add demo

TensorFlow2Paddle/demo/export_resnet_to_paddle_model.py

+import sys
+sys.path.append(".")
+from transformer import Transformer
+
+meta_file = sys.argv[1]
+ckpt_dir = sys.argv[2]
+export_dir = sys.argv[3]
+
+transformer = Transformer(meta_file, ckpt_dir, ['resnet_v1_50/pool5'],
+                          (224, 224, 3), ['inputs'])
+transformer.run(export_dir)
+
+open(export_dir + "/__init__.py", "w").close()

TensorFlow2Paddle/demo/save_resnet_ckpt_model.py

+from tensorflow.contrib.slim.nets import resnet_v1 as resnet_v1
+import tensorflow.contrib.slim as slim
+import tensorflow as tf
+import sys
+
+
+def load_model(ckpt_file):
+    img_size = resnet_v1.resnet_v1.default_image_size
+    img = tf.placeholder(
+        tf.float32, shape=[None, img_size, img_size, 3], name='inputs')
+    with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+        net, endpoint = resnet_v1.resnet_v1_50(
+            img, num_classes=None, is_training=False)
+
+    sess = tf.Session()
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(
+        ckpt_file, tf.contrib.slim.get_model_variables("resnet_v1_50"))
+    load_model(sess)
+    return sess
+
+
+def save_checkpoint(sess, save_dir):
+    saver = tf.train.Saver()
+    saver.save(sess, save_dir + "/resnet")
+
+
+if __name__ == "__main__":
+    ckpt_file = sys.argv[1]
+    save_dir = sys.argv[2]
+    sess = load_model(ckpt_file)
+    save_checkpoint(sess, save_dir)