add YOLOv3 infer scipt and change dataset to MindRecord

example/yolov3_coco2017/config.py

@@ -26,6 +26,7 @@ class ConfigYOLOV3ResNet18:
     img_shape = [352, 640]
     feature_shape = [32, 3, 352, 640]
     num_classes = 80
+    nms_max_num = 50
 
     backbone_input_shape = [64, 64, 128, 256]
     backbone_shape = [64, 128, 256, 512]
@@ -33,6 +34,8 @@ class ConfigYOLOV3ResNet18:
     backbone_stride = [1, 2, 2, 2]
 
     ignore_threshold = 0.5
+    obj_threshold = 0.3
+    nms_threshold = 0.4
 
     anchor_scales = [(10, 13),
                      (16, 30),

example/yolov3_coco2017/dataset.py

@@ -16,16 +16,14 @@
 """YOLOv3 dataset"""
 from __future__ import division
 
-import abc
-import io
 import os
-import math
-import json
 import numpy as np
 from PIL import Image
 from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
 import mindspore.dataset as de
+from mindspore.mindrecord import FileWriter
 import mindspore.dataset.transforms.vision.py_transforms as P
+import mindspore.dataset.transforms.vision.c_transforms as C
 from config import ConfigYOLOV3ResNet18
 
 iter_cnt = 0
@@ -114,6 +112,29 @@ def preprocess_fn(image, box, is_training):
 
         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):
@@ -124,32 +145,7 @@ def preprocess_fn(image, box, is_training):
         h, w = image_size
 
         if not is_training:
-            image = image.resize((w, h), Image.BICUBIC)
-            image_data = np.array(image) / 255.
-            if len(image_data.shape) == 2:
-                image_data = np.expand_dims(image_data, axis=-1)
-                image_data = np.concatenate([image_data, image_data, image_data], axis=-1)
-            image_data = image_data.astype(np.float32)
-
-            # correct boxes
-            box_data = np.zeros((max_boxes, 5))
-            if len(box) >= 1:
-                np.random.shuffle(box)
-                if len(box) > max_boxes:
-                    box = box[:max_boxes]
-                # xmin ymin xmax ymax
-                box[:, [0, 2]] = box[:, [0, 2]] * float(w) / float(iw)
-                box[:, [1, 3]] = box[:, [1, 3]] * float(h) / float(ih)
-                box_data[:len(box)] = box
-            else:
-                image_data, box_data = None, None
-
-            # 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
+            return _infer_data(image, image_size, box)
 
         flip = _rand() < .5
         # correct boxes
@@ -235,12 +231,16 @@ def preprocess_fn(image, box, is_training):
         return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
                ori_image_shape, gt_box1, gt_box2, gt_box3
 
-    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
+    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):
-    """Annotation parser."""
+    """Parse annotation from string to list."""
     annos = []
     for anno_str in annos_str:
         anno = list(map(int, anno_str.strip().split(',')))
@@ -248,142 +248,71 @@ def anno_parser(annos_str):
     return annos
 
 
-def expand_path(path):
-    """Get file list from path."""
-    files = []
-    if os.path.isdir(path):
-        for file in os.listdir(path):
-            if os.path.isfile(os.path.join(path, file)):
-                files.append(file)
-    else:
+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.")
-    return files
-
-
-def read_image(img_path):
-    """Read image with PIL."""
-    with open(img_path, "rb") as f:
-        img = f.read()
-    data = io.BytesIO(img)
-    img = Image.open(data)
-    return np.array(img)
-
-
-class BaseDataset():
-    """BaseDataset for GeneratorDataset iterator."""
-    def __init__(self, image_dir, anno_path):
-        self.image_dir = image_dir
-        self.anno_path = anno_path
-        self.cur_index = 0
-        self.samples = []
-        self.image_anno_dict = {}
-        self._load_samples()
-
-    def __getitem__(self, item):
-        sample = self.samples[item]
-        return self._next_data(sample, self.image_dir, self.image_anno_dict)
-
-    def __len__(self):
-        return len(self.samples)
-
-    @staticmethod
-    def _next_data(sample, image_dir, image_anno_dict):
-        """Get next data."""
-        image = read_image(os.path.join(image_dir, sample))
-        annos = image_anno_dict[sample]
-        return [np.array(image), np.array(annos)]
-
-    @abc.abstractmethod
-    def _load_samples(self):
-        """Base load samples."""
-
-
-class YoloDataset(BaseDataset):
-    """YoloDataset for GeneratorDataset iterator."""
-    def _load_samples(self):
-        """Load samples."""
-        image_files_raw = expand_path(self.image_dir)
-        self.samples = self._filter_valid_data(self.anno_path, image_files_raw)
-        self.dataset_size = len(self.samples)
-        if self.dataset_size == 0:
-            raise RuntimeError("Valid dataset is none!")
-
-    def _filter_valid_data(self, anno_path, image_files_raw):
-        """Filter valid data."""
-        image_files = []
-        anno_dict = {}
-        print("Start filter valid data.")
-        with open(anno_path, "rb") as f:
-            lines = f.readlines()
-            for line in lines:
-                line_str = line.decode("utf-8")
-                line_split = str(line_str).split(' ')
-                anno_dict[line_split[0].split("/")[-1]] = line_split[1:]
-        anno_set = set(anno_dict.keys())
-        image_set = set(image_files_raw)
-        for image_file in (anno_set & image_set):
-            image_files.append(image_file)
-            self.image_anno_dict[image_file] = anno_parser(anno_dict[image_file])
-        image_files.sort()
-        print("Filter valid data done!")
-        return image_files
-
-
-class DistributedSampler():
-    """DistributedSampler for YOLOv3"""
-    def __init__(self, dataset_size, batch_size, num_replicas=None, rank=None, shuffle=True):
-        if num_replicas is None:
-            num_replicas = 1
-        if rank is None:
-            rank = 0
-        self.dataset_size = dataset_size
-        self.num_replicas = num_replicas
-        self.rank = rank % num_replicas
-        self.epoch = 0
-        self.num_samples = max(batch_size, int(math.ceil(dataset_size * 1.0 / self.num_replicas)))
-        self.total_size = self.num_samples * self.num_replicas
-        self.shuffle = shuffle
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        if self.shuffle:
-            indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size)
-            indices = indices.tolist()
-        else:
-            indices = list(range(self.dataset_size))
-
-        # add extra samples to make it evenly divisible
-        indices += indices[:(self.total_size - len(indices))]
-        assert len(indices) == self.total_size
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
-
-
-def create_yolo_dataset(image_dir, anno_path, batch_size=32, repeat_num=10, device_num=1, rank=0,
+    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 GeneratorDataset."""
-    yolo_dataset = YoloDataset(image_dir=image_dir, anno_path=anno_path)
-    distributed_sampler = DistributedSampler(yolo_dataset.dataset_size, batch_size, device_num, rank)
-    ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"], sampler=distributed_sampler)
-    ds.set_dataset_size(len(distributed_sampler))
+    """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=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))
-    hwc_to_chw = P.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.shuffle(buffer_size=256)
-    ds = ds.batch(batch_size, drop_remainder=True)
-    ds = ds.repeat(repeat_num)
+
+    if is_training:
+        hwc_to_chw = P.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.shuffle(buffer_size=256)
+        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

example/yolov3_coco2017/eval.py

+# 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 yolo_v3"""
+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.yolov3 import yolov3_resnet18, YoloWithEval
+from dataset import create_yolo_dataset, data_to_mindrecord_byte_image
+from config import ConfigYOLOV3ResNet18
+from util import metrics
+
+def yolo_eval(dataset_path, ckpt_path):
+    """Yolov3 evaluation."""
+
+    ds = create_yolo_dataset(dataset_path, is_training=False)
+    config = ConfigYOLOV3ResNet18()
+    net = yolov3_resnet18(config)
+    eval_net = YoloWithEval(net, config)
+    print("Load Checkpoint!")
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+
+
+    eval_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']
+
+        eval_net.set_train(False)
+        output = eval_net(Tensor(img_np), Tensor(image_shape))
+        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})
+        percent = round(i / total * 100, 2)
+
+        print('    %s [%d/%d]' % (str(percent) + '%', i, total), end='\r')
+        i += 1
+    print('    %s [%d/%d] cost %d ms' % (str(100.0) + '%', total, total, int((time.time() - start) * 1000)), end='\n')
+
+    precisions, recalls = metrics(pred_data)
+    print("\n========================================\n")
+    for i in range(config.num_classes):
+        print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Yolov3 evaluation')
+    parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_eval",
+                        help="Mindrecord directory. 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. Default is ./Mindrecord_eval")
+    parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
+                                                                  "the absolute image path is joined by the image_dir "
+                                                                  "and the relative path in anno_path.")
+    parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
+    parser.add_argument("--ckpt_path", type=str, required=True, help="Checkpoint 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,
+                        enable_auto_mixed_precision=False)
+
+    # 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(args_opt.mindrecord_dir):
+        os.makedirs(args_opt.mindrecord_dir)
+
+    prefix = "yolo.mindrecord"
+    mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
+            print("Create Mindrecord")
+            data_to_mindrecord_byte_image(args_opt.image_dir,
+                                          args_opt.anno_path,
+                                          args_opt.mindrecord_dir,
+                                          prefix=prefix,
+                                          file_num=8)
+            print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
+        else:
+            print("image_dir or anno_path not exits")
+    print("Start Eval!")
+    yolo_eval(mindrecord_file, args_opt.ckpt_path)

example/yolov3_coco2017/run_distribute_train.sh

@@ -14,17 +14,26 @@
 # limitations under the License.
 # ============================================================================
 
+echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
-echo "for example: sh run_distribute_train.sh 8 100 ./dataset/coco/train2017 ./dataset/train.txt　./hccl.json"
-echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
+echo "for example: sh run_distribute_train.sh 8 100 /data/Mindrecord_train /data /data/train.txt /data/hccl.json"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
 
-export RANK_SIZE=$1
 EPOCH_SIZE=$2
-IMAGE_DIR=$3
-ANNO_PATH=$4
-export MINDSPORE_HCCL_CONFIG_PATH=$5
+MINDRECORD_DIR=$3
+IMAGE_DIR=$4
+ANNO_PATH=$5
+
+# Before start distribute train, first create mindrecord files.
+python train.py --only_create_dataset=1 --mindrecord_dir=$MINDRECORD_DIR --image_dir=$IMAGE_DIR  \
+--anno_path=$ANNO_PATH
 
+echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+
+export MINDSPORE_HCCL_CONFIG_PATH=$6
+export RANK_SIZE=$1
 
 for((i=0;i<RANK_SIZE;i++))
 do
@@ -40,6 +49,7 @@ do
     --distribute=1  \
     --device_num=$RANK_SIZE  \
     --device_id=$DEVICE_ID  \
+    --mindrecord_dir=$MINDRECORD_DIR  \
     --image_dir=$IMAGE_DIR  \
     --epoch_size=$EPOCH_SIZE  \
     --anno_path=$ANNO_PATH > log.txt 2>&1 &

example/yolov3_coco2017/run_eval.sh

+#!/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_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
+echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt"
+echo "=============================================================================================================="
+
+python eval.py --device_id=$1 --ckpt_path=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5

example/yolov3_coco2017/run_standalone_train.sh

@@ -14,8 +14,10 @@
 # limitations under the License.
 # ============================================================================
 
+echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE IMAGE_DIR ANNO_PATH"
-echo "for example: sh run_standalone_train.sh 0 50 ./dataset/coco/train2017 ./dataset/train.txt"
+echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
+echo "for example: sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt"
+echo "=============================================================================================================="
 
-python train.py --device_id=$1 --epoch_size=$2 --image_dir=$3 --anno_path=$4
+python train.py --device_id=$1 --epoch_size=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5

example/yolov3_coco2017/train.py

@@ -16,26 +16,30 @@
 """
 ######################## train YOLOv3 example ########################
 train YOLOv3 and get network model files(.ckpt) :
-python train.py --image_dir dataset/coco/coco/train2017 --anno_path dataset/coco/train_coco.txt
+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 argparse
 import numpy as np
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.common.initializer import initializer
 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.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
-from dataset import create_yolo_dataset
+from dataset import create_yolo_dataset, data_to_mindrecord_byte_image
 from config import ConfigYOLOV3ResNet18
 
 
 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
-    """Set learning rate"""
+    """Set learning rate."""
     lr_each_step = []
     lr = learning_rate
     for i in range(global_step):
@@ -57,7 +61,9 @@ def init_net_param(net, init='ones'):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="YOLOv3")
+    parser = argparse.ArgumentParser(description="YOLOv3 train")
+    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.")
@@ -67,12 +73,19 @@ if __name__ == '__main__':
     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.")
-    parser.add_argument("--image_dir", type=str, required=True, help="Dataset image dir.")
-    parser.add_argument("--anno_path", type=str, required=True, help="Dataset anno path.")
+    parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
+                        help="Mindrecord directory. 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. Default is ./Mindrecord_train")
+    parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
+                                                                  "the absolute image path is joined by the image_dir "
+                                                                  "and the relative path in anno_path")
+    parser.add_argument("--anno_path", type=str, default="", help="Annotation 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)
+    context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True,
+                        enable_auto_mixed_precision=False)
     if args_opt.distribute:
         device_num = args_opt.device_num
         context.reset_auto_parallel_context()
@@ -80,36 +93,65 @@ if __name__ == '__main__':
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                           device_num=device_num)
         init()
-        rank = args_opt.device_id
+        rank = args_opt.device_id % device_num
     else:
         context.set_context(enable_hccl=False)
         rank = 0
         device_num = 1
 
-    loss_scale = float(args_opt.loss_scale)
-    dataset = create_yolo_dataset(args_opt.image_dir, args_opt.anno_path, repeat_num=args_opt.epoch_size,
-                                  batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
-    dataset_size = dataset.get_dataset_size()
-    net = yolov3_resnet18(ConfigYOLOV3ResNet18())
-    net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
-    init_net_param(net, "XavierUniform")
-
-    # checkpoint
-    ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
-    ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
-    if args_opt.checkpoint_path != "":
-        param_dict = load_checkpoint(args_opt.checkpoint_path)
-        load_param_into_net(net, param_dict)
-
-    lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
-                       decay_step=1000, decay_rate=0.95))
-    opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
-    net = TrainingWrapper(net, opt, loss_scale)
-    callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
-
-    model = Model(net)
-    dataset_sink_mode = False
-    if args_opt.mode == "graph":
-        dataset_sink_mode = True
-    print("Start train YOLOv3.")
-    model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
+    print("Start create dataset!")
+
+    # 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(args_opt.mindrecord_dir):
+        os.makedirs(args_opt.mindrecord_dir)
+
+    prefix = "yolo.mindrecord"
+    mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
+    if not os.path.exists(mindrecord_file):
+        if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
+            print("Create Mindrecord.")
+            data_to_mindrecord_byte_image(args_opt.image_dir,
+                                          args_opt.anno_path,
+                                          args_opt.mindrecord_dir,
+                                          prefix=prefix,
+                                          file_num=8)
+            print("Create Mindrecord Done, at {}".format(args_opt.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 yolo.mindrecord0.
+        dataset = create_yolo_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!")
+
+        net = yolov3_resnet18(ConfigYOLOV3ResNet18())
+        net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
+        init_net_param(net, "XavierUniform")
+
+        # checkpoint
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
+        ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
+
+        lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
+                           decay_step=1000, decay_rate=0.95))
+        opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=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 == "graph":
+            print("In graph mode, one epoch return a loss.")
+            dataset_sink_mode = True
+        print("Start train YOLOv3, 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)

example/yolov3_coco2017/util.py

+# 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 ConfigYOLOV3ResNet18
+
+
+def calc_iou(bbox_pred, bbox_ground):
+    """Calculate iou of predicted bbox and ground truth."""
+    x1 = bbox_pred[0]
+    y1 = bbox_pred[1]
+    width1 = bbox_pred[2] - bbox_pred[0]
+    height1 = bbox_pred[3] - bbox_pred[1]
+
+    x2 = bbox_ground[0]
+    y2 = bbox_ground[1]
+    width2 = bbox_ground[2] - bbox_ground[0]
+    height2 = bbox_ground[3] - bbox_ground[1]
+
+    endx = max(x1 + width1, x2 + width2)
+    startx = min(x1, x2)
+    width = width1 + width2 - (endx - startx)
+
+    endy = max(y1 + height1, y2 + height2)
+    starty = min(y1, y2)
+    height = height1 + height2 - (endy - starty)
+
+    if width <= 0 or height <= 0:
+        iou = 0
+    else:
+        area = width * height
+        area1 = width1 * height1
+        area2 = width2 * height2
+        iou = area * 1. / (area1 + area2 - 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 metrics(pred_data):
+    """Calculate precision and recall of predicted bboxes."""
+    config = ConfigYOLOV3ResNet18()
+    num_classes = config.num_classes
+    count_corrects = [1e-6 for _ in range(num_classes)]
+    count_grounds = [1e-6 for _ in range(num_classes)]
+    count_preds = [1e-6 for _ in range(num_classes)]
+
+    for i, sample in enumerate(pred_data):
+        gt_anno = sample["annotation"]
+        box_scores = sample['box_scores']
+        boxes = sample['boxes']
+        mask = box_scores >= config.obj_threshold
+        boxes_ = []
+        scores_ = []
+        classes_ = []
+        max_boxes = config.nms_max_num
+        for c in range(num_classes):
+            class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])]
+            class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])]
+            nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes)
+            class_boxes = class_boxes[nms_index]
+            class_box_scores = class_box_scores[nms_index]
+            classes = np.ones_like(class_box_scores, 'int32') * c
+            boxes_.append(class_boxes)
+            scores_.append(class_box_scores)
+            classes_.append(classes)
+
+        boxes = np.concatenate(boxes_, axis=0)
+        classes = np.concatenate(classes_, axis=0)
+
+
+        # metric
+        count_correct = [1e-6 for _ in range(num_classes)]
+        count_ground = [1e-6 for _ in range(num_classes)]
+        count_pred = [1e-6 for _ in range(num_classes)]
+
+        for anno in gt_anno:
+            count_ground[anno[4]] += 1
+
+        for box_index, box in enumerate(boxes):
+            bbox_pred = [box[1], box[0], box[3], box[2]]
+            count_pred[classes[box_index]] += 1
+
+            for anno in gt_anno:
+                class_ground = anno[4]
+
+                if classes[box_index] == class_ground:
+                    iou = calc_iou(bbox_pred, anno)
+                    if iou >= 0.5:
+                        count_correct[class_ground] += 1
+                        break
+
+        count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)]
+        count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)]
+        count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)]
+
+    precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)])
+    recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)])
+    return precision, recall

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -34,6 +34,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"tensor_add", "add"},
   {"reduce_mean", "reduce_mean_d"},
   {"reduce_max", "reduce_max_d"},
+  {"reduce_min", "reduce_min_d"},
   {"conv2d_backprop_filter", "conv2d_backprop_filter_d"},
   {"conv2d_backprop_input", "conv2d_backprop_input_d"},
   {"top_kv2", "top_k"},

mindspore/model_zoo/yolov3.py

@@ -15,6 +15,7 @@
 
 """YOLOv3 based on ResNet18."""
 
+import numpy as np
 import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context, Tensor
@@ -31,19 +32,14 @@ def weight_variable():
     return TruncatedNormal(0.02)
 
 
-class _conv_with_pad(nn.Cell):
+class _conv2d(nn.Cell):
     """Create Conv2D with padding."""
     def __init__(self, in_channels, out_channels, kernel_size, stride=1):
-        super(_conv_with_pad, self).__init__()
-        total_pad = kernel_size - 1
-        pad_begin = total_pad // 2
-        pad_end = total_pad - pad_begin
-        self.pad = P.Pad(((0, 0), (0, 0), (pad_begin, pad_end), (pad_begin, pad_end)))
+        super(_conv2d, self).__init__()
         self.conv = nn.Conv2d(in_channels, out_channels,
-                              kernel_size=kernel_size, stride=stride, padding=0, pad_mode='valid',
+                              kernel_size=kernel_size, stride=stride, padding=0, pad_mode='same',
                               weight_init=weight_variable())
     def construct(self, x):
-        x = self.pad(x)
         x = self.conv(x)
         return x
 
@@ -101,15 +97,15 @@ class BasicBlock(nn.Cell):
                  momentum=0.99):
         super(BasicBlock, self).__init__()
 
-        self.conv1 = _conv_with_pad(in_channels, out_channels, 3, stride=stride)
+        self.conv1 = _conv2d(in_channels, out_channels, 3, stride=stride)
         self.bn1 = _fused_bn(out_channels, momentum=momentum)
-        self.conv2 = _conv_with_pad(out_channels, out_channels, 3)
+        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 = _conv_with_pad(in_channels, out_channels, 1, stride=stride)
+            self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride)
         self.add = P.TensorAdd()
 
     def construct(self, x):
@@ -166,7 +162,7 @@ class ResNet(nn.Cell):
             raise ValueError("the length of "
                              "layer_num, inchannel, outchannel list must be 4!")
 
-        self.conv1 = _conv_with_pad(3, 64, 7, stride=2)
+        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')
@@ -452,7 +448,7 @@ class DetectionBlock(nn.Cell):
 
         if self.training:
             return grid, prediction, box_xy, box_wh
-        return self.concat((box_xy, box_wh, box_confidence, box_probs))
+        return box_xy, box_wh, box_confidence, box_probs
 
 
 class Iou(nn.Cell):
@@ -675,3 +671,78 @@ class TrainingWrapper(nn.Cell):
             # 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

mindspore/ops/_op_impl/tbe/__init__.py

@@ -85,7 +85,9 @@ from .logical_and import _logical_and_tbe
 from .logical_not import _logical_not_tbe
 from .logical_or import _logical_or_tbe
 from .reduce_max import _reduce_max_tbe
+from .reduce_min import _reduce_min_tbe
 from .reduce_sum import _reduce_sum_tbe
+from .round import _round_tbe
 from .tanh import _tanh_tbe
 from .tanh_grad import _tanh_grad_tbe
 from .softmax import _softmax_tbe

mindspore/ops/_op_impl/tbe/reduce_min.py

+# 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.
+# ============================================================================
+
+"""ReduceMin op"""
+from mindspore.ops.op_info_register import op_info_register
+
+
+@op_info_register("""{
+    "op_name": "ReduceMin",
+    "imply_type": "TBE",
+    "fusion_type": "OPAQUE",
+    "async_flag": false,
+    "binfile_name": "reduce_min_d.so",
+    "compute_cost": 10,
+    "kernel_name": "reduce_min_d",
+    "partial_flag": true,
+    "attr": [
+        {
+            "name": "axis",
+            "param_type": "required",
+            "type": "listInt",
+            "value": "all"
+        },
+        {
+            "name": "keep_dims",
+            "param_type": "required",
+            "type": "bool",
+            "value": "all"
+        }
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
+            ],
+            "format": [
+                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
+            ],
+            "name": "x",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
+            ],
+            "format": [
+                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
+            ],
+            "name": "y",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ]
+}""")
+def _reduce_min_tbe():
+    """ReduceMin TBE register"""
+    return

mindspore/ops/_op_impl/tbe/round.py

+# 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.
+# ============================================================================
+
+"""Round op"""
+from mindspore.ops.op_info_register import op_info_register
+
+
+@op_info_register("""{
+    "op_name": "Round",
+    "imply_type": "TBE",
+    "fusion_type": "ELEMWISE",
+    "async_flag": false,
+    "binfile_name": "round.so",
+    "compute_cost": 10,
+    "kernel_name": "round",
+    "partial_flag": true,
+    "attr": [
+
+    ],
+    "inputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float16", "float", "float", "float"
+            ],
+            "format": [
+                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
+            ],
+            "name": "x",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ],
+    "outputs": [
+        {
+            "index": 0,
+            "dtype": [
+                "float16", "float16", "float16", "float", "float", "float"
+            ],
+            "format": [
+                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
+            ],
+            "name": "y",
+            "need_compile": false,
+            "param_type": "required",
+            "shape": "all"
+        }
+    ]
+}""")
+def _round_tbe():
+    """Round TBE register"""
+    return