api_server.py: report timing info, allow on the fly changing of model

api_server.py

@@ -1,4 +1,6 @@
 import json
+from datetime import datetime
+from typing import Union, Literal
 from io import BytesIO
 
 import numpy as np
@@ -16,10 +18,14 @@ app = FastAPI()
 
 reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
 reference_pattern = cv2.imread(reference_pattern_path)
-model_path = "train_log/models/latest.pth"
+device = torch.device('cuda:0')
-# model_path = "train_log/models/epoch-100.pth"
+model = None
-device = torch.device('cuda')
+
 
+def load_model(epoch):
+    global model
+    epoch = f'epoch-{epoch}.pth' if epoch != 'latest' else 'latest.pth'
+    model_path = f"train_log/models/{epoch}"
     model = Model(max_disp=256, mixed_precision=False, test_mode=True)
     model = nn.DataParallel(model, device_ids=[device])
     # model.load_state_dict(torch.load(model_path), strict=False)
@@ -27,6 +33,12 @@ state_dict = torch.load(model_path)['state_dict']
     model.load_state_dict(state_dict, strict=True)
     model.to(device)
     model.eval()
+    print(f'loaded model {epoch}')
+    return model
+
+
+model = load_model('latest')
+
 
 class NumpyEncoder(json.JSONEncoder):
     def default(self, obj):
@@ -45,7 +57,6 @@ def inference(left, right, model, n_iter=20):
     imgL = torch.tensor(imgL.astype("float32")).to(device)
     imgR = torch.tensor(imgR.astype("float32")).to(device)
 
-    # Funzt grob
     imgR = imgR.transpose(1,2)
     imgL = imgL.transpose(1,2)
 
@@ -71,6 +82,15 @@ def inference(left, right, model, n_iter=20):
     return pred_disp
 
 
+@app.post("/model/update/{epoch}")
+async def change_model(epoch: Union[int, Literal['latest']]):
+    global model
+    print(epoch)
+    print('updating model')
+    model = load_model(epoch)
+    return {'status': 'success'}
+
+
 @app.put("/ir")
 async def read_ir_input(file: UploadFile = File(...)):
     try:
@@ -89,11 +109,15 @@ async def read_ir_input(file: UploadFile = File(...)):
     img = img.transpose((1,2,0))
     ref_pat = reference_pattern.transpose((1,2,0))
 
-    pred_disp = inference(img, ref_pat, model)
+    start = datetime.now()
+    pred_disp = inference(img, ref_pat, model, 20)
+    duration = (datetime.now() - start).total_seconds()
 
-    return json.dumps({'disp': pred_disp, 'reference': ref_pat, 'input': img}, cls=NumpyEncoder)
+    return json.dumps({'disp': pred_disp, 'reference': ref_pat, 'input': img, 'duration': duration}, cls=NumpyEncoder)
+    # return json.dumps({'disp': pred_disp, 'duration': duration}, cls=NumpyEncoder)
 
 
 @app.get('/')
 def main():
     return {'test': 'abc'}
+