add ci test case for yolov3

tests/st/model_zoo_tests/yolov3/src/config.py

@@ -0,0 +1,49 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""Config parameters for YOLOv3 models."""
+
+
+class ConfigYOLOV3ResNet18:
+    """
+    Config parameters for YOLOv3.
+
+    Examples:
+        ConfigYoloV3ResNet18.
+    """
+    img_shape = [352, 640]
+    feature_shape = [32, 3, 352, 640]
+    num_classes = 2
+    nms_max_num = 50
+
+    backbone_input_shape = [64, 64, 128, 256]
+    backbone_shape = [64, 128, 256, 512]
+    backbone_layers = [2, 2, 2, 2]
+    backbone_stride = [1, 2, 2, 2]
+
+    ignore_threshold = 0.5
+    obj_threshold = 0.3
+    nms_threshold = 0.4
+
+    anchor_scales = [(10, 13),
+                     (16, 30),
+                     (33, 23),
+                     (30, 61),
+                     (62, 45),
+                     (59, 119),
+                     (116, 90),
+                     (156, 198),
+                     (163, 326)]
+    out_channel = int(len(anchor_scales) / 3 * (num_classes + 5))

tests/st/model_zoo_tests/yolov3/src/dataset.py

@@ -0,0 +1,318 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""YOLOv3 dataset"""
+from __future__ import division
+
+import os
+import numpy as np
+from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
+from PIL import Image
+import mindspore.dataset as de
+from mindspore.mindrecord import FileWriter
+import mindspore.dataset.transforms.vision.c_transforms as C
+from src.config import ConfigYOLOV3ResNet18
+
+iter_cnt = 0
+_NUM_BOXES = 50
+np.random.seed(1)
+de.config.set_seed(1)
+
+def preprocess_fn(image, box, is_training):
+    """Preprocess function for dataset."""
+    config_anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 163, 326]
+    anchors = np.array([float(x) for x in config_anchors]).reshape(-1, 2)
+    do_hsv = False
+    max_boxes = 20
+    num_classes = ConfigYOLOV3ResNet18.num_classes
+
+    def _rand(a=0., b=1.):
+        return np.random.rand() * (b - a) + a
+
+    def _preprocess_true_boxes(true_boxes, anchors, in_shape=None):
+        """Get true boxes."""
+        num_layers = anchors.shape[0] // 3
+        anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+        true_boxes = np.array(true_boxes, dtype='float32')
+        # input_shape = np.array([in_shape, in_shape], dtype='int32')
+        input_shape = np.array(in_shape, dtype='int32')
+        boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
+        boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
+        true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
+        true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
+
+        grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
+        y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]),
+                            5 + num_classes), dtype='float32') for l in range(num_layers)]
+
+        anchors = np.expand_dims(anchors, 0)
+        anchors_max = anchors / 2.
+        anchors_min = -anchors_max
+
+        valid_mask = boxes_wh[..., 0] >= 1
+
+        wh = boxes_wh[valid_mask]
+
+
+        if len(wh) >= 1:
+            wh = np.expand_dims(wh, -2)
+            boxes_max = wh / 2.
+            boxes_min = -boxes_max
+
+            intersect_min = np.maximum(boxes_min, anchors_min)
+            intersect_max = np.minimum(boxes_max, anchors_max)
+            intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
+            intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
+            box_area = wh[..., 0] * wh[..., 1]
+            anchor_area = anchors[..., 0] * anchors[..., 1]
+            iou = intersect_area / (box_area + anchor_area - intersect_area)
+
+            best_anchor = np.argmax(iou, axis=-1)
+            for t, n in enumerate(best_anchor):
+                for l in range(num_layers):
+                    if n in anchor_mask[l]:
+                        i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')
+                        j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')
+                        k = anchor_mask[l].index(n)
+
+                        c = true_boxes[t, 4].astype('int32')
+                        y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
+                        y_true[l][j, i, k, 4] = 1.
+                        y_true[l][j, i, k, 5 + c] = 1.
+
+        pad_gt_box0 = np.zeros(shape=[50, 4], dtype=np.float32)
+        pad_gt_box1 = np.zeros(shape=[50, 4], dtype=np.float32)
+        pad_gt_box2 = np.zeros(shape=[50, 4], dtype=np.float32)
+
+        mask0 = np.reshape(y_true[0][..., 4:5], [-1])
+        gt_box0 = np.reshape(y_true[0][..., 0:4], [-1, 4])
+        gt_box0 = gt_box0[mask0 == 1]
+        pad_gt_box0[:gt_box0.shape[0]] = gt_box0
+
+        mask1 = np.reshape(y_true[1][..., 4:5], [-1])
+        gt_box1 = np.reshape(y_true[1][..., 0:4], [-1, 4])
+        gt_box1 = gt_box1[mask1 == 1]
+        pad_gt_box1[:gt_box1.shape[0]] = gt_box1
+
+        mask2 = np.reshape(y_true[2][..., 4:5], [-1])
+        gt_box2 = np.reshape(y_true[2][..., 0:4], [-1, 4])
+        gt_box2 = gt_box2[mask2 == 1]
+        pad_gt_box2[:gt_box2.shape[0]] = gt_box2
+
+        return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2
+
+    def _infer_data(img_data, input_shape, box):
+        w, h = img_data.size
+        input_h, input_w = input_shape
+        scale = min(float(input_w) / float(w), float(input_h) / float(h))
+        nw = int(w * scale)
+        nh = int(h * scale)
+        img_data = img_data.resize((nw, nh), Image.BICUBIC)
+
+        new_image = np.zeros((input_h, input_w, 3), np.float32)
+        new_image.fill(128)
+        img_data = np.array(img_data)
+        if len(img_data.shape) == 2:
+            img_data = np.expand_dims(img_data, axis=-1)
+            img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+
+        dh = int((input_h - nh) / 2)
+        dw = int((input_w - nw) / 2)
+        new_image[dh:(nh + dh), dw:(nw + dw), :] = img_data
+        new_image /= 255.
+        new_image = np.transpose(new_image, (2, 0, 1))
+        new_image = np.expand_dims(new_image, 0)
+        return new_image, np.array([h, w], np.float32), box
+
+    def _data_aug(image, box, is_training, jitter=0.3, hue=0.1, sat=1.5, val=1.5, image_size=(352, 640)):
+        """Data augmentation function."""
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+
+        iw, ih = image.size
+        ori_image_shape = np.array([ih, iw], np.int32)
+        h, w = image_size
+
+        if not is_training:
+            return _infer_data(image, image_size, box)
+
+        flip = _rand() < .5
+        # correct boxes
+        box_data = np.zeros((max_boxes, 5))
+        while True:
+            # Prevent the situation that all boxes are eliminated
+            new_ar = float(w) / float(h) * _rand(1 - jitter, 1 + jitter) / \
+                     _rand(1 - jitter, 1 + jitter)
+            scale = _rand(0.25, 2)
+
+            if new_ar < 1:
+                nh = int(scale * h)
+                nw = int(nh * new_ar)
+            else:
+                nw = int(scale * w)
+                nh = int(nw / new_ar)
+
+            dx = int(_rand(0, w - nw))
+            dy = int(_rand(0, h - nh))
+
+            if len(box) >= 1:
+                t_box = box.copy()
+                np.random.shuffle(t_box)
+                t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(iw) + dx
+                t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(ih) + dy
+                if flip:
+                    t_box[:, [0, 2]] = w - t_box[:, [2, 0]]
+                t_box[:, 0:2][t_box[:, 0:2] < 0] = 0
+                t_box[:, 2][t_box[:, 2] > w] = w
+                t_box[:, 3][t_box[:, 3] > h] = h
+                box_w = t_box[:, 2] - t_box[:, 0]
+                box_h = t_box[:, 3] - t_box[:, 1]
+                t_box = t_box[np.logical_and(box_w > 1, box_h > 1)]  # discard invalid box
+
+            if len(t_box) >= 1:
+                box = t_box
+                break
+
+        box_data[:len(box)] = box
+        # resize image
+        image = image.resize((nw, nh), Image.BICUBIC)
+        # place image
+        new_image = Image.new('RGB', (w, h), (128, 128, 128))
+        new_image.paste(image, (dx, dy))
+        image = new_image
+
+        # flip image or not
+        if flip:
+            image = image.transpose(Image.FLIP_LEFT_RIGHT)
+
+        # convert image to gray or not
+        gray = _rand() < .25
+        if gray:
+            image = image.convert('L').convert('RGB')
+
+        # when the channels of image is 1
+        image = np.array(image)
+        if len(image.shape) == 2:
+            image = np.expand_dims(image, axis=-1)
+            image = np.concatenate([image, image, image], axis=-1)
+
+        # distort image
+        hue = _rand(-hue, hue)
+        sat = _rand(1, sat) if _rand() < .5 else 1 / _rand(1, sat)
+        val = _rand(1, val) if _rand() < .5 else 1 / _rand(1, val)
+        image_data = image / 255.
+        if do_hsv:
+            x = rgb_to_hsv(image_data)
+            x[..., 0] += hue
+            x[..., 0][x[..., 0] > 1] -= 1
+            x[..., 0][x[..., 0] < 0] += 1
+            x[..., 1] *= sat
+            x[..., 2] *= val
+            x[x > 1] = 1
+            x[x < 0] = 0
+            image_data = hsv_to_rgb(x)  # numpy array, 0 to 1
+        image_data = image_data.astype(np.float32)
+
+        # preprocess bounding boxes
+        bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
+            _preprocess_true_boxes(box_data, anchors, image_size)
+
+        return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
+               ori_image_shape, gt_box1, gt_box2, gt_box3
+
+    if is_training:
+        images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training)
+        return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3
+
+    images, shape, anno = _data_aug(image, box, is_training)
+    return images, shape, anno
+
+
+def anno_parser(annos_str):
+    """Parse annotation from string to list."""
+    annos = []
+    for anno_str in annos_str:
+        anno = list(map(int, anno_str.strip().split(',')))
+        annos.append(anno)
+    return annos
+
+
+def filter_valid_data(image_dir, anno_path):
+    """Filter valid image file, which both in image_dir and anno_path."""
+    image_files = []
+    image_anno_dict = {}
+    if not os.path.isdir(image_dir):
+        raise RuntimeError("Path given is not valid.")
+    if not os.path.isfile(anno_path):
+        raise RuntimeError("Annotation file is not valid.")
+
+    with open(anno_path, "rb") as f:
+        lines = f.readlines()
+    for line in lines:
+        line_str = line.decode("utf-8").strip()
+        line_split = str(line_str).split(' ')
+        file_name = line_split[0]
+        if os.path.isfile(os.path.join(image_dir, file_name)):
+            image_anno_dict[file_name] = anno_parser(line_split[1:])
+            image_files.append(file_name)
+    return image_files, image_anno_dict
+
+
+def data_to_mindrecord_byte_image(image_dir, anno_path, mindrecord_dir, prefix="yolo.mindrecord", file_num=8):
+    """Create MindRecord file by image_dir and anno_path."""
+    mindrecord_path = os.path.join(mindrecord_dir, prefix)
+    writer = FileWriter(mindrecord_path, file_num)
+    image_files, image_anno_dict = filter_valid_data(image_dir, anno_path)
+
+    yolo_json = {
+        "image": {"type": "bytes"},
+        "annotation": {"type": "int64", "shape": [-1, 5]},
+    }
+    writer.add_schema(yolo_json, "yolo_json")
+
+    for image_name in image_files:
+        image_path = os.path.join(image_dir, image_name)
+        with open(image_path, 'rb') as f:
+            img = f.read()
+        annos = np.array(image_anno_dict[image_name])
+        row = {"image": img, "annotation": annos}
+        writer.write_raw_data([row])
+    writer.commit()
+
+
+def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num=1, rank=0,
+                        is_training=True, num_parallel_workers=8):
+    """Creatr YOLOv3 dataset with MindDataset."""
+    ds = de.MindDataset(mindrecord_dir, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
+                        num_parallel_workers=num_parallel_workers, shuffle=False)
+    decode = C.Decode()
+    ds = ds.map(input_columns=["image"], operations=decode)
+    compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
+
+    if is_training:
+        hwc_to_chw = C.HWC2CHW()
+        ds = ds.map(input_columns=["image", "annotation"],
+                    output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
+                    columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
+                    operations=compose_map_func, num_parallel_workers=num_parallel_workers)
+        ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
+        ds = ds.batch(batch_size, drop_remainder=True)
+        ds = ds.repeat(repeat_num)
+    else:
+        ds = ds.map(input_columns=["image", "annotation"],
+                    output_columns=["image", "image_shape", "annotation"],
+                    columns_order=["image", "image_shape", "annotation"],
+                    operations=compose_map_func, num_parallel_workers=num_parallel_workers)
+    return ds

tests/st/model_zoo_tests/yolov3/src/yolov3.py

@@ -0,0 +1,748 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""YOLOv3 based on ResNet18."""
+
+import numpy as np
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import context, Tensor
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.communication.management import get_group_size
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+
+
+def weight_variable():
+    """Weight variable."""
+    return TruncatedNormal(0.02)
+
+
+class _conv2d(nn.Cell):
+    """Create Conv2D with padding."""
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1):
+        super(_conv2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels,
+                              kernel_size=kernel_size, stride=stride, padding=0, pad_mode='same',
+                              weight_init=weight_variable())
+    def construct(self, x):
+        x = self.conv(x)
+        return x
+
+
+def _fused_bn(channels, momentum=0.99):
+    """Get a fused batchnorm."""
+    return nn.BatchNorm2d(channels, momentum=momentum)
+
+
+def _conv_bn_relu(in_channel,
+                  out_channel,
+                  ksize,
+                  stride=1,
+                  padding=0,
+                  dilation=1,
+                  alpha=0.1,
+                  momentum=0.99,
+                  pad_mode="same"):
+    """Get a conv2d batchnorm and relu layer."""
+    return nn.SequentialCell(
+        [nn.Conv2d(in_channel,
+                   out_channel,
+                   kernel_size=ksize,
+                   stride=stride,
+                   padding=padding,
+                   dilation=dilation,
+                   pad_mode=pad_mode),
+         nn.BatchNorm2d(out_channel, momentum=momentum),
+         nn.LeakyReLU(alpha)]
+    )
+
+
+class BasicBlock(nn.Cell):
+    """
+    ResNet basic block.
+
+    Args:
+        in_channels (int): Input channel.
+        out_channels (int): Output channel.
+        stride (int): Stride size for the initial convolutional layer. Default:1.
+        momentum (float): Momentum for batchnorm layer. Default:0.1.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        BasicBlock(3,256,stride=2,down_sample=True).
+    """
+    expansion = 1
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 momentum=0.99):
+        super(BasicBlock, self).__init__()
+
+        self.conv1 = _conv2d(in_channels, out_channels, 3, stride=stride)
+        self.bn1 = _fused_bn(out_channels, momentum=momentum)
+        self.conv2 = _conv2d(out_channels, out_channels, 3)
+        self.bn2 = _fused_bn(out_channels, momentum=momentum)
+        self.relu = P.ReLU()
+        self.down_sample_layer = None
+        self.downsample = (in_channels != out_channels)
+        if self.downsample:
+            self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride)
+        self.add = P.TensorAdd()
+
+    def construct(self, x):
+        identity = x
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+
+        if self.downsample:
+            identity = self.down_sample_layer(identity)
+
+        out = self.add(x, identity)
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Cell):
+    """
+    ResNet network.
+
+    Args:
+        block (Cell): Block for network.
+        layer_nums (list): Numbers of different layers.
+        in_channels (int): Input channel.
+        out_channels (int): Output channel.
+        num_classes (int): Class number. Default:100.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        ResNet(ResidualBlock,
+               [3, 4, 6, 3],
+               [64, 256, 512, 1024],
+               [256, 512, 1024, 2048],
+               100).
+    """
+
+    def __init__(self,
+                 block,
+                 layer_nums,
+                 in_channels,
+                 out_channels,
+                 strides=None,
+                 num_classes=80):
+        super(ResNet, self).__init__()
+
+        if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
+            raise ValueError("the length of "
+                             "layer_num, inchannel, outchannel list must be 4!")
+
+        self.conv1 = _conv2d(3, 64, 7, stride=2)
+        self.bn1 = _fused_bn(64)
+        self.relu = P.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
+
+        self.layer1 = self._make_layer(block,
+                                       layer_nums[0],
+                                       in_channel=in_channels[0],
+                                       out_channel=out_channels[0],
+                                       stride=strides[0])
+        self.layer2 = self._make_layer(block,
+                                       layer_nums[1],
+                                       in_channel=in_channels[1],
+                                       out_channel=out_channels[1],
+                                       stride=strides[1])
+        self.layer3 = self._make_layer(block,
+                                       layer_nums[2],
+                                       in_channel=in_channels[2],
+                                       out_channel=out_channels[2],
+                                       stride=strides[2])
+        self.layer4 = self._make_layer(block,
+                                       layer_nums[3],
+                                       in_channel=in_channels[3],
+                                       out_channel=out_channels[3],
+                                       stride=strides[3])
+
+        self.num_classes = num_classes
+        if num_classes:
+            self.reduce_mean = P.ReduceMean(keep_dims=True)
+            self.end_point = nn.Dense(out_channels[3], num_classes, has_bias=True,
+                                      weight_init=weight_variable(),
+                                      bias_init=weight_variable())
+            self.squeeze = P.Squeeze(axis=(2, 3))
+
+    def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
+        """
+        Make Layer for ResNet.
+
+        Args:
+            block (Cell): Resnet block.
+            layer_num (int): Layer number.
+            in_channel (int): Input channel.
+            out_channel (int): Output channel.
+            stride (int): Stride size for the initial convolutional layer.
+
+        Returns:
+            SequentialCell, the output layer.
+
+        Examples:
+            _make_layer(BasicBlock, 3, 128, 256, 2).
+        """
+        layers = []
+
+        resblk = block(in_channel, out_channel, stride=stride)
+        layers.append(resblk)
+
+        for _ in range(1, layer_num - 1):
+            resblk = block(out_channel, out_channel, stride=1)
+            layers.append(resblk)
+
+        resblk = block(out_channel, out_channel, stride=1)
+        layers.append(resblk)
+
+        return nn.SequentialCell(layers)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        c1 = self.maxpool(x)
+
+        c2 = self.layer1(c1)
+        c3 = self.layer2(c2)
+        c4 = self.layer3(c3)
+        c5 = self.layer4(c4)
+
+        out = c5
+        if self.num_classes:
+            out = self.reduce_mean(c5, (2, 3))
+            out = self.squeeze(out)
+            out = self.end_point(out)
+
+        return c3, c4, out
+
+
+def resnet18(class_num=10):
+    """
+    Get ResNet18 neural network.
+
+    Args:
+        class_num (int): Class number.
+
+    Returns:
+        Cell, cell instance of ResNet18 neural network.
+
+    Examples:
+        resnet18(100).
+    """
+    return ResNet(BasicBlock,
+                  [2, 2, 2, 2],
+                  [64, 64, 128, 256],
+                  [64, 128, 256, 512],
+                  [1, 2, 2, 2],
+                  num_classes=class_num)
+
+
+class YoloBlock(nn.Cell):
+    """
+    YoloBlock for YOLOv3.
+
+    Args:
+        in_channels (int): Input channel.
+        out_chls (int): Middle channel.
+        out_channels (int): Output channel.
+
+    Returns:
+        Tuple, tuple of output tensor,(f1,f2,f3).
+
+    Examples:
+        YoloBlock(1024, 512, 255).
+
+    """
+    def __init__(self, in_channels, out_chls, out_channels):
+        super(YoloBlock, self).__init__()
+        out_chls_2 = out_chls * 2
+
+        self.conv0 = _conv_bn_relu(in_channels, out_chls, ksize=1)
+        self.conv1 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv2 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
+        self.conv3 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv4 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
+        self.conv5 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv6 = nn.Conv2d(out_chls_2, out_channels, kernel_size=1, stride=1, has_bias=True)
+
+    def construct(self, x):
+        c1 = self.conv0(x)
+        c2 = self.conv1(c1)
+
+        c3 = self.conv2(c2)
+        c4 = self.conv3(c3)
+
+        c5 = self.conv4(c4)
+        c6 = self.conv5(c5)
+
+        out = self.conv6(c6)
+        return c5, out
+
+
+class YOLOv3(nn.Cell):
+    """
+     YOLOv3 Network.
+
+     Note:
+         backbone = resnet18.
+
+     Args:
+         feature_shape (list): Input image shape, [N,C,H,W].
+         backbone_shape (list): resnet18 output channels shape.
+         backbone (Cell): Backbone Network.
+         out_channel (int): Output channel.
+
+     Returns:
+         Tensor, output tensor.
+
+     Examples:
+         YOLOv3(feature_shape=[1,3,416,416],
+                backbone_shape=[64, 128, 256, 512, 1024]
+                backbone=darknet53(),
+                out_channel=255).
+     """
+    def __init__(self, feature_shape, backbone_shape, backbone, out_channel):
+        super(YOLOv3, self).__init__()
+        self.out_channel = out_channel
+        self.net = backbone
+        self.backblock0 = YoloBlock(backbone_shape[-1], out_chls=backbone_shape[-2], out_channels=out_channel)
+
+        self.conv1 = _conv_bn_relu(in_channel=backbone_shape[-2], out_channel=backbone_shape[-2]//2, ksize=1)
+        self.upsample1 = P.ResizeNearestNeighbor((feature_shape[2]//16, feature_shape[3]//16))
+        self.backblock1 = YoloBlock(in_channels=backbone_shape[-2]+backbone_shape[-3],
+                                    out_chls=backbone_shape[-3],
+                                    out_channels=out_channel)
+
+        self.conv2 = _conv_bn_relu(in_channel=backbone_shape[-3], out_channel=backbone_shape[-3]//2, ksize=1)
+        self.upsample2 = P.ResizeNearestNeighbor((feature_shape[2]//8, feature_shape[3]//8))
+        self.backblock2 = YoloBlock(in_channels=backbone_shape[-3]+backbone_shape[-4],
+                                    out_chls=backbone_shape[-4],
+                                    out_channels=out_channel)
+        self.concat = P.Concat(axis=1)
+
+    def construct(self, x):
+        # input_shape of x is (batch_size, 3, h, w)
+        # feature_map1 is (batch_size, backbone_shape[2], h/8, w/8)
+        # feature_map2 is (batch_size, backbone_shape[3], h/16, w/16)
+        # feature_map3 is (batch_size, backbone_shape[4], h/32, w/32)
+        feature_map1, feature_map2, feature_map3 = self.net(x)
+        con1, big_object_output = self.backblock0(feature_map3)
+
+        con1 = self.conv1(con1)
+        ups1 = self.upsample1(con1)
+        con1 = self.concat((ups1, feature_map2))
+        con2, medium_object_output = self.backblock1(con1)
+
+        con2 = self.conv2(con2)
+        ups2 = self.upsample2(con2)
+        con3 = self.concat((ups2, feature_map1))
+        _, small_object_output = self.backblock2(con3)
+
+        return big_object_output, medium_object_output, small_object_output
+
+
+class DetectionBlock(nn.Cell):
+    """
+     YOLOv3 detection Network. It will finally output the detection result.
+
+     Args:
+         scale (str): Character, scale.
+         config (Class): YOLOv3 config.
+
+     Returns:
+         Tuple, tuple of output tensor,(f1,f2,f3).
+
+     Examples:
+         DetectionBlock(scale='l',stride=32).
+     """
+
+    def __init__(self, scale, config):
+        super(DetectionBlock, self).__init__()
+
+        self.config = config
+        if scale == 's':
+            idx = (0, 1, 2)
+        elif scale == 'm':
+            idx = (3, 4, 5)
+        elif scale == 'l':
+            idx = (6, 7, 8)
+        else:
+            raise KeyError("Invalid scale value for DetectionBlock")
+        self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
+        self.num_anchors_per_scale = 3
+        self.num_attrib = 4 + 1 + self.config.num_classes
+        self.ignore_threshold = 0.5
+        self.lambda_coord = 1
+
+        self.sigmoid = nn.Sigmoid()
+        self.reshape = P.Reshape()
+        self.tile = P.Tile()
+        self.concat = P.Concat(axis=-1)
+        self.input_shape = Tensor(tuple(config.img_shape[::-1]), ms.float32)
+
+    def construct(self, x):
+        num_batch = P.Shape()(x)[0]
+        grid_size = P.Shape()(x)[2:4]
+
+        # Reshape and transpose the feature to [n, 3, grid_size[0], grid_size[1], num_attrib]
+        prediction = P.Reshape()(x, (num_batch,
+                                     self.num_anchors_per_scale,
+                                     self.num_attrib,
+                                     grid_size[0],
+                                     grid_size[1]))
+        prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2))
+
+        range_x = range(grid_size[1])
+        range_y = range(grid_size[0])
+        grid_x = P.Cast()(F.tuple_to_array(range_x), ms.float32)
+        grid_y = P.Cast()(F.tuple_to_array(range_y), ms.float32)
+        # Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid
+        grid_x = self.tile(self.reshape(grid_x, (1, 1, -1, 1, 1)), (1, grid_size[0], 1, 1, 1))
+        grid_y = self.tile(self.reshape(grid_y, (1, -1, 1, 1, 1)), (1, 1, grid_size[1], 1, 1))
+        # Shape is [grid_size[0], grid_size[1], 1, 2]
+        grid = self.concat((grid_x, grid_y))
+
+        box_xy = prediction[:, :, :, :, :2]
+        box_wh = prediction[:, :, :, :, 2:4]
+        box_confidence = prediction[:, :, :, :, 4:5]
+        box_probs = prediction[:, :, :, :, 5:]
+
+        box_xy = (self.sigmoid(box_xy) + grid) / P.Cast()(F.tuple_to_array((grid_size[1], grid_size[0])), ms.float32)
+        box_wh = P.Exp()(box_wh) * self.anchors / self.input_shape
+        box_confidence = self.sigmoid(box_confidence)
+        box_probs = self.sigmoid(box_probs)
+
+        if self.training:
+            return grid, prediction, box_xy, box_wh
+        return box_xy, box_wh, box_confidence, box_probs
+
+
+class Iou(nn.Cell):
+    """Calculate the iou of boxes."""
+    def __init__(self):
+        super(Iou, self).__init__()
+        self.min = P.Minimum()
+        self.max = P.Maximum()
+
+    def construct(self, box1, box2):
+        box1_xy = box1[:, :, :, :, :, :2]
+        box1_wh = box1[:, :, :, :, :, 2:4]
+        box1_mins = box1_xy - box1_wh / F.scalar_to_array(2.0)
+        box1_maxs = box1_xy + box1_wh / F.scalar_to_array(2.0)
+
+        box2_xy = box2[:, :, :, :, :, :2]
+        box2_wh = box2[:, :, :, :, :, 2:4]
+        box2_mins = box2_xy - box2_wh / F.scalar_to_array(2.0)
+        box2_maxs = box2_xy + box2_wh / F.scalar_to_array(2.0)
+
+        intersect_mins = self.max(box1_mins, box2_mins)
+        intersect_maxs = self.min(box1_maxs, box2_maxs)
+        intersect_wh = self.max(intersect_maxs - intersect_mins, F.scalar_to_array(0.0))
+
+        intersect_area = P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 0:1]) * \
+                         P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 1:2])
+        box1_area = P.Squeeze(-1)(box1_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box1_wh[:, :, :, :, :, 1:2])
+        box2_area = P.Squeeze(-1)(box2_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box2_wh[:, :, :, :, :, 1:2])
+
+        iou = intersect_area / (box1_area + box2_area - intersect_area)
+        return iou
+
+
+class YoloLossBlock(nn.Cell):
+    """
+     YOLOv3 Loss block cell. It will finally output loss of the scale.
+
+     Args:
+         scale (str): Three scale here, 's', 'm' and 'l'.
+         config (Class): The default config of YOLOv3.
+
+     Returns:
+         Tensor, loss of the scale.
+
+     Examples:
+         YoloLossBlock('l', ConfigYOLOV3ResNet18()).
+     """
+
+    def __init__(self, scale, config):
+        super(YoloLossBlock, self).__init__()
+        self.config = config
+        if scale == 's':
+            idx = (0, 1, 2)
+        elif scale == 'm':
+            idx = (3, 4, 5)
+        elif scale == 'l':
+            idx = (6, 7, 8)
+        else:
+            raise KeyError("Invalid scale value for DetectionBlock")
+        self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
+        self.ignore_threshold = Tensor(self.config.ignore_threshold, ms.float32)
+        self.concat = P.Concat(axis=-1)
+        self.iou = Iou()
+        self.cross_entropy = P.SigmoidCrossEntropyWithLogits()
+        self.reduce_sum = P.ReduceSum()
+        self.reduce_max = P.ReduceMax(keep_dims=False)
+        self.input_shape = Tensor(tuple(config.img_shape[::-1]), ms.float32)
+
+    def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box):
+
+        object_mask = y_true[:, :, :, :, 4:5]
+        class_probs = y_true[:, :, :, :, 5:]
+
+        grid_shape = P.Shape()(prediction)[1:3]
+        grid_shape = P.Cast()(F.tuple_to_array(grid_shape[::-1]), ms.float32)
+
+        pred_boxes = self.concat((pred_xy, pred_wh))
+        true_xy = y_true[:, :, :, :, :2] * grid_shape - grid
+        true_wh = y_true[:, :, :, :, 2:4]
+        true_wh = P.Select()(P.Equal()(true_wh, 0.0),
+                             P.Fill()(P.DType()(true_wh), P.Shape()(true_wh), 1.0),
+                             true_wh)
+        true_wh = P.Log()(true_wh / self.anchors * self.input_shape)
+        box_loss_scale = 2 - y_true[:, :, :, :, 2:3] * y_true[:, :, :, :, 3:4]
+
+        gt_shape = P.Shape()(gt_box)
+        gt_box = P.Reshape()(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2]))
+
+        iou = self.iou(P.ExpandDims()(pred_boxes, -2), gt_box) # [batch, grid[0], grid[1], num_anchor, num_gt]
+        best_iou = self.reduce_max(iou, -1) # [batch, grid[0], grid[1], num_anchor]
+        ignore_mask = best_iou < self.ignore_threshold
+        ignore_mask = P.Cast()(ignore_mask, ms.float32)
+        ignore_mask = P.ExpandDims()(ignore_mask, -1)
+        ignore_mask = F.stop_gradient(ignore_mask)
+
+        xy_loss = object_mask * box_loss_scale * self.cross_entropy(prediction[:, :, :, :, :2], true_xy)
+        wh_loss = object_mask * box_loss_scale * 0.5 * P.Square()(true_wh - prediction[:, :, :, :, 2:4])
+        confidence_loss = self.cross_entropy(prediction[:, :, :, :, 4:5], object_mask)
+        confidence_loss = object_mask * confidence_loss + (1 - object_mask) * confidence_loss * ignore_mask
+        class_loss = object_mask * self.cross_entropy(prediction[:, :, :, :, 5:], class_probs)
+
+        # Get smooth loss
+        xy_loss = self.reduce_sum(xy_loss, ())
+        wh_loss = self.reduce_sum(wh_loss, ())
+        confidence_loss = self.reduce_sum(confidence_loss, ())
+        class_loss = self.reduce_sum(class_loss, ())
+
+        loss = xy_loss + wh_loss + confidence_loss + class_loss
+        return loss / P.Shape()(prediction)[0]
+
+
+class yolov3_resnet18(nn.Cell):
+    """
+    ResNet based YOLOv3 network.
+
+    Args:
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Cell, cell instance of ResNet based YOLOv3 neural network.
+
+    Examples:
+        yolov3_resnet18(80, [1,3,416,416]).
+    """
+
+    def __init__(self, config):
+        super(yolov3_resnet18, self).__init__()
+        self.config = config
+
+        # YOLOv3 network
+        self.feature_map = YOLOv3(feature_shape=self.config.feature_shape,
+                                  backbone=ResNet(BasicBlock,
+                                                  self.config.backbone_layers,
+                                                  self.config.backbone_input_shape,
+                                                  self.config.backbone_shape,
+                                                  self.config.backbone_stride,
+                                                  num_classes=None),
+                                  backbone_shape=self.config.backbone_shape,
+                                  out_channel=self.config.out_channel)
+
+        # prediction on the default anchor boxes
+        self.detect_1 = DetectionBlock('l', self.config)
+        self.detect_2 = DetectionBlock('m', self.config)
+        self.detect_3 = DetectionBlock('s', self.config)
+
+    def construct(self, x):
+        big_object_output, medium_object_output, small_object_output = self.feature_map(x)
+        output_big = self.detect_1(big_object_output)
+        output_me = self.detect_2(medium_object_output)
+        output_small = self.detect_3(small_object_output)
+
+        return output_big, output_me, output_small
+
+
+class YoloWithLossCell(nn.Cell):
+    """"
+    Provide YOLOv3 training loss through network.
+
+    Args:
+        network (Cell): The training network.
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Tensor, the loss of the network.
+    """
+    def __init__(self, network, config):
+        super(YoloWithLossCell, self).__init__()
+        self.yolo_network = network
+        self.config = config
+        self.loss_big = YoloLossBlock('l', self.config)
+        self.loss_me = YoloLossBlock('m', self.config)
+        self.loss_small = YoloLossBlock('s', self.config)
+
+    def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2):
+        yolo_out = self.yolo_network(x)
+        loss_l = self.loss_big(yolo_out[0][0], yolo_out[0][1], yolo_out[0][2], yolo_out[0][3], y_true_0, gt_0)
+        loss_m = self.loss_me(yolo_out[1][0], yolo_out[1][1], yolo_out[1][2], yolo_out[1][3], y_true_1, gt_1)
+        loss_s = self.loss_small(yolo_out[2][0], yolo_out[2][1], yolo_out[2][2], yolo_out[2][3], y_true_2, gt_2)
+        return loss_l + loss_m + loss_s
+
+
+class TrainingWrapper(nn.Cell):
+    """
+    Encapsulation class of YOLOv3 network training.
+
+    Append an optimizer to the training network after that the construct
+    function can be called to create the backward graph.
+
+    Args:
+        network (Cell): The training network. Note that loss function should have been added.
+        optimizer (Optimizer): Optimizer for updating the weights.
+        sens (Number): The adjust parameter. Default: 1.0.
+    """
+    def __init__(self, network, optimizer, sens=1.0):
+        super(TrainingWrapper, self).__init__(auto_prefix=False)
+        self.network = network
+        self.weights = ms.ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.sens = sens
+        self.reducer_flag = False
+        self.grad_reducer = None
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if self.parallel_mode in [ms.ParallelMode.DATA_PARALLEL, ms.ParallelMode.HYBRID_PARALLEL]:
+            self.reducer_flag = True
+        if self.reducer_flag:
+            mean = context.get_auto_parallel_context("mirror_mean")
+            if auto_parallel_context().get_device_num_is_set():
+                degree = context.get_auto_parallel_context("device_num")
+            else:
+                degree = get_group_size()
+            self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
+
+    def construct(self, *args):
+        weights = self.weights
+        loss = self.network(*args)
+        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
+        grads = self.grad(self.network, weights)(*args, sens)
+        if self.reducer_flag:
+            # apply grad reducer on grads
+            grads = self.grad_reducer(grads)
+        return F.depend(loss, self.optimizer(grads))
+
+
+class YoloBoxScores(nn.Cell):
+    """
+    Calculate the boxes of the original picture size and the score of each box.
+
+    Args:
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Tensor, the boxes of the original picture size.
+        Tensor, the score of each box.
+    """
+    def __init__(self, config):
+        super(YoloBoxScores, self).__init__()
+        self.input_shape = Tensor(np.array(config.img_shape), ms.float32)
+        self.num_classes = config.num_classes
+
+    def construct(self, box_xy, box_wh, box_confidence, box_probs, image_shape):
+        batch_size = F.shape(box_xy)[0]
+        x = box_xy[:, :, :, :, 0:1]
+        y = box_xy[:, :, :, :, 1:2]
+        box_yx = P.Concat(-1)((y, x))
+        w = box_wh[:, :, :, :, 0:1]
+        h = box_wh[:, :, :, :, 1:2]
+        box_hw = P.Concat(-1)((h, w))
+
+        new_shape = P.Round()(image_shape * P.ReduceMin()(self.input_shape / image_shape))
+        offset = (self.input_shape - new_shape) / 2.0 / self.input_shape
+        scale = self.input_shape / new_shape
+        box_yx = (box_yx - offset) * scale
+        box_hw = box_hw * scale
+
+        box_min = box_yx - box_hw / 2.0
+        box_max = box_yx + box_hw / 2.0
+        boxes = P.Concat(-1)((box_min[:, :, :, :, 0:1],
+                              box_min[:, :, :, :, 1:2],
+                              box_max[:, :, :, :, 0:1],
+                              box_max[:, :, :, :, 1:2]))
+        image_scale = P.Tile()(image_shape, (1, 2))
+        boxes = boxes * image_scale
+        boxes = F.reshape(boxes, (batch_size, -1, 4))
+        boxes_scores = box_confidence * box_probs
+        boxes_scores = F.reshape(boxes_scores, (batch_size, -1, self.num_classes))
+        return boxes, boxes_scores
+
+
+class YoloWithEval(nn.Cell):
+    """
+    Encapsulation class of YOLOv3 evaluation.
+
+    Args:
+        network (Cell): The training network. Note that loss function and optimizer must not be added.
+        config (Class): YOLOv3 config.
+
+    Returns:
+        Tensor, the boxes of the original picture size.
+        Tensor, the score of each box.
+        Tensor, the original picture size.
+    """
+    def __init__(self, network, config):
+        super(YoloWithEval, self).__init__()
+        self.yolo_network = network
+        self.box_score_0 = YoloBoxScores(config)
+        self.box_score_1 = YoloBoxScores(config)
+        self.box_score_2 = YoloBoxScores(config)
+
+    def construct(self, x, image_shape):
+        yolo_output = self.yolo_network(x)
+        boxes_0, boxes_scores_0 = self.box_score_0(*yolo_output[0], image_shape)
+        boxes_1, boxes_scores_1 = self.box_score_1(*yolo_output[1], image_shape)
+        boxes_2, boxes_scores_2 = self.box_score_2(*yolo_output[2], image_shape)
+        boxes = P.Concat(1)((boxes_0, boxes_1, boxes_2))
+        boxes_scores = P.Concat(1)((boxes_scores_0, boxes_scores_1, boxes_scores_2))
+        return boxes, boxes_scores, image_shape

tests/st/model_zoo_tests/yolov3/test_yolov3.py

@@ -0,0 +1,157 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# less 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.
+# ============================================================================
+
+"""
+######################## train YOLOv3 example ########################
+train YOLOv3 and get network model files(.ckpt) :
+python train.py --image_dir /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train
+
+If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path.
+Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.
+"""
+
+import os
+import time
+import pytest
+import numpy as np
+import mindspore.nn as nn
+from mindspore import context, Tensor
+from mindspore.train import Model
+from mindspore.common.initializer import initializer
+from mindspore.train.callback import Callback
+
+from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
+from src.dataset import create_yolo_dataset
+from src.config import ConfigYOLOV3ResNet18
+
+np.random.seed(1)
+def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
+    """Set learning rate."""
+    lr_each_step = []
+    for i in range(global_step):
+        if steps:
+            lr_each_step.append(learning_rate * (decay_rate ** (i // decay_step)))
+        else:
+            lr_each_step.append(learning_rate * (decay_rate ** (i / decay_step)))
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    lr_each_step = lr_each_step[start_step:]
+    return lr_each_step
+
+
+def init_net_param(network, init_value='ones'):
+    """Init:wq the parameters in network."""
+    params = network.trainable_params()
+    for p in params:
+        if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
+            p.set_parameter_data(initializer(init_value, p.data.shape(), p.data.dtype()))
+
+class ModelCallback(Callback):
+    def __init__(self):
+        super(ModelCallback, self).__init__()
+        self.loss_list = []
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        self.loss_list.append(cb_params.net_outputs.asnumpy())
+        print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
+
+class TimeMonitor(Callback):
+    """Time Monitor."""
+    def __init__(self, data_size):
+        super(TimeMonitor, self).__init__()
+        self.data_size = data_size
+        self.epoch_mseconds_list = []
+        self.per_step_mseconds_list = []
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+
+    def epoch_end(self, run_context):
+        epoch_mseconds = (time.time() - self.epoch_time) * 1000
+        self.epoch_mseconds_list.append(epoch_mseconds)
+        self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
+
+DATA_DIR = "/home/workspace/mindspore_dataset/coco/coco2017/mindrecord_train/yolov3"
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_yolov3():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    rank = 0
+    device_num = 1
+    lr_init = 0.001
+    epoch_size = 3
+    batch_size = 32
+    loss_scale = 1024
+    mindrecord_dir = DATA_DIR
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is yolo.mindrecord0, 1, ... file_num.
+    if not os.path.isdir(mindrecord_dir):
+        raise KeyError("mindrecord path is not exist.")
+
+    prefix = "yolo.mindrecord"
+    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    print("yolov3 mindrecord is ", mindrecord_file)
+    if not os.path.exists(mindrecord_file):
+        print("mindrecord file is not exist.")
+        assert False
+    else:
+        loss_scale = float(loss_scale)
+
+        # When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0.
+        dataset = create_yolo_dataset(mindrecord_file, repeat_num=epoch_size,
+                                      batch_size=batch_size, device_num=device_num, rank=rank)
+        dataset_size = dataset.get_dataset_size()
+        print("Create dataset done!")
+
+        net = yolov3_resnet18(ConfigYOLOV3ResNet18())
+        net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
+        init_net_param(net)
+
+        total_epoch_size = 60
+        lr = Tensor(get_lr(learning_rate=lr_init, start_step=0,
+                           global_step=total_epoch_size * dataset_size,
+                           decay_step=1000, decay_rate=0.95, steps=True))
+        opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
+        net = TrainingWrapper(net, opt, loss_scale)
+
+        model_callback = ModelCallback()
+        time_monitor_callback = TimeMonitor(data_size=dataset_size)
+        callback = [model_callback, time_monitor_callback]
+
+        model = Model(net)
+        print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
+        model.train(epoch_size, dataset, callbacks=callback, dataset_sink_mode=True)
+        # assertion occurs while the loss value, overflow state or loss_scale value is wrong
+        loss_value = np.array(model_callback.loss_list)
+
+        expect_loss_value = [6600, 4200, 2700]
+        print("loss value: {}".format(loss_value))
+        assert loss_value[0] < expect_loss_value[0]
+        assert loss_value[1] < expect_loss_value[1]
+        assert loss_value[2] < expect_loss_value[2]
+
+        epoch_mseconds = np.array(time_monitor_callback.epoch_mseconds_list)[2]
+        expect_epoch_mseconds = 950
+        print("epoch mseconds: {}".format(epoch_mseconds))
+        assert epoch_mseconds <= expect_epoch_mseconds
+
+        per_step_mseconds = np.array(time_monitor_callback.per_step_mseconds_list)[2]
+        expect_per_step_mseconds = 110
+        print("per step mseconds: {}".format(per_step_mseconds))
+        assert per_step_mseconds <= expect_per_step_mseconds
+        print("yolov3 test case passed.")