Merge pull request #266 from FlyingQianMM/develop_qh

add ppyolo

paddlex/cv/__init__.py

@@ -26,6 +26,7 @@ ResNet50 = models.ResNet50
 DarkNet53 = models.DarkNet53
 # detection
 YOLOv3 = models.YOLOv3
+PPYOLO = models.PPYOLO
 #EAST = models.EAST
 FasterRCNN = models.FasterRCNN
 MaskRCNN = models.MaskRCNN

paddlex/cv/datasets/dataset.py

@@ -115,7 +115,7 @@ def multithread_reader(mapper,
         while not isinstance(sample, EndSignal):
             batch_data.append(sample)
             if len(batch_data) == batch_size:
-                batch_data = generate_minibatch(batch_data)
+                batch_data = generate_minibatch(batch_data, mapper=mapper)
                 yield batch_data
                 batch_data = []
             sample = out_queue.get()
@@ -127,11 +127,11 @@ def multithread_reader(mapper,
             else:
                 batch_data.append(sample)
                 if len(batch_data) == batch_size:
-                    batch_data = generate_minibatch(batch_data)
+                    batch_data = generate_minibatch(batch_data, mapper=mapper)
                     yield batch_data
                     batch_data = []
         if not drop_last and len(batch_data) != 0:
-            batch_data = generate_minibatch(batch_data)
+            batch_data = generate_minibatch(batch_data, mapper=mapper)
             yield batch_data
             batch_data = []
 
@@ -188,18 +188,21 @@ def multiprocess_reader(mapper,
             else:
                 batch_data.append(sample)
                 if len(batch_data) == batch_size:
-                    batch_data = generate_minibatch(batch_data)
+                    batch_data = generate_minibatch(batch_data, mapper=mapper)
                     yield batch_data
                     batch_data = []
         if len(batch_data) != 0 and not drop_last:
-            batch_data = generate_minibatch(batch_data)
+            batch_data = generate_minibatch(batch_data, mapper=mapper)
             yield batch_data
             batch_data = []
 
     return queue_reader
 
 
-def generate_minibatch(batch_data, label_padding_value=255):
+def generate_minibatch(batch_data, label_padding_value=255, mapper=None):
+    if mapper is not None and mapper.batch_transforms is not None:
+        for op in mapper.batch_transforms:
+            batch_data = op(batch_data)
     # if batch_size is 1, do not pad the image
     if len(batch_data) == 1:
         return batch_data
@@ -218,14 +221,13 @@ def generate_minibatch(batch_data, label_padding_value=255):
             (im_c, max_shape[1], max_shape[2]), dtype=np.float32)
         padding_im[:, :im_h, :im_w] = data[0]
         if len(data) > 2:
-           # padding the image, label and insert 'padding' into `im_info` of segmentation during evaluating phase.
+            # padding the image, label and insert 'padding' into `im_info` of segmentation during evaluating phase.
             if len(data[1]) == 0 or 'padding' not in [
                     data[1][i][0] for i in range(len(data[1]))
             ]:
                 data[1].append(('padding', [im_h, im_w]))
             padding_batch.append((padding_im, data[1], data[2]))
 
-            
         elif len(data) > 1:
             if isinstance(data[1], np.ndarray) and len(data[1].shape) > 1:
                 # padding the image and label of segmentation during the training

paddlex/cv/models/__init__.py

@@ -38,6 +38,7 @@ from .classifier import HRNet_W18
 from .classifier import AlexNet
 from .base import BaseAPI
 from .yolo_v3 import YOLOv3
+from .ppyolo import PPYOLO
 from .faster_rcnn import FasterRCNN
 from .mask_rcnn import MaskRCNN
 from .unet import UNet

paddlex/cv/models/base.py

@@ -246,8 +246,8 @@ class BaseAPI:
             logging.info(
                 "Load pretrain weights from {}.".format(pretrain_weights),
                 use_color=True)
-            paddlex.utils.utils.load_pretrain_weights(self.exe, self.train_prog,
-                                                      pretrain_weights, fuse_bn)
+            paddlex.utils.utils.load_pretrain_weights(
+                self.exe, self.train_prog, pretrain_weights, fuse_bn)
         # 进行裁剪
         if sensitivities_file is not None:
             import paddleslim
@@ -351,7 +351,9 @@ class BaseAPI:
         logging.info("Model saved in {}.".format(save_dir))
 
     def export_inference_model(self, save_dir):
-        test_input_names = [var.name for var in list(self.test_inputs.values())]
+        test_input_names = [
+            var.name for var in list(self.test_inputs.values())
+        ]
         test_outputs = list(self.test_outputs.values())
         with fluid.scope_guard(self.scope):
             if self.__class__.__name__ == 'MaskRCNN':
@@ -389,7 +391,8 @@ class BaseAPI:
 
         # 模型保存成功的标志
         open(osp.join(save_dir, '.success'), 'w').close()
-        logging.info("Model for inference deploy saved in {}.".format(save_dir))
+        logging.info("Model for inference deploy saved in {}.".format(
+            save_dir))
 
     def train_loop(self,
                    num_epochs,
@@ -516,11 +519,13 @@ class BaseAPI:
                         eta = ((num_epochs - i) * total_num_steps - step - 1
                                ) * avg_step_time
                     if time_eval_one_epoch is not None:
-                        eval_eta = (total_eval_times - i // save_interval_epochs
-                                    ) * time_eval_one_epoch
+                        eval_eta = (
+                            total_eval_times - i // save_interval_epochs
+                        ) * time_eval_one_epoch
                     else:
-                        eval_eta = (total_eval_times - i // save_interval_epochs
-                                    ) * total_num_steps_eval * avg_step_time
+                        eval_eta = (
+                            total_eval_times - i // save_interval_epochs
+                        ) * total_num_steps_eval * avg_step_time
                     eta_str = seconds_to_hms(eta + eval_eta)
 
                     logging.info(
@@ -543,6 +548,8 @@ class BaseAPI:
                 current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1))
                 if not osp.isdir(current_save_dir):
                     os.makedirs(current_save_dir)
+                if hasattr(self, 'use_ema'):
+                    self.exe.run(self.ema.apply_program)
                 if eval_dataset is not None and eval_dataset.num_samples > 0:
                     self.eval_metrics, self.eval_details = self.evaluate(
                         eval_dataset=eval_dataset,
@@ -569,6 +576,8 @@ class BaseAPI:
                             log_writer.add_scalar(
                                 "Metrics/Eval(Epoch): {}".format(k), v, i + 1)
                 self.save_model(save_dir=current_save_dir)
+                if hasattr(self, 'use_ema'):
+                    self.exe.run(self.ema.restore_program)
                 time_eval_one_epoch = time.time() - eval_epoch_start_time
                 eval_epoch_start_time = time.time()
                 if best_model_epoch > 0:

paddlex/cv/models/yolo_v3.py

@@ -15,21 +15,11 @@
 from __future__ import absolute_import
 import math
 import tqdm
-import os.path as osp
-import numpy as np
-from multiprocessing.pool import ThreadPool
-import paddle.fluid as fluid
-import paddlex.utils.logging as logging
 import paddlex
-import copy
-from paddlex.cv.transforms import arrange_transforms
-from paddlex.cv.datasets import generate_minibatch
-from .base import BaseAPI
-from collections import OrderedDict
-from .utils.detection_eval import eval_results, bbox2out
+from .ppyolo import PPYOLO
 
 
-class YOLOv3(BaseAPI):
+class YOLOv3(PPYOLO):
     """构建YOLOv3，并实现其训练、评估、预测和模型导出。
 
     Args:
@@ -65,12 +55,12 @@ class YOLOv3(BaseAPI):
                      320, 352, 384, 416, 448, 480, 512, 544, 576, 608
                  ]):
         self.init_params = locals()
-        super(YOLOv3, self).__init__('detector')
         backbones = [
             'DarkNet53', 'ResNet34', 'MobileNetV1', 'MobileNetV3_large'
         ]
         assert backbone in backbones, "backbone should be one of {}".format(
             backbones)
+        super(YOLOv3, self).__init__('detector')
         self.backbone = backbone
         self.num_classes = num_classes
         self.anchors = anchors
@@ -84,6 +74,16 @@ class YOLOv3(BaseAPI):
         self.sync_bn = True
         self.train_random_shapes = train_random_shapes
         self.fixed_input_shape = None
+        self.use_fine_grained_loss = False
+        self.use_coord_conv = False
+        self.use_iou_aware = False
+        self.use_spp = False
+        self.use_drop_block = False
+        self.use_iou_loss = False
+        self.scale_x_y = 1.
+        self.use_matrix_nms = False
+        self.use_ema = False
+        self.with_dcn_v2 = False
 
     def _get_backbone(self, backbone_name):
         if backbone_name == 'DarkNet53':
@@ -104,59 +104,6 @@ class YOLOv3(BaseAPI):
                 norm_type='sync_bn', model_name=model_name)
         return backbone
 
-    def build_net(self, mode='train'):
-        model = paddlex.cv.nets.detection.YOLOv3(
-            backbone=self._get_backbone(self.backbone),
-            num_classes=self.num_classes,
-            mode=mode,
-            anchors=self.anchors,
-            anchor_masks=self.anchor_masks,
-            ignore_threshold=self.ignore_threshold,
-            label_smooth=self.label_smooth,
-            nms_score_threshold=self.nms_score_threshold,
-            nms_topk=self.nms_topk,
-            nms_keep_topk=self.nms_keep_topk,
-            nms_iou_threshold=self.nms_iou_threshold,
-            train_random_shapes=self.train_random_shapes,
-            fixed_input_shape=self.fixed_input_shape)
-        inputs = model.generate_inputs()
-        model_out = model.build_net(inputs)
-        outputs = OrderedDict([('bbox', model_out)])
-        if mode == 'train':
-            self.optimizer.minimize(model_out)
-            outputs = OrderedDict([('loss', model_out)])
-        return inputs, outputs
-
-    def default_optimizer(self, learning_rate, warmup_steps, warmup_start_lr,
-                          lr_decay_epochs, lr_decay_gamma,
-                          num_steps_each_epoch):
-        if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
-            logging.error(
-                "In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
-                exit=False)
-            logging.error(
-                "See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
-                exit=False)
-            logging.error(
-                "warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
-                format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
-                       // num_steps_each_epoch))
-        boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
-        values = [(lr_decay_gamma**i) * learning_rate
-                  for i in range(len(lr_decay_epochs) + 1)]
-        lr_decay = fluid.layers.piecewise_decay(
-            boundaries=boundaries, values=values)
-        lr_warmup = fluid.layers.linear_lr_warmup(
-            learning_rate=lr_decay,
-            warmup_steps=warmup_steps,
-            start_lr=warmup_start_lr,
-            end_lr=learning_rate)
-        optimizer = fluid.optimizer.Momentum(
-            learning_rate=lr_warmup,
-            momentum=0.9,
-            regularization=fluid.regularizer.L2DecayRegularizer(5e-04))
-        return optimizer
-
     def train(self,
               num_epochs,
               train_dataset,
@@ -214,259 +161,11 @@ class YOLOv3(BaseAPI):
             ValueError: 评估类型不在指定列表中。
             ValueError: 模型从inference model进行加载。
         """
-        if not self.trainable:
-            raise ValueError("Model is not trainable from load_model method.")
-        if metric is None:
-            if isinstance(train_dataset, paddlex.datasets.CocoDetection):
-                metric = 'COCO'
-            elif isinstance(train_dataset, paddlex.datasets.VOCDetection) or \
-                    isinstance(train_dataset, paddlex.datasets.EasyDataDet):
-                metric = 'VOC'
-            else:
-                raise ValueError(
-                    "train_dataset should be datasets.VOCDetection or datasets.COCODetection or datasets.EasyDataDet."
-                )
-        assert metric in ['COCO', 'VOC'], "Metric only support 'VOC' or 'COCO'"
-        self.metric = metric
-
-        self.labels = train_dataset.labels
-        # 构建训练网络
-        if optimizer is None:
-            # 构建默认的优化策略
-            num_steps_each_epoch = train_dataset.num_samples // train_batch_size
-            optimizer = self.default_optimizer(
-                learning_rate=learning_rate,
-                warmup_steps=warmup_steps,
-                warmup_start_lr=warmup_start_lr,
-                lr_decay_epochs=lr_decay_epochs,
-                lr_decay_gamma=lr_decay_gamma,
-                num_steps_each_epoch=num_steps_each_epoch)
-        self.optimizer = optimizer
-        # 构建训练、验证、预测网络
-        self.build_program()
-        # 初始化网络权重
-        self.net_initialize(
-            startup_prog=fluid.default_startup_program(),
-            pretrain_weights=pretrain_weights,
-            save_dir=save_dir,
-            sensitivities_file=sensitivities_file,
-            eval_metric_loss=eval_metric_loss,
-            resume_checkpoint=resume_checkpoint)
-        # 训练
-        self.train_loop(
-            num_epochs=num_epochs,
-            train_dataset=train_dataset,
-            train_batch_size=train_batch_size,
-            eval_dataset=eval_dataset,
-            save_interval_epochs=save_interval_epochs,
-            log_interval_steps=log_interval_steps,
-            save_dir=save_dir,
-            use_vdl=use_vdl,
-            early_stop=early_stop,
-            early_stop_patience=early_stop_patience)
-
-    def evaluate(self,
-                 eval_dataset,
-                 batch_size=1,
-                 epoch_id=None,
-                 metric=None,
-                 return_details=False):
-        """评估。
-
-        Args:
-            eval_dataset (paddlex.datasets): 验证数据读取器。
-            batch_size (int): 验证数据批大小。默认为1。
-            epoch_id (int): 当前评估模型所在的训练轮数。
-            metric (bool): 训练过程中评估的方式，取值范围为['COCO', 'VOC']。默认为None，
-                根据用户传入的Dataset自动选择，如为VOCDetection，则metric为'VOC';
-                如为COCODetection，则metric为'COCO'。
-            return_details (bool): 是否返回详细信息。
-
-        Returns:
-            tuple (metrics, eval_details) | dict (metrics): 当return_details为True时，返回(metrics, eval_details)，
-                当return_details为False时，返回metrics。metrics为dict，包含关键字：'bbox_mmap'或者’bbox_map‘，
-                分别表示平均准确率平均值在各个IoU阈值下的结果取平均值的结果（mmAP）、平均准确率平均值（mAP）。
-                eval_details为dict，包含关键字：'bbox'，对应元素预测结果列表，每个预测结果由图像id、
-                预测框类别id、预测框坐标、预测框得分；’gt‘：真实标注框相关信息。
-        """
-        arrange_transforms(
-            model_type=self.model_type,
-            class_name=self.__class__.__name__,
-            transforms=eval_dataset.transforms,
-            mode='eval')
-        if metric is None:
-            if hasattr(self, 'metric') and self.metric is not None:
-                metric = self.metric
-            else:
-                if isinstance(eval_dataset, paddlex.datasets.CocoDetection):
-                    metric = 'COCO'
-                elif isinstance(eval_dataset, paddlex.datasets.VOCDetection):
-                    metric = 'VOC'
-                else:
-                    raise Exception(
-                        "eval_dataset should be datasets.VOCDetection or datasets.COCODetection."
-                    )
-        assert metric in ['COCO', 'VOC'], "Metric only support 'VOC' or 'COCO'"
-
-        total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size)
-        results = list()
-
-        data_generator = eval_dataset.generator(
-            batch_size=batch_size, drop_last=False)
-        logging.info(
-            "Start to evaluating(total_samples={}, total_steps={})...".format(
-                eval_dataset.num_samples, total_steps))
-        for step, data in tqdm.tqdm(
-                enumerate(data_generator()), total=total_steps):
-            images = np.array([d[0] for d in data])
-            im_sizes = np.array([d[1] for d in data])
-            feed_data = {'image': images, 'im_size': im_sizes}
-            with fluid.scope_guard(self.scope):
-                outputs = self.exe.run(
-                    self.test_prog,
-                    feed=[feed_data],
-                    fetch_list=list(self.test_outputs.values()),
-                    return_numpy=False)
-            res = {
-                'bbox': (np.array(outputs[0]),
-                         outputs[0].recursive_sequence_lengths())
-            }
-            res_id = [np.array([d[2]]) for d in data]
-            res['im_id'] = (res_id, [])
-            if metric == 'VOC':
-                res_gt_box = [d[3].reshape(-1, 4) for d in data]
-                res_gt_label = [d[4].reshape(-1, 1) for d in data]
-                res_is_difficult = [d[5].reshape(-1, 1) for d in data]
-                res_id = [np.array([d[2]]) for d in data]
-                res['gt_box'] = (res_gt_box, [])
-                res['gt_label'] = (res_gt_label, [])
-                res['is_difficult'] = (res_is_difficult, [])
-            results.append(res)
-            logging.debug("[EVAL] Epoch={}, Step={}/{}".format(epoch_id, step +
-                                                               1, total_steps))
-        box_ap_stats, eval_details = eval_results(
-            results, metric, eval_dataset.coco_gt, with_background=False)
-        evaluate_metrics = OrderedDict(
-            zip(['bbox_mmap'
-                 if metric == 'COCO' else 'bbox_map'], box_ap_stats))
-        if return_details:
-            return evaluate_metrics, eval_details
-        return evaluate_metrics
-
-    @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_num=1):
-        arrange_transforms(
-            model_type=model_type,
-            class_name=class_name,
-            transforms=transforms,
-            mode='test')
-        pool = ThreadPool(thread_num)
-        batch_data = pool.map(transforms, images)
-        pool.close()
-        pool.join()
-        padding_batch = generate_minibatch(batch_data)
-        im = np.array(
-            [data[0] for data in padding_batch],
-            dtype=padding_batch[0][0].dtype)
-        im_size = np.array([data[1] for data in padding_batch], dtype=np.int32)
-
-        return im, im_size
-
-    @staticmethod
-    def _postprocess(res, batch_size, num_classes, labels):
-        clsid2catid = dict({i: i for i in range(num_classes)})
-        xywh_results = bbox2out([res], clsid2catid)
-        preds = [[] for i in range(batch_size)]
-        for xywh_res in xywh_results:
-            image_id = xywh_res['image_id']
-            del xywh_res['image_id']
-            xywh_res['category'] = labels[xywh_res['category_id']]
-            preds[image_id].append(xywh_res)
-
-        return preds
-
-    def predict(self, img_file, transforms=None):
-        """预测。
-
-        Args:
-            img_file (str|np.ndarray): 预测图像路径，或者是解码后的排列格式为（H, W, C）且类型为float32且为BGR格式的数组。
-            transforms (paddlex.det.transforms): 数据预处理操作。
-
-        Returns:
-            list: 预测结果列表，每个预测结果由预测框类别标签、
-              预测框类别名称、预测框坐标(坐标格式为[xmin, ymin, w, h]）、
-              预测框得分组成。
-        """
-        if transforms is None and not hasattr(self, 'test_transforms'):
-            raise Exception("transforms need to be defined, now is None.")
-        if isinstance(img_file, (str, np.ndarray)):
-            images = [img_file]
-        else:
-            raise Exception("img_file must be str/np.ndarray")
-
-        if transforms is None:
-            transforms = self.test_transforms
-        im, im_size = YOLOv3._preprocess(images, transforms, self.model_type,
-                                         self.__class__.__name__)
-
-        with fluid.scope_guard(self.scope):
-            result = self.exe.run(self.test_prog,
-                                  feed={'image': im,
-                                        'im_size': im_size},
-                                  fetch_list=list(self.test_outputs.values()),
-                                  return_numpy=False,
-                                  use_program_cache=True)
-
-        res = {
-            k: (np.array(v), v.recursive_sequence_lengths())
-            for k, v in zip(list(self.test_outputs.keys()), result)
-        }
-        res['im_id'] = (np.array(
-            [[i] for i in range(len(images))]).astype('int32'), [[]])
-        preds = YOLOv3._postprocess(res,
-                                    len(images), self.num_classes, self.labels)
-        return preds[0]
-
-    def batch_predict(self, img_file_list, transforms=None, thread_num=2):
-        """预测。
-
-        Args:
-            img_file_list (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径，也可以是解码后的排列格式为（H，W，C）
-                且类型为float32且为BGR格式的数组。
-            transforms (paddlex.det.transforms): 数据预处理操作。
-            thread_num (int): 并发执行各图像预处理时的线程数。
-        Returns:
-            list: 每个元素都为列表，表示各图像的预测结果。在各图像的预测结果列表中，每个预测结果由预测框类别标签、
-              预测框类别名称、预测框坐标(坐标格式为[xmin, ymin, w, h]）、
-              预测框得分组成。
-        """
-        if transforms is None and not hasattr(self, 'test_transforms'):
-            raise Exception("transforms need to be defined, now is None.")
-
-        if not isinstance(img_file_list, (list, tuple)):
-            raise Exception("im_file must be list/tuple")
-
-        if transforms is None:
-            transforms = self.test_transforms
-        im, im_size = YOLOv3._preprocess(img_file_list, transforms,
-                                         self.model_type,
-                                         self.__class__.__name__, thread_num)
-
-        with fluid.scope_guard(self.scope):
-            result = self.exe.run(self.test_prog,
-                                  feed={'image': im,
-                                        'im_size': im_size},
-                                  fetch_list=list(self.test_outputs.values()),
-                                  return_numpy=False,
-                                  use_program_cache=True)
 
-        res = {
-            k: (np.array(v), v.recursive_sequence_lengths())
-            for k, v in zip(list(self.test_outputs.keys()), result)
-        }
-        res['im_id'] = (np.array(
-            [[i] for i in range(len(img_file_list))]).astype('int32'), [[]])
-        preds = YOLOv3._postprocess(res,
-                                    len(img_file_list), self.num_classes,
-                                    self.labels)
-        return preds
+        return super(YOLOv3, self).train(
+            num_epochs, train_dataset, train_batch_size, eval_dataset,
+            save_interval_epochs, log_interval_steps, save_dir,
+            pretrain_weights, optimizer, learning_rate, warmup_steps,
+            warmup_start_lr, lr_decay_epochs, lr_decay_gamma, metric, use_vdl,
+            sensitivities_file, eval_metric_loss, early_stop,
+            early_stop_patience, resume_checkpoint, False)

paddlex/cv/nets/detection/iou_aware.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import fluid
+
+
+def _split_ioup(output, an_num, num_classes):
+    """
+    Split new output feature map to output, predicted iou
+    along channel dimension
+    """
+    ioup = fluid.layers.slice(output, axes=[1], starts=[0], ends=[an_num])
+    ioup = fluid.layers.sigmoid(ioup)
+
+    oriout = fluid.layers.slice(
+        output, axes=[1], starts=[an_num], ends=[an_num * (num_classes + 6)])
+
+    return (ioup, oriout)
+
+
+def _de_sigmoid(x, eps=1e-7):
+    x = fluid.layers.clip(x, eps, 1 / eps)
+    one = fluid.layers.fill_constant(
+        shape=[1, 1, 1, 1], dtype=x.dtype, value=1.)
+    x = fluid.layers.clip((one / x - 1.0), eps, 1 / eps)
+    x = -fluid.layers.log(x)
+    return x
+
+
+def _postprocess_output(ioup, output, an_num, num_classes, iou_aware_factor):
+    """
+    post process output objectness score
+    """
+    tensors = []
+    stride = output.shape[1] // an_num
+    for m in range(an_num):
+        tensors.append(
+            fluid.layers.slice(
+                output,
+                axes=[1],
+                starts=[stride * m + 0],
+                ends=[stride * m + 4]))
+        obj = fluid.layers.slice(
+            output, axes=[1], starts=[stride * m + 4], ends=[stride * m + 5])
+        obj = fluid.layers.sigmoid(obj)
+        ip = fluid.layers.slice(ioup, axes=[1], starts=[m], ends=[m + 1])
+
+        new_obj = fluid.layers.pow(obj, (
+            1 - iou_aware_factor)) * fluid.layers.pow(ip, iou_aware_factor)
+        new_obj = _de_sigmoid(new_obj)
+
+        tensors.append(new_obj)
+
+        tensors.append(
+            fluid.layers.slice(
+                output,
+                axes=[1],
+                starts=[stride * m + 5],
+                ends=[stride * m + 5 + num_classes]))
+
+    output = fluid.layers.concat(tensors, axis=1)
+
+    return output
+
+
+def get_iou_aware_score(output, an_num, num_classes, iou_aware_factor):
+    ioup, output = _split_ioup(output, an_num, num_classes)
+    output = _postprocess_output(ioup, output, an_num, num_classes,
+                                 iou_aware_factor)
+    return output

paddlex/cv/nets/detection/loss/iou_aware_loss.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.initializer import NumpyArrayInitializer
+
+from paddle import fluid
+from .iou_loss import IouLoss
+
+
+class IouAwareLoss(IouLoss):
+    """
+    iou aware loss, see https://arxiv.org/abs/1912.05992
+    Args:
+        loss_weight (float): iou aware loss weight, default is 1.0
+        max_height (int): max height of input to support random shape input
+        max_width (int): max width of input to support random shape input
+    """
+
+    def __init__(self, loss_weight=1.0, max_height=608, max_width=608):
+        super(IouAwareLoss, self).__init__(
+            loss_weight=loss_weight,
+            max_height=max_height,
+            max_width=max_width)
+
+    def __call__(self,
+                 ioup,
+                 x,
+                 y,
+                 w,
+                 h,
+                 tx,
+                 ty,
+                 tw,
+                 th,
+                 anchors,
+                 downsample_ratio,
+                 batch_size,
+                 scale_x_y,
+                 eps=1.e-10):
+        '''
+        Args:
+            ioup ([Variables]): the predicted iou
+            x  | y | w | h  ([Variables]): the output of yolov3 for encoded x|y|w|h
+            tx |ty |tw |th  ([Variables]): the target of yolov3 for encoded x|y|w|h
+            anchors ([float]): list of anchors for current output layer
+            downsample_ratio (float): the downsample ratio for current output layer
+            batch_size (int): training batch size
+            eps (float): the decimal to prevent the denominator eqaul zero
+        '''
+
+        pred = self._bbox_transform(x, y, w, h, anchors, downsample_ratio,
+                                    batch_size, False, scale_x_y, eps)
+        gt = self._bbox_transform(tx, ty, tw, th, anchors, downsample_ratio,
+                                  batch_size, True, scale_x_y, eps)
+        iouk = self._iou(pred, gt, ioup, eps)
+        iouk.stop_gradient = True
+
+        loss_iou_aware = fluid.layers.cross_entropy(
+            ioup, iouk, soft_label=True)
+        loss_iou_aware = loss_iou_aware * self._loss_weight
+        return loss_iou_aware

paddlex/cv/nets/detection/loss/iou_loss.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.initializer import NumpyArrayInitializer
+
+from paddle import fluid
+
+
+class IouLoss(object):
+    """
+    iou loss, see https://arxiv.org/abs/1908.03851
+    loss = 1.0 - iou * iou
+    Args:
+        loss_weight (float): iou loss weight, default is 2.5
+        max_height (int): max height of input to support random shape input
+        max_width (int): max width of input to support random shape input
+        ciou_term (bool): whether to add ciou_term
+        loss_square (bool): whether to square the iou term
+    """
+
+    def __init__(self,
+                 loss_weight=2.5,
+                 max_height=608,
+                 max_width=608,
+                 ciou_term=False,
+                 loss_square=True):
+        self._loss_weight = loss_weight
+        self._MAX_HI = max_height
+        self._MAX_WI = max_width
+        self.ciou_term = ciou_term
+        self.loss_square = loss_square
+
+    def __call__(self,
+                 x,
+                 y,
+                 w,
+                 h,
+                 tx,
+                 ty,
+                 tw,
+                 th,
+                 anchors,
+                 downsample_ratio,
+                 batch_size,
+                 scale_x_y=1.,
+                 ioup=None,
+                 eps=1.e-10):
+        '''
+        Args:
+            x  | y | w | h  ([Variables]): the output of yolov3 for encoded x|y|w|h
+            tx |ty |tw |th  ([Variables]): the target of yolov3 for encoded x|y|w|h
+            anchors ([float]): list of anchors for current output layer
+            downsample_ratio (float): the downsample ratio for current output layer
+            batch_size (int): training batch size
+            eps (float): the decimal to prevent the denominator eqaul zero
+        '''
+        pred = self._bbox_transform(x, y, w, h, anchors, downsample_ratio,
+                                    batch_size, False, scale_x_y, eps)
+        gt = self._bbox_transform(tx, ty, tw, th, anchors, downsample_ratio,
+                                  batch_size, True, scale_x_y, eps)
+        iouk = self._iou(pred, gt, ioup, eps)
+        if self.loss_square:
+            loss_iou = 1. - iouk * iouk
+        else:
+            loss_iou = 1. - iouk
+        loss_iou = loss_iou * self._loss_weight
+
+        return loss_iou
+
+    def _iou(self, pred, gt, ioup=None, eps=1.e-10):
+        x1, y1, x2, y2 = pred
+        x1g, y1g, x2g, y2g = gt
+        x2 = fluid.layers.elementwise_max(x1, x2)
+        y2 = fluid.layers.elementwise_max(y1, y2)
+
+        xkis1 = fluid.layers.elementwise_max(x1, x1g)
+        ykis1 = fluid.layers.elementwise_max(y1, y1g)
+        xkis2 = fluid.layers.elementwise_min(x2, x2g)
+        ykis2 = fluid.layers.elementwise_min(y2, y2g)
+
+        intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
+        intsctk = intsctk * fluid.layers.greater_than(
+            xkis2, xkis1) * fluid.layers.greater_than(ykis2, ykis1)
+        unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
+                                                        ) - intsctk + eps
+        iouk = intsctk / unionk
+        if self.ciou_term:
+            ciou = self.get_ciou_term(pred, gt, iouk, eps)
+            iouk = iouk - ciou
+        return iouk
+
+    def get_ciou_term(self, pred, gt, iouk, eps):
+        x1, y1, x2, y2 = pred
+        x1g, y1g, x2g, y2g = gt
+
+        cx = (x1 + x2) / 2
+        cy = (y1 + y2) / 2
+        w = (x2 - x1) + fluid.layers.cast((x2 - x1) == 0, 'float32')
+        h = (y2 - y1) + fluid.layers.cast((y2 - y1) == 0, 'float32')
+
+        cxg = (x1g + x2g) / 2
+        cyg = (y1g + y2g) / 2
+        wg = x2g - x1g
+        hg = y2g - y1g
+
+        # A or B
+        xc1 = fluid.layers.elementwise_min(x1, x1g)
+        yc1 = fluid.layers.elementwise_min(y1, y1g)
+        xc2 = fluid.layers.elementwise_max(x2, x2g)
+        yc2 = fluid.layers.elementwise_max(y2, y2g)
+
+        # DIOU term
+        dist_intersection = (cx - cxg) * (cx - cxg) + (cy - cyg) * (cy - cyg)
+        dist_union = (xc2 - xc1) * (xc2 - xc1) + (yc2 - yc1) * (yc2 - yc1)
+        diou_term = (dist_intersection + eps) / (dist_union + eps)
+        # CIOU term
+        ciou_term = 0
+        ar_gt = wg / hg
+        ar_pred = w / h
+        arctan = fluid.layers.atan(ar_gt) - fluid.layers.atan(ar_pred)
+        ar_loss = 4. / np.pi / np.pi * arctan * arctan
+        alpha = ar_loss / (1 - iouk + ar_loss + eps)
+        alpha.stop_gradient = True
+        ciou_term = alpha * ar_loss
+        return diou_term + ciou_term
+
+    def _bbox_transform(self, dcx, dcy, dw, dh, anchors, downsample_ratio,
+                        batch_size, is_gt, scale_x_y, eps):
+        grid_x = int(self._MAX_WI / downsample_ratio)
+        grid_y = int(self._MAX_HI / downsample_ratio)
+        an_num = len(anchors) // 2
+
+        shape_fmp = fluid.layers.shape(dcx)
+        shape_fmp.stop_gradient = True
+        # generate the grid_w x grid_h center of feature map
+        idx_i = np.array([[i for i in range(grid_x)]])
+        idx_j = np.array([[j for j in range(grid_y)]]).transpose()
+        gi_np = np.repeat(idx_i, grid_y, axis=0)
+        gi_np = np.reshape(gi_np, newshape=[1, 1, grid_y, grid_x])
+        gi_np = np.tile(gi_np, reps=[batch_size, an_num, 1, 1])
+        gj_np = np.repeat(idx_j, grid_x, axis=1)
+        gj_np = np.reshape(gj_np, newshape=[1, 1, grid_y, grid_x])
+        gj_np = np.tile(gj_np, reps=[batch_size, an_num, 1, 1])
+        gi_max = self._create_tensor_from_numpy(gi_np.astype(np.float32))
+        gi = fluid.layers.crop(x=gi_max, shape=dcx)
+        gi.stop_gradient = True
+        gj_max = self._create_tensor_from_numpy(gj_np.astype(np.float32))
+        gj = fluid.layers.crop(x=gj_max, shape=dcx)
+        gj.stop_gradient = True
+
+        grid_x_act = fluid.layers.cast(shape_fmp[3], dtype="float32")
+        grid_x_act.stop_gradient = True
+        grid_y_act = fluid.layers.cast(shape_fmp[2], dtype="float32")
+        grid_y_act.stop_gradient = True
+        if is_gt:
+            cx = fluid.layers.elementwise_add(dcx, gi) / grid_x_act
+            cx.gradient = True
+            cy = fluid.layers.elementwise_add(dcy, gj) / grid_y_act
+            cy.gradient = True
+        else:
+            dcx_sig = fluid.layers.sigmoid(dcx)
+            dcy_sig = fluid.layers.sigmoid(dcy)
+            if (abs(scale_x_y - 1.0) > eps):
+                dcx_sig = scale_x_y * dcx_sig - 0.5 * (scale_x_y - 1)
+                dcy_sig = scale_x_y * dcy_sig - 0.5 * (scale_x_y - 1)
+            cx = fluid.layers.elementwise_add(dcx_sig, gi) / grid_x_act
+            cy = fluid.layers.elementwise_add(dcy_sig, gj) / grid_y_act
+
+        anchor_w_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 0]
+        anchor_w_np = np.array(anchor_w_)
+        anchor_w_np = np.reshape(anchor_w_np, newshape=[1, an_num, 1, 1])
+        anchor_w_np = np.tile(
+            anchor_w_np, reps=[batch_size, 1, grid_y, grid_x])
+        anchor_w_max = self._create_tensor_from_numpy(
+            anchor_w_np.astype(np.float32))
+        anchor_w = fluid.layers.crop(x=anchor_w_max, shape=dcx)
+        anchor_w.stop_gradient = True
+        anchor_h_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 1]
+        anchor_h_np = np.array(anchor_h_)
+        anchor_h_np = np.reshape(anchor_h_np, newshape=[1, an_num, 1, 1])
+        anchor_h_np = np.tile(
+            anchor_h_np, reps=[batch_size, 1, grid_y, grid_x])
+        anchor_h_max = self._create_tensor_from_numpy(
+            anchor_h_np.astype(np.float32))
+        anchor_h = fluid.layers.crop(x=anchor_h_max, shape=dcx)
+        anchor_h.stop_gradient = True
+        # e^tw e^th
+        exp_dw = fluid.layers.exp(dw)
+        exp_dh = fluid.layers.exp(dh)
+        pw = fluid.layers.elementwise_mul(exp_dw, anchor_w) / \
+            (grid_x_act * downsample_ratio)
+        ph = fluid.layers.elementwise_mul(exp_dh, anchor_h) / \
+            (grid_y_act * downsample_ratio)
+        if is_gt:
+            exp_dw.stop_gradient = True
+            exp_dh.stop_gradient = True
+            pw.stop_gradient = True
+            ph.stop_gradient = True
+
+        x1 = cx - 0.5 * pw
+        y1 = cy - 0.5 * ph
+        x2 = cx + 0.5 * pw
+        y2 = cy + 0.5 * ph
+        if is_gt:
+            x1.stop_gradient = True
+            y1.stop_gradient = True
+            x2.stop_gradient = True
+            y2.stop_gradient = True
+
+        return x1, y1, x2, y2
+
+    def _create_tensor_from_numpy(self, numpy_array):
+        paddle_array = fluid.layers.create_parameter(
+            attr=ParamAttr(),
+            shape=numpy_array.shape,
+            dtype=numpy_array.dtype,
+            default_initializer=NumpyArrayInitializer(numpy_array))
+        paddle_array.stop_gradient = True
+        return paddle_array

paddlex/cv/nets/detection/loss/yolo_loss.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import fluid
+try:
+    from collections.abc import Sequence
+except Exception:
+    from collections import Sequence
+
+
+class YOLOv3Loss(object):
+    """
+    Combined loss for YOLOv3 network
+
+    Args:
+        batch_size (int): training batch size
+        ignore_thresh (float): threshold to ignore confidence loss
+        label_smooth (bool): whether to use label smoothing
+        use_fine_grained_loss (bool): whether use fine grained YOLOv3 loss
+                                      instead of fluid.layers.yolov3_loss
+    """
+
+    def __init__(self,
+                 batch_size=8,
+                 ignore_thresh=0.7,
+                 label_smooth=True,
+                 use_fine_grained_loss=False,
+                 iou_loss=None,
+                 iou_aware_loss=None,
+                 downsample=[32, 16, 8],
+                 scale_x_y=1.,
+                 match_score=False):
+        self._batch_size = batch_size
+        self._ignore_thresh = ignore_thresh
+        self._label_smooth = label_smooth
+        self._use_fine_grained_loss = use_fine_grained_loss
+        self._iou_loss = iou_loss
+        self._iou_aware_loss = iou_aware_loss
+        self.downsample = downsample
+        self.scale_x_y = scale_x_y
+        self.match_score = match_score
+
+    def __call__(self, outputs, gt_box, gt_label, gt_score, targets, anchors,
+                 anchor_masks, mask_anchors, num_classes, prefix_name):
+        if self._use_fine_grained_loss:
+            return self._get_fine_grained_loss(
+                outputs, targets, gt_box, self._batch_size, num_classes,
+                mask_anchors, self._ignore_thresh)
+        else:
+            losses = []
+            for i, output in enumerate(outputs):
+                scale_x_y = self.scale_x_y if not isinstance(
+                    self.scale_x_y, Sequence) else self.scale_x_y[i]
+                anchor_mask = anchor_masks[i]
+                loss = fluid.layers.yolov3_loss(
+                    x=output,
+                    gt_box=gt_box,
+                    gt_label=gt_label,
+                    gt_score=gt_score,
+                    anchors=anchors,
+                    anchor_mask=anchor_mask,
+                    class_num=num_classes,
+                    ignore_thresh=self._ignore_thresh,
+                    downsample_ratio=self.downsample[i],
+                    use_label_smooth=self._label_smooth,
+                    scale_x_y=scale_x_y,
+                    name=prefix_name + "yolo_loss" + str(i))
+
+                losses.append(fluid.layers.reduce_mean(loss))
+
+            return {'loss': sum(losses)}
+
+    def _get_fine_grained_loss(self,
+                               outputs,
+                               targets,
+                               gt_box,
+                               batch_size,
+                               num_classes,
+                               mask_anchors,
+                               ignore_thresh,
+                               eps=1.e-10):
+        """
+        Calculate fine grained YOLOv3 loss
+
+        Args:
+            outputs ([Variables]): List of Variables, output of backbone stages
+            targets ([Variables]): List of Variables, The targets for yolo
+                                   loss calculatation.
+            gt_box (Variable): The ground-truth boudding boxes.
+            batch_size (int): The training batch size
+            num_classes (int): class num of dataset
+            mask_anchors ([[float]]): list of anchors in each output layer
+            ignore_thresh (float): prediction bbox overlap any gt_box greater
+                                   than ignore_thresh, objectness loss will
+                                   be ignored.
+
+        Returns:
+            Type: dict
+                xy_loss (Variable): YOLOv3 (x, y) coordinates loss
+                wh_loss (Variable): YOLOv3 (w, h) coordinates loss
+                obj_loss (Variable): YOLOv3 objectness score loss
+                cls_loss (Variable): YOLOv3 classification loss
+
+        """
+
+        assert len(outputs) == len(targets), \
+            "YOLOv3 output layer number not equal target number"
+
+        loss_xys, loss_whs, loss_objs, loss_clss = [], [], [], []
+        if self._iou_loss is not None:
+            loss_ious = []
+        if self._iou_aware_loss is not None:
+            loss_iou_awares = []
+        for i, (output, target,
+                anchors) in enumerate(zip(outputs, targets, mask_anchors)):
+            downsample = self.downsample[i]
+            an_num = len(anchors) // 2
+            if self._iou_aware_loss is not None:
+                ioup, output = self._split_ioup(output, an_num, num_classes)
+            x, y, w, h, obj, cls = self._split_output(output, an_num,
+                                                      num_classes)
+            tx, ty, tw, th, tscale, tobj, tcls = self._split_target(target)
+
+            tscale_tobj = tscale * tobj
+
+            scale_x_y = self.scale_x_y if not isinstance(
+                self.scale_x_y, Sequence) else self.scale_x_y[i]
+
+            if (abs(scale_x_y - 1.0) < eps):
+                loss_x = fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x, tx) * tscale_tobj
+                loss_x = fluid.layers.reduce_sum(loss_x, dim=[1, 2, 3])
+                loss_y = fluid.layers.sigmoid_cross_entropy_with_logits(
+                    y, ty) * tscale_tobj
+                loss_y = fluid.layers.reduce_sum(loss_y, dim=[1, 2, 3])
+            else:
+                dx = scale_x_y * fluid.layers.sigmoid(x) - 0.5 * (scale_x_y -
+                                                                  1.0)
+                dy = scale_x_y * fluid.layers.sigmoid(y) - 0.5 * (scale_x_y -
+                                                                  1.0)
+                loss_x = fluid.layers.abs(dx - tx) * tscale_tobj
+                loss_x = fluid.layers.reduce_sum(loss_x, dim=[1, 2, 3])
+                loss_y = fluid.layers.abs(dy - ty) * tscale_tobj
+                loss_y = fluid.layers.reduce_sum(loss_y, dim=[1, 2, 3])
+
+            # NOTE: we refined loss function of (w, h) as L1Loss
+            loss_w = fluid.layers.abs(w - tw) * tscale_tobj
+            loss_w = fluid.layers.reduce_sum(loss_w, dim=[1, 2, 3])
+            loss_h = fluid.layers.abs(h - th) * tscale_tobj
+            loss_h = fluid.layers.reduce_sum(loss_h, dim=[1, 2, 3])
+            if self._iou_loss is not None:
+                loss_iou = self._iou_loss(x, y, w, h, tx, ty, tw, th, anchors,
+                                          downsample, self._batch_size,
+                                          scale_x_y)
+                loss_iou = loss_iou * tscale_tobj
+                loss_iou = fluid.layers.reduce_sum(loss_iou, dim=[1, 2, 3])
+                loss_ious.append(fluid.layers.reduce_mean(loss_iou))
+
+            if self._iou_aware_loss is not None:
+                loss_iou_aware = self._iou_aware_loss(
+                    ioup, x, y, w, h, tx, ty, tw, th, anchors, downsample,
+                    self._batch_size, scale_x_y)
+                loss_iou_aware = loss_iou_aware * tobj
+                loss_iou_aware = fluid.layers.reduce_sum(
+                    loss_iou_aware, dim=[1, 2, 3])
+                loss_iou_awares.append(
+                    fluid.layers.reduce_mean(loss_iou_aware))
+
+            loss_obj_pos, loss_obj_neg = self._calc_obj_loss(
+                output, obj, tobj, gt_box, self._batch_size, anchors,
+                num_classes, downsample, self._ignore_thresh, scale_x_y)
+
+            loss_cls = fluid.layers.sigmoid_cross_entropy_with_logits(cls,
+                                                                      tcls)
+            loss_cls = fluid.layers.elementwise_mul(loss_cls, tobj, axis=0)
+            loss_cls = fluid.layers.reduce_sum(loss_cls, dim=[1, 2, 3, 4])
+
+            loss_xys.append(fluid.layers.reduce_mean(loss_x + loss_y))
+            loss_whs.append(fluid.layers.reduce_mean(loss_w + loss_h))
+            loss_objs.append(
+                fluid.layers.reduce_mean(loss_obj_pos + loss_obj_neg))
+            loss_clss.append(fluid.layers.reduce_mean(loss_cls))
+
+        losses_all = {
+            "loss_xy": fluid.layers.sum(loss_xys),
+            "loss_wh": fluid.layers.sum(loss_whs),
+            "loss_obj": fluid.layers.sum(loss_objs),
+            "loss_cls": fluid.layers.sum(loss_clss),
+        }
+        if self._iou_loss is not None:
+            losses_all["loss_iou"] = fluid.layers.sum(loss_ious)
+        if self._iou_aware_loss is not None:
+            losses_all["loss_iou_aware"] = fluid.layers.sum(loss_iou_awares)
+        return losses_all
+
+    def _split_ioup(self, output, an_num, num_classes):
+        """
+        Split output feature map to output, predicted iou
+        along channel dimension
+        """
+        ioup = fluid.layers.slice(output, axes=[1], starts=[0], ends=[an_num])
+        ioup = fluid.layers.sigmoid(ioup)
+        oriout = fluid.layers.slice(
+            output,
+            axes=[1],
+            starts=[an_num],
+            ends=[an_num * (num_classes + 6)])
+        return (ioup, oriout)
+
+    def _split_output(self, output, an_num, num_classes):
+        """
+        Split output feature map to x, y, w, h, objectness, classification
+        along channel dimension
+        """
+        x = fluid.layers.strided_slice(
+            output,
+            axes=[1],
+            starts=[0],
+            ends=[output.shape[1]],
+            strides=[5 + num_classes])
+        y = fluid.layers.strided_slice(
+            output,
+            axes=[1],
+            starts=[1],
+            ends=[output.shape[1]],
+            strides=[5 + num_classes])
+        w = fluid.layers.strided_slice(
+            output,
+            axes=[1],
+            starts=[2],
+            ends=[output.shape[1]],
+            strides=[5 + num_classes])
+        h = fluid.layers.strided_slice(
+            output,
+            axes=[1],
+            starts=[3],
+            ends=[output.shape[1]],
+            strides=[5 + num_classes])
+        obj = fluid.layers.strided_slice(
+            output,
+            axes=[1],
+            starts=[4],
+            ends=[output.shape[1]],
+            strides=[5 + num_classes])
+        clss = []
+        stride = output.shape[1] // an_num
+        for m in range(an_num):
+            clss.append(
+                fluid.layers.slice(
+                    output,
+                    axes=[1],
+                    starts=[stride * m + 5],
+                    ends=[stride * m + 5 + num_classes]))
+        cls = fluid.layers.transpose(
+            fluid.layers.stack(
+                clss, axis=1), perm=[0, 1, 3, 4, 2])
+
+        return (x, y, w, h, obj, cls)
+
+    def _split_target(self, target):
+        """
+        split target to x, y, w, h, objectness, classification
+        along dimension 2
+
+        target is in shape [N, an_num, 6 + class_num, H, W]
+        """
+        tx = target[:, :, 0, :, :]
+        ty = target[:, :, 1, :, :]
+        tw = target[:, :, 2, :, :]
+        th = target[:, :, 3, :, :]
+
+        tscale = target[:, :, 4, :, :]
+        tobj = target[:, :, 5, :, :]
+
+        tcls = fluid.layers.transpose(
+            target[:, :, 6:, :, :], perm=[0, 1, 3, 4, 2])
+        tcls.stop_gradient = True
+
+        return (tx, ty, tw, th, tscale, tobj, tcls)
+
+    def _calc_obj_loss(self, output, obj, tobj, gt_box, batch_size, anchors,
+                       num_classes, downsample, ignore_thresh, scale_x_y):
+        # A prediction bbox overlap any gt_bbox over ignore_thresh,
+        # objectness loss will be ignored, process as follows:
+
+        # 1. get pred bbox, which is same with YOLOv3 infer mode, use yolo_box here
+        # NOTE: img_size is set as 1.0 to get noramlized pred bbox
+        bbox, prob = fluid.layers.yolo_box(
+            x=output,
+            img_size=fluid.layers.ones(
+                shape=[batch_size, 2], dtype="int32"),
+            anchors=anchors,
+            class_num=num_classes,
+            conf_thresh=0.,
+            downsample_ratio=downsample,
+            clip_bbox=False,
+            scale_x_y=scale_x_y)
+
+        # 2. split pred bbox and gt bbox by sample, calculate IoU between pred bbox
+        #    and gt bbox in each sample
+        if batch_size > 1:
+            preds = fluid.layers.split(bbox, batch_size, dim=0)
+            gts = fluid.layers.split(gt_box, batch_size, dim=0)
+        else:
+            preds = [bbox]
+            gts = [gt_box]
+            probs = [prob]
+        ious = []
+        for pred, gt in zip(preds, gts):
+
+            def box_xywh2xyxy(box):
+                x = box[:, 0]
+                y = box[:, 1]
+                w = box[:, 2]
+                h = box[:, 3]
+                return fluid.layers.stack(
+                    [
+                        x - w / 2.,
+                        y - h / 2.,
+                        x + w / 2.,
+                        y + h / 2.,
+                    ], axis=1)
+
+            pred = fluid.layers.squeeze(pred, axes=[0])
+            gt = box_xywh2xyxy(fluid.layers.squeeze(gt, axes=[0]))
+            ious.append(fluid.layers.iou_similarity(pred, gt))
+
+        iou = fluid.layers.stack(ious, axis=0)
+        # 3. Get iou_mask by IoU between gt bbox and prediction bbox,
+        #    Get obj_mask by tobj(holds gt_score), calculate objectness loss
+
+        max_iou = fluid.layers.reduce_max(iou, dim=-1)
+        iou_mask = fluid.layers.cast(max_iou <= ignore_thresh, dtype="float32")
+        if self.match_score:
+            max_prob = fluid.layers.reduce_max(prob, dim=-1)
+            iou_mask = iou_mask * fluid.layers.cast(
+                max_prob <= 0.25, dtype="float32")
+        output_shape = fluid.layers.shape(output)
+        an_num = len(anchors) // 2
+        iou_mask = fluid.layers.reshape(iou_mask, (-1, an_num, output_shape[2],
+                                                   output_shape[3]))
+        iou_mask.stop_gradient = True
+
+        # NOTE: tobj holds gt_score, obj_mask holds object existence mask
+        obj_mask = fluid.layers.cast(tobj > 0., dtype="float32")
+        obj_mask.stop_gradient = True
+
+        # For positive objectness grids, objectness loss should be calculated
+        # For negative objectness grids, objectness loss is calculated only iou_mask == 1.0
+        loss_obj = fluid.layers.sigmoid_cross_entropy_with_logits(obj,
+                                                                  obj_mask)
+        loss_obj_pos = fluid.layers.reduce_sum(loss_obj * tobj, dim=[1, 2, 3])
+        loss_obj_neg = fluid.layers.reduce_sum(
+            loss_obj * (1.0 - obj_mask) * iou_mask, dim=[1, 2, 3])
+
+        return loss_obj_pos, loss_obj_neg

paddlex/cv/nets/detection/ops.py

+# Copyright (c) 2019 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 numpy as np
+from numbers import Integral
+import math
+import six
+
+import paddle
+from paddle import fluid
+
+
+def DropBlock(input, block_size, keep_prob, is_test):
+    if is_test:
+        return input
+
+    def CalculateGamma(input, block_size, keep_prob):
+        input_shape = fluid.layers.shape(input)
+        feat_shape_tmp = fluid.layers.slice(input_shape, [0], [3], [4])
+        feat_shape_tmp = fluid.layers.cast(feat_shape_tmp, dtype="float32")
+        feat_shape_t = fluid.layers.reshape(feat_shape_tmp, [1, 1, 1, 1])
+        feat_area = fluid.layers.pow(feat_shape_t, factor=2)
+
+        block_shape_t = fluid.layers.fill_constant(
+            shape=[1, 1, 1, 1], value=block_size, dtype='float32')
+        block_area = fluid.layers.pow(block_shape_t, factor=2)
+
+        useful_shape_t = feat_shape_t - block_shape_t + 1
+        useful_area = fluid.layers.pow(useful_shape_t, factor=2)
+
+        upper_t = feat_area * (1 - keep_prob)
+        bottom_t = block_area * useful_area
+        output = upper_t / bottom_t
+        return output
+
+    gamma = CalculateGamma(input, block_size=block_size, keep_prob=keep_prob)
+    input_shape = fluid.layers.shape(input)
+    p = fluid.layers.expand_as(gamma, input)
+
+    input_shape_tmp = fluid.layers.cast(input_shape, dtype="int64")
+    random_matrix = fluid.layers.uniform_random(
+        input_shape_tmp, dtype='float32', min=0.0, max=1.0)
+    one_zero_m = fluid.layers.less_than(random_matrix, p)
+    one_zero_m.stop_gradient = True
+    one_zero_m = fluid.layers.cast(one_zero_m, dtype="float32")
+
+    mask_flag = fluid.layers.pool2d(
+        one_zero_m,
+        pool_size=block_size,
+        pool_type='max',
+        pool_stride=1,
+        pool_padding=block_size // 2)
+    mask = 1.0 - mask_flag
+
+    elem_numel = fluid.layers.reduce_prod(input_shape)
+    elem_numel_m = fluid.layers.cast(elem_numel, dtype="float32")
+    elem_numel_m.stop_gradient = True
+
+    elem_sum = fluid.layers.reduce_sum(mask)
+    elem_sum_m = fluid.layers.cast(elem_sum, dtype="float32")
+    elem_sum_m.stop_gradient = True
+
+    output = input * mask * elem_numel_m / elem_sum_m
+    return output
+
+
+class MultiClassNMS(object):
+    def __init__(self,
+                 score_threshold=.05,
+                 nms_top_k=-1,
+                 keep_top_k=100,
+                 nms_threshold=.5,
+                 normalized=False,
+                 nms_eta=1.0,
+                 background_label=0):
+        super(MultiClassNMS, self).__init__()
+        self.score_threshold = score_threshold
+        self.nms_top_k = nms_top_k
+        self.keep_top_k = keep_top_k
+        self.nms_threshold = nms_threshold
+        self.normalized = normalized
+        self.nms_eta = nms_eta
+        self.background_label = background_label
+
+    def __call__(self, bboxes, scores):
+        return fluid.layers.multiclass_nms(
+            bboxes=bboxes,
+            scores=scores,
+            score_threshold=self.score_threshold,
+            nms_top_k=self.nms_top_k,
+            keep_top_k=self.keep_top_k,
+            normalized=self.normalized,
+            nms_threshold=self.nms_threshold,
+            nms_eta=self.nms_eta,
+            background_label=self.background_label)
+
+
+class MatrixNMS(object):
+    def __init__(self,
+                 score_threshold=.05,
+                 post_threshold=.05,
+                 nms_top_k=-1,
+                 keep_top_k=100,
+                 use_gaussian=False,
+                 gaussian_sigma=2.,
+                 normalized=False,
+                 background_label=0):
+        super(MatrixNMS, self).__init__()
+        self.score_threshold = score_threshold
+        self.post_threshold = post_threshold
+        self.nms_top_k = nms_top_k
+        self.keep_top_k = keep_top_k
+        self.normalized = normalized
+        self.use_gaussian = use_gaussian
+        self.gaussian_sigma = gaussian_sigma
+        self.background_label = background_label
+
+    def __call__(self, bboxes, scores):
+        return paddle.fluid.layers.matrix_nms(
+            bboxes=bboxes,
+            scores=scores,
+            score_threshold=self.score_threshold,
+            post_threshold=self.post_threshold,
+            nms_top_k=self.nms_top_k,
+            keep_top_k=self.keep_top_k,
+            normalized=self.normalized,
+            use_gaussian=self.use_gaussian,
+            gaussian_sigma=self.gaussian_sigma,
+            background_label=self.background_label)
+
+
+class MultiClassSoftNMS(object):
+    def __init__(
+            self,
+            score_threshold=0.01,
+            keep_top_k=300,
+            softnms_sigma=0.5,
+            normalized=False,
+            background_label=0, ):
+        super(MultiClassSoftNMS, self).__init__()
+        self.score_threshold = score_threshold
+        self.keep_top_k = keep_top_k
+        self.softnms_sigma = softnms_sigma
+        self.normalized = normalized
+        self.background_label = background_label
+
+    def __call__(self, bboxes, scores):
+        def create_tmp_var(program, name, dtype, shape, lod_level):
+            return program.current_block().create_var(
+                name=name, dtype=dtype, shape=shape, lod_level=lod_level)
+
+        def _soft_nms_for_cls(dets, sigma, thres):
+            """soft_nms_for_cls"""
+            dets_final = []
+            while len(dets) > 0:
+                maxpos = np.argmax(dets[:, 0])
+                dets_final.append(dets[maxpos].copy())
+                ts, tx1, ty1, tx2, ty2 = dets[maxpos]
+                scores = dets[:, 0]
+                # force remove bbox at maxpos
+                scores[maxpos] = -1
+                x1 = dets[:, 1]
+                y1 = dets[:, 2]
+                x2 = dets[:, 3]
+                y2 = dets[:, 4]
+                eta = 0 if self.normalized else 1
+                areas = (x2 - x1 + eta) * (y2 - y1 + eta)
+                xx1 = np.maximum(tx1, x1)
+                yy1 = np.maximum(ty1, y1)
+                xx2 = np.minimum(tx2, x2)
+                yy2 = np.minimum(ty2, y2)
+                w = np.maximum(0.0, xx2 - xx1 + eta)
+                h = np.maximum(0.0, yy2 - yy1 + eta)
+                inter = w * h
+                ovr = inter / (areas + areas[maxpos] - inter)
+                weight = np.exp(-(ovr * ovr) / sigma)
+                scores = scores * weight
+                idx_keep = np.where(scores >= thres)
+                dets[:, 0] = scores
+                dets = dets[idx_keep]
+            dets_final = np.array(dets_final).reshape(-1, 5)
+            return dets_final
+
+        def _soft_nms(bboxes, scores):
+            class_nums = scores.shape[-1]
+
+            softnms_thres = self.score_threshold
+            softnms_sigma = self.softnms_sigma
+            keep_top_k = self.keep_top_k
+
+            cls_boxes = [[] for _ in range(class_nums)]
+            cls_ids = [[] for _ in range(class_nums)]
+
+            start_idx = 1 if self.background_label == 0 else 0
+            for j in range(start_idx, class_nums):
+                inds = np.where(scores[:, j] >= softnms_thres)[0]
+                scores_j = scores[inds, j]
+                rois_j = bboxes[inds, j, :] if len(
+                    bboxes.shape) > 2 else bboxes[inds, :]
+                dets_j = np.hstack((scores_j[:, np.newaxis], rois_j)).astype(
+                    np.float32, copy=False)
+                cls_rank = np.argsort(-dets_j[:, 0])
+                dets_j = dets_j[cls_rank]
+
+                cls_boxes[j] = _soft_nms_for_cls(
+                    dets_j, sigma=softnms_sigma, thres=softnms_thres)
+                cls_ids[j] = np.array([j] * cls_boxes[j].shape[0]).reshape(-1,
+                                                                           1)
+
+            cls_boxes = np.vstack(cls_boxes[start_idx:])
+            cls_ids = np.vstack(cls_ids[start_idx:])
+            pred_result = np.hstack([cls_ids, cls_boxes])
+
+            # Limit to max_per_image detections **over all classes**
+            image_scores = cls_boxes[:, 0]
+            if len(image_scores) > keep_top_k:
+                image_thresh = np.sort(image_scores)[-keep_top_k]
+                keep = np.where(cls_boxes[:, 0] >= image_thresh)[0]
+                pred_result = pred_result[keep, :]
+
+            return pred_result
+
+        def _batch_softnms(bboxes, scores):
+            batch_offsets = bboxes.lod()
+            bboxes = np.array(bboxes)
+            scores = np.array(scores)
+            out_offsets = [0]
+            pred_res = []
+            if len(batch_offsets) > 0:
+                batch_offset = batch_offsets[0]
+                for i in range(len(batch_offset) - 1):
+                    s, e = batch_offset[i], batch_offset[i + 1]
+                    pred = _soft_nms(bboxes[s:e], scores[s:e])
+                    out_offsets.append(pred.shape[0] + out_offsets[-1])
+                    pred_res.append(pred)
+            else:
+                assert len(bboxes.shape) == 3
+                assert len(scores.shape) == 3
+                for i in range(bboxes.shape[0]):
+                    pred = _soft_nms(bboxes[i], scores[i])
+                    out_offsets.append(pred.shape[0] + out_offsets[-1])
+                    pred_res.append(pred)
+
+            res = fluid.LoDTensor()
+            res.set_lod([out_offsets])
+            if len(pred_res) == 0:
+                pred_res = np.array([[1]], dtype=np.float32)
+            res.set(np.vstack(pred_res).astype(np.float32), fluid.CPUPlace())
+            return res
+
+        pred_result = create_tmp_var(
+            fluid.default_main_program(),
+            name='softnms_pred_result',
+            dtype='float32',
+            shape=[-1, 6],
+            lod_level=1)
+        fluid.layers.py_func(
+            func=_batch_softnms, x=[bboxes, scores], out=pred_result)
+        return pred_result

paddlex/cv/transforms/__init__.py

@@ -91,7 +91,10 @@ def arrange_transforms(model_type, class_name, transforms, mode='train'):
     elif model_type == 'segmenter':
         arrange_transform = seg_transforms.ArrangeSegmenter
     elif model_type == 'detector':
-        arrange_name = 'Arrange{}'.format(class_name)
+        if class_name == "PPYOLO":
+            arrange_name = 'ArrangeYOLOv3'
+        else:
+            arrange_name = 'Arrange{}'.format(class_name)
         arrange_transform = getattr(det_transforms, arrange_name)
     else:
         raise Exception("Unrecognized model type: {}".format(self.model_type))

paddlex/cv/transforms/cls_transforms.py

@@ -46,7 +46,7 @@ class Compose(ClsTransform):
             raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
         self.transforms = transforms
-
+        self.batch_transforms = None
         # 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):

paddlex/cv/transforms/det_transforms.py

@@ -55,6 +55,7 @@ class Compose(DetTransform):
             raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
         self.transforms = transforms
+        self.batch_transforms = None
         self.use_mixup = False
         for t in self.transforms:
             if type(t).__name__ == 'MixupImage':
@@ -1385,3 +1386,187 @@ class ComposedYOLOv3Transforms(Compose):
                         mean=mean, std=std)
             ]
         super(ComposedYOLOv3Transforms, self).__init__(transforms)
+
+
+class BatchRandomShape(DetTransform):
+    """调整图像大小（resize）。
+
+    对batch数据中的每张图像全部resize到random_shapes中任意一个大小。
+    注意：当插值方式为“RANDOM”时，则随机选取一种插值方式进行resize。
+
+    Args:
+        random_shapes (list): resize大小选择列表。
+            默认为[320, 352, 384, 416, 448, 480, 512, 544, 576, 608]。
+        interp (str): resize的插值方式，与opencv的插值方式对应，取值范围为
+            ['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM']。默认为"RANDOM"。
+    Raises:
+        ValueError: 插值方式不在['NEAREST', 'LINEAR', 'CUBIC',
+                    'AREA', 'LANCZOS4', 'RANDOM']中。
+    """
+
+    # The interpolation mode
+    interp_dict = {
+        'NEAREST': cv2.INTER_NEAREST,
+        'LINEAR': cv2.INTER_LINEAR,
+        'CUBIC': cv2.INTER_CUBIC,
+        'AREA': cv2.INTER_AREA,
+        'LANCZOS4': cv2.INTER_LANCZOS4
+    }
+
+    def __init__(
+            self,
+            random_shapes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
+            interp='RANDOM'):
+        if not (interp == "RANDOM" or interp in self.interp_dict):
+            raise ValueError("interp should be one of {}".format(
+                self.interp_dict.keys()))
+        self.random_shapes = random_shapes
+        self.interp = interp
+
+    def __call__(self, batch_data):
+        """
+        Args:
+            batch_data (list): 由与图像相关的各种信息组成的batch数据。
+        Returns:
+            list: 由与图像相关的各种信息组成的batch数据。
+        """
+        shape = np.random.choice(self.random_shapes)
+
+        if self.interp == "RANDOM":
+            interp = random.choice(list(self.interp_dict.keys()))
+        else:
+            interp = self.interp
+        for data_id, data in enumerate(batch_data):
+            data_list = list(data)
+            im = data_list[0]
+            im = np.swapaxes(im, 1, 0)
+            im = np.swapaxes(im, 1, 2)
+            im = resize(im, shape, self.interp_dict[interp])
+            im = np.swapaxes(im, 1, 2)
+            im = np.swapaxes(im, 1, 0)
+            data_list[0] = im
+            batch_data[data_id] = tuple(data_list)
+        return batch_data
+
+
+class GenerateYoloTarget(object):
+    """生成YOLOv3的ground truth（真实标注框）在不同特征层的位置转换信息。
+       该transform只在YOLOv3计算细粒度loss时使用。
+
+       Args:
+           anchors (list|tuple): anchor框的宽度和高度。
+           anchor_masks (list|tuple): 在计算损失时，使用anchor的mask索引。
+           num_classes (int): 类别数。默认为80。
+           iou_thresh (float): iou阈值，当anchor和真实标注框的iou大于该阈值时，计入target。默认为1.0。
+    """
+
+    def __init__(self,
+                 anchors,
+                 anchor_masks,
+                 downsample_ratios,
+                 num_classes=80,
+                 iou_thresh=1.):
+        super(GenerateYoloTarget, self).__init__()
+        self.anchors = anchors
+        self.anchor_masks = anchor_masks
+        self.downsample_ratios = downsample_ratios
+        self.num_classes = num_classes
+        self.iou_thresh = iou_thresh
+
+    def __call__(self, batch_data):
+        """
+        Args:
+            batch_data (list): 由与图像相关的各种信息组成的batch数据。
+        Returns:
+            list: 由与图像相关的各种信息组成的batch数据。
+                  其中，每个数据新添加的字段为：
+                           - target0 (np.ndarray): YOLOv3的ground truth在特征层0的位置转换信息，
+                                   形状为(特征层0的anchor数量, 6+类别数, 特征层0的h, 特征层0的w)。
+                           - target1 (np.ndarray): YOLOv3的ground truth在特征层1的位置转换信息，
+                                   形状为(特征层1的anchor数量, 6+类别数, 特征层1的h, 特征层1的w)。
+                           - ...
+                           -targetn (np.ndarray): YOLOv3的ground truth在特征层n的位置转换信息，
+                                   形状为(特征层n的anchor数量, 6+类别数, 特征层n的h, 特征层n的w)。
+                    n的是大小由anchor_masks的长度决定。
+        """
+        im = batch_data[0][0]
+        h = im.shape[1]
+        w = im.shape[2]
+        an_hw = np.array(self.anchors) / np.array([[w, h]])
+        for data_id, data in enumerate(batch_data):
+            gt_bbox = data[1]
+            gt_class = data[2]
+            gt_score = data[3]
+            im_shape = data[4]
+            origin_h = float(im_shape[0])
+            origin_w = float(im_shape[1])
+            data_list = list(data)
+            for i, (
+                    mask, downsample_ratio
+            ) in enumerate(zip(self.anchor_masks, self.downsample_ratios)):
+                grid_h = int(h / downsample_ratio)
+                grid_w = int(w / downsample_ratio)
+                target = np.zeros(
+                    (len(mask), 6 + self.num_classes, grid_h, grid_w),
+                    dtype=np.float32)
+                for b in range(gt_bbox.shape[0]):
+                    gx = gt_bbox[b, 0] / float(origin_w)
+                    gy = gt_bbox[b, 1] / float(origin_h)
+                    gw = gt_bbox[b, 2] / float(origin_w)
+                    gh = gt_bbox[b, 3] / float(origin_h)
+                    cls = gt_class[b]
+                    score = gt_score[b]
+                    if gw <= 0. or gh <= 0. or score <= 0.:
+                        continue
+                    # find best match anchor index
+                    best_iou = 0.
+                    best_idx = -1
+                    for an_idx in range(an_hw.shape[0]):
+                        iou = jaccard_overlap(
+                            [0., 0., gw, gh],
+                            [0., 0., an_hw[an_idx, 0], an_hw[an_idx, 1]])
+                        if iou > best_iou:
+                            best_iou = iou
+                            best_idx = an_idx
+                    gi = int(gx * grid_w)
+                    gj = int(gy * grid_h)
+                    # gtbox should be regresed in this layes if best match
+                    # anchor index in anchor mask of this layer
+                    if best_idx in mask:
+                        best_n = mask.index(best_idx)
+                        # x, y, w, h, scale
+                        target[best_n, 0, gj, gi] = gx * grid_w - gi
+                        target[best_n, 1, gj, gi] = gy * grid_h - gj
+                        target[best_n, 2, gj, gi] = np.log(
+                            gw * w / self.anchors[best_idx][0])
+                        target[best_n, 3, gj, gi] = np.log(
+                            gh * h / self.anchors[best_idx][1])
+                        target[best_n, 4, gj, gi] = 2.0 - gw * gh
+                        # objectness record gt_score
+                        target[best_n, 5, gj, gi] = score
+                        # classification
+                        target[best_n, 6 + cls, gj, gi] = 1.
+                    # For non-matched anchors, calculate the target if the iou
+                    # between anchor and gt is larger than iou_thresh
+                    if self.iou_thresh < 1:
+                        for idx, mask_i in enumerate(mask):
+                            if mask_i == best_idx: continue
+                            iou = jaccard_overlap(
+                                [0., 0., gw, gh],
+                                [0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
+                            if iou > self.iou_thresh:
+                                # x, y, w, h, scale
+                                target[idx, 0, gj, gi] = gx * grid_w - gi
+                                target[idx, 1, gj, gi] = gy * grid_h - gj
+                                target[idx, 2, gj, gi] = np.log(
+                                    gw * w / self.anchors[mask_i][0])
+                                target[idx, 3, gj, gi] = np.log(
+                                    gh * h / self.anchors[mask_i][1])
+                                target[idx, 4, gj, gi] = 2.0 - gw * gh
+                                # objectness record gt_score
+                                target[idx, 5, gj, gi] = score
+                                # classification
+                                target[idx, 6 + cls, gj, gi] = 1.
+                data_list.append(target)
+            batch_data[data_id] = tuple(data_list)
+        return batch_data

paddlex/cv/transforms/seg_transforms.py

@@ -49,6 +49,7 @@ class Compose(SegTransform):
             raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
         self.transforms = transforms
+        self.batch_transforms = None
         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:

paddlex/det.py

@@ -17,6 +17,7 @@ from . import cv
 
 FasterRCNN = cv.models.FasterRCNN
 YOLOv3 = cv.models.YOLOv3
+PPYOLO = cv.models.PPYOLO
 MaskRCNN = cv.models.MaskRCNN
 transforms = cv.transforms.det_transforms
 visualize = cv.models.utils.visualize.visualize_detection

tutorials/train/object_detection/ppyolo.py

+# 环境变量配置，用于控制是否使用GPU
+# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+from paddlex.det import transforms
+import paddlex as pdx
+
+# 下载和解压昆虫检测数据集
+insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
+pdx.utils.download_and_decompress(insect_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/det_transforms.html
+train_transforms = transforms.Compose([
+    transforms.MixupImage(mixup_epoch=250), transforms.RandomDistort(),
+    transforms.RandomExpand(), transforms.RandomCrop(), transforms.Resize(
+        target_size=608, interp='RANDOM'), transforms.RandomHorizontalFlip(),
+    transforms.Normalize()
+])
+
+eval_transforms = transforms.Compose([
+    transforms.Resize(
+        target_size=608, interp='CUBIC'), transforms.Normalize()
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-vocdetection
+train_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/train_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/val_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=eval_transforms)
+
+# 初始化模型，并进行训练
+# 可使用VisualDL查看训练指标，参考https://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html
+num_classes = len(train_dataset.labels)
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#paddlex-det-yolov3
+model = pdx.det.PPYOLO(num_classes=num_classes)
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#train
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=270,
+    train_dataset=train_dataset,
+    train_batch_size=8,
+    eval_dataset=eval_dataset,
+    learning_rate=0.000125,
+    lr_decay_epochs=[210, 240],
+    save_dir='output/ppyolo',
+    use_vdl=True)