!5214 yolov3_darknet53 script suit for gpu

Merge pull request !5214 from hanhuifeng/yolov3_gpu

model_zoo/official/cv/yolov3_darknet53/README.md

@@ -53,8 +53,8 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
 
 # [Environment Requirements](#contents)
 
-- Hardware（Ascend）
-  - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
+- Hardware（Ascend/GPU）
+  - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
   - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
 - For more information, please check the resources below：
@@ -65,7 +65,7 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
 
 # [Quick Start](#contents)
 
-After installing MindSpore via the official website, you can start training and evaluation in Ascend as follows: 
+After installing MindSpore via the official website, you can start training and evaluation in as follows. If running on GPU, please add `--device_target=GPU` in the python command or use the "_gpu" shell script ("xxx_gpu.sh").
 
 ```
 # The darknet53_backbone.ckpt in the follow script is got from darknet53 training like paper. 
@@ -87,9 +87,12 @@ python train.py \
 # standalone training example(1p) by shell script
 sh run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
 
-# distributed training example(8p) by shell script
+# For Ascend device, distributed training example(8p) by shell script
 sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 
+# For GPU device, distributed training example(8p) by shell script
+sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+
 # run evaluation by python command
 python eval.py \
     --data_dir=./dataset/coco2014 \
@@ -113,6 +116,9 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
     ├─run_standalone_train.sh         # launch standalone training(1p) in ascend
     ├─run_distribute_train.sh         # launch distributed training(8p) in ascend
     └─run_eval.sh                     # launch evaluating in ascend
+    ├─run_standalone_train_gpu.sh     # launch standalone training(1p) in gpu
+    ├─run_distribute_train_gpu.sh     # launch distributed training(8p) in gpu
+    └─run_eval_gpu.sh                 # launch evaluating in gpu
   ├─src
     ├─__init__.py                     # python init file
     ├─config.py                       # parameter configuration
@@ -138,6 +144,7 @@ Major parameters in train.py as follow.
 
 optional arguments:
   -h, --help            show this help message and exit
+  --device_target       device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
   --data_dir DATA_DIR   Train dataset directory.
   --per_batch_size PER_BATCH_SIZE
                         Batch size for Training. Default: 32.
@@ -212,7 +219,7 @@ python train.py \
     --lr_scheduler=cosine_annealing > log.txt 2>&1 &
 ```
 
-The python command above will run in the background, you can view the results through the file `log.txt`.
+The python command above will run in the background, you can view the results through the file `log.txt`. If running on GPU, please add `--device_target=GPU` in the python command.
 
 After training, you'll get some checkpoint files under the outputs folder by default. The loss value will be achieved as follows:
 
@@ -228,9 +235,14 @@ The model checkpoint will be saved in outputs directory.
 
 ### Distributed Training
 
+For Ascend device, distributed training example(8p) by shell script
 ```
 sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 ```
+For GPU device, distributed training example(8p) by shell script
+```
+sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+```
 
 The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log.txt`. The loss value will be achieved as follows:
 
@@ -254,7 +266,7 @@ epoch[319], iter[102300], loss:35.430038, 423.49 imgs/sec, lr:2.409552052995423e
 
 ### Evaluation
 
-Before running the command below.
+Before running the command below. If running on GPU, please add `--device_target=GPU` in the python command or use the "_gpu" shell script ("xxx_gpu.sh").
 
 ```
 python eval.py \

model_zoo/official/cv/yolov3_darknet53/eval.py

@@ -35,9 +35,6 @@ from src.logger import get_logger
 from src.yolo_dataset import create_yolo_dataset
 from src.config import ConfigYOLOV3DarkNet53
 
-devid = int(os.getenv('DEVICE_ID'))
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True, device_id=devid)
-
 
 class Redirct:
     def __init__(self):
@@ -208,6 +205,10 @@ def parse_args():
     """Parse arguments."""
     parser = argparse.ArgumentParser('mindspore coco testing')
 
+    # device related
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+
     # dataset related
     parser.add_argument('--data_dir', type=str, default='', help='train data dir')
     parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
@@ -243,10 +244,13 @@ def test():
     start_time = time.time()
     args = parse_args()
 
+    devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True, device_id=devid)
+
     # logger
     args.outputs_dir = os.path.join(args.log_path,
                                     datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-    rank_id = int(os.environ.get('RANK_ID'))
+    rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0
     args.logger = get_logger(args.outputs_dir, rank_id)
 
     context.reset_auto_parallel_context()

model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,67 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 2 ]
+then
+    echo "Usage: sh run_distribute_train_gpu.sh [DATASET_PATH] [PRETRAINED_BACKBONE]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+DATASET_PATH=$(get_real_path $1)
+PRETRAINED_BACKBONE=$(get_real_path $2)
+echo $DATASET_PATH
+echo $PRETRAINED_BACKBONE
+
+if [ ! -d $DATASET_PATH ]
+then
+    echo "error: DATASET_PATH=$DATASET_PATH is not a directory"
+exit 1
+fi
+
+if [ ! -f $PRETRAINED_BACKBONE ]
+then
+    echo "error: PRETRAINED_PATH=$PRETRAINED_BACKBONE is not a file"
+exit 1
+fi
+
+export DEVICE_NUM=8
+
+rm -rf ./train_parallel
+mkdir ./train_parallel
+cp ../*.py ./train_parallel
+cp -r ../src ./train_parallel
+cd ./train_parallel || exit
+env > env.log
+mpirun --allow-run-as-root -n ${DEVICE_NUM} python train.py \
+    --data_dir=$DATASET_PATH \
+    --pretrained_backbone=$PRETRAINED_BACKBONE \
+    --device_target=GPU \
+    --is_distributed=1 \
+    --lr=0.1 \
+    --T_max=320 \
+    --max_epoch=320 \
+    --warmup_epochs=4 \
+    --training_shape=416 \
+    --lr_scheduler=cosine_annealing  > log.txt 2>&1 &
+cd ..

model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh

@@ -0,0 +1,67 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 2 ]
+then
+    echo "Usage: sh run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+DATASET_PATH=$(get_real_path $1)
+CHECKPOINT_PATH=$(get_real_path $2)
+echo $DATASET_PATH
+echo $CHECKPOINT_PATH
+
+if [ ! -d $DATASET_PATH ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $CHECKPOINT_PATH ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start infering for device $DEVICE_ID"
+python eval.py \
+    --device_target="GPU" \
+    --data_dir=$DATASET_PATH \
+    --pretrained=$CHECKPOINT_PATH \
+    --testing_shape=416 > log.txt 2>&1 &
+cd ..

model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train_gpu.sh

@@ -0,0 +1,75 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 2 ]
+then
+    echo "Usage: sh run_standalone_train_gpu.sh [DATASET_PATH] [PRETRAINED_BACKBONE]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+DATASET_PATH=$(get_real_path $1)
+echo $DATASET_PATH
+PRETRAINED_BACKBONE=$(get_real_path $2)
+echo $PRETRAINED_BACKBONE
+
+if [ ! -d $DATASET_PATH ]
+then
+    echo "error: DATASET_PATH=$DATASET_PATH is not a directory"
+exit 1
+fi
+
+if [ ! -f $PRETRAINED_BACKBONE ]
+then
+    echo "error: PRETRAINED_PATH=$PRETRAINED_BACKBONE is not a file"
+exit 1
+fi
+
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+
+python train.py \
+    --device_targe="GPU" \
+    --data_dir=$DATASET_PATH \
+    --pretrained_backbone=$PRETRAINED_BACKBONE \
+    --is_distributed=0 \
+    --lr=0.1 \
+    --T_max=320 \
+    --max_epoch=320 \
+    --warmup_epochs=4 \
+    --training_shape=416 \
+    --lr_scheduler=cosine_annealing > log.txt 2>&1 &
+cd ..

model_zoo/official/cv/yolov3_darknet53/src/transforms.py

@@ -465,6 +465,11 @@ class MultiScaleTrans:
         self.seed_list = self.generate_seed_list(seed_num=self.seed_num)
         self.resize_count_num = int(np.ceil(self.dataset_size / self.resize_rate))
         self.device_num = device_num
+        self.anchor_scales = config.anchor_scales
+        self.num_classes = config.num_classes
+        self.max_box = config.max_box
+        self.label_smooth = config.label_smooth
+        self.label_smooth_factor = config.label_smooth_factor
 
     def generate_seed_list(self, init_seed=1234, seed_num=int(1e6), seed_range=(1, 1000)):
         seed_list = []
@@ -474,13 +479,20 @@ class MultiScaleTrans:
             seed_list.append(seed)
         return seed_list
 
-    def __call__(self, imgs, annos, batchInfo):
+    def __call__(self, imgs, annos, x1, x2, x3, x4, x5, x6, batchInfo):
         epoch_num = batchInfo.get_epoch_num()
         size_idx = int(batchInfo.get_batch_num() / self.resize_rate)
         seed_key = self.seed_list[(epoch_num * self.resize_count_num + size_idx) % self.seed_num]
         ret_imgs = []
         ret_annos = []
 
+        bbox1 = []
+        bbox2 = []
+        bbox3 = []
+        gt1 = []
+        gt2 = []
+        gt3 = []
+
         if self.size_dict.get(seed_key, None) is None:
             random.seed(seed_key)
             new_size = random.choice(self.config.multi_scale)
@@ -491,8 +503,19 @@ class MultiScaleTrans:
         for img, anno in zip(imgs, annos):
             img, anno = preprocess_fn(img, anno, self.config, input_size, self.device_num)
             ret_imgs.append(img.transpose(2, 0, 1).copy())
-            ret_annos.append(anno)
-        return np.array(ret_imgs), np.array(ret_annos)
+            bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
+                _preprocess_true_boxes(true_boxes=anno, anchors=self.anchor_scales, in_shape=img.shape[0:2],
+                                       num_classes=self.num_classes, max_boxes=self.max_box,
+                                       label_smooth=self.label_smooth, label_smooth_factor=self.label_smooth_factor)
+            bbox1.append(bbox_true_1)
+            bbox2.append(bbox_true_2)
+            bbox3.append(bbox_true_3)
+            gt1.append(gt_box1)
+            gt2.append(gt_box2)
+            gt3.append(gt_box3)
+            ret_annos.append(0)
+        return np.array(ret_imgs), np.array(ret_annos), np.array(bbox1), np.array(bbox2), np.array(bbox3), \
+               np.array(gt1), np.array(gt2), np.array(gt3)
 
 
 def thread_batch_preprocess_true_box(annos, config, input_shape, result_index, batch_bbox_true_1, batch_bbox_true_2,

model_zoo/official/cv/yolov3_darknet53/src/util.py

@@ -15,6 +15,9 @@
 """Util class or function."""
 from mindspore.train.serialization import load_checkpoint
 import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+
+from .yolo import YoloLossBlock
 
 
 class AverageMeter:
@@ -175,3 +178,10 @@ class ShapeRecord:
         for key in self.shape_record:
             rate = self.shape_record[key] / float(self.shape_record['total'])
             logger.info('shape {}: {:.2f}%'.format(key, rate*100))
+
+
+def keep_loss_fp32(network):
+    """Keep loss of network with float32"""
+    for _, cell in network.cells_and_names():
+        if isinstance(cell, (YoloLossBlock,)):
+            cell.to_float(mstype.float32)

model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py

@@ -15,6 +15,7 @@
 """YOLOV3 dataset."""
 import os
 
+import multiprocessing
 from PIL import Image
 from pycocotools.coco import COCO
 import mindspore.dataset as de
@@ -126,7 +127,7 @@ class COCOYoloDataset:
             tmp.append(int(label))
             # tmp [x_min y_min x_max y_max, label]
             out_target.append(tmp)
-        return img, out_target
+        return img, out_target, [], [], [], [], [], []
 
     def __len__(self):
         return len(self.img_ids)
@@ -155,20 +156,22 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, max_epoch, device_num,
     hwc_to_chw = CV.HWC2CHW()
 
     config.dataset_size = len(yolo_dataset)
-    num_parallel_workers1 = int(64 / device_num)
-    num_parallel_workers2 = int(16 / device_num)
+    cores = multiprocessing.cpu_count()
+    num_parallel_workers = int(cores / device_num)
     if is_training:
         multi_scale_trans = MultiScaleTrans(config, device_num)
+        dataset_column_names = ["image", "annotation", "bbox1", "bbox2", "bbox3",
+                                "gt_box1", "gt_box2", "gt_box3"]
         if device_num != 8:
-            ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"],
-                                     num_parallel_workers=num_parallel_workers1,
+            ds = de.GeneratorDataset(yolo_dataset, column_names=dataset_column_names,
+                                     num_parallel_workers=min(32, num_parallel_workers),
                                      sampler=distributed_sampler)
-            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=['image', 'annotation'],
-                          num_parallel_workers=num_parallel_workers2, drop_remainder=True)
+            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
+                          num_parallel_workers=min(32, num_parallel_workers), drop_remainder=True)
         else:
-            ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"], sampler=distributed_sampler)
-            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=['image', 'annotation'],
-                          num_parallel_workers=8, drop_remainder=True)
+            ds = de.GeneratorDataset(yolo_dataset, column_names=dataset_column_names, sampler=distributed_sampler)
+            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
+                          num_parallel_workers=min(8, num_parallel_workers), drop_remainder=True)
     else:
         ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "img_id"],
                                  sampler=distributed_sampler)

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -28,6 +28,8 @@ from mindspore.train.callback import ModelCheckpoint, RunContext
 from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
 import mindspore as ms
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore import amp
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
 
 from src.yolo import YOLOV3DarkNet53, YoloWithLossCell, TrainingWrapper
 from src.logger import get_logger
@@ -37,13 +39,7 @@ from src.lr_scheduler import warmup_step_lr, warmup_cosine_annealing_lr, \
 from src.yolo_dataset import create_yolo_dataset
 from src.initializer import default_recurisive_init
 from src.config import ConfigYOLOV3DarkNet53
-from src.transforms import batch_preprocess_true_box, batch_preprocess_true_box_single
-from src.util import ShapeRecord
-
-
-devid = int(os.getenv('DEVICE_ID'))
-context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
-                    device_target="Ascend", save_graphs=True, device_id=devid)
+from src.util import keep_loss_fp32
 
 
 class BuildTrainNetwork(nn.Cell):
@@ -62,6 +58,10 @@ def parse_args():
     """Parse train arguments."""
     parser = argparse.ArgumentParser('mindspore coco training')
 
+    # device related
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+
     # dataset related
     parser.add_argument('--data_dir', type=str, help='Train dataset directory.')
     parser.add_argument('--per_batch_size', default=32, type=int, help='Batch size for Training. Default: 32.')
@@ -136,9 +136,16 @@ def train():
     """Train function."""
     args = parse_args()
 
+    devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
+    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+                        device_target=args.device_target, save_graphs=True, device_id=devid)
+
     # init distributed
     if args.is_distributed:
-        init()
+        if args.device_target == "Ascend":
+            init()
+        else:
+            init("nccl")
         args.rank = get_rank()
         args.group_size = get_group_size()
 
@@ -259,9 +266,19 @@ def train():
                    momentum=args.momentum,
                    weight_decay=args.weight_decay,
                    loss_scale=args.loss_scale)
-
-    network = TrainingWrapper(network, opt)
-    network.set_train()
+    enable_amp = False
+    is_gpu = context.get_context("device_target") == "GPU"
+    if is_gpu:
+        enable_amp = True
+    if enable_amp:
+        loss_scale_value = 1.0
+        loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False)
+        network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale,
+                                          level="O2", keep_batchnorm_fp32=True)
+        keep_loss_fp32(network)
+    else:
+        network = TrainingWrapper(network, opt)
+        network.set_train()
 
     if args.rank_save_ckpt_flag:
         # checkpoint save
@@ -282,28 +299,19 @@ def train():
     t_end = time.time()
     data_loader = ds.create_dict_iterator()
 
-    shape_record = ShapeRecord()
     for i, data in enumerate(data_loader):
         images = data["image"]
         input_shape = images.shape[2:4]
         args.logger.info('iter[{}], shape{}'.format(i, input_shape[0]))
-        shape_record.set(input_shape)
 
         images = Tensor(images)
-        annos = data["annotation"]
-        if args.group_size == 1:
-            batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2 = \
-                batch_preprocess_true_box(annos, config, input_shape)
-        else:
-            batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2 = \
-                batch_preprocess_true_box_single(annos, config, input_shape)
-
-        batch_y_true_0 = Tensor(batch_y_true_0)
-        batch_y_true_1 = Tensor(batch_y_true_1)
-        batch_y_true_2 = Tensor(batch_y_true_2)
-        batch_gt_box0 = Tensor(batch_gt_box0)
-        batch_gt_box1 = Tensor(batch_gt_box1)
-        batch_gt_box2 = Tensor(batch_gt_box2)
+
+        batch_y_true_0 = Tensor(data['bbox1'])
+        batch_y_true_1 = Tensor(data['bbox2'])
+        batch_y_true_2 = Tensor(data['bbox3'])
+        batch_gt_box0 = Tensor(data['gt_box1'])
+        batch_gt_box1 = Tensor(data['gt_box2'])
+        batch_gt_box2 = Tensor(data['gt_box3'])
 
         input_shape = Tensor(tuple(input_shape[::-1]), ms.float32)
         loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1,