Fix evaluation in HoVerNet tutorial (#1127)

Part of [#5774](Project-MONAI/MONAI#5774).

### Description
Add `EnsureChannelFirstd`
Update to argmax

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Notebook runs automatically `./runner [-p <regex_pattern>]`

Signed-off-by: KumoLiu <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: KumoLiu

pathology/hovernet/evaluation.py

@@ -1,3 +1,14 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import glob
 import logging
@@ -8,7 +19,9 @@
 from monai.engines import SupervisedEvaluator
 from monai.transforms import (
     LoadImaged,
+    EnsureChannelFirstd,
     Lambdad,
+    AsDiscreted,
     Activationsd,
     Compose,
     CastToTyped,
@@ -54,6 +67,7 @@ def run(cfg):
     val_transforms = Compose(
         [
             LoadImaged(keys=["image", "label_inst", "label_type"], image_only=True),
+            EnsureChannelFirstd(keys=["image", "label_inst", "label_type"], channel_dim=-1),
             Lambdad(keys="label_inst", func=lambda x: measure.label(x)),
             CastToTyped(keys=["image", "label_inst"], dtype=torch.int),
             CenterSpatialCropd(
@@ -92,14 +106,14 @@ def run(cfg):
     post_process_np = Compose(
         [
             Activationsd(keys=HoVerNetBranch.NP.value, softmax=True),
-            Lambdad(keys=HoVerNetBranch.NP.value, func=lambda x: x[1:2, ...] > 0.5),
+            AsDiscreted(keys=HoVerNetBranch.NP.value, argmax=True),
         ]
     )
     post_process = Lambdad(keys="pred", func=post_process_np)
 
     # Evaluator
     val_handlers = [
-        CheckpointLoader(load_path=cfg["ckpt"], load_dict={"net": model}),
+        CheckpointLoader(load_path=cfg["ckpt"], load_dict={"model": model}),
         StatsHandler(output_transform=lambda x: None),
     ]
     evaluator = SupervisedEvaluator(

pathology/hovernet/inference.py

@@ -1,6 +1,16 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import logging
 import os
-import time
 from argparse import ArgumentParser
 from glob import glob
 
@@ -176,7 +186,7 @@ def main():
     parser.add_argument(
         "--root",
         type=str,
-        default="/workspace/Data/Pathology/CoNSeP/Test/Images",
+        default="/workspace/Data/CoNSeP/Test/Images",
         help="Images root dir",
     )
     parser.add_argument("--output", type=str, default="./eval/", dest="output", help="log directory")

pathology/hovernet/prepare_patches.py

@@ -1,3 +1,14 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import glob
 import math
 import os

pathology/hovernet/training.py

@@ -1,6 +1,16 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import glob
-import time
 import logging
 import torch
 import numpy as np