feat: create overlay instead of table

Former-commit-id: e6a0ea3ce845ab6ff524f98cebd49151da5765de [formerly 1fc368fdaba7b892357848fd47a8e53ed34a2c35]
Former-commit-id: afc34abfffe0e309d6e4ae77d4d56d4f372b9e9d

src/evaluate.py

@@ -1,9 +1,14 @@
+import numpy as np
 import torch
 from tqdm import tqdm
 
 import wandb
 from src.utils.dice import dice_coeff
 
+class_labels = {
+    1: "sphere",
+}
+
 
 def evaluate(net, dataloader, device):
     net.eval()
@@ -29,10 +34,28 @@ def evaluate(net, dataloader, device):
             pbar.update(images.shape[0])
 
     # save some images to wandb
-    table = wandb.Table(columns=["id", "image", "mask", "prediction"])
-    for i, (img, mask, pred) in enumerate(zip(images.to("cpu"), masks_true.to("cpu"), masks_pred.to("cpu"))):
-        table.add_data(i, wandb.Image(img), wandb.Image(mask), wandb.Image(pred))
-    wandb.log({"predictions_table": table}, commit=False)
+    overlays = []
+    for img, mask, pred in zip(images.to("cpu"), masks_true.to("cpu"), masks_pred.to("cpu")):
+        mask_img = np.asarray(mask > 0.5, np.uint8).squeeze(0)  # tester des trucs sans le threshold
+        pred_img = np.asarray(pred > 0.5, np.uint8).squeeze(0)
+
+        overlays.append(
+            wandb.Image(
+                img,
+                masks={
+                    "ground_truth": {
+                        "mask_data": mask_img,
+                        "class_labels": class_labels,
+                    },
+                    "predictions": {
+                        "mask_data": pred_img,
+                        "class_labels": class_labels,
+                    },
+                },
+            )
+        )
+
+    wandb.log({"val/images": overlays})
 
     net.train()