diff --git a/doc_dewarp/.gitignore b/doc_dewarp/.gitignore
deleted file mode 100644
index 7179701..0000000
--- a/doc_dewarp/.gitignore
+++ /dev/null
@@ -1,167 +0,0 @@
-input/
-output/
-
-runs/
-*_dump/
-*_log/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
diff --git a/doc_dewarp/.pre-commit-config.yaml b/doc_dewarp/.pre-commit-config.yaml
deleted file mode 100644
index 07921f0..0000000
--- a/doc_dewarp/.pre-commit-config.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-repos:
-# Common hooks
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-    -   id: check-added-large-files
-    -   id: check-merge-conflict
-    -   id: check-symlinks
-    -   id: detect-private-key
-    -   id: end-of-file-fixer
-    -   id: trailing-whitespace
--   repo: https://github.com/Lucas-C/pre-commit-hooks.git
-    rev: v1.5.1
-    hooks:
-    -   id: remove-crlf
-    -   id: remove-tabs
-        name: Tabs remover (Python)
-        files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
-        args: [--whitespaces-count, '4']
-# For Python files
--   repo: https://github.com/psf/black.git
-    rev: 23.3.0
-    hooks:
-    -   id: black
-        files: (.*\.(py|pyi|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
--   repo: https://github.com/pycqa/isort
-    rev: 5.11.5
-    hooks:
-    -   id: isort
--   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.272
-    hooks:
-    -   id: ruff
-        args: [--fix, --exit-non-zero-on-fix, --no-cache]
diff --git a/doc_dewarp/GeoTr.py b/doc_dewarp/GeoTr.py
deleted file mode 100644
index 44cd01a..0000000
--- a/doc_dewarp/GeoTr.py
+++ /dev/null
@@ -1,398 +0,0 @@
-import copy
-from typing import Optional
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-from .extractor import BasicEncoder
-from .position_encoding import build_position_encoding
-from .weight_init import weight_init_
-
-
-class attnLayer(nn.Layer):
-    def __init__(
-            self,
-            d_model,
-            nhead=8,
-            dim_feedforward=2048,
-            dropout=0.1,
-            activation="relu",
-            normalize_before=False,
-    ):
-        super().__init__()
-
-        self.self_attn = nn.MultiHeadAttention(d_model, nhead, dropout=dropout)
-        self.multihead_attn_list = nn.LayerList(
-            [
-                copy.deepcopy(nn.MultiHeadAttention(d_model, nhead, dropout=dropout))
-                for i in range(2)
-            ]
-        )
-
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(p=dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2_list = nn.LayerList(
-            [copy.deepcopy(nn.LayerNorm(d_model)) for i in range(2)]
-        )
-
-        self.norm3 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(p=dropout)
-        self.dropout2_list = nn.LayerList(
-            [copy.deepcopy(nn.Dropout(p=dropout)) for i in range(2)]
-        )
-        self.dropout3 = nn.Dropout(p=dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[paddle.Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(
-            self,
-            tgt,
-            memory_list,
-            tgt_mask=None,
-            memory_mask=None,
-            pos=None,
-            memory_pos=None,
-    ):
-        q = k = self.with_pos_embed(tgt, pos)
-        tgt2 = self.self_attn(
-            q.transpose((1, 0, 2)),
-            k.transpose((1, 0, 2)),
-            value=tgt.transpose((1, 0, 2)),
-            attn_mask=tgt_mask,
-        )
-        tgt2 = tgt2.transpose((1, 0, 2))
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        for memory, multihead_attn, norm2, dropout2, m_pos in zip(
-                memory_list,
-                self.multihead_attn_list,
-                self.norm2_list,
-                self.dropout2_list,
-                memory_pos,
-        ):
-            tgt2 = multihead_attn(
-                query=self.with_pos_embed(tgt, pos).transpose((1, 0, 2)),
-                key=self.with_pos_embed(memory, m_pos).transpose((1, 0, 2)),
-                value=memory.transpose((1, 0, 2)),
-                attn_mask=memory_mask,
-            ).transpose((1, 0, 2))
-
-            tgt = tgt + dropout2(tgt2)
-            tgt = norm2(tgt)
-
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout3(tgt2)
-        tgt = self.norm3(tgt)
-
-        return tgt
-
-    def forward_pre(
-            self,
-            tgt,
-            memory,
-            tgt_mask=None,
-            memory_mask=None,
-            pos=None,
-            memory_pos=None,
-    ):
-        tgt2 = self.norm1(tgt)
-
-        q = k = self.with_pos_embed(tgt2, pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt2 = self.norm2(tgt)
-
-        tgt2 = self.multihead_attn(
-            query=self.with_pos_embed(tgt2, pos),
-            key=self.with_pos_embed(memory, memory_pos),
-            value=memory,
-            attn_mask=memory_mask,
-        )
-        tgt = tgt + self.dropout2(tgt2)
-        tgt2 = self.norm3(tgt)
-
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-
-        return tgt
-
-    def forward(
-            self,
-            tgt,
-            memory_list,
-            tgt_mask=None,
-            memory_mask=None,
-            pos=None,
-            memory_pos=None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(
-                tgt,
-                memory_list,
-                tgt_mask,
-                memory_mask,
-                pos,
-                memory_pos,
-            )
-        return self.forward_post(
-            tgt,
-            memory_list,
-            tgt_mask,
-            memory_mask,
-            pos,
-            memory_pos,
-        )
-
-
-def _get_clones(module, N):
-    return nn.LayerList([copy.deepcopy(module) for i in range(N)])
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-class TransDecoder(nn.Layer):
-    def __init__(self, num_attn_layers: int, hidden_dim: int = 128):
-        super(TransDecoder, self).__init__()
-
-        attn_layer = attnLayer(hidden_dim)
-        self.layers = _get_clones(attn_layer, num_attn_layers)
-        self.position_embedding = build_position_encoding(hidden_dim)
-
-    def forward(self, image: paddle.Tensor, query_embed: paddle.Tensor):
-        pos = self.position_embedding(
-            paddle.ones([image.shape[0], image.shape[2], image.shape[3]], dtype="bool")
-        )
-
-        b, c, h, w = image.shape
-
-        image = image.flatten(2).transpose(perm=[2, 0, 1])
-        pos = pos.flatten(2).transpose(perm=[2, 0, 1])
-
-        for layer in self.layers:
-            query_embed = layer(query_embed, [image], pos=pos, memory_pos=[pos, pos])
-
-        query_embed = query_embed.transpose(perm=[1, 2, 0]).reshape([b, c, h, w])
-
-        return query_embed
-
-
-class TransEncoder(nn.Layer):
-    def __init__(self, num_attn_layers: int, hidden_dim: int = 128):
-        super(TransEncoder, self).__init__()
-
-        attn_layer = attnLayer(hidden_dim)
-        self.layers = _get_clones(attn_layer, num_attn_layers)
-        self.position_embedding = build_position_encoding(hidden_dim)
-
-    def forward(self, image: paddle.Tensor):
-        pos = self.position_embedding(
-            paddle.ones([image.shape[0], image.shape[2], image.shape[3]], dtype="bool")
-        )
-
-        b, c, h, w = image.shape
-
-        image = image.flatten(2).transpose(perm=[2, 0, 1])
-        pos = pos.flatten(2).transpose(perm=[2, 0, 1])
-
-        for layer in self.layers:
-            image = layer(image, [image], pos=pos, memory_pos=[pos, pos])
-
-        image = image.transpose(perm=[1, 2, 0]).reshape([b, c, h, w])
-
-        return image
-
-
-class FlowHead(nn.Layer):
-    def __init__(self, input_dim=128, hidden_dim=256):
-        super(FlowHead, self).__init__()
-
-        self.conv1 = nn.Conv2D(input_dim, hidden_dim, 3, padding=1)
-        self.conv2 = nn.Conv2D(hidden_dim, 2, 3, padding=1)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        return self.conv2(self.relu(self.conv1(x)))
-
-
-class UpdateBlock(nn.Layer):
-    def __init__(self, hidden_dim: int = 128):
-        super(UpdateBlock, self).__init__()
-
-        self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
-
-        self.mask = nn.Sequential(
-            nn.Conv2D(hidden_dim, 256, 3, padding=1),
-            nn.ReLU(),
-            nn.Conv2D(256, 64 * 9, 1, padding=0),
-        )
-
-    def forward(self, image, coords):
-        mask = 0.25 * self.mask(image)
-        dflow = self.flow_head(image)
-        coords = coords + dflow
-        return mask, coords
-
-
-def coords_grid(batch, ht, wd):
-    coords = paddle.meshgrid(paddle.arange(end=ht), paddle.arange(end=wd))
-    coords = paddle.stack(coords[::-1], axis=0).astype(dtype="float32")
-    return coords[None].tile([batch, 1, 1, 1])
-
-
-def upflow8(flow, mode="bilinear"):
-    new_size = 8 * flow.shape[2], 8 * flow.shape[3]
-    return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)
-
-
-class OverlapPatchEmbed(nn.Layer):
-    """Image to Patch Embedding"""
-
-    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
-        super().__init__()
-
-        img_size = img_size if isinstance(img_size, tuple) else (img_size, img_size)
-        patch_size = (
-            patch_size if isinstance(patch_size, tuple) else (patch_size, patch_size)
-        )
-
-        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
-        self.num_patches = self.H * self.W
-
-        self.proj = nn.Conv2D(
-            in_chans,
-            embed_dim,
-            patch_size,
-            stride=stride,
-            padding=(patch_size[0] // 2, patch_size[1] // 2),
-        )
-        self.norm = nn.LayerNorm(embed_dim)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            weight_init_(m, "trunc_normal_", std=0.02)
-        elif isinstance(m, nn.LayerNorm):
-            weight_init_(m, "Constant", value=1.0)
-        elif isinstance(m, nn.Conv2D):
-            weight_init_(
-                m.weight, "kaiming_normal_", mode="fan_out", nonlinearity="relu"
-            )
-
-    def forward(self, x):
-        x = self.proj(x)
-        _, _, H, W = x.shape
-        x = x.flatten(2)
-
-        perm = list(range(x.ndim))
-        perm[1] = 2
-        perm[2] = 1
-        x = x.transpose(perm=perm)
-
-        x = self.norm(x)
-
-        return x, H, W
-
-
-class GeoTr(nn.Layer):
-    def __init__(self):
-        super(GeoTr, self).__init__()
-
-        self.hidden_dim = hdim = 256
-
-        self.fnet = BasicEncoder(output_dim=hdim, norm_fn="instance")
-
-        self.encoder_block = [("encoder_block" + str(i)) for i in range(3)]
-        for i in self.encoder_block:
-            self.__setattr__(i, TransEncoder(2, hidden_dim=hdim))
-
-        self.down_layer = [("down_layer" + str(i)) for i in range(2)]
-        for i in self.down_layer:
-            self.__setattr__(i, nn.Conv2D(256, 256, 3, stride=2, padding=1))
-
-        self.decoder_block = [("decoder_block" + str(i)) for i in range(3)]
-        for i in self.decoder_block:
-            self.__setattr__(i, TransDecoder(2, hidden_dim=hdim))
-
-        self.up_layer = [("up_layer" + str(i)) for i in range(2)]
-        for i in self.up_layer:
-            self.__setattr__(
-                i, nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
-            )
-
-        self.query_embed = nn.Embedding(81, self.hidden_dim)
-
-        self.update_block = UpdateBlock(self.hidden_dim)
-
-    def initialize_flow(self, img):
-        N, _, H, W = img.shape
-        coodslar = coords_grid(N, H, W)
-        coords0 = coords_grid(N, H // 8, W // 8)
-        coords1 = coords_grid(N, H // 8, W // 8)
-        return coodslar, coords0, coords1
-
-    def upsample_flow(self, flow, mask):
-        N, _, H, W = flow.shape
-
-        mask = mask.reshape([N, 1, 9, 8, 8, H, W])
-        mask = F.softmax(mask, axis=2)
-
-        up_flow = F.unfold(8 * flow, [3, 3], paddings=1)
-        up_flow = up_flow.reshape([N, 2, 9, 1, 1, H, W])
-
-        up_flow = paddle.sum(mask * up_flow, axis=2)
-        up_flow = up_flow.transpose(perm=[0, 1, 4, 2, 5, 3])
-
-        return up_flow.reshape([N, 2, 8 * H, 8 * W])
-
-    def forward(self, image):
-        fmap = self.fnet(image)
-        fmap = F.relu(fmap)
-
-        fmap1 = self.__getattr__(self.encoder_block[0])(fmap)
-        fmap1d = self.__getattr__(self.down_layer[0])(fmap1)
-        fmap2 = self.__getattr__(self.encoder_block[1])(fmap1d)
-        fmap2d = self.__getattr__(self.down_layer[1])(fmap2)
-        fmap3 = self.__getattr__(self.encoder_block[2])(fmap2d)
-
-        query_embed0 = self.query_embed.weight.unsqueeze(1).tile([1, fmap3.shape[0], 1])
-
-        fmap3d_ = self.__getattr__(self.decoder_block[0])(fmap3, query_embed0)
-        fmap3du_ = (
-            self.__getattr__(self.up_layer[0])(fmap3d_)
-            .flatten(2)
-            .transpose(perm=[2, 0, 1])
-        )
-        fmap2d_ = self.__getattr__(self.decoder_block[1])(fmap2, fmap3du_)
-        fmap2du_ = (
-            self.__getattr__(self.up_layer[1])(fmap2d_)
-            .flatten(2)
-            .transpose(perm=[2, 0, 1])
-        )
-        fmap_out = self.__getattr__(self.decoder_block[2])(fmap1, fmap2du_)
-
-        coodslar, coords0, coords1 = self.initialize_flow(image)
-        coords1 = coords1.detach()
-        mask, coords1 = self.update_block(fmap_out, coords1)
-        flow_up = self.upsample_flow(coords1 - coords0, mask)
-        bm_up = coodslar + flow_up
-
-        return bm_up
diff --git a/doc_dewarp/README.md b/doc_dewarp/README.md
deleted file mode 100644
index 60127c2..0000000
--- a/doc_dewarp/README.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# DocTrPP: DocTr++ in PaddlePaddle
-
-## Introduction
-
-This is a PaddlePaddle implementation of DocTr++. The original paper is [DocTr++: Deep Unrestricted Document Image Rectification](https://arxiv.org/abs/2304.08796). The original code is [here](https://github.com/fh2019ustc/DocTr-Plus).
-
-![demo](https://github.com/GreatV/DocTrPP/assets/17264618/4e491512-bfc4-4e69-a833-fd1c6e17158c)
-
-## Requirements
-
-You need to install the latest version of PaddlePaddle, which is done through this [link](https://www.paddlepaddle.org.cn/).
-
-## Training
-
-1. Data Preparation
-
-To prepare datasets, refer to [doc3D](https://github.com/cvlab-stonybrook/doc3D-dataset).
-
-2. Training
-
-```shell
-sh train.sh
-```
-
-or
-
-```shell
-export OPENCV_IO_ENABLE_OPENEXR=1
-export CUDA_VISIBLE_DEVICES=0
-
-python train.py --img-size 288 \
-    --name "DocTr++" \
-    --batch-size 12 \
-    --lr 2.5e-5 \
-    --exist-ok \
-    --use-vdl
-```
-
-3. Load Trained Model and Continue Training
-
-```shell
-export OPENCV_IO_ENABLE_OPENEXR=1
-export CUDA_VISIBLE_DEVICES=0
-
-python train.py --img-size 288 \
-    --name "DocTr++" \
-    --batch-size 12 \
-    --lr 2.5e-5 \
-    --resume "runs/train/DocTr++/weights/last.ckpt" \
-    --exist-ok \
-    --use-vdl
-```
-
-## Test and Inference
-
-Test the dewarp result on a single image:
-
-```shell
-python predict.py -i "crop/12_2 copy.png" -m runs/train/DocTr++/weights/best.ckpt -o 12.2.png
-```
-![document image rectification](https://raw.githubusercontent.com/greatv/DocTrPP/main/doc/imgs/document_image_rectification.jpg)
-
-## Export to onnx
-
-```
-pip install paddle2onnx
-
-python export.py -m ./best.ckpt --format onnx
-```
-
-## Model Download
-
-The trained model can be downloaded from [here](https://github.com/GreatV/DocTrPP/releases/download/v0.0.2/best.ckpt).
diff --git a/doc_dewarp/__init__.py b/doc_dewarp/__init__.py
deleted file mode 100644
index c98d39c..0000000
--- a/doc_dewarp/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from onnxruntime import InferenceSession
-
-DOC_TR = InferenceSession("model/dewarp_model/doc_tr_pp.onnx",
-                          providers=["CUDAExecutionProvider"], provider_options=[{"device_id": 0}])
diff --git a/doc_dewarp/data_visualization.py b/doc_dewarp/data_visualization.py
deleted file mode 100644
index ad716ad..0000000
--- a/doc_dewarp/data_visualization.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import argparse
-import os
-import random
-
-import cv2
-import hdf5storage as h5
-import matplotlib.pyplot as plt
-import numpy as np
-import paddle
-import paddle.nn.functional as F
-
-os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--data_root",
-    nargs="?",
-    type=str,
-    default="~/datasets/doc3d/",
-    help="Path to the downloaded dataset",
-)
-parser.add_argument(
-    "--folder", nargs="?", type=int, default=1, help="Folder ID to read from"
-)
-parser.add_argument(
-    "--output",
-    nargs="?",
-    type=str,
-    default="output.png",
-    help="Output filename for the image",
-)
-
-args = parser.parse_args()
-
-root = os.path.expanduser(args.data_root)
-folder = args.folder
-dirname = os.path.join(root, "img", str(folder))
-
-choices = [f for f in os.listdir(dirname) if "png" in f]
-fname = random.choice(choices)
-
-# Read Image
-img_path = os.path.join(dirname, fname)
-img = cv2.imread(img_path)
-
-# Read 3D Coords
-wc_path = os.path.join(root, "wc", str(folder), fname[:-3] + "exr")
-wc = cv2.imread(wc_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
-# scale wc
-# value obtained from the entire dataset
-xmx, xmn, ymx, ymn, zmx, zmn = (
-    1.2539363,
-    -1.2442188,
-    1.2396319,
-    -1.2289206,
-    0.6436657,
-    -0.67492497,
-)
-wc[:, :, 0] = (wc[:, :, 0] - zmn) / (zmx - zmn)
-wc[:, :, 1] = (wc[:, :, 1] - ymn) / (ymx - ymn)
-wc[:, :, 2] = (wc[:, :, 2] - xmn) / (xmx - xmn)
-
-# Read Backward Map
-bm_path = os.path.join(root, "bm", str(folder), fname[:-3] + "mat")
-bm = h5.loadmat(bm_path)["bm"]
-
-# Read UV Map
-uv_path = os.path.join(root, "uv", str(folder), fname[:-3] + "exr")
-uv = cv2.imread(uv_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
-
-# Read Depth Map
-dmap_path = os.path.join(root, "dmap", str(folder), fname[:-3] + "exr")
-dmap = cv2.imread(dmap_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)[:, :, 0]
-# do some clipping and scaling to display it
-dmap[dmap > 30.0] = 30
-dmap = 1 - ((dmap - np.min(dmap)) / (np.max(dmap) - np.min(dmap)))
-
-# Read Normal Map
-norm_path = os.path.join(root, "norm", str(folder), fname[:-3] + "exr")
-norm = cv2.imread(norm_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
-
-# Read Albedo
-alb_path = os.path.join(root, "alb", str(folder), fname[:-3] + "png")
-alb = cv2.imread(alb_path)
-
-# Read Checkerboard Image
-recon_path = os.path.join(root, "recon", str(folder), fname[:-8] + "chess480001.png")
-recon = cv2.imread(recon_path)
-
-# Display image and GTs
-
-# use the backward mapping to dewarp the image
-# scale bm to -1.0 to 1.0
-bm_ = bm / np.array([448, 448])
-bm_ = (bm_ - 0.5) * 2
-bm_ = np.reshape(bm_, (1, 448, 448, 2))
-bm_ = paddle.to_tensor(bm_, dtype="float32")
-img_ = alb.transpose((2, 0, 1)).astype(np.float32) / 255.0
-img_ = np.expand_dims(img_, 0)
-img_ = paddle.to_tensor(img_, dtype="float32")
-uw = F.grid_sample(img_, bm_)
-uw = uw[0].numpy().transpose((1, 2, 0))
-
-f, axrr = plt.subplots(2, 5)
-for ax in axrr:
-    for a in ax:
-        a.set_xticks([])
-        a.set_yticks([])
-
-axrr[0][0].imshow(img)
-axrr[0][0].title.set_text("image")
-axrr[0][1].imshow(wc)
-axrr[0][1].title.set_text("3D coords")
-axrr[0][2].imshow(bm[:, :, 0])
-axrr[0][2].title.set_text("bm 0")
-axrr[0][3].imshow(bm[:, :, 1])
-axrr[0][3].title.set_text("bm 1")
-if uv is None:
-    uv = np.zeros_like(img)
-axrr[0][4].imshow(uv)
-axrr[0][4].title.set_text("uv map")
-axrr[1][0].imshow(dmap)
-axrr[1][0].title.set_text("depth map")
-axrr[1][1].imshow(norm)
-axrr[1][1].title.set_text("normal map")
-axrr[1][2].imshow(alb)
-axrr[1][2].title.set_text("albedo")
-axrr[1][3].imshow(recon)
-axrr[1][3].title.set_text("checkerboard")
-axrr[1][4].imshow(uw)
-axrr[1][4].title.set_text("gt unwarped")
-plt.tight_layout()
-plt.savefig(args.output)
diff --git a/doc_dewarp/dewarp.py b/doc_dewarp/dewarp.py
deleted file mode 100644
index 923f56f..0000000
--- a/doc_dewarp/dewarp.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import cv2
-import numpy as np
-import paddle
-
-from . import DOC_TR
-from .utils import to_tensor, to_image
-
-
-def dewarp_image(image):
-    img = cv2.resize(image, (288, 288)).astype(np.float32)
-    y = to_tensor(image)
-
-    img = np.transpose(img, (2, 0, 1))
-    bm = DOC_TR.run(None, {"image": img[None,]})[0]
-    bm = paddle.to_tensor(bm)
-    bm = paddle.nn.functional.interpolate(
-        bm, y.shape[2:], mode="bilinear", align_corners=False
-    )
-    bm_nhwc = np.transpose(bm, (0, 2, 3, 1))
-    out = paddle.nn.functional.grid_sample(y, (bm_nhwc / 288 - 0.5) * 2)
-    return to_image(out)
diff --git a/doc_dewarp/doc/download_dataset.sh b/doc_dewarp/doc/download_dataset.sh
deleted file mode 100644
index 7afe858..0000000
--- a/doc_dewarp/doc/download_dataset.sh
+++ /dev/null
@@ -1,161 +0,0 @@
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/img_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/wc_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/bm_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/uv_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/alb_8.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/recon_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/norm_21.zip"
-
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_1.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_2.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_3.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_4.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_5.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_6.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_7.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_8.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_9.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_10.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_11.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_12.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_13.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_14.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_15.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_16.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_17.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_18.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_19.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_20.zip"
-wget "https://bj.bcebos.com/paddleseg/paddleseg/datasets/doc3d/dmap_21.zip"
diff --git a/doc_dewarp/doc/imgs/document_image_rectification.jpg b/doc_dewarp/doc/imgs/document_image_rectification.jpg
deleted file mode 100644
index e5a8b26..0000000
Binary files a/doc_dewarp/doc/imgs/document_image_rectification.jpg and /dev/null differ
diff --git a/doc_dewarp/doc3d_dataset.py b/doc_dewarp/doc3d_dataset.py
deleted file mode 100644
index 49f64f4..0000000
--- a/doc_dewarp/doc3d_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import collections
-import os
-import random
-
-import albumentations as A
-import cv2
-import hdf5storage as h5
-import numpy as np
-import paddle
-from paddle import io
-
-# Set random seed
-random.seed(12345678)
-
-
-class Doc3dDataset(io.Dataset):
-    def __init__(self, root, split="train", is_augment=False, image_size=512):
-        self.root = os.path.expanduser(root)
-
-        self.split = split
-        self.is_augment = is_augment
-
-        self.files = collections.defaultdict(list)
-
-        self.image_size = (
-            image_size if isinstance(image_size, tuple) else (image_size, image_size)
-        )
-
-        # Augmentation
-        self.augmentation = A.Compose(
-            [
-                A.ColorJitter(),
-            ]
-        )
-
-        for split in ["train", "val"]:
-            path = os.path.join(self.root, split + ".txt")
-            file_list = []
-            with open(path, "r") as file:
-                file_list = [file_id.rstrip() for file_id in file.readlines()]
-            self.files[split] = file_list
-
-    def __len__(self):
-        return len(self.files[self.split])
-
-    def __getitem__(self, index):
-        image_name = self.files[self.split][index]
-
-        # Read image
-        image_path = os.path.join(self.root, "img", image_name + ".png")
-        image = cv2.imread(image_path)
-
-        # Read 3D Coordinates
-        wc_path = os.path.join(self.root, "wc", image_name + ".exr")
-        wc = cv2.imread(wc_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
-
-        # Read backward map
-        bm_path = os.path.join(self.root, "bm", image_name + ".mat")
-        bm = h5.loadmat(bm_path)["bm"]
-
-        image, bm = self.transform(wc, bm, image)
-
-        return image, bm
-
-    def tight_crop(self, wc: np.ndarray):
-        mask = ((wc[:, :, 0] != 0) & (wc[:, :, 1] != 0) & (wc[:, :, 2] != 0)).astype(
-            np.uint8
-        )
-        mask_size = mask.shape
-        [y, x] = mask.nonzero()
-        min_x = min(x)
-        max_x = max(x)
-        min_y = min(y)
-        max_y = max(y)
-
-        wc = wc[min_y : max_y + 1, min_x : max_x + 1, :]
-        s = 10
-        wc = np.pad(wc, ((s, s), (s, s), (0, 0)), "constant")
-
-        cx1 = random.randint(0, 2 * s)
-        cx2 = random.randint(0, 2 * s) + 1
-        cy1 = random.randint(0, 2 * s)
-        cy2 = random.randint(0, 2 * s) + 1
-
-        wc = wc[cy1:-cy2, cx1:-cx2, :]
-
-        top: int = min_y - s + cy1
-        bottom: int = mask_size[0] - max_y - s + cy2
-        left: int = min_x - s + cx1
-        right: int = mask_size[1] - max_x - s + cx2
-
-        top = np.clip(top, 0, mask_size[0])
-        bottom = np.clip(bottom, 1, mask_size[0] - 1)
-        left = np.clip(left, 0, mask_size[1])
-        right = np.clip(right, 1, mask_size[1] - 1)
-
-        return wc, top, bottom, left, right
-
-    def transform(self, wc, bm, img):
-        wc, top, bottom, left, right = self.tight_crop(wc)
-
-        img = img[top:-bottom, left:-right, :]
-
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        if self.is_augment:
-            img = self.augmentation(image=img)["image"]
-
-        # resize image
-        img = cv2.resize(img, self.image_size)
-        img = img.astype(np.float32) / 255.0
-        img = img.transpose(2, 0, 1)
-
-        # resize bm
-        bm = bm.astype(np.float32)
-        bm[:, :, 1] = bm[:, :, 1] - top
-        bm[:, :, 0] = bm[:, :, 0] - left
-        bm = bm / np.array([448.0 - left - right, 448.0 - top - bottom])
-        bm0 = cv2.resize(bm[:, :, 0], (self.image_size[0], self.image_size[1]))
-        bm1 = cv2.resize(bm[:, :, 1], (self.image_size[0], self.image_size[1]))
-        bm0 = bm0 * self.image_size[0]
-        bm1 = bm1 * self.image_size[1]
-
-        bm = np.stack([bm0, bm1], axis=-1)
-
-        img = paddle.to_tensor(img).astype(dtype="float32")
-        bm = paddle.to_tensor(bm).astype(dtype="float32")
-
-        return img, bm
diff --git a/doc_dewarp/export.py b/doc_dewarp/export.py
deleted file mode 100644
index 5b7e5d8..0000000
--- a/doc_dewarp/export.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import argparse
-import os
-
-import paddle
-
-from GeoTr import GeoTr
-
-
-def export(args):
-    model_path = args.model
-    imgsz = args.imgsz
-    format = args.format
-
-    model = GeoTr()
-    checkpoint = paddle.load(model_path)
-    model.set_state_dict(checkpoint["model"])
-    model.eval()
-
-    dirname = os.path.dirname(model_path)
-    if format == "static" or format == "onnx":
-        model = paddle.jit.to_static(
-            model,
-            input_spec=[
-                paddle.static.InputSpec(shape=[1, 3, imgsz, imgsz], dtype="float32")
-            ],
-            full_graph=True,
-        )
-        paddle.jit.save(model, os.path.join(dirname, "model"))
-
-    if format == "onnx":
-        onnx_path = os.path.join(dirname, "model.onnx")
-        os.system(
-            f"paddle2onnx --model_dir {dirname}"
-            " --model_filename model.pdmodel"
-            " --params_filename model.pdiparams"
-            f" --save_file {onnx_path}"
-            " --opset_version 11"
-        )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="export model")
-
-    parser.add_argument(
-        "--model",
-        "-m",
-        nargs="?",
-        type=str,
-        default="",
-        help="The path of model",
-    )
-
-    parser.add_argument(
-        "--imgsz", type=int, default=288, help="The size of input image"
-    )
-
-    parser.add_argument(
-        "--format",
-        type=str,
-        default="static",
-        help="The format of exported model, which can be static or onnx",
-    )
-
-    args = parser.parse_args()
-
-    export(args)
diff --git a/doc_dewarp/extractor.py b/doc_dewarp/extractor.py
deleted file mode 100644
index 5f3bace..0000000
--- a/doc_dewarp/extractor.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import paddle.nn as nn
-
-from .weight_init import weight_init_
-
-
-class ResidualBlock(nn.Layer):
-    """Residual Block with custom normalization."""
-
-    def __init__(self, in_planes, planes, norm_fn="group", stride=1):
-        super(ResidualBlock, self).__init__()
-
-        self.conv1 = nn.Conv2D(in_planes, planes, 3, padding=1, stride=stride)
-        self.conv2 = nn.Conv2D(planes, planes, 3, padding=1)
-        self.relu = nn.ReLU()
-
-        if norm_fn == "group":
-            num_groups = planes // 8
-            self.norm1 = nn.GroupNorm(num_groups, planes)
-            self.norm2 = nn.GroupNorm(num_groups, planes)
-            if not stride == 1:
-                self.norm3 = nn.GroupNorm(num_groups, planes)
-        elif norm_fn == "batch":
-            self.norm1 = nn.BatchNorm2D(planes)
-            self.norm2 = nn.BatchNorm2D(planes)
-            if not stride == 1:
-                self.norm3 = nn.BatchNorm2D(planes)
-        elif norm_fn == "instance":
-            self.norm1 = nn.InstanceNorm2D(planes)
-            self.norm2 = nn.InstanceNorm2D(planes)
-            if not stride == 1:
-                self.norm3 = nn.InstanceNorm2D(planes)
-        elif norm_fn == "none":
-            self.norm1 = nn.Sequential()
-            self.norm2 = nn.Sequential()
-            if not stride == 1:
-                self.norm3 = nn.Sequential()
-
-        if stride == 1:
-            self.downsample = None
-        else:
-            self.downsample = nn.Sequential(
-                nn.Conv2D(in_planes, planes, 1, stride=stride), self.norm3
-            )
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x + y)
-
-
-class BasicEncoder(nn.Layer):
-    """Basic Encoder with custom normalization."""
-
-    def __init__(self, output_dim=128, norm_fn="batch"):
-        super(BasicEncoder, self).__init__()
-
-        self.norm_fn = norm_fn
-
-        if self.norm_fn == "group":
-            self.norm1 = nn.GroupNorm(8, 64)
-        elif self.norm_fn == "batch":
-            self.norm1 = nn.BatchNorm2D(64)
-        elif self.norm_fn == "instance":
-            self.norm1 = nn.InstanceNorm2D(64)
-        elif self.norm_fn == "none":
-            self.norm1 = nn.Sequential()
-
-        self.conv1 = nn.Conv2D(3, 64, 7, stride=2, padding=3)
-        self.relu1 = nn.ReLU()
-
-        self.in_planes = 64
-        self.layer1 = self._make_layer(64, stride=1)
-        self.layer2 = self._make_layer(128, stride=2)
-        self.layer3 = self._make_layer(192, stride=2)
-
-        self.conv2 = nn.Conv2D(192, output_dim, 1)
-
-        for m in self.sublayers():
-            if isinstance(m, nn.Conv2D):
-                weight_init_(
-                    m.weight, "kaiming_normal_", mode="fan_out", nonlinearity="relu"
-                )
-            elif isinstance(m, (nn.BatchNorm2D, nn.InstanceNorm2D, nn.GroupNorm)):
-                weight_init_(m, "Constant", value=1, bias_value=0.0)
-
-    def _make_layer(self, dim, stride=1):
-        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
-        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
-        layers = layer1, layer2
-
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-
-        x = self.conv2(x)
-
-        return x
diff --git a/doc_dewarp/plots.py b/doc_dewarp/plots.py
deleted file mode 100644
index fa384e1..0000000
--- a/doc_dewarp/plots.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import copy
-import os
-
-import matplotlib.pyplot as plt
-import paddle.optimizer as optim
-
-from GeoTr import GeoTr
-
-
-def plot_lr_scheduler(optimizer, scheduler, epochs=65, save_dir=""):
-    """
-    Plot the learning rate scheduler
-    """
-
-    optimizer = copy.copy(optimizer)
-    scheduler = copy.copy(scheduler)
-
-    lr = []
-    for _ in range(epochs):
-        for _ in range(30):
-            lr.append(scheduler.get_lr())
-            optimizer.step()
-            scheduler.step()
-
-    epoch = [float(i) / 30.0 for i in range(len(lr))]
-
-    plt.figure()
-    plt.plot(epoch, lr, ".-", label="Learning Rate")
-    plt.xlabel("epoch")
-    plt.ylabel("Learning Rate")
-    plt.title("Learning Rate Scheduler")
-    plt.savefig(os.path.join(save_dir, "lr_scheduler.png"), dpi=300)
-    plt.close()
-
-
-if __name__ == "__main__":
-    model = GeoTr()
-
-    schaduler = optim.lr.OneCycleLR(
-        max_learning_rate=1e-4,
-        total_steps=1950,
-        phase_pct=0.1,
-        end_learning_rate=1e-4 / 2.5e5,
-    )
-    optimizer = optim.AdamW(learning_rate=schaduler, parameters=model.parameters())
-    plot_lr_scheduler(
-        scheduler=schaduler, optimizer=optimizer, epochs=65, save_dir="./"
-    )
diff --git a/doc_dewarp/position_encoding.py b/doc_dewarp/position_encoding.py
deleted file mode 100644
index 327f7ea..0000000
--- a/doc_dewarp/position_encoding.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import math
-from typing import Optional
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.initializer as init
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[paddle.Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-class PositionEmbeddingSine(nn.Layer):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-
-        if scale is None:
-            scale = 2 * math.pi
-
-        self.scale = scale
-
-    def forward(self, mask):
-        assert mask is not None
-
-        y_embed = mask.cumsum(axis=1, dtype="float32")
-        x_embed = mask.cumsum(axis=2, dtype="float32")
-
-        if self.normalize:
-            eps = 1e-06
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = paddle.arange(end=self.num_pos_feats, dtype="float32")
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-
-        pos_x = paddle.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), axis=4
-        ).flatten(3)
-        pos_y = paddle.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), axis=4
-        ).flatten(3)
-
-        pos = paddle.concat((pos_y, pos_x), axis=3).transpose(perm=[0, 3, 1, 2])
-
-        return pos
-
-
-class PositionEmbeddingLearned(nn.Layer):
-    """
-    Absolute pos embedding, learned.
-    """
-
-    def __init__(self, num_pos_feats=256):
-        super().__init__()
-        self.row_embed = nn.Embedding(50, num_pos_feats)
-        self.col_embed = nn.Embedding(50, num_pos_feats)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        init_Constant = init.Uniform()
-        init_Constant(self.row_embed.weight)
-        init_Constant(self.col_embed.weight)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-
-        h, w = x.shape[-2:]
-
-        i = paddle.arange(end=w)
-        j = paddle.arange(end=h)
-
-        x_emb = self.col_embed(i)
-        y_emb = self.row_embed(j)
-
-        pos = (
-            paddle.concat(
-                [
-                    x_emb.unsqueeze(0).tile([h, 1, 1]),
-                    y_emb.unsqueeze(1).tile([1, w, 1]),
-                ],
-                axis=-1,
-            )
-            .transpose([2, 0, 1])
-            .unsqueeze(0)
-            .tile([x.shape[0], 1, 1, 1])
-        )
-
-        return pos
-
-
-def build_position_encoding(hidden_dim=512, position_embedding="sine"):
-    N_steps = hidden_dim // 2
-
-    if position_embedding in ("v2", "sine"):
-        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
-    elif position_embedding in ("v3", "learned"):
-        position_embedding = PositionEmbeddingLearned(N_steps)
-    else:
-        raise ValueError(f"not supported {position_embedding}")
-    return position_embedding
diff --git a/doc_dewarp/predict.py b/doc_dewarp/predict.py
deleted file mode 100644
index bed8c63..0000000
--- a/doc_dewarp/predict.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import argparse
-
-import cv2
-import paddle
-
-from GeoTr import GeoTr
-from utils import to_image, to_tensor
-
-
-def run(args):
-    image_path = args.image
-    model_path = args.model
-    output_path = args.output
-
-    checkpoint = paddle.load(model_path)
-    state_dict = checkpoint["model"]
-    model = GeoTr()
-    model.set_state_dict(state_dict)
-    model.eval()
-
-    img_org = cv2.imread(image_path)
-    img = cv2.resize(img_org, (288, 288))
-    x = to_tensor(img)
-    y = to_tensor(img_org)
-    bm = model(x)
-    bm = paddle.nn.functional.interpolate(
-        bm, y.shape[2:], mode="bilinear", align_corners=False
-    )
-    bm_nhwc = bm.transpose([0, 2, 3, 1])
-    out = paddle.nn.functional.grid_sample(y, (bm_nhwc / 288 - 0.5) * 2)
-    out_image = to_image(out)
-    cv2.imwrite(output_path, out_image)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="predict")
-
-    parser.add_argument(
-        "--image",
-        "-i",
-        nargs="?",
-        type=str,
-        default="",
-        help="The path of image",
-    )
-
-    parser.add_argument(
-        "--model",
-        "-m",
-        nargs="?",
-        type=str,
-        default="",
-        help="The path of model",
-    )
-
-    parser.add_argument(
-        "--output",
-        "-o",
-        nargs="?",
-        type=str,
-        default="",
-        help="The path of output",
-    )
-
-    args = parser.parse_args()
-
-    print(args)
-
-    run(args)
diff --git a/doc_dewarp/requirements.txt b/doc_dewarp/requirements.txt
deleted file mode 100644
index 102d164..0000000
--- a/doc_dewarp/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-hdf5storage
-loguru
-numpy
-scipy
-opencv-python
-matplotlib
-albumentations
diff --git a/doc_dewarp/split_dataset.py b/doc_dewarp/split_dataset.py
deleted file mode 100644
index 6f83b3e..0000000
--- a/doc_dewarp/split_dataset.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import argparse
-import glob
-import os
-import random
-from pathlib import Path
-
-from loguru import logger
-
-random.seed(1234567)
-
-
-def run(args):
-    data_root = os.path.expanduser(args.data_root)
-    ratio = args.train_ratio
-
-    data_path = os.path.join(data_root, "img", "*", "*.png")
-    img_list = glob.glob(data_path, recursive=True)
-    sorted(img_list)
-    random.shuffle(img_list)
-
-    train_size = int(len(img_list) * ratio)
-
-    train_text_path = os.path.join(data_root, "train.txt")
-    with open(train_text_path, "w") as file:
-        for item in img_list[:train_size]:
-            parts = Path(item).parts
-            item = os.path.join(parts[-2], parts[-1])
-            file.write("%s\n" % item.split(".png")[0])
-
-    val_text_path = os.path.join(data_root, "val.txt")
-    with open(val_text_path, "w") as file:
-        for item in img_list[train_size:]:
-            parts = Path(item).parts
-            item = os.path.join(parts[-2], parts[-1])
-            file.write("%s\n" % item.split(".png")[0])
-
-    logger.info(f"TRAIN LABEL: {train_text_path}")
-    logger.info(f"VAL LABEL: {val_text_path}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--data_root",
-        type=str,
-        default="~/datasets/doc3d",
-        help="Data path to load data",
-    )
-    parser.add_argument(
-        "--train_ratio", type=float, default=0.8, help="Ratio of training data"
-    )
-
-    args = parser.parse_args()
-
-    logger.info(args)
-
-    run(args)
diff --git a/doc_dewarp/train.py b/doc_dewarp/train.py
deleted file mode 100644
index 264b4b9..0000000
--- a/doc_dewarp/train.py
+++ /dev/null
@@ -1,381 +0,0 @@
-import argparse
-import inspect
-import os
-import random
-from pathlib import Path
-from typing import Optional
-
-import numpy as np
-import paddle
-import paddle.nn as nn
-import paddle.optimizer as optim
-from loguru import logger
-from paddle.io import DataLoader
-from paddle.nn import functional as F
-from paddle_msssim import ms_ssim, ssim
-
-from doc3d_dataset import Doc3dDataset
-from GeoTr import GeoTr
-from utils import to_image
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]
-RANK = int(os.getenv("RANK", -1))
-
-
-def init_seeds(seed=0, deterministic=False):
-    random.seed(seed)
-    np.random.seed(seed)
-    paddle.seed(seed)
-    if deterministic:
-        os.environ["FLAGS_cudnn_deterministic"] = "1"
-        os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
-        os.environ["PYTHONHASHSEED"] = str(seed)
-
-
-def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code,
-    # i.e.  colorstr('blue', 'hello world')
-
-    *args, string = (
-        input if len(input) > 1 else ("blue", "bold", input[0])
-    )  # color arguments, string
-
-    colors = {
-        "black": "\033[30m",  # basic colors
-        "red": "\033[31m",
-        "green": "\033[32m",
-        "yellow": "\033[33m",
-        "blue": "\033[34m",
-        "magenta": "\033[35m",
-        "cyan": "\033[36m",
-        "white": "\033[37m",
-        "bright_black": "\033[90m",  # bright colors
-        "bright_red": "\033[91m",
-        "bright_green": "\033[92m",
-        "bright_yellow": "\033[93m",
-        "bright_blue": "\033[94m",
-        "bright_magenta": "\033[95m",
-        "bright_cyan": "\033[96m",
-        "bright_white": "\033[97m",
-        "end": "\033[0m",  # misc
-        "bold": "\033[1m",
-        "underline": "\033[4m",
-    }
-
-    return "".join(colors[x] for x in args) + f"{string}" + colors["end"]
-
-
-def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
-    # Print function arguments (optional args dict)
-    x = inspect.currentframe().f_back  # previous frame
-    file, _, func, _, _ = inspect.getframeinfo(x)
-    if args is None:  # get args automatically
-        args, _, _, frm = inspect.getargvalues(x)
-        args = {k: v for k, v in frm.items() if k in args}
-    try:
-        file = Path(file).resolve().relative_to(ROOT).with_suffix("")
-    except ValueError:
-        file = Path(file).stem
-    s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "")
-    logger.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items()))
-
-
-def increment_path(path, exist_ok=False, sep="", mkdir=False):
-    # Increment file or directory path,
-    # i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
-    path = Path(path)  # os-agnostic
-    if path.exists() and not exist_ok:
-        path, suffix = (
-            (path.with_suffix(""), path.suffix) if path.is_file() else (path, "")
-        )
-
-        for n in range(2, 9999):
-            p = f"{path}{sep}{n}{suffix}"  # increment path
-            if not os.path.exists(p):
-                break
-        path = Path(p)
-
-    if mkdir:
-        path.mkdir(parents=True, exist_ok=True)  # make directory
-
-    return path
-
-
-def train(args):
-    save_dir = Path(args.save_dir)
-
-    use_vdl = args.use_vdl
-    if use_vdl:
-        from visualdl import LogWriter
-
-        log_dir = save_dir / "vdl"
-        vdl_writer = LogWriter(str(log_dir))
-
-    # Directories
-    weights_dir = save_dir / "weights"
-    weights_dir.parent.mkdir(parents=True, exist_ok=True)
-
-    last = weights_dir / "last.ckpt"
-    best = weights_dir / "best.ckpt"
-
-    # Hyperparameters
-
-    # Config
-    init_seeds(args.seed)
-
-    # Train loader
-    train_dataset = Doc3dDataset(
-        args.data_root,
-        split="train",
-        is_augment=True,
-        image_size=args.img_size,
-    )
-
-    train_loader = DataLoader(
-        train_dataset,
-        batch_size=args.batch_size,
-        shuffle=True,
-        num_workers=args.workers,
-    )
-
-    # Validation loader
-    val_dataset = Doc3dDataset(
-        args.data_root,
-        split="val",
-        is_augment=False,
-        image_size=args.img_size,
-    )
-
-    val_loader = DataLoader(
-        val_dataset, batch_size=args.batch_size, num_workers=args.workers
-    )
-
-    # Model
-    model = GeoTr()
-
-    if use_vdl:
-        vdl_writer.add_graph(
-            model,
-            input_spec=[
-                paddle.static.InputSpec([1, 3, args.img_size, args.img_size], "float32")
-            ],
-        )
-
-    # Data Parallel Mode
-    if RANK == -1 and paddle.device.cuda.device_count() > 1:
-        model = paddle.DataParallel(model)
-
-    # Scheduler
-    scheduler = optim.lr.OneCycleLR(
-        max_learning_rate=args.lr,
-        total_steps=args.epochs * len(train_loader),
-        phase_pct=0.1,
-        end_learning_rate=args.lr / 2.5e5,
-    )
-
-    # Optimizer
-    optimizer = optim.AdamW(
-        learning_rate=scheduler,
-        parameters=model.parameters(),
-    )
-
-    # loss function
-    l1_loss_fn = nn.L1Loss()
-    mse_loss_fn = nn.MSELoss()
-
-    # Resume
-    best_fitness, start_epoch = 0.0, 0
-    if args.resume:
-        ckpt = paddle.load(args.resume)
-        model.set_state_dict(ckpt["model"])
-        optimizer.set_state_dict(ckpt["optimizer"])
-        scheduler.set_state_dict(ckpt["scheduler"])
-        best_fitness = ckpt["best_fitness"]
-        start_epoch = ckpt["epoch"] + 1
-
-    # Train
-    for epoch in range(start_epoch, args.epochs):
-        model.train()
-
-        for i, (img, target) in enumerate(train_loader):
-            img = paddle.to_tensor(img)  # NCHW
-            target = paddle.to_tensor(target)  # NHWC
-
-            pred = model(img)  # NCHW
-            pred_nhwc = pred.transpose([0, 2, 3, 1])
-
-            loss = l1_loss_fn(pred_nhwc, target)
-            mse_loss = mse_loss_fn(pred_nhwc, target)
-
-            if use_vdl:
-                vdl_writer.add_scalar(
-                    "Train/L1 Loss", float(loss), epoch * len(train_loader) + i
-                )
-                vdl_writer.add_scalar(
-                    "Train/MSE Loss", float(mse_loss), epoch * len(train_loader) + i
-                )
-                vdl_writer.add_scalar(
-                    "Train/Learning Rate",
-                    float(scheduler.get_lr()),
-                    epoch * len(train_loader) + i,
-                )
-
-            loss.backward()
-            optimizer.step()
-            scheduler.step()
-            optimizer.clear_grad()
-
-            if i % 10 == 0:
-                logger.info(
-                    f"[TRAIN MODE] Epoch: {epoch}, Iter: {i}, L1 Loss: {float(loss)}, "
-                    f"MSE Loss: {float(mse_loss)}, LR: {float(scheduler.get_lr())}"
-                )
-
-        # Validation
-        model.eval()
-
-        with paddle.no_grad():
-            avg_ssim = paddle.zeros([])
-            avg_ms_ssim = paddle.zeros([])
-            avg_l1_loss = paddle.zeros([])
-            avg_mse_loss = paddle.zeros([])
-
-            for i, (img, target) in enumerate(val_loader):
-                img = paddle.to_tensor(img)
-                target = paddle.to_tensor(target)
-
-                pred = model(img)
-                pred_nhwc = pred.transpose([0, 2, 3, 1])
-
-                # predict image
-                out = F.grid_sample(img, (pred_nhwc / args.img_size - 0.5) * 2)
-                out_gt = F.grid_sample(img, (target / args.img_size - 0.5) * 2)
-
-                # calculate ssim
-                ssim_val = ssim(out, out_gt, data_range=1.0)
-                ms_ssim_val = ms_ssim(out, out_gt, data_range=1.0)
-
-                loss = l1_loss_fn(pred_nhwc, target)
-                mse_loss = mse_loss_fn(pred_nhwc, target)
-
-                # calculate fitness
-                avg_ssim += ssim_val
-                avg_ms_ssim += ms_ssim_val
-                avg_l1_loss += loss
-                avg_mse_loss += mse_loss
-
-                if i % 10 == 0:
-                    logger.info(
-                        f"[VAL MODE] Epoch: {epoch}, VAL Iter: {i}, "
-                        f"L1 Loss: {float(loss)} MSE Loss: {float(mse_loss)}, "
-                        f"MS-SSIM: {float(ms_ssim_val)}, SSIM: {float(ssim_val)}"
-                    )
-
-                if use_vdl and i == 0:
-                    img_0 = to_image(out[0])
-                    img_gt_0 = to_image(out_gt[0])
-                    vdl_writer.add_image("Val/Predicted Image No.0", img_0, epoch)
-                    vdl_writer.add_image("Val/Target Image No.0", img_gt_0, epoch)
-
-                    img_1 = to_image(out[1])
-                    img_gt_1 = to_image(out_gt[1])
-                    img_gt_1 = img_gt_1.astype("uint8")
-                    vdl_writer.add_image("Val/Predicted Image No.1", img_1, epoch)
-                    vdl_writer.add_image("Val/Target Image No.1", img_gt_1, epoch)
-
-                    img_2 = to_image(out[2])
-                    img_gt_2 = to_image(out_gt[2])
-                    vdl_writer.add_image("Val/Predicted Image No.2", img_2, epoch)
-                    vdl_writer.add_image("Val/Target Image No.2", img_gt_2, epoch)
-
-            avg_ssim /= len(val_loader)
-            avg_ms_ssim /= len(val_loader)
-            avg_l1_loss /= len(val_loader)
-            avg_mse_loss /= len(val_loader)
-
-            if use_vdl:
-                vdl_writer.add_scalar("Val/L1 Loss", float(loss), epoch)
-                vdl_writer.add_scalar("Val/MSE Loss", float(mse_loss), epoch)
-                vdl_writer.add_scalar("Val/SSIM", float(ssim_val), epoch)
-                vdl_writer.add_scalar("Val/MS-SSIM", float(ms_ssim_val), epoch)
-
-        # Save
-        ckpt = {
-            "model": model.state_dict(),
-            "optimizer": optimizer.state_dict(),
-            "scheduler": scheduler.state_dict(),
-            "best_fitness": best_fitness,
-            "epoch": epoch,
-        }
-
-        paddle.save(ckpt, str(last))
-
-        if best_fitness < avg_ssim:
-            best_fitness = avg_ssim
-            paddle.save(ckpt, str(best))
-
-    if use_vdl:
-        vdl_writer.close()
-
-
-def main(args):
-    print_args(vars(args))
-
-    args.save_dir = str(
-        increment_path(Path(args.project) / args.name, exist_ok=args.exist_ok)
-    )
-
-    train(args)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Hyperparams")
-    parser.add_argument(
-        "--data-root",
-        nargs="?",
-        type=str,
-        default="~/datasets/doc3d",
-        help="The root path of the dataset",
-    )
-    parser.add_argument(
-        "--img-size",
-        nargs="?",
-        type=int,
-        default=288,
-        help="The size of the input image",
-    )
-    parser.add_argument(
-        "--epochs",
-        nargs="?",
-        type=int,
-        default=65,
-        help="The number of training epochs",
-    )
-    parser.add_argument(
-        "--batch-size", nargs="?", type=int, default=12, help="Batch Size"
-    )
-    parser.add_argument(
-        "--lr", nargs="?", type=float, default=1e-04, help="Learning Rate"
-    )
-    parser.add_argument(
-        "--resume",
-        nargs="?",
-        type=str,
-        default=None,
-        help="Path to previous saved model to restart from",
-    )
-    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers")
-    parser.add_argument(
-        "--project", default=ROOT / "runs/train", help="save to project/name"
-    )
-    parser.add_argument("--name", default="exp", help="save to project/name")
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="Global training seed")
-    parser.add_argument("--use-vdl", action="store_true", help="use VisualDL as logger")
-    args = parser.parse_args()
-    main(args)
diff --git a/doc_dewarp/train.sh b/doc_dewarp/train.sh
deleted file mode 100644
index 2b7da99..0000000
--- a/doc_dewarp/train.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-export OPENCV_IO_ENABLE_OPENEXR=1
-export FLAGS_logtostderr=0
-export CUDA_VISIBLE_DEVICES=0
-
-python train.py --img-size 288 \
-    --name "DocTr++" \
-    --batch-size 12 \
-    --lr 1e-4 \
-    --exist-ok \
-    --use-vdl
diff --git a/doc_dewarp/utils.py b/doc_dewarp/utils.py
deleted file mode 100644
index e887908..0000000
--- a/doc_dewarp/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import paddle
-from paddle.nn import functional as F
-
-
-def to_tensor(img: np.ndarray):
-    """
-    Converts a numpy array image (HWC) to a Paddle tensor (NCHW).
-
-    Args:
-        img (numpy.ndarray): The input image as a numpy array.
-
-    Returns:
-        out (paddle.Tensor): The output tensor.
-    """
-    img = img[:, :, ::-1]
-    img = img.astype("float32") / 255.0
-    img = img.transpose(2, 0, 1)
-    out: paddle.Tensor = paddle.to_tensor(img)
-    out = paddle.unsqueeze(out, axis=0)
-
-    return out
-
-
-def to_image(x: paddle.Tensor):
-    """
-    Converts a Paddle tensor (NCHW) to a numpy array image (HWC).
-
-    Args:
-        x (paddle.Tensor): The input tensor.
-
-    Returns:
-        out (numpy.ndarray): The output image as a numpy array.
-    """
-    out: np.ndarray = x.squeeze().numpy()
-    out = out.transpose(1, 2, 0)
-    out = out * 255.0
-    out = out.astype("uint8")
-    out = out[:, :, ::-1]
-
-    return out
-
-
-def unwarp(img, bm, bm_data_format="NCHW"):
-    """
-    Unwarp an image using a flow field.
-
-    Args:
-        img (paddle.Tensor): The input image.
-        bm (paddle.Tensor): The flow field.
-
-    Returns:
-        out (paddle.Tensor): The output image.
-    """
-    _, _, h, w = img.shape
-
-    if bm_data_format == "NHWC":
-        bm = bm.transpose([0, 3, 1, 2])
-
-    # NCHW
-    bm = F.upsample(bm, size=(h, w), mode="bilinear", align_corners=True)
-    # NHWC
-    bm = bm.transpose([0, 2, 3, 1])
-    # NCHW
-    out = F.grid_sample(img, bm)
-
-    return out
diff --git a/doc_dewarp/weight_init.py b/doc_dewarp/weight_init.py
deleted file mode 100644
index c83178c..0000000
--- a/doc_dewarp/weight_init.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import math
-
-import numpy as np
-import paddle
-import paddle.nn.initializer as init
-from scipy import special
-
-
-def weight_init_(
-    layer, func, weight_name=None, bias_name=None, bias_value=0.0, **kwargs
-):
-    """
-    In-place params init function.
-    Usage:
-    .. code-block:: python
-
-        import paddle
-        import numpy as np
-
-        data = np.ones([3, 4], dtype='float32')
-        linear = paddle.nn.Linear(4, 4)
-        input = paddle.to_tensor(data)
-        print(linear.weight)
-        linear(input)
-
-        weight_init_(linear, 'Normal', 'fc_w0', 'fc_b0', std=0.01, mean=0.1)
-        print(linear.weight)
-    """
-
-    if hasattr(layer, "weight") and layer.weight is not None:
-        getattr(init, func)(**kwargs)(layer.weight)
-        if weight_name is not None:
-            # override weight name
-            layer.weight.name = weight_name
-
-    if hasattr(layer, "bias") and layer.bias is not None:
-        init.Constant(bias_value)(layer.bias)
-        if bias_name is not None:
-            # override bias name
-            layer.bias.name = bias_name
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        print(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect."
-        )
-
-    with paddle.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        lower = norm_cdf((a - mean) / std)
-        upper = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to [2l-1, 2u-1].
-        tmp = np.random.uniform(
-            2 * lower - 1, 2 * upper - 1, size=list(tensor.shape)
-        ).astype(np.float32)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tmp = special.erfinv(tmp)
-
-        # Transform to proper mean, std
-        tmp *= std * math.sqrt(2.0)
-        tmp += mean
-
-        # Clamp to ensure it's in the proper range
-        tmp = np.clip(tmp, a, b)
-        tensor.set_value(paddle.to_tensor(tmp))
-
-        return tensor
-
-
-def _calculate_fan_in_and_fan_out(tensor):
-    dimensions = tensor.dim()
-    if dimensions < 2:
-        raise ValueError(
-            "Fan in and fan out can not be computed for tensor "
-            "with fewer than 2 dimensions"
-        )
-
-    num_input_fmaps = tensor.shape[1]
-    num_output_fmaps = tensor.shape[0]
-    receptive_field_size = 1
-    if tensor.dim() > 2:
-        receptive_field_size = tensor[0][0].numel()
-    fan_in = num_input_fmaps * receptive_field_size
-    fan_out = num_output_fmaps * receptive_field_size
-
-    return fan_in, fan_out
-
-
-def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def kaiming_normal_(tensor, a=0.0, mode="fan_in", nonlinearity="leaky_relu"):
-    def _calculate_correct_fan(tensor, mode):
-        mode = mode.lower()
-        valid_modes = ["fan_in", "fan_out"]
-        if mode not in valid_modes:
-            raise ValueError(
-                "Mode {} not supported, please use one of {}".format(mode, valid_modes)
-            )
-
-        fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-        return fan_in if mode == "fan_in" else fan_out
-
-    def calculate_gain(nonlinearity, param=None):
-        linear_fns = [
-            "linear",
-            "conv1d",
-            "conv2d",
-            "conv3d",
-            "conv_transpose1d",
-            "conv_transpose2d",
-            "conv_transpose3d",
-        ]
-        if nonlinearity in linear_fns or nonlinearity == "sigmoid":
-            return 1
-        elif nonlinearity == "tanh":
-            return 5.0 / 3
-        elif nonlinearity == "relu":
-            return math.sqrt(2.0)
-        elif nonlinearity == "leaky_relu":
-            if param is None:
-                negative_slope = 0.01
-            elif (
-                not isinstance(param, bool)
-                and isinstance(param, int)
-                or isinstance(param, float)
-            ):
-                negative_slope = param
-            else:
-                raise ValueError("negative_slope {} not a valid number".format(param))
-            return math.sqrt(2.0 / (1 + negative_slope**2))
-        else:
-            raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
-
-    fan = _calculate_correct_fan(tensor, mode)
-    gain = calculate_gain(nonlinearity, a)
-    std = gain / math.sqrt(fan)
-    with paddle.no_grad():
-        paddle.nn.initializer.Normal(0, std)(tensor)
-        return tensor