Merge branch 'pre-develop/v2.0.0' of https://github.com/PaddlePaddle/PaddleHub...

Merge branch 'pre-develop/v2.0.0' of https://github.com/PaddlePaddle/PaddleHub into pre-develop/v2.0.0

demo/colorization/predict.py

+import paddle
+import paddlehub as hub
+import paddle.nn as nn
+
+
+if __name__ == '__main__':
+    paddle.disable_static()
+    model = hub.Module(directory='user_guided_colorization')
+    model.eval()
+    result = model.predict(images='sea.jpg')
\ No newline at end of file

demo/colorization/train.py

+import paddle
+import paddlehub as hub
+import paddle.nn as nn
+from paddlehub.finetune.trainer import Trainer
+
+from paddlehub.datasets.colorizedataset import  Colorizedataset
+from paddlehub.process.transforms import Compose, Resize, RandomPaddingCrop, ConvertColorSpace,  ColorizePreprocess
+
+
+if __name__ == '__main__':
+    is_train = True
+    paddle.disable_static()
+    model = hub.Module(directory='user_guided_colorization')
+    transform = Compose([Resize((256,256),interp="RANDOM"),RandomPaddingCrop(crop_size=176), ConvertColorSpace(mode='RGB2LAB'), ColorizePreprocess(ab_thresh=0, p=1)], stay_rgb=True)
+    color_set = Colorizedataset(transform=transform, mode=is_train)
+    if is_train:
+        model.train()
+        optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
+        trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
+        trainer.train(color_set, epochs=3, batch_size=1, eval_dataset=color_set, save_interval=1)

hub_module/modules/image/colorization/user_guided_colorization/module.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import numpy
+import paddle.nn as nn
+from paddle.nn import Conv2d, ConvTranspose2d
+from paddlehub.module.module import moduleinfo
+
+from paddlehub.process.transforms import Compose, Resize, RandomPaddingCrop, ConvertColorSpace, ColorizePreprocess
+from paddlehub.module.cv_module import ImageColorizeModule
+
+
+@moduleinfo(
+    name="user_guided_colorization",
+    type="CV/image_editing",
+    author="paddlepaddle",
+    author_email="",
+    summary="User_guided_colorization is a image colorization model, this module is trained with ILSVRC2012 dataset.",
+    version="1.0.0",
+    meta=ImageColorizeModule)
+class UserGuidedColorization(nn.Layer):
+    """Userguidedcolorization, see https://github.com/haoyuying/colorization-pytorch
+
+    Args:
+        use_tanh (bool): Whether to use tanh as final activation function.
+        classification (bool): Whether to switch classification branch for optimization.
+        load_checkpoint (str): Pretrained checkpoint path.
+
+    """
+    def __init__(self, use_tanh: bool = True, classification: bool = True, load_checkpoint: str = None):
+        super(UserGuidedColorization, self).__init__()
+        self.input_nc = 4
+        self.output_nc = 2
+        self.classification = classification
+        # Conv1
+        model1 = (
+            Conv2d(self.input_nc, 64, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(64, 64, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(64),
+        )
+
+        # Conv2
+        model2 = (
+            Conv2d(64, 128, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(128, 128, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(128),
+        )
+
+        # Conv3
+        model3 = (
+            Conv2d(128, 256, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(256, 256, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(256, 256, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(256),
+        )
+
+        # Conv4
+        model4 = (
+            Conv2d(256, 512, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(512),
+        )
+
+        # Conv5
+        model5 = (
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            nn.BatchNorm(512),
+        )
+
+        # Conv6
+        model6 = (
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 2, 2),
+            nn.ReLU(),
+            nn.BatchNorm(512),
+        )
+
+        # Conv7
+        model7 = (
+            Conv2d(512, 512, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(512, 512, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(512),
+        )
+
+        # Conv8
+        model8up = (ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1), )
+        model3short8 = (Conv2d(256, 256, 3, 1, 1), )
+        model8 = (
+            nn.ReLU(),
+            Conv2d(256, 256, 3, 1, 1),
+            nn.ReLU(),
+            Conv2d(256, 256, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(256),
+        )
+
+        # Conv9
+        model9up = (ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), )
+        model2short9 = (Conv2d(
+            128,
+            128,
+            3,
+            1,
+            1,
+        ), )
+        model9 = (
+            nn.ReLU(),
+            Conv2d(128, 128, 3, 1, 1),
+            nn.ReLU(),
+            nn.BatchNorm(128),
+        )
+
+        # Conv10
+        model10up = (ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1), )
+        model1short10 = (Conv2d(64, 128, 3, 1, 1), )
+        model10 = (nn.ReLU(), Conv2d(128, 128, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2))
+        model_class = (Conv2d(256, 529, 1), )
+
+        if use_tanh:
+            model_out = (Conv2d(128, 2, 1, 1, 0, 1), nn.Tanh())
+        else:
+            model_out = (Conv2d(128, 2, 1, 1, 0, 1), )
+
+        self.model1 = nn.Sequential(*model1)
+        self.model2 = nn.Sequential(*model2)
+        self.model3 = nn.Sequential(*model3)
+        self.model4 = nn.Sequential(*model4)
+        self.model5 = nn.Sequential(*model5)
+        self.model6 = nn.Sequential(*model6)
+        self.model7 = nn.Sequential(*model7)
+        self.model8up = nn.Sequential(*model8up)
+        self.model8 = nn.Sequential(*model8)
+        self.model9up = nn.Sequential(*model9up)
+        self.model9 = nn.Sequential(*model9)
+        self.model10up = nn.Sequential(*model10up)
+        self.model10 = nn.Sequential(*model10)
+        self.model3short8 = nn.Sequential(*model3short8)
+        self.model2short9 = nn.Sequential(*model2short9)
+        self.model1short10 = nn.Sequential(*model1short10)
+        self.model_class = nn.Sequential(*model_class)
+        self.model_out = nn.Sequential(*model_out)
+
+        if load_checkpoint is not None:
+            model_dict = paddle.load(load_checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load pretrained model success")
+
+    def transforms(self, images: str, is_train: bool = True) -> callable:
+        if is_train:
+            transform = Compose([
+                Resize((256, 256), interp="RANDOM"),
+                RandomPaddingCrop(crop_size=176),
+                ConvertColorSpace(mode='RGB2LAB'),
+                ColorizePreprocess(ab_thresh=0, is_train=is_train)
+            ],
+                                stay_rgb=True)
+        else:
+            transform = Compose([
+                Resize((256, 256), interp="RANDOM"),
+                ConvertColorSpace(mode='RGB2LAB'),
+                ColorizePreprocess(ab_thresh=0, is_train=is_train)
+            ],
+                                stay_rgb=True)
+        return transform(images)
+
+    def forward(self,
+                input_A: paddle.Tensor,
+                input_B: paddle.Tensor,
+                mask_B: paddle.Tensor,
+                real_b: paddle.Tensor = None,
+                real_B_enc: paddle.Tensor = None) -> paddle.Tensor:
+        conv1_2 = self.model1(paddle.concat([input_A, input_B, mask_B], axis=1))
+        conv2_2 = self.model2(conv1_2[:, :, ::2, ::2])
+        conv3_3 = self.model3(conv2_2[:, :, ::2, ::2])
+        conv4_3 = self.model4(conv3_3[:, :, ::2, ::2])
+        conv5_3 = self.model5(conv4_3)
+        conv6_3 = self.model6(conv5_3)
+        conv7_3 = self.model7(conv6_3)
+        conv8_up = self.model8up(conv7_3) + self.model3short8(conv3_3)
+        conv8_3 = self.model8(conv8_up)
+
+        if self.classification:
+            out_class = self.model_class(conv8_3)
+            conv9_up = self.model9up(conv8_3.detach()) + self.model2short9(conv2_2.detach())
+            conv9_3 = self.model9(conv9_up)
+            conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2.detach())
+            conv10_2 = self.model10(conv10_up)
+            out_reg = self.model_out(conv10_2)
+        else:
+            out_class = self.model_class(conv8_3.detach())
+            conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)
+            conv9_3 = self.model9(conv9_up)
+            conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2)
+            conv10_2 = self.model10(conv10_up)
+            out_reg = self.model_out(conv10_2)
+
+        return out_class, out_reg
+
+
+if __name__ == "__main__":
+    place = paddle.CUDAPlace(0)
+    paddle.disable_static()
+    model = UserGuidedColorization()
+    model.eval()

paddlehub/datasets/colorizedataset.py

+# coding:utf-8
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import numpy
+import paddle
+
+from paddlehub.process.functional import get_img_file
+from paddlehub.env import DATA_HOME
+from typing import Callable
+
+class Colorizedataset(paddle.io.Dataset):
+    """
+    Dataset for colorization.
+    Args:
+       transform(callmethod) : The method of preprocess images.
+       mode(str): The mode for preparing dataset.
+    Returns:
+        DataSet: An iterable object for data iterating
+    """
+    def __init__(self, transform: Callable, mode: str = 'train'):
+        self.mode = mode
+        self.transform = transform
+        
+        if self.mode == 'train':
+            self.file = 'train'
+        elif self.mode == 'test':
+            self.file = 'test'
+        else:
+            self.file = 'validation'
+            
+        self.file = os.path.join(DATA_HOME, 'canvas', self.file)
+        self.data = get_img_file(self.file)
+
+    def __getitem__(self, idx: int) -> numpy.ndarray:
+        img_path = self.data[idx]
+        im = self.transform(img_path)
+        return im['A'], im['hint_B'], im['mask_B'], im['B'], im['real_B_enc']
+
+    def __len__(self):
+        return len(self.data)
\ No newline at end of file

paddlehub/module/cv_module.py

@@ -13,14 +13,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import time
+import os
 from typing import List
+from collections import OrderedDict
 
 import numpy as np
 import paddle
+import paddle.nn as nn
 import paddle.nn.functional as F
+from PIL import Image
 
 from paddlehub.module.module import serving, RunModule
 from paddlehub.utils.utils import base64_to_cv2
+from paddlehub.process.transforms import ConvertColorSpace, ColorPostprocess, Resize
 
 
 class ImageServing(object):
@@ -91,3 +97,96 @@ class ImageClassifierModule(RunModule, ImageServing):
                 res_dict[class_name] = preds[i][k]
             res.append(res_dict)
         return res
+
+
+class ImageColorizeModule(RunModule, ImageServing):
+    def training_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for training, which should be called as forward computation.
+        
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
+            batch_idx(int): The index of batch.
+            
+        Returns:
+            results(dict) : The model outputs, such as loss and metrics.
+        '''
+        return self.validation_step(batch, batch_idx)
+
+    def validation_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for validation, which should be called as forward computation.
+        
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
+            batch_idx(int): The index of batch.
+            
+        Returns:
+            results(dict) : The model outputs, such as metrics.
+        '''
+        out_class, out_reg = self(batch[0], batch[1], batch[2])
+        
+        criterionCE = nn.loss.CrossEntropyLoss()
+        loss_ce = criterionCE(out_class, batch[4][:, 0, :, :])
+        loss_G_L1_reg = paddle.sum(paddle.abs(batch[3] - out_reg), axis=1, keepdim=True)
+        loss_G_L1_reg = paddle.mean(loss_G_L1_reg)
+        loss = loss_ce + loss_G_L1_reg
+        
+        visual_ret = OrderedDict()
+        psnrs = []
+        lab2rgb = ConvertColorSpace(mode='LAB2RGB')
+        process = ColorPostprocess()
+        for i in range(batch[0].numpy().shape[0]):
+            real = lab2rgb(np.concatenate((batch[0].numpy(), batch[3].numpy()), axis=1))[i]
+            visual_ret['real'] = process(real)
+            fake = lab2rgb(np.concatenate((batch[0].numpy(), out_reg.numpy()), axis=1))[i]
+            visual_ret['fake_reg'] = process(fake)
+            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0) ** 2)
+            psnr_value = 20 * np.log10(255. / np.sqrt(mse))
+            psnrs.append(psnr_value)
+        psnr = paddle.to_variable(np.array(psnrs))
+        return {'loss': loss, 'metrics': {'psnr': psnr}}
+
+    def predict(self, images: str, visualization: bool = True, save_path: str = 'result'):
+        '''
+        Colorize images
+        
+        Args:
+            images(str) : Images path to be colorized.
+            visualization(bool): Whether to save colorized images.
+            save_path(str) : Path to save colorized images.
+            
+        Returns:
+            results(list[dict]) : The prediction result of each input image
+        '''
+        lab2rgb = ConvertColorSpace(mode='LAB2RGB')
+        process = ColorPostprocess()
+        resize = Resize((256, 256))
+        visual_ret = OrderedDict()
+        im = self.transforms(images, is_train=False)
+        out_class, out_reg = self(paddle.to_tensor(im['A']), paddle.to_variable(im['hint_B']),
+                                  paddle.to_variable(im['mask_B']))
+        result = []
+
+        for i in range(im['A'].shape[0]):
+            gray = lab2rgb(np.concatenate((im['A'], np.zeros(im['B'].shape)), axis=1))[i]
+            visual_ret['gray'] = resize(process(gray))
+            hint = lab2rgb(np.concatenate((im['A'], im['hint_B']), axis=1))[i]
+            visual_ret['hint'] = resize(process(hint))
+            real = lab2rgb(np.concatenate((im['A'], im['B']), axis=1))[i]
+            visual_ret['real'] = resize(process(real))
+            fake = lab2rgb(np.concatenate((im['A'], out_reg.numpy()), axis=1))[i]
+            visual_ret['fake_reg'] = resize(process(fake))
+            
+            if visualization:
+                fake_name = "fake_" + str(time.time()) + ".png"
+                if not os.path.exists(save_path):
+                    os.mkdir(save_path)
+                fake_path = os.path.join(save_path, fake_name)
+                visual_gray = Image.fromarray(visual_ret['fake_reg'])
+                visual_gray.save(fake_path)
+                
+            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0) ** 2)
+            psnr_value = 20 * np.log10(255. / np.sqrt(mse))
+            result.append(visual_ret)
+        return result

paddlehub/process/functional.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+
 import cv2
 import numpy as np
 from PIL import Image, ImageEnhance
@@ -96,3 +98,23 @@ def rotate(im, rotate_lower, rotate_upper):
     rotate_delta = np.random.uniform(rotate_lower, rotate_upper)
     im = im.rotate(int(rotate_delta))
     return im
+
+
+def is_image_file(filename: str) -> bool:
+    '''Determine whether the input file name is a valid image file name.'''
+    ext = os.path.splitext(filename)[-1].lower()
+    return ext in ['.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff']
+
+
+def get_img_file(dir_name: str) -> list:
+    '''Get all image file paths in several directories which have the same parent directory.'''
+    images = []
+    for parent, dirnames, filenames in os.walk(dir_name):
+        for filename in filenames:
+            if not is_image_file(filename):
+                continue
+            img_path = os.path.join(parent, filename)
+            print(img_path)
+            images.append(img_path)
+    images.sort()
+    return images
\ No newline at end of file

paddlehub/process/transforms.py

@@ -16,15 +16,15 @@
 import random
 from collections import OrderedDict
 
+import cv2
 import numpy as np
 from PIL import Image
-import cv2
 
 from paddlehub.process.functional import *
 
 
 class Compose:
-    def __init__(self, transforms, to_rgb=True):
+    def __init__(self, transforms, to_rgb=True, stay_rgb=False):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
         if len(transforms) < 1:
@@ -32,6 +32,7 @@ class Compose:
                             'must be equal or larger than 1!')
         self.transforms = transforms
         self.to_rgb = to_rgb
+        self.stay_rgb = stay_rgb
 
     def __call__(self, im):
         if isinstance(im, str):
@@ -44,10 +45,15 @@ class Compose:
 
         for op in self.transforms:
             im = op(im)
+            
+        if not self.stay_rgb:
             im = permute(im)
+
         return im
 
 
+
+
 class RandomHorizontalFlip:
     def __init__(self, prob=0.5):
         self.prob = prob
@@ -396,3 +402,314 @@ class RandomDistort:
         im = np.asarray(im).astype('float32')
 
         return im
+
+    
+class ConvertColorSpace:
+    """
+    Convert color space from RGB to LAB or from LAB to RGB.
+    
+    Args:
+       mode(str): Color space convert mode, it can be 'RGB2LAB' or 'LAB2RGB'.
+       
+    Return:
+        img(np.ndarray): converted image.
+    """
+    def __init__(self, mode: str = 'RGB2LAB'):
+        self.mode = mode
+
+    def rgb2xyz(self, rgb: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from RGB to XYZ.
+
+        Args:
+           img(np.ndarray): Original RGB image.
+
+        Return:
+            img(np.ndarray): Converted XYZ image.
+        """
+        mask = (rgb > 0.04045)
+        np.seterr(invalid='ignore')
+        rgb = (((rgb + .055) / 1.055) ** 2.4) * mask + rgb / 12.92 * (1 - mask)
+        rgb = np.nan_to_num(rgb)
+        x = .412453 * rgb[:, 0, :, :] + .357580 * rgb[:, 1, :, :] + .180423 * rgb[:, 2, :, :]
+        y = .212671 * rgb[:, 0, :, :] + .715160 * rgb[:, 1, :, :] + .072169 * rgb[:, 2, :, :]
+        z = .019334 * rgb[:, 0, :, :] + .119193 * rgb[:, 1, :, :] + .950227 * rgb[:, 2, :, :]
+        out = np.concatenate((x[:, None, :, :], y[:, None, :, :], z[:, None, :, :]), axis=1)
+        return out
+
+    def xyz2lab(self, xyz: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from XYZ to LAB.
+
+        Args:
+           img(np.ndarray): Original XYZ image.
+
+        Return:
+            img(np.ndarray): Converted LAB image.
+        """
+        sc = np.array((0.95047, 1., 1.08883))[None, :, None, None]
+        xyz_scale = xyz / sc
+        mask = (xyz_scale > .008856).astype(np.float32)
+        xyz_int = np.cbrt(xyz_scale) * mask + (7.787 * xyz_scale + 16. / 116.) * (1 - mask)
+        L = 116. * xyz_int[:, 1, :, :] - 16.
+        a = 500. * (xyz_int[:, 0, :, :] - xyz_int[:, 1, :, :])
+        b = 200. * (xyz_int[:, 1, :, :] - xyz_int[:, 2, :, :])
+        out = np.concatenate((L[:, None, :, :], a[:, None, :, :], b[:, None, :, :]), axis=1)
+        return out
+
+    def rgb2lab(self, rgb: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from RGB to LAB.
+
+        Args:
+           img(np.ndarray): Original RGB image.
+
+        Return:
+            img(np.ndarray): Converted LAB image.
+        """
+        lab = self.xyz2lab(self.rgb2xyz(rgb))
+        l_rs = (lab[:, [0], :, :] - 50) / 100
+        ab_rs = lab[:, 1:, :, :] / 110
+        out = np.concatenate((l_rs, ab_rs), axis=1)
+        return out
+
+    def xyz2rgb(self, xyz: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from XYZ to RGB.
+
+        Args:
+           img(np.ndarray): Original XYZ image.
+
+        Return:
+            img(np.ndarray): Converted RGB image.
+        """
+        r = 3.24048134 * xyz[:, 0, :, :] - 1.53715152 * xyz[:, 1, :, :] - 0.49853633 * xyz[:, 2, :, :]
+        g = -0.96925495 * xyz[:, 0, :, :] + 1.87599 * xyz[:, 1, :, :] + .04155593 * xyz[:, 2, :, :]
+        b = .05564664 * xyz[:, 0, :, :] - .20404134 * xyz[:, 1, :, :] + 1.05731107 * xyz[:, 2, :, :]
+        rgb = np.concatenate((r[:, None, :, :], g[:, None, :, :], b[:, None, :, :]), axis=1)
+        rgb = np.maximum(rgb, 0)  # sometimes reaches a small negative number, which causes NaNs
+        mask = (rgb > .0031308).astype(np.float32)
+        np.seterr(invalid='ignore')
+        out = (1.055 * (rgb ** (1. / 2.4)) - 0.055) * mask + 12.92 * rgb * (1 - mask)
+        out = np.nan_to_num(out)
+        return out
+
+    def lab2xyz(self, lab: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from LAB to XYZ.
+
+        Args:
+           img(np.ndarray): Original LAB image.
+
+        Return:
+            img(np.ndarray): Converted XYZ image.
+        """
+        y_int = (lab[:, 0, :, :] + 16.) / 116.
+        x_int = (lab[:, 1, :, :] / 500.) + y_int
+        z_int = y_int - (lab[:, 2, :, :] / 200.)
+        z_int = np.maximum(z_int, 0)
+        out = np.concatenate((x_int[:, None, :, :], y_int[:, None, :, :], z_int[:, None, :, :]), axis=1)
+        mask = (out > .2068966).astype(np.float32)
+        np.seterr(invalid='ignore')
+        out = (out ** 3.) * mask + (out - 16. / 116.) / 7.787 * (1 - mask)
+        out = np.nan_to_num(out)
+        sc = np.array((0.95047, 1., 1.08883))[None, :, None, None]
+        out = out * sc
+        return out
+
+    def lab2rgb(self, lab_rs: np.ndarray) -> np.ndarray:
+        """
+        Convert color space from LAB to RGB.
+
+        Args:
+           img(np.ndarray): Original LAB image.
+
+        Return:
+            img(np.ndarray): Converted RGB image.
+        """
+        l = lab_rs[:, [0], :, :] * 100 + 50
+        ab = lab_rs[:, 1:, :, :] * 110
+        lab = np.concatenate((l, ab), axis=1)
+        out = self.xyz2rgb(self.lab2xyz(lab))
+        return out
+
+    def __call__(self, img: np.ndarray) -> np.ndarray:
+        if self.mode == 'RGB2LAB':
+            img = np.expand_dims(img / 255, 0)
+            img = np.array(img).transpose(0, 3, 1, 2)
+            return self.rgb2lab(img)
+        elif self.mode == 'LAB2RGB':
+            return self.lab2rgb(img)
+        else:
+            raise ValueError('The mode should be RGB2LAB or LAB2RGB')
+
+
+class ColorizeHint:
+    """Get hint and mask images for colorization.
+    
+    This method is prepared for user guided colorization tasks. Take the original RGB images as imput, we will obtain the local hints and correspoding mask to guid colorization process.
+    
+    Args:
+       percent(float): Probability for ignoring hint in an iteration.
+       num_points(int): Number of selected hints in an iteration.
+       samp(str): Sample method, default is normal.
+       use_avg(bool): Whether to use mean in selected hint area.
+       
+    Return:
+        hint(np.ndarray): hint images
+        mask(np.ndarray): mask images
+    """
+    def __init__(self,  percent: float, num_points: int = None, samp: str = 'normal', use_avg: bool = True):
+        self.percent = percent
+        self.num_points = num_points
+        self.samp = samp
+        self.use_avg = use_avg
+
+    def __call__(self, data: np.ndarray, hint: np.ndarray, mask: np.ndarray):
+        sample_Ps = [1, 2, 3, 4, 5, 6, 7, 8, 9, ]
+        self.data = data
+        self.hint = hint
+        self.mask = mask
+        N, C, H, W = data.shape
+        for nn in range(N):
+            pp = 0
+            cont_cond = True
+            while cont_cond:
+                if self.num_points is None:  # draw from geometric
+                    # embed()
+                    cont_cond = np.random.rand() < (1 - self.percent)
+                else:  # add certain number of points
+                    cont_cond = pp < self.num_points
+                if not cont_cond:  # skip out of loop if condition not met
+                    continue
+                P = np.random.choice(sample_Ps)  # patch size
+                # sample location
+                if self.samp == 'normal':  # geometric distribution
+                    h = int(np.clip(np.random.normal((H - P + 1) / 2., (H - P + 1) / 4.), 0, H - P))
+                    w = int(np.clip(np.random.normal((W - P + 1) / 2., (W - P + 1) / 4.), 0, W - P))
+                else:  # uniform distribution
+                    h = np.random.randint(H - P + 1)
+                    w = np.random.randint(W - P + 1)
+                # add color point
+                if self.use_avg:
+                    # embed()
+                    hint[nn, :, h:h + P, w:w + P] = np.mean(
+                        np.mean(data[nn, :, h:h + P, w:w + P], axis=2, keepdims=True), axis=1, keepdims=True).reshape(
+                        1, C, 1, 1)
+                else:
+                    hint[nn, :, h:h + P, w:w + P] = data[nn, :, h:h + P, w:w + P]
+                mask[nn, :, h:h + P, w:w + P] = 1
+                # increment counter
+                pp += 1
+
+        mask -= 0.5
+        return hint, mask
+
+
+class SqueezeAxis:
+    """
+    Squeeze the specific axis when it equal to 1.
+    
+    Args:
+       axis(int): Which axis should be squeezed.
+       
+    """
+    def __init__(self, axis: int):
+        self.axis = axis
+
+    def __call__(self, data: dict):
+        if isinstance(data, dict):
+            for key in data.keys():
+                data[key] = np.squeeze(data[key], 0).astype(np.float32)
+            return data
+        else:
+            raise TypeError("Type of data is invalid. Must be Dict or List or tuple, now is {}".format(type(data)))
+
+
+class ColorizePreprocess:
+    """Prepare dataset for image Colorization.
+    
+    Args:
+       ab_thresh(float): Thresh value for setting mask value.
+       p(float): Probability for ignoring hint in an iteration.
+       num_points(int): Number of selected hints in an iteration.
+       samp(str): Sample method, default is normal.
+       use_avg(bool): Whether to use mean in selected hint area.
+       is_train(bool): Training process or not.
+       
+    Return:
+        data(dict)：The preprocessed data for colorization.
+
+    """
+    def __init__(self, ab_thresh: float = 0.,
+                 p: float = .125,
+                 num_points: int = None,
+                 samp: str = 'normal',
+                 use_avg: bool = True,
+                 is_train: bool = True):
+        self.ab_thresh = ab_thresh
+        self.p = p
+        self.num_points = num_points
+        self.samp = samp
+        self.use_avg = use_avg
+        self.is_train = is_train
+        self.gethint = ColorizeHint(percent=self.p, num_points=self.num_points, samp=self.samp, use_avg=self.use_avg)
+        self.squeeze = SqueezeAxis(0)
+
+    def __call__(self, data_lab: np.ndarray):
+        """
+        This method seperates the L channel and AB channel, obtain hint, mask and real_B_enc as the input for colorization task.
+
+        Args:
+           img(np.ndarray): LAB image.
+
+        Returns:
+            data(dict)：The preprocessed data for colorization.
+        """
+        data = {}
+        A = 2 * 110 / 10 + 1
+        data['A'] = data_lab[:, [0, ], :, :]
+        data['B'] = data_lab[:, 1:, :, :]
+        if self.ab_thresh > 0:  # mask out grayscale images
+            thresh = 1. * self.ab_thresh / 110
+            mask = np.sum(np.abs(np.max(np.max(data['B'], axis=3), axis=2) - np.min(np.min(data['B'], axis=3), axis=2)),axis=1)
+            mask = (mask >= thresh)
+            data['A'] = data['A'][mask, :, :, :]
+            data['B'] = data['B'][mask, :, :, :]
+            if np.sum(mask) == 0:
+                return None
+        data_ab_rs = np.round((data['B'][:, :, ::4, ::4] * 110. + 110.) / 10.)  # normalized bin number
+        data['real_B_enc'] = data_ab_rs[:, [0], :, :] * A + data_ab_rs[:, [1], :, :]
+        data['hint_B'] = np.zeros(shape=data['B'].shape)
+        data['mask_B'] = np.zeros(shape=data['A'].shape)
+        data['hint_B'], data['mask_B'] = self.gethint(data['B'], data['hint_B'], data['mask_B'])
+        if self.is_train:
+            data = self.squeeze(data)
+            data['real_B_enc'] = data['real_B_enc'].astype(np.int64)
+        else:
+            data['A'] = data['A'].astype(np.float32)
+            data['B'] = data['B'].astype(np.float32)
+            data['real_B_enc'] = data['real_B_enc'].astype(np.int64)
+            data['hint_B'] = data['hint_B'].astype(np.float32)
+            data['mask_B'] = data['mask_B'].astype(np.float32)
+        return data
+
+
+class ColorPostprocess:
+    """
+    Transform images from [0, 1] to [0, 255]
+    
+    Args:
+       type(type): Type of Image value.
+       
+    Return:
+        img(np.ndarray): Image in range of 0-255.
+    """
+    def __init__(self, type: type = np.uint8):
+        self.type = type
+
+    def __call__(self, img: np.ndarray):
+        img = np.transpose(img, (1, 2, 0))
+        img = np.clip(img, 0, 1) * 255
+        img = img.astype(self.type)
+        return img