add ssd scipt

example/ssd_coco2017/README.md

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+# SSD Example
+
+## Description
+
+SSD network based on MobileNetV2, with support for training and evaluation.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Dataset
+
+    We use coco2017 as training dataset in this example by default, and you can also use your own datasets.
+
+    1. If coco dataset is used. **Select dataset to coco when run script.**
+        Download coco2017: [train2017](http://images.cocodataset.org/zips/train2017.zip), [val2017](http://images.cocodataset.org/zips/val2017.zip), [test2017](http://images.cocodataset.org/zips/test2017.zip), [annotations](http://images.cocodataset.org/annotations/annotations_trainval2017.zip). Install pycocotool.
+
+        ```
+        pip install Cython
+
+        pip install pycocotools
+        ```
+        And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
+
+
+        ```
+        └─coco2017
+            ├── annotations  # annotation jsons
+            ├── train2017    # train dataset
+            └── val2017      # infer dataset
+        ```
+
+    2. If your own dataset is used. **Select dataset to other when run script.**
+        Organize the dataset infomation into a TXT file, each row in the file is as follows:
+
+        ```
+        train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
+        ```
+
+        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `IMAGE_DIR`(dataset directory) and the relative path in `ANNO_PATH`(the TXT file path), `IMAGE_DIR` and `ANNO_PATH` are setting in `config.py`.
+
+
+## Running the example
+
+### Training
+
+To train the model, run `train.py`. If the `MINDRECORD_DIR` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/converting_datasets.html) files by `COCO_ROOT`(coco dataset) or `IMAGE_DIR` and `ANNO_PATH`(own dataset). **Note if MINDRECORD_DIR isn't empty, it will use MINDRECORD_DIR instead of raw images.**
+
+
+- Stand alone mode
+
+    ```
+    python train.py --dataset coco
+
+    ```
+
+    You can run ```python train.py -h```  to get more information.
+
+
+- Distribute mode
+
+    ```
+    sh run_distribute_train.sh 8 150 coco /data/hccl.json
+    ```
+
+    The input parameters are device numbers, epoch size, dataset mode and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.** 
+
+You will get the loss value of each step as following:
+
+```
+epoch: 1 step: 455, loss is 5.8653416
+epoch: 2 step: 455, loss is 5.4292373
+epoch: 3 step: 455, loss is 5.458992
+...
+epoch: 148 step: 455, loss is 1.8340507
+epoch: 149 step: 455, loss is 2.0876894
+epoch: 150 step: 455, loss is 2.239692
+```
+
+### Evaluation
+
+for evaluation , run `eval.py` with `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html) file.
+
+```
+python eval.py --ckpt_path ssd.ckpt --dataset coco
+```
+
+You can run ```python eval.py -h```  to get more information.

example/ssd_coco2017/config.py

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+# 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 SSD models."""
+
+
+class ConfigSSD:
+    """
+    Config parameters for SSD.
+
+    Examples:
+        ConfigSSD().
+    """
+    IMG_SHAPE = [300, 300]
+    NUM_SSD_BOXES = 1917
+    NEG_PRE_POSITIVE = 3
+    MATCH_THRESHOLD = 0.5
+
+    NUM_DEFAULT = [3, 6, 6, 6, 6, 6]
+    EXTRAS_IN_CHANNELS = [256, 576, 1280, 512, 256, 256]
+    EXTRAS_OUT_CHANNELS = [576, 1280, 512, 256, 256, 128]
+    EXTRAS_STRIDES = [1, 1, 2, 2, 2, 2]
+    EXTRAS_RATIO = [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
+    FEATURE_SIZE = [19, 10, 5, 3, 2, 1]
+    SCALES = [21, 45, 99, 153, 207, 261, 315]
+    ASPECT_RATIOS = [(1,), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)]
+    STEPS = (16, 32, 64, 100, 150, 300)
+    PRIOR_SCALING = (0.1, 0.2)
+
+
+    # `MINDRECORD_DIR` and `COCO_ROOT` are better to use absolute path.
+    MINDRECORD_DIR = "MindRecord_COCO"
+    COCO_ROOT = "coco2017"
+    TRAIN_DATA_TYPE = "train2017"
+    VAL_DATA_TYPE = "val2017"
+    INSTANCES_SET = "annotations/instances_{}.json"
+    COCO_CLASSES = ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+                    'train', 'truck', 'boat', 'traffic light', 'fire', 'hydrant',
+                    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+                    'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
+                    'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
+                    'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+                    'kite', 'baseball bat', 'baseball glove', 'skateboard',
+                    'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
+                    'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+                    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+                    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
+                    'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+                    'keyboard', 'cell phone', 'microwave oven', 'toaster', 'sink',
+                    'refrigerator', 'book', 'clock', 'vase', 'scissors',
+                    'teddy bear', 'hair drier', 'toothbrush')
+    NUM_CLASSES = len(COCO_CLASSES)

example/ssd_coco2017/dataset.py

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+# 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.
+# ============================================================================
+
+"""SSD dataset"""
+from __future__ import division
+
+import os
+import math
+import itertools as it
+import numpy as np
+import cv2
+
+import mindspore.dataset as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+from mindspore.mindrecord import FileWriter
+from config import ConfigSSD
+
+config = ConfigSSD()
+
+class GeneratDefaultBoxes():
+    """
+    Generate Default boxes for SSD, follows the order of (W, H, archor_sizes).
+    `self.default_boxes` has a shape of [archor_sizes, H, W, 4], the last dimension is [x, y, w, h].
+    `self.default_boxes_ltrb` has a shape as `self.default_boxes`, the last dimension is [x1, y1, x2, y2].
+    """
+    def __init__(self):
+        fk = config.IMG_SHAPE[0] / np.array(config.STEPS)
+        self.default_boxes = []
+        for idex, feature_size in enumerate(config.FEATURE_SIZE):
+            sk1 = config.SCALES[idex] / config.IMG_SHAPE[0]
+            sk2 = config.SCALES[idex + 1] / config.IMG_SHAPE[0]
+            sk3 = math.sqrt(sk1 * sk2)
+
+            if config.NUM_DEFAULT[idex] == 3:
+                all_sizes = [(0.5, 1.0), (1.0, 1.0), (1.0, 0.5)]
+            else:
+                all_sizes = [(sk1, sk1), (sk3, sk3)]
+                for aspect_ratio in config.ASPECT_RATIOS[idex]:
+                    w, h = sk1 * math.sqrt(aspect_ratio), sk1 / math.sqrt(aspect_ratio)
+                    all_sizes.append((w, h))
+                    all_sizes.append((h, w))
+
+            assert len(all_sizes) == config.NUM_DEFAULT[idex]
+
+            for i, j in it.product(range(feature_size), repeat=2):
+                for w, h in all_sizes:
+                    cx, cy = (j + 0.5) / fk[idex], (i + 0.5) / fk[idex]
+                    box = [np.clip(k, 0, 1) for k in (cx, cy, w, h)]
+                    self.default_boxes.append(box)
+
+        def to_ltrb(cx, cy, w, h):
+            return cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
+
+        # For IoU calculation
+        self.default_boxes_ltrb = np.array(tuple(to_ltrb(*i) for i in self.default_boxes), dtype='float32')
+        self.default_boxes = np.array(self.default_boxes, dtype='float32')
+
+
+default_boxes_ltrb = GeneratDefaultBoxes().default_boxes_ltrb
+default_boxes = GeneratDefaultBoxes().default_boxes
+x1, y1, x2, y2 = np.split(default_boxes_ltrb[:, :4], 4, axis=-1)
+vol_anchors = (x2 - x1) * (y2 - y1)
+matching_threshold = config.MATCH_THRESHOLD
+
+
+def ssd_bboxes_encode(boxes):
+    """
+    Labels anchors with ground truth inputs.
+
+    Args:
+        boxex: ground truth with shape [N, 5], for each row, it stores [x, y, w, h, cls].
+
+    Returns:
+        gt_loc: location ground truth with shape [num_anchors, 4].
+        gt_label: class ground truth with shape [num_anchors, 1].
+        num_matched_boxes: number of positives in an image.
+    """
+
+    def jaccard_with_anchors(bbox):
+        """Compute jaccard score a box and the anchors."""
+        # Intersection bbox and volume.
+        xmin = np.maximum(x1, bbox[0])
+        ymin = np.maximum(y1, bbox[1])
+        xmax = np.minimum(x2, bbox[2])
+        ymax = np.minimum(y2, bbox[3])
+        w = np.maximum(xmax - xmin, 0.)
+        h = np.maximum(ymax - ymin, 0.)
+
+        # Volumes.
+        inter_vol = h * w
+        union_vol = vol_anchors + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) - inter_vol
+        jaccard = inter_vol / union_vol
+        return np.squeeze(jaccard)
+
+    pre_scores = np.zeros((config.NUM_SSD_BOXES), dtype=np.float32)
+    t_boxes = np.zeros((config.NUM_SSD_BOXES, 4), dtype=np.float32)
+    t_label = np.zeros((config.NUM_SSD_BOXES), dtype=np.int64)
+    for bbox in boxes:
+        label = int(bbox[4])
+        scores = jaccard_with_anchors(bbox)
+        mask = (scores > matching_threshold)
+        if not np.any(mask):
+            mask[np.argmax(scores)] = True
+
+        mask = mask & (scores > pre_scores)
+        pre_scores = np.maximum(pre_scores, scores)
+        t_label = mask * label + (1 - mask) * t_label
+        for i in range(4):
+            t_boxes[:, i] = mask * bbox[i] + (1 - mask) * t_boxes[:, i]
+
+    index = np.nonzero(t_label)
+
+    # Transform to ltrb.
+    bboxes = np.zeros((config.NUM_SSD_BOXES, 4), dtype=np.float32)
+    bboxes[:, [0, 1]] = (t_boxes[:, [0, 1]] + t_boxes[:, [2, 3]]) / 2
+    bboxes[:, [2, 3]] = t_boxes[:, [2, 3]] - t_boxes[:, [0, 1]]
+
+    # Encode features.
+    bboxes_t = bboxes[index]
+    default_boxes_t = default_boxes[index]
+    bboxes_t[:, :2] = (bboxes_t[:, :2] - default_boxes_t[:, :2]) / (default_boxes_t[:, 2:] * config.PRIOR_SCALING[0])
+    bboxes_t[:, 2:4] = np.log(bboxes_t[:, 2:4] / default_boxes_t[:, 2:4]) / config.PRIOR_SCALING[1]
+    bboxes[index] = bboxes_t
+
+    num_match_num = np.array([len(np.nonzero(t_label)[0])], dtype=np.int32)
+    return bboxes, t_label.astype(np.int32), num_match_num
+
+def ssd_bboxes_decode(boxes, index, image_shape):
+    """Decode predict boxes to [x, y, w, h]"""
+    boxes_t = boxes[index]
+    default_boxes_t = default_boxes[index]
+    boxes_t[:, :2] = boxes_t[:, :2] * config.PRIOR_SCALING[0] * default_boxes_t[:, 2:] + default_boxes_t[:, :2]
+    boxes_t[:, 2:4] = np.exp(boxes_t[:, 2:4] * config.PRIOR_SCALING[1]) * default_boxes_t[:, 2:4]
+
+    bboxes = np.zeros((len(boxes_t), 4), dtype=np.float32)
+
+    bboxes[:, [0, 1]] = boxes_t[:, [0, 1]] - boxes_t[:, [2, 3]] / 2
+    bboxes[:, [2, 3]] = boxes_t[:, [0, 1]] + boxes_t[:, [2, 3]] / 2
+
+    return bboxes
+
+def preprocess_fn(image, box, is_training):
+    """Preprocess function for dataset."""
+
+    def _rand(a=0., b=1.):
+        """Generate random."""
+        return np.random.rand() * (b - a) + a
+
+    def _infer_data(image, input_shape, box):
+        img_h, img_w, _ = image.shape
+        input_h, input_w = input_shape
+
+        scale = min(float(input_w) / float(img_w), float(input_h) / float(img_h))
+        nw = int(img_w * scale)
+        nh = int(img_h * scale)
+
+        image = cv2.resize(image, (nw, nh))
+
+        new_image = np.zeros((input_h, input_w, 3), np.float32)
+        dh = (input_h - nh) // 2
+        dw = (input_w - nw) // 2
+        new_image[dh: (nh + dh), dw: (nw + dw), :] = image
+        image = new_image
+
+        #When the channels of image is 1
+        if len(image.shape) == 2:
+            image = np.expand_dims(image, axis=-1)
+            image = np.concatenate([image, image, image], axis=-1)
+
+        box = box.astype(np.float32)
+
+        box[:, [0, 2]] = (box[:, [0, 2]] * scale + dw) / input_w
+        box[:, [1, 3]] = (box[:, [1, 3]] * scale + dh) / input_h
+        return image, np.array((img_h, img_w), np.float32), box
+
+    def _data_aug(image, box, is_training, image_size=(300, 300)):
+        """Data augmentation function."""
+        ih, iw, _ = image.shape
+        w, h = image_size
+
+        if not is_training:
+            return _infer_data(image, image_size, box)
+        # Random settings
+        scale_w = _rand(0.75, 1.25)
+        scale_h = _rand(0.75, 1.25)
+
+        flip = _rand() < .5
+        nw = iw * scale_w
+        nh = ih * scale_h
+        scale = min(w / nw, h / nh)
+        nw = int(scale * nw)
+        nh = int(scale * nh)
+
+        # Resize image
+        image = cv2.resize(image, (nw, nh))
+
+        # place image
+        new_image = np.zeros((h, w, 3), dtype=np.float32)
+        dw = (w - nw) // 2
+        dh = (h - nh) // 2
+        new_image[dh:dh + nh, dw:dw + nw, :] = image
+        image = new_image
+
+        # Flip image or not
+        if flip:
+            image = cv2.flip(image, 1, dst=None)
+
+        # Convert image to gray or not
+        gray = _rand() < .25
+        if gray:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # When the channels of image is 1
+        if len(image.shape) == 2:
+            image = np.expand_dims(image, axis=-1)
+            image = np.concatenate([image, image, image], axis=-1)
+
+        box = box.astype(np.float32)
+
+        # Transform box with shape[x1, y1, x2, y2].
+        box[:, [0, 2]] = (box[:, [0, 2]] * scale * scale_w + dw) / w
+        box[:, [1, 3]] = (box[:, [1, 3]] * scale * scale_h + dh) / h
+
+        if flip:
+            box[:, [0, 2]] = 1 - box[:, [2, 0]]
+
+        box, label, num_match_num = ssd_bboxes_encode(box)
+        return image, box, label, num_match_num
+    return _data_aug(image, box, is_training, image_size=config.IMG_SHAPE)
+
+
+def create_coco_label(is_training):
+    """Get image path and annotation from COCO."""
+    from pycocotools.coco import COCO
+
+    coco_root = config.COCO_ROOT
+    data_type = config.VAL_DATA_TYPE
+    if is_training:
+        data_type = config.TRAIN_DATA_TYPE
+
+    #Classes need to train or test.
+    train_cls = config.COCO_CLASSES
+    train_cls_dict = {}
+    for i, cls in enumerate(train_cls):
+        train_cls_dict[cls] = i
+
+    anno_json = os.path.join(coco_root, config.INSTANCES_SET.format(data_type))
+
+    coco = COCO(anno_json)
+    classs_dict = {}
+    cat_ids = coco.loadCats(coco.getCatIds())
+    for cat in cat_ids:
+        classs_dict[cat["id"]] = cat["name"]
+
+    image_ids = coco.getImgIds()
+    image_files = []
+    image_anno_dict = {}
+
+    for img_id in image_ids:
+        image_info = coco.loadImgs(img_id)
+        file_name = image_info[0]["file_name"]
+        anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
+        anno = coco.loadAnns(anno_ids)
+        image_path = os.path.join(coco_root, data_type, file_name)
+        annos = []
+        for label in anno:
+            bbox = label["bbox"]
+            class_name = classs_dict[label["category_id"]]
+            if class_name in train_cls:
+                x_min, x_max = bbox[0], bbox[0] + bbox[2]
+                y_min, y_max = bbox[1], bbox[1] + bbox[3]
+                annos.append(list(map(round, [x_min, y_min, x_max, y_max])) + [train_cls_dict[class_name]])
+        if len(annos) >= 1:
+            image_files.append(image_path)
+            image_anno_dict[image_path] = np.array(annos)
+    return image_files, image_anno_dict
+
+
+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]
+        image_path = os.path.join(image_dir, file_name)
+        if os.path.isfile(image_path):
+            image_anno_dict[image_path] = anno_parser(line_split[1:])
+            image_files.append(image_path)
+    return image_files, image_anno_dict
+
+
+def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="ssd.mindrecord", file_num=8):
+    """Create MindRecord file."""
+    mindrecord_dir = config.MINDRECORD_DIR
+    mindrecord_path = os.path.join(mindrecord_dir, prefix)
+    writer = FileWriter(mindrecord_path, file_num)
+    if dataset == "coco":
+        image_files, image_anno_dict = create_coco_label(is_training)
+    else:
+        image_files, image_anno_dict = filter_valid_data(config.IMAGE_DIR, config.ANNO_PATH)
+
+    ssd_json = {
+        "image": {"type": "bytes"},
+        "annotation": {"type": "int32", "shape": [-1, 5]},
+    }
+    writer.add_schema(ssd_json, "ssd_json")
+
+    for image_name in image_files:
+        with open(image_name, 'rb') as f:
+            img = f.read()
+        annos = np.array(image_anno_dict[image_name], dtype=np.int32)
+        row = {"image": img, "annotation": annos}
+        writer.write_raw_data([row])
+    writer.commit()
+
+
+def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num=1, rank=0,
+                       is_training=True, num_parallel_workers=4):
+    """Creatr SSD dataset with MindDataset."""
+    ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
+                        num_parallel_workers=num_parallel_workers, shuffle=is_training)
+    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", "box", "label", "num_match_num"],
+                    columns_order=["image", "box", "label", "num_match_num"],
+                    operations=compose_map_func, python_multiprocessing=True, num_parallel_workers=num_parallel_workers)
+        ds = ds.map(input_columns=["image"], operations=hwc_to_chw, python_multiprocessing=True,
+                    num_parallel_workers=num_parallel_workers)
+        ds = ds.batch(batch_size, drop_remainder=True)
+        ds = ds.repeat(repeat_num)
+    else:
+        hwc_to_chw = C.HWC2CHW()
+        ds = ds.map(input_columns=["image", "annotation"],
+                    output_columns=["image", "image_shape", "annotation"],
+                    columns_order=["image", "image_shape", "annotation"],
+                    operations=compose_map_func)
+        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)
+    return ds

example/ssd_coco2017/eval.py

@@ -0,0 +1,99 @@
+# 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.
+# ============================================================================
+
+"""Evaluation for SSD"""
+import os
+import argparse
+import time
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.model_zoo.ssd import SSD300, ssd_mobilenet_v2
+from dataset import create_ssd_dataset, data_to_mindrecord_byte_image
+from config import ConfigSSD
+from util import metrics
+
+def ssd_eval(dataset_path, ckpt_path):
+    """SSD evaluation."""
+
+    ds = create_ssd_dataset(dataset_path, batch_size=1, repeat_num=1, is_training=False)
+    net = SSD300(ssd_mobilenet_v2(), ConfigSSD(), is_training=False)
+    print("Load Checkpoint!")
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+
+    net.set_train(False)
+    i = 1.
+    total = ds.get_dataset_size()
+    start = time.time()
+    pred_data = []
+    print("\n========================================\n")
+    print("total images num: ", total)
+    print("Processing, please wait a moment.")
+    for data in ds.create_dict_iterator():
+        img_np = data['image']
+        image_shape = data['image_shape']
+        annotation = data['annotation']
+
+        output = net(Tensor(img_np))
+        for batch_idx in range(img_np.shape[0]):
+            pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
+                              "box_scores": output[1].asnumpy()[batch_idx],
+                              "annotation": annotation,
+                              "image_shape": image_shape})
+        percent = round(i / total * 100, 2)
+
+        print(f'    {str(percent)} [{i}/{total}]', end='\r')
+        i += 1
+    cost_time = int((time.time() - start) * 1000)
+    print(f'    100% [{total}/{total}] cost {cost_time} ms')
+    mAP = metrics(pred_data)
+    print("\n========================================\n")
+    print(f"mAP: {mAP}")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='SSD evaluation')
+    parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
+    parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True)
+
+    config = ConfigSSD()
+    prefix = "ssd_eval.mindrecord"
+    mindrecord_dir = config.MINDRECORD_DIR
+    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        if args_opt.dataset == "coco":
+            if os.path.isdir(config.COCO_ROOT):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("coco", False, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("COCO_ROOT not exits.")
+        else:
+            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("other", False, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("IMAGE_DIR or ANNO_PATH not exits.")
+
+    print("Start Eval!")
+    ssd_eval(mindrecord_file, args_opt.checkpoint_path)

example/ssd_coco2017/run_distribute_train.sh

@@ -0,0 +1,54 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the scipt as: "
+echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDSPORE_HCCL_CONFIG_PATH"
+echo "for example: sh run_distribute_train.sh 8 150 coco /data/hccl.json"
+echo "It is better to use absolute path."
+echo "The learning rate is 0.4 as default, if you want other lr, please change the value in this script."
+echo "=============================================================================================================="
+
+# Before start distribute train, first create mindrecord files.
+python train.py --only_create_dataset=1
+
+echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+
+export RANK_SIZE=$1
+EPOCH_SIZE=$2
+DATASET=$3
+export MINDSPORE_HCCL_CONFIG_PATH=$4
+
+
+for((i=0;i<RANK_SIZE;i++))
+do
+    export DEVICE_ID=$i
+    rm -rf LOG$i
+    mkdir ./LOG$i
+    cp  *.py ./LOG$i
+    cd ./LOG$i || exit
+    export RANK_ID=$i
+    echo "start training for rank $i, device $DEVICE_ID"
+    env > env.log
+    python ../train.py  \
+    --distribute=1  \
+    --lr=0.4 \
+    --dataset=$DATASET \
+    --device_num=$RANK_SIZE  \
+    --device_id=$DEVICE_ID  \
+    --epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
+    cd ../
+done

example/ssd_coco2017/train.py

@@ -0,0 +1,176 @@
+# 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 SSD and get checkpoint files."""
+
+import os
+import math
+import argparse
+import numpy as np
+import mindspore.nn as nn
+from mindspore import context, Tensor
+from mindspore.communication.management import init
+from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
+from mindspore.train import Model, ParallelMode
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common.initializer import initializer
+
+from mindspore.model_zoo.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2
+from config import ConfigSSD
+from dataset import create_ssd_dataset, data_to_mindrecord_byte_image
+
+
+def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
+    """
+    generate learning rate array
+
+    Args:
+       global_step(int): total steps of the training
+       lr_init(float): init learning rate
+       lr_end(float): end learning rate
+       lr_max(float): max learning rate
+       warmup_epochs(int): number of warmup epochs
+       total_epochs(int): total epoch of training
+       steps_per_epoch(int): steps of one epoch
+
+    Returns:
+       np.array, learning rate array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+        else:
+            lr = lr_end + (lr_max - lr_end) * \
+                 (1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
+        if lr < 0.0:
+            lr = 0.0
+        lr_each_step.append(lr)
+
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate
+
+
+def init_net_param(network, initialize_mode='XavierUniform'):
+    """Init the parameters in net."""
+    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(initialize_mode, p.data.shape(), p.data.dtype()))
+
+def main():
+    parser = argparse.ArgumentParser(description="SSD training")
+    parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create "
+                                                                                "Mindrecord, default is false.")
+    parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
+    parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
+    parser.add_argument("--lr", type=float, default=0.25, help="Learning rate, default is 0.25.")
+    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink.")
+    parser.add_argument("--dataset", type=str, default="coco", help="Dataset, defalut is coco.")
+    parser.add_argument("--epoch_size", type=int, default=70, help="Epoch size, default is 70.")
+    parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
+    parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path.")
+    parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
+    parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True)
+
+    if args_opt.distribute:
+        device_num = args_opt.device_num
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
+                                          device_num=device_num)
+        init()
+        rank = args_opt.device_id % device_num
+    else:
+        rank = 0
+        device_num = 1
+
+    print("Start create dataset!")
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is ssd.mindrecord0, 1, ... file_num.
+
+    config = ConfigSSD()
+    prefix = "ssd.mindrecord"
+    mindrecord_dir = config.MINDRECORD_DIR
+    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        if args_opt.dataset == "coco":
+            if os.path.isdir(config.COCO_ROOT):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("coco", True, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("COCO_ROOT not exits.")
+        else:
+            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
+                print("Create Mindrecord.")
+                data_to_mindrecord_byte_image("other", True, prefix)
+                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+            else:
+                print("IMAGE_DIR or ANNO_PATH not exits.")
+
+    if not args_opt.only_create_dataset:
+        loss_scale = float(args_opt.loss_scale)
+
+        # When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0.
+        dataset = create_ssd_dataset(mindrecord_file, repeat_num=args_opt.epoch_size,
+                                     batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
+
+        dataset_size = dataset.get_dataset_size()
+        print("Create dataset done!")
+
+        ssd = SSD300(backbone=ssd_mobilenet_v2(), config=config)
+        net = SSDWithLossCell(ssd, config)
+        init_net_param(net)
+
+        # checkpoint
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
+        ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=None, config=ckpt_config)
+
+        lr = Tensor(get_lr(global_step=0, lr_init=0, lr_end=0, lr_max=args_opt.lr,
+                           warmup_epochs=max(args_opt.epoch_size // 20, 1),
+                           total_epochs=args_opt.epoch_size,
+                           steps_per_epoch=dataset_size))
+        opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, 0.9, 0.0001, loss_scale)
+        net = TrainingWrapper(net, opt, loss_scale)
+
+        if args_opt.checkpoint_path != "":
+            param_dict = load_checkpoint(args_opt.checkpoint_path)
+            load_param_into_net(net, param_dict)
+
+        callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
+
+        model = Model(net)
+        dataset_sink_mode = False
+        if args_opt.mode == "sink":
+            print("In sink mode, one epoch return a loss.")
+            dataset_sink_mode = True
+        print("Start train SSD, the first epoch will be slower because of the graph compilation.")
+        model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
+
+if __name__ == '__main__':
+    main()

example/ssd_coco2017/util.py

@@ -0,0 +1,208 @@
+# 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.
+# ============================================================================
+"""metrics utils"""
+
+import numpy as np
+from config import ConfigSSD
+from dataset import ssd_bboxes_decode
+
+
+def calc_iou(bbox_pred, bbox_ground):
+    """Calculate iou of predicted bbox and ground truth."""
+    bbox_pred = np.expand_dims(bbox_pred, axis=0)
+
+    pred_w = bbox_pred[:, 2] - bbox_pred[:, 0]
+    pred_h = bbox_pred[:, 3] - bbox_pred[:, 1]
+    pred_area = pred_w * pred_h
+
+    gt_w = bbox_ground[:, 2] - bbox_ground[:, 0]
+    gt_h = bbox_ground[:, 3] - bbox_ground[:, 1]
+    gt_area = gt_w * gt_h
+
+    iw = np.minimum(bbox_pred[:, 2], bbox_ground[:, 2]) - np.maximum(bbox_pred[:, 0], bbox_ground[:, 0])
+    ih = np.minimum(bbox_pred[:, 3], bbox_ground[:, 3]) - np.maximum(bbox_pred[:, 1], bbox_ground[:, 1])
+
+    iw = np.maximum(iw, 0)
+    ih = np.maximum(ih, 0)
+    intersection_area = iw * ih
+
+    union_area = pred_area + gt_area - intersection_area
+    union_area = np.maximum(union_area, np.finfo(float).eps)
+
+    iou = intersection_area *  1. / union_area
+    return iou
+
+
+def apply_nms(all_boxes, all_scores, thres, max_boxes):
+    """Apply NMS to bboxes."""
+    x1 = all_boxes[:, 0]
+    y1 = all_boxes[:, 1]
+    x2 = all_boxes[:, 2]
+    y2 = all_boxes[:, 3]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+    order = all_scores.argsort()[::-1]
+    keep = []
+
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        if len(keep) >= max_boxes:
+            break
+
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thres)[0]
+
+        order = order[inds + 1]
+    return keep
+
+
+def calc_ap(recall, precision):
+    """Calculate AP."""
+    correct_recall = np.concatenate(([0.], recall, [1.]))
+    correct_precision = np.concatenate(([0.], precision, [0.]))
+
+    for i in range(correct_recall.size - 1, 0, -1):
+        correct_precision[i - 1] = np.maximum(correct_precision[i - 1], correct_precision[i])
+
+    i = np.where(correct_recall[1:] != correct_recall[:-1])[0]
+
+    ap = np.sum((correct_recall[i + 1] - correct_recall[i]) * correct_precision[i + 1])
+
+    return ap
+
+def metrics(pred_data):
+    """Calculate mAP of predicted bboxes."""
+    config = ConfigSSD()
+    num_classes = config.NUM_CLASSES
+
+    all_detections = [None for i in range(num_classes)]
+    all_pred_scores = [None for i in range(num_classes)]
+    all_annotations = [None for i in range(num_classes)]
+    average_precisions = {}
+    num = [0 for i in range(num_classes)]
+    accurate_num = [0 for i in range(num_classes)]
+
+    for sample in pred_data:
+        pred_boxes = sample['boxes']
+        boxes_scores = sample['box_scores']
+        annotation = sample['annotation']
+        image_shape = sample['image_shape']
+
+        annotation = np.squeeze(annotation, axis=0)
+        image_shape = np.squeeze(image_shape, axis=0)
+
+        pred_labels = np.argmax(boxes_scores, axis=-1)
+        index = np.nonzero(pred_labels)
+        pred_boxes = ssd_bboxes_decode(pred_boxes, index, image_shape)
+
+        pred_boxes = pred_boxes.clip(0, 1)
+        boxes_scores = np.max(boxes_scores, axis=-1)
+        boxes_scores = boxes_scores[index]
+        pred_labels = pred_labels[index]
+
+        top_k = 50
+
+        for c in range(1, num_classes):
+            if len(pred_labels) >= 1:
+                class_box_scores = boxes_scores[pred_labels == c]
+                class_boxes = pred_boxes[pred_labels == c]
+
+                nms_index = apply_nms(class_boxes, class_box_scores, config.MATCH_THRESHOLD, top_k)
+
+                class_boxes = class_boxes[nms_index]
+                class_box_scores = class_box_scores[nms_index]
+
+                cmask = class_box_scores > 0.5
+                class_boxes = class_boxes[cmask]
+                class_box_scores = class_box_scores[cmask]
+
+                all_detections[c] = class_boxes
+                all_pred_scores[c] = class_box_scores
+
+        for c in range(1, num_classes):
+            if len(annotation) >= 1:
+                all_annotations[c] = annotation[annotation[:, 4] == c, :4]
+
+        for c in range(1, num_classes):
+            false_positives = np.zeros((0,))
+            true_positives = np.zeros((0,))
+            scores = np.zeros((0,))
+            num_annotations = 0.0
+
+            annotations = all_annotations[c]
+            num_annotations += annotations.shape[0]
+            detections = all_detections[c]
+            pred_scores = all_pred_scores[c]
+
+            for index, detection in enumerate(detections):
+                scores = np.append(scores, pred_scores[index])
+                if len(annotations) >= 1:
+                    IoUs = calc_iou(detection, annotations)
+                    assigned_anno = np.argmax(IoUs)
+                    max_overlap = IoUs[assigned_anno]
+
+                    if max_overlap >= 0.5:
+                        false_positives = np.append(false_positives, 0)
+                        true_positives = np.append(true_positives, 1)
+                    else:
+                        false_positives = np.append(false_positives, 1)
+                        true_positives = np.append(true_positives, 0)
+                else:
+                    false_positives = np.append(false_positives, 1)
+                    true_positives = np.append(true_positives, 0)
+
+            if num_annotations == 0:
+                if c not in average_precisions.keys():
+                    average_precisions[c] = 0
+                continue
+            accurate_num[c] = 1
+            indices = np.argsort(-scores)
+            false_positives = false_positives[indices]
+            true_positives = true_positives[indices]
+
+            false_positives = np.cumsum(false_positives)
+            true_positives = np.cumsum(true_positives)
+
+            recall = true_positives * 1. / num_annotations
+            precision = true_positives * 1. / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)
+
+            average_precision = calc_ap(recall, precision)
+
+            if c not in average_precisions.keys():
+                average_precisions[c] = average_precision
+            else:
+                average_precisions[c] += average_precision
+
+            num[c] += 1
+
+    count = 0
+    for key in average_precisions:
+        if num[key] != 0:
+            count += (average_precisions[key] / num[key])
+
+    mAP = count * 1. / accurate_num.count(1)
+    return mAP

mindspore/model_zoo/ssd.py

@@ -0,0 +1,367 @@
+# 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.
+# ============================================================================
+
+"""SSD net based MobilenetV2."""
+import mindspore.common.dtype as mstype
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.communication.management import get_group_size
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.common.initializer import initializer
+from .mobilenet import InvertedResidual, ConvBNReLU
+
+
+def _conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same'):
+    weight_shape = (out_channel, in_channel, kernel_size, kernel_size)
+    weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
+    return nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=stride,
+                     padding=0, pad_mode=pad_mod, weight_init=weight)
+
+
+def _make_divisible(v, divisor, min_value=None):
+    """nsures that all layers have a channel number that is divisible by 8."""
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class FlattenConcat(nn.Cell):
+    """
+    Concatenate predictions into a single tensor.
+
+    Args:
+        config (Class): The default config of SSD.
+
+    Returns:
+        Tensor, flatten predictions.
+    """
+    def __init__(self, config):
+        super(FlattenConcat, self).__init__()
+        self.sizes = config.FEATURE_SIZE
+        self.length = len(self.sizes)
+        self.num_default = config.NUM_DEFAULT
+        self.concat = P.Concat(axis=-1)
+        self.transpose = P.Transpose()
+    def construct(self, x):
+        output = ()
+        for i in range(self.length):
+            shape = F.shape(x[i])
+            mid_shape = (shape[0], -1, self.num_default[i], self.sizes[i], self.sizes[i])
+            final_shape = (shape[0], -1, self.num_default[i] * self.sizes[i] * self.sizes[i])
+            output += (F.reshape(F.reshape(x[i], mid_shape), final_shape),)
+        res = self.concat(output)
+        return self.transpose(res, (0, 2, 1))
+
+
+class MultiBox(nn.Cell):
+    """
+    Multibox conv layers. Each multibox layer contains class conf scores and localization predictions.
+
+    Args:
+        config (Class): The default config of SSD.
+
+    Returns:
+        Tensor, localization predictions.
+        Tensor, class conf scores.
+    """
+    def __init__(self, config):
+        super(MultiBox, self).__init__()
+        num_classes = config.NUM_CLASSES
+        out_channels = config.EXTRAS_OUT_CHANNELS
+        num_default = config.NUM_DEFAULT
+
+        loc_layers = []
+        cls_layers = []
+        for k, out_channel in enumerate(out_channels):
+            loc_layers += [_conv2d(out_channel, 4 * num_default[k],
+                                   kernel_size=3, stride=1, pad_mod='same')]
+            cls_layers += [_conv2d(out_channel, num_classes * num_default[k],
+                                   kernel_size=3, stride=1, pad_mod='same')]
+
+        self.multi_loc_layers = nn.layer.CellList(loc_layers)
+        self.multi_cls_layers = nn.layer.CellList(cls_layers)
+        self.flatten_concat = FlattenConcat(config)
+
+    def construct(self, inputs):
+        loc_outputs = ()
+        cls_outputs = ()
+        for i in range(len(self.multi_loc_layers)):
+            loc_outputs += (self.multi_loc_layers[i](inputs[i]),)
+            cls_outputs += (self.multi_cls_layers[i](inputs[i]),)
+        return self.flatten_concat(loc_outputs), self.flatten_concat(cls_outputs)
+
+
+class SSD300(nn.Cell):
+    """
+    SSD300 Network. Default backbone is resnet34.
+
+    Args:
+        backbone (Cell): Backbone Network.
+        config (Class): The default config of SSD.
+
+    Returns:
+        Tensor, localization predictions.
+        Tensor, class conf scores.
+
+    Examples:backbone
+         SSD300(backbone=resnet34(num_classes=None),
+                config=ConfigSSDResNet34()).
+    """
+    def __init__(self, backbone, config, is_training=True):
+        super(SSD300, self).__init__()
+
+        self.backbone = backbone
+        in_channels = config.EXTRAS_IN_CHANNELS
+        out_channels = config.EXTRAS_OUT_CHANNELS
+        ratios = config.EXTRAS_RATIO
+        strides = config.EXTRAS_STRIDES
+        residual_list = []
+        for i in range(2, len(in_channels)):
+            residual = InvertedResidual(in_channels[i], out_channels[i], stride=strides[i], expand_ratio=ratios[i])
+            residual_list.append(residual)
+        self.multi_residual = nn.layer.CellList(residual_list)
+        self.multi_box = MultiBox(config)
+        self.is_training = is_training
+        if not is_training:
+            self.softmax = P.Softmax()
+
+
+    def construct(self, x):
+        layer_out_13, output = self.backbone(x)
+        multi_feature = (layer_out_13, output)
+        feature = output
+        for residual in self.multi_residual:
+            feature = residual(feature)
+            multi_feature += (feature,)
+        pred_loc, pred_label = self.multi_box(multi_feature)
+        if not self.is_training:
+            pred_label = self.softmax(pred_label)
+        return pred_loc, pred_label
+
+
+class LocalizationLoss(nn.Cell):
+    """"
+    Computes the localization loss with SmoothL1Loss.
+
+    Returns:
+        Tensor, box regression loss.
+    """
+    def __init__(self):
+        super(LocalizationLoss, self).__init__()
+        self.reduce_sum = P.ReduceSum()
+        self.reduce_mean = P.ReduceMean()
+        self.loss = nn.SmoothL1Loss()
+        self.expand_dims = P.ExpandDims()
+        self.less = P.Less()
+
+    def construct(self, pred_loc, gt_loc, gt_label, num_matched_boxes):
+        mask = F.cast(self.less(0, gt_label), mstype.float32)
+        mask = self.expand_dims(mask, -1)
+        smooth_l1 = self.loss(gt_loc, pred_loc) * mask
+        box_loss = self.reduce_sum(smooth_l1, 1)
+        return self.reduce_mean(box_loss / F.cast(num_matched_boxes, mstype.float32), (0, 1))
+
+
+class ClassificationLoss(nn.Cell):
+    """"
+    Computes the classification loss with hard example mining.
+
+    Args:
+        config (Class): The default config of SSD.
+
+    Returns:
+        Tensor, classification loss.
+    """
+    def __init__(self, config):
+        super(ClassificationLoss, self).__init__()
+        self.num_classes = config.NUM_CLASSES
+        self.num_boxes = config.NUM_SSD_BOXES
+        self.neg_pre_positive = config.NEG_PRE_POSITIVE
+        self.minimum = P.Minimum()
+        self.less = P.Less()
+        self.sort = P.TopK()
+        self.tile = P.Tile()
+        self.reduce_sum = P.ReduceSum()
+        self.reduce_mean = P.ReduceMean()
+        self.expand_dims = P.ExpandDims()
+        self.sort_descend = P.TopK(True)
+        self.cross_entropy = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
+
+    def construct(self, pred_label, gt_label, num_matched_boxes):
+        gt_label = F.cast(gt_label, mstype.int32)
+        mask = F.cast(self.less(0, gt_label), mstype.float32)
+        gt_label_shape = F.shape(gt_label)
+        pred_label = F.reshape(pred_label, (-1, self.num_classes))
+        gt_label = F.reshape(gt_label, (-1,))
+        cross_entropy = self.cross_entropy(pred_label, gt_label)
+        cross_entropy = F.reshape(cross_entropy, gt_label_shape)
+
+        # Hard example mining
+        num_matched_boxes = F.reshape(num_matched_boxes, (-1,))
+        neg_masked_cross_entropy = F.cast(cross_entropy * (1- mask), mstype.float16)
+        _, loss_idx = self.sort_descend(neg_masked_cross_entropy, self.num_boxes)
+        _, relative_position = self.sort(F.cast(loss_idx, mstype.float16), self.num_boxes)
+        num_neg_boxes = self.minimum(num_matched_boxes * self.neg_pre_positive, self.num_boxes)
+        tile_num_neg_boxes = self.tile(self.expand_dims(num_neg_boxes, -1), (1, self.num_boxes))
+        top_k_neg_mask = F.cast(self.less(relative_position, tile_num_neg_boxes), mstype.float32)
+        class_loss = self.reduce_sum(cross_entropy * (mask + top_k_neg_mask), 1)
+        return self.reduce_mean(class_loss / F.cast(num_matched_boxes, mstype.float32), 0)
+
+
+class SSDWithLossCell(nn.Cell):
+    """"
+    Provide SSD training loss through network.
+
+    Args:
+        network (Cell): The training network.
+        config (Class): SSD config.
+
+    Returns:
+        Tensor, the loss of the network.
+    """
+    def __init__(self, network, config):
+        super(SSDWithLossCell, self).__init__()
+        self.network = network
+        self.class_loss = ClassificationLoss(config)
+        self.box_loss = LocalizationLoss()
+
+    def construct(self, x, gt_loc, gt_label, num_matched_boxes):
+        pred_loc, pred_label = self.network(x)
+        loss_cls = self.class_loss(pred_label, gt_label, num_matched_boxes)
+        loss_loc = self.box_loss(pred_loc, gt_loc, gt_label, num_matched_boxes)
+        return loss_cls + loss_loc
+
+
+class TrainingWrapper(nn.Cell):
+    """
+    Encapsulation class of SSD 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 SSDWithMobileNetV2(nn.Cell):
+    """
+    MobileNetV2 architecture for SSD backbone.
+
+    Args:
+        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
+        inverted_residual_setting (list): Inverted residual settings. Default is None
+        round_nearest (list): Channel round to. Default is 8
+    Returns:
+        Tensor, the 13th feature after ConvBNReLU in MobileNetV2.
+        Tensor, the last feature in MobileNetV2.
+
+    Examples:
+        >>> SSDWithMobileNetV2()
+    """
+    def __init__(self, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
+        super(SSDWithMobileNetV2, self).__init__()
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+
+        if inverted_residual_setting is None:
+            inverted_residual_setting = [
+                # t, c, n, s
+                [1, 16, 1, 1],
+                [6, 24, 2, 2],
+                [6, 32, 3, 2],
+                [6, 64, 4, 2],
+                [6, 96, 3, 1],
+                [6, 160, 3, 2],
+                [6, 320, 1, 1],
+            ]
+        if len(inverted_residual_setting[0]) != 4:
+            raise ValueError("inverted_residual_setting should be non-empty "
+                             "or a 4-element list, got {}".format(inverted_residual_setting))
+
+        #building first layer
+        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
+        features = [ConvBNReLU(3, input_channel, stride=2)]
+        # building inverted residual blocks
+        layer_index = 0
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = _make_divisible(c * width_mult, round_nearest)
+            for i in range(n):
+                if layer_index == 13:
+                    hidden_dim = int(round(input_channel * t))
+                    self.expand_layer_conv_13 = ConvBNReLU(input_channel, hidden_dim, kernel_size=1)
+                stride = s if i == 0 else 1
+                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+                input_channel = output_channel
+                layer_index += 1
+        # building last several layers
+        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
+
+        self.features_1 = nn.SequentialCell(features[:14])
+        self.features_2 = nn.SequentialCell(features[14:])
+
+    def construct(self, x):
+        out = self.features_1(x)
+        expand_layer_conv_13 = self.expand_layer_conv_13(out)
+        out = self.features_2(out)
+        return expand_layer_conv_13, out
+
+    def get_out_channels(self):
+        return self.last_channel
+
+def ssd_mobilenet_v2(**kwargs):
+    return SSDWithMobileNetV2(**kwargs)