Rename paddlehub.process -> paddlehub.transforms

demo/colorization/predict.py

@@ -2,5 +2,5 @@ import paddle
 import paddlehub as hub
 
 if __name__ == '__main__':
-    model = hub.Module(name='user_guided_colorization', load_checkpoint='/PATH/TO/CHECKPOINT')
+    model = hub.Module(name='user_guided_colorization')
     result = model.predict(images='house.png')

demo/colorization/train.py

 import paddle
 import paddlehub as hub
-import paddlehub.process.transforms as T
+import paddlehub.transforms.transforms as T
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets import Canvas
 

demo/image_classification/train.py

 import paddle
 import paddlehub as hub
-import paddlehub.process.transforms as T
+import paddlehub.transforms.transforms as T
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets import Flowers
 

demo/style_transfer/train.py

 import paddle
 import paddlehub as hub
-import paddlehub.process.transforms as T
+import paddlehub.transforms.transforms as T
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets import MiniCOCO
 

modules/image/colorization/user_guided_colorization/module.py

@@ -18,7 +18,7 @@ import paddle
 import paddle.nn as nn
 from paddle.nn import Conv2D, Conv2DTranspose
 from paddlehub.module.module import moduleinfo
-import paddlehub.process.transforms as T
+import paddlehub.transforms.transforms as T
 from paddlehub.module.cv_module import ImageColorizeModule
 from user_guided_colorization.data_feed import ColorizePreprocess
 

modules/image/keypoint_detection/openpose_body_estimation/module.py

@@ -20,18 +20,19 @@ import cv2
 import paddle
 import paddle.nn as nn
 import numpy as np
-from paddlehub.module.module import moduleinfo
-import paddlehub.process.transforms as T
+from paddlehub.transforms.module import moduleinfo
+import paddlehub.transforms.transforms as T
 import openpose_body_estimation.processor as P
 
 
-@moduleinfo(name="openpose_body_estimation",
-            type="CV/image_editing",
-            author="paddlepaddle",
-            author_email="",
-            summary="Openpose_body_estimation is a body pose estimation model based on Realtime Multi-Person 2D Pose \
+@moduleinfo(
+    name="openpose_body_estimation",
+    type="CV/image_editing",
+    author="paddlepaddle",
+    author_email="",
+    summary="Openpose_body_estimation is a body pose estimation model based on Realtime Multi-Person 2D Pose \
             Estimation using Part Affinity Fields.",
-            version="1.0.0")
+    version="1.0.0")
 class BodyPoseModel(nn.Layer):
     """
     BodyposeModel
@@ -40,6 +41,7 @@ class BodyPoseModel(nn.Layer):
         load_checkpoint(str): Checkpoint save path, default is None.
         visualization (bool): Whether to save the estimation result. Default is True.
     """
+
     def __init__(self, load_checkpoint: str = None, visualization: bool = True):
         super(BodyPoseModel, self).__init__()
 

modules/image/keypoint_detection/openpose_hands_estimation/module.py

@@ -25,18 +25,19 @@ from skimage.measure import label
 from scipy.ndimage.filters import gaussian_filter
 from paddlehub.module.module import moduleinfo
 from paddlehub.process.functional import npmax
-import paddlehub.process.transforms as T
+import paddlehub.transforms.transforms as T
 
 import openpose_hands_estimation.processor as P
 
 
-@moduleinfo(name="openpose_hands_estimation",
-            type="CV/image_editing",
-            author="paddlepaddle",
-            author_email="",
-            summary="Openpose_hands_estimation is a hand pose estimation model based on Hand Keypoint Detection in \
+@moduleinfo(
+    name="openpose_hands_estimation",
+    type="CV/image_editing",
+    author="paddlepaddle",
+    author_email="",
+    summary="Openpose_hands_estimation is a hand pose estimation model based on Hand Keypoint Detection in \
             Single Images using Multiview Bootstrapping.",
-            version="1.0.0")
+    version="1.0.0")
 class HandPoseModel(nn.Layer):
     """
     HandposeModel
@@ -45,6 +46,7 @@ class HandPoseModel(nn.Layer):
         load_checkpoint(str): Checkpoint save path, default is None.
         visualization (bool): Whether to save the estimation result. Default is True.
     """
+
     def __init__(self, load_checkpoint: str = None, visualization: bool = True):
         super(HandPoseModel, self).__init__()
         self.visualization = visualization

modules/image/style_transfer/msgnet/module.py

@@ -7,12 +7,13 @@ import paddle.nn.functional as F
 
 from paddlehub.env import MODULE_HOME
 from paddlehub.module.module import moduleinfo
-from paddlehub.process.transforms import Compose, Resize, CenterCrop, SetType
+from paddlehub.transforms.transforms import Compose, Resize, CenterCrop, SetType
 from paddlehub.module.cv_module import StyleTransferModule
 
 
 class GramMatrix(nn.Layer):
     """Calculate gram matrix"""
+
     def forward(self, y):
         (b, ch, h, w) = y.shape
         features = y.reshape((b, ch, w * h))
@@ -23,6 +24,7 @@ class GramMatrix(nn.Layer):
 
 class ConvLayer(nn.Layer):
     """Basic conv layer with reflection padding layer"""
+
     def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int):
         super(ConvLayer, self).__init__()
         pad = int(np.floor(kernel_size / 2))
@@ -50,6 +52,7 @@ class UpsampleConvLayer(nn.Layer):
     Return:
         img(paddle.Tensor): UpsampleConvLayer output.
     """
+
     def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int, upsample=None):
         super(UpsampleConvLayer, self).__init__()
         self.upsample = upsample
@@ -84,6 +87,7 @@ class Bottleneck(nn.Layer):
     Return:
         img(paddle.Tensor): Bottleneck output.
     """
+
     def __init__(self,
                  inplanes: int,
                  planes: int,
@@ -97,8 +101,8 @@ class Bottleneck(nn.Layer):
             self.residual_layer = nn.Conv2D(inplanes, planes * self.expansion, kernel_size=1, stride=stride)
         conv_block = (norm_layer(inplanes), nn.ReLU(), nn.Conv2D(inplanes, planes, kernel_size=1, stride=1),
                       norm_layer(planes), nn.ReLU(), ConvLayer(planes, planes, kernel_size=3, stride=stride),
-                      norm_layer(planes), nn.ReLU(), nn.Conv2D(planes, planes * self.expansion, kernel_size=1,
-                                                               stride=1))
+                      norm_layer(planes), nn.ReLU(), nn.Conv2D(
+                          planes, planes * self.expansion, kernel_size=1, stride=1))
         self.conv_block = nn.Sequential(*conv_block)
 
     def forward(self, x: paddle.Tensor):
@@ -124,14 +128,12 @@ class UpBottleneck(nn.Layer):
     Return:
         img(paddle.Tensor): UpBottleneck output.
     """
+
     def __init__(self, inplanes: int, planes: int, stride: int = 2, norm_layer: nn.Layer = nn.BatchNorm2D):
         super(UpBottleneck, self).__init__()
         self.expansion = 4
-        self.residual_layer = UpsampleConvLayer(inplanes,
-                                                planes * self.expansion,
-                                                kernel_size=1,
-                                                stride=1,
-                                                upsample=stride)
+        self.residual_layer = UpsampleConvLayer(
+            inplanes, planes * self.expansion, kernel_size=1, stride=1, upsample=stride)
         conv_block = []
         conv_block += [norm_layer(inplanes), nn.ReLU(), nn.Conv2D(inplanes, planes, kernel_size=1, stride=1)]
         conv_block += [
@@ -162,6 +164,7 @@ class Inspiration(nn.Layer):
     Return:
         img(paddle.Tensor): UpBottleneck output.
     """
+
     def __init__(self, C: int, B: int = 1):
         super(Inspiration, self).__init__()
 
@@ -178,8 +181,8 @@ class Inspiration(nn.Layer):
         self.P = paddle.bmm(self.weight.expand_as(self.G), self.G)
 
         x = paddle.bmm(
-            self.P.transpose((0, 2, 1)).expand((X.shape[0], self.C, self.C)), X.reshape(
-                (X.shape[0], X.shape[1], -1))).reshape(X.shape)
+            self.P.transpose((0, 2, 1)).expand((X.shape[0], self.C, self.C)), X.reshape((X.shape[0], X.shape[1],
+                                                                                         -1))).reshape(X.shape)
         return x
 
     def __repr__(self):
@@ -189,6 +192,7 @@ class Inspiration(nn.Layer):
 
 class Vgg16(nn.Layer):
     """ First four layers from Vgg16."""
+
     def __init__(self):
         super(Vgg16, self).__init__()
         self.conv1_1 = nn.Conv2D(3, 64, kernel_size=3, stride=1, padding=1)
@@ -263,12 +267,8 @@ class MSGNet(nn.Layer):
     Return:
         img(paddle.Tensor): MSGNet output.
     """
-    def __init__(self,
-                 input_nc=3,
-                 output_nc=3,
-                 ngf=128,
-                 n_blocks=6,
-                 norm_layer=nn.InstanceNorm2D,
+
+    def __init__(self, input_nc=3, output_nc=3, ngf=128, n_blocks=6, norm_layer=nn.InstanceNorm2D,
                  load_checkpoint=None):
         super(MSGNet, self).__init__()
         self.gram = GramMatrix()

paddlehub/datasets/canvas.py

@@ -18,7 +18,7 @@ import os
 import numpy as np
 import paddle
 
-from paddlehub.process.functional import get_img_file
+from paddlehub.transforms.functional import get_img_file
 from paddlehub.env import DATA_HOME
 from typing import Callable
 from paddlehub.utils.download import download_data

paddlehub/datasets/minicoco.py

@@ -17,7 +17,7 @@ import os
 from typing import Callable
 
 import paddle
-from paddlehub.process.functional import get_img_file
+from paddlehub.transforms.functional import get_img_file
 from paddlehub.env import DATA_HOME
 from paddlehub.utils.download import download_data
 

paddlehub/module/cv_module.py

@@ -27,8 +27,8 @@ from PIL import Image
 
 from paddlehub.module.module import serving, RunModule
 from paddlehub.utils.utils import base64_to_cv2
-import paddlehub.process.transforms as T
-import paddlehub.process.functional as Func
+import paddlehub.transforms.transforms as T
+import paddlehub.transforms.functional as Func
 
 
 class ImageServing(object):

paddlehub/process/detect_transforms.py → paddlehub/transforms/detect_transforms.py

@@ -9,7 +9,7 @@ import PIL
 from PIL import Image, ImageEnhance
 from matplotlib import pyplot as plt
 
-from paddlehub.process.functional import *
+from paddlehub.transforms.functional import *
 
 matplotlib.use('Agg')
 
@@ -27,6 +27,7 @@ class RandomDistort:
         data(dict): Image info and label info.
 
     """
+
     def __init__(self, lower: float = 0.5, upper: float = 1.5):
         self.lower = lower
         self.upper = upper
@@ -70,6 +71,7 @@ class RandomExpand:
         data(dict): Image info and label info.
 
     """
+
     def __init__(self, max_ratio: float = 4., fill: list = None, keep_ratio: bool = True, thresh: float = 0.5):
 
         self.max_ratio = max_ratio
@@ -129,10 +131,8 @@ class RandomCrop:
         data(dict): Image info and label info.
 
     """
-    def __init__(self,
-                 scales: list = [0.3, 1.0],
-                 max_ratio: float = 2.0,
-                 constraints: list = None,
+
+    def __init__(self, scales: list = [0.3, 1.0], max_ratio: float = 2.0, constraints: list = None,
                  max_trial: int = 50):
         self.scales = scales
         self.max_ratio = max_ratio
@@ -198,6 +198,7 @@ class RandomFlip:
         img(np.ndarray): Distorted image.
         data(dict): Image info and label info.
     """
+
     def __init__(self, thresh: float = 0.5):
         self.thresh = thresh
 
@@ -221,6 +222,7 @@ class Compose:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
     """
+
     def __init__(self, transforms: list):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
@@ -263,6 +265,7 @@ class Resize:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
     """
+
     def __init__(self, target_size: int = 512, interp: str = 'RANDOM'):
         self.interp_dict = {
             'NEAREST': cv2.INTER_NEAREST,
@@ -309,6 +312,7 @@ class Normalize:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
     """
+
     def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
         self.mean = mean
         self.std = std
@@ -332,6 +336,7 @@ class Normalize:
 
 class ShuffleBox:
     """Shuffle detection information for corresponding input image."""
+
     def __call__(self, img, data):
         gt = np.concatenate([data['gt_boxes'], data['gt_labels'][:, np.newaxis], data['gt_scores'][:, np.newaxis]],
                             axis=1)

paddlehub/process/transforms.py → paddlehub/transforms/transforms.py

@@ -29,7 +29,7 @@ from matplotlib import pyplot as plt
 from matplotlib.figure import Figure
 from scipy.ndimage.filters import gaussian_filter
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from paddlehub.process.functional import *
+from paddlehub.transforms.functional import *
 
 matplotlib.use('Agg')
 
@@ -252,13 +252,8 @@ 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)
@@ -313,12 +308,13 @@ 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
 
@@ -425,6 +421,7 @@ class RGB2LAB:
     """
     Convert color space from RGB to LAB.
     """
+
     def rgb2xyz(self, rgb: np.ndarray) -> np.ndarray:
         """
         Convert color space from RGB to XYZ.
@@ -491,6 +488,7 @@ class LAB2RGB:
     """
     Convert color space from LAB to RGB.
     """
+
     def __init__(self, mode: str = 'RGB2LAB'):
         self.mode = mode
 
@@ -568,6 +566,7 @@ class ColorPostprocess:
     Return:
         img(np.ndarray): Image in range of 0-255.
     """
+
     def __init__(self, type: type = np.uint8):
         self.type = type
 
@@ -588,6 +587,7 @@ class CenterCrop:
         Return:
             img(np.ndarray): Croped image.
     """
+
     def __init__(self, crop_size: int):
         self.crop_size = crop_size
 
@@ -608,6 +608,7 @@ class SetType:
     Return:
         img(np.ndarray): Transformed image.
     """
+
     def __init__(self, datatype: type = 'float32'):
         self.type = datatype
 
@@ -623,6 +624,7 @@ class ResizeScaling:
         target(int): Target image size.
         interp(Callable): Interpolation method.
     """
+
     def __init__(self, target: int = 368, interp: Callable = cv2.INTER_CUBIC):
         self.target = target
         self.interp = interp