Add AMR demo of inference API

api_example/amr_demo.py

@@ -0,0 +1,41 @@
+"""
+Copyright $today.year LY Corporation
+
+LY Corporation licenses this file to you under the Apache License,
+version 2.0 (the "License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at:
+
+  https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+License for the specific language governing permissions and limitations
+under the License.
+"""
+import os
+import subprocess
+import torch
+
+from lighthouse.models import QDDETRPredictor
+from typing import Dict, List, Optional
+
+def load_weights(weight_dir: str) -> None:
+    if not os.path.exists(os.path.join(weight_dir, 'clap_qd_detr_clotho-moment.ckpt')):  
+        command = 'wget -P gradio_demo/weights/ https://zenodo.org/records/13961029/files/clap_qd_detr_clotho-moment.ckpt'
+        subprocess.run(command, shell=True)
+
+# use GPU if available
+device: str = 'cpu'
+weight_dir: str = 'gradio_demo/weights'
+weight_path: str = os.path.join(weight_dir, 'clap_qd_detr_clotho-moment.ckpt')
+load_weights(weight_dir)
+model: QDDETRPredictor = QDDETRPredictor(weight_path, device=device, feature_name='clap')
+
+# encode audio features
+model.encode_audio('api_example/1a-ODBWMUAE.wav')
+
+# moment retrieval
+query: str = 'Water cascades down from a waterfall.'
+prediction: Optional[Dict[str, List[float]]] = model.predict(query)
+print(prediction)

api_example/demo.py

@@ -35,6 +35,7 @@ def load_weights(weight_dir: str) -> None:
 device: str = 'cpu'
 weight_dir: str = 'gradio_demo/weights'
 weight_path: str = os.path.join(weight_dir, 'clip_slowfast_cg_detr_qvhighlight.ckpt')
+load_weights(weight_dir)
 model: CGDETRPredictor = CGDETRPredictor(weight_path, device=device, feature_name='clip_slowfast', 
                                          slowfast_path='SLOWFAST_8x8_R50.pkl', pann_path=None)
 
@@ -44,4 +45,4 @@ def load_weights(weight_dir: str) -> None:
 # moment retrieval & highlight detection
 query: str = 'A woman wearing a glass is speaking in front of the camera'
 prediction: Optional[Dict[str, List[float]]] = model.predict(query)
-print(prediction)
+print(prediction)