e24\u4e2atensor\u5728\u62fc\u63a5\u7ef4\u5ea6\u4ee5\u5916\u7684\u5c3a\u5bf8\u4e0d\u4e00\u81f4\u3002\n\n\u5177\u4f53\u5230\u4f60\u7684\u65e5\u5fd7\uff1a\n\n```\ndepth.shape=torch.Size([1, 64, 32, 128])  # B, C, H, W \u8fd9\u91ccH=32\nguidance.shape=torch.Size([1, 64, 48, 128])  # B, C, H, W \u8fd9\u91ccH=48\n```\n\n\u7b2c3\u7ef4\uff08height\uff09\u4e0d\u5339\u914d\uff0c\u5206\u522b\u662f`32`\u548c`48`\u3002\n\n---\n\n## \u7ed3\u5408\u4f60\u7684\u4ee3\u7801\u548c\u8bf4\u660e\uff0c\u6392\u67e5\u539f\u56e0\uff1a\n\n### 1. \u67e5\u770b\u8f93\u5165\u6570\u636e\u6d41\u7a0b\u662f\u5426\u4e00\u81f4\u3001\u5bf9\u5e94\u7684\u7a7a\u95f4\u5c3a\u5bf8\u9519\u5f00\u4e86\n\n\u4f60\u5b9a\u4e49\u7684 `mod_crop` \u5bf9\u6df1\u5ea6\u56fe\u548crgb\u7684\u5904\u7406\u662f\uff1a\n\n```python\ndep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm = mod_crop(rgb_norm, modulo=32)\n```\n\n\u8fd9\u91cc `dep_norm` \u662f `[1, H, W]`\uff0c`rgb_norm` \u662f `[C, H, W]`\u3002`mod_crop`\u51fd\u6570\u9488\u5bf9\u4e8c\u7ef4\u5355\u901a\u9053\u548c\u4e09\u7ef4\u56fe\uff08C,H,W\uff09\u5904\u7406\u633a\u5408\u7406\u3002\n\n\u95ee\u9898\u53ef\u80fd\u662f\uff1a\n\n- \u4f60\u5bf9`dep_norm`\u5148`expand_dims`\u6210`(1, H, W)`\uff0c\u7136\u540e\u88c1\u526a\u540e\u4f9d\u7136\u662f`(1, h_crop, w_crop)`\n- `rgb_norm`\u662f`(3, H, W)`\uff0c\u88c1\u526a\u540e`(3, h_crop, w_crop)`\uff0c\u6ce8\u610f\u8fd9\u91cc\u7ef4\u5ea6\u5dee\u5f02\u3002\n\n\u4f46\u6700\u7ec8\u7ed9\u6a21\u578b\u7684\u662f `dep_t` \u548c `rgb_t`\uff0c\u7ef4\u5ea6\u5e94\u8be5\u662f\u4e00\u81f4\u7684\u3002\n\n----\n\n### 2. \u4f60\u6253\u5370\u7684`depth.shape=torch.Size([1, 64, 32, 128])`\u548c`guidance.shape=torch.Size([1, 64, 48, 128])`\n\n\u4e00\u822c\u901a\u9053\u657064\uff0c\u7a7a\u95f4\u5c3a\u5bf832*128 vs 48*128\uff0c\u8fd9\u8868\u660e\uff1a\n\n- \u4f60\u7684\u6df1\u5ea6\u56fe\u5bf9\u5e94\u7684`H`\u662f32\n- \u6307\u5bfc\u56fe\u7684`H`\u662f48\n\n\u4e3a\u5565\u5c3a\u5bf8\u4f1a\u5dee\u8fd9\u4e48\u591a\uff1f\n\n---\n\n### 3. \u539f\u56e0\u5f88\u53ef\u80fd\u662f`rgb_norm`\u548c`dep_norm`\u8d77\u521d\u7a7a\u95f4\u5c3a\u5bf8\u4e0d\u5339\u914d\n\n\u53ef\u80fd\u5728\u539f\u59cb\u56fe\u7247\u7684\u88c1\u526a\uff0c\u5f52\u4e00\u5316\uff0c\u6216\u8005resize\u73af\u8282\u5bfc\u81f4\u7a7a\u95f4\u5927\u5c0f\u4e0d\u4e00\u81f4\u3002\n\n---\n\n### 4. \u7ed3\u5408\u4f60\u4ee3\u7801`main`\u4e2d\u76f8\u5173\u90e8\u5206\uff1a\n\n```python\ndep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm = mod_crop(rgb_norm, modulo=32)\nlr_norm = mod_crop(lr_norm, modulo=32)\n```\n\n\u5982\u679c`dep_norm`\u662f `(1, H, W)`\uff0c\u88c1\u526a\u540eH\u662f32\uff1b\u800c`rgb_norm`\u662f `(3, H, W)`\uff0c\u4f46\u4ece\u6253\u5370\u7ed3\u679c\u770bH\u662f48\u3002\n\n\u610f\u5473\u7740\uff1a\n\n- `dep_norm`\u7684\u5c3a\u5bf8\u662f`32x128`\n- `rgb_norm`\u88c1\u526a\u8fc7\u540e\u5c3a\u5bf8\u662f`48x128`\n\n\u8fd9\u91cc\u660e\u663e\u4e0d\u5339\u914d\u3002\n\n---\n\n### 5. \u53ef\u80fd\u662f\u56e0\u4e3a\u4f60\u7684`rgb_norm`\u5728`normalize_img`\u4e4b\u524d\u6ca1\u6709\u88c1\u526a\uff0c\u4e5f\u53ef\u80fd\u662f`rgb_norm`\u8fd8\u6ca1\u8f6c\u6210`CHW`\u4e4b\u524d\u505a\u4e86\u88c1\u526a\uff0c\u987a\u5e8f\u95ee\u9898\u3002\n\n---\n\n### 6. \u5efa\u8bae\u4f60\u4fee\u6539**\u88c1\u526a\u987a\u5e8f**\uff0c\u786e\u4fdd`rgb`\u8f6c`CHW`\u540e\uff0c\u518d\u7edf\u4e00\u88c1\u526a\uff1a\n\n```python\nrgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0  # \u8f6cCHW\u5f52\u4e00\u5316\n\n# \u7136\u540e\u518d\u7edf\u4e00\u88c1\u526a\uff1a\ndep_norm, rgb_norm, lr_norm = mod_crop(np.expand_dims(dep_norm, 0), rgb_norm, lr_norm, modulo=32)\n```\n\n\u8fd9\u6837`dep_norm`\u662f(1, h, w)\uff0c`rgb_norm`\u662f(3, h, w)\uff0c`lr_norm`\u662f(1, h, w)\uff0c\u90fd\u88ab\u4e00\u81f4\u88c1\u526a\uff0c\u4fdd\u8bc1`h,w`\u76f8\u540c\u3002\n\n---\n\n### 7. \u4f60\u5f53\u524d\u7684\u4ee3\u7801\u662f\uff1a\n\n```python\ndep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm = mod_crop(rgb_norm, modulo=32)\nlr_norm = mod_crop(lr_norm, modulo=32)\n```\n\n\u628a\u4e0a\u9762\u6539\u6210\uff1a\n\n```python\ndep_norm, rgb_norm, lr_norm = mod_crop(np.expand_dims(dep_norm, 0), rgb_norm, lr_norm, modulo=32)\n```\n\n---\n\n### 8. \u53e6\u4e00\u70b9\u662f\u4f60\u6253\u5370\u7684`depth.shape=torch.Size([1, 64, 32, 128])`\uff0c\u8fd9\u91cc`64`\u662f\u901a\u9053\uff0c`32 x 128`\u662f\u7a7a\u95f4\u5c3a\u5bf8\uff0c`guidance.shape`\u662f`[1, 64, 48, 128]`\uff0c\u901a\u9053\u6570\u4e5f\u76f8\u540c\uff0c\u4f46\u662f\u7a7a\u95f4\u9ad8\u5ea6\u4e0d\u540c\u3002\n\n\u8fd9\u8bf4\u660e\u6a21\u578b\u5185\u90e8\u5bf9`rgb`\u505a\u4e86\u67d0\u4e9b\u4e0b\u91c7\u6837\u6216\u8005padding\uff0c\u53ef\u80fd\u4e5f\u548c\u6570\u636e\u8f93\u5165\u5c3a\u5bf8\u6709\u5173\uff0c\u4fdd\u6301\u8f93\u5165\u5c3a\u5bf8\u4e00\u81f4\u6781\u5176\u91cd\u8981\u3002\n\n---\n\n### 9. \u5982\u679cmod_crop\u8c03\u6574\u540e\u4ecd\u51fa\u73b0\uff0c\u5219\u5efa\u8bae\u4f60\u6253\u5370\u6240\u6709\u8f93\u5165\u5f20\u91cf\u7684shape\uff0c\u7279\u522b\u662f\u8f93\u5165\u6a21\u578b\u4e4b\u524d\u7684\uff1a\n\n```python\nprint(f\"dep_t shape: {dep_t.shape}\")\nprint(f\"rgb_t shape: {rgb_t.shape}\")\nprint(f\"lr_t shape: {lr_t.shape}\")\nprint(f\"lr_up_t shape: {lr_up_t.shape}\")\n```\n\n\u786e\u8ba4\u5b83\u4eec\u7a7a\u95f4\u5927\u5c0f\u662f\u4e0d\u662f\u4e00\u81f4\u3002\n\n---\n\n## \u603b\u7ed3\n\n**\u6700\u91cd\u8981\u4fee\u6539**\uff1a\n\n```python\n# \u8f6c\u5230CHW\u4e4b\u540e\uff0c\u4e00\u8d77\u88c1\u526a\nrgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\ndep_norm, rgb_norm, lr_norm = mod_crop(np.expand_dims(dep_norm, 0), rgb_norm, lr_norm, modulo=32)\n```\n\n\u800c\u4e0d\u662f\u5206\u5f00\u5355\u72ec\u88c1\u526a\u3002\n\n---\n\n## \u9644\u5b8c\u6574\u88c1\u526a\u90e8\u5206\u793a\u4f8b\n\n```python\n# \u5f52\u4e00\u5316\ndep_norm, dep_max = normalize_img(dep)\nlr_norm, lr_max = normalize_img(lr)\nrgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n# \u7edf\u4e00\u88c1\u526a\ndep_norm, rgb_norm, lr_norm = mod_crop(np.expand_dims(dep_norm, 0), rgb_norm, lr_norm, modulo=32)\n```\n\n---\n\n\u8fd9\u6837\u4f60\u5c31\u80fd\u4fdd\u8bc1\u8f93\u5165\u6a21\u578b\u7684`dep_t`\u548c`rgb_t`\u7684\u7a7a\u95f4\u5c3a\u5bf8\u4e00\u81f4\uff0c\u9632\u6b62\u5728\u6a21\u578b\u5185\u90e8\u62fc\u63a5\u65f6\u62a5\u9519\u3002  \n\u786e\u8ba4\u4fee\u6539\u540e\u91cd\u65b0\u8fd0\u884c\u8bd5\u8bd5\u3002\n\n---\n\n\u5982\u679c\u95ee\u9898\u4f9d\u65e7\uff0c\u8bf7\u8d34\u51fa`dep_norm.shape`, `rgb_norm.shape`, `lr_norm.shape`\u88c1\u526a\u524d\u540e\u7684\u5c3a\u5bf8\uff0c\u6211\u5e2e\u4f60\u8be6\u7ec6\u5206\u6790\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":4844,"n_turns_in_conversation":16,"n_words":470,"source_row_offset":54849,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"0abcaebb5f13da2dd2db4af6","turn_index":7,"user_text":"Found 6 image groups.\nsintel_frame_0001_bix2: Input dep dtype = uint8, shape = (218, 512)\nsintel_frame_0001_bix2: Input rgb dtype = uint8, shape = (218, 512, 3)\nsintel_frame_0001_bix2: Input lr dtype = uint8, shape = (54, 128)\ndepth.shape=torch.Size([1, 64, 32, 128]), guidance.shape=torch.Size([1, 64, 48, 128])\nTraceback (most recent call last):\n  File \"test3.py\", line 169, in <module>\n    main()\n  File \"test3.py\", line 141, in main\n    out = model(inputs)['img_out']\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 364, in forward\n    tmp = self.fuse_conv[i](depth=depth_out[3 - i],\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 293, in forward\n    depth, guide = self.attention_layer(depth=depth, guidance=guide)\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 273, in forward\n    fuse_feature = self.squeeze(torch.cat((depth, guidance), 1))\nRuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 48 for tensor number 1 in the list."},{"assistant_initiates":false,"asst_text":"`mod_crop` \u8fd9\u4e2a\u51fd\u6570\u7684\u4f5c\u7528\u662f**\u88c1\u526a\u56fe\u50cf\u7684\u9ad8\u548c\u5bbd\uff0c\u4f7f\u5176\u5c3a\u5bf8\u53d8\u6210\u6307\u5b9a\u6570\u5b57 `modulo` \u7684\u6574\u6570\u500d**\uff0c\u907f\u514d\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u56e0\u4e3a\u4e0d\u80fd\u88ab\u7f51\u7edc\u7ed3\u6784\u4e2d\u7279\u5b9a\u6a21\u5757\uff08\u5982\u5377\u79ef\u3001\u4e0b\u91c7\u6837\u3001\u91d1\u5b57\u5854\u3001\u5bf9\u9f50\u7b49\uff09\u6574\u9664\u800c\u53d1\u751f\u5f62\u72b6\u4e0d\u5339\u914d\u7684\u95ee\u9898\u3002\n\n---\n\n### \u4e3a\u4ec0\u4e48\u9700\u8981`mod_crop`\uff1f\n\n\u591a\u6570\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5377\u79ef\u7ed3\u6784\uff0c\u6bd4\u5982\u6709\u6c60\u5316\u6216\u8005\u6b65\u957f\u4e3a2\u7684\u5377\u79ef\u5c42\uff0c\u591a\u6b21\u4e0b\u91c7\u6837\u540e\uff0c\u8f93\u5165\u5c3a\u5bf8\u5fc5\u987b\u6ee1\u8db3\u4e00\u5b9a\u7684\u201c\u5bf9\u9f50\u201d\u6761\u4ef6\uff08\u5c3a\u5bf8\u5fc5\u987b\u662f\u67d0\u4e2a\u6570\u7684\u500d\u6570\uff09\uff0c\u5426\u5219\u7f51\u7edc\u4e2d\u95f4\u7684feature map\u5c3a\u5bf8\u4f1a\u5bfc\u81f4shape\u4e0d\u5339\u914d\u9519\u8bef\u3002\n\n\u4e3e\u4e2a\u4f8b\u5b50\uff1a\n\n- \u7f51\u7edc\u67094\u5c42\u4e0b\u91c7\u6837\uff0c\u6bcf\u6b21\u91c7\u6837\u5c3a\u5ea6\u7f29\u5c0f2\u500d\uff0c\u5219\u8f93\u5165\u9ad8\u5bbd\u987b\u662f \\(2^4 = 16\\) \u7684\u500d\u6570\u3002\n- \u5982\u679c\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u662f `(218, 512)`\uff0c218 \u4e0d\u662f16\u7684\u500d\u6570\uff0c\u5c31\u4f1a\u5bfc\u81f4\u7f51\u7edc\u4e2d\u95f4\u5c3a\u5bf8\u9519\u8bef\u3002\n- `mod_crop`\u51fd\u6570\u4f1a\u5207\u6389\u8fb9\u754c\u51e0\u884c\u51e0\u5217\uff0c\u88c1\u526a\u6210\u6ee1\u8db3\u6761\u4ef6\u7684\u5c3a\u5bf8\u3002\n\n---\n\n### `mod_crop`\u600e\u4e48\u505a\u7684\uff1f\n\n\u5bf9\u6bcf\u5f20\u56fe\u50cf\u7684\u9ad8\u548c\u5bbd\u8ba1\u7b97 `crop_h = h % modulo`\uff0c\u5373\u9ad8\u7ef4\u5ea6\u9664\u4ee5 `modulo` \u7684\u4f59\u6570\uff0c\u7136\u540e\u628a\u4f59\u6570\u5747\u5300\u4ece\u4e24\u8fb9\u88c1\u526a\uff1a\n\n\u793a\u4f8b\uff08\u4e8c\u7ef4\u56fe\u50cf\uff09\uff1a\n\n```python\ndef crop(img):\n    h, w = img.shape\n    crop_h, crop_w = h % modulo, w % modulo\n    # \u4ece\u4e0a\u4e0b\u88c1\u526a crop_h \u884c\uff08\u5c3d\u91cf\u5747\u5300\u88c1\uff09\n    img_cropped = img[\n        crop_h // 2 : h - (crop_h - crop_h // 2),\n        crop_w // 2 : w - (crop_w - crop_w // 2)\n    ]\n    return img_cropped\n```\n\n\u4e09\u7ef4\uff08C,H,W\uff09\u65f6\u9ad8\u5bbd\u540c\u7406\uff1a\n\n```python\ndef crop(img):\n    c, h, w = img.shape\n    crop_h, crop_w = h % modulo, w % modulo\n    img_cropped = img[\n        :,\n        crop_h // 2 : h - (crop_h - crop_h // 2),\n        crop_w // 2 : w - (crop_w - crop_w // 2)\n    ]\n    return img_cropped\n```\n\n---\n\n### \u603b\u7ed3\n\n- `mod_crop`\u7528\u6765\u4fdd\u8bc1\u7f51\u7edc\u8f93\u5165\u5c3a\u5bf8\u6ee1\u8db3\u88ab`modulo`\u6574\u9664\u7684\u8981\u6c42\u3002\n- \u5b83\u901a\u8fc7\u88c1\u526a\u56fe\u50cf\u8fb9\u754c\u50cf\u7d20\uff0c\u5b9e\u73b0\u628a\u56fe\u50cf\u9ad8\u5bbd\u8c03\u6574\u4e3a`modulo`\u7684\u500d\u6570\u3002\n- \u8fd9\u662f\u6df1\u5ea6\u5b66\u4e60\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u7ecf\u5178\u9884\u5904\u7406\u6b65\u9aa4\uff0c\u5c24\u5176\u662f\u5bf9\u591a\u5c3a\u5ea6\u3001\u591a\u5c42\u7f51\u7edc\u7ed3\u6784\u5f88\u91cd\u8981\u3002\n\n\u5982\u679c\u6ca1\u6709\u8fd9\u4e2a\u88c1\u526a\uff0c\u7f51\u7edc\u7684\u5377\u79ef\u4e0b\u91c7\u6837\u8fc7\u7a0b\u53ef\u80fd\u51fa\u73b0\u5c3a\u5bf8\u4e0d\u5bf9\u9f50\uff0c\u62a5\u9519\u6216\u8005\u6a21\u578b\u6027\u80fd\u4e0b\u964d\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":1235,"n_turns_in_conversation":16,"n_words":143,"source_row_offset":54850,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"3360ea54c02cd094f0d576e3","turn_index":8,"user_text":"mod_crop\u8fd9\u4e2a\u51fd\u6570\u662f\u8d77\u4ec0\u4e48\u4f5c\u7528\u7684"},{"assistant_initiates":false,"asst_text":"\u8fd9\u6bb5\u4ee3\u7801\uff1a\n\n```python\nmodule = max(int(math.pow(2, 1 + args.num_pyramid)), args.scale)\n```\n\n\u7684\u542b\u4e49\u662f\u8ba1\u7b97\u4e00\u4e2a\u201c\u6a21\u6570\u201d\uff08`module`\uff09\uff0c\u7528\u4e8e\u4fdd\u8bc1\u56fe\u50cf\u5c3a\u5bf8\u7b26\u5408\u7f51\u7edc\u7ed3\u6784\u5bf9\u8f93\u5165\u5927\u5c0f\u7684\u8981\u6c42\u3002\n\n---\n\n### \u5177\u4f53\u89e3\u91ca\uff1a\n\n- `args.num_pyramid` \u662f\u7f51\u7edc\u91cc**\u91d1\u5b57\u5854\u5c42\u6570**\uff08pyramid\u5c42\u6570\uff0c\u901a\u5e38\u8868\u793a\u591a\u5c3a\u5ea6\u7279\u5f81\u63d0\u53d6\u7684\u5c42\u7ea7\u6570\uff09\uff0c\u6bd4\u5982\u91d1\u5b57\u5854\u5c42\u6570\u4e3a2\u62163\u3002\n- `math.pow(2, 1 + args.num_pyramid)`\uff1a\u8ba1\u7b97 \\(2^{1 + \\text{num_pyramid}}\\)\uff0c\u4e5f\u5c31\u662f2\u7684`(1 + num_pyramid)`\u6b21\u65b9\u3002\n\n\u4e3e\u4e2a\u4f8b\u5b50\uff1a\n\n| `args.num_pyramid` | \u8ba1\u7b97\u7ed3\u679c (2^(1 + num_pyramid)) |\n|-------------------|-------------------------------|\n| 1                 | \\(2^{2}=4\\)                   |\n| 2                 | \\(2^{3}=8\\)                   |\n| 3                 | \\(2^{4}=16\\)                  |\n\n- \u7136\u540e\u5c06\u8fd9\u4e2a\u503c\u548c `args.scale` \uff08\u901a\u5e38\u662f\u8d85\u5206\u8fa8\u7387\u7f29\u653e\u500d\u6570\uff0c\u59824\u30018\u7b49\uff09\u505a\u6bd4\u8f83\uff0c\u53d6\u8f83\u5927\u503c\u4f5c\u4e3a `module`\u3002\n\n---\n\n### \u4e3a\u4ec0\u4e48\u8981\u8fd9\u4e48\u505a\uff1f\n\n- \u901a\u5e38\u91d1\u5b57\u5854\u7ed3\u6784\u4e0b\uff0c\u7f51\u7edc\u5bf9\u8f93\u5165\u5c3a\u5bf8\u6709\u8981\u6c42\uff1a\u8f93\u5165\u7684\u9ad8\u5bbd\u5fc5\u987b\u662f \\(2^{1 + \\text{num_pyramid}}\\) \u7684\u500d\u6570\uff0c\u5426\u5219\u591a\u6b21\u4e0b\u91c7\u6837\u3001\u7279\u5f81\u878d\u5408\u65f6\uff0c\u5c3a\u5bf8\u4f1a\u4e0d\u5339\u914d\u3002\n- \u540c\u65f6\uff0c\u56e0\u4e3a\u662f\u8d85\u5206\u7f51\u7edc\uff0c\u6700\u7ec8\u8f93\u51fa\u5c3a\u5bf8\u4f1a\u653e\u5927 `args.scale` \u500d\uff0c\u6240\u4ee5\u5c3a\u5bf8\u4e5f\u81f3\u5c11\u5e94\u8be5\u662f `args.scale` \u7684\u500d\u6570\u3002\n- \u53d6\u4e24\u8005\u4e2d\u8f83\u5927\u8005\uff0c\u4fdd\u8bc1\uff1a\n  - \u6ee1\u8db3\u7f51\u7edc\u91d1\u5b57\u5854\u7ed3\u6784\u5c3a\u5bf8\u9650\u5236\uff1b\n  - \u540c\u65f6\u6ee1\u8db3\u5c3a\u5ea6\u653e\u5927\u7ea6\u675f\u3002\n\n---\n\n### \u4ee3\u7801\u4f5c\u7528\u603b\u7ed3\uff1a\n\n```python\nmodule = max(2^{1 + num_pyramid}, scale)\n```\n\n- `module` \u5373\u8fd9\u4e2a\u201c\u6a21\u6570\u201d\uff0c\u7528\u6765\u88c1\u526a\u8f93\u5165\u56fe\u50cf\u7684\u9ad8\u5bbd\uff0c\u4f7f\u5176\u662f `module` \u7684\u6574\u6570\u500d\u3002\n\n- \u4e3e\u4f8b\uff1a\u5982\u679c `num_pyramid=2`\uff0c `scale=8`\uff0c\u5219\n\n```python\nmodule = max(2^{3}, 8) = max(8, 8) = 8\n```\n\n\u6b64\u65f6\u8f93\u5165\u56fe\u7684\u9ad8\u5bbd\u5fc5\u987b\u662f8\u7684\u500d\u6570\u3002\n\n- \u5982\u679c `num_pyramid=3`\uff0c`scale=4`\uff0c\u5219\n\n```python\nmodule = max(2^{4}, 4) = max(16, 4) = 16\n```\n\n\u8f93\u5165\u5c3a\u5bf8\u9700\u662f16\u7684\u500d\u6570\u3002\n\n---\n\n### \u4f7f\u7528\u573a\u666f\n\n\u914d\u5408\u524d\u9762`mod_crop`\u51fd\u6570\uff0c\u5c06\u8f93\u5165\u56fe\u50cf\u88c1\u526a\u5230\u9ad8\u5bbd\u4e3a `module` \u7684\u6574\u6570\u500d\uff0c\u4ece\u800c\u4fdd\u8bc1\u6a21\u578b\u8fd0\u884c\u65f6\u5c3a\u5bf8\u5339\u914d\uff0c\u65e0shape\u62a5\u9519\u3002\n\n---\n\n\u5982\u679c\u8fd8\u60f3\u66f4\u6df1\u5165\u7406\u89e3\uff0c\u53ef\u4ee5\u544a\u8bc9\u6211 `args.num_pyramid`\u3001`args.scale` \u7684\u5177\u4f53\u53d6\u503c\uff0c\u6211\u53ef\u4ee5\u5e2e\u4f60\u7b97\u7b97\u5b9e\u9645\u5c3a\u5bf8\u8981\u6c42\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":1461,"n_turns_in_conversation":16,"n_words":142,"source_row_offset":54851,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"d1696c84fe530bfc2094ba60","turn_index":9,"user_text":"module = max(int(math.pow(2, 1 + args.num_pyramid)), args.scale)\u8fd9\u4e2a\u4ee3\u7801\u53c8\u662f\u4ec0\u4e48\u610f\u601d"},{"assistant_initiates":false,"asst_text":"\u4f60\u76ee\u524d\u7684\u4ee3\u7801\u4e2d\u4f7f\u7528\u4e86 `mod_crop` \u51fd\u6570\u6765\u88c1\u526a\u56fe\u50cf\uff0c\u4f7f\u5176\u5c3a\u5bf8\u80fd\u88ab `module` \u6574\u9664\u3002\u4f46\u4f60\u5e0c\u671b\u6539\u6210**\u5bf9\u4e0d\u80fd\u88ab32\u6574\u9664\u7684\u56fe\u50cf\u8fdb\u884c\u586b\u5145\uff08padding\uff09\u5230\u80fd\u88ab32\u6574\u9664\u7684\u5927\u5c0f**\uff0c\u5e76\u4e14**\u4fdd\u8bc1\u6700\u7ec8\u8f93\u51fa\u4e0d\u53d7\u586b\u5145\u7684\u5f71\u54cd**\uff08\u5373\u8fd8\u539f\u5230\u539f\u59cb\u5c3a\u5bf8\uff09\u3002\u4e0b\u9762\u5e2e\u4f60\u4fee\u6539\u4ee3\u7801\u5b9e\u73b0\u8fd9\u4e2a\u529f\u80fd\u3002\n\n---\n\n## \u65b9\u6848\u601d\u8def\n\n1. **\u5b9a\u4e49 `mod_pad` \u51fd\u6570**  \n   \u5c06\u8f93\u5165\u56fe\u50cf\u586b\u5145\u5230\u9ad8\u5bbd\u80fd\u88ab32\u6574\u9664\uff08\u5373`module=32`\uff09\uff0c\u8fb9\u754c\u5728\u56fe\u50cf\u53f3\u4fa7\u548c\u4e0b\u4fa7\u88650\uff08\u6216\u5176\u4ed6\u503c\uff09\uff0c\u540c\u65f6\u8bb0\u5f55\u586b\u5145\u50cf\u7d20\u7684\u6570\u91cf\u3002\n\n2. **\u6a21\u578b\u8f93\u5165\u4f7f\u7528\u586b\u5145\u540e\u7684\u56fe\u50cf\uff0c\u4fdd\u8bc1\u5c3a\u5bf8\u7b26\u5408\u7f51\u7edc\u9700\u6c42\u3002**\n\n3. **\u6a21\u578b\u63a8\u7406\u7ed3\u675f\u540e\uff0c\u88c1\u526a\u8f93\u51fa\u7ed3\u679c\u56de\u539f\u59cb\u5927\u5c0f\uff0c\u53bb\u9664\u586b\u5145\u90e8\u5206\u3002**\n\n---\n\n## \u4ee3\u7801\u793a\u8303\n\n```python\nimport numpy as np\nimport torch\nimport cv2\n\ndef mod_pad(img, modulo=32):\n    \"\"\"\n    \u5bf9\u8f93\u5165\u56fe\u50cf\u586b\u5145\uff08pad\uff09\u4f7f\u5176H\u548cW\u53d8\u6210modulo\u7684\u6574\u6570\u500d\n    pad\u65b9\u5f0f\u662f\u5728\u4e0b\u8fb9\u548c\u53f3\u8fb9\u88650\n    img: numpy array, \u652f\u63012D (H,W)\u62163D (C,H,W)\n    \u8fd4\u56de\uff1apad\u540e\u56fe\u50cf\u548c\u88c1\u526a\u4fe1\u606f (pad_bottom, pad_right)\n    \"\"\"\n    if img.ndim == 2:\n        h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,pad_h),(0,pad_w)), mode='constant')\n    elif img.ndim == 3:\n        c, h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,0),(0,pad_h),(0,pad_w)), mode='constant')\n    else:\n        raise ValueError(f\"Unsupported img ndim {img.ndim}\")\n    return padded_img, pad_h, pad_w\n\n# \u793a\u4f8b\u8c03\u7528\uff1a\n# dep_norm_padded, pad_h, pad_w = mod_pad(np.expand_dims(dep_norm, 0), modulo=32)\n\n# \u63a8\u7406\u540e\u88c1\u526a\u56de\u539f\u59cb\u5c3a\u5bf8\ndef crop_to_original(img, pad_h, pad_w):\n    \"\"\"\n    \u8f93\u5165\u5f20\u91cf\u6216numpy\u6570\u7ec4\uff0c\u88c1\u526a\u56de\u53bbpadding\u4e4b\u524d\u5927\u5c0f\n    \u652f\u6301numpy\u6570\u7ec4\u6216torch tensor\uff0c\u7ef4\u5ea6\u652f\u6301[B,C,H,W]\u6216[C,H,W]\u6216[H,W]\n    \"\"\"\n    if isinstance(img, torch.Tensor):\n        if img.dim() == 4:\n            return img[..., :-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        elif img.dim() == 3:\n            return img[:, :-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        elif img.dim() == 2:\n            return img[:-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        else:\n            raise ValueError(f\"Unsupported tensor dim {img.dim()}\")\n    else:\n        # numpy\u6570\u7ec4\n        if img.ndim == 4:\n            return img[..., :-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        elif img.ndim ==3:\n            return img[:, :-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        elif img.ndim == 2:\n            return img[:-pad_h if pad_h !=0 else None, :-pad_w if pad_w !=0 else None]\n        else:\n            raise ValueError(f\"Unsupported numpy ndim {img.ndim}\")\n\n# -----------------\n# \u4e0b\u9762\u662f\u4f60\u4ee3\u7801\u4e2d\u6838\u5fc3\u90e8\u5206\u7684\u793a\u8303\u4fee\u6539\n\n# \u5f52\u4e00\u5316\ndep_norm, dep_max = normalize_img(dep)\nlr_norm, lr_max = normalize_img(lr)\nrgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n# \u5148pad\u586b\u5145\u523032\u7684\u500d\u6570\ndep_norm_padded, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm_padded, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=32)\nlr_norm_padded, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=32)\n\n# \u4e09\u4e2apad\u5927\u5c0f\u5e94\u8be5\u4e00\u6837\uff0c\u5efa\u8bae\u65ad\u8a00\u4e00\u4e0b\nassert pad_h_dep == pad_h_rgb == pad_h_lr, \"\u586b\u5145h\u4e0d\u4e00\u81f4\uff01\"\nassert pad_w_dep == pad_w_rgb == pad_w_lr, \"\u586b\u5145w\u4e0d\u4e00\u81f4\uff01\"\n\n# \u4e0a\u91c7\u6837 lr\nimport cv2\nh_lr, w_lr = lr_norm_padded.shape[-2], lr_norm_padded.shape[-1]\nlr_up = cv2.resize(lr_norm_padded, (w_lr * scale, h_lr * scale), interpolation=cv2.INTER_LINEAR)\nif lr_up.ndim == 2:\n    lr_up = np.expand_dims(lr_up, 0)\n\n# \u8f6ctensor\u5e76\u52a0batch\u7ef4\ndep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm_padded, rgb_norm_padded, lr_norm_padded, lr_up)\ndep_t = dep_t.unsqueeze(0).cuda()\nrgb_t = rgb_t.unsqueeze(0).cuda()\nlr_t = lr_t.unsqueeze(0).cuda()\nlr_up_t = lr_up_t.unsqueeze(0).cuda()\n\ninputs = {\n    'img_gt': dep_t,\n    'img_rgb': rgb_t,\n    'img_lr': lr_t,\n    'lr_up': lr_up_t,\n}\n\nwith torch.no_grad():\n    out = model(inputs)['img_out']\n\n# \u88c1\u526a\u56de\u539f\u59cb\u5c3a\u5bf8\nout = crop_to_original(out, pad_h_dep, pad_w_dep)\n\n# \u53cd\u5f52\u4e00\u5316\nout_img = out.squeeze().cpu().numpy()\nout_img = np.clip(out_img, 0, 1) * dep_max\n\n# \u6839\u636e\u8f93\u5165\u7c7b\u578b\u8f6c\u6574\u578b\nif dep.dtype == np.uint8:\n    out_img_int = np.round(out_img).astype(np.uint8)\n    dep_int = dep\nelif dep.dtype == np.uint16:\n    out_img_int = np.round(out_img).astype(np.uint16)\n    dep_int = dep\nelse:\n    out_img_int = out_img.astype(np.float32)\n    dep_int = dep.astype(np.float32)\n\n# \u8ba1\u7b97RMSE\nrmse_val = calc_rmse(out_img_int, dep_int)\nprint(f'{prefix}: RMSE = {rmse_val:.4f}')\n\n# \u4fdd\u5b58\u56fe\u50cf\uff08\u6ce8\u610fsize\u5df2\u7ecf\u662f\u539f\u59cb\u5927\u5c0f\uff09\ncv2.imwrite(os.path.join(output_dir, f'{prefix}_sr.png'), out_img_int)\n```\n\n---\n\n# \u603b\u7ed3\n\n- \u4f7f\u7528`mod_pad`\u5bf9\u8f93\u5165\u56fe\u7247\u505a\u586b\u5145\uff0c\u4fdd\u8bc1\u5c3a\u5bf8\u4e3a32\u7684\u500d\u6570\uff08\u6216\u5176\u4ed6modulo\uff09\u3002\n- \u6574\u4e2a\u63a8\u7406\u6d41\u7a0b\u4f7f\u7528\u586b\u5145\u540e\u7684\u56fe\u7247\u8f93\u5165\u6a21\u578b\uff0c\u4fdd\u8bc1\u4e0d\u62a5\u9519\u3002\n- \u63a8\u7406\u5b8c\u6210\u540e\u7528`crop_to_original`\u88c1\u526a\u6389\u591a\u4f59\u7684\u586b\u5145\u90e8\u5206\uff0c\u4fdd\u8bc1\u8f93\u51fasize\u548c\u539f\u59cb\u8f93\u5165\u4e00\u81f4\u3002\n- \u8fd9\u6837\u6539\u4e0d\u4f1a\u5f71\u54cd\u6700\u7ec8\u6548\u679c\u4f46\u53ef\u4ee5\u907f\u514d\u5c3a\u5bf8\u4e0d\u5339\u914d\u53d1\u751f\u9519\u8bef\u3002\n\n\u5982\u679c\u9700\u8981\uff0c\u6211\u53ef\u4ee5\u5e2e\u4f60\u628a\u5b8c\u6574\u4ee3\u7801\u7528\u8fd9\u4e2a\u65b9\u5f0f\u5b9e\u73b0\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":4381,"n_turns_in_conversation":16,"n_words":466,"source_row_offset":54852,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"290afdbe906576125cc2c2ba","turn_index":10,"user_text":"\u8bf7\u5e2e\u6211\u4fee\u6539\u4e0b\u8ff0\u4ee3\u7801\uff0c\u4f7f\u5f97\u4e0d\u80fd\u88ab32\u6574\u9664\u7684\u56fe\u50cf\u88ab\u586b\u5145\u5230\u80fd\u88ab32\u6574\u9664\u7684\u5927\u5c0f\uff0c\u5e76\u4e14\u4e0d\u5f71\u54cd\u6700\u7ec8\u56fe\u50cf\u8f93\u51fa"},{"assistant_initiates":false,"asst_text":"\u4f60\u63d0\u4f9b\u7684\u4ee3\u7801\u7528\u4e86 `mod_crop` \u51fd\u6570\u6765\u88c1\u526a\u56fe\u50cf\uff0c\u8fd9\u4f1a\u88c1\u6389\u8fb9\u754c\u7684\u50cf\u7d20\uff0c\u5bfc\u81f4\u8f93\u51fa\u7ed3\u679c\u6bd4\u539f\u59cb\u56fe\u50cf\u5c0f\u3002\u4f60\u5e0c\u671b\u6539\u6210**\u5bf9\u4e0d\u80fd\u88ab32\u6574\u9664\u7684\u56fe\u50cf\u8fdb\u884c\u201c\u586b\u5145\u201d**\uff08pad\uff09\u5230\u80fd\u88ab32\u6574\u9664\u7684\u5927\u5c0f\uff0c\u4e14**\u63a8\u7406\u8f93\u51fa\u540e\u88c1\u526a\u56de\u539f\u59cb\u5927\u5c0f\uff0c\u4e0d\u5f71\u54cd\u6700\u7ec8\u8f93\u51fa**\u3002\n\n---\n\n\u6211\u5728\u4f60\u7684\u4ee3\u7801\u57fa\u7840\u4e0a\u5e2e\u4f60\u5199\u4e86 `mod_pad` \u548c\u8f93\u51fa\u7aef\u7684\u88c1\u526a\u51fd\u6570 `crop_to_original`\uff0c\u5e76\u66ff\u6362\u4e86`mod_crop`\u90e8\u5206\u3002\u4fee\u6539\u540e\u7684\u4ee3\u7801\u5982\u4e0b\uff1a\n\n```python\nimport os\nimport glob\nimport torch\nimport numpy as np\nfrom ahmf import AHMF\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\ndef mod_pad(img, modulo=32):\n    \"\"\"\n    \u5bf9\u8f93\u5165\u56fe\u50cf\u586b\u5145\uff08pad\uff09\u4f7f\u5176H\u548cW\u53d8\u6210modulo\u7684\u6574\u6570\u500d\u3002\n    pad\u65b9\u5f0f\uff1a\u5728\u4e0b\u8fb9\u548c\u53f3\u8fb9\u88650\u3002\n    img\u652f\u63012D (H,W) \u6216 3D (C,H,W) numpy\u6570\u7ec4\u3002\n    \u8fd4\u56depadding\u540e\u7684\u56fe\u50cf\uff0c\u4ee5\u53ca\u586b\u5145\u7684\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u6570\u91cf\u3002\n    \"\"\"\n    if img.ndim == 2:\n        h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,pad_h),(0,pad_w)), mode='constant')\n    elif img.ndim == 3:\n        c, h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,0),(0,pad_h),(0,pad_w)), mode='constant')\n    else:\n        raise ValueError(f\"Unsupported img ndim {img.ndim}\")\n    return padded_img, pad_h, pad_w\n\n\ndef crop_to_original(img, pad_h, pad_w):\n    \"\"\"\n    \u8f93\u5165\u7684img\u53ef\u4ee5\u4e3anumpy\u6570\u7ec4 \u6216 pytorch tensor\u3002\n    \u88c1\u526a\u6389\u4e4b\u524dpad\u589e\u52a0\u7684\u90e8\u5206\uff0c\u6062\u590d\u4e3a\u539f\u59cb\u5927\u5c0f\u3002\n    \u652f\u6301numpy\u6570\u7ec4\u53catensor\uff0c\u4e14shape\u652f\u6301[B,C,H,W]\u3001[C,H,W]\u3001[H,W]\u3002\n    \"\"\"\n    if isinstance(img, torch.Tensor):\n        if img.dim() == 4:\n            return img[..., :img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        elif img.dim() == 3:\n            return img[:, :img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        elif img.dim() == 2:\n            return img[:img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        else:\n            raise ValueError(f\"Unsupported tensor dim {img.dim()}\")\n    else:\n        # numpy \u6570\u7ec4\n        if img.ndim == 4:\n            return img[..., :img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        elif img.ndim == 3:\n            return img[:, :img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        elif img.ndim == 2:\n            return img[:img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        else:\n            raise ValueError(f\"Unsupported numpy ndim {img.ndim}\")\n\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef normalize_img(img):\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        max_val = float(np.max(img)) if np.max(img) > 1.0 else 1.0\n        return img.astype(np.float32) / max_val, max_val\n\ndef calc_rmse(pred, target):\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n\n\ndef main():\n    scale = 4    \n    model = AHMF(scale=scale, act='PReLU', in_channels=1, guidance_channels=3)\n    ckpt_path = f'/home/jianruobing/code/AHMF-main/AHMF-test/model_x{scale}.pth'\n    checkpoint = torch.load(ckpt_path, map_location='cpu')\n    model.load_state_dict(checkpoint['state_dict'])\n    model = model.cuda().eval()\n    \n    input_dir = '/home/jianruobing/code/AHMF-main/imgs2npy'\n    output_dir = './output'\n    os.makedirs(output_dir, exist_ok=True)\n\n    all_files = []\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth_x4.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_rgb.npy')))\n\n    groups = {}\n    for fpath in all_files:\n        fname = os.path.basename(fpath)\n        if '_depth_x4' in fname:\n            prefix = fname.split('_depth_x4')[0]\n            key = 'depth_x4'\n        elif '_depth' in fname:\n            prefix = fname.split('_depth')[0]\n            key = 'depth'\n        elif '_rgb' in fname:\n            prefix = fname.split('_rgb')[0]\n            key = 'rgb'\n        else:\n            continue\n        groups.setdefault(prefix, {})[key] = fpath\n\n    print(f'Found {len(groups)} image groups.')\n\n    for prefix, files in groups.items():\n        if not {'depth', 'depth_x4', 'rgb'}.issubset(files.keys()):\n            print(f'Warning: missing files for {prefix}, skipped.')\n            continue\n\n        dep = np.load(files['depth'])\n        lr = np.load(files['depth_x4'])\n        rgb = np.load(files['rgb'])\n\n        print(f\"{prefix}: Input dep dtype = {dep.dtype}, shape = {dep.shape}\")\n        print(f\"{prefix}: Input rgb dtype = {rgb.dtype}, shape = {rgb.shape}\")\n        print(f\"{prefix}: Input lr dtype = {lr.dtype}, shape = {lr.shape}\")\n\n        dep_norm, dep_max = normalize_img(dep)\n        lr_norm, lr_max = normalize_img(lr)\n        rgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n        # \u4f7f\u7528mod_pad\u586b\u5145\u81f332\u7684\u500d\u6570\n        dep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=32)\n        rgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=32)\n        lr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=32)\n\n        assert pad_h_dep == pad_h_rgb == pad_h_lr, \"\u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\"\n        assert pad_w_dep == pad_w_rgb == pad_w_lr, \"\u586b\u5145\u5bbd\u5ea6\u4e0d\u4e00\u81f4\"\n\n        import cv2\n        h_lr, w_lr = lr_norm_pad.shape[-2], lr_norm_pad.shape[-1]\n        lr_up = cv2.resize(lr_norm_pad, (w_lr * scale, h_lr * scale), interpolation=cv2.INTER_LINEAR)\n        if lr_up.ndim == 2:\n            lr_up = np.expand_dims(lr_up, 0)\n\n        dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm_pad, rgb_norm_pad, lr_norm_pad, lr_up)\n        dep_t = dep_t.unsqueeze(0).cuda()\n        rgb_t = rgb_t.unsqueeze(0).cuda()\n        lr_t = lr_t.unsqueeze(0).cuda()\n        lr_up_t = lr_up_t.unsqueeze(0).cuda()\n\n        inputs = {\n            'img_gt': dep_t,\n            'img_rgb': rgb_t,\n            'img_lr': lr_t,\n            'lr_up': lr_up_t,\n        }\n\n        with torch.no_grad():\n            out = model(inputs)['img_out']\n\n        # \u88c1\u526a\u56de\u539f\u59cb\u5c3a\u5bf8\n        out = crop_to_original(out, pad_h_dep, pad_w_dep)\n\n        out_img = out.squeeze().cpu().numpy()\n        out_img = np.clip(out_img, 0, 1) * dep_max\n\n        if dep.dtype == np.uint8:\n            out_img_int = np.round(out_img).astype(np.uint8)\n            dep_int = dep\n        elif dep.dtype == np.uint16:\n            out_img_int = np.round(out_img).astype(np.uint16)\n            dep_int = dep\n        else:\n            out_img_int = out_img.astype(np.float32)\n            dep_int = dep.astype(np.float32)\n\n        rmse_val = calc_rmse(out_img_int, dep_int)\n        print(f'{prefix}: RMSE = {rmse_val:.4f}')\n\n        save_path = os.path.join(output_dir, f'{prefix}_sr.png')\n        import cv2\n        cv2.imwrite(save_path, out_img_int)\n        print(f'Saved SR image to {save_path}')\n\nif __name__ == '__main__':\n    main()\n```\n\n---\n\n### \u4e3b\u8981\u6539\u52a8\u70b9\n\n- \u79fb\u9664\u4e86`mod_crop`\u88c1\u526a\uff0c\u5c06\u5176\u66ff\u6362\u4e3a`mod_pad`\u586b\u5145\uff0c\u4f7f\u8f93\u5165\u5c3a\u5bf8\u53d8\u4e3a32\u7684\u500d\u6570\n- \u6a21\u578b\u8f93\u51fa\u540e\uff0c\u4f7f\u7528`crop_to_original`\u53bb\u9664padding\u90e8\u5206\uff0c\u8fd8\u539f\u5230\u539f\u59cb\u5927\u5c0f\n- \u4fdd\u8bc1`dep_norm`\u3001`rgb_norm`\u3001`lr_norm`\u5747\u4f7f\u7528\u76f8\u540c\u7684padding\u4fdd\u6301\u4e00\u81f4\u5c3a\u5bf8\uff0c\u907f\u514dshape\u4e0d\u5339\u914d\u9519\u8bef\u53d1\u751f\n\n---\n\n\u8fd9\u6837\u80fd\u4fdd\u8bc1\uff1a\n\n- \u8f93\u5165\u6ee1\u8db3\u6a21\u578b\u5c3a\u5bf8\u8981\u6c42\n- \u4e0d\u88c1\u526a\u539f\u59cb\u6709\u6548\u533a\u57df\uff0c\u907f\u514d\u7cbe\u5ea6\u635f\u5931\n- \u8f93\u51fa\u56fe\u50cf\u5927\u5c0f\u4e0e\u8f93\u5165\u5927\u5c0f\u4e00\u81f4\uff0c\u65b9\u4fbf\u540e\u7eed\u4fdd\u5b58\u548c\u8bc4\u4ef7\n\n---\n\n\u5982\u679c\u4f60\u9700\u8981\uff0c\u6211\u53ef\u4ee5\u5e2e\u4f60\u5199\u6210\u4e00\u4e2a\u51fd\u6570\u7248\u672c\uff0c\u65b9\u4fbf\u96c6\u6210\u548c\u590d\u7528\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":13540,"n_turns_in_conversation":16,"n_words":1258,"source_row_offset":54853,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"b4e59c901e576a7f2adb44be","turn_index":11,"user_text":"\u628a\u4e0a\u8ff0\u529f\u80fd\u52a0\u5165\u8fd9\u4e2a\u4ee3\u7801\uff1a\nimport os\nimport glob\nimport torch\nimport numpy as np\nfrom ahmf import AHMF\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\ndef mod_crop(*imgs, modulo):\n    def crop(img):\n        if img.ndim == 2:\n            h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        elif img.ndim == 3:\n            c, h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[:, crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        else:\n            raise ValueError(f\"Unsupported image dimension: {img.ndim}\")\n    out = [crop(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef normalize_img(img):\n    # \u4fdd\u6301\u539f\u6570\u636e\u7c7b\u578b\uff0c\u5e76\u5f52\u4e00\u5316\uff08\u548cquick_test\u7c7b\u4f3c\uff09\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        # float\u7c7b\u578b\u6216\u8005\u5176\u5b83\n        max_val = float(np.max(img)) if np.max(img) > 1.0 else 1.0\n        return img.astype(np.float32) / max_val, max_val\n\ndef calc_rmse(pred, target):\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n\ndef main():\n    scale = 4    \n    model = AHMF(scale=scale, act='PReLU', in_channels=1, guidance_channels=3)\n    ckpt_path = f'/home/jianruobing/code/AHMF-main/AHMF-test/model_x{scale}.pth'\n    checkpoint = torch.load(ckpt_path, map_location='cpu')\n    model.load_state_dict(checkpoint['state_dict'])\n    model = model.cuda().eval()\n    \n    input_dir = '/home/jianruobing/code/AHMF-main/imgs2npy'\n    output_dir = './output'\n    os.makedirs(output_dir, exist_ok=True)\n\n    # \u67e5\u627enpy\u6587\u4ef6\n    all_files = []\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth_x4.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_rgb.npy')))\n\n    # \u6309\u524d\u7f00\u5206\u7ec4\n    groups = {}\n    for fpath in all_files:\n        fname = os.path.basename(fpath)\n        if '_depth_x4' in fname:\n            prefix = fname.split('_depth_x4')[0]\n            key = 'depth_x4'\n        elif '_depth' in fname:\n            prefix = fname.split('_depth')[0]\n            key = 'depth'\n        elif '_rgb' in fname:\n            prefix = fname.split('_rgb')[0]\n            key = 'rgb'\n        else:\n            continue\n        groups.setdefault(prefix, {})[key] = fpath\n\n    print(f'Found {len(groups)} image groups.')\n\n    for prefix, files in groups.items():\n        if not {'depth', 'depth_x4', 'rgb'}.issubset(files.keys()):\n            print(f'Warning: missing files for {prefix}, skipped.')\n            continue\n\n        dep = np.load(files['depth'])\n        lr = np.load(files['depth_x4'])\n        rgb = np.load(files['rgb'])\n\n        print(f\"{prefix}: Input dep dtype = {dep.dtype}, shape = {dep.shape}\")\n        print(f\"{prefix}: Input rgb dtype = {rgb.dtype}, shape = {rgb.shape}\")\n        print(f\"{prefix}: Input lr dtype = {lr.dtype}, shape = {lr.shape}\")\n\n        # \u5f52\u4e00\u5316\n        dep_norm, dep_max = normalize_img(dep)\n        lr_norm, lr_max = normalize_img(lr)\n        # rgb\u5047\u8bbeshape H, W, C\uff0c\u8f6cCHW\u5e76\u5f52\u4e00\u5316\uff080~1\uff09\n        rgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n        # mod_crop\uff0c\u6df1\u5ea6\u548crgb\u90fd\u62691\u7ef4\uff08channel\uff09\uff0crgb\u8f6cCHW\u5728\u524d\u9762\u5b8c\u6210\u4e86\n        dep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\n        rgb_norm = mod_crop(rgb_norm, modulo=32)\n        lr_norm = mod_crop(lr_norm, modulo=32)\n\n        # lr\u4e0a\u91c7\u6837\u7528cv2\u4e00\u6837\u53cc\u7ebf\u6027\u63d2\u503c\n        h, w = lr_norm.shape[-2], lr_norm.shape[-1]  # \u5bf9\u5e94\u6df1\u5ea6h,w\n        import cv2\n        lr_up = cv2.resize(lr_norm, (w * scale, h * scale), interpolation=cv2.INTER_LINEAR)\n\n        # \u786e\u4fddshape\u7ef4\u5ea6\u5bf9\u5e94\n        if lr_norm.ndim == 2:\n            lr_norm = np.expand_dims(lr_norm, 0)\n        if lr_up.ndim == 2:\n            lr_up = np.expand_dims(lr_up, 0)\n\n        # \u8f6ctensor\u5e76\u589e\u52a0batch\u7ef4\n        dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm, rgb_norm, lr_norm, lr_up)\n        dep_t = dep_t.unsqueeze(0).cuda()\n        rgb_t = rgb_t.unsqueeze(0).cuda()\n        lr_t = lr_t.unsqueeze(0).cuda()\n        lr_up_t = lr_up_t.unsqueeze(0).cuda()\n\n        inputs = {\n            'img_gt': dep_t,\n            'img_rgb': rgb_t,\n            'img_lr': lr_t,\n            'lr_up': lr_up_t,\n        }\n\n        with torch.no_grad():\n            out = model(inputs)['img_out']\n\n        # \u53cd\u5f52\u4e00\u5316\u8fd8\u539f\n        out_img = out.squeeze().cpu().numpy()\n        out_img = np.clip(out_img, 0, 1) * dep_max\n\n        # \u8f6c\u56de\u6574\u6570\u683c\u5f0f\u505aRMSE\u8ba1\u7b97\n        if dep.dtype == np.uint8:\n            out_img_int = np.round(out_img).astype(np.uint8)\n            dep_int = dep\n        elif dep.dtype == np.uint16:\n            out_img_int = np.round(out_img).astype(np.uint16)\n            dep_int = dep\n        else:\n            # float\u6216\u8005\u5176\u5b83\u7c7b\u578b\uff08\u5982int32\uff09\uff0c\u4fdd\u6301float32\uff0cRMSE\u65f6\u4f1a\u81ea\u52a8\u8f6c\u6362\n            out_img_int = out_img.astype(np.float32)\n            dep_int = dep.astype(np.float32)\n\n        rmse_val = calc_rmse(out_img_int, dep_int)\n        print(f'{prefix}: RMSE = {rmse_val:.4f}')\n\n        # \u4fdd\u5b58\u7ed3\u679c\u56fe\u4e3apng\uff0c\u9700\u5148\u8f6cuint8\u6216uint16\n        save_path = os.path.join(output_dir, f'{prefix}_sr.png')\n        import cv2\n        cv2.imwrite(save_path, out_img_int)\n        print(f'Saved SR image to {save_path}')\n\nif __name__ == '__main__':\n    main()"},{"assistant_initiates":false,"asst_text":"\u597d\u7684\uff01\u6211\u5e2e\u4f60\u628a\u201c\u586b\u5145 + \u63a8\u7406 + \u88c1\u526a\u590d\u539f + \u8ba1\u7b97RMSE + \u4fdd\u5b58\u7ed3\u679c\u201d\u5199\u6210\u4e00\u4e2a\u51fd\u6570\uff0c\u65b9\u4fbf\u4f60\u5728\u9879\u76ee\u91cc\u8c03\u7528\u3002\u8fd9\u4e2a\u51fd\u6570\u63a5\u53e3\u8bbe\u8ba1\u6210\uff1a\n\n```python\ndef inference_with_padding(model, dep, lr, rgb, scale=4, modulo=32, device='cuda', save_path=None):\n    \"\"\"\n    \u8bf4\u660e\uff1a\n    - model: \u4f60\u7684\u8d85\u5206\u6a21\u578b\uff0c\u5df2\u7ecf\u5207\u6362\u5230eval\u6a21\u5f0f\u5e76\u52a0\u8f7d\u6743\u91cd\n    - dep: np.array\uff0c\u539f\u59cb\u6df1\u5ea6\u56fe\uff08H,W\uff09\n    - lr: np.array\uff0c\u4f4e\u5206\u8fa8\u7387\u6df1\u5ea6\u56fe\uff08H_lr, W_lr\uff09\n    - rgb: np.array\uff0cRGB\u5f15\u5bfc\u56fe\uff08H, W, 3\uff09\n    - scale: int\uff0c\u8d85\u5206\u6bd4\u4f8b\n    - modulo: int\uff0cpad\u5230\u80fd\u88ab\u8fd9\u4e2a\u6570\u6574\u9664\n    - device: str\uff0ctorch\u8fd0\u884c\u8bbe\u5907\n    - save_path: str\u6216None\uff0c\u82e5\u63d0\u4f9b\u8def\u5f84\uff0c\u5219\u4fdd\u5b58\u8d85\u5206\u540e\u7684\u7ed3\u679c\u56fe\u50cf\n\n    \u8fd4\u56de\uff1a\n    - rmse_val: float\uff0c\u8d85\u5206\u7ed3\u679c\u4e0e\u539f\u59cb\u6df1\u5ea6\u7684RMSE\n    - out_img_int: np.array\uff0c\u8d85\u5206\u7ed3\u679c\u7684\u6574\u6570\u683c\u5f0f\u56fe\u50cf\n    \"\"\"\n\n```\n\n\u4e0b\u9762\u662f\u5b8c\u6574\u4ee3\u7801\uff1a\n\n```python\nimport numpy as np\nimport torch\nimport cv2\n\ndef mod_pad(img, modulo=32):\n    if img.ndim == 2:\n        h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,pad_h),(0,pad_w)), mode='constant')\n    elif img.ndim == 3:\n        c, h, w = img.shape\n        pad_h = (modulo - h % modulo) % modulo\n        pad_w = (modulo - w % modulo) % modulo\n        padded_img = np.pad(img, ((0,0),(0,pad_h),(0,pad_w)), mode='constant')\n    else:\n        raise ValueError(f\"Unsupported img ndim {img.ndim}\")\n    return padded_img, pad_h, pad_w\n\ndef crop_to_original(img, pad_h, pad_w):\n    if isinstance(img, torch.Tensor):\n        if img.dim() == 4:\n            return img[..., :img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        elif img.dim() == 3:\n            return img[:, :img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        elif img.dim() == 2:\n            return img[:img.size(-2)-pad_h if pad_h != 0 else img.size(-2),\n                       :img.size(-1)-pad_w if pad_w != 0 else img.size(-1)]\n        else:\n            raise ValueError(f\"Unsupported tensor dim {img.dim()}\")\n    else:\n        if img.ndim == 4:\n            return img[..., :img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        elif img.ndim == 3:\n            return img[:, :img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        elif img.ndim == 2:\n            return img[:img.shape[-2]-pad_h if pad_h != 0 else img.shape[-2],\n                       :img.shape[-1]-pad_w if pad_w != 0 else img.shape[-1]]\n        else:\n            raise ValueError(f\"Unsupported numpy ndim {img.ndim}\")\n\ndef normalize_img(img):\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        max_val = float(np.max(img)) if np.max(img) > 1.0 else 1.0\n        return img.astype(np.float32) / max_val, max_val\n\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n\ndef calc_rmse(pred, target):\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n\ndef inference_with_padding(model, dep, lr, rgb, scale=4, modulo=32, device='cuda', save_path=None):\n    dep_norm, dep_max = normalize_img(dep)\n    lr_norm, _ = normalize_img(lr)\n    rgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n\n    dep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=modulo)\n    rgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=modulo)\n    lr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=modulo)\n\n    assert pad_h_dep == pad_h_rgb == pad_h_lr, \"Height padding not consistent\"\n    assert pad_w_dep == pad_w_rgb == pad_w_lr, \"Width padding not consistent\"\n\n    h_lr, w_lr = lr_norm_pad.shape[-2], lr_norm_pad.shape[-1]\n    lr_up = cv2.resize(lr_norm_pad, (w_lr * scale, h_lr * scale), interpolation=cv2.INTER_LINEAR)\n    if lr_up.ndim == 2:\n        lr_up = np.expand_dims(lr_up, 0)\n\n    dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm_pad, rgb_norm_pad, lr_norm_pad, lr_up)\n    dep_t = dep_t.unsqueeze(0).to(device)\n    rgb_t = rgb_t.unsqueeze(0).to(device)\n    lr_t = lr_t.unsqueeze(0).to(device)\n    lr_up_t = lr_up_t.unsqueeze(0).to(device)\n\n    inputs = {\n        'img_gt': dep_t,\n        'img_rgb': rgb_t,\n        'img_lr': lr_t,\n        'lr_up': lr_up_t,\n    }\n\n    with torch.no_grad():\n        out = model(inputs)['img_out']\n\n    out = crop_to_original(out, pad_h_dep, pad_w_dep)\n    out_img = out.squeeze().cpu().numpy()\n    out_img = np.clip(out_img, 0, 1) * dep_max\n\n    if dep.dtype == np.uint8:\n        out_img_int = np.round(out_img).astype(np.uint8)\n        dep_int = dep\n    elif dep.dtype == np.uint16:\n        out_img_int = np.round(out_img).astype(np.uint16)\n        dep_int = dep\n    else:\n        out_img_int = out_img.astype(np.float32)\n        dep_int = dep.astype(np.float32)\n\n    rmse_val = calc_rmse(out_img_int, dep_int)\n\n    if save_path is not None:\n        cv2.imwrite(save_path, out_img_int)\n        print(f\"Saved SR image to {save_path}\")\n\n    return rmse_val, out_img_int\n```\n\n---\n\n## \u4f7f\u7528\u793a\u4f8b\n\n```python\n# \u52a0\u8f7d\u6a21\u578b\uff0c\u8bbe\u7f6eeval\u7b49\u7565\n# dep, lr, rgb \u5df2\u901a\u8fc7 np.load \u52a0\u8f7d\n\nrmse, sr_img = inference_with_padding(\n    model,\n    dep,\n    lr,\n    rgb,\n    scale=4,\n    modulo=32,\n    device='cuda',\n    save_path='./output/sample_sr.png'\n)\nprint(f\"RMSE: {rmse}\")\n```\n\n---\n\n\u8fd9\u6bb5\u51fd\u6570\u5c01\u88c5\u4e86\u6700\u6838\u5fc3\u7684\u9884\u5904\u7406\u3001padding\u3001\u63a8\u7406\u3001\u53bbpadding\u548c\u7ed3\u679c\u540e\u5904\u7406\u6d41\u7a0b\uff0c\u53ef\u4ee5\u76f4\u63a5\u8c03\u7528\u4e14\u65b9\u4fbf\u96c6\u6210\u3002  \n\u5982\u679c\u8981\u6279\u91cf\u5904\u7406\uff0c\u53ea\u8981\u5faa\u73af\u8c03\u7528\u8be5\u51fd\u6570\uff0c\u4f20\u5165\u4e0d\u540c\u8f93\u5165\u6570\u636e\u5373\u53ef\u3002\n\n\u5982\u679c\u9700\u8981\uff0c\u6211\u4e5f\u53ef\u4ee5\u5e2e\u4f60\u5199\u6279\u91cf\u63a8\u7406\u7684\u5b8c\u6574\u8c03\u7528\u793a\u4f8b\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":5831,"n_turns_in_conversation":16,"n_words":594,"source_row_offset":54854,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"f01eb7f17deeb101d6fe8fa4","turn_index":12,"user_text":"\u5e2e\u6211\u5199\u6210\u4e00\u4e2a\u51fd\u6570\u7248\u672c\uff0c\u65b9\u4fbf\u96c6\u6210\u548c\u590d\u7528\u3002"},{"assistant_initiates":false,"asst_text":"\u4ece\u9519\u8bef\u4fe1\u606f\u6765\u770b\uff1a\n\n```plain\nAssertionError: \u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\n```\n\n\u8bf4\u660e\u4f60\u8c03\u7528\u7684 `mod_pad` \u51fd\u6570\u5bf9 `dep_norm`\uff0c`rgb_norm`\uff0c`lr_norm` \u4e09\u4e2a\u6570\u7ec4\u8ba1\u7b97\u586b\u5145\u9ad8\u5ea6 (`pad_h_XXX`) \u65f6\uff0c\u8fd4\u56de\u7684\u9ad8\u5ea6\u4e0d\u4e00\u81f4\uff0c\u4e5f\u5c31\u662f\u8bf4\uff1a\n\n```python\npad_h_dep != pad_h_rgb or pad_h_dep != pad_h_lr\n```\n\n---\n\n# \u95ee\u9898\u539f\u56e0\u5206\u6790\n\n- \u4e09\u4e2a\u8f93\u5165\u6570\u636e (`dep_norm`, `rgb_norm`, `lr_norm`) \u539f\u59cb\u5c3a\u5bf8\u4e0d\u4e00\u6837\uff0c\u539f\u56e0\u662f\uff1a\n  - `dep_norm` \u662f\u6df1\u5ea6\u56fe\uff0cshape\u5f62\u5982 `[H, W]`\u3002\n  - `rgb_norm` \u662f\u4e09\u901a\u9053\u56fe\uff0cshape\u662f `[C, H, W]`\u3002\n  - `lr_norm` \u662f\u4f4e\u5206\u8fa8\u7387\u56fe\uff0cshape\u662f `[H_lr, W_lr]`\uff0c\u901a\u5e38\u5c3a\u5bf8\u662f\u539f\u56fe\u5c3a\u5bf8 `H, W` \u9664\u4ee5 `scale`\u3002\n- \u7531\u4e8e `lr_norm` \u662f\u4f4e\u5206\u8fa8\u7387\u56fe\uff0c\u901a\u5e38\u9ad8\u5bbd\u548c `dep_norm`\uff0c`rgb_norm` \u4e0d\u4e00\u6837\uff08\u7f29\u653e\u4e86\uff09\u3002\n- \u4f60\u7528\u540c\u6837\u7684`mod_pad`\u548c\u671f\u5f85\u4e00\u81f4\u7684\u586b\u5145\u5927\u5c0f\u81ea\u7136\u5931\u8d25\u4e86\u3002\n\n---\n\n# \u89e3\u51b3\u65b9\u6848\n\n## 1. \u53ea\u5bf9\u9700\u8981\u4fdd\u6301\u4e00\u81f4\u5c3a\u5bf8\u7684\u4e24\u4e2a\u505apad\n\n- `dep_norm` \u548c `rgb_norm` \u662f\u540c\u4e00\u5206\u8fa8\u7387\uff0c**\u5fc5\u987b\u4fdd\u8bc1\u5b83\u4eecpad\u540e\u5c3a\u5bf8\u4e00\u81f4**\u3002\n- `lr_norm` \u662f\u4f4e\u5206\u8fa8\u7387\uff0c\u5c3a\u5bf8\u66f4\u5c0f\uff0c\u7528\u5b83\u8f6c\u6210\u4e0a\u91c7\u6837\uff08`lr_up`\uff09\u540e\u5c3a\u5bf8\u624d\u5e94\u8be5\u548c`dep_norm`\u4fdd\u6301\u4e00\u81f4\u3002\n- \u6240\u4ee5\u53ea\u8981\u4fdd\u8bc1`dep_norm`\u548c`rgb_norm`\u7684pad\u5c3a\u5bf8\u4e00\u81f4\u5373\u53ef\u3002`lr_norm`\u53ef\u5355\u72ecpad\u3002\n\n## 2. \u4fee\u6539\u4ee3\u7801\u793a\u4f8b\n\n\u6309\u4e0a\u9762\u601d\u8def\u6539\u4f60pad\u4ee3\u7801\uff1a\n\n```python\n# dep_norm \u548c rgb_norm\u4e00\u8d77padding\u4fdd\u8bc1\u5c3a\u5bf8\u4e00\u81f4\ndep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=32)\n\nassert pad_h_dep == pad_h_rgb, \"dep\u548crgb\u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\"\nassert pad_w_dep == pad_w_rgb, \"dep\u548crgb\u586b\u5145\u5bbd\u5ea6\u4e0d\u4e00\u81f4\"\n\n# lr_norm\u5355\u72ecpadding\uff08\u56e0\u4e3a\u5c3a\u5bf8\u672c\u6765\u4e0d\u540c\uff09\nlr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=32)\n```\n\n\u7136\u540e\u7ee7\u7eed\u540e\u7eed\u5904\u7406\uff1a\n\n```python\nimport cv2\nh_lr, w_lr = lr_norm_pad.shape[-2], lr_norm_pad.shape[-1]\nlr_up = cv2.resize(lr_norm_pad, (w_lr * scale, h_lr * scale), interpolation=cv2.INTER_LINEAR)\nif lr_up.ndim == 2:\n    lr_up = np.expand_dims(lr_up, 0)\n```\n\n---\n\n# \u53e6\u5916\u6ce8\u610f\n\n- \u5982\u679c`lr_up`\u7684\u5c3a\u5bf8\u4e0d\u662f`dep_norm_pad` / `rgb_norm_pad`\u7684\u500d\u6570\uff0c\u5bfc\u81f4\u540e\u7eed\u6a21\u578b\u8f93\u5165\u4e5f\u51fa\u73b0\u5c3a\u5bf8\u4e0d\u5339\u914d\uff0c\u9700\u8981\u5bf9`lr_up`\u6267\u884c\u76f8\u540c\u7684pad\u6216\u8005\u88c1\u526a\uff0c\u786e\u4fdd\u548c\u4e3b\u5206\u8fa8\u7387\u8f93\u5165\u5c3a\u5bf8\u4e00\u81f4\u3002\n- \u4f46\u5e38\u89c4\u8d85\u5206\u6a21\u578b\u90fd\u4f1a\u4fdd\u8bc1`lr_up`\u7ecf\u8fc7\u63d2\u503c\u540e\u521a\u597d\u548c\u8f93\u5165`dep_norm_pad`\u76f8\u5339\u914d\u3002\n\n\n---\n\n# \u603b\u7ed3\n\n\u6539\u4e3a\uff1a\n\n```python\ndep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=32)\nrgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=32)\n\nassert pad_h_dep == pad_h_rgb, \"dep\u548crgb\u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\"\nassert pad_w_dep == pad_w_rgb, \"dep\u548crgb\u586b\u5145\u5bbd\u5ea6\u4e0d\u4e00\u81f4\"\n\nlr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=32)\n```\n\n---\n\n\u5982\u679c\u8fd8\u662f\u51fa\u73b0\u5176\u4ed6\u5f62\u72b6\u4e0d\u5339\u914d\u62a5\u9519\uff0c\u8bf7\u7ee7\u7eed\u53cd\u9988\u7ed9\u6211\uff0c\u6211\u5e2e\u4f60\u5b9a\u4f4d\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":2155,"n_turns_in_conversation":16,"n_words":216,"source_row_offset":54855,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"69c9b2f55d091326a096e855","turn_index":13,"user_text":"Traceback (most recent call last):\n  File \"test3.py\", line 197, in <module>\n    main()\n  File \"test3.py\", line 147, in main\n    assert pad_h_dep == pad_h_rgb == pad_h_lr, \"\u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\"\nAssertionError: \u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4"},{"assistant_initiates":false,"asst_text":"\u8fd9\u4e2a\u62a5\u9519\u8bf4\u660e\u6a21\u578b\u5185\u90e8`depth`\u548c`guidance`\u4e24\u4e2a\u5f20\u91cf\u5728\u9664\u62fc\u63a5\u7ef4\u5ea6\uff08dim=1\uff0c\u901a\u9053\u6570\uff09\u5916\u7684\u7a7a\u95f4\u5c3a\u5bf8\u4e0d\u5339\u914d\uff0c\u5177\u4f53\uff1a\n\n```\nExpected size 64 but got size 56 for tensor number 1 in the list\n```\n\n\u8868\u793a\u4e24\u5f20\u91cf\u5728\u67d0\u4e2a\u7a7a\u95f4\u5c3a\u5bf8\u4e0a\u4e00\u4e2a\u662f64\uff0c\u53e6\u4e00\u4e2a\u662f56\u3002\n\n---\n\n## \u4f60\u4e4b\u524dpadding\u53d8\u621032\u7684\u500d\u6570\uff0c\u6a21\u578b\u91cc\u7684\u5c3a\u5bf8\u5e94\u8be5\u662f\u591a\u5c42\u4e0b\u91c7\u6837\u540e\u5c3a\u5bf8\u4e0d\u5339\u914d\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5f88\u53ef\u80fd\u662f\uff1a\n\n- `dep`\uff08\u6df1\u5ea6\u56fe\uff09\u548c\uff08\u7b49\u540c\u4e8e`lr_up`\u6216\uff09\u6307\u5bfc\u56fe`rgb`\u5728\u7f51\u7edc\u591a\u5c42\u4e0b\u91c7\u6837\u548c\u7279\u5f81\u878d\u5408\u65f6\u5c3a\u5bf8\u4e0d\u7edf\u4e00\u3002\n- \u8fd9\u79cd\u60c5\u51b5\u901a\u5e38\u548c\u8f93\u5165\u7684\u9ad8\u5bbd\u4e0d\u662f\u5bf9\u5e94\u591a\u5c42\u4e0b\u91c7\u6837\u7684\u6574\u6570\u500d\u6709\u5173\uff0c\u6bd4\u5982 `2^(num_pyramid +1)` \u500d\u6570\u3002\n- \u4ec5\u4ec5\u4fdd\u8bc1\u8f93\u5165\u662f`32`\u7684\u500d\u53ef\u80fd\u8fd8\u4e0d\u591f\uff0c\u53ef\u80fd\u4f60\u7684\u7f51\u7edc\u67095\u6b21\u4e0b\u91c7\u6837\uff0c\u5373\u9700\u898164\u500d\u6574\u6570\u6216\u8005\u5176\u4ed6\u5c3a\u5bf8\u3002\n\n---\n\n## \u89e3\u51b3\u529e\u6cd5\n\n### 1. \u786e\u8ba4\u4f60\u7684\u7f51\u7edc\u4e0b\u91c7\u6837\u500d\u7387\n\n- \u67e5\u770b\u7f51\u7edc\u6e90\u7801\u6216\u53c2\u6570\u914d\u7f6e\uff0c\u786e\u8ba4`num_pyramid`\u6216\u8005\u7f51\u7edc\u5c42\u7ea7\u4e0b\u91c7\u6837\u500d\u7387\uff0c\u6bd4\u5982\u662f\u4e0d\u662f5\u5c42\u4e0b\u91c7\u6837\uff082^5=32\uff09\uff0c\u6216\u80056\u5c42\u4e0b\u91c7\u6837\uff082^6=64\uff09\u3002\n- \u8ba1\u7b97\u9700\u8981\u4fdd\u8bc1\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u662f\u591a\u5927\u500d\u6570\u7684\u6574\u6570\uff0c\u901a\u5e38\u662f\uff1a\n\n```python\nmodule = max(int(math.pow(2, 1 + args.num_pyramid)), args.scale)\n```\n\n\u6bd4\u5982`num_pyramid=4`\uff0c\u5219 `module=2^(1+4)=32`\uff0c`num_pyramid=5`\uff0c\u5219`module=64`\u3002\n\n### 2. \u4fee\u6539padding\u500d\u6570\n\n\u5982\u679c\u4f60\u4e4b\u524d\u56fa\u5b9a\u4f7f\u7528`modulo=32`\uff0c\u5e94\u5f53\u6539\u6210\u7528**\u52a8\u6001\u8ba1\u7b97\u7684`module`**\u3002\u4f8b\u5982\uff1a\n\n```python\nmodule = max(2 ** (1 + num_pyramid), scale)\n# \u4e3e\u4f8b\uff0c\u5982\u679c num_pyramid=4\uff0c scale=4\uff0c\u5219 module=32\n# \u5982\u679c num_pyramid=5\uff0c scale=4\uff0c\u5219 module=64\n```\n\n\u7528\u586b\u5145\u6539\u4e3a\uff1a\n\n```python\ndep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=module)\nrgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=module)\nlr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=module)\n```\n\n### 3. \u4ecd\u7136\u4fdd\u8bc1 `dep` \u548c `rgb` pad\u5927\u5c0f\u4e00\u81f4\uff0c`lr` \u72ec\u7acbpad\n\n```python\nassert pad_h_dep == pad_h_rgb, \"dep\u548crgb\u586b\u5145\u9ad8\u5ea6\u4e0d\u4e00\u81f4\"\nassert pad_w_dep == pad_w_rgb, \"dep\u548crgb\u586b\u5145\u5bbd\u5ea6\u4e0d\u4e00\u81f4\"\n```\n\n### 4. \u4fdd\u8bc1`lr_up`\u5c3a\u5bf8\u548c\u9ad8\u5206\u8fa8\u7387\u8f93\u5165\u5c3a\u5bf8\u5339\u914d\n\n```python\nimport cv2\nh_lr, w_lr = lr_norm_pad.shape[-2], lr_norm_pad.shape[-1]\nlr_up = cv2.resize(lr_norm_pad, (w_lr * scale, h_lr * scale), interpolation=cv2.INTER_LINEAR)\nif lr_up.ndim == 2:\n    lr_up = np.expand_dims(lr_up, 0)\n```\n\n\u63a8\u7406\u540e\u4ecd\u7136\u88c1\u526a\u56de\u539f\u59cb\u5c3a\u5bf8\u3002\n\n---\n\n## \u7b80\u5355\u603b\u7ed3\uff1a\n\n- \u67e5\u770b\u6216\u786e\u8ba4\u4f60\u5b9e\u9645\u4ee3\u7801\u4e2d\u7684`num_pyramid`\u548c`scale`\u53c2\u6570\uff08\u5e94\u5728`args`\u6216\u6a21\u578b\u5b9a\u4e49\u4e2d\uff09\uff0c\u786e\u8ba4\u9700\u8981\u7684`module`\u503c\u3002\n- \u52a8\u6001\u8bbe\u5b9a`mod_pad`\u7684`modulo=module`\uff0c\u800c\u4e0d\u8981\u56fa\u5b9a32\u3002\n- \u4fdd\u8bc1\u4f9d\u7167`module`\u586b\u5145\u7684\u5c3a\u5bf8\u548c\u4f60\u7684\u7f51\u7edc\u7ed3\u6784\u4e0b\u91c7\u6837\u8981\u6c42\u5339\u914d\uff0c\u907f\u514d\u5c3a\u5bf8\u4e0d\u517c\u5bb9\u95ee\u9898\u3002\n\n---\n\n## \u4f60\u8c03\u7528\u7684\u793a\u4f8b\u4fee\u6539\uff08\u793a\u610f\uff09\n\n```python\nnum_pyramid = 4  # \u9700\u8981\u6839\u636e\u4f60\u9879\u76ee\u5b9e\u9645\u8bbe\u5b9a\u6b63\u786e\u8d4b\u503c\nscale = 4\nmodule = max(2 ** (1 + num_pyramid), scale)\n\ndep_norm_pad, pad_h_dep, pad_w_dep = mod_pad(np.expand_dims(dep_norm, 0), modulo=module)\nrgb_norm_pad, pad_h_rgb, pad_w_rgb = mod_pad(rgb_norm, modulo=module)\n\nassert pad_h_dep == pad_h_rgb\nassert pad_w_dep == pad_w_rgb\n\nlr_norm_pad, pad_h_lr, pad_w_lr = mod_pad(lr_norm, modulo=module)\n```\n\n\u8bf7\u786e\u8ba4\u4f60\u7684`num_pyramid`\u548c`scale`\u6570\u503c\uff0c\u7136\u540e\u5c1d\u8bd5\u6539\u6210\u52a8\u6001`module`\u586b\u5145\u3002\n\n---\n\n\u5982\u679c\u4f60\u7ed9\u6211\u4f60\u4f7f\u7528\u6a21\u578b\u7684`num_pyramid`\u6216\u8005\u76f8\u5173\u53c2\u6570\uff0c\u6211\u53ef\u4ee5\u5e2e\u4f60\u5177\u4f53\u7b97\u51fa\u9700\u8981\u586b\u5145\u6210\u591a\u5c11\u500d\u6570\u3002","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":3506,"n_turns_in_conversation":16,"n_words":313,"source_row_offset":54856,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"2ae3dd5d89f5e10f2facba99","turn_index":14,"user_text":"Traceback (most recent call last):\n  File \"test3.py\", line 199, in <module>\n    main()\n  File \"test3.py\", line 172, in main\n    out = model(inputs)['img_out']\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 364, in forward\n    tmp = self.fuse_conv[i](depth=depth_out[3 - i],\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 293, in forward\n    depth, guide = self.attention_layer(depth=depth, guidance=guide)\n  File \"/home/jianruobing/anaconda3/envs/UHDFour/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/jianruobing/code/AHMF-main/AHMF-test/ahmf.py\", line 273, in forward\n    fuse_feature = self.squeeze(torch.cat((depth, guidance), 1))\nRuntimeError: Sizes of tensors must match except in dimension 1. Expected size 64 but got size 56 for tensor number 1 in the list."},{"assistant_initiates":false,"asst_text":"\u597d\u7684\uff0c\u6211\u5e2e\u4f60\u8be6\u7ec6\u89e3\u91ca\u4e00\u4e0b\u4f60\u7ed9\u51fa\u7684\u4ee3\u7801\u7684\u7ed3\u6784\u548c\u6bcf\u90e8\u5206\u4f5c\u7528\u3002\n\n---\n\n```python\nimport os\nimport glob\nimport torch\nimport numpy as np\nfrom ahmf import AHMF\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n```\n- \u5bfc\u5165\u57fa\u672c\u5e93\uff1a`os`\u548c`glob`\u7528\u4e8e\u6587\u4ef6\u7cfb\u7edf\u64cd\u4f5c\uff0c`torch`\u7528\u4e8e\u6a21\u578b\u63a8\u7406\uff0c`numpy`\u8fdb\u884c\u6570\u503c\u8fd0\u7b97\uff0c\n- `AHMF` \u662f\u4f60\u8d85\u5206\u6a21\u578b\u7684\u5b9a\u4e49\u6587\u4ef6\u6a21\u5757\u3002\n- \u8b66\u544a\u5ffd\u7565\uff0c\u907f\u514d\u7a0b\u5e8f\u8f93\u51fa\u8fc7\u591a\u65e0\u5173\u8b66\u544a\u3002\n\n---\n\n### `mod_crop` \u51fd\u6570\n```python\ndef mod_crop(*imgs, modulo):\n    def crop(img):\n        if img.ndim == 2:\n            h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        elif img.ndim == 3:\n            c, h, w = img.shape\n            crop_h, crop_w = h % modulo, w % modulo\n            return img[:, crop_h // 2 : h - (crop_h - crop_h // 2),\n                       crop_w // 2 : w - (crop_w - crop_w // 2)]\n        else:\n            raise ValueError(f\"Unsupported image dimension: {img.ndim}\")\n    out = [crop(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n```\n- **\u4f5c\u7528**\uff1a\u88c1\u526a\u56fe\u50cf\uff0c\u4f7f\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u9ad8\u548c\u5bbd\u80fd\u88ab `modulo` \u6574\u9664\uff0c\u89e3\u51b3\u5377\u79ef\u7f51\u7edc\u4e2d\u500d\u6570\u5bf9\u9f50\u95ee\u9898\u3002\n- `imgs`\u53ef\u4ee5\u4f20\u5165\u591a\u4e2a\u56fe\u50cf\uff0c\u4f1a\u4f9d\u6b21\u88c1\u526a\u3002\n- \u652f\u63012\u7ef4\uff08H,W\uff09\u548c3\u7ef4\uff08C,H,W\uff09\u56fe\u50cf\u3002\n- \u88c1\u526a\u5747\u5300\u5206\u5e03\u5728\u56fe\u50cf\u8fb9\u7f18\u3002\n\n---\n\n### `np_to_tensor` \u51fd\u6570\n```python\ndef np_to_tensor(*imgs, input_range=1.0, output_range=1.0):\n    def _to_tensor(img):\n        arr = img.astype(np.float32)\n        if arr.ndim == 2:\n            arr = np.expand_dims(arr, 0)  # \u589e\u52a0\u901a\u9053\u7ef4\n        tensor = torch.from_numpy(arr).float()\n        tensor *= output_range / input_range  # \u5f52\u4e00\u5316\u6bd4\u4f8b\u8c03\u6574\n        return tensor\n    out = [_to_tensor(img) for img in imgs]\n    return out if len(out) > 1 else out[0]\n```\n- \u4f5c\u7528\uff1a\u628a numpy \u683c\u5f0f\u7684\u56fe\u7247\u6570\u636e\u8f6c\u6362\u6210 PyTorch \u5f20\u91cf\u3002\n- \u81ea\u52a8\u5c06 HxW \u6570\u7ec4\u8f6c\u6362\u6210 1xHxW (\u5355\u901a\u9053)\uff0c\u9002\u914d\u6a21\u578b\u8f93\u5165\u3002\n- \u80fd\u540c\u65f6\u8f6c\u6362\u591a\u4e2a\u56fe\u7247\u3002\n- \u4e58\u4e0a\u7f29\u653e\u6bd4\u4f8b\u4fdd\u6301\u8f93\u5165\u8303\u56f4\u6b63\u786e\u3002\n\n---\n\n### `normalize_img` \u51fd\u6570\n```python\ndef normalize_img(img):\n    # \u4fdd\u6301\u539f\u6570\u636e\u7c7b\u578b\uff0c\u5e76\u5f52\u4e00\u5316\uff08\u548cquick_test\u7c7b\u4f3c\uff09\n    if img.dtype == np.uint8:\n        return img.astype(np.float32) / 255.0, 255\n    elif img.dtype == np.uint16:\n        return img.astype(np.float32) / 65535.0, 65535\n    else:\n        # float\u7c7b\u578b\u6216\u8005\u5176\u5b83\n        max_val = float(np.max(img)) if np.max(img) > 1.0 else 1.0\n        return img.astype(np.float32) / max_val, max_val\n```\n- \u8bfb\u53d6\u56fe\u50cf\u540e\u5c06\u5176\u5f52\u4e00\u5316\u5230 [0,1] \u7684\u8303\u56f4\u3002\n- \u652f\u6301 uint8\uff088\u4f4d\u56fe\u50cf\uff09\u3001uint16\uff0816\u4f4d\u56fe\u50cf\uff09\u548c\u6d6e\u70b9\u56fe\u50cf\u3002\n- \u8fd4\u56de\u5f52\u4e00\u5316\u7ed3\u679c\u548c\u6700\u5927\u503c\uff08\u540e\u7eed\u53cd\u5f52\u4e00\u5316\u65f6\u7528\uff09\u3002\n\n---\n\n### `calc_rmse` \u51fd\u6570\n```python\ndef calc_rmse(pred, target):\n    pred = pred.astype(np.float64)\n    target = target.astype(np.float64)\n    return np.sqrt(np.mean((pred - target) ** 2))\n```\n- \u8ba1\u7b97\u4e24\u4e2a\u56fe\u50cf\u4e4b\u95f4\u7684\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\uff0c\u7528\u4e8e\u8bc4\u4f30\u8d85\u5206\u6548\u679c\u3002\n- \u8f6c\u6362\u621064\u4f4d\u6d6e\u70b9\u7cbe\u5ea6\u907f\u514d\u8bef\u5dee\u8ba1\u7b97\u95ee\u9898\u3002\n\n---\n\n### `main()` \u51fd\u6570 - \u7a0b\u5e8f\u5165\u53e3\n\n```python\ndef main():\n    scale = 4    \n    model = AHMF(scale=scale, act='PReLU', in_channels=1, guidance_channels=3)\n    ckpt_path = f'/home/jianruobing/code/AHMF-main/AHMF-test/model_x{scale}.pth'\n    checkpoint = torch.load(ckpt_path, map_location='cpu')\n    model.load_state_dict(checkpoint['state_dict'])\n    model = model.cuda().eval()\n```\n- \u521b\u5efa\u8d85\u5206\u6a21\u578b\u5b9e\u4f8b\uff0c\u8bbe\u7f6e\u8d85\u5206\u6bd4\u4f8b\u548c\u6fc0\u6d3b\u51fd\u6570\u3002\n- \u52a0\u8f7d\u6a21\u578b\u6743\u91cd\u6587\u4ef6\u3002\n- \u5207\u6362\u5230GPU\uff0c\u5e76\u8bbe\u6210\u8bc4\u4f30\u6a21\u5f0f\u3002\n\n---\n\n```python\n    input_dir = '/home/jianruobing/code/AHMF-main/imgs2npy'\n    output_dir = './output'\n    os.makedirs(output_dir, exist_ok=True)\n```\n- \u5b9a\u4e49\u6570\u636e\u8f93\u5165\u548c\u7ed3\u679c\u8f93\u51fa\u8def\u5f84\u3002\n- \u5982\u679c\u8f93\u51fa\u76ee\u5f55\u4e0d\u5b58\u5728\u5219\u521b\u5efa\u3002\n\n---\n\n```python\n    # \u67e5\u627e\u5168\u90e8\u6d4b\u8bd5\u7528\u7684npy\u6587\u4ef6\uff0c\u4ee5\u6df1\u5ea6\u56fe\u3001\u4f4e\u5206\u8fa8\u7387\u56fe\u548cRGB\u56fe\u5f52\u7c7b\u5206\u7ec4\n    all_files = []\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_depth_x4.npy')))\n    all_files.extend(glob.glob(os.path.join(input_dir, '*_rgb.npy')))\n\n    groups = {}\n    for fpath in all_files:\n        fname = os.path.basename(fpath)\n        if '_depth_x4' in fname:\n            prefix = fname.split('_depth_x4')[0]\n            key = 'depth_x4'\n        elif '_depth' in fname:\n            prefix = fname.split('_depth')[0]\n            key = 'depth'\n        elif '_rgb' in fname:\n            prefix = fname.split('_rgb')[0]\n            key = 'rgb'\n        else:\n            continue\n        groups.setdefault(prefix, {})[key] = fpath\n\n    print(f'Found {len(groups)} image groups.')\n```\n- \u8bfb\u53d6`input_dir`\u4e2d\u6240\u6709`*_depth.npy`\u3001`*_depth_x4.npy`\u3001`*_rgb.npy`\u6587\u4ef6\u3002\n- \u6839\u636e\u6587\u4ef6\u540d\u524d\u7f00\u5206\u7ec4\uff0c\u628a\u5bf9\u5e94\u6df1\u5ea6\u56fe\u3001\u4f4e\u5206\u8fa8\u7387\u56fe\u548cRGB\u56fe\u653e\u5230\u540c\u4e00\u7ec4\u3002\n- \u6700\u540e\u8f93\u51fa\u627e\u5230\u7684\u7ec4\u6570\u91cf\u3002\n\n---\n\n### \u5bf9\u6bcf\u7ec4\u6570\u636e\u8fdb\u884c\u8d85\u5206\u63a8\u7406\uff1a\n\n```python\n    for prefix, files in groups.items():\n        if not {'depth', 'depth_x4', 'rgb'}.issubset(files.keys()):\n            print(f'Warning: missing files for {prefix}, skipped.')\n            continue\n\n        dep = np.load(files['depth'])\n        lr = np.load(files['depth_x4'])\n        rgb = np.load(files['rgb'])\n\n        print(f\"{prefix}: Input dep dtype = {dep.dtype}, shape = {dep.shape}\")\n        print(f\"{prefix}: Input rgb dtype = {rgb.dtype}, shape = {rgb.shape}\")\n        print(f\"{prefix}: Input lr dtype = {lr.dtype}, shape = {lr.shape}\")\n```\n- \u68c0\u67e5\u6bcf\u7ec4\u5fc5\u987b\u5305\u542b\u5b8c\u6574\u7684\u4e09\u79cd\u6587\u4ef6\uff0c\u6ca1\u6709\u5219\u8df3\u8fc7\u3002\n- \u4f7f\u7528`np.load`\u52a0\u8f7dnpy\u6587\u4ef6\u5230\u5185\u5b58\u3002\n- \u6253\u5370\u56fe\u50cf\u6570\u636e\u7c7b\u578b\u548c\u5c3a\u5bf8\u3002\n\n---\n\n### \u8f93\u5165\u56fe\u50cf\u5f52\u4e00\u5316\u548c\u53d8\u6362\n\n```python\n        dep_norm, dep_max = normalize_img(dep)\n        lr_norm, lr_max = normalize_img(lr)\n        rgb_norm = np.float32(np.transpose(rgb, (2, 0, 1))) / 255.0\n```\n- \u6df1\u5ea6\u56fe`dep`\u548c\u4f4e\u5206\u8fa8\u7387`lr`\u5f52\u4e00\u5316\u3002\n- RGB\u56fe\u8f6c\u6362\u6210 CHW \u5f62\u5f0f\uff0c\u5e76\u5f52\u4e00\u5316\u52300~1\u3002\n\n---\n\n### \u88c1\u526a\u8f93\u5165\u56fe\u50cf\u5927\u5c0f\n\n```python\n        dep_norm = mod_crop(np.expand_dims(dep_norm, 0), modulo=32)\n        rgb_norm = mod_crop(rgb_norm, modulo=32)\n        lr_norm = mod_crop(lr_norm, modulo=32)\n```\n- \u5bf9\u6df1\u5ea6\u3001RGB\u3001\u4f4e\u5206\u8fa8\u7387\u8fdb\u884c `mod_crop`\uff0c\u88c1\u526a\u6210\u80fd\u88ab32\u6574\u9664\u5927\u5c0f\uff0c\u907f\u514d\u6a21\u578b\u5c3a\u5bf8\u4e0d\u5339\u914d\u3002\n\n---\n\n### \u4f4e\u5206\u8fa8\u7387\u56fe\u50cf\u4e0a\u91c7\u6837\n\n```python\n        import cv2\n        h, w = lr_norm.shape[-2], lr_norm.shape[-1]\n        lr_up = cv2.resize(lr_norm, (w * scale, h * scale), interpolation=cv2.INTER_LINEAR)\n\n        if lr_norm.ndim == 2:\n            lr_norm = np.expand_dims(lr_norm, 0)\n        if lr_up.ndim == 2:\n            lr_up = np.expand_dims(lr_up, 0)\n```\n- \u4f7f\u7528OpenCV\u53cc\u7ebf\u6027\u63d2\u503c\uff0c\u5c06`lr_norm`\u653e\u5927\u5230\u539f\u59cb\u6df1\u5ea6\u5c3a\u5ea6\u5927\u5c0f\u3002\n- \u786e\u4fdd`lr_norm`\u548c`lr_up`\u53d8\u6210`(C=1, H, W)`\u683c\u5f0f\u3002\n\n---\n\n### \u8f6ctensor + \u6dfb\u52a0batch\u7ef4\n\n```python\n        dep_t, rgb_t, lr_t, lr_up_t = np_to_tensor(dep_norm, rgb_norm, lr_norm, lr_up)\n        dep_t = dep_t.unsqueeze(0).cuda()\n        rgb_t = rgb_t.unsqueeze(0).cuda()\n        lr_t = lr_t.unsqueeze(0).cuda()\n        lr_up_t = lr_up_t.unsqueeze(0).cuda()\n```\n- \u8f6c\u6362numpy\u4e3apytorch tensor\uff0c\u6dfb\u52a0batch\u7ef4\u3002\n- \u5e76\u8f6c\u79fb\u81f3GPU\u3002\n\n---\n\n### \u63a8\u7406+\u540e\u5904\u7406+\u8ba1\u7b97RMSE\n\n```python\n        inputs = {\n            'img_gt': dep_t,\n            'img_rgb': rgb_t,\n            'img_lr': lr_t,\n            'lr_up': lr_up_t,\n        }\n\n        with torch.no_grad():\n            out = model(inputs)['img_out']\n\n        out_img = out.squeeze().cpu().numpy()\n        out_img = np.clip(out_img, 0, 1) * dep_max\n\n        if dep.dtype == np.uint8:\n            out_img_int = np.round(out_img).astype(np.uint8)\n            dep_int = dep\n        elif dep.dtype == np.uint16:\n            out_img_int = np.round(out_img).astype(np.uint16)\n            dep_int = dep\n        else:\n            out_img_int = out_img.astype(np.float32)\n            dep_int = dep.astype(np.float32)\n\n        rmse_val = calc_rmse(out_img_int, dep_int)\n        print(f'{prefix}: RMSE = {rmse_val:.4f}')\n```\n- \u5c06inputs\u4f20\u7ed9\u6a21\u578b\uff0c\u6267\u884c\u524d\u5411\u63a8\u7406\uff0c\u83b7\u5f97\u8d85\u5206\u7ed3\u679c\u3002\n- \u5c06\u7ed3\u679c\u5f52\u4e00\u5316\u53cd\u8f6c\u56de\u539f\u59cb\u6df1\u5ea6\u8303\u56f4\u3002\n- \u6839\u636e\u539f\u59cb\u6df1\u5ea6\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u8d85\u5206\u7ed3\u679c\u3002\n- \u8ba1\u7b97\u9884\u6d4b\u4e0e\u771f\u5b9e\u6df1\u5ea6\u56fe\u7684RMSE\u8bef\u5dee\uff0c\u6253\u5370\u3002\n\n---\n\n### \u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf\n\n```python\n        save_path = os.path.join(output_dir, f'{prefix}_sr.png')\n        import cv2\n        cv2.imwrite(save_path, out_img_int)\n        print(f'Saved SR image to {save_path}')\n```\n- \u4fdd\u5b58\u8d85\u5206\u7ed3\u679c\u4e3apng\u56fe\u50cf\u6587\u4ef6\u3002\n- \u7528OpenCV\u4ee5\u517c\u5bb9\u683c\u5f0f\u5199\u76d8\u3002\n\n---\n\n### \u603b\u7ed3\uff1a\n\n- \u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8enumpy\u548ctorch\u7684\u6d4b\u8bd5\u6d41\u7a0b\u811a\u672c\u3002\n- \u4f9d\u6b21\u904d\u5386\u76ee\u5f55\u4e2d\u7684npy\u6df1\u5ea6\u56fe\u3001\u4f4e\u5206\u8fa8\u7387\u56fe\u3001RGB\u56fe\u3002\n- \u5bf9\u6bcf\u7ec4\u6570\u636e\u5f52\u4e00\u5316\u3001\u88c1\u526a\u3001\u4e0a\u91c7\u6837\uff0c\u5582\u7ed9\u6a21\u578b\u505a\u8d85\u5206\u63a8\u7406\u3002\n- \u8f93\u51fa\u8d85\u5206\u7ed3\u679c\uff0c\u505aRMSE\u8bc4\u4f30\uff0c\u4fdd\u5b58\u8d85\u5206\u56fe\u50cf\u3002\n- \u517c\u987e\u591a\u79cd\u8f93\u5165\u6df1\u5ea6\u56fe\u6570\u636e\u7c7b\u578b\uff08uint8\u3001uint16\u53ca\u6d6e\u70b9\uff09\u3002\n\n---\n\n\u5982\u679c\u4f60\u9700\u8981\uff0c\u6211\u53ef\u4ee5\u5e2e\u4f60\u5199\u4e2a\u52a0padding\u66ff\u4ee3`mod_crop`\u9632\u6b62\u88c1\u526a\u7248\u672c\uff0c\u6216\u8005\u5e2e\u4f60\u9488\u5bf9\u4ee3\u7801\u7684\u67d0\u90e8\u5206\u505a\u66f4\u8be6\u7ec6\u8bb2\u89e3~","behavior":"degenerate_repetition","conversation_hash":"547e7f0f183501c64eb2c14d94869279","conversation_redacted":true,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4.1-mini-2025-04-14","n_chars":7341,"n_turns_in_conversation":16,"n_words":723,"source_row_offset":54857,"source_shard":"turns-00085.parquet","timestamp":"Fri, 25 Jul 2025 14:57:09 GMT","turn_id":"06adbdd727107a1cbd895fe8","turn_index":15,"user_text":"\u8bf7\u5e2e\u6211\u89e3\u91ca\u4e00\u4e0b\u8fd9\u4e2a\u4ee3\u7801"}],"shard":{"index":85,"name":"turns-00085.parquet","rows":65589}}