add caffe export script

tools/export_caffe.py

@@ -0,0 +1,65 @@
+import argparse
+import os
+import sys
+import numpy as np
+from pathlib import Path
+import torch
+
+from brocolli.converter.pytorch_caffe_parser import PytorchCaffeParser
+
+from torchreid.utils.feature_extractor import FeatureExtractor
+
+
+__model_types = [
+    'resnet50', 'mlfn', 'hacnn', 'mobilenetv2_x1_0', 'mobilenetv2_x1_4',
+    'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25',
+    'osnet_ibn_x1_0', 'osnet_ain_x1_0']
+
+
+def get_model_name(model):
+    model = str(model).rsplit('/', 1)[-1].split('.')[0]
+    for x in __model_types:
+        if x in model:
+            return x
+    return None
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Caffe Model Converter")
+    parser.add_argument(
+        "-p",
+        "--weights",
+        type=Path,
+        default="./osnet_x0_75_imagenet.pth",
+        help="Path to weights",
+    )
+    parser.add_argument(
+        '--imgsz', '--img', '--img-size',
+        nargs='+',
+        type=int,
+        default=[256, 128],
+        help='image (h, w)'
+    )
+    parser.add_argument('--include',
+                        nargs='+',
+                        default=['onnx', 'openvino', 'tflite'],
+                        help='onnx, openvino, tflite')
+    args = parser.parse_args()
+
+    # Build model
+    extractor = FeatureExtractor(
+        # get rid of dataset information DeepSort model name
+        model_name=get_model_name(args.weights),
+        model_path=args.weights,
+        device=str('cpu')
+    )
+
+    im = torch.zeros(1, 3, args.imgsz[0], args.imgsz[1]).to('cpu')  # image size(1,3,640,480) BCHW iDetection
+
+    concrete_args = {"return_featuremaps": False}
+    runner = PytorchCaffeParser(extractor.model, im, concrete_args=concrete_args)
+    runner.convert()
+    runner.save(get_model_name(args.weights))
+    runner.check_result()
+
+