yolov3 network directory rectification

model_zoo/yolov3/eval.py

@@ -19,10 +19,10 @@ 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
+from src.yolov3 import yolov3_resnet18, YoloWithEval
+from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
+from src.config import ConfigYOLOV3ResNet18
+from src.utils import metrics
 
 def yolo_eval(dataset_path, ckpt_path):
     """Yolov3 evaluation."""

model_zoo/yolov3/scripts/run_distribute_train.sh

@@ -45,6 +45,9 @@ echo "After running the scipt, the network runs in the background. The log will
 export MINDSPORE_HCCL_CONFIG_PATH=$6
 export RANK_SIZE=$1
 
+BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
+cd $BASE_PATH/../ || exit
+
 for((i=0;i<RANK_SIZE;i++))
 do
     export DEVICE_ID=$i
@@ -56,6 +59,7 @@ do
     rm -rf LOG$i
     mkdir ./LOG$i
     cp  *.py ./LOG$i
+    cp -r ./src ./LOG$i
     cd ./LOG$i || exit
     export RANK_ID=$i
     echo "start training for rank $i, device $DEVICE_ID"
@@ -63,7 +67,7 @@ do
 
     if [ $# == 6 ]
     then
-        taskset -c $cmdopt python ../train.py  \
+        taskset -c $cmdopt python train.py  \
         --distribute=1  \
         --lr=0.005 \
         --device_num=$RANK_SIZE  \
@@ -76,7 +80,7 @@ do
 
     if [ $# == 8 ]
     then
-        taskset -c $cmdopt python ../train.py  \
+        taskset -c $cmdopt python train.py  \
         --distribute=1  \
         --lr=0.005 \
         --device_num=$RANK_SIZE  \

model_zoo/yolov3/scripts/run_eval.sh

@@ -20,4 +20,7 @@ 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 "=============================================================================================================="
 
+BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
+cd $BASE_PATH/../ || exit
+
 python eval.py --device_id=$1 --ckpt_path=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5

model_zoo/yolov3/scripts/run_standalone_train.sh

@@ -27,6 +27,9 @@ then
     exit 1
 fi
 
+BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
+cd $BASE_PATH/../ || exit
+
 if [ $# == 5 ]
 then
     python train.py --device_id=$1 --epoch_size=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5

model_zoo/yolov3/src/config.py

@@ -25,7 +25,7 @@ class ConfigYOLOV3ResNet18:
     """
     img_shape = [352, 640]
     feature_shape = [32, 3, 352, 640]
-    num_classes = 80
+    num_classes = 2
     nms_max_num = 50
 
     backbone_input_shape = [64, 64, 128, 256]

model_zoo/yolov3/src/dataset.py

@@ -23,7 +23,7 @@ from PIL import Image
 import mindspore.dataset as de
 from mindspore.mindrecord import FileWriter
 import mindspore.dataset.transforms.vision.c_transforms as C
-from config import ConfigYOLOV3ResNet18
+from src.config import ConfigYOLOV3ResNet18
 
 iter_cnt = 0
 _NUM_BOXES = 50

model_zoo/yolov3/src/utils.py

@@ -15,7 +15,7 @@
 """metrics utils"""
 
 import numpy as np
-from config import ConfigYOLOV3ResNet18
+from src.config import ConfigYOLOV3ResNet18
 
 
 def calc_iou(bbox_pred, bbox_ground):

model_zoo/yolov3/train.py

@@ -33,9 +33,9 @@ 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, data_to_mindrecord_byte_image
-from config import ConfigYOLOV3ResNet18
+from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
+from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
+from src.config import ConfigYOLOV3ResNet18
 
 
 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):