clould

model_zoo/official/cv/maskrcnn/eval.py

@@ -162,16 +162,16 @@ def modelarts_process():
         print("#" * 200, os.listdir(save_dir_1))
         print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
 
-    config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
-    config.coco_root = config.dataset_path
-    config.checkpoint_path = os.path.join(config.dataset_path, config.ckpt_path)
-    config.ann_file = os.path.join(config.dataset_path, config.ann_file)
-    config.mindrecord_dir = os.path.join(config.dataset_path, config.mindrecord_dir)
+        config.coco_root = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
+    config.checkpoint_path = os.path.join(config.output_path, config.ckpt_path)
+    config.ann_file = os.path.join(config.coco_root, config.ann_file)
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
 
 @moxing_wrapper(pre_process=modelarts_process)
 def eval_():
+    config.mindrecord_dir = os.path.join(config.coco_root, config.mindrecord_dir)
+    print('\neval.py config:\n', config)
     prefix = "MaskRcnn_eval.mindrecord"
     mindrecord_dir = config.mindrecord_dir
     mindrecord_file = os.path.join(mindrecord_dir, prefix)

model_zoo/official/cv/maskrcnn/scripts/run_distribute_train.sh

@@ -77,6 +77,6 @@ do
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
     taskset -c $cmdopt python train.py --do_train=True  --device_id=$i --rank_id=$i --run_distribute=True --device_num=$DEVICE_NUM \
-    --pre_trained=$PATH2 --data_path=$PATH3 &> log &
+    --pre_trained=$PATH2 --coco_root=$PATH3 &> log &
     cd ..
 done

model_zoo/official/cv/maskrcnn/scripts/run_eval.sh

@@ -65,5 +65,5 @@ cd ./eval || exit
 env > env.log_eval
 echo "start eval for device $DEVICE_ID"
 python ./eval.py --device_id=$DEVICE_ID --ann_file=$PATH1 --checkpoint_path=$PATH2 \
---data_path=$PATH3 &> log_eval.txt &
+--coco_root=$PATH3 &> log_eval.txt &
 cd ..

model_zoo/official/cv/maskrcnn/scripts/run_standalone_train.sh

@@ -56,5 +56,5 @@ cp -r ../src ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
-python ./train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 --data_path=$PATH2 &> log.txt &
+python ./train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 --coco_root=$PATH2 &> log.txt &
 cd ..

model_zoo/official/cv/maskrcnn/src/dataset.py

@@ -31,12 +31,6 @@ from .model_utils.config import config
 
 config.mask_shape = (28, 28)
 
-if config.enable_modelarts and config.need_modelarts_dataset_unzip:
-    config.coco_root = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
-else:
-    config.coco_root = config.data_path
-config.mindrecord_dir = os.path.join(config.coco_root, config.mindrecord_dir)
-
 def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
     """Calculate the ious between each bbox of bboxes1 and bboxes2.
 

model_zoo/official/cv/maskrcnn/train.py

@@ -98,16 +98,17 @@ def modelarts_pre_process():
         print("#" * 200, os.listdir(save_dir_1))
         print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
 
-    config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
-    config.pre_trained = os.path.join(config.dataset_path, config.ckpt_path)
+        config.coco_root = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
+    config.pre_trained = os.path.join(config.coco_root, config.pre_trained)
     config.save_checkpoint_path = config.output_path
-    config.mindrecord_dir = os.path.join(config.dataset_path, config.mindrecord_dir)
 
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
 
 @moxing_wrapper(pre_process=modelarts_pre_process)
 def train_maskrcnn():
+    config.mindrecord_dir = os.path.join(config.coco_root, config.mindrecord_dir)
+    print('\ntrain.py config:\n', config)
     print("Start train for maskrcnn!")
     if not config.do_eval and config.run_distribute:
         init()
@@ -130,12 +131,12 @@ def train_maskrcnn():
         if not os.path.isdir(mindrecord_dir):
             os.makedirs(mindrecord_dir)
         if config.dataset == "coco":
-            if os.path.isdir(config.data_path):
+            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:
-                raise Exception("data_path not exits.")
+                raise Exception("coco_root not exits.")
         else:
             if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
                 print("Create Mindrecord.")

model_zoo/official/cv/mobilenetv1/default_config.yaml

@@ -11,7 +11,7 @@ device_target: Ascend
 enable_profiling: False
 
 # ==============================================================================
-modelarts_dataset_unzip_name: 'ImageNet_Original'
+modelarts_dataset_unzip_name: 'cifar10'
 need_modelarts_dataset_unzip: True
 
 # config for mobilenet, cifar10
@@ -34,11 +34,10 @@ lr_max: 0.1
 
 # Image classification - train
 dataset: 'cifar10'
-run_distribute: True
+run_distribute: False
 device_num: 1
 dataset_path: "/cache/data"
-device_target: 'Ascend'
-pre_trained: ""
+pre_trained: ''
 parameter_server: False
 
 # Image classification - eval

model_zoo/official/cv/mobilenetv1/default_config_imagenet.yaml

@@ -36,11 +36,10 @@ lr_end: 0.0
 
 # Image classification - train
 dataset: 'imagenet2012'
-run_distribute: True
+run_distribute: False
 device_num: 1
 dataset_path: "/cache/data"
-device_target: 'Ascend'
-pre_trained: ""
+pre_trained: ''
 parameter_server: False
 
 # Image classification - eval

model_zoo/official/cv/mobilenetv1/train.py

@@ -97,8 +97,8 @@ def modelarts_pre_process():
         print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
 
         config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
-    config.ckpt_path = config.output_path
-    config.pre_trained = os.path.join(config.dataset_path, config.pre_trained)
+    config.save_checkpoint_path = config.output_path
+    # config.pre_trained = os.path.join(config.dataset_path, config.pre_trained)
 
 
 @moxing_wrapper(pre_process=modelarts_pre_process)

model_zoo/research/audio/fcn-4/default_config.yaml

@@ -19,7 +19,6 @@ audio_path: "/cache/data"
 npy_path: "/cache/data"
 info_path: "/cache/data"
 info_name: 'annotations_final.csv'
-device_target: 'Ascend'
 device_id: 0
 mr_path: "/cache/data"
 mr_name: ['train', 'val']
@@ -41,7 +40,7 @@ prefix: 'MusicTagger'
 model_name: 'MusicTagger-10_543.ckpt'
 
 # export
-file_name: "/cache/data/musicTagger/fcn-4.air" 
+file_name: "fcn-4"
 file_format: "MINDIR"
 
 # 310 infer

model_zoo/research/audio/fcn-4/train.py

@@ -92,5 +92,5 @@ if __name__ == "__main__":
           save_checkpoint_steps=config.save_step,
           keep_checkpoint_max=config.keep_checkpoint_max,
           prefix=config.prefix,
-          directory=config.checkpoint_path + "_{}".format(get_device_id()))
+          directory=config.checkpoint_path) #  + "_{}".format(get_device_id())
     print("train success")