tinydarknet pass parameter modification

model_zoo/official/cv/cnnctc/ascend310_infer/src/main.cc

@@ -61,9 +61,8 @@ DEFINE_int32(image_height, 32, "image height");
 DEFINE_int32(image_width, 100, "image width");
 
 int PadImage(const MSTensor &input, MSTensor *output) {
-  std::shared_ptr<TensorTransform> normalize(new Normalize({127.5, 127.5, 127.5},
-                                                           {127.5, 127.5, 127.5}));
-  Execute composeNormalize({normalize});
+  auto normalize = Normalize({127.5, 127.5, 127.5}, {127.5, 127.5, 127.5});
+  Execute composeNormalize(normalize);
   std::vector<int64_t> shape = input.Shape();
   auto imgResize = MSTensor();
   auto imgNormalize = MSTensor();
@@ -74,19 +73,17 @@ int PadImage(const MSTensor &input, MSTensor *output) {
   NewWidth = ceil(FLAGS_image_height * ratio);
   paddingSize = FLAGS_image_width - NewWidth;
   if (NewWidth > FLAGS_image_width) {
-    std::shared_ptr<TensorTransform> resize(new Resize({FLAGS_image_height, FLAGS_image_width},
-                                                       InterpolationMode::kCubicPil));
-    Execute composeResize({resize});
+    auto resize = Resize({FLAGS_image_height, FLAGS_image_width}, InterpolationMode::kArea);
+    Execute composeResize(resize);
     composeResize(input, &imgResize);
     composeNormalize(imgResize, output);
   } else {
-    std::shared_ptr<TensorTransform> resize(new Resize({FLAGS_image_height, NewWidth},
-                                                       InterpolationMode::kCubicPil));
-    Execute composeResize({resize});
+    auto resize = Resize({FLAGS_image_height, NewWidth}, InterpolationMode::kArea);
+    Execute composeResize(resize);
     composeResize(input, &imgResize);
     composeNormalize(imgResize, &imgNormalize);
-    std::shared_ptr<TensorTransform> pad(new Pad({0, 0, paddingSize, 0}));
-    Execute composePad({pad});
+    auto pad = Pad({0, 0, paddingSize, 0});
+    Execute composePad(pad);
     composePad(imgNormalize, output);
   }
   return 0;
@@ -118,10 +115,10 @@ int main(int argc, char **argv) {
   auto all_files = GetAllFiles(FLAGS_dataset_path);
   std::map<double, double> costTime_map;
   size_t size = all_files.size();
-  std::shared_ptr<TensorTransform> decode(new Decode());
-  std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
-  Execute composeDecode({decode});
-  Execute composeTranspose({hwc2chw});
+  auto decode = Decode();
+  auto hwc2chw = HWC2CHW();
+  Execute composeDecode(decode);
+  Execute composeTranspose(hwc2chw);
   for (size_t i = 0; i < size; ++i) {
     struct timeval start = {0};
     struct timeval end = {0};

model_zoo/official/cv/tinydarknet/ascend310_infer/src/main.cc

@@ -88,14 +88,12 @@ int main(int argc, char **argv) {
     return 1;
   }
 
-  std::shared_ptr<TensorTransform> decode(new Decode());
-  std::shared_ptr<TensorTransform> resize(new Resize({256}));
-  std::shared_ptr<TensorTransform> dvpp_resize(new Resize({256, 256}));
-  auto crop_size = {FLAGS_image_height, FLAGS_image_width};
-  std::shared_ptr<TensorTransform> center_crop(new CenterCrop(crop_size));
-  std::shared_ptr<TensorTransform> normalize(new Normalize({123.675, 116.28, 103.53},
-                                                           {58.395, 57.120, 57.375}));
-  std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
+  auto decode = Decode();
+  auto resize = Resize({256});
+  auto dvpp_resize = Resize({256, 256});
+  auto center_crop = CenterCrop({FLAGS_image_height, FLAGS_image_width});
+  auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375});
+  auto hwc2chw = HWC2CHW();
   Execute transform({decode, resize, center_crop, normalize, hwc2chw});
   Execute dvpptransform({decode, dvpp_resize});
 

model_zoo/official/cv/tinydarknet/export.py

@@ -16,35 +16,24 @@
 ##############export checkpoint file into air and onnx models#################
 python export.py
 """
-import argparse
 import numpy as np
 
 import mindspore as ms
 from mindspore import Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
 
-from src.model_utils.config import config as imagenet_cfg
+from src.model_utils.config import config
 from src.tinydarknet import TinyDarkNet
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Classification')
-    parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet', 'cifar10'],
-                        help='dataset name.')
-    parser.add_argument('--file_format', type=str, default='AIR', choices=['MINDIR', 'AIR'],
-                        help='file format.')
-    parser.add_argument('--file_name', type=str, default='tinydarknet', help='output file name.')
-    args_opt = parser.parse_args()
-
-    if args_opt.dataset_name == 'imagenet':
-        cfg = imagenet_cfg
-    else:
+    if config.dataset_name != 'imagenet':
         raise ValueError("Dataset is not support.")
 
-    net = TinyDarkNet(num_classes=cfg.num_classes)
+    net = TinyDarkNet(num_classes=config.num_classes)
 
-    assert cfg.checkpoint_path is not None, "cfg.checkpoint_path is None."
-    param_dict = load_checkpoint(cfg.checkpoint_path)
+    assert config.checkpoint_path is not None, "config.checkpoint_path is None."
+    param_dict = load_checkpoint(config.checkpoint_path)
     load_param_into_net(net, param_dict)
 
-    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]), ms.float32)
-    export(net, input_arr, file_name=args_opt.file_name, file_format=args_opt.file_format)
+    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[config.batch_size, 3, 224, 224]), ms.float32)
+    export(net, input_arr, file_name=config.file_name, file_format=config.file_format)

model_zoo/official/cv/tinydarknet/imagenet_config.yaml

@@ -30,8 +30,8 @@ train_data_dir: './dataset/imagenet_original/train/'
 val_data_dir: './dataset/imagenet_original/val/'
 keep_checkpoint_max: 1
 checkpoint_path: './scripts/train_parallel4/ckpt_4/train_tinydarknet_imagenet-300_1251.ckpt'
-onnx_filename: 'tinydarknet.onnx'
-air_filename: 'tinydarknet.air'
+file_name: 'tinydarknet'
+file_format: 'MINDIR'
 # optimizer and lr related
 lr_scheduler: 'exponential'
 lr_epochs: [70, 140, 210, 280]
@@ -44,6 +44,9 @@ is_dynamic_loss_scale: False
 loss_scale: 1024
 label_smooth_factor: 0.1
 use_label_smooth: True
+#310infer postprocess
+result_path: ''
+label_file: ''
 
 ---
 
@@ -55,3 +58,4 @@ data_path: "The location of the input data."
 output_path: "The location of the output file."
 device_target: "Running platform, choose from Ascend, GPU or CPU, and default is Ascend."
 enable_profiling: 'Whether enable profiling while training, default: False'
+file_format: '["MINDIR", "AIR"]'

model_zoo/official/cv/tinydarknet/postprocess.py

@@ -14,13 +14,8 @@
 # ============================================================================
 """post process for 310 inference"""
 import os
-import argparse
 import numpy as np
-
-parser = argparse.ArgumentParser(description='tinydarknet calcul top1 and top5 acc')
-parser.add_argument("--result_path", type=str, required=True, default='', help="result file path")
-parser.add_argument("--label_file", type=str, required=True, default='', help="label file")
-args = parser.parse_args()
+from src.model_utils.config import config
 
 
 def get_top5_acc(top_arg, gt_class):
@@ -69,4 +64,4 @@ def cal_acc(result_path, label_file):
 
 
 if __name__ == '__main__':
-    cal_acc(args.result_path, args.label_file)
+    cal_acc(config.result_path, config.label_file)