[DLMED] add more features

Signed-off-by: Nic Ma <nma@nvidia.com>

Author: Nic-Ma

modules/model_package/spleen_segmentation/commands/inference_v2.sh

@@ -0,0 +1,4 @@
+python ../scripts/inference.py
+    --base_config ../configs/inference.json
+    --config ../configs/inference_v2.json
+    --meta ../configs/metadata.json

modules/model_package/spleen_segmentation/commands/trtinfer.sh

@@ -0,0 +1,3 @@
+python ../scripts/inference.py
+    --config ../configs/trtinfer.json
+    --meta ../configs/metadata.json

modules/model_package/spleen_segmentation/configs/inference.json

@@ -1,7 +1,7 @@
 {
     "multi_gpu": false,
     "amp": true,
-    "model": "monai.data.load_net_with_metadata('../models/model.ts')[0]",
+    "model": "#monai.data.load_net_with_metadata('../models/model.ts')[0]",
     "network": {
         "name": "UNet",
         "args": {

modules/model_package/spleen_segmentation/configs/inference_v2.json

@@ -0,0 +1,23 @@
+{
+    "amp": false,
+    "network": {
+        "name": "UNet",
+        "args": {
+            "spatial_dims": 3,
+            "in_channels": 1,
+            "out_channels": 2,
+            "channels": [32, 64, 128, 256, 512],
+            "strides": [2, 2, 2, 2],
+            "num_res_units": 2,
+            "norm": "group"
+        }
+    },
+    "inferer": {
+        "name": "SlidingWindowInferer",
+        "args": {
+            "roi_size": [96, 96, 96],
+            "sw_batch_size": 4,
+            "overlap": 0.6
+        }
+    }
+}

modules/model_package/spleen_segmentation/configs/trtinfer.json

@@ -0,0 +1,19 @@
+{
+    "preprocessing": {
+        "ref": {
+            "path": "../inference.json/preprocessing"
+        }
+    },
+    "dataset": {
+        "ref": {
+            "path": "../inference.json/dataset"
+        }
+    },
+    "model": "#load_trt_model(...)",
+    "dataloader": {
+        "name": "DALIpipeline"
+    },
+    "inferer": {
+        "name": "TensorRTInferer"
+    }
+}

modules/model_package/spleen_segmentation/scripts/export.py

@@ -14,6 +14,7 @@
 import json
 
 import torch
+from monai.apps import ConfigParser
 from ignite.handlers import Checkpoint
 from monai.data import save_net_with_metadata
 from monai.networks import convert_to_torchscript
@@ -28,15 +29,15 @@ def main():
 
     # load config file
     with open(args.config, "r") as f:
-        cofnig_dict = json.load(f)
+        config_dict = json.load(f)
     # load meta data
     with open(args.meta, "r") as f:
         meta_dict = json.load(f)
 
     net: torch.nn.Module = None
     # TODO: parse network definiftion from config file and construct network instance
-    # config_parser = ConfigParser(config_dict, meta_dict)
-    # net = config_parser.get_component("network")
+    config_parser = ConfigParser(config_dict)
+    net = config_parser.get_instance("network")
 
     checkpoint = torch.load(args.weights)
     # here we use ignite Checkpoint to support nested weights and be compatible with MONAI CheckpointSaver
@@ -51,7 +52,7 @@ def main():
         include_config_vals=False,
         append_timestamp=False,
         meta_values=meta_dict,
-        more_extra_files={args.config: json.dumps(cofnig_dict).encode()},
+        more_extra_files={args.config: json.dumps(config_dict).encode()},
     )
 
 

modules/model_package/spleen_segmentation/scripts/inference.py

@@ -14,6 +14,7 @@
 import json
 
 import torch
+from monai.apps import ConfigParser
 from monai.data import decollate_batch
 from monai.inferers import Inferer
 from monai.transforms import Transform
@@ -22,38 +23,45 @@
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--config', '-c', type=str, help='file path of config file that defines network', required=True)
+    parser.add_argument('--config', '-c', type=str, help='config file that defines components', required=True)
     parser.add_argument('--meta', '-e', type=str, help='file path of the meta data')
     args = parser.parse_args()
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    configs = {}
 
-    # load config file
-    with open(args.config, "r") as f:
-        cofnig_dict = json.load(f)
     # load meta data
     with open(args.meta, "r") as f:
-        meta_dict = json.load(f)
+        configs.update(json.load(f))
+    # load config file, can override meta data in config
+    with open(args.config, "r") as f:
+        configs.update(json.load(f))
 
-    net: torch.nn.Module = None
+    model: torch.nn.Module = None
     dataloader: torch.utils.data.DataLoader = None
     inferer: Inferer = None
     postprocessing: Transform = None
     # TODO: parse inference config file and construct instances
-    # config_parser = ConfigParser(config_dict, meta_dict)
-    # net = config_parser.get_component("model").to(device)
-    # dataloader = config_parser.get_component("dataloader")
-    # inferer = config_parser.get_component("inferer")
-    # postprocessing = config_parser.get_component("postprocessing")
+    config_parser = ConfigParser(configs)
+
+    # change JSON config content in python code, lazy instantiation
+    model_conf = config_parser.get_config("model")
+    model_conf["disabled"] = False
+    model = config_parser.build(model_conf).to(device)
+
+    # instantialize the components immediately
+    dataloader = config_parser.get_instance("dataloader")
+    inferer = config_parser.get_instance("inferer")
+    postprocessing = config_parser.get_instance("postprocessing")
 
-    net.eval()
+    model.eval()
     with torch.no_grad():
         for d in dataloader:
             images = d[CommonKeys.IMAGE].to(device)
             # define sliding window size and batch size for windows inference
-            d[CommonKeys.PRED] = inferer(inputs=images, predictor=net)
+            d[CommonKeys.PRED] = inferer(inputs=images, predictor=model)
             # decollate the batch data into a list of dictionaries, then execute postprocessing transforms
-            d = [postprocessing(i) for i in decollate_batch(d)]
+            [postprocessing(i) for i in decollate_batch(d)]
 
 
 if __name__ == '__main__':

modules/model_package/spleen_segmentation/scripts/inference_v2.py

@@ -0,0 +1,67 @@
+
+# Copyright 2020 - 2021 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 argparse
+import json
+
+import torch
+from monai.apps import ConfigParser
+from monai.data import decollate_batch
+from monai.inferers import Inferer
+from monai.transforms import Transform
+from monai.utils.enums import CommonKeys
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--base_config', '-c', type=str, help='file path of base config', required=False)
+    parser.add_argument('--config', '-c', type=str, help='config file to override base config', required=True)
+    parser.add_argument('--meta', '-e', type=str, help='file path of the meta data')
+    args = parser.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    configs = {}
+
+    # load meta data
+    with open(args.meta, "r") as f:
+        configs.update(json.load(f))
+    # load base config file, can override meta data in config
+    with open(args.base_config, "r") as f:
+        configs.update(json.load(f))
+    # load config file, add or override the content of base config
+    with open(args.config, "r") as f:
+        configs.update(json.load(f))
+
+    model: torch.nn.Module = None
+    dataloader: torch.utils.data.DataLoader = None
+    inferer: Inferer = None
+    postprocessing: Transform = None
+    # TODO: parse inference config file and construct instances
+    config_parser = ConfigParser(configs)
+    # instantialize the components immediately
+    model = config_parser.get_instance("model").to(device)
+    dataloader = config_parser.get_instance("dataloader")
+    inferer = config_parser.get_instance("inferer")
+    postprocessing = config_parser.get_instance("postprocessing")
+
+    model.eval()
+    with torch.no_grad():
+        for d in dataloader:
+            images = d[CommonKeys.IMAGE].to(device)
+            # define sliding window size and batch size for windows inference
+            d[CommonKeys.PRED] = inferer(inputs=images, predictor=model)
+            # decollate the batch data into a list of dictionaries, then execute postprocessing transforms
+            [postprocessing(i) for i in decollate_batch(d)]
+
+
+if __name__ == '__main__':
+    main()

modules/model_package/spleen_segmentation/scripts/trtinfer.py

@@ -0,0 +1,59 @@
+
+# Copyright 2020 - 2021 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 argparse
+import json
+
+import torch
+from monai.apps import ConfigParser
+from monai.data import decollate_batch
+from monai.transforms import Transform
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', '-c', type=str, help='config file that defines components', required=True)
+    parser.add_argument('--meta', '-e', type=str, help='file path of the meta data')
+    args = parser.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    configs = {}
+
+    # load meta data
+    with open(args.meta, "r") as f:
+        configs.update(json.load(f))
+    # load config file, can override meta data in config
+    with open(args.config, "r") as f:
+        configs.update(json.load(f))
+
+    # fake code to simulate TensorRT and DALI logic
+    model: TRTModel = None
+    dataloader: DALIpipeline = None
+    inferer: TRTInfer = None
+    postprocessing: Transform = None
+    # TODO: parse inference config file and construct instances
+    config_parser = ConfigParser(configs)
+
+    # instantialize the components immediately
+    model = config_parser.get_instance("model").to(device)
+    dataloader = config_parser.get_instance("dataloader")
+    inferer = config_parser.get_instance("inferer")
+    postprocessing = config_parser.get_instance("postprocessing")
+
+    # simuluate TensorRT and DALI logic
+    for d in dataloader:
+        r = inferer(inputs=d, predictor=model)
+        [postprocessing(i) for i in decollate_batch(r)]
+
+
+if __name__ == '__main__':
+    main()