fix python openvino

deploy/openvino/python/convertor.py

+# 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
 from six import text_type as _text_type
 import argparse

deploy/openvino/python/demo.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.

deploy/openvino/python/deploy.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.
@@ -21,7 +21,7 @@ import numpy as np
 import yaml
 from six import text_type as _text_type
 from openvino.inference_engine import IECore
-from utils import logging 
+
 
 
 
@@ -33,11 +33,11 @@ class Predictor:
                  device="CPU"):
         self.device = device
         if not osp.exists(model_xml):
-            logging.error("model xml file is not exists in {}".format(model_xml))
+            print("model xml file is not exists in {}".format(model_xml))
         self.model_xml = model_xml
         self.model_bin = osp.splitext(model_xml)[0] + ".bin"
         if not osp.exists(model_yaml):
-            logging,error("model yaml file is not exists in {}".format(model_yaml))
+            print("model yaml file is not exists in {}".format(model_yaml))
         with open(model_yaml) as f:
             self.info = yaml.load(f.read(), Loader=yaml.Loader)
         self.model_type = self.info['_Attributes']['model_type']
@@ -65,9 +65,9 @@ class Predictor:
     def create_predictor(self):
 
         #initialization for specified device
-        logging.info("Creating Inference Engine")
+        print("Creating Inference Engine")
         ie = IECore()
-        logging.info("Loading network files:\n\t{}\n\t{}".format(self.model_xml, self.model_bin))
+        print("Loading network files:\n\t{}\n\t{}".format(self.model_xml, self.model_bin))
         net = ie.read_network(model=self.model_xml, weights=self.model_bin)
         net.batch_size = 1
         network_config = {}
@@ -135,11 +135,11 @@ class Predictor:
             input_blob = next(iter(self.net.inputs))
             feed_dict[input_blob] = preprocessed_input['image']
         #Start sync inference
-        logging.info("Starting inference in synchronous mode")
+        print("Starting inference in synchronous mode")
         res = self.predictor.infer(inputs=feed_dict)
  
         #Processing output blob
-        logging.info("Processing output blob")
+        print("Processing output blob")
         return res
         
 
@@ -167,7 +167,6 @@ class Predictor:
         elif self.model_type == "segmenter":
             im, im_info = self.transforms(image)
             im = np.expand_dims(im, axis=0).copy()
-            #np.savetxt('./input_data.txt',im.flatten())
             res['image'] = im
             res['im_info'] = im_info
         return res
@@ -193,11 +192,9 @@ class Predictor:
         it = iter(self.net.outputs)
         next(it)
         score_name = next(it)
-        #np.savetxt('./score_map.txt',preds[score_name].flatten())
         score_map = np.squeeze(preds[score_name])
         score_map = np.transpose(score_map, (1, 2, 0))
         label_name = next(it)
-        #np.savetxt('./label_map.txt',preds[label_name].flatten())
         label_map = np.squeeze(preds[label_name]).astype('uint8')
         im_info = preprocessed_inputs['im_info']
         for info in im_info[::-1]:

deploy/openvino/python/transforms/__init__.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.

deploy/openvino/python/transforms/box_utils.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 numpy as np
-import random
-import math
-import cv2
-import scipy
-
-
-def bbox_area(src_bbox):
-    if src_bbox[2] < src_bbox[0] or src_bbox[3] < src_bbox[1]:
-        return 0.
-    else:
-        width = src_bbox[2] - src_bbox[0]
-        height = src_bbox[3] - src_bbox[1]
-        return width * height
-
-
-def jaccard_overlap(sample_bbox, object_bbox):
-    if sample_bbox[0] >= object_bbox[2] or \
-        sample_bbox[2] <= object_bbox[0] or \
-        sample_bbox[1] >= object_bbox[3] or \
-        sample_bbox[3] <= object_bbox[1]:
-        return 0
-    intersect_xmin = max(sample_bbox[0], object_bbox[0])
-    intersect_ymin = max(sample_bbox[1], object_bbox[1])
-    intersect_xmax = min(sample_bbox[2], object_bbox[2])
-    intersect_ymax = min(sample_bbox[3], object_bbox[3])
-    intersect_size = (intersect_xmax - intersect_xmin) * (
-        intersect_ymax - intersect_ymin)
-    sample_bbox_size = bbox_area(sample_bbox)
-    object_bbox_size = bbox_area(object_bbox)
-    overlap = intersect_size / (
-        sample_bbox_size + object_bbox_size - intersect_size)
-    return overlap
-
-
-def iou_matrix(a, b):
-    tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
-    br_i = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
-
-    area_i = np.prod(br_i - tl_i, axis=2) * (tl_i < br_i).all(axis=2)
-    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
-    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
-    area_o = (area_a[:, np.newaxis] + area_b - area_i)
-    return area_i / (area_o + 1e-10)
-
-
-def crop_box_with_center_constraint(box, crop):
-    cropped_box = box.copy()
-
-    cropped_box[:, :2] = np.maximum(box[:, :2], crop[:2])
-    cropped_box[:, 2:] = np.minimum(box[:, 2:], crop[2:])
-    cropped_box[:, :2] -= crop[:2]
-    cropped_box[:, 2:] -= crop[:2]
-
-    centers = (box[:, :2] + box[:, 2:]) / 2
-    valid = np.logical_and(crop[:2] <= centers, centers < crop[2:]).all(axis=1)
-    valid = np.logical_and(
-        valid, (cropped_box[:, :2] < cropped_box[:, 2:]).all(axis=1))
-
-    return cropped_box, np.where(valid)[0]
-
-
-def is_poly(segm):
-    if not isinstance(segm, (list, dict)):
-        raise Exception("Invalid segm type: {}".format(type(segm)))
-    return isinstance(segm, list)
-
-
-def crop_image(img, crop):
-    x1, y1, x2, y2 = crop
-    return img[y1:y2, x1:x2, :]
-
-
-def crop_segms(segms, valid_ids, crop, height, width):
-    def _crop_poly(segm, crop):
-        xmin, ymin, xmax, ymax = crop
-        crop_coord = [xmin, ymin, xmin, ymax, xmax, ymax, xmax, ymin]
-        crop_p = np.array(crop_coord).reshape(4, 2)
-        crop_p = Polygon(crop_p)
-
-        crop_segm = list()
-        for poly in segm:
-            poly = np.array(poly).reshape(len(poly) // 2, 2)
-            polygon = Polygon(poly)
-            if not polygon.is_valid:
-                exterior = polygon.exterior
-                multi_lines = exterior.intersection(exterior)
-                polygons = shapely.ops.polygonize(multi_lines)
-                polygon = MultiPolygon(polygons)
-            multi_polygon = list()
-            if isinstance(polygon, MultiPolygon):
-                multi_polygon = copy.deepcopy(polygon)
-            else:
-                multi_polygon.append(copy.deepcopy(polygon))
-            for per_polygon in multi_polygon:
-                inter = per_polygon.intersection(crop_p)
-                if not inter:
-                    continue
-                if isinstance(inter, (MultiPolygon, GeometryCollection)):
-                    for part in inter:
-                        if not isinstance(part, Polygon):
-                            continue
-                        part = np.squeeze(
-                            np.array(part.exterior.coords[:-1]).reshape(1, -1))
-                        part[0::2] -= xmin
-                        part[1::2] -= ymin
-                        crop_segm.append(part.tolist())
-                elif isinstance(inter, Polygon):
-                    crop_poly = np.squeeze(
-                        np.array(inter.exterior.coords[:-1]).reshape(1, -1))
-                    crop_poly[0::2] -= xmin
-                    crop_poly[1::2] -= ymin
-                    crop_segm.append(crop_poly.tolist())
-                else:
-                    continue
-        return crop_segm
-
-    def _crop_rle(rle, crop, height, width):
-        if 'counts' in rle and type(rle['counts']) == list:
-            rle = mask_util.frPyObjects(rle, height, width)
-        mask = mask_util.decode(rle)
-        mask = mask[crop[1]:crop[3], crop[0]:crop[2]]
-        rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
-        return rle
-
-    crop_segms = []
-    for id in valid_ids:
-        segm = segms[id]
-        if is_poly(segm):
-            import copy
-            import shapely.ops
-            import logging
-            from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
-            logging.getLogger("shapely").setLevel(logging.WARNING)
-            # Polygon format
-            crop_segms.append(_crop_poly(segm, crop))
-        else:
-            # RLE format
-            import pycocotools.mask as mask_util
-            crop_segms.append(_crop_rle(segm, crop, height, width))
-    return crop_segms
-
-
-def expand_segms(segms, x, y, height, width, ratio):
-    def _expand_poly(poly, x, y):
-        expanded_poly = np.array(poly)
-        expanded_poly[0::2] += x
-        expanded_poly[1::2] += y
-        return expanded_poly.tolist()
-
-    def _expand_rle(rle, x, y, height, width, ratio):
-        if 'counts' in rle and type(rle['counts']) == list:
-            rle = mask_util.frPyObjects(rle, height, width)
-        mask = mask_util.decode(rle)
-        expanded_mask = np.full((int(height * ratio), int(width * ratio)),
-                                0).astype(mask.dtype)
-        expanded_mask[y:y + height, x:x + width] = mask
-        rle = mask_util.encode(
-            np.array(expanded_mask, order='F', dtype=np.uint8))
-        return rle
-
-    expanded_segms = []
-    for segm in segms:
-        if is_poly(segm):
-            # Polygon format
-            expanded_segms.append([_expand_poly(poly, x, y) for poly in segm])
-        else:
-            # RLE format
-            import pycocotools.mask as mask_util
-            expanded_segms.append(
-                _expand_rle(segm, x, y, height, width, ratio))
-    return expanded_segms
-
-
-def box_horizontal_flip(bboxes, width):
-    oldx1 = bboxes[:, 0].copy()
-    oldx2 = bboxes[:, 2].copy()
-    bboxes[:, 0] = width - oldx2 - 1
-    bboxes[:, 2] = width - oldx1 - 1
-    if bboxes.shape[0] != 0 and (bboxes[:, 2] < bboxes[:, 0]).all():
-        raise ValueError(
-            "RandomHorizontalFlip: invalid box, x2 should be greater than x1")
-    return bboxes
-
-
-def segms_horizontal_flip(segms, height, width):
-    def _flip_poly(poly, width):
-        flipped_poly = np.array(poly)
-        flipped_poly[0::2] = width - np.array(poly[0::2]) - 1
-        return flipped_poly.tolist()
-
-    def _flip_rle(rle, height, width):
-        if 'counts' in rle and type(rle['counts']) == list:
-            rle = mask_util.frPyObjects([rle], height, width)
-        mask = mask_util.decode(rle)
-        mask = mask[:, ::-1]
-        rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
-        return rle
-
-    flipped_segms = []
-    for segm in segms:
-        if is_poly(segm):
-            # Polygon format
-            flipped_segms.append([_flip_poly(poly, width) for poly in segm])
-        else:
-            # RLE format
-            import pycocotools.mask as mask_util
-            flipped_segms.append(_flip_rle(segm, height, width))
-    return flipped_segms

deploy/openvino/python/transforms/cls_transforms.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.
@@ -18,7 +18,6 @@ import random
 import os.path as osp
 import numpy as np
 from PIL import Image, ImageEnhance
-import utils.logging as logging
 
 
 class ClsTransform:
@@ -49,14 +48,7 @@ class Compose(ClsTransform):
                             'must be equal or larger than 1!')
         self.transforms = transforms
 
-        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
-        for op in self.transforms:
-            if not isinstance(op, ClsTransform):
-                import imgaug.augmenters as iaa
-                if not isinstance(op, iaa.Augmenter):
-                    raise Exception(
-                        "Elements in transforms should be defined in 'paddlex.cls.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
-                    )
+
 
     def __call__(self, im, label=None):
         """
@@ -79,18 +71,10 @@ class Compose(ClsTransform):
                 raise TypeError('Can\'t read The image file {}!'.format(im))
         im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
         for op in self.transforms:
-            if isinstance(op, ClsTransform):
             outputs = op(im, label)
             im = outputs[0]
             if len(outputs) == 2:
                 label = outputs[1]
-            else:
-                import imgaug.augmenters as iaa
-                if isinstance(op, iaa.Augmenter):
-                    im = execute_imgaug(op, im)
-                outputs = (im, )
-                if label is not None:
-                    outputs = (im, label)
         return outputs
 
     def add_augmenters(self, augmenters):
@@ -100,109 +84,10 @@ class Compose(ClsTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                print("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
-class RandomCrop(ClsTransform):
-    """对图像进行随机剪裁，模型训练时的数据增强操作。
-
-    1. 根据lower_scale、lower_ratio、upper_ratio计算随机剪裁的高、宽。
-    2. 根据随机剪裁的高、宽随机选取剪裁的起始点。
-    3. 剪裁图像。
-    4. 调整剪裁后的图像的大小到crop_size*crop_size。
-
-    Args:
-        crop_size (int): 随机裁剪后重新调整的目标边长。默认为224。
-        lower_scale (float): 裁剪面积相对原面积比例的最小限制。默认为0.08。
-        lower_ratio (float): 宽变换比例的最小限制。默认为3. / 4。
-        upper_ratio (float): 宽变换比例的最大限制。默认为4. / 3。
-    """
-
-    def __init__(self,
-                 crop_size=224,
-                 lower_scale=0.08,
-                 lower_ratio=3. / 4,
-                 upper_ratio=4. / 3):
-        self.crop_size = crop_size
-        self.lower_scale = lower_scale
-        self.lower_ratio = lower_ratio
-        self.upper_ratio = upper_ratio
-
-    def __call__(self, im, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            label (int): 每张图像所对应的类别序号。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
-                   当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
-        """
-        im = random_crop(im, self.crop_size, self.lower_scale,
-                         self.lower_ratio, self.upper_ratio)
-        if label is None:
-            return (im, )
-        else:
-            return (im, label)
-
-
-class RandomHorizontalFlip(ClsTransform):
-    """以一定的概率对图像进行随机水平翻转，模型训练时的数据增强操作。
-
-    Args:
-        prob (float): 随机水平翻转的概率。默认为0.5。
-    """
-
-    def __init__(self, prob=0.5):
-        self.prob = prob
-
-    def __call__(self, im, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            label (int): 每张图像所对应的类别序号。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
-                   当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
-        """
-        if random.random() < self.prob:
-            im = horizontal_flip(im)
-        if label is None:
-            return (im, )
-        else:
-            return (im, label)
-
-
-class RandomVerticalFlip(ClsTransform):
-    """以一定的概率对图像进行随机垂直翻转，模型训练时的数据增强操作。
-
-    Args:
-        prob (float): 随机垂直翻转的概率。默认为0.5。
-    """
-
-    def __init__(self, prob=0.5):
-        self.prob = prob
-
-    def __call__(self, im, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            label (int): 每张图像所对应的类别序号。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
-                   当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
-        """
-        if random.random() < self.prob:
-            im = vertical_flip(im)
-        if label is None:
-            return (im, )
-        else:
-            return (im, label)
-
-
 class Normalize(ClsTransform):
     """对图像进行标准化。
 
@@ -315,131 +200,6 @@ class CenterCrop(ClsTransform):
             return (im, label)
 
 
-class RandomRotate(ClsTransform):
-    def __init__(self, rotate_range=30, prob=0.5):
-        """以一定的概率对图像在[-rotate_range, rotaterange]角度范围内进行旋转，模型训练时的数据增强操作。
-
-        Args:
-            rotate_range (int): 旋转度数的范围。默认为30。
-            prob (float): 随机旋转的概率。默认为0.5。
-        """
-        self.rotate_range = rotate_range
-        self.prob = prob
-
-    def __call__(self, im, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            label (int): 每张图像所对应的类别序号。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
-                   当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
-        """
-        rotate_lower = -self.rotate_range
-        rotate_upper = self.rotate_range
-        im = im.astype('uint8')
-        im = Image.fromarray(im)
-        if np.random.uniform(0, 1) < self.prob:
-            im = rotate(im, rotate_lower, rotate_upper)
-        im = np.asarray(im).astype('float32')
-        if label is None:
-            return (im, )
-        else:
-            return (im, label)
-
-
-class RandomDistort(ClsTransform):
-    """以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
-
-    1. 对变换的操作顺序进行随机化操作。
-    2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
-
-    Args:
-        brightness_range (float): 明亮度因子的范围。默认为0.9。
-        brightness_prob (float): 随机调整明亮度的概率。默认为0.5。
-        contrast_range (float): 对比度因子的范围。默认为0.9。
-        contrast_prob (float): 随机调整对比度的概率。默认为0.5。
-        saturation_range (float): 饱和度因子的范围。默认为0.9。
-        saturation_prob (float): 随机调整饱和度的概率。默认为0.5。
-        hue_range (int): 色调因子的范围。默认为18。
-        hue_prob (float): 随机调整色调的概率。默认为0.5。
-    """
-
-    def __init__(self,
-                 brightness_range=0.9,
-                 brightness_prob=0.5,
-                 contrast_range=0.9,
-                 contrast_prob=0.5,
-                 saturation_range=0.9,
-                 saturation_prob=0.5,
-                 hue_range=18,
-                 hue_prob=0.5):
-        self.brightness_range = brightness_range
-        self.brightness_prob = brightness_prob
-        self.contrast_range = contrast_range
-        self.contrast_prob = contrast_prob
-        self.saturation_range = saturation_range
-        self.saturation_prob = saturation_prob
-        self.hue_range = hue_range
-        self.hue_prob = hue_prob
-
-    def __call__(self, im, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            label (int): 每张图像所对应的类别序号。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
-                   当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
-        """
-        brightness_lower = 1 - self.brightness_range
-        brightness_upper = 1 + self.brightness_range
-        contrast_lower = 1 - self.contrast_range
-        contrast_upper = 1 + self.contrast_range
-        saturation_lower = 1 - self.saturation_range
-        saturation_upper = 1 + self.saturation_range
-        hue_lower = -self.hue_range
-        hue_upper = self.hue_range
-        ops = [brightness, contrast, saturation, hue]
-        random.shuffle(ops)
-        params_dict = {
-            'brightness': {
-                'brightness_lower': brightness_lower,
-                'brightness_upper': brightness_upper
-            },
-            'contrast': {
-                'contrast_lower': contrast_lower,
-                'contrast_upper': contrast_upper
-            },
-            'saturation': {
-                'saturation_lower': saturation_lower,
-                'saturation_upper': saturation_upper
-            },
-            'hue': {
-                'hue_lower': hue_lower,
-                'hue_upper': hue_upper
-            }
-        }
-        prob_dict = {
-            'brightness': self.brightness_prob,
-            'contrast': self.contrast_prob,
-            'saturation': self.saturation_prob,
-            'hue': self.hue_prob,
-        }
-        for id in range(len(ops)):
-            params = params_dict[ops[id].__name__]
-            prob = prob_dict[ops[id].__name__]
-            params['im'] = im
-            if np.random.uniform(0, 1) < prob:
-                im = ops[id](**params)
-        if label is None:
-            return (im, )
-        else:
-            return (im, label)
-
-
 class ArrangeClassifier(ClsTransform):
     """获取训练/验证/预测所需信息。注意：此操作不需用户自己显示调用
 
@@ -510,12 +270,7 @@ class ComposedClsTransforms(Compose):
             )
 
         if mode == 'train':
-            # 训练时的transforms，包含数据增强
-            transforms = [
-                RandomCrop(crop_size=width), RandomHorizontalFlip(prob=0.5),
-                Normalize(
-                    mean=mean, std=std)
-            ]
+            pass
         else:
             # 验证/预测时的transforms
             transforms = [

deploy/openvino/python/transforms/det_transforms.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.
@@ -24,10 +24,8 @@ import numpy as np
 import cv2
 from PIL import Image, ImageEnhance
 
-from .imgaug_support import execute_imgaug
 from .ops import *
 from .box_utils import *
-import utils.logging as logging
 
 
 class DetTransform:
@@ -61,14 +59,6 @@ class Compose(DetTransform):
         for t in self.transforms:
             if type(t).__name__ == 'MixupImage':
                 self.use_mixup = True
-        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
-        for op in self.transforms:
-            if not isinstance(op, DetTransform):
-                import imgaug.augmenters as iaa
-                if not isinstance(op, iaa.Augmenter):
-                    raise Exception(
-                        "Elements in transforms should be defined in 'paddlex.det.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
-                    )
 
     def __call__(self, im, im_info=None, label_info=None):
         """
@@ -142,15 +132,8 @@ class Compose(DetTransform):
         for op in self.transforms:
             if im is None:
                 return None
-            if isinstance(op, DetTransform):
             outputs = op(im, im_info, label_info)
             im = outputs[0]
-            else:
-                im = execute_imgaug(op, im)
-                if label_info is not None:
-                    outputs = (im, im_info, label_info)
-                else:
-                    outputs = (im, im_info)
         return outputs
 
     def add_augmenters(self, augmenters):
@@ -160,7 +143,7 @@ class Compose(DetTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                print("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
@@ -411,82 +394,6 @@ class Resize(DetTransform):
             return (im, im_info, label_info)
 
 
-class RandomHorizontalFlip(DetTransform):
-    """随机翻转图像、标注框、分割信息，模型训练时的数据增强操作。
-
-    1. 随机采样一个0-1之间的小数，当小数小于水平翻转概率时，
-       执行2-4步操作，否则直接返回。
-    2. 水平翻转图像。
-    3. 计算翻转后的真实标注框的坐标，更新label_info中的gt_bbox信息。
-    4. 计算翻转后的真实分割区域的坐标，更新label_info中的gt_poly信息。
-
-    Args:
-        prob (float): 随机水平翻转的概率。默认为0.5。
-
-    Raises:
-        TypeError: 形参数据类型不满足需求。
-    """
-
-    def __init__(self, prob=0.5):
-        self.prob = prob
-        if not isinstance(self.prob, float):
-            raise TypeError("RandomHorizontalFlip: input type is invalid.")
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                   当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
-                   存储与标注框相关信息的字典。
-                   其中，im_info更新字段为：
-                       - gt_bbox (np.ndarray): 水平翻转后的标注框坐标[x1, y1, x2, y2]，形状为(n, 4)，
-                                          其中n代表真实标注框的个数。
-                       - gt_poly (list): 水平翻转后的多边形分割区域的x、y坐标，长度为n，
-                                         其中n代表真实标注框的个数。
-
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-            ValueError: 数据长度不匹配。
-        """
-        if not isinstance(im, np.ndarray):
-            raise TypeError(
-                "RandomHorizontalFlip: image is not a numpy array.")
-        if len(im.shape) != 3:
-            raise ValueError(
-                "RandomHorizontalFlip: image is not 3-dimensional.")
-        if im_info is None or label_info is None:
-            raise TypeError(
-                'Cannot do RandomHorizontalFlip! ' +
-                'Becasuse the im_info and label_info can not be None!')
-        if 'gt_bbox' not in label_info:
-            raise TypeError('Cannot do RandomHorizontalFlip! ' + \
-                            'Becasuse gt_bbox is not in label_info!')
-        image_shape = im_info['image_shape']
-        gt_bbox = label_info['gt_bbox']
-        height = image_shape[0]
-        width = image_shape[1]
-
-        if np.random.uniform(0, 1) < self.prob:
-            im = horizontal_flip(im)
-            if gt_bbox.shape[0] == 0:
-                if label_info is None:
-                    return (im, im_info)
-                else:
-                    return (im, im_info, label_info)
-            label_info['gt_bbox'] = box_horizontal_flip(gt_bbox, width)
-            if 'gt_poly' in label_info and \
-                    len(label_info['gt_poly']) != 0:
-                label_info['gt_poly'] = segms_horizontal_flip(
-                    label_info['gt_poly'], height, width)
-        if label_info is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label_info)
 
 
 class Normalize(DetTransform):
@@ -533,642 +440,6 @@ class Normalize(DetTransform):
             return (im, im_info, label_info)
 
 
-class RandomDistort(DetTransform):
-    """以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作
-
-    1. 对变换的操作顺序进行随机化操作。
-    2. 按照1中的顺序以一定的概率在范围[-range, range]对图像进行随机像素内容变换。
-
-    Args:
-        brightness_range (float): 明亮度因子的范围。默认为0.5。
-        brightness_prob (float): 随机调整明亮度的概率。默认为0.5。
-        contrast_range (float): 对比度因子的范围。默认为0.5。
-        contrast_prob (float): 随机调整对比度的概率。默认为0.5。
-        saturation_range (float): 饱和度因子的范围。默认为0.5。
-        saturation_prob (float): 随机调整饱和度的概率。默认为0.5。
-        hue_range (int): 色调因子的范围。默认为18。
-        hue_prob (float): 随机调整色调的概率。默认为0.5。
-    """
-
-    def __init__(self,
-                 brightness_range=0.5,
-                 brightness_prob=0.5,
-                 contrast_range=0.5,
-                 contrast_prob=0.5,
-                 saturation_range=0.5,
-                 saturation_prob=0.5,
-                 hue_range=18,
-                 hue_prob=0.5):
-        self.brightness_range = brightness_range
-        self.brightness_prob = brightness_prob
-        self.contrast_range = contrast_range
-        self.contrast_prob = contrast_prob
-        self.saturation_range = saturation_range
-        self.saturation_prob = saturation_prob
-        self.hue_range = hue_range
-        self.hue_prob = hue_prob
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                   当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
-                   存储与标注框相关信息的字典。
-        """
-        brightness_lower = 1 - self.brightness_range
-        brightness_upper = 1 + self.brightness_range
-        contrast_lower = 1 - self.contrast_range
-        contrast_upper = 1 + self.contrast_range
-        saturation_lower = 1 - self.saturation_range
-        saturation_upper = 1 + self.saturation_range
-        hue_lower = -self.hue_range
-        hue_upper = self.hue_range
-        ops = [brightness, contrast, saturation, hue]
-        random.shuffle(ops)
-        params_dict = {
-            'brightness': {
-                'brightness_lower': brightness_lower,
-                'brightness_upper': brightness_upper
-            },
-            'contrast': {
-                'contrast_lower': contrast_lower,
-                'contrast_upper': contrast_upper
-            },
-            'saturation': {
-                'saturation_lower': saturation_lower,
-                'saturation_upper': saturation_upper
-            },
-            'hue': {
-                'hue_lower': hue_lower,
-                'hue_upper': hue_upper
-            }
-        }
-        prob_dict = {
-            'brightness': self.brightness_prob,
-            'contrast': self.contrast_prob,
-            'saturation': self.saturation_prob,
-            'hue': self.hue_prob
-        }
-        for id in range(4):
-            params = params_dict[ops[id].__name__]
-            prob = prob_dict[ops[id].__name__]
-            params['im'] = im
-
-            if np.random.uniform(0, 1) < prob:
-                im = ops[id](**params)
-        if label_info is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label_info)
-
-
-class MixupImage(DetTransform):
-    """对图像进行mixup操作,模型训练时的数据增强操作，目前仅YOLOv3模型支持该transform。
-
-    当label_info中不存在mixup字段时，直接返回，否则进行下述操作：
-    1. 从随机beta分布中抽取出随机因子factor。
-    2.
-        - 当factor>=1.0时，去除label_info中的mixup字段，直接返回。
-        - 当factor<=0.0时，直接返回label_info中的mixup字段，并在label_info中去除该字段。
-        - 其余情况，执行下述操作：
-            （1）原图像乘以factor，mixup图像乘以(1-factor)，叠加2个结果。
-            （2）拼接原图像标注框和mixup图像标注框。
-            （3）拼接原图像标注框类别和mixup图像标注框类别。
-            （4）原图像标注框混合得分乘以factor，mixup图像标注框混合得分乘以(1-factor)，叠加2个结果。
-    3. 更新im_info中的image_shape信息。
-
-    Args:
-        alpha (float): 随机beta分布的下限。默认为1.5。
-        beta (float): 随机beta分布的上限。默认为1.5。
-        mixup_epoch (int): 在前mixup_epoch轮使用mixup增强操作；当该参数为-1时，该策略不会生效。
-            默认为-1。
-
-    Raises:
-        ValueError: 数据长度不匹配。
-    """
-
-    def __init__(self, alpha=1.5, beta=1.5, mixup_epoch=-1):
-        self.alpha = alpha
-        self.beta = beta
-        if self.alpha <= 0.0:
-            raise ValueError("alpha shold be positive in MixupImage")
-        if self.beta <= 0.0:
-            raise ValueError("beta shold be positive in MixupImage")
-        self.mixup_epoch = mixup_epoch
-
-    def _mixup_img(self, img1, img2, factor):
-        h = max(img1.shape[0], img2.shape[0])
-        w = max(img1.shape[1], img2.shape[1])
-        img = np.zeros((h, w, img1.shape[2]), 'float32')
-        img[:img1.shape[0], :img1.shape[1], :] = \
-            img1.astype('float32') * factor
-        img[:img2.shape[0], :img2.shape[1], :] += \
-            img2.astype('float32') * (1.0 - factor)
-        return img.astype('float32')
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                   当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
-                   存储与标注框相关信息的字典。
-                   其中，im_info更新字段为：
-                       - image_shape (np.ndarray): mixup后的图像高、宽二者组成的np.ndarray，形状为(2,)。
-                   im_info删除的字段：
-                       - mixup (list): 与当前字段进行mixup的图像相关信息。
-                   label_info更新字段为：
-                       - gt_bbox (np.ndarray): mixup后真实标注框坐标，形状为(n, 4)，
-                                          其中n代表真实标注框的个数。
-                       - gt_class (np.ndarray): mixup后每个真实标注框对应的类别序号，形状为(n, 1)，
-                                           其中n代表真实标注框的个数。
-                       - gt_score (np.ndarray): mixup后每个真实标注框对应的混合得分，形状为(n, 1)，
-                                           其中n代表真实标注框的个数。
-
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-        """
-        if im_info is None:
-            raise TypeError('Cannot do MixupImage! ' +
-                            'Becasuse the im_info can not be None!')
-        if 'mixup' not in im_info:
-            if label_info is None:
-                return (im, im_info)
-            else:
-                return (im, im_info, label_info)
-        factor = np.random.beta(self.alpha, self.beta)
-        factor = max(0.0, min(1.0, factor))
-        if im_info['epoch'] > self.mixup_epoch \
-                or factor >= 1.0:
-            im_info.pop('mixup')
-            if label_info is None:
-                return (im, im_info)
-            else:
-                return (im, im_info, label_info)
-        if factor <= 0.0:
-            return im_info.pop('mixup')
-        im = self._mixup_img(im, im_info['mixup'][0], factor)
-        if label_info is None:
-            raise TypeError('Cannot do MixupImage! ' +
-                            'Becasuse the label_info can not be None!')
-        if 'gt_bbox' not in label_info or \
-                'gt_class' not in label_info or \
-                'gt_score' not in label_info:
-            raise TypeError('Cannot do MixupImage! ' + \
-                            'Becasuse gt_bbox/gt_class/gt_score is not in label_info!')
-        gt_bbox1 = label_info['gt_bbox']
-        gt_bbox2 = im_info['mixup'][2]['gt_bbox']
-        gt_class1 = label_info['gt_class']
-        gt_class2 = im_info['mixup'][2]['gt_class']
-        gt_score1 = label_info['gt_score']
-        gt_score2 = im_info['mixup'][2]['gt_score']
-        if 'gt_poly' in label_info:
-            gt_poly1 = label_info['gt_poly']
-            gt_poly2 = im_info['mixup'][2]['gt_poly']
-        is_crowd1 = label_info['is_crowd']
-        is_crowd2 = im_info['mixup'][2]['is_crowd']
-        
-        if 0 not in gt_class1 and 0 not in gt_class2:
-            gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
-            gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
-            gt_score = np.concatenate(
-                (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
-            if 'gt_poly' in label_info:
-                label_info['gt_poly'] = gt_poly1 + gt_poly2
-            is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0)
-        elif 0 in gt_class1:
-            gt_bbox = gt_bbox2
-            gt_class = gt_class2
-            gt_score = gt_score2 * (1. - factor)
-            if 'gt_poly' in label_info:
-                label_info['gt_poly'] = gt_poly2
-            is_crowd = is_crowd2
-        else:
-            gt_bbox = gt_bbox1
-            gt_class = gt_class1
-            gt_score = gt_score1 * factor
-            if 'gt_poly' in label_info:
-                label_info['gt_poly'] = gt_poly1
-            is_crowd = is_crowd1
-        label_info['gt_bbox'] = gt_bbox
-        label_info['gt_score'] = gt_score
-        label_info['gt_class'] = gt_class
-        label_info['is_crowd'] = is_crowd
-        im_info['image_shape'] = np.array([im.shape[0],
-                                           im.shape[1]]).astype('int32')
-        im_info.pop('mixup')
-        if label_info is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label_info)
-
-
-class RandomExpand(DetTransform):
-    """随机扩张图像，模型训练时的数据增强操作。
-    1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
-    2. 计算扩张后图像大小。
-    3. 初始化像素值为输入填充值的图像，并将原图像随机粘贴于该图像上。
-    4. 根据原图像粘贴位置换算出扩张后真实标注框的位置坐标。
-    5. 根据原图像粘贴位置换算出扩张后真实分割区域的位置坐标。
-    Args:
-        ratio (float): 图像扩张的最大比例。默认为4.0。
-        prob (float): 随机扩张的概率。默认为0.5。
-        fill_value (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
-    """
-
-    def __init__(self,
-                 ratio=4.,
-                 prob=0.5,
-                 fill_value=[123.675, 116.28, 103.53]):
-        super(RandomExpand, self).__init__()
-        assert ratio > 1.01, "expand ratio must be larger than 1.01"
-        self.ratio = ratio
-        self.prob = prob
-        assert isinstance(fill_value, Sequence), \
-            "fill value must be sequence"
-        if not isinstance(fill_value, tuple):
-            fill_value = tuple(fill_value)
-        self.fill_value = fill_value
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-        Returns:
-            tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                   当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
-                   存储与标注框相关信息的字典。
-                   其中，im_info更新字段为：
-                       - image_shape (np.ndarray): 扩张后的图像高、宽二者组成的np.ndarray，形状为(2,)。
-                   label_info更新字段为：
-                       - gt_bbox (np.ndarray): 随机扩张后真实标注框坐标，形状为(n, 4)，
-                                          其中n代表真实标注框的个数。
-                       - gt_class (np.ndarray): 随机扩张后每个真实标注框对应的类别序号，形状为(n, 1)，
-                                           其中n代表真实标注框的个数。
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-        """
-        if im_info is None or label_info is None:
-            raise TypeError(
-                'Cannot do RandomExpand! ' +
-                'Becasuse the im_info and label_info can not be None!')
-        if 'gt_bbox' not in label_info or \
-                'gt_class' not in label_info:
-            raise TypeError('Cannot do RandomExpand! ' + \
-                            'Becasuse gt_bbox/gt_class is not in label_info!')
-        if np.random.uniform(0., 1.) < self.prob:
-            return (im, im_info, label_info)
-
-        if 'gt_class' in label_info and 0 in label_info['gt_class']:
-            return (im, im_info, label_info)
-        image_shape = im_info['image_shape']
-        height = int(image_shape[0])
-        width = int(image_shape[1])
-
-        expand_ratio = np.random.uniform(1., self.ratio)
-        h = int(height * expand_ratio)
-        w = int(width * expand_ratio)
-        if not h > height or not w > width:
-            return (im, im_info, label_info)
-        y = np.random.randint(0, h - height)
-        x = np.random.randint(0, w - width)
-        canvas = np.ones((h, w, 3), dtype=np.float32)
-        canvas *= np.array(self.fill_value, dtype=np.float32)
-        canvas[y:y + height, x:x + width, :] = im
-
-        im_info['image_shape'] = np.array([h, w]).astype('int32')
-        if 'gt_bbox' in label_info and len(label_info['gt_bbox']) > 0:
-            label_info['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32)
-        if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
-            label_info['gt_poly'] = expand_segms(label_info['gt_poly'], x, y,
-                                                 height, width, expand_ratio)
-        return (canvas, im_info, label_info)
-
-
-class RandomCrop(DetTransform):
-    """随机裁剪图像。
-    1. 若allow_no_crop为True，则在thresholds加入’no_crop’。
-    2. 随机打乱thresholds。
-    3. 遍历thresholds中各元素：
-        (1) 如果当前thresh为’no_crop’，则返回原始图像和标注信息。
-        (2) 随机取出aspect_ratio和scaling中的值并由此计算出候选裁剪区域的高、宽、起始点。
-        (3) 计算真实标注框与候选裁剪区域IoU，若全部真实标注框的IoU都小于thresh，则继续第3步。
-        (4) 如果cover_all_box为True且存在真实标注框的IoU小于thresh，则继续第3步。
-        (5) 筛选出位于候选裁剪区域内的真实标注框，若有效框的个数为0，则继续第3步，否则进行第4步。
-    4. 换算有效真值标注框相对候选裁剪区域的位置坐标。
-    5. 换算有效分割区域相对候选裁剪区域的位置坐标。
-
-    Args:
-        aspect_ratio (list): 裁剪后短边缩放比例的取值范围，以[min, max]形式表示。默认值为[.5, 2.]。
-        thresholds (list): 判断裁剪候选区域是否有效所需的IoU阈值取值列表。默认值为[.0, .1, .3, .5, .7, .9]。
-        scaling (list): 裁剪面积相对原面积的取值范围，以[min, max]形式表示。默认值为[.3, 1.]。
-        num_attempts (int): 在放弃寻找有效裁剪区域前尝试的次数。默认值为50。
-        allow_no_crop (bool): 是否允许未进行裁剪。默认值为True。
-        cover_all_box (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。
-    """
-
-    def __init__(self,
-                 aspect_ratio=[.5, 2.],
-                 thresholds=[.0, .1, .3, .5, .7, .9],
-                 scaling=[.3, 1.],
-                 num_attempts=50,
-                 allow_no_crop=True,
-                 cover_all_box=False):
-        self.aspect_ratio = aspect_ratio
-        self.thresholds = thresholds
-        self.scaling = scaling
-        self.num_attempts = num_attempts
-        self.allow_no_crop = allow_no_crop
-        self.cover_all_box = cover_all_box
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                   当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
-                   存储与标注框相关信息的字典。
-                   其中，im_info更新字段为：
-                           - image_shape (np.ndarray): 扩裁剪的图像高、宽二者组成的np.ndarray，形状为(2,)。
-                       label_info更新字段为：
-                           - gt_bbox (np.ndarray): 随机裁剪后真实标注框坐标，形状为(n, 4)，
-                                          其中n代表真实标注框的个数。
-                           - gt_class (np.ndarray): 随机裁剪后每个真实标注框对应的类别序号，形状为(n, 1)，
-                                           其中n代表真实标注框的个数。
-                           - gt_score (np.ndarray): 随机裁剪后每个真实标注框对应的混合得分，形状为(n, 1)，
-                                           其中n代表真实标注框的个数。
-
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-        """
-        if im_info is None or label_info is None:
-            raise TypeError(
-                'Cannot do RandomCrop! ' +
-                'Becasuse the im_info and label_info can not be None!')
-        if 'gt_bbox' not in label_info or \
-                'gt_class' not in label_info:
-            raise TypeError('Cannot do RandomCrop! ' + \
-                            'Becasuse gt_bbox/gt_class is not in label_info!')
-
-        if len(label_info['gt_bbox']) == 0:
-            return (im, im_info, label_info)
-        if 'gt_class' in label_info and 0 in label_info['gt_class']:
-            return (im, im_info, label_info)
-
-        image_shape = im_info['image_shape']
-        w = image_shape[1]
-        h = image_shape[0]
-        gt_bbox = label_info['gt_bbox']
-        thresholds = list(self.thresholds)
-        if self.allow_no_crop:
-            thresholds.append('no_crop')
-        np.random.shuffle(thresholds)
-
-        for thresh in thresholds:
-            if thresh == 'no_crop':
-                return (im, im_info, label_info)
-
-            found = False
-            for i in range(self.num_attempts):
-                scale = np.random.uniform(*self.scaling)
-                min_ar, max_ar = self.aspect_ratio
-                aspect_ratio = np.random.uniform(
-                    max(min_ar, scale**2), min(max_ar, scale**-2))
-                crop_h = int(h * scale / np.sqrt(aspect_ratio))
-                crop_w = int(w * scale * np.sqrt(aspect_ratio))
-                crop_y = np.random.randint(0, h - crop_h)
-                crop_x = np.random.randint(0, w - crop_w)
-                crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
-                iou = iou_matrix(
-                    gt_bbox, np.array(
-                        [crop_box], dtype=np.float32))
-                if iou.max() < thresh:
-                    continue
-
-                if self.cover_all_box and iou.min() < thresh:
-                    continue
-
-                cropped_box, valid_ids = crop_box_with_center_constraint(
-                    gt_bbox, np.array(
-                        crop_box, dtype=np.float32))
-                if valid_ids.size > 0:
-                    found = True
-                    break
-
-            if found:
-                if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
-                    crop_polys = crop_segms(
-                        label_info['gt_poly'],
-                        valid_ids,
-                        np.array(
-                            crop_box, dtype=np.int64),
-                        h,
-                        w)
-                    if [] in crop_polys:
-                        delete_id = list()
-                        valid_polys = list()
-                        for id, crop_poly in enumerate(crop_polys):
-                            if crop_poly == []:
-                                delete_id.append(id)
-                            else:
-                                valid_polys.append(crop_poly)
-                        valid_ids = np.delete(valid_ids, delete_id)
-                        if len(valid_polys) == 0:
-                            return (im, im_info, label_info)
-                        label_info['gt_poly'] = valid_polys
-                    else:
-                        label_info['gt_poly'] = crop_polys
-                im = crop_image(im, crop_box)
-                label_info['gt_bbox'] = np.take(cropped_box, valid_ids, axis=0)
-                label_info['gt_class'] = np.take(
-                    label_info['gt_class'], valid_ids, axis=0)
-                im_info['image_shape'] = np.array(
-                    [crop_box[3] - crop_box[1],
-                     crop_box[2] - crop_box[0]]).astype('int32')
-                if 'gt_score' in label_info:
-                    label_info['gt_score'] = np.take(
-                        label_info['gt_score'], valid_ids, axis=0)
-
-                if 'is_crowd' in label_info:
-                    label_info['is_crowd'] = np.take(
-                        label_info['is_crowd'], valid_ids, axis=0)
-                return (im, im_info, label_info)
-
-        return (im, im_info, label_info)
-
-
-class ArrangeFasterRCNN(DetTransform):
-    """获取FasterRCNN模型训练/验证/预测所需信息。
-
-    Args:
-        mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
-
-    Raises:
-        ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内。
-    """
-
-    def __init__(self, mode=None):
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode must be in ['train', 'eval', 'test', 'quant']!")
-        self.mode = mode
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当mode为'train'时，返回(im, im_resize_info, gt_bbox, gt_class, is_crowd)，分别对应
-                图像np.ndarray数据、图像相当对于原图的resize信息、真实标注框、真实标注框对应的类别、真实标注框内是否是一组对象；
-                当mode为'eval'时，返回(im, im_resize_info, im_id, im_shape, gt_bbox, gt_class, is_difficult)，
-                分别对应图像np.ndarray数据、图像相当对于原图的resize信息、图像id、图像大小信息、真实标注框、真实标注框对应的类别、
-                真实标注框是否为难识别对象；当mode为'test'或'quant'时，返回(im, im_resize_info, im_shape)，分别对应图像np.ndarray数据、
-                图像相当对于原图的resize信息、图像大小信息。
-
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-            ValueError: 数据长度不匹配。
-        """
-        im = permute(im, False)
-        if self.mode == 'train':
-            if im_info is None or label_info is None:
-                raise TypeError(
-                    'Cannot do ArrangeFasterRCNN! ' +
-                    'Becasuse the im_info and label_info can not be None!')
-            if len(label_info['gt_bbox']) != len(label_info['gt_class']):
-                raise ValueError("gt num mismatch: bbox and class.")
-            im_resize_info = im_info['im_resize_info']
-            gt_bbox = label_info['gt_bbox']
-            gt_class = label_info['gt_class']
-            is_crowd = label_info['is_crowd']
-            outputs = (im, im_resize_info, gt_bbox, gt_class, is_crowd)
-        elif self.mode == 'eval':
-            if im_info is None or label_info is None:
-                raise TypeError(
-                    'Cannot do ArrangeFasterRCNN! ' +
-                    'Becasuse the im_info and label_info can not be None!')
-            im_resize_info = im_info['im_resize_info']
-            im_id = im_info['im_id']
-            im_shape = np.array(
-                (im_info['image_shape'][0], im_info['image_shape'][1], 1),
-                dtype=np.float32)
-            gt_bbox = label_info['gt_bbox']
-            gt_class = label_info['gt_class']
-            is_difficult = label_info['difficult']
-            outputs = (im, im_resize_info, im_id, im_shape, gt_bbox, gt_class,
-                       is_difficult)
-        else:
-            if im_info is None:
-                raise TypeError('Cannot do ArrangeFasterRCNN! ' +
-                                'Becasuse the im_info can not be None!')
-            im_resize_info = im_info['im_resize_info']
-            im_shape = np.array(
-                (im_info['image_shape'][0], im_info['image_shape'][1], 1),
-                dtype=np.float32)
-            outputs = (im, im_resize_info, im_shape)
-        return outputs
-
-
-class ArrangeMaskRCNN(DetTransform):
-    """获取MaskRCNN模型训练/验证/预测所需信息。
-
-    Args:
-        mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
-
-    Raises:
-        ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内。
-    """
-
-    def __init__(self, mode=None):
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode must be in ['train', 'eval', 'test', 'quant']!")
-        self.mode = mode
-
-    def __call__(self, im, im_info=None, label_info=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict, 可选): 存储与图像相关的信息。
-            label_info (dict, 可选): 存储与标注框相关的信息。
-
-        Returns:
-            tuple: 当mode为'train'时，返回(im, im_resize_info, gt_bbox, gt_class, is_crowd, gt_masks)，分别对应
-                图像np.ndarray数据、图像相当对于原图的resize信息、真实标注框、真实标注框对应的类别、真实标注框内是否是一组对象、
-                真实分割区域；当mode为'eval'时，返回(im, im_resize_info, im_id, im_shape)，分别对应图像np.ndarray数据、
-                图像相当对于原图的resize信息、图像id、图像大小信息；当mode为'test'或'quant'时，返回(im, im_resize_info, im_shape)，
-                分别对应图像np.ndarray数据、图像相当对于原图的resize信息、图像大小信息。
-
-        Raises:
-            TypeError: 形参数据类型不满足需求。
-            ValueError: 数据长度不匹配。
-        """
-        im = permute(im, False)
-        if self.mode == 'train':
-            if im_info is None or label_info is None:
-                raise TypeError(
-                    'Cannot do ArrangeTrainMaskRCNN! ' +
-                    'Becasuse the im_info and label_info can not be None!')
-            if len(label_info['gt_bbox']) != len(label_info['gt_class']):
-                raise ValueError("gt num mismatch: bbox and class.")
-            im_resize_info = im_info['im_resize_info']
-            gt_bbox = label_info['gt_bbox']
-            gt_class = label_info['gt_class']
-            is_crowd = label_info['is_crowd']
-            assert 'gt_poly' in label_info
-            segms = label_info['gt_poly']
-            if len(segms) != 0:
-                assert len(segms) == is_crowd.shape[0]
-            gt_masks = []
-            valid = True
-            for i in range(len(segms)):
-                segm = segms[i]
-                gt_segm = []
-                if is_crowd[i]:
-                    gt_segm.append([[0, 0]])
-                else:
-                    for poly in segm:
-                        if len(poly) == 0:
-                            valid = False
-                            break
-                        gt_segm.append(np.array(poly).reshape(-1, 2))
-                if (not valid) or len(gt_segm) == 0:
-                    break
-                gt_masks.append(gt_segm)
-            outputs = (im, im_resize_info, gt_bbox, gt_class, is_crowd,
-                       gt_masks)
-        else:
-            if im_info is None:
-                raise TypeError('Cannot do ArrangeMaskRCNN! ' +
-                                'Becasuse the im_info can not be None!')
-            im_resize_info = im_info['im_resize_info']
-            im_shape = np.array(
-                (im_info['image_shape'][0], im_info['image_shape'][1], 1),
-                dtype=np.float32)
-            if self.mode == 'eval':
-                im_id = im_info['im_id']
-                outputs = (im, im_resize_info, im_id, im_shape)
-            else:
-                outputs = (im, im_resize_info, im_shape)
-        return outputs
 
 
 class ArrangeYOLOv3(DetTransform):
@@ -1208,50 +479,9 @@ class ArrangeYOLOv3(DetTransform):
         """
         im = permute(im, False)
         if self.mode == 'train':
-            if im_info is None or label_info is None:
-                raise TypeError(
-                    'Cannot do ArrangeYolov3! ' +
-                    'Becasuse the im_info and label_info can not be None!')
-            im_shape = im_info['image_shape']
-            if len(label_info['gt_bbox']) != len(label_info['gt_class']):
-                raise ValueError("gt num mismatch: bbox and class.")
-            if len(label_info['gt_bbox']) != len(label_info['gt_score']):
-                raise ValueError("gt num mismatch: bbox and score.")
-            gt_bbox = np.zeros((50, 4), dtype=im.dtype)
-            gt_class = np.zeros((50, ), dtype=np.int32)
-            gt_score = np.zeros((50, ), dtype=im.dtype)
-            gt_num = min(50, len(label_info['gt_bbox']))
-            if gt_num > 0:
-                label_info['gt_class'][:gt_num, 0] = label_info[
-                    'gt_class'][:gt_num, 0] - 1
-                if -1 not in label_info['gt_class']:
-                    gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
-                    gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
-                    gt_score[:gt_num] = label_info['gt_score'][:gt_num, 0]
-            # parse [x1, y1, x2, y2] to [x, y, w, h]
-            gt_bbox[:, 2:4] = gt_bbox[:, 2:4] - gt_bbox[:, :2]
-            gt_bbox[:, :2] = gt_bbox[:, :2] + gt_bbox[:, 2:4] / 2.
-            outputs = (im, gt_bbox, gt_class, gt_score, im_shape)
+            pass
         elif self.mode == 'eval':
-            if im_info is None or label_info is None:
-                raise TypeError(
-                    'Cannot do ArrangeYolov3! ' +
-                    'Becasuse the im_info and label_info can not be None!')
-            im_shape = im_info['image_shape']
-            if len(label_info['gt_bbox']) != len(label_info['gt_class']):
-                raise ValueError("gt num mismatch: bbox and class.")
-            im_id = im_info['im_id']
-            gt_bbox = np.zeros((50, 4), dtype=im.dtype)
-            gt_class = np.zeros((50, ), dtype=np.int32)
-            difficult = np.zeros((50, ), dtype=np.int32)
-            gt_num = min(50, len(label_info['gt_bbox']))
-            if gt_num > 0:
-                label_info['gt_class'][:gt_num, 0] = label_info[
-                    'gt_class'][:gt_num, 0] - 1
-                gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
-                gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
-                difficult[:gt_num] = label_info['difficult'][:gt_num, 0]
-            outputs = (im, im_shape, im_id, gt_bbox, gt_class, difficult)
+            pass
         else:
             if im_info is None:
                 raise TypeError('Cannot do ArrangeYolov3! ' +
@@ -1261,51 +491,6 @@ class ArrangeYOLOv3(DetTransform):
         return outputs
 
 
-class ComposedRCNNTransforms(Compose):
-    """ RCNN模型(faster-rcnn/mask-rcnn)图像处理流程，具体如下，
-        训练阶段：
-        1. 随机以0.5的概率将图像水平翻转
-        2. 图像归一化
-        3. 图像按比例Resize，scale计算方式如下
-            scale = min_max_size[0] / short_size_of_image
-            if max_size_of_image * scale > min_max_size[1]:
-                scale = min_max_size[1] / max_size_of_image
-        4. 将3步骤的长宽进行padding，使得长宽为32的倍数
-        验证阶段：
-        1. 图像归一化
-        2. 图像按比例Resize，scale计算方式同上训练阶段
-        3. 将2步骤的长宽进行padding，使得长宽为32的倍数
-
-        Args:
-            mode(str): 图像处理流程所处阶段，训练/验证/预测，分别对应'train', 'eval', 'test'
-            min_max_size(list): 图像在缩放时，最小边和最大边的约束条件
-            mean(list): 图像均值
-            std(list): 图像方差
-    """
-
-    def __init__(self,
-                 mode,
-                 min_max_size=[800, 1333],
-                 mean=[0.485, 0.456, 0.406],
-                 std=[0.229, 0.224, 0.225]):
-        if mode == 'train':
-            # 训练时的transforms，包含数据增强
-            transforms = [
-                RandomHorizontalFlip(prob=0.5), Normalize(
-                    mean=mean, std=std), ResizeByShort(
-                        short_size=min_max_size[0], max_size=min_max_size[1]),
-                Padding(coarsest_stride=32)
-            ]
-        else:
-            # 验证/预测时的transforms
-            transforms = [
-                Normalize(
-                    mean=mean, std=std), ResizeByShort(
-                        short_size=min_max_size[0], max_size=min_max_size[1]),
-                Padding(coarsest_stride=32)
-            ]
-
-        super(ComposedRCNNTransforms, self).__init__(transforms)
 
 
 class ComposedYOLOv3Transforms(Compose):
@@ -1349,13 +534,7 @@ class ComposedYOLOv3Transforms(Compose):
 
         if mode == 'train':
             # 训练时的transforms，包含数据增强
-            transforms = [
-                MixupImage(mixup_epoch=mixup_epoch), RandomDistort(),
-                RandomExpand(), RandomCrop(), Resize(
-                    target_size=width,
-                    interp='RANDOM'), RandomHorizontalFlip(), Normalize(
-                        mean=mean, std=std)
-            ]
+            pass
         else:
             # 验证/预测时的transforms
             transforms = [

deploy/openvino/python/transforms/imgaug_support.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 numpy as np
-import copy
-
-
-def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
-                   segment_map=None):
-    # 预处理，将bboxes, polygons转换成imgaug格式
-    import imgaug.augmentables.kps as kps
-    import imgaug.augmentables.bbs as bbs
-
-    aug_im = im.astype('uint8')
-    aug_im = augmenter.augment(image=aug_im).astype('float32')
-    return aug_im
-
-    # TODO imgaug的标注处理逻辑与paddlex已存的transform存在部分差异
-    # 目前仅支持对原图进行处理，因此只能使用pixlevel的imgaug增强操作
-    # 以下代码暂不会执行
-    aug_bboxes = None
-    if bboxes is not None:
-        aug_bboxes = list()
-        for i in range(len(bboxes)):
-            x1 = bboxes[i, 0]
-            y1 = bboxes[i, 1]
-            x2 = bboxes[i, 2]
-            y2 = bboxes[i, 3]
-            aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
-
-    aug_points = None
-    if polygons is not None:
-        aug_points = list()
-        for i in range(len(polygons)):
-            num = len(polygons[i])
-            for j in range(num):
-                tmp = np.reshape(polygons[i][j], (-1, 2))
-                for k in range(len(tmp)):
-                    aug_points.append(kps.Keypoint(tmp[k, 0], tmp[k, 1]))
-
-    aug_segment_map = None
-    if segment_map is not None:
-        if len(segment_map.shape) == 2:
-            h, w = segment_map.shape
-            aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
-        elif len(segment_map.shape) == 3:
-            h, w, c = segment_map.shape
-            aug_segment_map = np.reshape(segment_map, (1, h, w, c))
-        else:
-            raise Exception(
-                "Only support 2-dimensions for 3-dimensions for segment_map")
-
-    unnormalized_batch = augmenter.augment(
-        image=aug_im,
-        bounding_boxes=aug_bboxes,
-        keypoints=aug_points,
-        segmentation_maps=aug_segment_map,
-        return_batch=True)
-    aug_im = unnormalized_batch.images_aug[0]
-    aug_bboxes = unnormalized_batch.bounding_boxes_aug
-    aug_points = unnormalized_batch.keypoints_aug
-    aug_seg_map = unnormalized_batch.segmentation_maps_aug
-
-    aug_im = aug_im.astype('float32')
-
-    if aug_bboxes is not None:
-        converted_bboxes = list()
-        for i in range(len(aug_bboxes)):
-            converted_bboxes.append([
-                aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
-                aug_bboxes[i].y2
-            ])
-        aug_bboxes = converted_bboxes
-
-    aug_polygons = None
-    if aug_points is not None:
-        aug_polygons = copy.deepcopy(polygons)
-        idx = 0
-        for i in range(len(aug_polygons)):
-            num = len(aug_polygons[i])
-            for j in range(num):
-                num_points = len(aug_polygons[i][j]) // 2
-                for k in range(num_points):
-                    aug_polygons[i][j][k * 2] = aug_points[idx].x
-                    aug_polygons[i][j][k * 2 + 1] = aug_points[idx].y
-                    idx += 1
-
-    result = [aug_im]
-    if aug_bboxes is not None:
-        result.append(np.array(aug_bboxes))
-    if aug_polygons is not None:
-        result.append(aug_polygons)
-    if aug_seg_map is not None:
-        n, h, w, c = aug_seg_map.shape
-        if len(segment_map.shape) == 2:
-            aug_seg_map = np.reshape(aug_seg_map, (h, w))
-        elif len(segment_map.shape) == 3:
-            aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
-        result.append(aug_seg_map)
-    return result

deploy/openvino/python/transforms/ops.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.

deploy/openvino/python/transforms/seg_transforms.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# 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.
@@ -14,14 +14,12 @@
 # limitations under the License.
 
 from .ops import *
-from .imgaug_support import execute_imgaug
 import random
 import os.path as osp
 import numpy as np
 from PIL import Image
 import cv2
 from collections import OrderedDict
-import utils.logging as logging
 
 
 class SegTransform:
@@ -53,14 +51,7 @@ class Compose(SegTransform):
                             'must be equal or larger than 1!')
         self.transforms = transforms
         self.to_rgb = False
-        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
-        for op in self.transforms:
-            if not isinstance(op, SegTransform):
-                import imgaug.augmenters as iaa
-                if not isinstance(op, iaa.Augmenter):
-                    raise Exception(
-                        "Elements in transforms should be defined in 'paddlex.seg.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
-                    )
+
 
     def __call__(self, im, im_info=None, label=None):
         """
@@ -116,7 +107,7 @@ class Compose(SegTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                print("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
@@ -828,76 +819,6 @@ class RandomBlur(SegTransform):
             return (im, im_info, label)
 
 
-class RandomRotate(SegTransform):
-    """对图像进行随机旋转, 模型训练时的数据增强操作。
-    在旋转区间[-rotate_range, rotate_range]内，对图像进行随机旋转，当存在标注图像时，同步进行，
-    并对旋转后的图像和标注图像进行相应的padding。
-
-    Args:
-        rotate_range (float): 最大旋转角度。默认为15度。
-        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
-        label_padding_value (int): 标注图像padding的值。默认为255。
-
-    """
-
-    def __init__(self,
-                 rotate_range=15,
-                 im_padding_value=[127.5, 127.5, 127.5],
-                 label_padding_value=255):
-        self.rotate_range = rotate_range
-        self.im_padding_value = im_padding_value
-        self.label_padding_value = label_padding_value
-
-    def __call__(self, im, im_info=None, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (list): 存储图像reisze或padding前的shape信息，如
-                [('resize', [200, 300]), ('padding', [400, 600])]表示
-                图像在过resize前shape为(200, 300)， 过padding前shape为
-                (400, 600)
-            label (np.ndarray): 标注图像np.ndarray数据。
-
-        Returns:
-            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
-                存储与图像相关信息的字典和标注图像np.ndarray数据。
-        """
-        if self.rotate_range > 0:
-            (h, w) = im.shape[:2]
-            do_rotation = np.random.uniform(-self.rotate_range,
-                                            self.rotate_range)
-            pc = (w // 2, h // 2)
-            r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
-            cos = np.abs(r[0, 0])
-            sin = np.abs(r[0, 1])
-
-            nw = int((h * sin) + (w * cos))
-            nh = int((h * cos) + (w * sin))
-
-            (cx, cy) = pc
-            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)
-            label = cv2.warpAffine(
-                label,
-                r,
-                dsize=dsize,
-                flags=cv2.INTER_NEAREST,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=self.label_padding_value)
-
-        if label is None:
-            return (im, im_info)
-        else:
-            return (im, im_info, label)
 
 
 class RandomScaleAspect(SegTransform):
@@ -1125,11 +1046,7 @@ class ComposedSegTransforms(Compose):
                  std=[0.5, 0.5, 0.5]):
         if mode == 'train':
             # 训练时的transforms，包含数据增强
-            transforms = [
-                RandomHorizontalFlip(prob=0.5), ResizeStepScaling(),
-                RandomPaddingCrop(crop_size=train_crop_size), Normalize(
-                    mean=mean, std=std)
-            ]
+            pass
         else:
             # 验证/预测时的transforms
             transforms = [Normalize(mean=mean, std=std)]

deploy/openvino/python/utils/__init__.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.
-
-

deploy/openvino/python/utils/logging.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 time
-import os
-import sys
-import colorama
-from colorama import init
-
-init(autoreset=True)
-levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
-log_level = 2
-
-def log(level=2, message="", use_color=False):
-    current_time = time.time()
-    time_array = time.localtime(current_time)
-    current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
-    if log_level >= level:
-        if use_color:
-            print("\033[1;31;40m{} [{}]\t{}\033[0m".format(current_time, levels[
-                level], message).encode("utf-8").decode("latin1"))
-        else:
-            print("{} [{}]\t{}".format(current_time, levels[level], message)
-                  .encode("utf-8").decode("latin1"))
-        sys.stdout.flush()
-
-
-def debug(message="", use_color=False):
-    log(level=3, message=message, use_color=use_color)
-
-
-def info(message="", use_color=False):
-    log(level=2, message=message, use_color=use_color)
-
-
-def warning(message="", use_color=True):
-    log(level=1, message=message, use_color=use_color)
-
-
-def error(message="", use_color=True, exit=True):
-    log(level=0, message=message, use_color=use_color)
-    if exit:
-        sys.exit(-1)

docs/deploy/openvino/introduction.md

@@ -6,11 +6,11 @@ PaddleX支持将训练好的Paddle模型通过OpenVINO实现模型的预测加
 
 |硬件平台|Linux|Windows|Raspbian OS|c++|python |分类|检测|分割|
 | ----|  ---- | ---- | ----|  ---- | ---- |---- | ---- |---- |
-|CPU|支持|支持|不支持|支持|支持|支持|支持|支持|
+|CPU|支持|支持|不支持|支持|支持|支持|支持|不支持|
 |VPU|支持|支持|支持|支持|支持|支持|不支持|不支持|  
 
 
-其中Raspbian OS为树莓派操作系统。
+**注意**：其中Raspbian OS为树莓派操作系统。检测模型仅支持YOLOV3，由于OpenVINO不支持ONNX的resize-11 OP的原因，目前还不支持Paddle的分割模型
 
 ## 部署流程
 **PaddleX到OpenVINO的部署流程可以分为如下两步**： 
@@ -29,4 +29,4 @@ CPU或者VPU的情况下使用OpenVINO进行预测加速
 
 **[Windows](./windows.md)**:介绍了PaddleX在操作系统为Window，编程语言为C++，硬件平台为CPU或者VPU的情况下使用OpenVINO进行预测加速  
 
-**[python](./windows.md)**:介绍了PaddleX在python下使用OpenVINO进行预测加速
+**[Python](./python.md)**:介绍了PaddleX在python下使用OpenVINO进行预测加速
\ No newline at end of file

docs/deploy/openvino/linux.md

@@ -63,7 +63,7 @@ ARCH=x86
 
 ### Step4: 预测
 
-编译成功后，分类任务的预测可执行程序为`classifier`，分割任务的预测可执行程序为`segmenter`，检测任务的预测可执行程序为`detector`，其主要命令参数说明如下：
+编译成功后，分类任务的预测可执行程序为`classifier`，检测任务的预测可执行程序为`detector`，其主要命令参数说明如下：
 
 |  参数   | 说明  |
 |  ----  | ----  |
@@ -72,7 +72,7 @@ ARCH=x86
 | --image_list  | 按行存储图片路径的.txt文件 |
 | --device  | 运行的平台，可选项{"CPU"，"MYRIAD"}，默认值为"CPU"，如在VPU上请使用"MYRIAD"|
 | --cfg_dir | PaddleX model 的.yml配置文件 |
-| --save_dir | 可视化结果图片保存地址，仅适用于检测和分割任务，默认值为" "既不保存可视化结果 |
+| --save_dir | 可视化结果图片保存地址，仅适用于检测任务，默认值为" "既不保存可视化结果 |
 
 ### 样例
 `样例一`：
@@ -85,7 +85,7 @@ linux系统在CPU下做单张图片的分类任务预测
 
 
 `样例二`:
-linux系统在CPU下做多张图片的分割任务预测，并保存预测可视化结果
+linux系统在CPU下做多张图片的检测任务预测，并保存预测可视化结果
 预测的多个图片`/path/to/image_list.txt`，image_list.txt内容的格式如下：
 ```
 /path/to/images/test_img1.jpeg
@@ -95,7 +95,7 @@ linux系统在CPU下做多张图片的分割任务预测，并保存预测可视
 ```
 
 ```shell
-./build/segmenter --model_dir=/path/to/models/openvino_model --image_list=/root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --save_dir ./output
+./build/detector --model_dir=/path/to/models/openvino_model --image_list=/root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --save_dir ./output
 ```
 
 `样例三`:  
@@ -118,9 +118,6 @@ linux系统在CPU下做多张图片的分割任务预测，并保存预测可视
 |---|---|---|---|
 |resnet-50 | 20.56 | 16.12 | 224*224 |
 |mobilenet-V2 | 5.16 | 2.31 |224*224|
-|hrnet | None | 63.35 |512*512|
-|unet | 276.40 | 211.49 |512*512|
-|deeplabv3 | None | 25.91 |512*512|
 |yolov3-mobilnetv1 |76.63| 46.26|608*608 |   
 
 `测试二`:

docs/deploy/openvino/windows.md

@@ -73,7 +73,7 @@ D:
 cd D:\projects\PaddleX\deploy\openvino\out\build\x64-Release
 ```
 
-* 编译成功后，图片预测demo的入口程序为`detector.exe`，`classifier.exe`，`segmenter.exe`，用户可根据自己的模型类型选择，其主要命令参数说明如下：
+* 编译成功后，图片预测demo的入口程序为`detector.exe`，`classifier.exe`，用户可根据自己的模型类型选择，其主要命令参数说明如下：
 
 |  参数   | 说明  |
 |  ----  | ----  |
@@ -82,7 +82,7 @@ cd D:\projects\PaddleX\deploy\openvino\out\build\x64-Release
 | --image_list  | 按行存储图片路径的.txt文件 |
 | --device  | 运行的平台，可选项{"CPU"，"MYRIAD"}，默认值为"CPU"，如在VPU上请使用"MYRIAD"|
 | --cfg_dir | PaddleX model 的.yml配置文件 |
-| --save_dir | 可视化结果图片保存地址，仅适用于检测和分割任务，默认值为" "既不保存可视化结果 |
+| --save_dir | 可视化结果图片保存地址，仅适用于检测任务，默认值为" "既不保存可视化结果 |
 
 ### 样例
 `样例一`：
@@ -94,7 +94,7 @@ cd D:\projects\PaddleX\deploy\openvino\out\build\x64-Release
 ```
 
 `样例二`:
-在CPU下做多张图片的分割任务预测，并保存预测可视化结果
+在CPU下做多张图片的检测任务预测，并保存预测可视化结果
 预测多个图片`/path/to/image_list.txt`，image_list.txt内容的格式如下：
 ```
 /path/to/images/test_img1.jpeg
@@ -104,7 +104,7 @@ cd D:\projects\PaddleX\deploy\openvino\out\build\x64-Release
 ```
 
 ```shell
-./segmenter.exe --model_dir=/path/to/models/openvino_model --image_list=/root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --save_dir ./output
+./detector.exe --model_dir=/path/to/models/openvino_model --image_list=/root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --save_dir ./output
 ```
 
 `样例三`:  

docs/deploy/raspberry/Raspberry.md

@@ -23,7 +23,7 @@ sudo apt-get upgrade
 ```
 
 ## Paddle-Lite部署
-基于Paddle-Lite的部署目前可以支持PaddleX的分类、分割与检测模型。   
+基于Paddle-Lite的部署目前可以支持PaddleX的分类、分割与检测模型，其实检测模型仅支持YOLOV3   
 部署的流程包括：PaddleX模型转换与转换后的模型部署  
 
 **说明**：PaddleX安装请参考[PaddleX](https://paddlex.readthedocs.io/zh_CN/develop/install.html)，Paddle-Lite详细资料请参考[Paddle-Lite](https://paddle-lite.readthedocs.io/zh/latest/index.html)