Added unit test to test the CXR sampling, which works now. Modified v…

…erify_bundle to pass the check for model.pt, since the requirement for two models (autoencoder and diffusion_model) makes sense for them to keep their specific names.

Modification of inference.json to add dummy attributes to pass the ConfigWorkflow check.
Modification of large_files.yml so that models are .pt and not .pth.

ci/unit_tests/test_cxr_image_synthesis_latent_diffusion_model.py

@@ -0,0 +1,45 @@
+# 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 shutil
+import tempfile
+import unittest
+from parameterized import parameterized
+import numpy as np
+from monai.bundle import ConfigWorkflow
+from utils import check_workflow
+
+TEST_CASE_1 = [  # inference
+    {
+        "bundle_root": "models/cxr_image_synthesis_latent_diffusion_model",
+        "prompt": "Big right-sided pleural effusion. Normal left lung.",
+        "guidance_scale": 7.0,
+    }
+]
+
+class TestCXRLatentDiffusionInference(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_inference(self, params):
+        bundle_root = params["bundle_root"]
+        inference_file = os.path.join(bundle_root, "configs/inference.json")
+        trainer = ConfigWorkflow(
+            workflow_type="inference",
+            config_file=inference_file,
+            logging_file=os.path.join(bundle_root, "configs/logging.conf"),
+            meta_file=os.path.join(bundle_root, "configs/metadata.json"),
+            **params,
+        )
+        check_workflow(trainer, check_properties=True)
+
+if __name__ == "__main__":
+    loader = unittest.TestLoader()
+    unittest.main(testLoader=loader)

ci/verify_bundle.py

@@ -54,6 +54,8 @@ def _get_weights_names(bundle: str):
     if bundle == "pediatric_abdominal_ct_segmentation":
         # skip test for this bundle's ts file
         return "dynunet_FT.pt", None
+    if bundle == "cxr_image_synthesis_latent_diffusion_model":
+        return "autoencoder.pt", None
     return "model.pt", "model.ts"
 
 

models/cxr_image_synthesis_latent_diffusion_model/configs/inference.json

@@ -7,6 +7,10 @@
         "$from transformers import CLIPTokenizer"
     ],
     "bundle_root": ".",
+    "dataset_dir": "",
+    "dataset": "",
+    "evaluator": "",
+    "inferer": "",
     "load_old": 1,
     "model_dir": "$@bundle_root + '/models'",
     "output_dir": "$@bundle_root + '/output'",
@@ -44,6 +48,7 @@
         "with_encoder_nonlocal_attn": false,
         "with_decoder_nonlocal_attn": false
     },
+    "network_def": "@autoencoder_def",
     "load_autoencoder_path": "$@model_dir + '/autoencoder.pth'",
     "load_autoencoder_func": "$@autoencoder_def.load_old_state_dict if bool(@load_old) else @autoencoder_def.load_state_dict",
     "load_autoencoder": "$@load_autoencoder_func(torch.load(@load_autoencoder_path))",

models/cxr_image_synthesis_latent_diffusion_model/large_files.yml

@@ -1,9 +1,9 @@
 large_files:
-  - path: "models/autoencoder.pth"
+  - path: "models/autoencoder.pt"
     url: "https://drive.google.com/uc?export=download&id=1paDN1m-Q_Oy8d_BanPkRTi3RlNB_Sv_h"
     hash_val: "7f579cb789597db7bb5de1488f54bc6c"
     hash_type: "md5"
-  - path: "models/diffusion_model.pth"
+  - path: "models/diffusion_model.pt"
     url: "https://drive.google.com/uc?export=download&id=1CjcmiPu5_QWr-f7wDJsXrCCcVeczneGT"
     hash_val: "c3fd4c8e38cd1d7250a8903cca935823"
     hash_type: "md5"