Colorize (#897)

demo/colorization/predict.py

@@ -2,9 +2,8 @@ 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 = hub.Module(name='user_guided_colorization')
     model.eval()
-    result = model.predict(images='sea.jpg')
\ No newline at end of file
+    result = model.predict(images='house.png')

demo/colorization/train.py

@@ -3,18 +3,24 @@ 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
-
+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)
+    model = hub.Module(name='user_guided_colorization')
+    transform = Compose([
+        Resize((256, 256), interp='NEAREST'),
+        RandomPaddingCrop(crop_size=176),
+        ConvertColorSpace(mode='RGB2LAB'),
+        ColorizePreprocess(ab_thresh=0, is_train=is_train),
+    ],
+                        stay_rgb=True,
+                        is_permute=False)
+    color_set = Colorizedataset(transform=transform, mode='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)
+        trainer.train(color_set, epochs=101, batch_size=5, eval_dataset=color_set, log_interval=10, save_interval=10)

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

@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+
 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
 
@@ -178,24 +179,31 @@ class UserGuidedColorization(nn.Layer):
         if load_checkpoint is not None:
             model_dict = paddle.load(load_checkpoint)[0]
             self.set_dict(model_dict)
-            print("load pretrained model success")
+            print("load custom checkpoint success")
+        else:
+            checkpoint = os.path.join(self.directory, 'user_guided.pdparams')
+            model_dict = paddle.load(checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load pretrained checkpoint success")
 
     def transforms(self, images: str, is_train: bool = True) -> callable:
         if is_train:
             transform = Compose([
-                Resize((256, 256), interp="RANDOM"),
+                Resize((256, 256), interp='NEAREST'),
                 RandomPaddingCrop(crop_size=176),
                 ConvertColorSpace(mode='RGB2LAB'),
                 ColorizePreprocess(ab_thresh=0, is_train=is_train)
             ],
-                                stay_rgb=True)
+                                stay_rgb=True,
+                                is_permute=False)
         else:
             transform = Compose([
-                Resize((256, 256), interp="RANDOM"),
+                Resize((256, 256), interp='NEAREST'),
                 ConvertColorSpace(mode='RGB2LAB'),
                 ColorizePreprocess(ab_thresh=0, is_train=is_train)
             ],
-                                stay_rgb=True)
+                                stay_rgb=True,
+                                is_permute=False)
         return transform(images)
 
     def forward(self,

paddlehub/datasets/colorizedataset.py

@@ -22,6 +22,7 @@ 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.
@@ -34,14 +35,12 @@ class Colorizedataset(paddle.io.Dataset):
     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)
 
@@ -51,4 +50,4 @@ class Colorizedataset(paddle.io.Dataset):
         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
+        return len(self.data)

paddlehub/module/cv_module.py

@@ -18,6 +18,7 @@ import os
 from typing import List
 from collections import OrderedDict
 
+import cv2
 import numpy as np
 import paddle
 import paddle.nn as nn
@@ -27,6 +28,7 @@ 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
+from paddlehub.process.functional import subtract_imagenet_mean_batch, gram_matrix
 
 
 class ImageServing(object):
@@ -192,3 +194,87 @@ class ImageColorizeModule(RunModule, ImageServing):
             psnr_value = 20 * np.log10(255. / np.sqrt(mse))
             result.append(visual_ret)
         return result
+
+
+class StyleTransferModule(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.
+        '''
+        mse_loss = nn.MSELoss()
+        N, C, H, W = batch[0].shape
+        batch[1] = batch[1][0].unsqueeze(0)
+        self.setTarget(batch[1])
+
+        y = self(batch[0])
+        xc = paddle.to_tensor(batch[0].numpy().copy())
+        y = subtract_imagenet_mean_batch(y)
+        xc = subtract_imagenet_mean_batch(xc)
+        features_y = self.getFeature(y)
+        features_xc = self.getFeature(xc)
+        f_xc_c = paddle.to_tensor(features_xc[1].numpy(), stop_gradient=True)
+        content_loss = mse_loss(features_y[1], f_xc_c)
+
+        batch[1] = subtract_imagenet_mean_batch(batch[1])
+        features_style = self.getFeature(batch[1])
+        gram_style = [gram_matrix(y) for y in features_style]
+        style_loss = 0.
+        for m in range(len(features_y)):
+            gram_y = gram_matrix(features_y[m])
+            gram_s = paddle.to_tensor(np.tile(gram_style[m].numpy(), (N, 1, 1, 1)))
+            style_loss += mse_loss(gram_y, gram_s[:N, :, :])
+
+        loss = content_loss + style_loss
+
+        return {'loss': loss, 'metrics': {'content gap': content_loss, 'style gap': style_loss}}
+
+    def predict(self, origin_path: str, style_path: str, visualization: bool = True, save_path: str = 'result'):
+        '''
+        Colorize images
+
+        Args:
+            origin_path(str): Content image path .
+            style_path(str): Style image path.
+            visualization(bool): Whether to save colorized images.
+            save_path(str) : Path to save colorized images.
+
+        Returns:
+            output(np.ndarray) : The style transformed images with bgr mode.
+        '''
+        content = paddle.to_tensor(self.transform(origin_path))
+        style = paddle.to_tensor(self.transform(style_path))
+        content = content.unsqueeze(0)
+        style = style.unsqueeze(0)
+
+        self.setTarget(style)
+        output = self(content)
+        output = paddle.clip(output[0].transpose((1, 2, 0)), 0, 255).numpy()
+
+        if visualization:
+            output = output.astype(np.uint8)
+            style_name = "style_" + str(time.time()) + ".png"
+            if not os.path.exists(save_path):
+                os.mkdir(save_path)
+            path = os.path.join(save_path, style_name)
+            cv2.imwrite(path, output)
+        return output

paddlehub/process/functional.py

@@ -15,6 +15,7 @@
 import os
 
 import cv2
+import paddle
 import numpy as np
 from PIL import Image, ImageEnhance
 
@@ -114,7 +115,25 @@ def get_img_file(dir_name: str) -> list:
             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
+    return images
+
+
+def subtract_imagenet_mean_batch(batch: paddle.Tensor) -> paddle.Tensor:
+    """Subtract ImageNet mean pixel-wise from a BGR image."""
+    mean = np.zeros(shape=batch.shape, dtype='float32')
+    mean[:, 0, :, :] = 103.939
+    mean[:, 1, :, :] = 116.779
+    mean[:, 2, :, :] = 123.680
+    mean = paddle.to_tensor(mean)
+    return batch - mean
+
+
+def gram_matrix(data: paddle.Tensor) -> paddle.Tensor:
+    """Get gram matrix"""
+    b, ch, h, w = data.shape
+    features = data.reshape((b, ch, w * h))
+    features_t = features.transpose((0, 2, 1))
+    gram = features.bmm(features_t) / (ch * h * w)
+    return gram

paddlehub/process/transforms.py

@@ -24,15 +24,16 @@ from paddlehub.process.functional import *
 
 
 class Compose:
-    def __init__(self, transforms, to_rgb=True, stay_rgb=False):
+    def __init__(self, transforms, to_rgb=True, stay_rgb=False, is_permute=True):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
         if len(transforms) < 1:
             raise ValueError('The length of transforms ' + \
-                            'must be equal or larger than 1!')
+                             'must be equal or larger than 1!')
         self.transforms = transforms
         self.to_rgb = to_rgb
         self.stay_rgb = stay_rgb
+        self.is_permute = is_permute
 
     def __call__(self, im):
         if isinstance(im, str):
@@ -45,15 +46,16 @@ class Compose:
 
         for op in self.transforms:
             im = op(im)
-            
+
         if not self.stay_rgb:
+            im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
+
+        if self.is_permute:
             im = permute(im)
 
         return im
 
 
-
-
 class RandomHorizontalFlip:
     def __init__(self, prob=0.5):
         self.prob = prob
@@ -239,8 +241,13 @@ class RandomPaddingCrop:
             pad_height = max(crop_height - img_height, 0)
             pad_width = max(crop_width - img_width, 0)
             if (pad_height > 0 or pad_width > 0):
-                im = cv2.copyMakeBorder(
-                    im, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, value=self.im_padding_value)
+                im = cv2.copyMakeBorder(im,
+                                        0,
+                                        pad_height,
+                                        0,
+                                        pad_width,
+                                        cv2.BORDER_CONSTANT,
+                                        value=self.im_padding_value)
 
             if crop_height > 0 and crop_width > 0:
                 h_off = np.random.randint(img_height - crop_height + 1)
@@ -295,13 +302,12 @@ class RandomRotation:
             r[0, 2] += (nw / 2) - cx
             r[1, 2] += (nh / 2) - cy
             dsize = (nw, nh)
-            im = cv2.warpAffine(
-                im,
-                r,
-                dsize=dsize,
-                flags=cv2.INTER_LINEAR,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=self.im_padding_value)
+            im = cv2.warpAffine(im,
+                                r,
+                                dsize=dsize,
+                                flags=cv2.INTER_LINEAR,
+                                borderMode=cv2.BORDER_CONSTANT,
+                                borderValue=self.im_padding_value)
 
         return im
 
@@ -403,14 +409,14 @@ class RandomDistort:
 
         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.
     """
@@ -429,7 +435,7 @@ class ConvertColorSpace:
         """
         mask = (rgb > 0.04045)
         np.seterr(invalid='ignore')
-        rgb = (((rgb + .055) / 1.055) ** 2.4) * mask + rgb / 12.92 * (1 - mask)
+        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, :, :]
@@ -490,7 +496,7 @@ class ConvertColorSpace:
         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 = (1.055 * (rgb**(1. / 2.4)) - 0.055) * mask + 12.92 * rgb * (1 - mask)
         out = np.nan_to_num(out)
         return out
 
@@ -511,7 +517,7 @@ class ConvertColorSpace:
         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 = (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
@@ -546,27 +552,27 @@ class ConvertColorSpace:
 
 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):
+    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, ]
+        sample_Ps = [1, 2, 3, 4, 5, 6, 7, 8, 9]
         self.data = data
         self.hint = hint
         self.mask = mask
@@ -577,7 +583,7 @@ class ColorizeHint:
             while cont_cond:
                 if self.num_points is None:  # draw from geometric
                     # embed()
-                    cont_cond = np.random.rand() < (1 - self.percent)
+                    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
@@ -593,9 +599,11 @@ class ColorizeHint:
                 # 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)
+                    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
@@ -609,10 +617,10 @@ class ColorizeHint:
 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
@@ -628,7 +636,7 @@ class SqueezeAxis:
 
 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.
@@ -636,13 +644,14 @@ class ColorizePreprocess:
        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,
+    def __init__(self,
+                 ab_thresh: float = 0.,
+                 p: float = 0.,
                  num_points: int = None,
                  samp: str = 'normal',
                  use_avg: bool = True,
@@ -668,11 +677,14 @@ class ColorizePreprocess:
         """
         data = {}
         A = 2 * 110 / 10 + 1
-        data['A'] = data_lab[:, [0, ], :, :]
+        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 = 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, :, :, :]
@@ -698,10 +710,10 @@ class ColorizePreprocess:
 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.
     """
@@ -713,3 +725,41 @@ class ColorPostprocess:
         img = np.clip(img, 0, 1) * 255
         img = img.astype(self.type)
         return img
+
+
+class CenterCrop:
+    """
+        Crop the middle part of the image to the specified size.
+
+        Args:
+           crop_size(int): Crop size.
+
+        Return:
+            img(np.ndarray): Croped image.
+    """
+    def __init__(self, crop_size: int):
+        self.crop_size = crop_size
+
+    def __call__(self, img: np.ndarray):
+        img_width, img_height, chanel = img.shape
+        crop_top = int((img_height - self.crop_size) / 2.)
+        crop_left = int((img_width - self.crop_size) / 2.)
+        return img[crop_left:crop_left + self.crop_size, crop_top:crop_top + self.crop_size, :]
+
+
+class SetType:
+    """
+    Set image type.
+
+    Args:
+       type(type): Type of Image value.
+
+    Return:
+        img(np.ndarray): Transformed image.
+    """
+    def __init__(self, datatype: type = 'float32'):
+        self.type = datatype
+
+    def __call__(self, img: np.ndarray):
+        img = img.astype(self.type)
+        return img