!303 change some settings in YOLOv3

Merge pull request !303 from zhaoting/add-YOLOv3-infer-scipt-and-change-dataset-to-MindRecord

example/yolov3_coco2017/dataset.py

@@ -22,7 +22,6 @@ 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
 
@@ -301,13 +300,12 @@ def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num
     compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
 
     if is_training:
-        hwc_to_chw = P.HWC2CHW()
+        hwc_to_chw = C.HWC2CHW()
         ds = ds.map(input_columns=["image", "annotation"],
                     output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
                     columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
                     operations=compose_map_func, num_parallel_workers=num_parallel_workers)
         ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
-        ds = ds.shuffle(buffer_size=256)
         ds = ds.batch(batch_size, drop_remainder=True)
         ds = ds.repeat(repeat_num)
     else:

example/yolov3_coco2017/run_distribute_train.sh

@@ -19,6 +19,7 @@ echo "Please run the scipt as: "
 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 "The learning rate is 0.005 as default, if you want other lr, please change the value in this script."
 echo "=============================================================================================================="
 
 EPOCH_SIZE=$2
@@ -38,6 +39,11 @@ export RANK_SIZE=$1
 for((i=0;i<RANK_SIZE;i++))
 do
     export DEVICE_ID=$i
+
+    start=`expr $i \* 12`
+    end=`expr $start \+ 11`
+    cmdopt=$start"-"$end
+
     rm -rf LOG$i
     mkdir ./LOG$i
     cp  *.py ./LOG$i
@@ -45,8 +51,9 @@ do
     export RANK_ID=$i
     echo "start training for rank $i, device $DEVICE_ID"
     env > env.log
-    python ../train.py  \
+    taskset -c $cmdopt python ../train.py  \
     --distribute=1  \
+    --lr=0.005 \
     --device_num=$RANK_SIZE  \
     --device_id=$DEVICE_ID  \
     --mindrecord_dir=$MINDRECORD_DIR  \

example/yolov3_coco2017/train.py

@@ -67,6 +67,7 @@ if __name__ == '__main__':
     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=0.001, help="Learning rate, default is 0.001.")
     parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
@@ -137,8 +138,8 @@ if __name__ == '__main__':
         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))
+        lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=0, global_step=args_opt.epoch_size * dataset_size,
+                           decay_step=1000, decay_rate=0.95, steps=True))
         opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
         net = TrainingWrapper(net, opt, loss_scale)
 

mindspore/ops/operations/nn_ops.py

@@ -2331,7 +2331,11 @@ class Adam(PrimitiveWithInfer):
         - **gradient** (Tensor) - Gradients.
 
     Outputs:
-        Tensor, has the same shape and data type as `var`.
+        Tuple of 3 Tensor, the updated parameters.
+
+        - **var** (Tensor) - The same shape and data type as `var`.
+        - **m** (Tensor) - The same shape and data type as `m`.
+        - **v** (Tensor) - The same shape and data type as `v`.
     """
 
     @prim_attr_register