modified two parameters to one parameter in network

model_zoo/official/cv/yolov3_darknet53/eval.py

@@ -24,11 +24,9 @@ import numpy as np
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
 
-from mindspore import Tensor
 from mindspore.context import ParallelMode
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore as ms
 
 from src.yolo import YOLOV3DarkNet53
 from src.logger import get_logger
@@ -297,7 +295,6 @@ def test():
     # init detection engine
     detection = DetectionEngine(args)
 
-    input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
     args.logger.info('Start inference....')
     for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):
         image = data["image"]
@@ -305,7 +302,7 @@ def test():
         image_shape = data["image_shape"]
         image_id = data["img_id"]
 
-        prediction = network(image, input_shape)
+        prediction = network(image)
         output_big, output_me, output_small = prediction
         output_big = output_big.asnumpy()
         output_me = output_me.asnumpy()
@@ -324,7 +321,7 @@ def test():
     eval_result = detection.get_eval_result()
 
     cost_time = time.time() - start_time
-    args.logger.info('\n=============coco eval reulst=========\n' + eval_result)
+    args.logger.info('\n=============coco eval result=========\n' + eval_result)
     args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
 
 

model_zoo/official/cv/yolov3_darknet53/export.py

@@ -45,6 +45,5 @@ if __name__ == "__main__":
 
     shape = [args.batch_size, 3] + config.test_img_shape
     input_data = Tensor(np.zeros(shape), ms.float32)
-    input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
 
-    export(network, input_data, input_shape, file_name=args.file_name, file_format=args.file_format)
+    export(network, input_data, file_name=args.file_name, file_format=args.file_format)

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

@@ -365,6 +365,7 @@ class YOLOV3DarkNet53(nn.Cell):
     def __init__(self, is_training):
         super(YOLOV3DarkNet53, self).__init__()
         self.config = ConfigYOLOV3DarkNet53()
+        self.tenser_to_array = P.TupleToArray()
 
         # YOLOv3 network
         self.feature_map = YOLOv3(backbone=DarkNet(ResidualBlock, self.config.backbone_layers,
@@ -379,7 +380,9 @@ class YOLOV3DarkNet53(nn.Cell):
         self.detect_2 = DetectionBlock('m', is_training=is_training)
         self.detect_3 = DetectionBlock('s', is_training=is_training)
 
-    def construct(self, x, input_shape):
+    def construct(self, x):
+        input_shape = F.shape(x)[2:4]
+        input_shape = F.cast(self.tenser_to_array(input_shape), ms.float32)
         big_object_output, medium_object_output, small_object_output = self.feature_map(x)
         output_big = self.detect_1(big_object_output, input_shape)
         output_me = self.detect_2(medium_object_output, input_shape)
@@ -394,12 +397,15 @@ class YoloWithLossCell(nn.Cell):
         super(YoloWithLossCell, self).__init__()
         self.yolo_network = network
         self.config = ConfigYOLOV3DarkNet53()
+        self.tenser_to_array = P.TupleToArray()
         self.loss_big = YoloLossBlock('l', self.config)
         self.loss_me = YoloLossBlock('m', self.config)
         self.loss_small = YoloLossBlock('s', self.config)
 
-    def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2, input_shape):
-        yolo_out = self.yolo_network(x, input_shape)
+    def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2):
+        input_shape = F.shape(x)[2:4]
+        input_shape = F.cast(self.tenser_to_array(input_shape), ms.float32)
+        yolo_out = self.yolo_network(x)
         loss_l = self.loss_big(*yolo_out[0], y_true_0, gt_0, input_shape)
         loss_m = self.loss_me(*yolo_out[1], y_true_1, gt_1, input_shape)
         loss_s = self.loss_small(*yolo_out[2], y_true_2, gt_2, input_shape)

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -26,7 +26,6 @@ from mindspore import context
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import ModelCheckpoint, RunContext
 from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
-import mindspore as ms
 from mindspore import amp
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.common import set_seed
@@ -254,9 +253,8 @@ def train():
         batch_gt_box1 = Tensor.from_numpy(data['gt_box2'])
         batch_gt_box2 = Tensor.from_numpy(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,
-                       batch_gt_box2, input_shape)
+                       batch_gt_box2)
         loss_meter.update(loss.asnumpy())
 
         if args.rank_save_ckpt_flag: