From bb15dcd0a12911ce4fbcb23d3ce53047e330ffcd Mon Sep 17 00:00:00 2001
From: Nils Koch <nkoch@seibert-media.net>
Date: Thu, 2 Jun 2022 12:12:53 +0200
Subject: [PATCH] api_server.py: report timing info, allow on the fly changing
 of model

---
 api_server.py | 54 +++++++++++++++++++++++++++++++++++++--------------
 1 file changed, 39 insertions(+), 15 deletions(-)

diff --git a/api_server.py b/api_server.py
index 26227e7..78e09f3 100644
--- a/api_server.py
+++ b/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
@@ -16,17 +18,27 @@ 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"
-# model_path = "train_log/models/epoch-100.pth"
-device = torch.device('cuda')
-
-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)
-state_dict = torch.load(model_path)['state_dict']
-model.load_state_dict(state_dict, strict=True)
-model.to(device)
-model.eval()
+device = torch.device('cuda:0')
+model = None
+
+
+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)
+    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):
@@ -45,10 +57,9 @@ 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)
-    
+
     imgL_dw2 = F.interpolate(
         imgL,
         size=(imgL.shape[2] // 2, imgL.shape[3] // 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'}
+