From f93103caff5fcf69050b0a9aa7ccd4c44f88f2bc Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Mon, 20 Mar 2023 10:52:24 -0700
Subject: [PATCH 01/17] consume biovil-t weights

---
 .../health_multimodal/image/model/__init__.py |  6 +-
 .../health_multimodal/image/model/model.py    | 42 +---------
 .../image/model/pretrained.py                 | 81 +++++++++++++++++++
 .../test_multimodal/image/model/test_model.py |  7 +-
 .../vlp/test_vlp_inference_engine.py          |  2 +-
 .../vlp/test_zero_shot_classification.py      |  2 +-
 6 files changed, 93 insertions(+), 47 deletions(-)
 create mode 100644 hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
index 9e060d72b..e3bc1a03d 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
@@ -6,9 +6,9 @@
 from .types import ImageEncoderType
 from .model import BaseImageModel
 from .model import ImageModel
-from .model import get_biovil_resnet
-from .model import CXR_BERT_COMMIT_TAG
-from .model import BIOMED_VLP_CXR_BERT_SPECIALIZED
+from .pretrained import BIOMED_VLP_CXR_BERT_SPECIALIZED
+from .pretrained import CXR_BERT_COMMIT_TAG
+from .pretrained import get_biovil_resnet
 
 __all__ = [
     "BaseImageModel",
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/model.py b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
index b63fc2c69..a056d0295 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/model.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
@@ -5,7 +5,6 @@
 
 from __future__ import annotations
 
-import tempfile
 from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Any, Optional, Union
@@ -14,51 +13,12 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from health_multimodal.common.device import get_module_device
-from torchvision.datasets.utils import download_url
 
 from .encoder import get_encoder_from_type, get_encoder_output_dim, MultiImageEncoder
 from .modules import MLP, MultiTaskModel
-from .types import ImageEncoderType, ImageModelOutput
+from .types import ImageModelOutput
 
 
-MODEL_TYPE = ImageEncoderType.RESNET50
-JOINT_FEATURE_SIZE = 128
-
-BIOMED_VLP_CXR_BERT_SPECIALIZED = "microsoft/BiomedVLP-CXR-BERT-specialized"
-REPO_URL = f"https://huggingface.co/{BIOMED_VLP_CXR_BERT_SPECIALIZED}"
-CXR_BERT_COMMIT_TAG = "v1.1"
-
-BIOVIL_IMAGE_WEIGHTS_NAME = "biovil_image_resnet50_proj_size_128.pt"
-BIOVIL_IMAGE_WEIGHTS_URL = f"{REPO_URL}/resolve/{CXR_BERT_COMMIT_TAG}/{BIOVIL_IMAGE_WEIGHTS_NAME}"
-BIOVIL_IMAGE_WEIGHTS_MD5 = "02ce6ee460f72efd599295f440dbb453"
-
-
-def _download_biovil_image_model_weights() -> Path:
-    """Download image model weights from Hugging Face.
-
-    More information available at https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized.
-    """
-    root_dir = tempfile.gettempdir()
-    download_url(
-        BIOVIL_IMAGE_WEIGHTS_URL,
-        root=root_dir,
-        filename=BIOVIL_IMAGE_WEIGHTS_NAME,
-        md5=BIOVIL_IMAGE_WEIGHTS_MD5,
-    )
-    return Path(root_dir, BIOVIL_IMAGE_WEIGHTS_NAME)
-
-
-def get_biovil_resnet(pretrained: bool = True) -> ImageModel:
-    """Download weights from Hugging Face and instantiate the image model."""
-    resnet_checkpoint_path = _download_biovil_image_model_weights() if pretrained else None
-
-    image_model = ImageModel(
-        img_encoder_type=MODEL_TYPE,
-        joint_feature_size=JOINT_FEATURE_SIZE,
-        pretrained_model_path=resnet_checkpoint_path,
-    )
-    return image_model
-
 
 class BaseImageModel(nn.Module, ABC):
     """Abstract class for image models."""
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
new file mode 100644
index 000000000..b22e12b6e
--- /dev/null
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -0,0 +1,81 @@
+#  -------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  -------------------------------------------------------------------------------------------
+
+from __future__ import annotations
+
+import tempfile
+from pathlib import Path
+
+from torchvision.datasets.utils import download_url
+
+from .model import ImageModel
+from .types import ImageEncoderType
+
+
+JOINT_FEATURE_SIZE = 128
+
+BIOMED_VLP_CXR_BERT_SPECIALIZED = "microsoft/BiomedVLP-CXR-BERT-specialized"
+BIOMED_VLP_BIOVIL_T = "microsoft/BiomedVLP-BioViL-T"
+HF_URL = f"https://huggingface.co/"
+
+CXR_BERT_COMMIT_TAG = "v1.1"
+BIOVIL_T_COMMIT_TAG = "v1.0"
+
+BIOVIL_IMAGE_WEIGHTS_NAME = "biovil_image_resnet50_proj_size_128.pt"
+BIOVIL_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_CXR_BERT_SPECIALIZED}/resolve/{CXR_BERT_COMMIT_TAG}/{BIOVIL_IMAGE_WEIGHTS_NAME}"
+BIOVIL_IMAGE_WEIGHTS_MD5 = "02ce6ee460f72efd599295f440dbb453"
+
+BIOVIL_T_IMAGE_WEIGHTS_NAME = "biovil_t_image_model_proj_size_128.pt"
+BIOVIL_T_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_BIOVIL_T}/resolve/{BIOVIL_T_COMMIT_TAG}/{BIOVIL_T_IMAGE_WEIGHTS_NAME}"
+BIOVIL_T_IMAGE_WEIGHTS_MD5 = "a83080e2f23aa584a4f2b24c39b1bb64"
+
+
+def _download_biovil_image_model_weights() -> Path:
+    """Download image model weights from Hugging Face.
+
+    More information available at https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized.
+    """
+    root_dir = tempfile.gettempdir()
+    download_url(
+        BIOVIL_IMAGE_WEIGHTS_URL,
+        root=root_dir,
+        filename=BIOVIL_IMAGE_WEIGHTS_NAME,
+        md5=BIOVIL_IMAGE_WEIGHTS_MD5,
+    )
+    return Path(root_dir, BIOVIL_IMAGE_WEIGHTS_NAME)
+
+
+def _download_biovil_t_image_model_weights() -> Path:
+    root_dir = tempfile.gettempdir()
+    download_url(
+        BIOVIL_T_IMAGE_WEIGHTS_URL,
+        root=root_dir,
+        filename=BIOVIL_T_IMAGE_WEIGHTS_NAME,
+        md5=BIOVIL_T_IMAGE_WEIGHTS_MD5
+    )
+    return Path(root_dir, BIOVIL_T_IMAGE_WEIGHTS_NAME)
+
+
+def get_biovil_resnet(pretrained: bool = True) -> ImageModel:
+    """Download weights from Hugging Face and instantiate the image model."""
+    resnet_checkpoint_path = _download_biovil_image_model_weights() if pretrained else None
+
+    image_model = ImageModel(
+        img_encoder_type=ImageEncoderType.RESNET50,
+        joint_feature_size=JOINT_FEATURE_SIZE,
+        pretrained_model_path=resnet_checkpoint_path,
+    )
+    return image_model
+
+
+def get_biovilt_image_encoder() -> ImageModel:
+    """
+    """
+    biovilt_checkpoint_path = _download_biovil_t_image_model_weights()
+    model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
+    image_model = ImageModel(img_encoder_type=model_type,
+                             joint_feature_size=JOINT_FEATURE_SIZE,
+                             pretrained_model_path=biovilt_checkpoint_path)
+    return image_model
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_model.py b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
index d295b518b..d04cc5c8b 100644
--- a/hi-ml-multimodal/test_multimodal/image/model/test_model.py
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
@@ -7,11 +7,16 @@
 
 import pytest
 import torch
-from health_multimodal.image.model.model import ImageModel, get_biovil_resnet, MultiImageModel
+from health_multimodal.image.model.model import ImageModel, MultiImageModel
 from health_multimodal.image.model.modules import MultiTaskModel
+from health_multimodal.image.model.pretrained import get_biovil_resnet, get_biovilt_image_encoder
 from health_multimodal.image.model.types import ImageEncoderType, ImageModelOutput
 
 
+def test_biovilt_pretrained_model() -> None:
+    model = get_biovilt_image_encoder()
+
+
 def test_frozen_cnn_model() -> None:
     """
     Checks if the mode of module parameters is set correctly.
diff --git a/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py b/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
index 97b09bb25..ba20f5814 100644
--- a/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
+++ b/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
@@ -12,7 +12,7 @@
 import torch
 from health_multimodal.image import ImageInferenceEngine, ImageModel, ImageEncoderType
 from health_multimodal.image.data.transforms import create_chest_xray_transform_for_inference
-from health_multimodal.image.model.model import JOINT_FEATURE_SIZE
+from health_multimodal.image.model.pretrained import JOINT_FEATURE_SIZE
 from health_multimodal.text.utils import get_cxr_bert_inference
 from health_multimodal.vlp.inference_engine import ImageTextInferenceEngine
 from PIL import Image
diff --git a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
index a5dd54d1e..7ad596919 100644
--- a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
+++ b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
@@ -8,7 +8,7 @@
 
 from health_multimodal.image import ImageInferenceEngine
 from health_multimodal.image.data.transforms import create_chest_xray_transform_for_inference
-from health_multimodal.image.model.model import get_biovil_resnet
+from health_multimodal.image.model.pretrained import get_biovil_resnet
 from health_multimodal.text.utils import get_cxr_bert_inference
 from health_multimodal.vlp.inference_engine import ImageTextInferenceEngine
 

From e9c56e64591e53338abc72bd405de417c3f8b503 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Mon, 20 Mar 2023 11:08:26 -0700
Subject: [PATCH 02/17] import biovil-t text model

---
 .../health_multimodal/image/model/__init__.py   |  4 ----
 .../health_multimodal/image/model/pretrained.py | 10 +++++++---
 .../src/health_multimodal/text/utils.py         | 17 +++++++++++------
 .../test_multimodal/image/model/test_model.py   |  2 +-
 .../text/test_inference_engine.py               | 15 +++++++++------
 5 files changed, 28 insertions(+), 20 deletions(-)

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
index e3bc1a03d..b6b97b1c9 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
@@ -6,8 +6,6 @@
 from .types import ImageEncoderType
 from .model import BaseImageModel
 from .model import ImageModel
-from .pretrained import BIOMED_VLP_CXR_BERT_SPECIALIZED
-from .pretrained import CXR_BERT_COMMIT_TAG
 from .pretrained import get_biovil_resnet
 
 __all__ = [
@@ -15,6 +13,4 @@
     "ImageEncoderType",
     "ImageModel",
     "get_biovil_resnet",
-    "CXR_BERT_COMMIT_TAG",
-    "BIOMED_VLP_CXR_BERT_SPECIALIZED",
 ]
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index b22e12b6e..3670e0b8f 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -18,7 +18,7 @@
 
 BIOMED_VLP_CXR_BERT_SPECIALIZED = "microsoft/BiomedVLP-CXR-BERT-specialized"
 BIOMED_VLP_BIOVIL_T = "microsoft/BiomedVLP-BioViL-T"
-HF_URL = f"https://huggingface.co/"
+HF_URL = f"https://huggingface.co"
 
 CXR_BERT_COMMIT_TAG = "v1.1"
 BIOVIL_T_COMMIT_TAG = "v1.0"
@@ -48,6 +48,10 @@ def _download_biovil_image_model_weights() -> Path:
 
 
 def _download_biovil_t_image_model_weights() -> Path:
+    """Download image model weights from Hugging Face.
+
+    More information available at https://huggingface.co/microsoft/microsoft/BiomedVLP-BioViL-T.
+    """
     root_dir = tempfile.gettempdir()
     download_url(
         BIOVIL_T_IMAGE_WEIGHTS_URL,
@@ -71,8 +75,8 @@ def get_biovil_resnet(pretrained: bool = True) -> ImageModel:
 
 
 def get_biovilt_image_encoder() -> ImageModel:
-    """
-    """
+    """Download weights from Hugging Face and instantiate the image model."""
+
     biovilt_checkpoint_path = _download_biovil_t_image_model_weights()
     model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
     image_model = ImageModel(img_encoder_type=model_type,
diff --git a/hi-ml-multimodal/src/health_multimodal/text/utils.py b/hi-ml-multimodal/src/health_multimodal/text/utils.py
index ff8207320..1654effb7 100644
--- a/hi-ml-multimodal/src/health_multimodal/text/utils.py
+++ b/hi-ml-multimodal/src/health_multimodal/text/utils.py
@@ -6,19 +6,24 @@
 
 from typing import Tuple
 
-from ..image.model import CXR_BERT_COMMIT_TAG
-from ..image.model import BIOMED_VLP_CXR_BERT_SPECIALIZED
+from ..image.model.pretrained import BIOVIL_T_COMMIT_TAG, CXR_BERT_COMMIT_TAG
+from ..image.model.pretrained import BIOMED_VLP_BIOVIL_T, BIOMED_VLP_CXR_BERT_SPECIALIZED
 from .inference_engine import TextInferenceEngine
 from .model import CXRBertModel
 from .model import CXRBertTokenizer
 
 
+def get_biovil_t_bert() -> Tuple[CXRBertTokenizer, CXRBertModel]:
+    """Load the BioViL-T Bert model and tokenizer from the `Hugging Face Hub <https://huggingface.co/microsoft/BiomedVLP-BioViL-T>`_."""  # noqa: E501
+    tokenizer = CXRBertTokenizer.from_pretrained(BIOMED_VLP_BIOVIL_T, revision=BIOVIL_T_COMMIT_TAG)
+    text_model = CXRBertModel.from_pretrained(BIOMED_VLP_BIOVIL_T, revision=BIOVIL_T_COMMIT_TAG)
+    return tokenizer, text_model
+
+
 def get_cxr_bert() -> Tuple[CXRBertTokenizer, CXRBertModel]:
     """Load the CXR-BERT model and tokenizer from the `Hugging Face Hub <https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized>`_."""  # noqa: E501
-    model_name = BIOMED_VLP_CXR_BERT_SPECIALIZED
-    revision = CXR_BERT_COMMIT_TAG
-    tokenizer = CXRBertTokenizer.from_pretrained(model_name, revision=revision)
-    text_model = CXRBertModel.from_pretrained(model_name, revision=revision)
+    tokenizer = CXRBertTokenizer.from_pretrained(BIOMED_VLP_CXR_BERT_SPECIALIZED, revision=CXR_BERT_COMMIT_TAG)
+    text_model = CXRBertModel.from_pretrained(BIOMED_VLP_CXR_BERT_SPECIALIZED, revision=CXR_BERT_COMMIT_TAG)
     return tokenizer, text_model
 
 
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_model.py b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
index d04cc5c8b..6b61bdeff 100644
--- a/hi-ml-multimodal/test_multimodal/image/model/test_model.py
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
@@ -13,7 +13,7 @@
 from health_multimodal.image.model.types import ImageEncoderType, ImageModelOutput
 
 
-def test_biovilt_pretrained_model() -> None:
+def test_loading_biovilt_pretrained_model() -> None:
     model = get_biovilt_image_encoder()
 
 
diff --git a/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py b/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
index ebb69d47a..118cf3ec0 100644
--- a/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
+++ b/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
@@ -3,19 +3,21 @@
 #  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
 #  -------------------------------------------------------------------------------------------
 
+from typing import Callable
 
 import pytest
 import torch
 
 from health_multimodal.text.inference_engine import TextInferenceEngine
-from health_multimodal.text.utils import get_cxr_bert
+from health_multimodal.text.utils import get_biovil_t_bert, get_cxr_bert
 
 
-def test_text_inference_init_model_type() -> None:
+@pytest.mark.parametrize("text_model_inst_callable", [get_cxr_bert, get_biovil_t_bert])
+def test_text_inference_init_model_type(text_model_inst_callable: Callable) -> None:
     """
     Test that init fails if the wrong model type is passed in
     """
-    tokenizer, _ = get_cxr_bert()
+    tokenizer, _ = text_model_inst_callable()
     false_model = torch.nn.Linear(4, 4)
     with pytest.raises(AssertionError) as ex:
         TextInferenceEngine(tokenizer=tokenizer, text_model=false_model)  # type: ignore[arg-type]
@@ -26,7 +28,7 @@ def test_l2_normalization() -> None:
     """
     Test that the text embeddings (CLS token) are l2 normalized.
     """
-    tokenizer, text_model = get_cxr_bert()
+    tokenizer, text_model = get_biovil_t_bert()
 
     text_inference = TextInferenceEngine(tokenizer=tokenizer, text_model=text_model)
     input_query = ["There is a tumor in the left lung", "Lungs are all clear"]
@@ -35,11 +37,12 @@ def test_l2_normalization() -> None:
     assert torch.allclose(norm, torch.ones_like(norm))
 
 
-def test_sentence_semantic_similarity() -> None:
+@pytest.mark.parametrize("text_model_inst_callable", [get_cxr_bert, get_biovil_t_bert])
+def test_sentence_semantic_similarity(text_model_inst_callable: Callable) -> None:
     """
     Test that the sentence embedding similarity computed by the text model is meaningful.
     """
-    tokenizer, text_model = get_cxr_bert()
+    tokenizer, text_model = text_model_inst_callable()
 
     # CLS token has no dedicated meaning, but we can expect vector similarity due to token overlap between the sentences
     text_inference = TextInferenceEngine(tokenizer=tokenizer, text_model=text_model)

From 938146dc71b087bf6fb1c449242203a799e51d9d Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Mon, 20 Mar 2023 11:17:57 -0700
Subject: [PATCH 03/17] add inference engine for biovil-t bert

---
 .../src/health_multimodal/text/__init__.py    |  4 +--
 .../src/health_multimodal/text/utils.py       | 26 ++++++++++++++-----
 .../test_multimodal/text/data/test_text_io.py |  4 +--
 .../vlp/test_vlp_inference_engine.py          |  4 +--
 .../vlp/test_zero_shot_classification.py      |  4 +--
 5 files changed, 27 insertions(+), 15 deletions(-)

diff --git a/hi-ml-multimodal/src/health_multimodal/text/__init__.py b/hi-ml-multimodal/src/health_multimodal/text/__init__.py
index 7ab4c297b..8e0c05632 100644
--- a/hi-ml-multimodal/src/health_multimodal/text/__init__.py
+++ b/hi-ml-multimodal/src/health_multimodal/text/__init__.py
@@ -33,7 +33,7 @@
 """
 
 from .data.io import TypePrompts
-from .utils import get_cxr_bert_inference
+from .utils import get_bert_inference
 from .inference_engine import TextInferenceEngine
 from .model import CXRBertModel
 from .model import CXRBertOutput
@@ -48,5 +48,5 @@
     "CXRBertTokenizer",
     "CXRBertModel",
     "CXRBertOutput",
-    "get_cxr_bert_inference",
+    "get_bert_inference",
 ]
diff --git a/hi-ml-multimodal/src/health_multimodal/text/utils.py b/hi-ml-multimodal/src/health_multimodal/text/utils.py
index 1654effb7..bae562925 100644
--- a/hi-ml-multimodal/src/health_multimodal/text/utils.py
+++ b/hi-ml-multimodal/src/health_multimodal/text/utils.py
@@ -4,13 +4,19 @@
 #  -------------------------------------------------------------------------------------------
 
 
+from enum import Enum, unique
 from typing import Tuple
 
-from ..image.model.pretrained import BIOVIL_T_COMMIT_TAG, CXR_BERT_COMMIT_TAG
-from ..image.model.pretrained import BIOMED_VLP_BIOVIL_T, BIOMED_VLP_CXR_BERT_SPECIALIZED
+from ..image.model.pretrained import (BIOMED_VLP_BIOVIL_T, BIOMED_VLP_CXR_BERT_SPECIALIZED, BIOVIL_T_COMMIT_TAG,
+                                      CXR_BERT_COMMIT_TAG)
 from .inference_engine import TextInferenceEngine
-from .model import CXRBertModel
-from .model import CXRBertTokenizer
+from .model import CXRBertModel, CXRBertTokenizer
+
+
+@unique
+class BertEncoderType(str, Enum):
+    CXR_BERT = "cxr_bert"
+    BIOVIL_T_BERT = "biovil_t_bert"
 
 
 def get_biovil_t_bert() -> Tuple[CXRBertTokenizer, CXRBertModel]:
@@ -27,12 +33,18 @@ def get_cxr_bert() -> Tuple[CXRBertTokenizer, CXRBertModel]:
     return tokenizer, text_model
 
 
-def get_cxr_bert_inference() -> TextInferenceEngine:
-    """Create a :class:`TextInferenceEngine` for the CXR-BERT model.
+def get_bert_inference(bert_encoder_type: BertEncoderType = BertEncoderType.BIOVIL_T_BERT) -> TextInferenceEngine:
+    """Create a :class:`TextInferenceEngine` for a text encoder model.
 
     The model is downloaded from the Hugging Face Hub.
     The engine can be used to get embeddings from text prompts or masked token predictions.
     """
-    tokenizer, text_model = get_cxr_bert()
+    if bert_encoder_type == BertEncoderType.CXR_BERT:
+        tokenizer, text_model = get_cxr_bert()
+    elif bert_encoder_type == BertEncoderType.BIOVIL_T_BERT:
+        tokenizer, text_model = get_biovil_t_bert()
+    else:
+        raise ValueError(f"Unknown bert_encoder_type: {bert_encoder_type}")
+
     text_inference = TextInferenceEngine(tokenizer=tokenizer, text_model=text_model)
     return text_inference
diff --git a/hi-ml-multimodal/test_multimodal/text/data/test_text_io.py b/hi-ml-multimodal/test_multimodal/text/data/test_text_io.py
index 9d115d2cb..2ce046799 100644
--- a/hi-ml-multimodal/test_multimodal/text/data/test_text_io.py
+++ b/hi-ml-multimodal/test_multimodal/text/data/test_text_io.py
@@ -6,9 +6,9 @@
 import pytest
 
 from health_multimodal.text import TypePrompts
-from health_multimodal.text.utils import get_cxr_bert_inference
+from health_multimodal.text.utils import get_bert_inference
 
-text_inference = get_cxr_bert_inference()
+text_inference = get_bert_inference()
 
 
 @pytest.mark.parametrize("prompts", ("", "hello", "this is a test", ["this is", "also a test"]))
diff --git a/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py b/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
index ba20f5814..2c39bbd58 100644
--- a/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
+++ b/hi-ml-multimodal/test_multimodal/vlp/test_vlp_inference_engine.py
@@ -13,7 +13,7 @@
 from health_multimodal.image import ImageInferenceEngine, ImageModel, ImageEncoderType
 from health_multimodal.image.data.transforms import create_chest_xray_transform_for_inference
 from health_multimodal.image.model.pretrained import JOINT_FEATURE_SIZE
-from health_multimodal.text.utils import get_cxr_bert_inference
+from health_multimodal.text.utils import get_bert_inference
 from health_multimodal.vlp.inference_engine import ImageTextInferenceEngine
 from PIL import Image
 
@@ -28,7 +28,7 @@ def _get_vlp_inference_engine() -> ImageTextInferenceEngine:
         transform=create_chest_xray_transform_for_inference(resize=512, center_crop_size=CENTER_CROP_SIZE))
     img_txt_inference = ImageTextInferenceEngine(
         image_inference_engine=image_inference,
-        text_inference_engine=get_cxr_bert_inference(),
+        text_inference_engine=get_bert_inference(),
     )
 
     return img_txt_inference
diff --git a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
index 7ad596919..209beb7ec 100644
--- a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
+++ b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
@@ -9,7 +9,7 @@
 from health_multimodal.image import ImageInferenceEngine
 from health_multimodal.image.data.transforms import create_chest_xray_transform_for_inference
 from health_multimodal.image.model.pretrained import get_biovil_resnet
-from health_multimodal.text.utils import get_cxr_bert_inference
+from health_multimodal.text.utils import get_bert_inference
 from health_multimodal.vlp.inference_engine import ImageTextInferenceEngine
 
 RESIZE = 512
@@ -29,7 +29,7 @@ def _get_vlp_inference_engine() -> ImageTextInferenceEngine:
         transform=create_chest_xray_transform_for_inference(resize=RESIZE, center_crop_size=CENTER_CROP_SIZE))
     img_txt_inference = ImageTextInferenceEngine(
         image_inference_engine=image_inference,
-        text_inference_engine=get_cxr_bert_inference(),
+        text_inference_engine=get_bert_inference(),
     )
     return img_txt_inference
 

From 01963bbec604a84ed9bf3706ade83fd1de5a36ee Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Mon, 20 Mar 2023 11:54:12 -0700
Subject: [PATCH 04/17] consume the biovil-t in notebook and update the
 inference engine helper functions

---
 .../notebooks/phrase_grounding.ipynb          | 27 ++++++++++++---
 .../src/health_multimodal/image/__init__.py   |  6 ++--
 .../health_multimodal/image/model/__init__.py |  2 --
 .../image/model/pretrained.py                 |  2 +-
 .../src/health_multimodal/image/utils.py      | 33 ++++++++++++++-----
 .../src/health_multimodal/text/utils.py       |  2 ++
 .../test_multimodal/image/model/test_model.py |  4 +--
 7 files changed, 54 insertions(+), 22 deletions(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index 428ee029f..604e80710 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -64,6 +64,16 @@
     "%pip install {pip_source}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0,'/home/ozoktay/workspace/hi-ml/hi-ml-multimodal/src')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -75,8 +85,8 @@
     "\n",
     "import torch\n",
     "\n",
-    "from health_multimodal.text import get_cxr_bert_inference\n",
-    "from health_multimodal.image import get_biovil_resnet_inference\n",
+    "from health_multimodal.text import get_bert_inference\n",
+    "from health_multimodal.image import get_image_inference\n",
     "from health_multimodal.vlp import ImageTextInferenceEngine\n",
     "from health_multimodal.common.visualization import plot_phrase_grounding_similarity_map"
    ]
@@ -101,8 +111,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "text_inference = get_cxr_bert_inference()\n",
-    "image_inference = get_biovil_resnet_inference()"
+    "text_inference = get_bert_inference(\"biovil_t_bert\")\n",
+    "image_inference = get_image_inference(\"biovil_t\")"
    ]
   },
   {
@@ -184,6 +194,13 @@
     "text_prompt = \"there is cardiomegaly\"\n",
     "plot_phrase_grounding_from_url(image_url, text_prompt)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -202,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.7.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/hi-ml-multimodal/src/health_multimodal/image/__init__.py b/hi-ml-multimodal/src/health_multimodal/image/__init__.py
index e35f49886..5836892c4 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/__init__.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/__init__.py
@@ -40,9 +40,8 @@
 from .model import BaseImageModel
 from .model import ImageModel
 from .model import ImageEncoderType
-from .model import get_biovil_resnet
 from .inference_engine import ImageInferenceEngine
-from .utils import get_biovil_resnet_inference
+from .utils import get_image_inference
 
 
 __all__ = [
@@ -50,6 +49,5 @@
     "ImageModel",
     "ImageEncoderType",
     "ImageInferenceEngine",
-    "get_biovil_resnet",
-    "get_biovil_resnet_inference",
+    "get_image_inference",
 ]
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
index b6b97b1c9..ceb15d0e1 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/__init__.py
@@ -6,11 +6,9 @@
 from .types import ImageEncoderType
 from .model import BaseImageModel
 from .model import ImageModel
-from .pretrained import get_biovil_resnet
 
 __all__ = [
     "BaseImageModel",
     "ImageEncoderType",
     "ImageModel",
-    "get_biovil_resnet",
 ]
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index 3670e0b8f..61018e582 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -74,7 +74,7 @@ def get_biovil_resnet(pretrained: bool = True) -> ImageModel:
     return image_model
 
 
-def get_biovilt_image_encoder() -> ImageModel:
+def get_biovil_t_image_encoder() -> ImageModel:
     """Download weights from Hugging Face and instantiate the image model."""
 
     biovilt_checkpoint_path = _download_biovil_t_image_model_weights()
diff --git a/hi-ml-multimodal/src/health_multimodal/image/utils.py b/hi-ml-multimodal/src/health_multimodal/image/utils.py
index 9504b8e1e..9cd57b3ad 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/utils.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/utils.py
@@ -3,25 +3,42 @@
 #  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
 #  -------------------------------------------------------------------------------------------
 
-from .inference_engine import ImageInferenceEngine
-from .data.transforms import create_chest_xray_transform_for_inference
-from .model import get_biovil_resnet
+from enum import Enum, unique
 
+from .data.transforms import create_chest_xray_transform_for_inference
+from .inference_engine import ImageInferenceEngine
+from .model.pretrained import get_biovil_resnet, get_biovil_t_image_encoder
 
 TRANSFORM_RESIZE = 512
-TRANSFORM_CENTER_CROP_SIZE = 480
 
 
-def get_biovil_resnet_inference() -> ImageInferenceEngine:
+@unique
+class ImageModelType(str, Enum):
+    BIOVIL = "biovil"
+    BIOVIL_T = "biovil_t"
+
+
+def get_image_inference(image_model_type: ImageModelType = ImageModelType.BIOVIL_T) -> ImageInferenceEngine:
     """Create a :class:`ImageInferenceEngine` for the image model.
 
+    :param image_model_type: The type of image model to use, `BIOVIL` or `BIOVIL_T`.
+
     The model is downloaded from the Hugging Face Hub.
     The engine can be used to get embeddings from text prompts or masked token predictions.
     """
-    image_model = get_biovil_resnet()
+
+    if image_model_type == ImageModelType.BIOVIL_T:
+        image_model = get_biovil_t_image_encoder()
+        transform_center_crop_size = 448
+    elif image_model_type == ImageModelType.BIOVIL:
+        image_model = get_biovil_resnet()
+        transform_center_crop_size = 480
+    else:
+        raise ValueError(f"Unknown image_model_type: {image_model_type}")
+
     transform = create_chest_xray_transform_for_inference(
         resize=TRANSFORM_RESIZE,
-        center_crop_size=TRANSFORM_CENTER_CROP_SIZE,
-    )
+        center_crop_size=transform_center_crop_size)
     image_inference = ImageInferenceEngine(image_model=image_model, transform=transform)
+
     return image_inference
diff --git a/hi-ml-multimodal/src/health_multimodal/text/utils.py b/hi-ml-multimodal/src/health_multimodal/text/utils.py
index bae562925..8b9a0d063 100644
--- a/hi-ml-multimodal/src/health_multimodal/text/utils.py
+++ b/hi-ml-multimodal/src/health_multimodal/text/utils.py
@@ -36,6 +36,8 @@ def get_cxr_bert() -> Tuple[CXRBertTokenizer, CXRBertModel]:
 def get_bert_inference(bert_encoder_type: BertEncoderType = BertEncoderType.BIOVIL_T_BERT) -> TextInferenceEngine:
     """Create a :class:`TextInferenceEngine` for a text encoder model.
 
+    :param bert_encoder_type: The type of text encoder model to use, `CXR_BERT` or `BIOVIL_T_BERT`.
+
     The model is downloaded from the Hugging Face Hub.
     The engine can be used to get embeddings from text prompts or masked token predictions.
     """
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_model.py b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
index 6b61bdeff..ad0f00319 100644
--- a/hi-ml-multimodal/test_multimodal/image/model/test_model.py
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_model.py
@@ -9,12 +9,12 @@
 import torch
 from health_multimodal.image.model.model import ImageModel, MultiImageModel
 from health_multimodal.image.model.modules import MultiTaskModel
-from health_multimodal.image.model.pretrained import get_biovil_resnet, get_biovilt_image_encoder
+from health_multimodal.image.model.pretrained import get_biovil_resnet, get_biovil_t_image_encoder
 from health_multimodal.image.model.types import ImageEncoderType, ImageModelOutput
 
 
 def test_loading_biovilt_pretrained_model() -> None:
-    model = get_biovilt_image_encoder()
+    get_biovil_t_image_encoder()
 
 
 def test_frozen_cnn_model() -> None:

From 9d2ed78f68a47e5733cd17399ecebb21f3000da5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Mar 2023 18:56:44 +0000
Subject: [PATCH 05/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 hi-ml-multimodal/src/health_multimodal/image/model/model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/model.py b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
index a056d0295..3974be83f 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/model.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
@@ -19,7 +19,6 @@
 from .types import ImageModelOutput
 
 
-
 class BaseImageModel(nn.Module, ABC):
     """Abstract class for image models."""
     @abstractmethod

From f2f02d96ecb5c4570eec119f31ad7967f33423a9 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Tue, 21 Mar 2023 10:45:25 -0700
Subject: [PATCH 06/17] fix flake8 errors

---
 hi-ml-multimodal/src/health_multimodal/image/model/model.py | 1 -
 .../src/health_multimodal/image/model/pretrained.py         | 6 +++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/model.py b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
index a056d0295..3974be83f 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/model.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/model.py
@@ -19,7 +19,6 @@
 from .types import ImageModelOutput
 
 
-
 class BaseImageModel(nn.Module, ABC):
     """Abstract class for image models."""
     @abstractmethod
diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index 61018e582..7246edb66 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -18,17 +18,17 @@
 
 BIOMED_VLP_CXR_BERT_SPECIALIZED = "microsoft/BiomedVLP-CXR-BERT-specialized"
 BIOMED_VLP_BIOVIL_T = "microsoft/BiomedVLP-BioViL-T"
-HF_URL = f"https://huggingface.co"
+HF_URL = "https://huggingface.co"
 
 CXR_BERT_COMMIT_TAG = "v1.1"
 BIOVIL_T_COMMIT_TAG = "v1.0"
 
 BIOVIL_IMAGE_WEIGHTS_NAME = "biovil_image_resnet50_proj_size_128.pt"
-BIOVIL_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_CXR_BERT_SPECIALIZED}/resolve/{CXR_BERT_COMMIT_TAG}/{BIOVIL_IMAGE_WEIGHTS_NAME}"
+BIOVIL_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_CXR_BERT_SPECIALIZED}/resolve/{CXR_BERT_COMMIT_TAG}/{BIOVIL_IMAGE_WEIGHTS_NAME}"  # noqa: E501
 BIOVIL_IMAGE_WEIGHTS_MD5 = "02ce6ee460f72efd599295f440dbb453"
 
 BIOVIL_T_IMAGE_WEIGHTS_NAME = "biovil_t_image_model_proj_size_128.pt"
-BIOVIL_T_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_BIOVIL_T}/resolve/{BIOVIL_T_COMMIT_TAG}/{BIOVIL_T_IMAGE_WEIGHTS_NAME}"
+BIOVIL_T_IMAGE_WEIGHTS_URL = f"{HF_URL}/{BIOMED_VLP_BIOVIL_T}/resolve/{BIOVIL_T_COMMIT_TAG}/{BIOVIL_T_IMAGE_WEIGHTS_NAME}"  # noqa: E501
 BIOVIL_T_IMAGE_WEIGHTS_MD5 = "a83080e2f23aa584a4f2b24c39b1bb64"
 
 

From 05576f6f342aa97e701e6f9be2811332c904e4f5 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Tue, 21 Mar 2023 11:17:27 -0700
Subject: [PATCH 07/17] fix pytest error

---
 .../test_multimodal/vlp/test_zero_shot_classification.py  | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
index 209beb7ec..25dd0a5ca 100644
--- a/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
+++ b/hi-ml-multimodal/test_multimodal/vlp/test_zero_shot_classification.py
@@ -8,8 +8,8 @@
 
 from health_multimodal.image import ImageInferenceEngine
 from health_multimodal.image.data.transforms import create_chest_xray_transform_for_inference
-from health_multimodal.image.model.pretrained import get_biovil_resnet
-from health_multimodal.text.utils import get_bert_inference
+from health_multimodal.image.model.pretrained import get_biovil_t_image_encoder
+from health_multimodal.text.utils import BertEncoderType, get_bert_inference
 from health_multimodal.vlp.inference_engine import ImageTextInferenceEngine
 
 RESIZE = 512
@@ -25,11 +25,11 @@ class ClassType(str, Enum):
 
 def _get_vlp_inference_engine() -> ImageTextInferenceEngine:
     image_inference = ImageInferenceEngine(
-        image_model=get_biovil_resnet(pretrained=True),
+        image_model=get_biovil_t_image_encoder(),
         transform=create_chest_xray_transform_for_inference(resize=RESIZE, center_crop_size=CENTER_CROP_SIZE))
     img_txt_inference = ImageTextInferenceEngine(
         image_inference_engine=image_inference,
-        text_inference_engine=get_bert_inference(),
+        text_inference_engine=get_bert_inference(BertEncoderType.BIOVIL_T_BERT),
     )
     return img_txt_inference
 

From 5d7d17f8947755640c2b5e6dc0be7dd68dcbe1c0 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Tue, 21 Mar 2023 11:25:50 -0700
Subject: [PATCH 08/17] PR comments - renaming variable

---
 .../test_multimodal/text/test_inference_engine.py    | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py b/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
index 118cf3ec0..87984a6c1 100644
--- a/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
+++ b/hi-ml-multimodal/test_multimodal/text/test_inference_engine.py
@@ -12,12 +12,12 @@
 from health_multimodal.text.utils import get_biovil_t_bert, get_cxr_bert
 
 
-@pytest.mark.parametrize("text_model_inst_callable", [get_cxr_bert, get_biovil_t_bert])
-def test_text_inference_init_model_type(text_model_inst_callable: Callable) -> None:
+@pytest.mark.parametrize("create_text_model_and_tokenizer", [get_cxr_bert, get_biovil_t_bert])
+def test_text_inference_init_model_type(create_text_model_and_tokenizer: Callable) -> None:
     """
     Test that init fails if the wrong model type is passed in
     """
-    tokenizer, _ = text_model_inst_callable()
+    tokenizer, _ = create_text_model_and_tokenizer()
     false_model = torch.nn.Linear(4, 4)
     with pytest.raises(AssertionError) as ex:
         TextInferenceEngine(tokenizer=tokenizer, text_model=false_model)  # type: ignore[arg-type]
@@ -37,12 +37,12 @@ def test_l2_normalization() -> None:
     assert torch.allclose(norm, torch.ones_like(norm))
 
 
-@pytest.mark.parametrize("text_model_inst_callable", [get_cxr_bert, get_biovil_t_bert])
-def test_sentence_semantic_similarity(text_model_inst_callable: Callable) -> None:
+@pytest.mark.parametrize("create_text_model_and_tokenizer", [get_cxr_bert, get_biovil_t_bert])
+def test_sentence_semantic_similarity(create_text_model_and_tokenizer: Callable) -> None:
     """
     Test that the sentence embedding similarity computed by the text model is meaningful.
     """
-    tokenizer, text_model = text_model_inst_callable()
+    tokenizer, text_model = create_text_model_and_tokenizer()
 
     # CLS token has no dedicated meaning, but we can expect vector similarity due to token overlap between the sentences
     text_inference = TextInferenceEngine(tokenizer=tokenizer, text_model=text_model)

From 291eeae5098db4b164bb2a868cf50ec6788db3dd Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Wed, 22 Mar 2023 08:58:45 -0700
Subject: [PATCH 09/17] fix the example in notebook and add bounding boxes

---
 .../notebooks/phrase_grounding.ipynb          | 24 +++++++++----
 hi-ml-multimodal/requirements_run.txt         |  1 +
 hi-ml-multimodal/requirements_test.txt        |  1 +
 .../health_multimodal/common/visualization.py | 34 ++++++++++++++++---
 .../common/test_visualization.py              | 15 ++++++++
 5 files changed, 65 insertions(+), 10 deletions(-)
 create mode 100644 hi-ml-multimodal/test_multimodal/common/test_visualization.py

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index 604e80710..7db0b6f15 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -80,6 +80,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import List, Tuple\n",
+    "\n",
     "import tempfile\n",
     "from pathlib import Path\n",
     "\n",
@@ -149,7 +151,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_phrase_grounding(image_path: Path, text_prompt: str) -> None:\n",
+    "def plot_phrase_grounding(image_path: Path, text_prompt: str, bboxes: List[Tuple[float, float, float, float]]) -> None:\n",
     "    similarity_map = image_text_inference.get_similarity_map_from_raw_data(\n",
     "        image_path=image_path,\n",
     "        query_text=text_prompt,\n",
@@ -158,12 +160,13 @@
     "    plot_phrase_grounding_similarity_map(\n",
     "        image_path=image_path,\n",
     "        similarity_map=similarity_map,\n",
+    "        bboxes=bboxes\n",
     "    )\n",
     "\n",
-    "def plot_phrase_grounding_from_url(image_url: str, text_prompt: str) -> None:\n",
+    "def plot_phrase_grounding_from_url(image_url: str, text_prompt: str, bboxes: List[Tuple[float, float, float, float]]) -> None:\n",
     "    image_path = Path(tempfile.tempdir, \"downloaded_chest_xray.jpg\")\n",
     "    !curl -s -L -o {image_path} {image_url}\n",
-    "    plot_phrase_grounding(image_path, text_prompt)"
+    "    plot_phrase_grounding(image_path, text_prompt, bboxes)"
    ]
   },
   {
@@ -182,7 +185,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image_url = \"https://openi.nlm.nih.gov/imgs/512/177/177/CXR177_IM-0503-1001.png\""
+    "image_url = \"https://openi.nlm.nih.gov/imgs/512/242/1445/CXR1445_IM-0287-4004.png\"\n",
+    "text_prompt = \"Left basilar consolidation seen.\"\n",
+    "\n",
+    "# Ground-truth bounding box annotations for the phrase \"Left basilar consolidation seen.\"\n",
+    "bboxes = [[306, 168, 124, 101]]\n",
+    "\n",
+    "plot_phrase_grounding_from_url(image_url, text_prompt, bboxes)"
    ]
   },
   {
@@ -191,8 +200,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "text_prompt = \"there is cardiomegaly\"\n",
-    "plot_phrase_grounding_from_url(image_url, text_prompt)"
+    "from ipyannotations import text\n",
+    "widget = text.TextTagger()\n",
+    "widget.display(\"This is a text tagging widget. Highlight words \"\n",
+    "               \"or phrases to tag them with a class.\")\n",
+    "widget"
    ]
   },
   {
diff --git a/hi-ml-multimodal/requirements_run.txt b/hi-ml-multimodal/requirements_run.txt
index 962496a80..827159a85 100644
--- a/hi-ml-multimodal/requirements_run.txt
+++ b/hi-ml-multimodal/requirements_run.txt
@@ -1,3 +1,4 @@
+ipyannotations==0.5.1
 huggingface-hub==0.6.0
 pillow==9.3.0
 pydicom==2.2.2
diff --git a/hi-ml-multimodal/requirements_test.txt b/hi-ml-multimodal/requirements_test.txt
index 7b29988d0..74391e3f9 100644
--- a/hi-ml-multimodal/requirements_test.txt
+++ b/hi-ml-multimodal/requirements_test.txt
@@ -1,6 +1,7 @@
 bump2version==1.0.1
 coverage==6.3.2
 flake8==5.0.2
+ipyannotations==0.5.1
 ipykernel==6.15.0
 ipython==7.34.0
 mypy==0.931
diff --git a/hi-ml-multimodal/src/health_multimodal/common/visualization.py b/hi-ml-multimodal/src/health_multimodal/common/visualization.py
index 04138ab9b..a5b837429 100644
--- a/hi-ml-multimodal/src/health_multimodal/common/visualization.py
+++ b/hi-ml-multimodal/src/health_multimodal/common/visualization.py
@@ -4,12 +4,13 @@
 #  -------------------------------------------------------------------------------------------
 
 from pathlib import Path
-from typing import Union, Optional
+from typing import List, Optional, Tuple, Union
 
-import numpy as np
-from PIL import Image
+import matplotlib.patches as patches
 import matplotlib.pyplot as plt
+import numpy as np
 from mpl_toolkits.axes_grid1 import make_axes_locatable
+from PIL import Image
 
 from health_multimodal.image.data.io import load_image
 
@@ -106,15 +107,40 @@ def _plot_heatmap(
         axis.set_title(title)
 
 
-def plot_phrase_grounding_similarity_map(image_path: Path, similarity_map: np.ndarray) -> plt.Figure:
+def plot_phrase_grounding_similarity_map(
+        image_path: Path,
+        similarity_map: np.ndarray,
+        bboxes: Optional[List[Tuple[float, float, float, float]]] = None) -> plt.Figure:
     """Plot visualization of the input image, the similarity heatmap and the heatmap isolines.
 
     :param image_path: Path to the input image.
     :param similarity_map: Phase grounding similarity map of the same size as the image.
+    :param bboxes: Optional list of bounding boxes to plot on the image.
     """
     fig, axes = plt.subplots(1, 3, figsize=(15, 6))
     image = load_image(image_path).convert("RGB")
     _plot_image(image, axis=axes[0], title="Input image")
     _plot_isolines(image, similarity_map, axis=axes[1], title="Similarity isolines")
     _plot_heatmap(image, similarity_map, figure=fig, axis=axes[2], title="Similarity heatmap")
+    if bboxes is not None:
+        _plot_bounding_boxes(ax=axes[1], bboxes=bboxes)
     return fig
+
+
+def _plot_bounding_boxes(ax: plt.Axes,
+                         bboxes: List[Tuple[float, float, float, float]],
+                         linewidth: float = 1.5,
+                         alpha: float = 0.45) -> None:
+    """
+    Plot bounding boxes on an existing axes object.
+
+    :param ax: The axes object to plot the bounding boxes on.
+    :param bboxes: A list of bounding box coordinates as (x, y, width, height) tuples.
+    :param linewidth: Optional line width for the bounding box edges (default is 2).
+    :param alpha: Optional opacity for the bounding box edges (default is 1.0).
+    """
+    for bbox in bboxes:
+        x, y, width, height = bbox
+        rect = patches.Rectangle((x, y), width, height, linewidth=linewidth, edgecolor='k',
+                                 facecolor='none', linestyle='--', alpha=alpha)
+        ax.add_patch(rect)
diff --git a/hi-ml-multimodal/test_multimodal/common/test_visualization.py b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
new file mode 100644
index 000000000..af3ceb49f
--- /dev/null
+++ b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
@@ -0,0 +1,15 @@
+import matplotlib.pyplot as plt
+
+from health_multimodal.common.visualization import plot_bounding_boxes
+
+
+def test_plot_bounding_boxes():
+    fig, ax = plt.subplots()
+    bboxes = [
+        (1, 1, 3, 3),
+        (5, 2, 2, 4),
+    ]
+    initial_patches_count = len(ax.patches)
+    plot_bounding_boxes(ax, bboxes)
+    final_patches_count = len(ax.patches)
+    assert final_patches_count - initial_patches_count == len(bboxes)

From 47883aada47b5bcee7553ca9b3ca9fa33ba2eb52 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Wed, 22 Mar 2023 09:05:56 -0700
Subject: [PATCH 10/17] update the notebook

---
 .../notebooks/phrase_grounding.ipynb          | 141 ++++++++++++++----
 hi-ml-multimodal/requirements_run.txt         |   1 -
 hi-ml-multimodal/requirements_test.txt        |   1 -
 3 files changed, 116 insertions(+), 27 deletions(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index 7db0b6f15..b738dde0c 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "tags": [
      "parameters"
@@ -57,16 +57,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting hi-ml-multimodal\n",
+      "  Downloading hi_ml_multimodal-0.1.3-py3-none-any.whl (27 kB)\n",
+      "Requirement already satisfied: torchvision<=0.10.0,>0.9 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.10.0)\n",
+      "Requirement already satisfied: transformers==4.17.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (4.17.0)\n",
+      "Requirement already satisfied: huggingface-hub==0.6.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.6.0)\n",
+      "Requirement already satisfied: SimpleITK==2.1.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (2.1.1)\n",
+      "Requirement already satisfied: torch==1.9.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (1.9.0)\n",
+      "Requirement already satisfied: pydicom==2.2.2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (2.2.2)\n",
+      "Requirement already satisfied: scikit-image==0.18.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.18.1)\n",
+      "Requirement already satisfied: pillow==9.3.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (9.3.0)\n",
+      "Requirement already satisfied: numpy in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from torchvision<=0.10.0,>0.9->hi-ml-multimodal) (1.21.5)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,>=0.11.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (0.12.1)\n",
+      "Requirement already satisfied: tqdm>=4.27 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (4.64.1)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (2022.9.13)\n",
+      "Requirement already satisfied: filelock in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (3.8.0)\n",
+      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (4.2.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (21.3)\n",
+      "Requirement already satisfied: sacremoses in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (0.0.53)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (6.0)\n",
+      "Requirement already satisfied: requests in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (2.28.1)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from huggingface-hub==0.6.0->hi-ml-multimodal) (4.3.0)\n",
+      "Requirement already satisfied: PyWavelets>=1.1.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (1.3.0)\n",
+      "Requirement already satisfied: scipy>=1.0.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (1.7.3)\n",
+      "Requirement already satisfied: networkx>=2.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2.6.3)\n",
+      "Requirement already satisfied: tifffile>=2019.7.26 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2021.11.2)\n",
+      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (3.5.3)\n",
+      "Requirement already satisfied: imageio>=2.3.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2.21.3)\n",
+      "Requirement already satisfied: zipp>=0.5 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.17.0->hi-ml-multimodal) (3.8.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from packaging>=20.0->transformers==4.17.0->hi-ml-multimodal) (3.0.9)\n",
+      "Requirement already satisfied: joblib in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (1.1.0)\n",
+      "Requirement already satisfied: six in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (1.16.0)\n",
+      "Requirement already satisfied: click in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (8.1.3)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (3.4)\n",
+      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (2.1.1)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (1.26.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (2022.12.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (1.4.4)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (0.11.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (2.8.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (4.37.1)\n",
+      "Installing collected packages: hi-ml-multimodal\n",
+      "Successfully installed hi-ml-multimodal-0.1.3\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install {pip_source}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,9 +159,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
+      "The tokenizer class you load from this checkpoint is 'BertTokenizer'. \n",
+      "The class this function is called from is 'CXRBertTokenizer'.\n",
+      "You are using a model of type bert to instantiate a model of type cxr-bert. This is not supported for all configurations of models and can yield errors.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using downloaded and verified file: /tmp/biovil_t_image_model_proj_size_128.pt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /tmp/pip-req-build-eoxszjt0/c10/core/TensorImpl.h:1156.)\n",
+      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
+     ]
+    }
+   ],
    "source": [
     "text_inference = get_bert_inference(\"biovil_t_bert\")\n",
     "image_inference = get_image_inference(\"biovil_t\")"
@@ -126,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,9 +257,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "Phrase grounding results\n",
+       " -- Ground-truth bounding box annotations for the phrase 'Left basilar consolidation seen.' is shown in black"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "image_url = \"https://openi.nlm.nih.gov/imgs/512/242/1445/CXR1445_IM-0287-4004.png\"\n",
     "text_prompt = \"Left basilar consolidation seen.\"\n",
@@ -191,22 +291,13 @@
     "# Ground-truth bounding box annotations for the phrase \"Left basilar consolidation seen.\"\n",
     "bboxes = [[306, 168, 124, 101]]\n",
     "\n",
+    "# Import ipython display and show the following text \"hello world\"\n",
+    "from IPython.display import display, Markdown\n",
+    "display(Markdown(\"Phrase grounding results -- Ground-truth bounding box annotations for the phrase 'Left basilar consolidation seen.' is shown in black\"))\n",
+    "\n",
     "plot_phrase_grounding_from_url(image_url, text_prompt, bboxes)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ipyannotations import text\n",
-    "widget = text.TextTagger()\n",
-    "widget.display(\"This is a text tagging widget. Highlight words \"\n",
-    "               \"or phrases to tag them with a class.\")\n",
-    "widget"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/hi-ml-multimodal/requirements_run.txt b/hi-ml-multimodal/requirements_run.txt
index 827159a85..962496a80 100644
--- a/hi-ml-multimodal/requirements_run.txt
+++ b/hi-ml-multimodal/requirements_run.txt
@@ -1,4 +1,3 @@
-ipyannotations==0.5.1
 huggingface-hub==0.6.0
 pillow==9.3.0
 pydicom==2.2.2
diff --git a/hi-ml-multimodal/requirements_test.txt b/hi-ml-multimodal/requirements_test.txt
index 74391e3f9..7b29988d0 100644
--- a/hi-ml-multimodal/requirements_test.txt
+++ b/hi-ml-multimodal/requirements_test.txt
@@ -1,7 +1,6 @@
 bump2version==1.0.1
 coverage==6.3.2
 flake8==5.0.2
-ipyannotations==0.5.1
 ipykernel==6.15.0
 ipython==7.34.0
 mypy==0.931

From 7e77b17bae45d6deecb03bc8cbfc2d6eb6f6b727 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Wed, 22 Mar 2023 09:16:48 -0700
Subject: [PATCH 11/17] finalise the notebook

---
 .../notebooks/phrase_grounding.ipynb          | 133 +++---------------
 1 file changed, 17 insertions(+), 116 deletions(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index b738dde0c..c991b3766 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "parameters"
@@ -57,66 +57,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting hi-ml-multimodal\n",
-      "  Downloading hi_ml_multimodal-0.1.3-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: torchvision<=0.10.0,>0.9 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.10.0)\n",
-      "Requirement already satisfied: transformers==4.17.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (4.17.0)\n",
-      "Requirement already satisfied: huggingface-hub==0.6.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.6.0)\n",
-      "Requirement already satisfied: SimpleITK==2.1.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (2.1.1)\n",
-      "Requirement already satisfied: torch==1.9.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (1.9.0)\n",
-      "Requirement already satisfied: pydicom==2.2.2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (2.2.2)\n",
-      "Requirement already satisfied: scikit-image==0.18.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (0.18.1)\n",
-      "Requirement already satisfied: pillow==9.3.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from hi-ml-multimodal) (9.3.0)\n",
-      "Requirement already satisfied: numpy in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from torchvision<=0.10.0,>0.9->hi-ml-multimodal) (1.21.5)\n",
-      "Requirement already satisfied: tokenizers!=0.11.3,>=0.11.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (0.12.1)\n",
-      "Requirement already satisfied: tqdm>=4.27 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (4.64.1)\n",
-      "Requirement already satisfied: regex!=2019.12.17 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (2022.9.13)\n",
-      "Requirement already satisfied: filelock in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (3.8.0)\n",
-      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (4.2.0)\n",
-      "Requirement already satisfied: packaging>=20.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (21.3)\n",
-      "Requirement already satisfied: sacremoses in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (0.0.53)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (6.0)\n",
-      "Requirement already satisfied: requests in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from transformers==4.17.0->hi-ml-multimodal) (2.28.1)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from huggingface-hub==0.6.0->hi-ml-multimodal) (4.3.0)\n",
-      "Requirement already satisfied: PyWavelets>=1.1.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (1.3.0)\n",
-      "Requirement already satisfied: scipy>=1.0.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (1.7.3)\n",
-      "Requirement already satisfied: networkx>=2.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2.6.3)\n",
-      "Requirement already satisfied: tifffile>=2019.7.26 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2021.11.2)\n",
-      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (3.5.3)\n",
-      "Requirement already satisfied: imageio>=2.3.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from scikit-image==0.18.1->hi-ml-multimodal) (2.21.3)\n",
-      "Requirement already satisfied: zipp>=0.5 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.17.0->hi-ml-multimodal) (3.8.1)\n",
-      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from packaging>=20.0->transformers==4.17.0->hi-ml-multimodal) (3.0.9)\n",
-      "Requirement already satisfied: joblib in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (1.1.0)\n",
-      "Requirement already satisfied: six in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (1.16.0)\n",
-      "Requirement already satisfied: click in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from sacremoses->transformers==4.17.0->hi-ml-multimodal) (8.1.3)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (3.4)\n",
-      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (2.1.1)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (1.26.12)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from requests->transformers==4.17.0->hi-ml-multimodal) (2022.12.7)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (1.4.4)\n",
-      "Requirement already satisfied: cycler>=0.10 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (0.11.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (2.8.2)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.18.1->hi-ml-multimodal) (4.37.1)\n",
-      "Installing collected packages: hi-ml-multimodal\n",
-      "Successfully installed hi-ml-multimodal-0.1.3\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install {pip_source}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,35 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
-      "The tokenizer class you load from this checkpoint is 'BertTokenizer'. \n",
-      "The class this function is called from is 'CXRBertTokenizer'.\n",
-      "You are using a model of type bert to instantiate a model of type cxr-bert. This is not supported for all configurations of models and can yield errors.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using downloaded and verified file: /tmp/biovil_t_image_model_proj_size_128.pt\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ozoktay/.conda/envs/himl/lib/python3.7/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /tmp/pip-req-build-eoxszjt0/c10/core/TensorImpl.h:1156.)\n",
-      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "text_inference = get_bert_inference(\"biovil_t_bert\")\n",
     "image_inference = get_image_inference(\"biovil_t\")"
@@ -202,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,43 +181,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Phrase grounding results\n",
-       " -- Ground-truth bounding box annotations for the phrase 'Left basilar consolidation seen.' is shown in black"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_url = \"https://openi.nlm.nih.gov/imgs/512/242/1445/CXR1445_IM-0287-4004.png\"\n",
-    "text_prompt = \"Left basilar consolidation seen.\"\n",
+    "text_prompt = \"Left basilar consolidation seen\"\n",
     "\n",
-    "# Ground-truth bounding box annotations for the phrase \"Left basilar consolidation seen.\"\n",
+    "# Ground-truth bounding box annotation(s) for the input text prompt.\n",
     "bboxes = [[306, 168, 124, 101]]\n",
     "\n",
-    "# Import ipython display and show the following text \"hello world\"\n",
+    "# Phrase-grounding experiment\n",
     "from IPython.display import display, Markdown\n",
-    "display(Markdown(\"Phrase grounding results -- Ground-truth bounding box annotations for the phrase 'Left basilar consolidation seen.' is shown in black\"))\n",
+    "display(Markdown(\"Phrase Grounding Experiment: \\n\\n\"\n",
+    "                 f\"Ground-truth bounding box annotation(s) for the phrase \\\"{text_prompt}\\\" is shown in the middle figure (in black).\"))\n",
     "\n",
     "plot_phrase_grounding_from_url(image_url, text_prompt, bboxes)"
    ]

From dea646458ce633092b398ce9d49d2b1e93c18ead Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Wed, 22 Mar 2023 09:18:53 -0700
Subject: [PATCH 12/17] fix the test error

---
 .../test_multimodal/common/test_visualization.py          | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/hi-ml-multimodal/test_multimodal/common/test_visualization.py b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
index af3ceb49f..56ca5440b 100644
--- a/hi-ml-multimodal/test_multimodal/common/test_visualization.py
+++ b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
@@ -1,15 +1,15 @@
 import matplotlib.pyplot as plt
 
-from health_multimodal.common.visualization import plot_bounding_boxes
+from health_multimodal.common.visualization import _plot_bounding_boxes
 
 
-def test_plot_bounding_boxes():
-    fig, ax = plt.subplots()
+def test_plot_bounding_boxes() -> None:
+    _, ax = plt.subplots()
     bboxes = [
         (1, 1, 3, 3),
         (5, 2, 2, 4),
     ]
     initial_patches_count = len(ax.patches)
-    plot_bounding_boxes(ax, bboxes)
+    _plot_bounding_boxes(ax, bboxes)
     final_patches_count = len(ax.patches)
     assert final_patches_count - initial_patches_count == len(bboxes)

From d9cc5959aab0ce7de7b12492219452e30666cfab Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Wed, 22 Mar 2023 09:22:20 -0700
Subject: [PATCH 13/17] add copyright and remove temporary code from the
 notebook

---
 hi-ml-multimodal/notebooks/phrase_grounding.ipynb      | 10 ----------
 .../test_multimodal/common/test_visualization.py       |  5 +++++
 2 files changed, 5 insertions(+), 10 deletions(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index c991b3766..7af31a3ff 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -64,16 +64,6 @@
     "%pip install {pip_source}"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.insert(0,'/home/ozoktay/workspace/hi-ml/hi-ml-multimodal/src')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/hi-ml-multimodal/test_multimodal/common/test_visualization.py b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
index 56ca5440b..257d354f1 100644
--- a/hi-ml-multimodal/test_multimodal/common/test_visualization.py
+++ b/hi-ml-multimodal/test_multimodal/common/test_visualization.py
@@ -1,3 +1,8 @@
+#  -------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  -------------------------------------------------------------------------------------------
+
 import matplotlib.pyplot as plt
 
 from health_multimodal.common.visualization import _plot_bounding_boxes

From aa51c39731ae7cafb8c425635121321d05409df8 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Thu, 30 Mar 2023 07:43:28 -0700
Subject: [PATCH 14/17] add linear classifier

---
 .../image/model/pretrained.py                 | 34 ++++++++++++++++++
 .../image/model/test_pretrained.py            | 36 +++++++++++++++++++
 2 files changed, 70 insertions(+)
 create mode 100644 hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index 7246edb66..520989935 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -83,3 +83,37 @@ def get_biovil_t_image_encoder() -> ImageModel:
                              joint_feature_size=JOINT_FEATURE_SIZE,
                              pretrained_model_path=biovilt_checkpoint_path)
     return image_model
+
+
+def get_biovil_t_linear_image_classifier(biovilt_checkpoint_path: str) -> ImageModel:
+    """
+    Download weights from Hugging Face and instantiate the image model.
+
+    The model is initialized with a linear classifier on top of the
+    BiomedVLP-BioViL-T image encoder.
+
+    Binary classification tasks in order:
+    ['Enlarged Cardiomediastinum', 'Cardiomegaly', 'Edema', 'Consolidation',
+     'Pneumonia',  'Pneumothorax', 'Pleural Effusion', 'No Finding']
+
+    :param biovilt_checkpoint_path: Path to the checkpoint file.
+
+    Example:
+    >>> checkpoint_path = "..."
+    >>> image_model = get_biovil_t_linear_image_classifier(checkpoint_path)
+    >>> image_model(torch.Tensor(batch_size, 3, 448, 448)).class_logits.shape
+    torch.Size([batch_size, num_classes, num_tasks])
+    """
+
+    num_classes = 2
+    num_tasks = 8
+
+    model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
+    image_model = ImageModel(img_encoder_type=model_type,
+                             joint_feature_size=JOINT_FEATURE_SIZE,
+                             pretrained_model_path=biovilt_checkpoint_path,
+                             num_classes=num_classes,
+                             num_tasks=num_tasks,
+                             classifier_hidden_dim=None)
+
+    return image_model
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
new file mode 100644
index 000000000..c7cd5bdcb
--- /dev/null
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
@@ -0,0 +1,36 @@
+import pytest
+import torch
+
+from health_multimodal.image.model.model import ImageModel
+from health_multimodal.image.model.pretrained import get_biovil_t_linear_image_classifier
+
+
+@pytest.fixture
+def dummy_input_tensor() -> torch.Tensor:
+    batch_size = 2
+    return torch.randn(batch_size, 3, 448, 448)
+
+@pytest.fixture
+def biovil_t_linear_image_classifier() -> ImageModel:
+    # Set the path to None to initialize the model weights with random values
+    biovil_t_checkpoint_path = None
+    return get_biovil_t_linear_image_classifier(biovil_t_checkpoint_path)
+
+def test_get_biovil_t_linear_image_classifier_shape(biovil_t_linear_image_classifier: ImageModel,
+                                                    dummy_input_tensor: torch.Tensor) -> None:
+    num_classes = 2
+    num_tasks = 8
+
+    output = biovil_t_linear_image_classifier(dummy_input_tensor)
+    expected_shape = torch.Size([dummy_input_tensor.shape[0], num_classes, num_tasks])
+
+    assert output.class_logits.shape == expected_shape, \
+        f"Unexpected output shape. Expected {expected_shape} but got {output.class_logits.shape}"
+
+def test_get_biovil_t_linear_image_classifier_inference(biovil_t_linear_image_classifier: ImageModel,
+                                                        dummy_input_tensor: torch.Tensor) -> None:
+    with torch.no_grad():
+        try:
+            _ = biovil_t_linear_image_classifier(dummy_input_tensor)
+        except Exception as e:
+            pytest.fail(f"Model inference failed: {e}")

From 095d4e9706920320cd1f85373ed29c27f220bc96 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 30 Mar 2023 14:51:25 +0000
Subject: [PATCH 15/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../image/model/pretrained.py                  | 14 ++++++++------
 .../image/model/test_pretrained.py             | 18 ++++++++++++------
 2 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index 0d9cccb97..0ef67f2c3 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -109,11 +109,13 @@ def get_biovil_t_linear_image_classifier(biovilt_checkpoint_path: str) -> ImageM
     num_tasks = 8
 
     model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
-    image_model = ImageModel(img_encoder_type=model_type,
-                             joint_feature_size=JOINT_FEATURE_SIZE,
-                             pretrained_model_path=biovilt_checkpoint_path,
-                             num_classes=num_classes,
-                             num_tasks=num_tasks,
-                             classifier_hidden_dim=None)
+    image_model = ImageModel(
+        img_encoder_type=model_type,
+        joint_feature_size=JOINT_FEATURE_SIZE,
+        pretrained_model_path=biovilt_checkpoint_path,
+        num_classes=num_classes,
+        num_tasks=num_tasks,
+        classifier_hidden_dim=None,
+    )
 
     return image_model
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
index c7cd5bdcb..515886cdb 100644
--- a/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
@@ -10,25 +10,31 @@ def dummy_input_tensor() -> torch.Tensor:
     batch_size = 2
     return torch.randn(batch_size, 3, 448, 448)
 
+
 @pytest.fixture
 def biovil_t_linear_image_classifier() -> ImageModel:
     # Set the path to None to initialize the model weights with random values
     biovil_t_checkpoint_path = None
     return get_biovil_t_linear_image_classifier(biovil_t_checkpoint_path)
 
-def test_get_biovil_t_linear_image_classifier_shape(biovil_t_linear_image_classifier: ImageModel,
-                                                    dummy_input_tensor: torch.Tensor) -> None:
+
+def test_get_biovil_t_linear_image_classifier_shape(
+    biovil_t_linear_image_classifier: ImageModel, dummy_input_tensor: torch.Tensor
+) -> None:
     num_classes = 2
     num_tasks = 8
 
     output = biovil_t_linear_image_classifier(dummy_input_tensor)
     expected_shape = torch.Size([dummy_input_tensor.shape[0], num_classes, num_tasks])
 
-    assert output.class_logits.shape == expected_shape, \
-        f"Unexpected output shape. Expected {expected_shape} but got {output.class_logits.shape}"
+    assert (
+        output.class_logits.shape == expected_shape
+    ), f"Unexpected output shape. Expected {expected_shape} but got {output.class_logits.shape}"
+
 
-def test_get_biovil_t_linear_image_classifier_inference(biovil_t_linear_image_classifier: ImageModel,
-                                                        dummy_input_tensor: torch.Tensor) -> None:
+def test_get_biovil_t_linear_image_classifier_inference(
+    biovil_t_linear_image_classifier: ImageModel, dummy_input_tensor: torch.Tensor
+) -> None:
     with torch.no_grad():
         try:
             _ = biovil_t_linear_image_classifier(dummy_input_tensor)

From ab50f14d376d1fb9321c7896088770fe41f1ea3b Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Thu, 30 Mar 2023 07:52:30 -0700
Subject: [PATCH 16/17] revert the notebook changes

---
 hi-ml-multimodal/notebooks/phrase_grounding.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index 7b521d5d6..701835115 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -212,7 +212,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.7.3"
+            "version": "3.8.16"
         },
         "vscode": {
             "interpreter": {

From f9e99fac31a9ae83ba1192de408f0d4dec12e698 Mon Sep 17 00:00:00 2001
From: Ozan Oktay <ozan.oktay@microsoft.com>
Date: Thu, 30 Mar 2023 07:54:54 -0700
Subject: [PATCH 17/17] revert the notebook changes

---
 .../notebooks/phrase_grounding.ipynb          | 446 +++++++++---------
 1 file changed, 223 insertions(+), 223 deletions(-)

diff --git a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
index 701835115..c7337eca3 100644
--- a/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
+++ b/hi-ml-multimodal/notebooks/phrase_grounding.ipynb
@@ -1,225 +1,225 @@
 {
-    "cells": [
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "#  -------------------------------------------------------------------------------------------\n",
-                "#  Copyright (c) Microsoft Corporation. All rights reserved.\n",
-                "#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n",
-                "#  -------------------------------------------------------------------------------------------"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Phrase grounding\n",
-                "\n",
-                "This notebook demonstrates the usage of the BioViL-T image and text models in a multimodal phrase grounding setting.\n",
-                "Given a chest X-ray and a radiology text phrase, the joint model grounds the phrase in the image, i.e., highlights the regions of the image that share features similar to the phrase.\n",
-                "Please refer to [our ECCV and CVPR papers](https://hi-ml.readthedocs.io/en/latest/multimodal.html#credit) for further details.\n",
-                "\n",
-                "The notebook can also be run on Binder without the need of any coding or local installation:\n",
-                "\n",
-                "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/hi-ml/HEAD?labpath=hi-ml-multimodal%2Fnotebooks%2Fphrase_grounding.ipynb)\n",
-                "\n",
-                "This demo is solely for research evaluation purposes, not intended to be a medical product or clinical use."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Setup\n",
-                "\n",
-                "Let's first install the `hi-ml-multimodal` Python package, which will allow us to import the `health_multimodal` Python module."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {
-                "tags": [
-                    "parameters"
-                ]
-            },
-            "outputs": [],
-            "source": [
-                "pip_source = \"hi-ml-multimodal\""
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%pip install {pip_source}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from typing import List\n",
-                "from typing import Tuple\n",
-                "\n",
-                "import tempfile\n",
-                "from pathlib import Path\n",
-                "\n",
-                "import torch\n",
-                "from IPython.display import display\n",
-                "from IPython.display import Markdown\n",
-                "\n",
-                "from health_multimodal.common.visualization import plot_phrase_grounding_similarity_map\n",
-                "from health_multimodal.text import get_bert_inference\n",
-                "from health_multimodal.text.utils import BertEncoderType\n",
-                "from health_multimodal.image import get_image_inference\n",
-                "from health_multimodal.image.utils import ImageModelType\n",
-                "from health_multimodal.vlp import ImageTextInferenceEngine"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Load multimodal model"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Load the text and image models from [Hugging Face 🤗](https://aka.ms/biovil-models) and instantiate the inference engines:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "text_inference = get_bert_inference(BertEncoderType.BIOVIL_T_BERT)\n",
-                "image_inference = get_image_inference(ImageModelType.BIOVIL_T)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Instantiate the joint inference engine:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "image_text_inference = ImageTextInferenceEngine(\n",
-                "    image_inference_engine=image_inference,\n",
-                "    text_inference_engine=text_inference,\n",
-                ")\n",
-                "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-                "image_text_inference.to(device)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Helper visualization functions"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "TypeBox = Tuple[float, float, float, float]\n",
-                "\n",
-                "def plot_phrase_grounding(image_path: Path, text_prompt: str, bboxes: List[TypeBox]) -> None:\n",
-                "    similarity_map = image_text_inference.get_similarity_map_from_raw_data(\n",
-                "        image_path=image_path,\n",
-                "        query_text=text_prompt,\n",
-                "        interpolation=\"bilinear\",\n",
-                "    )\n",
-                "    plot_phrase_grounding_similarity_map(\n",
-                "        image_path=image_path,\n",
-                "        similarity_map=similarity_map,\n",
-                "        bboxes=bboxes\n",
-                "    )\n",
-                "\n",
-                "def plot_phrase_grounding_from_url(image_url: str, text_prompt: str, bboxes: List[TypeBox]) -> None:\n",
-                "    image_path = Path(tempfile.tempdir, \"downloaded_chest_xray.jpg\")\n",
-                "    !curl -s -L -o {image_path} {image_url}\n",
-                "    plot_phrase_grounding(image_path, text_prompt, bboxes)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Inference\n",
-                "\n",
-                "We will run inference on a chest X-ray from [Open-i](https://openi.nlm.nih.gov/detailedresult?img=CXR111_IM-0076-1001&req=4), but any other chest X-ray image in DICOM or JPEG format can be used for research purposes.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "image_url = \"https://openi.nlm.nih.gov/imgs/512/242/1445/CXR1445_IM-0287-4004.png\"\n",
-                "text_prompt = \"Left basilar consolidation seen\"\n",
-                "# Ground-truth bounding box annotation(s) for the input text prompt\n",
-                "bboxes = [\n",
-                "    (306, 168, 124, 101),\n",
-                "]\n",
-                "\n",
-                "text = (\n",
-                "    'The ground-truth bounding box annotation for the phrase'\n",
-                "    f' *{text_prompt}* is shown in the middle figure (in black).'\n",
-                ")\n",
-                "\n",
-                "display(Markdown(text))\n",
-                "plot_phrase_grounding_from_url(image_url, text_prompt, bboxes)"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.3 ('multimodal')",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.16"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "b3ed4ebeb13ecf0e023e91854a12f6980e67b335857d302451652e7fbb2d2298"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  -------------------------------------------------------------------------------------------\n",
+    "#  Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n",
+    "#  -------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phrase grounding\n",
+    "\n",
+    "This notebook demonstrates the usage of the BioViL-T image and text models in a multimodal phrase grounding setting.\n",
+    "Given a chest X-ray and a radiology text phrase, the joint model grounds the phrase in the image, i.e., highlights the regions of the image that share features similar to the phrase.\n",
+    "Please refer to [our ECCV and CVPR papers](https://hi-ml.readthedocs.io/en/latest/multimodal.html#credit) for further details.\n",
+    "\n",
+    "The notebook can also be run on Binder without the need of any coding or local installation:\n",
+    "\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/hi-ml/HEAD?labpath=hi-ml-multimodal%2Fnotebooks%2Fphrase_grounding.ipynb)\n",
+    "\n",
+    "This demo is solely for research evaluation purposes, not intended to be a medical product or clinical use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Let's first install the `hi-ml-multimodal` Python package, which will allow us to import the `health_multimodal` Python module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "pip_source = \"hi-ml-multimodal\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install {pip_source}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from typing import Tuple\n",
+    "\n",
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import torch\n",
+    "from IPython.display import display\n",
+    "from IPython.display import Markdown\n",
+    "\n",
+    "from health_multimodal.common.visualization import plot_phrase_grounding_similarity_map\n",
+    "from health_multimodal.text import get_bert_inference\n",
+    "from health_multimodal.text.utils import BertEncoderType\n",
+    "from health_multimodal.image import get_image_inference\n",
+    "from health_multimodal.image.utils import ImageModelType\n",
+    "from health_multimodal.vlp import ImageTextInferenceEngine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load multimodal model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the text and image models from [Hugging Face 🤗](https://aka.ms/biovil-models) and instantiate the inference engines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_inference = get_bert_inference(BertEncoderType.BIOVIL_T_BERT)\n",
+    "image_inference = get_image_inference(ImageModelType.BIOVIL_T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instantiate the joint inference engine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_text_inference = ImageTextInferenceEngine(\n",
+    "    image_inference_engine=image_inference,\n",
+    "    text_inference_engine=text_inference,\n",
+    ")\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "image_text_inference.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper visualization functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TypeBox = Tuple[float, float, float, float]\n",
+    "\n",
+    "def plot_phrase_grounding(image_path: Path, text_prompt: str, bboxes: List[TypeBox]) -> None:\n",
+    "    similarity_map = image_text_inference.get_similarity_map_from_raw_data(\n",
+    "        image_path=image_path,\n",
+    "        query_text=text_prompt,\n",
+    "        interpolation=\"bilinear\",\n",
+    "    )\n",
+    "    plot_phrase_grounding_similarity_map(\n",
+    "        image_path=image_path,\n",
+    "        similarity_map=similarity_map,\n",
+    "        bboxes=bboxes\n",
+    "    )\n",
+    "\n",
+    "def plot_phrase_grounding_from_url(image_url: str, text_prompt: str, bboxes: List[TypeBox]) -> None:\n",
+    "    image_path = Path(tempfile.tempdir, \"downloaded_chest_xray.jpg\")\n",
+    "    !curl -s -L -o {image_path} {image_url}\n",
+    "    plot_phrase_grounding(image_path, text_prompt, bboxes)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference\n",
+    "\n",
+    "We will run inference on a chest X-ray from [Open-i](https://openi.nlm.nih.gov/detailedresult?img=CXR111_IM-0076-1001&req=4), but any other chest X-ray image in DICOM or JPEG format can be used for research purposes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_url = \"https://openi.nlm.nih.gov/imgs/512/242/1445/CXR1445_IM-0287-4004.png\"\n",
+    "text_prompt = \"Left basilar consolidation seen\"\n",
+    "# Ground-truth bounding box annotation(s) for the input text prompt\n",
+    "bboxes = [\n",
+    "    (306, 168, 124, 101),\n",
+    "]\n",
+    "\n",
+    "text = (\n",
+    "    'The ground-truth bounding box annotation for the phrase'\n",
+    "    f' *{text_prompt}* is shown in the middle figure (in black).'\n",
+    ")\n",
+    "\n",
+    "display(Markdown(text))\n",
+    "plot_phrase_grounding_from_url(image_url, text_prompt, bboxes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.3 ('multimodal')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b3ed4ebeb13ecf0e023e91854a12f6980e67b335857d302451652e7fbb2d2298"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }