Merge branch 'main' into mingxinz/adapt_eval

Project-MONAI · Oct 22, 2024 · 14a864a · 14a864a
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diff --git a/Makefile b/Makefile
@@ -1,4 +1,23 @@
-demo_monai_vila2d:
+demo_monai_vila2d: cxr_download
 	cd thirdparty/VILA; \
 	./environment_setup.sh
-	pip install -U python-dotenv deepspeed gradio monai[nibabel,pynrrd,skimage]
+	pip install -U python-dotenv deepspeed gradio monai[nibabel,pynrrd,skimage] torchxrayvision
+
+cxr_download:
+	mkdir -p $(HOME)/.torchxrayvision/models_data/ \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/nih-pc-chex-mimic_ch-google-openi-kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/nih-pc-chex-mimic_ch-google-openi-kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/chex-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/chex-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/mimic_ch-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/mimic_ch-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/mimic_nb-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/mimic_nb-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/nih-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/nih-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/pc-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/pc-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    -O $(HOME)/.torchxrayvision/models_data/kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt \
+    && wget https://github.com/mlmed/torchxrayvision/releases/download/v1/pc-nih-rsna-siim-vin-resnet50-test512-e400-state.pt \
+    -O $(HOME)/.torchxrayvision/models_data/pc-nih-rsna-siim-vin-resnet50-test512-e400-state.pt
diff --git a/README.md b/README.md
@@ -39,10 +39,15 @@ For details, see [here](./monai_vila2d/README.md).
     To install these, run
     ```bash
     sudo apt-get update
-    sudo apt-get install -y python3.10 python3.10-venv python3.10-dev git
+    sudo apt-get install -y wget python3.10 python3.10-venv python3.10-dev git
     ```
     NOTE: The commands are tailored for the Docker image `nvidia/cuda:12.2.2-devel-ubuntu22.04`. If using a different setup, adjust the commands accordingly.
 
+1. **GPU Memory**: Ensure that the GPU has sufficient memory to run the models:
+    - **VILA-M3**: 8B: ~18GB, 13B: ~30GB
+    - **CXR**: This expert loads various [TorchXRayVision](https://github.com/mlmed/torchxrayvision) models and performs ensemble predictions. The memory requirement is roughly 1.5GB in total.
+    - **VISTA3D**: (TBD)
+    - **BRATS**: (TBD)
 
 #### Setup Environment
 
@@ -64,7 +69,6 @@ For details, see [here](./monai_vila2d/README.md).
 
 1. Set the API keys for calling the expert models:
     ```bash
-    export api_key=<your nvcf key>
     export NIM_API_KEY=<your NIM key>
     ```
 
@@ -77,6 +81,9 @@ For details, see [here](./monai_vila2d/README.md).
         --port 7860
     ```
 
+1. Adding your own model
+    - This is still a work in progress. Please refer to the [README](./monai_vila2d/demo/experts/README.md) for more details.
+
 ## Contributing
 
 To lint the code, please install these packages:

diff --git a/monai_vila2d/data_prepare/experts/torchxrayvision/README.md b/monai_vila2d/data_prepare/experts/torchxrayvision/README.md
@@ -2,6 +2,7 @@
 
 For both training and inference, `pip install` packages `torchxrayvision` (for the chest X-ray models), `monai` (for json files writing) and `scikit-image` (for image reading)
 are required.  The steps were tested with `torchxrayvision==1.2.4`, `monai==1.3.2` and `scikit-image==0.24.0`.
+The corresponding image data are described in the `data_prepare` folder's [readme file](../../README.md).
 
 
 #### Generate classification scores using all models

diff --git a/monai_vila2d/demo/experts/README.md b/monai_vila2d/demo/experts/README.md
@@ -0,0 +1,33 @@
+# Adding a new expert model
+
+[BaseExpert](./base_expert.py) is a template for adding a new expert model to the repository. Please follow the instructions below to add a new expert model.
+
+1. Create a new python file in the `monai_vila2d/demo/experts` directory. The file name should be the name of the expert model you are adding. For example, if you are adding a new expert model called `MyExpert`, the file name should be `my_expert.py`.
+
+2. Implement the expert model `mentioned_by` method in the new python file. The method should return a boolean value indicating whether the expert model is mentioned by the given text from the language model. For example:
+
+```python
+def mentioned_by(text: str) -> bool:
+    return "my expert" in text
+```
+
+3. Implement the expert model `run` method in the new python file. The method should return the expert model's prediction given the input image. For example:
+
+```python
+
+def run(image_url: str) -> Tuple[str, str, str, str]:
+    # Load the image
+    image = load_image(image_url)
+
+    # Perform inference
+    prediction = my_model(image)
+
+    # Return the prediction
+    text_output = "The expert model prediction is a segmentation mask."
+    # save the prediction to a file
+    save_image(prediction, "prediction.png")
+    image_output = "prediction.png"
+    instruction = "Use this mask to answer: what is the object in the image?"
+    seg_file = "prediction.png"
+    return text_output, image_output, instruction, seg_file
+```
diff --git a/monai_vila2d/demo/experts/expert_torchxrayvision.py b/monai_vila2d/demo/experts/expert_torchxrayvision.py
@@ -9,21 +9,92 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
+"""
+The implementation of TorchXRayVision expert model is adapted from the Get-Started example in TorchXRayVision:
+https://github.com/mlmed/torchxrayvision
+"""
+
 import re
 
-import requests
+import skimage.io
+import torch
+import torchxrayvision as xrv
 from experts.base_expert import BaseExpert
 
+MODEL_NAMES = [
+    "densenet121-res224-all",
+    "densenet121-res224-chex",
+    "densenet121-res224-mimic_ch",
+    "densenet121-res224-mimic_nb",
+    "densenet121-res224-nih",
+    "densenet121-res224-pc",
+    "densenet121-res224-rsna",
+    "resnet50-res512-all",
+]
+
+# Taken from https://github.com/mlmed/torchxrayvision/blob/master/torchxrayvision/models.py
+valid_labels_model = {
+    "densenet121-res224-all": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
+    "densenet121-res224-chex": ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
+    "densenet121-res224-mimic_ch": ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
+    "densenet121-res224-mimic_nb":  ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
+    "densenet121-res224-nih": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', '', '', '', ''],
+    "densenet121-res224-pc": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', '', 'Fracture', '', ''],
+    "densenet121-res224-rsna": ['', '', '', '', '', '', '', '', 'Pneumonia', '', '', '', '', '', '', '', 'Lung Opacity', ''],
+    "resnet50-res512-all": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
+}
+
+
+# Copied from https://github.com/Project-MONAI/VLM/blob/b74d7e444c6604ea83846b67bb98b5172a7e5495/monai_vila2d/data_prepare/experts/torchxrayvision/torchxray_cls.py#L47-L54
+group_0 = ["resnet50-res512-all"]
+group_1 = [
+    "densenet121-res224-all",
+    "densenet121-res224-nih",
+    "densenet121-res224-chex",
+    "densenet121-res224-mimic_ch",
+    "densenet121-res224-mimic_nb",
+    "densenet121-res224-rsna",
+    "densenet121-res224-pc",
+    "resnet50-res512-all",
+]
+group_2 = [
+    "densenet121-res224-all",
+    "densenet121-res224-chex",
+    "densenet121-res224-pc",
+    "resnet50-res512-all",
+]
+group_4 = [
+    "densenet121-res224-all",
+    "densenet121-res224-nih",
+    "densenet121-res224-chex",
+    "resnet50-res512-all",
+]
+
+cls_models = {
+    "Fracture": group_4,
+    "Pneumothorax": group_0,
+    "Lung Opacity": group_1,
+    "Atelectasis": group_2,
+    "Cardiomegaly": group_2,
+    "Consolidation": group_2,
+    "Edema": group_2,
+    "Effusion": group_2,
+}
+
 
 class ExpertTXRV(BaseExpert):
     """Expert model for the TorchXRayVision model."""
 
-    NIM_CXR = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/5ac6a152-6d3f-4691-aef8-9e656557ee45"
-
     def __init__(self) -> None:
         """Initialize the CXR expert model."""
         self.model_name = "CXR"
+        self.models = {}
+        for name in MODEL_NAMES:
+            if "densenet" in name:
+                self.models[name] = xrv.models.DenseNet(weights=name).to("cuda")
+            elif "resnet" in name:
+                self.models[name] = xrv.models.ResNet(weights=name).to("cuda")
+
 
     def classification_to_string(self, outputs):
         """Format the classification outputs to a string."""
@@ -64,19 +135,43 @@ def run(self, image_url: str = "", prompt: str = "", **kwargs):
         Returns:
             tuple: The classification string, file path, and the next step instruction.
         """
-
-        api_key = os.getenv("api_key", "Invalid")
-        if api_key == "Invalid":
-            raise ValueError("API key not found. Please set the 'api_key' environment variable.")
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}",
-            "accept": "application/json",
+
+        img = skimage.io.imread(image_url)
+        img = xrv.datasets.normalize(img, 255)
+
+        # Check that images are 2D arrays
+        if len(img.shape) > 2:
+            img = img[:, :, 0]
+        elif len(img.shape) < 2:
+            raise ValueError("error, dimension lower than 2 for image")  # FIX TBD
+
+        # Add color channel
+        img = img[None, :, :]
+        img = xrv.datasets.XRayCenterCrop()(img)
+
+        preds_label = {
+            label: []
+            for label in xrv.datasets.default_pathologies
         }
-        response = requests.post(self.NIM_CXR, headers=headers, json={"image": image_url})
-        response.raise_for_status()
+
+        with torch.no_grad():
+            img = torch.from_numpy(img).unsqueeze(0).to("cuda")
+            for name, model in self.models.items():
+                preds = model(img).cpu()
+                for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy()):
+                    # TODO: Exclude invalid labels, which may be different from training
+                    # if k not in valid_labels_model[name]:
+                    #     continue
+                    # skip if k is one of the 8 pre-selected classes but the model isn't in the group
+                    if k in cls_models and name not in cls_models[k]:
+                        continue
+                    preds_label[k].append(float(v))
+            output = {
+                k: float(sum(v) / len(v))
+                for k, v in preds_label.items()
+            }
         return (
-            self.classification_to_string(response.json()),
+            self.classification_to_string(output),
             None,
             "Use this result to respond to this prompt:\n" + prompt,
             "",  # no file needs to be downloaded

diff --git a/monai_vila2d/demo/gradio_monai_vila2d.py b/monai_vila2d/demo/gradio_monai_vila2d.py
@@ -99,28 +99,32 @@
 CACHED_IMAGES = {}
 
 TITLE = """
-    <div style="text-align: center; max-width: 800px; margin: 0 auto;">
-        <p>
-        <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" alt="project monai" style="width: 50%; min-width: 500px; max-width: 800px; margin: auto; display: block;">
-        </p>
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-        "
-        >
-        <h1 style="font-weight: 900; margin-bottom: 7px;">
-            MONAI Multi-Modal Medical (M3) VLM Demo
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        VILA-M3 is a vision-language model for medical applications that interprets medical images and text prompts to generate relevant responses.
-        Disclaimer: AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate, harmful, biased or indecent. By testing this model, you assume the risk of any harm caused by any response or output of the model. This model is for research purposes and not for clinical usage.
-        </p>
-
+<div style="text-align: center; max-width: 800px; margin: 0 auto; padding: 20px;">
+    <p>
+        <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" alt="Project MONAI logo" 
+            style="width: 50%; max-width: 600px; min-width: 300px; margin: auto; display: block;">
+    </p>
+    
+    <h1 style="font-weight: 900; font-size: 1.5rem; margin-bottom: 10px;">
+        MONAI Multi-Modal (M3) VLM Demo
+    </h1>
+    
+    <div style="font-size: 0.95rem; text-align: left; max-width: 800px; margin: 0 auto;">
+        <span>
+            VILA-M3 is a vision-language model for medical applications that interprets medical images and text prompts to generate relevant responses.
+        </span>
+        <details style="display: inline; cursor: pointer;">
+            <summary style="display: inline; font-weight: bold;">
+                <strong>DISCLAIMER</strong>
+            </summary>
+            <span style="font-size: 0.95rem;">
+                AI models generate responses and outputs based on complex algorithms and machine learning techniques, 
+                and those responses or outputs may be inaccurate, harmful, biased, or indecent. By testing this model, you assume the risk of any 
+                harm caused by any response or output of the model. This model is for research purposes and not for clinical usage.
+            </span>
+        </details>
     </div>
+</div>
 """
 
 CSS_STYLES = (
@@ -723,9 +727,9 @@ def create_demo(source, model_path, conv_mode, server_port):
 
             with gr.Column():
                 with gr.Tab("In front of the scene"):
-                    history_text = gr.HTML(HTML_PLACEHOLDER, label="Previous prompts")
+                    history_text = gr.HTML(HTML_PLACEHOLDER, label="Previous prompts", max_height=600)
                 with gr.Tab("Behind the scene"):
-                    history_text_full = gr.HTML(HTML_PLACEHOLDER, label="Previous prompts full")
+                    history_text_full = gr.HTML(HTML_PLACEHOLDER, label="Previous prompts full", max_height=600)
                 image_download = gr.DownloadButton("Download the file", visible=False)
                 clear_btn = gr.Button("Clear Conversation")
                 with gr.Row(variant="compact"):