Fix breast density example application to make it work with SDK v0.6+

examples/apps/breast_density_classifier_app/README.md

@@ -0,0 +1,23 @@
+## A MONAI Application Package to deploy breast density classification algorithm
+This MAP is based on the Breast Density Model in MONAI [Model-Zoo](https://github.com/Project-MONAI/model-zoo). This model is developed at the Center for Augmented Intelligence in Imaging at the Mayo Clinic, Florida.
+For any questions, feel free to contact Vikash Gupta ([email protected])
+Sample data and a torchscript model can be downloaded from https://drive.google.com/drive/folders/1Dryozl2MwNunpsGaFPVoaKBLkNbVM3Hu?usp=sharing
+
+
+## Run the application code with Python interpreter
+```
+python app.py -i <input_dir> -o <out_dir> -m <breast_density_model>
+```
+
+## Package the application as a MONAI Application Package (contianer image)
+In order to build the MONAI App Package, go a level up and execute the following command.
+```
+monai-deploy package breast_density_classification_app -m <breast_density_model> -c breast_density_classifer_app/app.yaml --tag breast_density:0.1.0 --platform x64-workstation -l DEBUG
+```
+
+## Run the MONAI Application Package using MONAI Deploy CLI
+```
+monai-deploy run breast_density-x64-workstation-dgpu-linux-amd64:0.1.0 -i <input_dir> -o <output_dir>
+```
+
+Once the container exits successfully, check the results in the output directory. There should be a newly creeated DICOM instance file and a `output.json` file containing the classification results.

examples/apps/breast_density_classifer_app/app.py → examples/apps/breast_density_classifier_app/app.py

@@ -26,14 +26,16 @@ def __init__(self, *args, **kwargs):
 
     def compose(self):
         """Creates the app specific operators and chain them up in the processing DAG."""
-        logging.info(f"Begin {self.compose.__name__}")
+        self._logger.info(f"Begin {self.compose.__name__}")
 
         # Use command line options over environment variables to init context.
         app_context: AppContext = Application.init_app_context(self.argv)
         app_input_path = Path(app_context.input_path)
         app_output_path = Path(app_context.output_path)
         model_path = Path(app_context.model_path)
 
+        self._logger.info(f"App input, output path, & model path: {app_input_path}, {app_output_path}, {model_path}")
+
         model_info = ModelInfo(
             "MONAI Model for Breast Density",
             "BreastDensity",

examples/apps/breast_density_classifier_app/app.yaml

@@ -0,0 +1,27 @@
+%YAML 1.2
+# SPDX-FileCopyrightText: Copyright (c) 2022-2023 MONAI. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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.
+---
+application:
+  title: MONAI Deploy App Package - Spleen Seg Inference
+  version: 1.0
+  inputFormats: ["file"]
+  outputFormats: ["file"]
+
+resources:
+  cpu: 1
+  gpu: 1
+  memory: 1Gi
+  gpuMemory: 2Gi

examples/apps/breast_density_classifer_app/breast_density_classifier_operator.py → examples/apps/breast_density_classifier_app/breast_density_classifier_operator.py

@@ -1,4 +1,5 @@
 import json
+import logging
 import os
 from pathlib import Path
 from typing import Dict, Optional
@@ -94,7 +95,9 @@ def _get_model(self, app_context: AppContext, model_path: Path, model_name: str)
             # `app_context.models.get(model_name)` returns a model instance if exists.
             # If model_name is not specified and only one model exists, it returns that model.
             model = app_context.models.get(model_name)
+            logging.info("Got the model network from the app context.")
         else:
+            logging.info("Model network not in context. JIT loading from file...")
             model = torch.jit.load(
                 ClassifierOperator.MODEL_LOCAL_PATH,
                 map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
@@ -149,9 +152,7 @@ def compute(self, op_input, op_output, context):
 
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        # Need to get the model from context, when it is re-implemented, and for now, load it directly here.
-        # model = context.models.get()
-        model = torch.jit.load(self.model_path, map_location=device)
+        # Model network loading has been handled during init.
 
         pre_transforms = self.pre_process(_reader)
         post_transforms = self.post_process()
@@ -162,7 +163,7 @@ def compute(self, op_input, op_output, context):
         with torch.no_grad():
             for d in dataloader:
                 image = d[0].to(device)
-                outputs = model(image)
+                outputs = self.model(image)
                 out = post_transforms(outputs).data.cpu().numpy()[0]
                 print(out)
 

examples/apps/breast_density_classifier_app/requirements.txt

@@ -0,0 +1,4 @@
+highdicom>=0.18.2
+monai>=1.2.0
+pydicom>=2.3.0
+torch>=1.12.0