Migrate Brain Image Synthesis Latent Diffusion from GenerativeModels to model-zoo

.gitignore

@@ -129,3 +129,5 @@ temp/
 # VSCode
 .vscode/
 *.zip
+models/*/models/*
+models/*/output/*

ci/unit_tests/test_brain_image_synthesis_latent_diffusion_model.py

@@ -0,0 +1,47 @@
+# 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 unittest
+
+from monai.bundle import ConfigWorkflow
+from parameterized import parameterized
+from utils import check_workflow
+
+TEST_CASE_1 = [  # inference
+    {
+        "bundle_root": "models/brain_image_synthesis_latent_diffusion_model",
+        "gender": 1.0,
+        "age": 0.7,
+        "ventricular_vol": 0.7,
+        "brain_vol": 0.5,
+    }
+]
+
+
+class BrainImageSynthesisLatentDiffusionModel(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 == "brain_image_synthesis_latent_diffusion":
+        return "autoencoder.pt", "model.pt"
     return "model.pt", "model.ts"
 
 

models/brain_image_synthesis_latent_diffusion_model/LICENSE

@@ -0,0 +1,201 @@
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models/brain_image_synthesis_latent_diffusion_model/configs/inference.json

@@ -0,0 +1,112 @@
+{
+    "imports": [
+        "$import torch",
+        "$from datetime import datetime",
+        "$from pathlib import Path"
+    ],
+    "bundle_root": ".",
+    "dataset_dir": "",
+    "dataset": "",
+    "evaluator": "",
+    "inferer": "",
+    "load_old": 1,
+    "model_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/output'",
+    "create_output_dir": "$Path(@output_dir).mkdir(exist_ok=True)",
+    "gender": 0.0,
+    "age": 0.1,
+    "ventricular_vol": 0.2,
+    "brain_vol": 0.4,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "conditioning": "$torch.tensor([[@gender, @age, @ventricular_vol, @brain_vol]]).to(@device).unsqueeze(1)",
+    "out_file": "$datetime.now().strftime('sample_%H%M%S_%d%m%Y') + '_' + str(@gender) + '_' + str(@age) + '_' + str(@ventricular_vol) + '_' + str(@brain_vol)",
+    "autoencoder_def": {
+        "_target_": "monai.networks.nets.AutoencoderKL",
+        "spatial_dims": 3,
+        "in_channels": 1,
+        "out_channels": 1,
+        "latent_channels": 3,
+        "channels": [
+            64,
+            128,
+            128,
+            128
+        ],
+        "num_res_blocks": 2,
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "attention_levels": [
+            false,
+            false,
+            false,
+            false
+        ],
+        "with_encoder_nonlocal_attn": false,
+        "with_decoder_nonlocal_attn": false
+    },
+    "network_def": "@autoencoder_def",
+    "load_autoencoder_path": "$@model_dir + '/autoencoder.pt'",
+    "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))",
+    "autoencoder": "$@autoencoder_def.to(@device)",
+    "diffusion_def": {
+        "_target_": "monai.networks.nets.DiffusionModelUNet",
+        "spatial_dims": 3,
+        "in_channels": 7,
+        "out_channels": 3,
+        "channels": [
+            256,
+            512,
+            768
+        ],
+        "num_res_blocks": 2,
+        "attention_levels": [
+            false,
+            true,
+            true
+        ],
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "resblock_updown": true,
+        "num_head_channels": [
+            0,
+            512,
+            768
+        ],
+        "with_conditioning": true,
+        "transformer_num_layers": 1,
+        "cross_attention_dim": 4,
+        "upcast_attention": true,
+        "use_flash_attention": false
+    },
+    "load_diffusion_path": "$@model_dir + '/model.pt'",
+    "load_diffusion_func": "$@diffusion_def.load_old_state_dict if bool(@load_old) else @diffusion_def.load_state_dict",
+    "load_diffusion": "$@load_diffusion_func(torch.load(@load_diffusion_path))",
+    "diffusion": "$@diffusion_def.to(@device)",
+    "scheduler": {
+        "_target_": "monai.networks.schedulers.DDIMScheduler",
+        "_requires_": [
+            "@load_diffusion",
+            "@load_autoencoder"
+        ],
+        "beta_start": 0.0015,
+        "beta_end": 0.0205,
+        "num_train_timesteps": 1000,
+        "schedule": "scaled_linear_beta",
+        "clip_sample": false
+    },
+    "noise": "$torch.randn((1, 3, 20, 28, 20)).to(@device)",
+    "set_timesteps": "[email protected]_timesteps(num_inference_steps=50)",
+    "sampler": {
+        "_target_": "scripts.sampler.Sampler",
+        "_requires_": "@set_timesteps"
+    },
+    "sample": "[email protected]_fn(@noise, @autoencoder, @diffusion, @scheduler, @conditioning)",
+    "saver": {
+        "_target_": "SaveImage",
+        "_requires_": "@create_output_dir",
+        "output_dir": "@output_dir",
+        "output_postfix": "@out_file"
+    },
+    "run": "$@saver(@sample[0][0])"
+}

models/brain_image_synthesis_latent_diffusion_model/configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s