add yolov3_darknet_pascalvoc

demo/detection/yolov3_darknet53_pascalvoc/predict.py

+import paddle
+import paddlehub as hub
+
+if __name__ == '__main__':
+    place = paddle.CUDAPlace(0)
+    paddle.disable_static()
+    model = model = hub.Module(name='yolov3_darknet53_pascalvoc', is_train=False)
+    model.eval()
+    model.predict(imgpath="/PATH/TO/IMAGE", filelist="/PATH/TO/JSON/FILE")

demo/detection/yolov3_darknet53_pascalvoc/train.py

+import paddle
+import paddlehub as hub
+import paddle.nn as nn
+from paddlehub.finetune.trainer import Trainer
+from paddlehub.datasets.pascalvoc import DetectionData
+from paddlehub.process.transforms import DetectTrainReader, DetectTestReader
+
+if __name__ == "__main__":
+    place = paddle.CUDAPlace(0)
+    paddle.disable_static()
+    is_train = True
+    if is_train:
+        transform = DetectTrainReader()
+        train_reader = DetectionData(transform)
+    else:
+        transform = DetectTestReader()
+        test_reader = DetectionData(transform)
+    model = hub.Module(name='yolov3_darknet53_pascalvoc')
+    model.train()
+    optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
+    trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
+    trainer.train(train_reader, epochs=5, batch_size=4, eval_dataset=train_reader, log_interval=1, save_interval=1)

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

+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import Normal
+
+
+class ConvBNLayer(nn.Layer):
+    """Basic block for Darknet"""
+    def __init__(self,
+                 ch_in: int,
+                 ch_out: int,
+                 filter_size: int = 3,
+                 stride: int = 1,
+                 groups: int = 1,
+                 padding: int = 0,
+                 act: str = 'leakly',
+                 is_test: bool = False):
+        super(ConvBNLayer, self).__init__()
+
+        self.conv = nn.Conv2d(ch_in,
+                              ch_out,
+                              filter_size,
+                              padding=padding,
+                              stride=stride,
+                              groups=groups,
+                              weight_attr=paddle.ParamAttr(initializer=Normal(0., 0.02)),
+                              bias_attr=False)
+
+        self.batch_norm = nn.BatchNorm(num_channels=ch_out,
+                                       is_test=is_test,
+                                       param_attr=paddle.ParamAttr(initializer=Normal(0., 0.02),
+                                                                   regularizer=L2Decay(0.)))
+        self.act = act
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        out = self.conv(inputs)
+        out = self.batch_norm(out)
+        if self.act == "leakly":
+            out = F.leaky_relu(x=out, negative_slope=0.1)
+        return out
+
+
+class DownSample(nn.Layer):
+    """Downsample block for Darknet"""
+    def __init__(self,
+                 ch_in: int,
+                 ch_out: int,
+                 filter_size: int = 3,
+                 stride: int = 2,
+                 padding: int = 1,
+                 is_test: bool = False):
+        super(DownSample, self).__init__()
+
+        self.conv_bn_layer = ConvBNLayer(ch_in=ch_in,
+                                         ch_out=ch_out,
+                                         filter_size=filter_size,
+                                         stride=stride,
+                                         padding=padding,
+                                         is_test=is_test)
+        self.ch_out = ch_out
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        out = self.conv_bn_layer(inputs)
+        return out
+
+
+class BasicBlock(nn.Layer):
+    """Basic residual block for Darknet"""
+    def __init__(self, ch_in: int, ch_out: int, is_test: bool = False):
+        super(BasicBlock, self).__init__()
+
+        self.conv1 = ConvBNLayer(ch_in=ch_in, ch_out=ch_out, filter_size=1, stride=1, padding=0, is_test=is_test)
+        self.conv2 = ConvBNLayer(ch_in=ch_out, ch_out=ch_out * 2, filter_size=3, stride=1, padding=1, is_test=is_test)
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        conv1 = self.conv1(inputs)
+        conv2 = self.conv2(conv1)
+        out = paddle.elementwise_add(x=inputs, y=conv2, act=None)
+        return out
+
+
+class LayerWarp(nn.Layer):
+    """Warp layer composed by basic residual blocks"""
+    def __init__(self, ch_in: int, ch_out: int, count: int, is_test: bool = False):
+        super(LayerWarp, self).__init__()
+        self.basicblock0 = BasicBlock(ch_in, ch_out, is_test=is_test)
+        self.res_out_list = []
+        for i in range(1, count):
+            res_out = self.add_sublayer("basic_block_%d" % (i), BasicBlock(ch_out * 2, ch_out, is_test=is_test))
+            self.res_out_list.append(res_out)
+        self.ch_out = ch_out
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        y = self.basicblock0(inputs)
+        for basic_block_i in self.res_out_list:
+            y = basic_block_i(y)
+        return y
+
+
+DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
+
+
+class DarkNet53_conv_body(nn.Layer):
+    """Darknet53
+    Args:
+        ch_in(int): Input channels, default is 3.
+        is_test (bool): Set the test mode, default is True.
+    """
+    def __init__(self, ch_in: int = 3, is_test: bool = False):
+        super(DarkNet53_conv_body, self).__init__()
+        self.stages = DarkNet_cfg[53]
+        self.stages = self.stages[0:5]
+
+        self.conv0 = ConvBNLayer(ch_in=ch_in, ch_out=32, filter_size=3, stride=1, padding=1, is_test=is_test)
+
+        self.downsample0 = DownSample(ch_in=32, ch_out=32 * 2, is_test=is_test)
+        self.darknet53_conv_block_list = []
+        self.downsample_list = []
+        ch_in = [64, 128, 256, 512, 1024]
+
+        for i, stage in enumerate(self.stages):
+            conv_block = self.add_sublayer("stage_%d" % (i),
+                                           LayerWarp(int(ch_in[i]), 32 * (2**i), stage, is_test=is_test))
+            self.darknet53_conv_block_list.append(conv_block)
+
+        for i in range(len(self.stages) - 1):
+            downsample = self.add_sublayer(
+                "stage_%d_downsample" % i, DownSample(ch_in=32 * (2**(i + 1)),
+                                                      ch_out=32 * (2**(i + 2)),
+                                                      is_test=is_test))
+            self.downsample_list.append(downsample)
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        out = self.conv0(inputs)
+        out = self.downsample0(out)
+        blocks = []
+        for i, conv_block_i in enumerate(self.darknet53_conv_block_list):
+            out = conv_block_i(out)
+            blocks.append(out)
+            if i < len(self.stages) - 1:
+                out = self.downsample_list[i](out)
+        return blocks[-1:-4:-1]

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

+import os
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.initializer import Normal, Constant
+from paddle.regularizer import L2Decay
+from pycocotools.coco import COCO
+
+from darknet import DarkNet53_conv_body
+from darknet import ConvBNLayer
+from paddlehub.module.cv_module import Yolov3Module
+from paddlehub.process.transforms import DetectTrainReader, DetectTestReader
+from paddlehub.module.module import moduleinfo
+
+
+class YoloDetectionBlock(nn.Layer):
+    """Basic block for Yolov3"""
+    def __init__(self, ch_in: int, channel: int, is_test: bool = True):
+        super(YoloDetectionBlock, self).__init__()
+
+        assert channel % 2 == 0, \
+            "channel {} cannot be divided by 2".format(channel)
+
+        self.conv0 = ConvBNLayer(ch_in=ch_in, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test)
+        self.conv1 = ConvBNLayer(ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test)
+        self.conv2 = ConvBNLayer(ch_in=channel * 2, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test)
+        self.conv3 = ConvBNLayer(ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test)
+        self.route = ConvBNLayer(ch_in=channel * 2, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test)
+        self.tip = ConvBNLayer(ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test)
+
+    def forward(self, inputs):
+        out = self.conv0(inputs)
+        out = self.conv1(out)
+        out = self.conv2(out)
+        out = self.conv3(out)
+        route = self.route(out)
+        tip = self.tip(route)
+        return route, tip
+
+
+class Upsample(nn.Layer):
+    """Upsample block for Yolov3"""
+    def __init__(self, scale: int = 2):
+        super(Upsample, self).__init__()
+        self.scale = scale
+
+    def forward(self, inputs: paddle.Tensor):
+        shape_nchw = paddle.to_tensor(inputs.shape)
+        shape_hw = paddle.slice(shape_nchw, axes=[0], starts=[2], ends=[4])
+        shape_hw.stop_gradient = True
+        in_shape = paddle.cast(shape_hw, dtype='int32')
+        out_shape = in_shape * self.scale
+        out_shape.stop_gradient = True
+        out = F.resize_nearest(input=inputs, scale=self.scale, actual_shape=out_shape)
+        return out
+
+
+@moduleinfo(name="yolov3_darknet53_pascalvoc",
+            type="CV/image_editing",
+            author="paddlepaddle",
+            author_email="",
+            summary="Yolov3 is a detection model, this module is trained with VOC dataset.",
+            version="1.0.0",
+            meta=Yolov3Module)
+class YOLOv3(nn.Layer):
+    """YOLOV3 for detection
+
+    Args:
+        ch_in(int): Input channels, default is 3.
+        class_num(int): Categories for detection,if dataset is voc, class_num is 20.
+        ignore_thresh(float): The ignore threshold to ignore confidence loss.
+        valid_thresh(float): Threshold to filter out bounding boxes with low confidence score.
+        nms_topk(int): Maximum number of detections to be kept according to the confidences after the filtering
+                       detections based on score_threshold.
+        nms_posk(int): Number of total bboxes to be kept per image after NMS step. -1 means keeping all bboxes after NMS
+                       step.
+        nms_thresh (float): The threshold to be used in NMS. Default: 0.3.
+        is_train (bool): Set the train mode, default is True.
+        load_checkpoint(str): Whether to load checkpoint.
+    """
+    def __init__(self,
+                 ch_in: int = 3,
+                 class_num: int = 20,
+                 ignore_thresh: float = 0.7,
+                 valid_thresh: float = 0.005,
+                 nms_topk: int = 400,
+                 nms_posk: int = 100,
+                 nms_thresh: float = 0.45,
+                 is_train: bool = True,
+                 load_checkpoint: str = None):
+        super(YOLOv3, self).__init__()
+
+        self.is_train = is_train
+        self.block = DarkNet53_conv_body(ch_in=ch_in, is_test=not self.is_train)
+        self.block_outputs = []
+        self.yolo_blocks = []
+        self.route_blocks_2 = []
+        self.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+        self.anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
+        self.class_num = class_num
+        self.ignore_thresh = ignore_thresh
+        self.valid_thresh = valid_thresh
+        self.nms_topk = nms_topk
+        self.nms_posk = nms_posk
+        self.nms_thresh = nms_thresh
+        ch_in_list = [1024, 768, 384]
+
+        for i in range(3):
+            yolo_block = self.add_sublayer(
+                "yolo_detecton_block_%d" % (i),
+                YoloDetectionBlock(ch_in_list[i], channel=512 // (2**i), is_test=not self.is_train))
+            self.yolo_blocks.append(yolo_block)
+
+            num_filters = len(self.anchor_masks[i]) * (self.class_num + 5)
+            block_out = self.add_sublayer(
+                "block_out_%d" % (i),
+                nn.Conv2d(1024 // (2**i),
+                          num_filters,
+                          1,
+                          stride=1,
+                          padding=0,
+                          weight_attr=paddle.ParamAttr(initializer=Normal(0., 0.02)),
+                          bias_attr=paddle.ParamAttr(initializer=Constant(0.0), regularizer=L2Decay(0.))))
+            self.block_outputs.append(block_out)
+
+            if i < 2:
+                route = self.add_sublayer(
+                    "route2_%d" % i,
+                    ConvBNLayer(ch_in=512 // (2**i),
+                                ch_out=256 // (2**i),
+                                filter_size=1,
+                                stride=1,
+                                padding=0,
+                                is_test=(not self.is_train)))
+                self.route_blocks_2.append(route)
+            self.upsample = Upsample()
+
+        if load_checkpoint is not None:
+            model_dict = paddle.load(load_checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load custom checkpoint success")
+
+        else:
+            checkpoint = os.path.join(self.directory, 'yolov3_70000.pdparams')
+            if not os.path.exists(checkpoint):
+                os.system(
+                    'wget https://bj.bcebos.com/paddlehub/model/image/object_detection/yolov3_70000.pdparams -O ' \
+                    + checkpoint)
+            model_dict = paddle.load(checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load pretrained checkpoint success")
+
+    def transform(self, img: paddle.Tensor, size: int):
+        if self.is_train:
+            transforms = DetectTrainReader()
+        else:
+            transforms = DetectTestReader()
+        return transforms(img, size)
+
+    def get_label_infos(self, file_list: str):
+        self.COCO = COCO(file_list)
+        label_names = []
+        categories = self.COCO.loadCats(self.COCO.getCatIds())
+        for category in categories:
+            label_names.append(category['name'])
+        return label_names
+
+    def forward(self,
+                inputs: paddle.Tensor,
+                gtbox: paddle.Tensor = None,
+                gtlabel: paddle.Tensor = None,
+                gtscore: paddle.Tensor = None,
+                im_shape: paddle.Tensor = None):
+
+        self.gtbox = gtbox
+        self.gtlabel = gtlabel
+        self.gtscore = gtscore
+        self.im_shape = im_shape
+        self.outputs = []
+        self.boxes = []
+        self.scores = []
+        self.losses = []
+        self.pred = []
+        self.downsample = 32
+        blocks = self.block(inputs)
+        route = None
+        for i, block in enumerate(blocks):
+            if i > 0:
+                block = paddle.concat([route, block], axis=1)
+            route, tip = self.yolo_blocks[i](block)
+            block_out = self.block_outputs[i](tip)
+            self.outputs.append(block_out)
+            if i < 2:
+                route = self.route_blocks_2[i](route)
+                route = self.upsample(route)
+
+        for i, out in enumerate(self.outputs):
+            anchor_mask = self.anchor_masks[i]
+
+            if self.is_train:
+                loss = F.yolov3_loss(x=out,
+                                     gt_box=self.gtbox,
+                                     gt_label=self.gtlabel,
+                                     gt_score=self.gtscore,
+                                     anchors=self.anchors,
+                                     anchor_mask=anchor_mask,
+                                     class_num=self.class_num,
+                                     ignore_thresh=self.ignore_thresh,
+                                     downsample_ratio=self.downsample,
+                                     use_label_smooth=False)
+            else:
+                loss = paddle.to_tensor(0.0)
+            self.losses.append(paddle.reduce_mean(loss))
+
+            mask_anchors = []
+            for m in anchor_mask:
+                mask_anchors.append((self.anchors[2 * m]))
+                mask_anchors.append(self.anchors[2 * m + 1])
+
+            boxes, scores = F.yolo_box(x=out,
+                                       img_size=self.im_shape,
+                                       anchors=mask_anchors,
+                                       class_num=self.class_num,
+                                       conf_thresh=self.valid_thresh,
+                                       downsample_ratio=self.downsample,
+                                       name="yolo_box" + str(i))
+
+            self.boxes.append(boxes)
+            self.scores.append(paddle.transpose(scores, perm=[0, 2, 1]))
+            self.downsample //= 2
+
+        for i in range(self.boxes[0].shape[0]):
+            yolo_boxes = paddle.unsqueeze(paddle.concat([self.boxes[0][i], self.boxes[1][i], self.boxes[2][i]], axis=0),
+                                          0)
+            yolo_scores = paddle.unsqueeze(
+                paddle.concat([self.scores[0][i], self.scores[1][i], self.scores[2][i]], axis=1), 0)
+            pred = F.multiclass_nms(bboxes=yolo_boxes,
+                                    scores=yolo_scores,
+                                    score_threshold=self.valid_thresh,
+                                    nms_top_k=self.nms_topk,
+                                    keep_top_k=self.nms_posk,
+                                    nms_threshold=self.nms_thresh,
+                                    background_label=-1)
+            self.pred.append(pred)
+
+        return sum(self.losses), self.pred

paddlehub/datasets/pascalvoc.py

+# coding:utf-8
+# 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 typing import Callable
+
+import paddle
+from paddlehub.env import DATA_HOME
+from pycocotools.coco import COCO
+
+from paddlehub.process.transforms import DetectCatagory, ParseImages
+
+
+class DetectionData(paddle.io.Dataset):
+    """
+    Dataset for image detection.
+
+    Args:
+       transform(callmethod) : The method of preprocess images.
+       mode(str): The mode for preparing dataset.
+
+    Returns:
+        DataSet: An iterable object for data iterating
+    """
+    def __init__(self, transform: Callable, size: int = 416, mode: str = 'train'):
+        self.mode = mode
+        self.transform = transform
+        self.size = size
+
+        if self.mode == 'train':
+            train_file_list = 'annotations/instances_train2017.json'
+            train_data_dir = 'train2017'
+            self.train_file_list = os.path.join(DATA_HOME, 'voc', train_file_list)
+            self.train_data_dir = os.path.join(DATA_HOME, 'voc', train_data_dir)
+            self.COCO = COCO(self.train_file_list)
+            self.img_dir = self.train_data_dir
+
+        elif self.mode == 'test':
+            val_file_list = 'annotations/instances_val2017.json'
+            val_data_dir = 'val2017'
+            self.val_file_list = os.path.join(DATA_HOME, 'voc', val_file_list)
+            self.val_data_dir = os.path.join(DATA_HOME, 'voc', val_data_dir)
+            self.COCO = COCO(self.val_file_list)
+            self.img_dir = self.val_data_dir
+
+        parse_dataset_catagory = DetectCatagory(self.COCO, self.img_dir)
+        self.label_names, self.label_ids, self.category_to_id_map = parse_dataset_catagory()
+        parse_images = ParseImages(self.COCO, self.mode, self.img_dir, self.category_to_id_map)
+        self.data = parse_images()
+
+    def __getitem__(self, idx: int):
+        if self.mode == "train":
+            img = self.data[idx]
+            out_img, gt_boxes, gt_labels, gt_scores = self.transform(img, 416)
+            return out_img, gt_boxes, gt_labels, gt_scores
+        elif self.mode == "test":
+            img = self.data[idx]
+            out_img, id, (h, w) = self.transform(img)
+            return out_img, id, (h, w)
+
+    def __len__(self):
+        return len(self.data)

paddlehub/module/cv_module.py

@@ -26,7 +26,7 @@ from PIL import Image
 
 from paddlehub.module.module import serving, RunModule
 from paddlehub.utils.utils import base64_to_cv2
-from paddlehub.process.transforms import ConvertColorSpace, ColorPostprocess, Resize
+from paddlehub.process.transforms import ConvertColorSpace, ColorPostprocess, Resize, BoxTool
 
 
 class ImageServing(object):
@@ -103,11 +103,11 @@ class ImageColorizeModule(RunModule, ImageServing):
     def training_step(self, batch: int, batch_idx: int) -> dict:
         '''
         One step for training, which should be called as forward computation.
-        
+
         Args:
             batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
             batch_idx(int): The index of batch.
-            
+
         Returns:
             results(dict) : The model outputs, such as loss and metrics.
         '''
@@ -116,22 +116,22 @@ class ImageColorizeModule(RunModule, ImageServing):
     def validation_step(self, batch: int, batch_idx: int) -> dict:
         '''
         One step for validation, which should be called as forward computation.
-        
+
         Args:
             batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
             batch_idx(int): The index of batch.
-            
+
         Returns:
             results(dict) : The model outputs, such as metrics.
         '''
         out_class, out_reg = self(batch[0], batch[1], batch[2])
-        
+
         criterionCE = nn.loss.CrossEntropyLoss()
         loss_ce = criterionCE(out_class, batch[4][:, 0, :, :])
         loss_G_L1_reg = paddle.sum(paddle.abs(batch[3] - out_reg), axis=1, keepdim=True)
         loss_G_L1_reg = paddle.mean(loss_G_L1_reg)
         loss = loss_ce + loss_G_L1_reg
-        
+
         visual_ret = OrderedDict()
         psnrs = []
         lab2rgb = ConvertColorSpace(mode='LAB2RGB')
@@ -141,7 +141,7 @@ class ImageColorizeModule(RunModule, ImageServing):
             visual_ret['real'] = process(real)
             fake = lab2rgb(np.concatenate((batch[0].numpy(), out_reg.numpy()), axis=1))[i]
             visual_ret['fake_reg'] = process(fake)
-            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0) ** 2)
+            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0)**2)
             psnr_value = 20 * np.log10(255. / np.sqrt(mse))
             psnrs.append(psnr_value)
         psnr = paddle.to_variable(np.array(psnrs))
@@ -150,12 +150,12 @@ class ImageColorizeModule(RunModule, ImageServing):
     def predict(self, images: str, visualization: bool = True, save_path: str = 'result'):
         '''
         Colorize images
-        
+
         Args:
             images(str) : Images path to be colorized.
             visualization(bool): Whether to save colorized images.
             save_path(str) : Path to save colorized images.
-            
+
         Returns:
             results(list[dict]) : The prediction result of each input image
         '''
@@ -177,7 +177,7 @@ class ImageColorizeModule(RunModule, ImageServing):
             visual_ret['real'] = resize(process(real))
             fake = lab2rgb(np.concatenate((im['A'], out_reg.numpy()), axis=1))[i]
             visual_ret['fake_reg'] = resize(process(fake))
-            
+
             if visualization:
                 fake_name = "fake_" + str(time.time()) + ".png"
                 if not os.path.exists(save_path):
@@ -185,8 +185,107 @@ class ImageColorizeModule(RunModule, ImageServing):
                 fake_path = os.path.join(save_path, fake_name)
                 visual_gray = Image.fromarray(visual_ret['fake_reg'])
                 visual_gray.save(fake_path)
-                
-            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0) ** 2)
+
+            mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0)**2)
             psnr_value = 20 * np.log10(255. / np.sqrt(mse))
             result.append(visual_ret)
         return result
+
+
+class Yolov3Module(RunModule, ImageServing):
+    def training_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for training, which should be called as forward computation.
+
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images, ground truth boxes, labels and scores.
+            batch_idx(int): The index of batch.
+
+        Returns:
+            results(dict): The model outputs, such as loss.
+        '''
+
+        return self.validation_step(batch, batch_idx)
+
+    def validation_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for validation, which should be called as forward computation.
+
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images, ground truth boxes, labels and scores.
+            batch_idx(int): The index of batch.
+
+        Returns:
+            results(dict) : The model outputs, such as metrics.
+        '''
+        ious = []
+        boxtool = BoxTool()
+        img = batch[0].astype('float32')
+        B, C, W, H = img.shape
+        im_shape = np.array([(W, H)] * B).astype('int32')
+        im_shape = paddle.to_tensor(im_shape)
+
+        gt_box = batch[1].astype('float32')
+        gt_label = batch[2].astype('int32')
+        gt_score = batch[3].astype("float32")
+        loss, pred = self(img, gt_box, gt_label, gt_score, im_shape)
+
+        for i in range(len(pred)):
+            bboxes = pred[i].numpy()
+            labels = bboxes[:, 0].astype('int32')
+            scores = bboxes[:, 1].astype('float32')
+            boxes = bboxes[:, 2:].astype('float32')
+            iou = []
+
+            for j, (box, score, label) in enumerate(zip(boxes, scores, labels)):
+                x1, y1, x2, y2 = box
+                w = x2 - x1 + 1
+                h = y2 - y1 + 1
+                bbox = [x1, y1, w, h]
+                bbox = np.expand_dims(boxtool.coco_anno_box_to_center_relative(bbox, H, W), 0)
+                gt = gt_box[i].numpy()
+                iou.append(max(boxtool.box_iou_xywh(bbox, gt)))
+
+            ious.append(max(iou))
+        ious = paddle.to_tensor(np.array(ious))
+
+        return {'loss': loss, 'metrics': {'iou': ious}}
+
+    def predict(self, imgpath: str, filelist: str, visualization: bool = True, save_path: str = 'result'):
+        '''
+        Detect images
+
+        Args:
+            imgpath(str): Image path .
+            filelist(str): Path to get label name.
+            visualization(bool): Whether to save result image.
+            save_path(str) : Path to save detected images.
+
+        Returns:
+            boxes(np.ndarray): Predict box information.
+            scores(np.ndarray): Predict score.
+            labels(np.ndarray): Predict labels.
+        '''
+        boxtool = BoxTool()
+        img = {}
+        img['image'] = imgpath
+        img['id'] = 0
+        im, im_id, im_shape = self.transform(img, 416)
+        label_names = self.get_label_infos(filelist)
+        img_data = np.array([im]).astype('float32')
+        img_data = paddle.to_tensor(img_data)
+        im_shape = np.array([im_shape]).astype('int32')
+        im_shape = paddle.to_tensor(im_shape)
+
+        output, pred = self(img_data, None, None, None, im_shape)
+
+        for i in range(len(pred)):
+            bboxes = pred[i].numpy()
+            labels = bboxes[:, 0].astype('int32')
+            scores = bboxes[:, 1].astype('float32')
+            boxes = bboxes[:, 2:].astype('float32')
+
+            if visualization:
+                boxtool.draw_boxes_on_image(imgpath, boxes, scores, labels, label_names, 0.5)
+
+        return boxes, scores, labels