update conifgs

HelmholtzAI-Consultants-Munich · Mar 13, 2024 · 7c7500b · 7c7500b
1 parent 4cbad70
commit 7c7500b
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Showing 4 changed files with 8 additions and 7 deletions.
diff --git a/src/client/dcp_client/utils/bentoml_model.py b/src/client/dcp_client/utils/bentoml_model.py
@@ -2,6 +2,7 @@
 from typing import Optional, List
 from bentoml.client import Client as BentoClient
 from bentoml.exceptions import BentoMLException
+import numpy as np
 
 from dcp_client.app import Model
 

diff --git a/src/server/dcp_server/config_instance.yaml b/src/server/dcp_server/config_instance.yaml
@@ -1,9 +1,7 @@
 {
     "setup": {
         "segmentation": "GeneralSegmentation",
-        "model_to_use": "CustomCellpose", 
-        "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
-        "seg_name_string": "_seg"
+        "model_to_use": "CustomCellpose"
     },
 
     "service": {
@@ -21,6 +19,8 @@
 
     "data": {
         "data_root": "data",
+        "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
+        "seg_name_string": "_seg",
         "gray": True,
         "rescale": True
     },

diff --git a/src/server/dcp_server/config_semantic.yaml b/src/server/dcp_server/config_semantic.yaml
@@ -1,9 +1,7 @@
 {
     "setup": {
         "segmentation": "GeneralSegmentation",
-        "model_to_use": "UNet", 
-        "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
-        "seg_name_string": "_seg"
+        "model_to_use": "UNet"
     },
 
     "service": {
@@ -23,6 +21,8 @@
 
     "data": {
         "data_root": "data",
+        "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
+        "seg_name_string": "_seg",
         "gray": True,
         "rescale": True
     },

diff --git a/src/server/dcp_server/models/unet.py b/src/server/dcp_server/models/unet.py
@@ -152,7 +152,7 @@ def train(self, imgs: List[np.ndarray], masks: List[np.ndarray]) -> None:
         # compute metric on test set after train is complete
         for imgs, masks in train_dataloader:
             pred_masks = self.forward(imgs.float())
-            self.metric += self.metric_f(masks, pred_masks)
+            self.metric += self.metric_f(pred_masks, masks)
         self.metric /= len(train_dataloader)
 
     def eval(self, img: np.ndarray) -> np.ndarray: