revise transform and pascalvoc dataset

demo/detection/yolov3_darknet53_pascalvoc/train.py

@@ -3,20 +3,21 @@ import paddlehub as hub
 import paddle.nn as nn
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets.pascalvoc import DetectionData
-from paddlehub.process.detect_transforms import Compose, RandomDistort, RandomExpand, RandomCrop, RandomFlip, Normalize, Resize, ShuffleBox
-
+import paddlehub.process.detect_transforms as T
 if __name__ == "__main__":
     place = paddle.CUDAPlace(0)
     paddle.disable_static()
-    transform = Compose([
-        RandomDistort(),
-        RandomExpand(fill=[0.485, 0.456, 0.406]),
-        RandomCrop(),
-        Resize(target_size=416),
-        RandomFlip(),
-        ShuffleBox(),
-        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    transform = T.Compose([
+        T.RandomDistort(),
+        T.RandomExpand(fill=[0.485, 0.456, 0.406]),
+        T.RandomCrop(),
+        T.Resize(target_size=416),
+        T.RandomFlip(),
+        T.ShuffleBox(),
+        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     ])
+
     train_reader = DetectionData(transform)
     model = hub.Module(name='yolov3_darknet53_pascalvoc')
     model.train()

hub_module/modules/image/object_detection/yolov3_darknet53_pascalvoc/module.py

@@ -7,7 +7,7 @@ from paddle.nn.initializer import Normal, Constant
 from paddle.regularizer import L2Decay
 from pycocotools.coco import COCO
 from paddlehub.module.cv_module import Yolov3Module
-from paddlehub.process.detect_transforms import Compose, RandomDistort, RandomExpand, RandomCrop, Resize, RandomFlip, ShuffleBox, Normalize
+import paddlehub.process.detect_transforms as T
 from paddlehub.module.module import moduleinfo
 
 
@@ -288,19 +288,19 @@ class YOLOv3(nn.Layer):
 
     def transform(self, img):
         if self.is_train:
-            transform = Compose([
-                RandomDistort(),
-                RandomExpand(fill=[0.485, 0.456, 0.406]),
-                RandomCrop(),
-                Resize(target_size=416),
-                RandomFlip(),
-                ShuffleBox(),
-                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            transform = T.Compose([
+                T.RandomDistort(),
+                T.RandomExpand(fill=[0.485, 0.456, 0.406]),
+                T.RandomCrop(),
+                T.Resize(target_size=416),
+                T.RandomFlip(),
+                T.ShuffleBox(),
+                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
             ])
         else:
-            transform = Compose([
-                Resize(target_size=416, interp='CUBIC'),
-                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            transform = T.Compose([
+                T.Resize(target_size=416, interp='CUBIC'),
+                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
             ])
 
         return transform(img)

paddlehub/datasets/pascalvoc.py

@@ -14,15 +14,137 @@
 # limitations under the License.
 
 import os
+import copy
 from typing import Callable
 
 import paddle
+import numpy as np
 from paddlehub.env import DATA_HOME
 from pycocotools.coco import COCO
 
 from paddlehub.process.transforms import DetectCatagory, ParseImages
 
 
+class DetectCatagory:
+    """Load label name, id and map from detection dataset.
+
+    Args:
+        attrbox(Callable): Method to get detection attributes of images.
+        data_dir(str): Image dataset path.
+
+    Returns:
+        label_names(List(str)): The dataset label names.
+        label_ids(List(int)): The dataset label ids.
+        category_to_id_map(dict): Mapping relations of category and id for images.
+    """
+    def __init__(self, attrbox: Callable, data_dir: str):
+        self.attrbox = attrbox
+        self.img_dir = data_dir
+
+    def __call__(self):
+        self.categories = self.attrbox.loadCats(self.attrbox.getCatIds())
+        self.num_category = len(self.categories)
+        label_names = []
+        label_ids = []
+        for category in self.categories:
+            label_names.append(category['name'])
+            label_ids.append(int(category['id']))
+        category_to_id_map = {v: i for i, v in enumerate(label_ids)}
+        return label_names, label_ids, category_to_id_map
+
+
+class ParseImages:
+    """Prepare images for detection.
+
+    Args:
+        attrbox(Callable): Method to get detection attributes of images.
+        data_dir(str): Image dataset path.
+        category_to_id_map(dict): Mapping relations of category and id for images.
+
+    Returns:
+        imgs(dict): The input for detection model, it is a dict.
+    """
+    def __init__(self, attrbox: Callable, data_dir: str, category_to_id_map: dict):
+        self.attrbox = attrbox
+        self.img_dir = data_dir
+        self.category_to_id_map = category_to_id_map
+        self.parse_gt_annotations = GTAnotations(self.attrbox, self.category_to_id_map)
+
+    def __call__(self):
+        image_ids = self.attrbox.getImgIds()
+        image_ids.sort()
+        imgs = copy.deepcopy(self.attrbox.loadImgs(image_ids))
+
+        for img in imgs:
+            img['image'] = os.path.join(self.img_dir, img['file_name'])
+            assert os.path.exists(img['image']), "image {} not found.".format(img['image'])
+            box_num = 50
+            img['gt_boxes'] = np.zeros((box_num, 4), dtype=np.float32)
+            img['gt_labels'] = np.zeros((box_num), dtype=np.int32)
+            img = self.parse_gt_annotations(img)
+        return imgs
+
+
+class GTAnotations:
+    """Set gt boxes and gt labels for train.
+
+    Args:
+        attrbox(Callable): Method for get detection attributes for images.
+        category_to_id_map(dict): Mapping relations of category and id for images.
+        img(dict): Input for detection model.
+
+    Returns:
+        img(dict): Set specific value on the attributes of 'gt boxes' and 'gt labels' for input.
+    """
+    def __init__(self, attrbox: Callable, category_to_id_map: dict):
+        self.attrbox = attrbox
+        self.category_to_id_map = category_to_id_map
+
+    def box_to_center_relative(self, box: list, img_height: int, img_width: int) -> np.ndarray:
+        """
+            Convert COCO annotations box with format [x1, y1, w, h] to
+            center mode [center_x, center_y, w, h] and divide image width
+            and height to get relative value in range[0, 1]
+            """
+        assert len(box) == 4, "box should be a len(4) list or tuple"
+        x, y, w, h = box
+
+        x1 = max(x, 0)
+        x2 = min(x + w - 1, img_width - 1)
+        y1 = max(y, 0)
+        y2 = min(y + h - 1, img_height - 1)
+
+        x = (x1 + x2) / 2 / img_width
+        y = (y1 + y2) / 2 / img_height
+        w = (x2 - x1) / img_width
+        h = (y2 - y1) / img_height
+
+        return np.array([x, y, w, h])
+
+    def __call__(self, img: dict):
+        img_height = img['height']
+        img_width = img['width']
+        anno = self.attrbox.loadAnns(self.attrbox.getAnnIds(imgIds=img['id'], iscrowd=None))
+        gt_index = 0
+
+        for target in anno:
+            if target['area'] < -1:
+                continue
+            if 'ignore' in target and target['ignore']:
+                continue
+            box = self.box_to_center_relative(target['bbox'], img_height, img_width)
+
+            if box[2] <= 0 and box[3] <= 0:
+                continue
+            img['gt_boxes'][gt_index] = box
+            img['gt_labels'][gt_index] = \
+                self.category_to_id_map[target['category_id']]
+            gt_index += 1
+            if gt_index >= 50:
+                break
+        return img
+
+
 class DetectionData(paddle.io.Dataset):
     """
     Dataset for image detection.
@@ -57,7 +179,7 @@ class DetectionData(paddle.io.Dataset):
 
         parse_dataset_catagory = DetectCatagory(self.COCO, self.img_dir)
         self.label_names, self.label_ids, self.category_to_id_map = parse_dataset_catagory()
-        parse_images = ParseImages(self.COCO, self.mode, self.img_dir, self.category_to_id_map)
+        parse_images = ParseImages(self.COCO, self.img_dir, self.category_to_id_map)
         self.data = parse_images()
 
     def __getitem__(self, idx: int):

paddlehub/process/detect_transforms.py

-import copy
 import os
 import random
 from typing import Callable
@@ -15,108 +14,10 @@ from paddlehub.process.functional import *
 matplotlib.use('Agg')
 
 
-class DetectCatagory:
-    """Load label name, id and map from detection dataset.
-
-    Args:
-        attrbox(Callable): Method to get detection attributes of images.
-        data_dir(str): Image dataset path.
-
-    Returns:
-        label_names(List(str)): The dataset label names.
-        label_ids(List(int)): The dataset label ids.
-        category_to_id_map(dict): Mapping relations of category and id for images.
-    """
-    def __init__(self, attrbox: Callable, data_dir: str):
-        self.attrbox = attrbox
-        self.img_dir = data_dir
-
-    def __call__(self):
-        self.categories = self.attrbox.loadCats(self.attrbox.getCatIds())
-        self.num_category = len(self.categories)
-        label_names = []
-        label_ids = []
-        for category in self.categories:
-            label_names.append(category['name'])
-            label_ids.append(int(category['id']))
-        category_to_id_map = {v: i for i, v in enumerate(label_ids)}
-        return label_names, label_ids, category_to_id_map
-
-
-class ParseImages:
-    """Prepare images for detection.
-
-    Args:
-        attrbox(Callable): Method to get detection attributes of images.
-        is_train(bool): Select the mode for train or test.
-        data_dir(str): Image dataset path.
-        category_to_id_map(dict): Mapping relations of category and id for images.
-
-    Returns:
-        imgs(dict): The input for detection model, it is a dict.
+class RandomDistort:
     """
-    def __init__(self, attrbox: Callable, data_dir: str, category_to_id_map: dict):
-        self.attrbox = attrbox
-        self.img_dir = data_dir
-        self.category_to_id_map = category_to_id_map
-        self.parse_gt_annotations = GTAnotations(self.attrbox, self.category_to_id_map)
-
-    def __call__(self):
-        image_ids = self.attrbox.getImgIds()
-        image_ids.sort()
-        imgs = copy.deepcopy(self.attrbox.loadImgs(image_ids))
-
-        for img in imgs:
-            img['image'] = os.path.join(self.img_dir, img['file_name'])
-            assert os.path.exists(img['image']), "image {} not found.".format(img['image'])
-            box_num = 50
-            img['gt_boxes'] = np.zeros((box_num, 4), dtype=np.float32)
-            img['gt_labels'] = np.zeros((box_num), dtype=np.int32)
-            img = self.parse_gt_annotations(img)
-        return imgs
-
-
-class GTAnotations:
-    """Set gt boxes and gt labels for train.
+    Distort the input image randomly.
 
-    Args:
-        attrbox(Callable): Method for get detection attributes for images.
-        category_to_id_map(dict): Mapping relations of category and id for images.
-        img(dict): Input for detection model.
-
-    Returns:
-        img(dict): Set specific value on the attributes of 'gt boxes' and 'gt labels' for input.
-    """
-    def __init__(self, attrbox: Callable, category_to_id_map: dict):
-        self.attrbox = attrbox
-        self.category_to_id_map = category_to_id_map
-
-    def __call__(self, img: dict):
-        img_height = img['height']
-        img_width = img['width']
-        anno = self.attrbox.loadAnns(self.attrbox.getAnnIds(imgIds=img['id'], iscrowd=None))
-        gt_index = 0
-
-        for target in anno:
-            if target['area'] < -1:
-                continue
-            if 'ignore' in target and target['ignore']:
-                continue
-            box = coco_anno_box_to_center_relative(target['bbox'], img_height, img_width)
-
-            if box[2] <= 0 and box[3] <= 0:
-                continue
-            img['gt_boxes'][gt_index] = box
-            img['gt_labels'][gt_index] = \
-                self.category_to_id_map[target['category_id']]
-            gt_index += 1
-            if gt_index >= 50:
-                break
-        return img
-
-
-class RandomDistort:
-    """ Distort the input image randomly.
     Args:
         lower(float): The lower bound value for enhancement, default is 0.5.
         upper(float): The upper bound value for enhancement, default is 1.5.
@@ -155,7 +56,9 @@ class RandomDistort:
 
 
 class RandomExpand:
-    """Randomly expand images and gt boxes by random ratio. It is a data enhancement operation for model training.
+    """
+    Randomly expand images and gt boxes by random ratio. It is a data enhancement operation for model training.
+
     Args:
         max_ratio(float): Max value for expansion ratio, default is 4.
         fill(list): Initialize the pixel value of the image with the input fill value, default is None.
@@ -213,6 +116,7 @@ class RandomExpand:
 class RandomCrop:
     """
     Random crop the input image according to constraints.
+
     Args:
         scales(list): The value of the cutting area relative to the original area, expressed in the form of \
                       [min, max]. The default value is [.3, 1.].
@@ -276,6 +180,7 @@ class RandomCrop:
             data['gt_labels'] = crop_labels
             data['gt_scores'] = crop_scores
             return img, data
+
         img = np.asarray(img)
         data['gt_boxes'] = boxes
         data['gt_labels'] = labels
@@ -285,8 +190,10 @@ class RandomCrop:
 
 class RandomFlip:
     """Flip the images and gt boxes randomly.
+
     Args:
         thresh: Probability for random flip.
+
     Returns:
         img(np.ndarray): Distorted image.
         data(dict): Image info and label info.
@@ -304,9 +211,12 @@ class RandomFlip:
 
 
 class Compose:
-    """Preprocess the input data according to the operators.
+    """
+    Preprocess the input data according to the operators.
+
     Args:
         transforms(list): Preprocessing operators.
+
     Returns:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
@@ -342,10 +252,13 @@ class Compose:
 
 
 class Resize:
-    """Resize the input images.
+    """
+    Resize the input images.
+
     Args:
         target_size(int): Targeted input size.
         interp(str): Interpolation method.
+
     Returns:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
@@ -385,10 +298,13 @@ class Resize:
 
 
 class Normalize:
-    """Normalize the input images.
+    """
+    Normalize the input images.
+
     Args:
         mean(list): Mean values for normalization, default is [0.5, 0.5, 0.5].
         std(list): Standard deviation for normalization, default is [0.5, 0.5, 0.5].
+
     Returns:
         img(np.ndarray): Preprocessed image.
         data(dict): Image info and label info, default is None.
@@ -403,20 +319,19 @@ class Normalize:
             raise ValueError('{}: std is invalid!'.format(self))
 
     def __call__(self, im, data=None):
+
+        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+        std = np.array(self.std)[np.newaxis, np.newaxis, :]
+        im = normalize(im, mean, std)
+
         if data is not None:
-            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-            std = np.array(self.std)[np.newaxis, np.newaxis, :]
-            im = normalize(im, mean, std)
             return im, data
         else:
-            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-            std = np.array(self.std)[np.newaxis, np.newaxis, :]
-            im = normalize(im, mean, std)
             return im
 
 
 class ShuffleBox:
-    """Shuffle data information."""
+    """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/functional.py

@@ -124,28 +124,6 @@ def get_img_file(dir_name: str) -> list:
     return images
 
 
-def coco_anno_box_to_center_relative(box: list, img_height: int, img_width: int) -> np.ndarray:
-    """
-    Convert COCO annotations box with format [x1, y1, w, h] to
-    center mode [center_x, center_y, w, h] and divide image width
-    and height to get relative value in range[0, 1]
-    """
-    assert len(box) == 4, "box should be a len(4) list or tuple"
-    x, y, w, h = box
-
-    x1 = max(x, 0)
-    x2 = min(x + w - 1, img_width - 1)
-    y1 = max(y, 0)
-    y2 = min(y + h - 1, img_height - 1)
-
-    x = (x1 + x2) / 2 / img_width
-    y = (y1 + y2) / 2 / img_height
-    w = (x2 - x1) / img_width
-    h = (y2 - y1) / img_height
-
-    return np.array([x, y, w, h])
-
-
 def box_crop(boxes: np.ndarray, labels: np.ndarray, scores: np.ndarray, crop: list, img_shape: list):
     """Crop the boxes ,labels, scores according to the given shape"""