add task/solution split for task 3.2

solution.py

@@ -625,9 +625,10 @@ def get_inner_mask(pred, threshold):
 
 # %% [markdown]
 # <div class="alert alert-block alert-info">
-# <b>Task 3.2</b>: <br> Evaluate metrics for the validation dataset.
+# <b>Task 3.2</b>: <br> Evaluate metrics for the validation dataset. Fill in the blanks
 # </div>
-# %%
+
+# %% tags=["task"]
 from local import evaluate
 
 # Need to re-initialize the dataloader to return masks in addition to SDTs.
@@ -671,6 +672,49 @@ def get_inner_mask(pred, threshold):
 print(f"Mean Precision is {np.mean(precision_list):.3f}")
 print(f"Mean Recall is {np.mean(recall_list):.3f}")
 print(f"Mean Accuracy is {np.mean(accuracy_list):.3f}")
+
+# %% tags=["solution"]
+from local import evaluate
+
+# Need to re-initialize the dataloader to return masks in addition to SDTs.
+val_dataset = SDTDataset("tissuenet_data/test", return_mask=True)
+val_dataloader = DataLoader(
+    val_dataset, batch_size=1, shuffle=False, num_workers=NUM_THREADS
+)
+unet.eval()
+
+(
+    precision_list,
+    recall_list,
+    accuracy_list,
+) = (
+    [],
+    [],
+    [],
+)
+for idx, (image, mask, sdt) in enumerate(tqdm(val_dataloader)):
+    image = image.to(device)
+    pred = unet(image)
+
+    image = np.squeeze(image.cpu())
+    gt_labels = np.squeeze(mask.cpu().numpy())
+    pred = np.squeeze(pred.cpu().detach().numpy())
+
+    # feel free to try different thresholds
+    thresh = ...
+
+    # get boundary mask
+    inner_mask = ...
+    pred_labels = ...
+    precision, recall, accuracy = evaluate(gt_labels, pred_labels)
+    precision_list.append(precision)
+    recall_list.append(recall)
+    accuracy_list.append(accuracy)
+
+print(f"Mean Precision is {np.mean(precision_list):.3f}")
+print(f"Mean Recall is {np.mean(recall_list):.3f}")
+print(f"Mean Accuracy is {np.mean(accuracy_list):.3f}")
+
 # %% [markdown]
 # <hr style="height:2px;">
 #