diff --git a/__pycache__/utils.cpython-39.pyc b/__pycache__/utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7449f931f43f1ca40265185d8dbd1958770abbf9
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1ef41a6b8adc162c08434691b4ce190fdb394b8
--- /dev/null
+++ b/app.py
@@ -0,0 +1,295 @@
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+from streamlit_image_coordinates import streamlit_image_coordinates
+
+
+from model.data_process.demo_data_process import process_ct_gt
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image, ImageDraw
+import monai.transforms as transforms
+from utils import show_points, make_fig, reflect_points_into_model, initial_rectangle, reflect_json_data_to_3D_box, reflect_box_into_model, run
+
+print('script run')
+
+#############################################
+# init session_state
+if 'option' not in  st.session_state:
+    st.session_state.option = None
+if 'text_prompt' not in st.session_state:
+    st.session_state.text_prompt = None
+
+if 'reset_demo_case' not in st.session_state:
+    st.session_state.reset_demo_case = False
+
+if 'preds_3D' not in st.session_state:
+    st.session_state.preds_3D = None
+
+if 'data_item' not in st.session_state:
+    st.session_state.data_item = None
+
+if 'points' not in st.session_state:
+    st.session_state.points = []
+
+if 'use_text_prompt' not in st.session_state:
+    st.session_state.use_text_prompt = False
+
+if 'use_point_prompt' not in st.session_state:
+    st.session_state.use_point_prompt = False
+
+if 'use_box_prompt' not in st.session_state:
+    st.session_state.use_box_prompt = False
+
+if 'rectangle_3Dbox' not in st.session_state:
+    st.session_state.rectangle_3Dbox = [0,0,0,0,0,0]
+
+if 'irregular_box' not in st.session_state:
+    st.session_state.irregular_box = False
+
+if 'running' not in st.session_state:
+    st.session_state.running = False
+
+if 'transparency' not in st.session_state:
+    st.session_state.transparency = 0.25
+
+case_list = [
+    'model/asset/Case_image_00001_0000.nii.gz',
+    'cases/FLARE22_Tr_0002_0000.nii.gz',
+    'cases/FLARE22_Tr_0005_0000.nii.gz',
+    'cases/FLARE22_Tr_0034_0000.nii.gz',
+    'cases/FLARE22_Tr_0045_0000.nii.gz'
+]
+#############################################
+
+#############################################
+# reset functions
+def clear_prompts():
+    st.session_state.points = []
+    st.session_state.rectangle_3Dbox = [0,0,0,0,0,0]
+
+def reset_demo_case():
+    st.session_state.data_item = None
+    st.session_state.reset_demo_case = True
+    clear_prompts()
+
+def clear_file():
+    st.session_state.option = None
+    process_ct_gt.clear()
+    reset_demo_case()
+    clear_prompts()
+
+#############################################
+
+st.image(Image.open('model/asset/overview back.png'), use_column_width=True)
+# modify demo case here
+demo_type = st.radio(
+        "Demo case source",
+        ["Select", "Upload"],
+        on_change=clear_file
+    )
+
+if demo_type=="Select":
+    uploaded_file = st.selectbox(
+        "Select a demo case",
+        case_list,
+        index=None,
+        placeholder="Select a demo case...",
+        on_change=reset_demo_case
+    )
+else:
+    uploaded_file = st.file_uploader("Upload demo case(nii.gz)", type='nii.gz', on_change=reset_demo_case)
+
+st.session_state.option = uploaded_file
+
+if  st.session_state.option is not None and \
+    st.session_state.reset_demo_case or (st.session_state.data_item is None and st.session_state.option is not None):
+
+    st.session_state.data_item = process_ct_gt(st.session_state.option)
+    st.session_state.reset_demo_case = False
+    st.session_state.preds_3D = None
+
+prompt_col1, prompt_col2 = st.columns(2)
+
+with prompt_col1:
+    st.session_state.use_text_prompt = st.toggle('Sematic prompt')
+    text_prompt_type = st.radio(
+        "Sematic prompt type",
+        ["Predefined", "Custom"],
+        disabled=(not st.session_state.use_text_prompt)
+    )
+    if text_prompt_type == "Predefined":
+        pre_text = st.selectbox(
+            "Predefined anatomical category:",
+            ['liver', 'right kidney', 'spleen', 'pancreas', 'aorta', 'inferior vena cava', 'right adrenal gland', 'left adrenal gland', 'gallbladder', 'esophagus', 'stomach', 'duodenum', 'left kidney'],
+            index=None,
+            disabled=(not st.session_state.use_text_prompt)
+        )
+    else:
+        pre_text = st.text_input('Enter an Anatomical word or phrase:', None, max_chars=20,
+                                                     disabled=(not st.session_state.use_text_prompt))
+    if pre_text is None or len(pre_text) > 0:
+        st.session_state.text_prompt = pre_text
+    else:
+        st.session_state.text_prompt = None
+
+
+with prompt_col2:
+    spatial_prompt_on = st.toggle('Spatial prompt', on_change=clear_prompts)
+    spatial_prompt = st.radio(
+        "Spatial prompt type",
+        ["Point prompt", "Box prompt"],
+        on_change=clear_prompts,
+        disabled=(not spatial_prompt_on))
+
+if spatial_prompt == "Point prompt":
+    st.session_state.use_point_prompt = True
+    st.session_state.use_box_prompt = False
+elif spatial_prompt == "Box prompt":
+    st.session_state.use_box_prompt = True
+    st.session_state.use_point_prompt = False
+else:
+    st.session_state.use_point_prompt = False
+    st.session_state.use_box_prompt = False
+
+if not spatial_prompt_on:
+    st.session_state.use_point_prompt = False
+    st.session_state.use_box_prompt = False
+
+if not st.session_state.use_text_prompt:
+    st.session_state.text_prompt = None
+
+if st.session_state.option is None:
+    st.write('please select demo case first')
+else:
+    image_3D = st.session_state.data_item['z_image'][0].numpy()
+    col_control1, col_control2 = st.columns(2)
+
+    with col_control1:
+        selected_index_z = st.slider('X-Y view', 0, image_3D.shape[0] - 1, 0, key='xy')
+
+    with col_control2:
+        selected_index_y = st.slider('X-Z view', 0, image_3D.shape[1] - 1, 0, key='xz')
+        if st.session_state.use_box_prompt:
+            top, bottom = st.select_slider(
+                'Top and bottom of box',
+                options=range(0, 325),
+                value=(0, 324)
+            )
+            st.session_state.rectangle_3Dbox[0] = top
+            st.session_state.rectangle_3Dbox[3] = bottom
+    col_image1, col_image2 = st.columns(2)
+
+    if st.session_state.preds_3D is not None:
+        st.session_state.transparency = st.slider('Mask opacity', 0.0, 1.0, 0.5)
+
+    with col_image1:
+        
+        image_z_array = image_3D[selected_index_z]
+
+        preds_z_array = None
+        if st.session_state.preds_3D is not None:
+            preds_z_array = st.session_state.preds_3D[selected_index_z]
+            
+        image_z = make_fig(image_z_array, preds_z_array, st.session_state.points, selected_index_z, 'xy')
+        
+        
+        if st.session_state.use_point_prompt:
+            value_xy = streamlit_image_coordinates(image_z, width=325)
+            
+            if value_xy is not None:
+                point_ax_xy = (selected_index_z, value_xy['y'], value_xy['x'])
+                if len(st.session_state.points) >= 3:
+                    st.warning('Max point num is 3', icon="⚠️")
+                elif point_ax_xy not in st.session_state.points:
+                    st.session_state.points.append(point_ax_xy)
+                    print('point_ax_xy add rerun')
+                    st.rerun()
+        elif st.session_state.use_box_prompt:
+            canvas_result_xy = st_canvas(
+                fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
+                stroke_width=3,
+                stroke_color='#2909F1',
+                background_image=image_z,
+                update_streamlit=True,
+                height=325,
+                width=325,
+                drawing_mode='transform',
+                point_display_radius=0,
+                key="canvas_xy",
+                initial_drawing=initial_rectangle,
+                display_toolbar=True
+            )
+            try:
+                print(canvas_result_xy.json_data['objects'][0]['angle'])
+                if canvas_result_xy.json_data['objects'][0]['angle'] != 0:
+                    st.warning('Rotating is undefined behavior', icon="⚠️")
+                    st.session_state.irregular_box = True
+                else:
+                    st.session_state.irregular_box = False
+                reflect_json_data_to_3D_box(canvas_result_xy.json_data, view='xy')
+            except:
+                print('exception')
+                pass
+        else:
+            st.image(image_z, use_column_width=False)
+
+    with col_image2:
+        image_y_array = image_3D[:, selected_index_y, :]
+        
+        preds_y_array = None
+        if st.session_state.preds_3D is not None:
+            preds_y_array = st.session_state.preds_3D[:, selected_index_y, :]
+        
+        image_y = make_fig(image_y_array, preds_y_array, st.session_state.points, selected_index_y, 'xz')
+        
+        if st.session_state.use_point_prompt:
+            value_yz = streamlit_image_coordinates(image_y, width=325)
+            
+            if value_yz is not None:
+                point_ax_xz = (value_yz['y'], selected_index_y, value_yz['x'])
+                if len(st.session_state.points) >= 3:
+                    st.warning('Max point num is 3', icon="⚠️")
+                elif point_ax_xz not in st.session_state.points:
+                    st.session_state.points.append(point_ax_xz)
+                    print('point_ax_xz add rerun')
+                    st.rerun()
+        elif st.session_state.use_box_prompt:
+            if st.session_state.rectangle_3Dbox[1] <= selected_index_y and selected_index_y <= st.session_state.rectangle_3Dbox[4]:
+                draw = ImageDraw.Draw(image_y)
+                #rectangle xz view (upper-left and lower-right)
+                rectangle_coords = [(st.session_state.rectangle_3Dbox[2], st.session_state.rectangle_3Dbox[0]),
+                                    (st.session_state.rectangle_3Dbox[5], st.session_state.rectangle_3Dbox[3])]
+                # Draw the rectangle on the image
+                draw.rectangle(rectangle_coords, outline='#2909F1', width=3)
+            st.image(image_y, use_column_width=False)
+        else:
+            st.image(image_y, use_column_width=False)
+
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    if st.button("Clear", use_container_width=True,
+                 disabled=(st.session_state.option is None or (len(st.session_state.points)==0 and not st.session_state.use_box_prompt and st.session_state.preds_3D is None))):
+        clear_prompts()
+        st.session_state.preds_3D = None
+        st.rerun()
+
+with col3:
+    if st.button("Run", type="primary", use_container_width=True,
+            disabled=(
+                st.session_state.data_item is None or
+                (st.session_state.text_prompt is None and len(st.session_state.points) == 0 and st.session_state.use_box_prompt is False) or 
+                st.session_state.irregular_box or 
+                st.session_state.running
+                )):
+        st.session_state.running = True
+        st.rerun()
+
+# if len(st.session_state.points) > 0:
+#     st.write(st.session_state.points)
+
+if st.session_state.running:
+    st.session_state.running = False
+    run()
+    st.rerun()
\ No newline at end of file
diff --git a/model/LICENSE b/model/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..046bedb384179804fa1676f29edee7eef6756969
--- /dev/null
+++ b/model/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 BAAI-DCAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/model/README.md b/model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0ef6f5638f174463072799e9ea537786a176b352
--- /dev/null
+++ b/model/README.md
@@ -0,0 +1,74 @@
+# SegVol: Universal and Interactive Volumetric Medical Image Segmentation
+This repo is the official implementation of [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://arxiv.org/abs/2311.13385).
+
+## News🚀
+(2023.11.24) *You can download weight files of SegVol and ViT(CTs pre-train) [here](https://drive.google.com/drive/folders/1TEJtgctH534Ko5r4i79usJvqmXVuLf54?usp=drive_link).* 🔥
+
+(2023.11.23) *The brief introduction and instruction have been uploaded.*
+
+(2023.11.23) *The inference demo code has been uploaded.*
+
+(2023.11.22) *The first edition of our paper has been uploaded to arXiv.* 📃
+
+## Introduction
+<img src="https://github.com/BAAI-DCAI/SegVol/blob/main/asset/overview.png" width="60%" height="60%">
+
+The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts **point**, **box** and **text** prompt while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories.
+
+We will release SegVol's **inference code**, **training code**, **model params** and **ViT pre-training params** (pre-training is performed over 2,000 epochs on 96k  CTs). 
+
+## Usage
+### Requirements
+The [pytorch v1.11.0](https://pytorch.org/get-started/previous-versions/) (or higher virsion) is needed first. Following install key requirements using commands:
+
+```
+pip install 'monai[all]==0.9.0'
+pip install einops==0.6.1
+pip install transformers==4.18.0
+pip install matplotlib
+``` 
+### Config and run demo script
+1. You can download the demo case [here](https://drive.google.com/drive/folders/1TEJtgctH534Ko5r4i79usJvqmXVuLf54?usp=drive_link), or download the whole demo dataset  [AbdomenCT-1K](https://github.com/JunMa11/AbdomenCT-1K) and choose any demo case you want.
+2. Please set CT path and Ground Truth path of the case in the [config_demo.json](https://github.com/BAAI-DCAI/SegVol/blob/main/config/config_demo.json).
+3. After that, config the [inference_demo.sh](https://github.com/BAAI-DCAI/SegVol/blob/main/script/inference_demo.sh) file for execution:
+
+    - `$segvol_ckpt`: the path of SegVol's checkpoint (Download from [here](https://drive.google.com/drive/folders/1TEJtgctH534Ko5r4i79usJvqmXVuLf54?usp=drive_link)).
+
+    - `$work_dir`: any path of folder you want to save the log files and visualizaion results.
+
+4. Finally, you can control the **prompt type**, **zoom-in-zoom-out mechanism** and **visualizaion switch** [here](https://github.com/BAAI-DCAI/SegVol/blob/35f3ff9c943a74f630e6948051a1fe21aaba91bc/inference_demo.py#L208C11-L208C11).
+5. Now, just run `bash script/inference_demo.sh` to infer your demo case.
+
+## Citation
+If you find this repository helpful, please consider citing:
+```
+@misc{du2023segvol,
+      title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation}, 
+      author={Yuxin Du and Fan Bai and Tiejun Huang and Bo Zhao},
+      year={2023},
+      eprint={2311.13385},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+## Acknowledgement
+Thanks for the following amazing works:
+
+[HuggingFace](https://huggingface.co/).
+
+[CLIP](https://github.com/openai/CLIP).
+
+[MONAI](https://github.com/Project-MONAI/MONAI).
+
+[Image by brgfx](https://www.freepik.com/free-vector/anatomical-structure-human-bodies_26353260.htm) on Freepik.
+
+[Image by muammark](https://www.freepik.com/free-vector/people-icon-collection_1157380.htm#query=user&position=2&from_view=search&track=sph) on Freepik.
+
+[Image by pch.vector](https://www.freepik.com/free-vector/different-phone-hand-gestures-set_9649376.htm#query=Vector%20touch%20screen%20hand%20gestures&position=4&from_view=search&track=ais) on Freepik.
+
+[Image by starline](https://www.freepik.com/free-vector/set-three-light-bulb-represent-effective-business-idea-concept_37588597.htm#query=idea&position=0&from_view=search&track=sph) on Freepik.
+
+
+
+
diff --git a/model/__pycache__/inference_cpu.cpython-39.pyc b/model/__pycache__/inference_cpu.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..736b994e269d40f09a1c9df30b37a0e4adb9891d
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diff --git a/model/asset/overview back.png b/model/asset/overview back.png
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diff --git a/model/config/clip/config.json b/model/config/clip/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2a88b96561196777ca173b15309ea859f4d2ce0
--- /dev/null
+++ b/model/config/clip/config.json
@@ -0,0 +1,157 @@
+{
+  "_name_or_path": "openai/clip-vit-base-patch32",
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 512,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 512,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 2048,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 8,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "projection_dim": 512,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.16.0.dev0",
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": null,
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
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diff --git a/model/config/clip/special_tokens_map.json b/model/config/clip/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bfb42aa97dcd61e89f279ccaee988bccb4fabae
--- /dev/null
+++ b/model/config/clip/special_tokens_map.json
@@ -0,0 +1 @@
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diff --git a/model/config/clip/tokenizer.json b/model/config/clip/tokenizer.json
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index 0000000000000000000000000000000000000000..564c0ebd5ce29c4ee4864004aee693deadd3128c
--- /dev/null
+++ b/model/config/clip/tokenizer.json
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+      "bar ton",
+      "authori zation</w>",
+      "imperi alism</w>",
+      "home of",
+      "to do",
+      "foot path</w>",
+      "band width</w>",
+      "visit spain</w>",
+      "moh sin</w>",
+      "erup ted</w>",
+      "mi ki</w>",
+      "insig nia</w>",
+      "mike l</w>",
+      "ss h</w>",
+      "ger a</w>",
+      "bank holiday",
+      "aw an",
+      "t weak</w>",
+      "star craft</w>",
+      "e al",
+      "construc tion",
+      "skelet ons</w>",
+      "le ep",
+      "ine m</w>",
+      "bar clay",
+      "ship wreck</w>",
+      "monsi eur</w>",
+      "yo h</w>",
+      "ron t</w>",
+      "form ative</w>",
+      "ser o",
+      "le p",
+      "horse man</w>",
+      "hoo sier</w>",
+      "haz mat</w>",
+      "cylin ders</w>",
+      "cen ti",
+      "ðŁĴ¥ðŁĴ¥ ðŁĴ¥</w>",
+      "re em</w>",
+      "na ire</w>",
+      "mus ically</w>",
+      "gras shopper</w>",
+      "est onian</w>",
+      "termin ology</w>",
+      "ro main</w>",
+      "blogger rt</w>",
+      "tox in</w>",
+      "stan ce",
+      "cultiv ated</w>",
+      "an ast",
+      "ðŁIJ į",
+      "shi mano</w>",
+      "go pher</w>",
+      "ene i</w>",
+      "recycla ble</w>",
+      "gam ification</w>",
+      "fight for",
+      "c q",
+      "avoc ados</w>",
+      "ke ys",
+      "eli ke",
+      "gly cer",
+      "shak ur</w>",
+      "mobili zation</w>",
+      "gal ley</w>",
+      "expla in",
+      "ex changed</w>",
+      "pe th</w>",
+      "obe dience</w>",
+      "illa ge</w>",
+      "en nis",
+      "ãĥ ŀ",
+      "wi v</w>",
+      "walla bies</w>",
+      "ma ar</w>",
+      "ig ers</w>",
+      "fin tech",
+      "fin alized</w>",
+      "wo j",
+      "meaning less</w>",
+      "in field</w>",
+      "onna ise</w>",
+      "e et</w>",
+      "bron te</w>",
+      "pass ages</w>",
+      "ðŁij §",
+      "strick land</w>",
+      "northern lights</w>",
+      "lom ond</w>",
+      "h tc",
+      "wr ay</w>",
+      "shi fter</w>",
+      "di alog</w>",
+      "ðŁį į</w>",
+      ">> >>>></w>",
+      "te atime</w>",
+      "ste ch",
+      "sic huan</w>",
+      "qu ill</w>",
+      "fran ca",
+      "comple mentary</w>",
+      "bar rington</w>",
+      "marcu s",
+      "mal am</w>",
+      "goo oo</w>",
+      "for sa",
+      "elec tra</w>",
+      "af s</w>",
+      "âĹ Ĩ</w>",
+      "tri fe",
+      "sn azzy</w>",
+      "fo lia</w>",
+      "and olan</w>",
+      "after dark</w>",
+      "wood son</w>",
+      "stra de</w>",
+      "litt lest</w>",
+      "o gun",
+      "con wy</w>",
+      "co wards</w>",
+      "ðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤ</w>",
+      "íĬ ¸",
+      "se ul",
+      "mur phy",
+      "dun ks</w>",
+      "kapil shar",
+      "jo achim</w>",
+      "wom ack</w>",
+      "equal ity",
+      "aver ages</w>",
+      "a ine",
+      "ðŁ¦ Ī</w>",
+      "tac ular</w>",
+      "dis ability",
+      "u ked",
+      "mid century</w>",
+      "bar thol",
+      "teas ers</w>",
+      "tab ern",
+      "nj caa</w>",
+      "sp out</w>",
+      "op i</w>",
+      "ku bball</w>",
+      "bl om",
+      "so ar",
+      "popu lism</w>",
+      "meth yl",
+      "ðŁijĬ ðŁı¼",
+      "o spre",
+      "alo ils</w>",
+      "ðŁĵ ĸ",
+      "ðŁĮ ļ",
+      "x er",
+      "sp illing</w>",
+      "publ ica</w>",
+      "car dam",
+      "adi sh</w>",
+      "sa cha</w>",
+      "p kg</w>",
+      "bu da</w>",
+      "lyric ist</w>",
+      "i bc</w>",
+      "gru mp",
+      "ho ver</w>",
+      "hal ep</w>",
+      "anti body</w>",
+      "anem one</w>",
+      "âĻ¥âĻ¥ âĻ¥âĻ¥",
+      "m cl",
+      "litho graph</w>",
+      "cc u</w>",
+      "s fest</w>",
+      "path ic</w>",
+      "calli ster</w>",
+      "otta wa",
+      "gun sn",
+      "rut ger",
+      "hali but</w>",
+      "en vision</w>",
+      "differenti ate</w>",
+      "ðŁļĢ ðŁļĢ",
+      "pir an",
+      "lat el",
+      "uc n</w>",
+      "trou bad",
+      "ra ine",
+      "fierc ely</w>",
+      "learn english</w>",
+      "lea se",
+      "wex mondays</w>",
+      "em it</w>",
+      "dray ton</w>",
+      "bur rell</w>",
+      "scuba diving</w>",
+      "hol ler</w>",
+      "dr u</w>",
+      "clo cked</w>",
+      "w ral</w>",
+      "ap ro</w>",
+      "trans lucent</w>",
+      "w bo</w>",
+      "patri arch</w>",
+      "mo ja",
+      "lan nister</w>",
+      "fish ery</w>",
+      "ne derland</w>",
+      "mil dly</w>",
+      "mi rai</w>",
+      "ma ko</w>",
+      "ja p</w>",
+      "ðŁĺ©ðŁĺ© ðŁĺ©</w>",
+      "pro statec",
+      "p anna</w>",
+      "ar ama</w>",
+      "under taking</w>",
+      "tomp kins</w>",
+      "ne op",
+      "soli ds</w>",
+      "sav oury</w>",
+      "e ames</w>",
+      "cut lery</w>",
+      "wood bridge</w>",
+      "steam er</w>",
+      "ri zzo</w>",
+      "wild cat",
+      "rat na</w>",
+      "lamin ated</w>",
+      "kin eni</w>",
+      "jal ap",
+      "ai des</w>",
+      "acknowle dges</w>",
+      "?! ?!?!</w>",
+      "! ðŁİī</w>",
+      "w afc</w>",
+      "mag gio</w>",
+      "ha ves</w>",
+      "dar je",
+      "of i</w>",
+      "gr il",
+      "v asi",
+      "bru x",
+      "mo hd</w>",
+      "fake speare</w>",
+      "arn old",
+      "r mb</w>",
+      "for be",
+      "wal leye</w>",
+      "ro di",
+      "therapeu tics</w>",
+      "strate gi",
+      "ob ste",
+      "mu dder</w>",
+      "download able</w>",
+      "dd ings</w>",
+      "d ca</w>",
+      "asi angames</w>",
+      "campe on",
+      "appropri ation</w>",
+      "th century</w>",
+      "ram atta</w>",
+      "dra ped</w>",
+      "bul lion</w>",
+      "mu c</w>",
+      "one x</w>",
+      "se greg",
+      "ophel ia</w>",
+      "bod ily</w>",
+      "âĿ¤ ðŁĺį</w>",
+      "wi zar",
+      "te ased</w>",
+      "ade my</w>",
+      "to id</w>",
+      "sur a</w>",
+      "lazar us</w>",
+      "sn ickers</w>",
+      "ma se",
+      "lo h",
+      "bow ed</w>",
+      "bibli o",
+      "x change</w>",
+      "har lan</w>",
+      "gho shal</w>",
+      "flavor ful</w>",
+      "bha gat</w>",
+      "alle z</w>",
+      "whiche ver</w>",
+      "ten stein</w>",
+      "disc er",
+      "organ iser</w>",
+      "mt g",
+      "dream liner</w>",
+      "t se",
+      "hok kaido</w>",
+      "mo k",
+      "indulg ent</w>",
+      "hick man</w>",
+      "blin ded</w>",
+      "al yn",
+      "aaa ah</w>",
+      "sp ool</w>",
+      "lough borough</w>",
+      "inter pret",
+      "et v",
+      "aristo tle</w>",
+      "optimi zing</w>",
+      "avici i</w>",
+      "madu rai</w>",
+      "ju li</w>",
+      "naw az",
+      "mat chups</w>",
+      "ab ide</w>",
+      "paint ing",
+      "w elling</w>",
+      "vel i</w>",
+      "octag on</w>",
+      "in scribed</w>",
+      "po king</w>",
+      "plac er</w>",
+      "life cycle</w>",
+      "kili g</w>",
+      "g sp</w>",
+      "eli ves</w>",
+      "cle ments</w>",
+      "na sheed</w>",
+      "me sut</w>",
+      "incarcer ated</w>",
+      "dist illed</w>",
+      "wal ang</w>",
+      "delic acy</w>",
+      "del gado</w>",
+      "che z",
+      "ch ita</w>",
+      "ad ero</w>",
+      "tu x</w>",
+      "pati l</w>",
+      "o do",
+      "abh cosmetics</w>",
+      "tv c</w>",
+      "p bc</w>",
+      "in accurate</w>",
+      "hardwork paysoff</w>",
+      "ball er",
+      "quot ation</w>",
+      "merchandi sing</w>",
+      "ga stri",
+      "defen ses</w>",
+      "dro gba</w>",
+      "bex hill</w>",
+      "ban kno",
+      "win ona</w>",
+      "si eg",
+      "p gs</w>",
+      "hahah ha</w>",
+      "agu chi</w>",
+      "su bram",
+      "mirac le",
+      "de sch",
+      "li bre",
+      "ba cher</w>",
+      "ent ine</w>",
+      "bbcra di",
+      "lou dest</w>",
+      "r ps</w>",
+      "pi erc",
+      "fr yer</w>",
+      "storm trooper</w>",
+      "rafael nadal</w>",
+      "pas co</w>",
+      "exhau stion</w>",
+      "epic onetsy</w>",
+      "rc tid</w>",
+      "kel lie</w>",
+      "ga ines</w>",
+      "d bz</w>",
+      "sm riti",
+      "s bridge</w>",
+      "lim ited",
+      "cla w",
+      "technic al",
+      "bio graphical</w>",
+      "ado red</w>",
+      "ภ°</w>",
+      "exclu de</w>",
+      "ac adia</w>",
+      "key boards</w>",
+      "fur man</w>",
+      "so ca</w>",
+      "sur u</w>",
+      "ni ps</w>",
+      "sw aps</w>",
+      "server less</w>",
+      "run e</w>",
+      "pu ffy</w>",
+      "north ampton",
+      "nish ings</w>",
+      "hen der",
+      "cartri dges</w>",
+      "gun shot</w>",
+      "ðŁĵ ¹</w>",
+      "fil ament</w>",
+      "respon dents</w>",
+      "pey ton",
+      "mountaine er</w>",
+      "mer ging</w>",
+      "life span</w>",
+      "intimid ation</w>",
+      "p afc</w>",
+      "nl wx</w>",
+      "expan sive</w>",
+      "pur r",
+      "f ck</w>",
+      "ca e</w>",
+      "at ti",
+      "tele thon</w>",
+      "so hn</w>",
+      "mend el",
+      "lo pes</w>",
+      "dor i</w>",
+      "un broken</w>",
+      "te red",
+      "tast ings</w>",
+      "in active</w>",
+      "disin tegr",
+      "t assel</w>",
+      "share the",
+      "pi ano",
+      "is lay</w>",
+      "air space</w>",
+      "z awa</w>",
+      "ricci ardo</w>",
+      "ming ton",
+      "fresh er</w>",
+      "cur ry",
+      "re vs</w>",
+      "pharo ah</w>",
+      "h mv</w>",
+      "exhilar ating</w>",
+      "wh oo</w>",
+      "lin kin</w>",
+      "kri spy</w>",
+      "competen cy</w>",
+      "ste wards</w>",
+      "ne bu",
+      "kat su",
+      "ad mins</w>",
+      "baz ar</w>",
+      "as ar</w>",
+      "giving back</w>",
+      "s summit</w>",
+      "song z</w>",
+      "lin us</w>",
+      "raj kumar</w>",
+      "farm ington</w>",
+      "fanta sia</w>",
+      "ðŁĺ´ ðŁĺ´</w>",
+      "so bri",
+      "lis se</w>",
+      "barry more</w>",
+      "pri sm",
+      "blo b</w>",
+      "sen ew",
+      "mono xide</w>",
+      "exp ire</w>",
+      "eigh teen</w>",
+      "di pper</w>",
+      "xi ao</w>",
+      "kil t</w>",
+      "hin ch",
+      "bbc sport</w>",
+      "bam boo",
+      "p ter",
+      "ex al",
+      "ðŁ¦ ĭ",
+      "ham lin</w>",
+      "expe ditions</w>",
+      "star gazing</w>",
+      "food security</w>",
+      "wy lie</w>",
+      "ul f</w>",
+      "st ingly</w>",
+      "on storm</w>",
+      "lo eb</w>",
+      "bro ome</w>",
+      "bn ha</w>",
+      "pancre atic</w>",
+      "eli ve",
+      "!!!!!!!! !!!</w>",
+      "ther apper</w>",
+      "ortho pedic</w>",
+      "avengers endgame</w>",
+      "antit rust</w>",
+      "ìļ °</w>",
+      "go te</w>",
+      "om d</w>",
+      "off side</w>",
+      "gy llen",
+      "win eries</w>",
+      "white water</w>",
+      "ad l</w>",
+      "lu pita</w>",
+      "exce eds</w>",
+      "consi sted</w>",
+      "chew bacca</w>",
+      "ash leigh</w>",
+      "nhl jets</w>",
+      "is san",
+      "sh ld</w>",
+      "hay at</w>",
+      "cran berries</w>",
+      "ðŁ¤ĺ ðŁı½</w>",
+      "rock the",
+      "spring training</w>",
+      "fall out",
+      "dairy free</w>",
+      "wa j</w>",
+      "un decided</w>",
+      "so wn</w>",
+      "rc n</w>",
+      "north wales</w>",
+      "htt r</w>",
+      "fu mble</w>",
+      "d its</w>",
+      "comp elled</w>",
+      "popu list</w>",
+      "min ted</w>",
+      "blan chett</w>",
+      ". ''</w>",
+      "pro pulsion</w>",
+      "m illa</w>",
+      "au berg",
+      "her tz</w>",
+      "h ta</w>",
+      "u daipur</w>",
+      "serendip ity</w>",
+      "azte cs</w>",
+      "als ace</w>",
+      "ðŁIJ ij</w>",
+      "lu n</w>",
+      "sho es",
+      "char li</w>",
+      "gar za</w>",
+      "ðŁĴ Ł",
+      "pro biotics</w>",
+      "fox tv</w>",
+      "ol is</w>",
+      "mi ff",
+      "loc alized</w>",
+      "diffu ser</w>",
+      "si gue</w>",
+      "fun ko",
+      "rend ous</w>",
+      "ðŁĴ ij</w>",
+      "jeky ll</w>"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/model/config/clip/tokenizer_config.json b/model/config/clip/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab0ca294e9ef2a950015496bc13b84e1bb462b09
--- /dev/null
+++ b/model/config/clip/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": "<|endoftext|>", "add_prefix_space": false, "errors": "replace", "do_lower_case": true, "name_or_path": "./clip_ViT_B_32/"}
\ No newline at end of file
diff --git a/model/config/clip/vocab.json b/model/config/clip/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..182766ce89b439768edadda342519f33802f5364
--- /dev/null
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diff --git a/model/config/config_demo.json b/model/config/config_demo.json
new file mode 100644
index 0000000000000000000000000000000000000000..395acb0169c2425a5a7ea6636c8e476515c34edc
--- /dev/null
+++ b/model/config/config_demo.json
@@ -0,0 +1,8 @@
+{
+    "dataset_name": "AbdomenCT-1k",
+    "categories": ["liver", "kidney", "spleen", "pancreas"],
+    "demo_case": {
+        "ct_path": "path/to/Case_image",
+        "gt_path": "path/to/Case_label" 
+    }
+}
diff --git a/model/data_process/__pycache__/demo_data_process.cpython-39.pyc b/model/data_process/__pycache__/demo_data_process.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f46ca09857aacae63c8f5efdbaf1d7d0ca372bd5
Binary files /dev/null and b/model/data_process/__pycache__/demo_data_process.cpython-39.pyc differ
diff --git a/model/data_process/demo_data_process.py b/model/data_process/demo_data_process.py
new file mode 100644
index 0000000000000000000000000000000000000000..faea513d344420219bc34ff4cd00eb8f889695af
--- /dev/null
+++ b/model/data_process/demo_data_process.py
@@ -0,0 +1,91 @@
+import numpy as np
+import monai.transforms as transforms
+import streamlit as st
+import tempfile
+
+class MinMaxNormalization(transforms.Transform):
+    def __call__(self, data):
+        d = dict(data)
+        k = "image"
+        d[k] = d[k] - d[k].min()
+        d[k] = d[k] / np.clip(d[k].max(), a_min=1e-8, a_max=None)
+        return d
+
+class DimTranspose(transforms.Transform):
+    def __init__(self, keys):
+        self.keys = keys
+    
+    def __call__(self, data):
+        d = dict(data)
+        for key in self.keys:
+            d[key] = np.swapaxes(d[key], -1, -3)
+        return d
+
+class ForegroundNormalization(transforms.Transform):
+    def __init__(self, keys):
+        self.keys = keys
+    
+    def __call__(self, data):
+        d = dict(data)
+        
+        for key in self.keys:
+            d[key] = self.normalize(d[key])
+        return d
+    
+    def normalize(self, ct_narray):
+        ct_voxel_ndarray = ct_narray.copy()
+        ct_voxel_ndarray = ct_voxel_ndarray.flatten()
+        thred = np.mean(ct_voxel_ndarray)
+        voxel_filtered = ct_voxel_ndarray[(ct_voxel_ndarray > thred)]
+        upper_bound = np.percentile(voxel_filtered, 99.95)
+        lower_bound = np.percentile(voxel_filtered, 00.05)
+        mean = np.mean(voxel_filtered)
+        std = np.std(voxel_filtered)
+        ### transform ###
+        ct_narray = np.clip(ct_narray, lower_bound, upper_bound)
+        ct_narray = (ct_narray - mean) / max(std, 1e-8)
+        return ct_narray
+    
+@st.cache_data
+def process_ct_gt(case_path, spatial_size=(32,256,256)):
+    if case_path is None:
+        return None
+    print('Data preprocessing...')
+    # transform
+    img_loader = transforms.LoadImage(dtype=np.float32)
+    transform = transforms.Compose(
+        [
+            transforms.Orientationd(keys=["image"], axcodes="RAS"),
+            ForegroundNormalization(keys=["image"]),
+            DimTranspose(keys=["image"]),
+            MinMaxNormalization(),
+            transforms.SpatialPadd(keys=["image"], spatial_size=spatial_size, mode='constant'),
+            transforms.CropForegroundd(keys=["image"], source_key="image"),
+            transforms.ToTensord(keys=["image"]),
+        ]
+    )
+    zoom_out_transform = transforms.Resized(keys=["image"], spatial_size=spatial_size, mode='nearest-exact')
+    z_transform = transforms.Resized(keys=["image"], spatial_size=(325,325,325), mode='nearest-exact')
+    ###
+    item = {}
+    # generate ct_voxel_ndarray
+    if type(case_path) is str:
+        ct_voxel_ndarray, _ = img_loader(case_path)
+    else:
+        bytes_data = case_path.read()
+        with tempfile.NamedTemporaryFile(suffix='.nii.gz') as tmp:
+            tmp.write(bytes_data)
+            tmp.seek(0)
+            ct_voxel_ndarray, _ = img_loader(tmp.name)
+    ct_voxel_ndarray = np.array(ct_voxel_ndarray).squeeze()
+    ct_voxel_ndarray = np.expand_dims(ct_voxel_ndarray, axis=0)
+    item['image'] = ct_voxel_ndarray
+
+    # transform
+    item = transform(item)
+    item_zoom_out = zoom_out_transform(item)
+    item['zoom_out_image'] = item_zoom_out['image']
+    
+    item_z = z_transform(item)
+    item['z_image'] = item_z['image']
+    return item
diff --git a/model/inference_cpu.py b/model/inference_cpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..c33305d3527cfc82703ffe673507164b695376f8
--- /dev/null
+++ b/model/inference_cpu.py
@@ -0,0 +1,171 @@
+import argparse
+import os
+import torch
+import torch.nn.functional as F
+import json
+import monai.transforms as transforms
+
+from model.segment_anything_volumetric import sam_model_registry
+from model.network.model import SegVol
+from model.data_process.demo_data_process import process_ct_gt
+from model.utils.monai_inferers_utils import sliding_window_inference, generate_box, select_points, build_binary_cube, build_binary_points, logits2roi_coor
+from model.utils.visualize import draw_result
+import streamlit as st
+
+def set_parse():
+    # %% set up parser
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--test_mode", default=True, type=bool)
+    parser.add_argument("--resume", type = str, default = 'model/asset/SegVol_v1.pth')
+    parser.add_argument("-infer_overlap", default=0.0, type=float, help="sliding window inference overlap")
+    parser.add_argument("-spatial_size", default=(32, 256, 256), type=tuple)
+    parser.add_argument("-patch_size", default=(4, 16, 16), type=tuple)
+    parser.add_argument('-work_dir', type=str, default='./work_dir')
+    ### demo
+    parser.add_argument("--clip_ckpt", type = str, default = 'model/config/clip')
+    args = parser.parse_args()
+    return args
+
+def zoom_in_zoom_out(args, segvol_model, image, image_resize, text_prompt, point_prompt, box_prompt):
+    image_single_resize = image_resize
+    image_single = image[0,0]
+    ori_shape = image_single.shape
+
+    # generate prompts
+    text_single = None if text_prompt is None else [text_prompt]
+    points_single = None
+    box_single = None
+
+    if args.use_point_prompt:
+        point, point_label = point_prompt
+        points_single = (point.unsqueeze(0).float(), point_label.unsqueeze(0).float()) 
+        binary_points_resize = build_binary_points(point, point_label, ori_shape)
+    if args.use_box_prompt:
+        box_single = box_prompt.unsqueeze(0).float()
+        binary_cube_resize = build_binary_cube(box_single, binary_cube_shape=ori_shape)
+    
+    ####################
+    # zoom-out inference:
+    print('--- zoom out inference ---')
+    print(text_single)
+    print(f'use text-prompt [{text_single!=None}], use box-prompt [{box_single!=None}], use point-prompt [{points_single!=None}]')
+    with torch.no_grad():
+        logits_global_single = segvol_model(image_single_resize,
+                                            text=text_single, 
+                                            boxes=box_single, 
+                                            points=points_single)
+    
+    # resize back global logits
+    logits_global_single = F.interpolate(
+            logits_global_single.cpu(),
+            size=ori_shape, mode='nearest')[0][0]
+    
+    # build prompt reflection for zoom-in
+    if args.use_point_prompt:
+        binary_points = F.interpolate(
+            binary_points_resize.unsqueeze(0).unsqueeze(0).float(),
+            size=ori_shape, mode='nearest')[0][0]
+    if args.use_box_prompt:
+        binary_cube = F.interpolate(
+            binary_cube_resize.unsqueeze(0).unsqueeze(0).float(),
+            size=ori_shape, mode='nearest')[0][0]
+    # draw_result('unknow', image_single_resize, None, point_prompt, logits_global_single, logits_global_single)
+    if not args.use_zoom_in:
+        return logits_global_single
+
+    ####################
+    # zoom-in inference:
+    min_d, min_h, min_w, max_d, max_h, max_w = logits2roi_coor(args.spatial_size, logits_global_single)
+    if min_d is None:
+        print('Fail to detect foreground!')
+        return logits_global_single
+
+    # Crop roi
+    image_single_cropped = image_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1].unsqueeze(0).unsqueeze(0)
+    global_preds = (torch.sigmoid(logits_global_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1])>0.5).long()
+    
+    assert not (args.use_box_prompt and args.use_point_prompt)
+    # label_single_cropped = label_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1].unsqueeze(0).unsqueeze(0)
+    prompt_reflection = None
+    if args.use_box_prompt:
+        binary_cube_cropped = binary_cube[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1]
+        prompt_reflection = (
+            binary_cube_cropped.unsqueeze(0).unsqueeze(0),
+            global_preds.unsqueeze(0).unsqueeze(0)
+        )
+    if args.use_point_prompt:
+        binary_points_cropped = binary_points[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1]
+        prompt_reflection = (
+            binary_points_cropped.unsqueeze(0).unsqueeze(0),
+            global_preds.unsqueeze(0).unsqueeze(0)
+        )
+
+    ## inference
+    with torch.no_grad():
+        logits_single_cropped = sliding_window_inference(
+                image_single_cropped, prompt_reflection,
+                args.spatial_size, 1, segvol_model, args.infer_overlap,
+                text=text_single,
+                use_box=args.use_box_prompt,
+                use_point=args.use_point_prompt,
+                logits_global_single=logits_global_single,
+            )
+        logits_single_cropped = logits_single_cropped.cpu().squeeze()
+        if logits_single_cropped.shape != logits_global_single.shape:
+            logits_global_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1] = logits_single_cropped
+
+    return logits_global_single
+
+@st.cache_resource
+def build_model():
+    # build model
+    clip_ckpt = 'model/config/clip'
+    resume = 'model/asset/SegVol_v1.pth'
+    sam_model = sam_model_registry['vit']()
+    segvol_model = SegVol(
+                        image_encoder=sam_model.image_encoder, 
+                        mask_decoder=sam_model.mask_decoder,
+                        prompt_encoder=sam_model.prompt_encoder,
+                        clip_ckpt=clip_ckpt,
+                        roi_size=(32,256,256),
+                        patch_size=(4,16,16),
+                        test_mode=True,
+                        )
+    segvol_model = torch.nn.DataParallel(segvol_model)
+    segvol_model.eval()
+    # load param
+    if os.path.isfile(resume):
+        ## Map model to be loaded to specified single GPU
+        loc = 'cpu'
+        checkpoint = torch.load(resume, map_location=loc)
+        segvol_model.load_state_dict(checkpoint['model'], strict=True)
+        print("loaded checkpoint '{}' (epoch {})".format(resume, checkpoint['epoch']))
+    print('model build done!')
+    return segvol_model
+
+@st.cache_data
+def inference_case(_image, _image_zoom_out, _point_prompt, text_prompt, _box_prompt):
+    # seg config
+    args = set_parse()
+    args.use_zoom_in = True
+    args.use_text_prompt = text_prompt is not None
+    args.use_box_prompt = _box_prompt is not None
+    args.use_point_prompt = _point_prompt is not None
+
+    segvol_model = build_model()
+
+    # run inference
+    logits = zoom_in_zoom_out(
+        args, segvol_model, 
+        _image.unsqueeze(0), _image_zoom_out.unsqueeze(0), 
+        text_prompt, _point_prompt, _box_prompt)
+    print(logits.shape)
+    resize_transform = transforms.Compose([
+        transforms.AddChannel(),
+        transforms.Resize((325,325,325), mode='trilinear')
+    ]
+    )
+    logits = resize_transform(logits)[0]
+    print(logits.shape)
+    return (torch.sigmoid(logits) > 0.5).int().numpy()
+    
diff --git a/model/inference_demo.py b/model/inference_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6f6b1378d071126b4bee0cf455f72999f320a0f
--- /dev/null
+++ b/model/inference_demo.py
@@ -0,0 +1,219 @@
+import argparse
+import os
+import torch
+import torch.nn.functional as F
+import json
+from segment_anything_volumetric import sam_model_registry
+from network.model import SegVol
+from data_process.demo_data_process import process_ct_gt
+import monai.transforms as transforms
+from utils.monai_inferers_utils import sliding_window_inference, generate_box, select_points, build_binary_cube, build_binary_points, logits2roi_coor
+from utils.visualize import draw_result
+
+def set_parse():
+    # %% set up parser
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--test_mode", default=True, type=bool)
+    parser.add_argument("--resume", type = str, default = '')
+    parser.add_argument("-infer_overlap", default=0.5, type=float, help="sliding window inference overlap")
+    parser.add_argument("-spatial_size", default=(32, 256, 256), type=tuple)
+    parser.add_argument("-patch_size", default=(4, 16, 16), type=tuple)
+    parser.add_argument('-work_dir', type=str, default='./work_dir')
+    ### demo
+    parser.add_argument('--demo_config', type=str, required=True)
+    parser.add_argument("--clip_ckpt", type = str, default = './config/clip')
+    args = parser.parse_args()
+    return args
+
+def dice_score(preds, labels):  # on GPU
+    assert preds.shape[0] == labels.shape[0], "predict & target batch size don't match\n" + str(preds.shape) + str(labels.shape)
+    predict = preds.view(1, -1)
+    target = labels.view(1, -1)
+    if target.shape[1] < 1e8:
+        predict = predict.cuda()
+        target = target.cuda()
+    predict = torch.sigmoid(predict)
+    predict = torch.where(predict > 0.5, 1., 0.)
+    
+    tp = torch.sum(torch.mul(predict, target))
+    den = torch.sum(predict) + torch.sum(target) + 1
+    dice = 2 * tp / den
+
+    if target.shape[1] < 1e8:
+        predict = predict.cpu()
+        target = target.cpu()
+    return dice
+
+def zoom_in_zoom_out(args, segvol_model, image, image_resize, gt3D, gt3D_resize, categories=None):
+    logits_labels_record = {}
+    image_single_resize = image_resize
+    image_single = image[0,0]
+    ori_shape = image_single.shape
+    for item_idx in range(len(categories)):
+        # get label to generate prompts
+        label_single = gt3D[0][item_idx]
+        label_single_resize = gt3D_resize[0][item_idx]
+        # skip meaningless categories
+        if torch.sum(label_single) == 0:
+            print('No object, skip')
+            continue
+        # generate prompts
+        text_single = categories[item_idx] if args.use_text_prompt else None
+        if categories is not None: print(f'inference |{categories[item_idx]}| target...')
+        points_single = None
+        box_single = None
+        if args.use_point_prompt:
+            point, point_label = select_points(label_single_resize, num_positive_extra=3, num_negative_extra=3)
+            points_single = (point.unsqueeze(0).float().cuda(), point_label.unsqueeze(0).float().cuda()) 
+            binary_points_resize = build_binary_points(point, point_label, label_single_resize.shape)
+        if args.use_box_prompt:
+            box_single = generate_box(label_single_resize).unsqueeze(0).float().cuda()
+            binary_cube_resize = build_binary_cube(box_single, binary_cube_shape=label_single_resize.shape)
+        
+        ####################
+        # zoom-out inference:
+        print('--- zoom out inference ---')
+        print(f'use text-prompt [{text_single!=None}], use box-prompt [{box_single!=None}], use point-prompt [{points_single!=None}]')
+        with torch.no_grad():
+            logits_global_single = segvol_model(image_single_resize.cuda(),
+                                                text=text_single, 
+                                                boxes=box_single, 
+                                                points=points_single)
+        
+        # resize back global logits
+        logits_global_single = F.interpolate(
+                logits_global_single.cpu(),
+                size=ori_shape, mode='nearest')[0][0]
+        
+        # build prompt reflection for zoom-in
+        if args.use_point_prompt:
+            binary_points = F.interpolate(
+                binary_points_resize.unsqueeze(0).unsqueeze(0).float(),
+                size=ori_shape, mode='nearest')[0][0]
+        if args.use_box_prompt:
+            binary_cube = F.interpolate(
+                binary_cube_resize.unsqueeze(0).unsqueeze(0).float(),
+                size=ori_shape, mode='nearest')[0][0]
+        zoom_out_dice = dice_score(logits_global_single.squeeze(), label_single.squeeze())
+        logits_labels_record[categories[item_idx]] = (
+            zoom_out_dice,
+            image_single, 
+            points_single,
+            box_single,
+            logits_global_single, 
+            label_single)
+        print(f'zoom out inference done with zoom_out_dice: {zoom_out_dice:.4f}')
+        if not args.use_zoom_in:
+            continue
+
+        ####################
+        # zoom-in inference:
+        min_d, min_h, min_w, max_d, max_h, max_w = logits2roi_coor(args.spatial_size, logits_global_single)
+        if min_d is None:
+            print('Fail to detect foreground!')
+            continue
+
+        # Crop roi
+        image_single_cropped = image_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1].unsqueeze(0).unsqueeze(0)
+        global_preds = (torch.sigmoid(logits_global_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1])>0.5).long()
+        
+        assert not (args.use_box_prompt and args.use_point_prompt)
+        # label_single_cropped = label_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1].unsqueeze(0).unsqueeze(0)
+        prompt_reflection = None
+        if args.use_box_prompt:
+            binary_cube_cropped = binary_cube[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1]
+            prompt_reflection = (
+                binary_cube_cropped.unsqueeze(0).unsqueeze(0),
+                global_preds.unsqueeze(0).unsqueeze(0)
+            )
+        if args.use_point_prompt:
+            binary_points_cropped = binary_points[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1]
+            prompt_reflection = (
+                binary_points_cropped.unsqueeze(0).unsqueeze(0),
+                global_preds.unsqueeze(0).unsqueeze(0)
+            )
+    
+        ## inference
+        with torch.no_grad():
+            logits_single_cropped = sliding_window_inference(
+                    image_single_cropped.cuda(), prompt_reflection,
+                    args.spatial_size, 1, segvol_model, args.infer_overlap,
+                    text=text_single,
+                    use_box=args.use_box_prompt,
+                    use_point=args.use_point_prompt,
+                )
+            logits_single_cropped = logits_single_cropped.cpu().squeeze()
+        logits_global_single[min_d:max_d+1, min_h:max_h+1, min_w:max_w+1] = logits_single_cropped
+        zoom_in_dice = dice_score(logits_global_single.squeeze(), label_single.squeeze())
+        logits_labels_record[categories[item_idx]] = (
+            zoom_in_dice,
+            image_single, 
+            points_single,
+            box_single,
+            logits_global_single, 
+            label_single)
+        print(f'===> zoom out dice {zoom_out_dice:.4f} -> zoom-out-zoom-in dice {zoom_in_dice:.4f} <===')
+    return logits_labels_record
+
+def inference_single_ct(args, segvol_model, data_item, categories):
+    segvol_model.eval()
+    image, gt3D = data_item["image"].float(), data_item["label"]
+    image_zoom_out, gt3D__zoom_out = data_item["zoom_out_image"].float(), data_item['zoom_out_label']
+
+    logits_labels_record = zoom_in_zoom_out(
+        args, segvol_model, 
+        image.unsqueeze(0), image_zoom_out.unsqueeze(0), 
+        gt3D.unsqueeze(0), gt3D__zoom_out.unsqueeze(0),     # add batch dim
+        categories=categories)
+    
+    # visualize
+    if args.visualize:
+        for target, values in logits_labels_record.items():
+            dice_score, image, point_prompt, box_prompt, logits, labels = values
+            print(f'{target} result with Dice score {dice_score:.4f} visualizing')
+            draw_result(target + f"-Dice {dice_score:.4f}", image, box_prompt, point_prompt, logits, labels, args.spatial_size, args.work_dir)
+
+def main(args):
+    gpu = 0
+    torch.cuda.set_device(gpu)
+    # build model
+    sam_model = sam_model_registry['vit'](args=args)
+    segvol_model = SegVol(
+                        image_encoder=sam_model.image_encoder, 
+                        mask_decoder=sam_model.mask_decoder,
+                        prompt_encoder=sam_model.prompt_encoder,
+                        clip_ckpt=args.clip_ckpt,
+                        roi_size=args.spatial_size,
+                        patch_size=args.patch_size,
+                        test_mode=args.test_mode,
+                        ).cuda()
+    segvol_model = torch.nn.DataParallel(segvol_model, device_ids=[gpu])
+
+    # load param
+    if os.path.isfile(args.resume):
+        ## Map model to be loaded to specified single GPU
+        loc = 'cuda:{}'.format(gpu)
+        checkpoint = torch.load(args.resume, map_location=loc)
+        segvol_model.load_state_dict(checkpoint['model'], strict=True)
+        print("loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
+
+    # load demo config
+    with open(args.demo_config, 'r') as file:
+        config_dict = json.load(file)
+    ct_path, gt_path, categories = config_dict['demo_case']['ct_path'], config_dict['demo_case']['gt_path'], config_dict['categories']
+
+    # preprocess for data
+    data_item = process_ct_gt(ct_path, gt_path, categories, args.spatial_size)   # keys: image, label
+
+    # seg config for prompt & zoom-in-zoom-out
+    args.use_zoom_in = True
+    args.use_text_prompt = True
+    args.use_box_prompt = True
+    args.use_point_prompt = False
+    args.visualize = False
+
+    inference_single_ct(args, segvol_model, data_item, categories)
+
+if __name__ == "__main__":
+    args = set_parse()
+    main(args)
diff --git a/model/network/__pycache__/model.cpython-39.pyc b/model/network/__pycache__/model.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a91bf94da98e0da0849568223e943043092b2f67
Binary files /dev/null and b/model/network/__pycache__/model.cpython-39.pyc differ
diff --git a/model/network/model.py b/model/network/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc655f151bf4cf5406662b8857b66d3261b5a054
--- /dev/null
+++ b/model/network/model.py
@@ -0,0 +1,91 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from transformers import AutoTokenizer, CLIPTextModel, CLIPTextConfig
+
+#%% set up model
+class SegVol(nn.Module):
+    def __init__(self, 
+                image_encoder, 
+                mask_decoder,
+                prompt_encoder,
+                clip_ckpt,
+                roi_size,
+                patch_size,
+                test_mode=False,
+                ):
+        super().__init__()
+        self.image_encoder = image_encoder
+        self.mask_decoder = mask_decoder
+        self.prompt_encoder = prompt_encoder
+        self.text_encoder = TextEncoder(clip_ckpt)
+        self.feat_shape = np.array(roi_size)/np.array(patch_size)
+        self.test_mode = test_mode
+
+    def forward(self, image, text=None, boxes=None, points=None, **kwargs):
+        bs = image.shape[0]
+        img_shape = (image.shape[2], image.shape[3], image.shape[4])
+        image_embedding, _ = self.image_encoder(image)
+        image_embedding = image_embedding.transpose(1, 2).view(bs, -1, 
+            int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))
+        # test mode
+        if self.test_mode:
+            return self.forward_decoder(image_embedding, img_shape, text, boxes, points)
+        # train mode
+        # future release
+
+    def forward_decoder(self, image_embedding, img_shape, text=None, boxes=None, points=None):
+        with torch.no_grad():
+            if boxes is not None:
+                if len(boxes.shape) == 2:
+                    boxes = boxes[:, None, :] # (B, 1, 6)
+            if text is not None:
+                text_embedding = self.text_encoder(text)  # (B, 768)
+            else:
+                text_embedding = None
+        sparse_embeddings, dense_embeddings = self.prompt_encoder(
+            points=points,
+            boxes=boxes,
+            masks=None,
+            text_embedding=text_embedding,
+        )
+
+        dense_pe = self.prompt_encoder.get_dense_pe()
+        low_res_masks, _ = self.mask_decoder(
+            image_embeddings=image_embedding,
+            text_embedding = text_embedding,
+            image_pe=dense_pe,
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=False,
+          )
+        logits = F.interpolate(low_res_masks, size=img_shape, mode='trilinear', align_corners=False)
+        return logits
+
+class TextEncoder(nn.Module):
+    def __init__(self, clip_ckpt):
+        super().__init__()
+        config = CLIPTextConfig()
+        self.clip_text_model = CLIPTextModel(config)
+        self.tokenizer = AutoTokenizer.from_pretrained(clip_ckpt)
+        self.dim_align = nn.Linear(512, 768)
+        # freeze text encoder
+        for param in self.clip_text_model.parameters():
+            param.requires_grad = False
+
+    def organ2tokens(self, organ_names):
+        text_list = ['A computerized tomography of a {}.'.format(organ_name) for organ_name in organ_names]
+        tokens = self.tokenizer(text_list, padding=True, return_tensors="pt")
+        return tokens
+    
+    def forward(self, text):
+        if text is None:
+            return None
+        if type(text) is str:
+            text = [text]
+        tokens = self.organ2tokens(text)
+        clip_outputs = self.clip_text_model(**tokens)
+        text_embedding = clip_outputs.pooler_output
+        text_embedding = self.dim_align(text_embedding)
+        return text_embedding
diff --git a/model/script/inference_demo.sh b/model/script/inference_demo.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d805dfa77c934074bdab1b6d3eb4aaba44c2c8ae
--- /dev/null
+++ b/model/script/inference_demo.sh
@@ -0,0 +1,8 @@
+export segvol_ckpt="path/to/SegVol_v1.pth"
+export work_dir="path/to/work_dir"
+export demo_config_path="./config/config_demo.json"
+
+CUDA_VISIBLE_DEVICES=0 python inference_demo.py \
+--resume $segvol_ckpt \
+-work_dir $work_dir \
+--demo_config $demo_config_path 
diff --git a/model/segment_anything_volumetric/.ipynb_checkpoints/build_sam-checkpoint.py b/model/segment_anything_volumetric/.ipynb_checkpoints/build_sam-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..5885de4ddb055c14582a75cd7f3314b7027e8213
--- /dev/null
+++ b/model/segment_anything_volumetric/.ipynb_checkpoints/build_sam-checkpoint.py
@@ -0,0 +1,172 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from functools import partial
+from pathlib import Path
+import urllib.request
+import torch
+
+from .modeling import (
+    ImageEncoderViT,
+    MaskDecoder,
+    PromptEncoder,
+    Sam,
+    TwoWayTransformer,
+)
+
+from .modeling.image_encoder_swin import SwinTransformer
+
+from monai.utils import ensure_tuple_rep, optional_import
+
+def build_sam_vit_h(checkpoint=None, image_size=1024):
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+        image_size=image_size,
+    )
+
+
+build_sam = build_sam_vit_h
+
+
+def build_sam_vit_l(checkpoint=None, image_size=1024):
+    return _build_sam(
+        encoder_embed_dim=1024,
+        encoder_depth=24,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[5, 11, 17, 23],
+        checkpoint=checkpoint,
+        image_size=image_size,
+    )
+
+
+def build_sam_vit_b(checkpoint=None, image_size=1024):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+        image_size=image_size,
+    )
+"""
+Examples::
+            # for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48.
+            >>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48)
+            # for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage.
+            >>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2))
+            # for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing.
+            >>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2)
+"""
+
+def build_sam_vit_swin(checkpoint=None, image_size=96):
+    print('==> build_sam_vit_swin')
+    return _build_sam(
+        encoder_embed_dim=48,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+        image_size=image_size,
+    )
+
+sam_model_registry = {
+    "default": build_sam_vit_h,
+    "vit_h": build_sam_vit_h,
+    "vit_l": build_sam_vit_l,
+    "vit_b": build_sam_vit_b,
+    "swin_vit": build_sam_vit_swin,
+}
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+    image_size=None,
+    spatial_dims=3,
+):
+    prompt_embed_dim = 768
+    patch_size = ensure_tuple_rep(2, spatial_dims)
+    window_size = ensure_tuple_rep(7, spatial_dims)
+    image_embedding_size = [size // 32 for size in image_size]
+    sam = Sam(
+        image_encoder=SwinTransformer(
+            in_chans=1,
+            embed_dim=encoder_embed_dim,
+            window_size=window_size,
+            patch_size=patch_size,
+            depths=(2, 2, 6, 2), #(2, 2, 6, 2),
+            num_heads=(3, 6, 12, 24),
+            mlp_ratio=4.0,
+            qkv_bias=True,
+            spatial_dims=spatial_dims,
+        ),
+        prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=image_embedding_size,
+            input_image_size=image_size,
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    sam.eval()
+    if checkpoint is not None:
+        checkpoint = Path(checkpoint)
+        if checkpoint.name == "sam_vit_b_01ec64.pth" and not checkpoint.exists():
+            cmd = input("Download sam_vit_b_01ec64.pth from facebook AI? [y]/n: ")
+            if len(cmd) == 0 or cmd.lower() == 'y':
+                checkpoint.parent.mkdir(parents=True, exist_ok=True)
+                print("Downloading SAM ViT-B checkpoint...")
+                urllib.request.urlretrieve(
+                    "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
+                    checkpoint,
+                )
+                print(checkpoint.name, " is downloaded!")
+        elif checkpoint.name == "sam_vit_h_4b8939.pth" and not checkpoint.exists():
+            cmd = input("Download sam_vit_h_4b8939.pth from facebook AI? [y]/n: ")
+            if len(cmd) == 0 or cmd.lower() == 'y':
+                checkpoint.parent.mkdir(parents=True, exist_ok=True)
+                print("Downloading SAM ViT-H checkpoint...")
+                urllib.request.urlretrieve(
+                    "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+                    checkpoint,
+                )
+                print(checkpoint.name, " is downloaded!")
+        elif checkpoint.name == "sam_vit_l_0b3195.pth" and not checkpoint.exists():
+            cmd = input("Download sam_vit_l_0b3195.pth from facebook AI? [y]/n: ")
+            if len(cmd) == 0 or cmd.lower() == 'y':
+                checkpoint.parent.mkdir(parents=True, exist_ok=True)
+                print("Downloading SAM ViT-L checkpoint...")
+                urllib.request.urlretrieve(
+                    "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
+                    checkpoint,
+                )
+                print(checkpoint.name, " is downloaded!")
+
+        
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)
+        sam.load_state_dict(state_dict)
+    return sam
diff --git a/model/segment_anything_volumetric/__init__.py b/model/segment_anything_volumetric/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..073ec3592234ed09afa0d3170156fb4fee56c3fe
--- /dev/null
+++ b/model/segment_anything_volumetric/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .build_sam import (
+    build_sam_vit_3d,
+    sam_model_registry,
+)
+from .predictor import SamPredictor
+from .automatic_mask_generator import SamAutomaticMaskGenerator
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diff --git a/model/segment_anything_volumetric/automatic_mask_generator.py b/model/segment_anything_volumetric/automatic_mask_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5a8c969207f119feff7087f94e044403acdff00
--- /dev/null
+++ b/model/segment_anything_volumetric/automatic_mask_generator.py
@@ -0,0 +1,372 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torchvision.ops.boxes import batched_nms, box_area  # type: ignore
+
+from typing import Any, Dict, List, Optional, Tuple
+
+from .modeling import Sam
+from .predictor import SamPredictor
+from .utils.amg import (
+    MaskData,
+    area_from_rle,
+    batch_iterator,
+    batched_mask_to_box,
+    box_xyxy_to_xywh,
+    build_all_layer_point_grids,
+    calculate_stability_score,
+    coco_encode_rle,
+    generate_crop_boxes,
+    is_box_near_crop_edge,
+    mask_to_rle_pytorch,
+    remove_small_regions,
+    rle_to_mask,
+    uncrop_boxes_xyxy,
+    uncrop_masks,
+    uncrop_points,
+)
+
+
+class SamAutomaticMaskGenerator:
+    def __init__(
+        self,
+        model: Sam,
+        points_per_side: Optional[int] = 32,
+        points_per_batch: int = 64,
+        pred_iou_thresh: float = 0.88,
+        stability_score_thresh: float = 0.95,
+        stability_score_offset: float = 1.0,
+        box_nms_thresh: float = 0.7,
+        crop_n_layers: int = 0,
+        crop_nms_thresh: float = 0.7,
+        crop_overlap_ratio: float = 512 / 1500,
+        crop_n_points_downscale_factor: int = 1,
+        point_grids: Optional[List[np.ndarray]] = None,
+        min_mask_region_area: int = 0,
+        output_mode: str = "binary_mask",
+    ) -> None:
+        """
+        Using a SAM model, generates masks for the entire image.
+        Generates a grid of point prompts over the image, then filters
+        low quality and duplicate masks. The default settings are chosen
+        for SAM with a ViT-H backbone.
+
+        Arguments:
+          model (Sam): The SAM model to use for mask prediction.
+          points_per_side (int or None): The number of points to be sampled
+            along one side of the image. The total number of points is
+            points_per_side**2. If None, 'point_grids' must provide explicit
+            point sampling.
+          points_per_batch (int): Sets the number of points run simultaneously
+            by the model. Higher numbers may be faster but use more GPU memory.
+          pred_iou_thresh (float): A filtering threshold in [0,1], using the
+            model's predicted mask quality.
+          stability_score_thresh (float): A filtering threshold in [0,1], using
+            the stability of the mask under changes to the cutoff used to binarize
+            the model's mask predictions.
+          stability_score_offset (float): The amount to shift the cutoff when
+            calculated the stability score.
+          box_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks.
+          crop_n_layers (int): If >0, mask prediction will be run again on
+            crops of the image. Sets the number of layers to run, where each
+            layer has 2**i_layer number of image crops.
+          crop_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks between different crops.
+          crop_overlap_ratio (float): Sets the degree to which crops overlap.
+            In the first crop layer, crops will overlap by this fraction of
+            the image length. Later layers with more crops scale down this overlap.
+          crop_n_points_downscale_factor (int): The number of points-per-side
+            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
+          point_grids (list(np.ndarray) or None): A list over explicit grids
+            of points used for sampling, normalized to [0,1]. The nth grid in the
+            list is used in the nth crop layer. Exclusive with points_per_side.
+          min_mask_region_area (int): If >0, postprocessing will be applied
+            to remove disconnected regions and holes in masks with area smaller
+            than min_mask_region_area. Requires opencv.
+          output_mode (str): The form masks are returned in. Can be 'binary_mask',
+            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
+            For large resolutions, 'binary_mask' may consume large amounts of
+            memory.
+        """
+
+        assert (points_per_side is None) != (
+            point_grids is None
+        ), "Exactly one of points_per_side or point_grid must be provided."
+        if points_per_side is not None:
+            self.point_grids = build_all_layer_point_grids(
+                points_per_side,
+                crop_n_layers,
+                crop_n_points_downscale_factor,
+            )
+        elif point_grids is not None:
+            self.point_grids = point_grids
+        else:
+            raise ValueError("Can't have both points_per_side and point_grid be None.")
+
+        assert output_mode in [
+            "binary_mask",
+            "uncompressed_rle",
+            "coco_rle",
+        ], f"Unknown output_mode {output_mode}."
+        if output_mode == "coco_rle":
+            from pycocotools import mask as mask_utils  # type: ignore # noqa: F401
+
+        if min_mask_region_area > 0:
+            import cv2  # type: ignore # noqa: F401
+
+        self.predictor = SamPredictor(model)
+        self.points_per_batch = points_per_batch
+        self.pred_iou_thresh = pred_iou_thresh
+        self.stability_score_thresh = stability_score_thresh
+        self.stability_score_offset = stability_score_offset
+        self.box_nms_thresh = box_nms_thresh
+        self.crop_n_layers = crop_n_layers
+        self.crop_nms_thresh = crop_nms_thresh
+        self.crop_overlap_ratio = crop_overlap_ratio
+        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
+        self.min_mask_region_area = min_mask_region_area
+        self.output_mode = output_mode
+
+    @torch.no_grad()
+    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Generates masks for the given image.
+
+        Arguments:
+          image (np.ndarray): The image to generate masks for, in HWC uint8 format.
+
+        Returns:
+           list(dict(str, any)): A list over records for masks. Each record is
+             a dict containing the following keys:
+               segmentation (dict(str, any) or np.ndarray): The mask. If
+                 output_mode='binary_mask', is an array of shape HW. Otherwise,
+                 is a dictionary containing the RLE.
+               bbox (list(float)): The box around the mask, in XYWH format.
+               area (int): The area in pixels of the mask.
+               predicted_iou (float): The model's own prediction of the mask's
+                 quality. This is filtered by the pred_iou_thresh parameter.
+               point_coords (list(list(float))): The point coordinates input
+                 to the model to generate this mask.
+               stability_score (float): A measure of the mask's quality. This
+                 is filtered on using the stability_score_thresh parameter.
+               crop_box (list(float)): The crop of the image used to generate
+                 the mask, given in XYWH format.
+        """
+
+        # Generate masks
+        mask_data = self._generate_masks(image)
+
+        # Filter small disconnected regions and holes in masks
+        if self.min_mask_region_area > 0:
+            mask_data = self.postprocess_small_regions(
+                mask_data,
+                self.min_mask_region_area,
+                max(self.box_nms_thresh, self.crop_nms_thresh),
+            )
+
+        # Encode masks
+        if self.output_mode == "coco_rle":
+            mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
+        elif self.output_mode == "binary_mask":
+            mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
+        else:
+            mask_data["segmentations"] = mask_data["rles"]
+
+        # Write mask records
+        curr_anns = []
+        for idx in range(len(mask_data["segmentations"])):
+            ann = {
+                "segmentation": mask_data["segmentations"][idx],
+                "area": area_from_rle(mask_data["rles"][idx]),
+                "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
+                "predicted_iou": mask_data["iou_preds"][idx].item(),
+                "point_coords": [mask_data["points"][idx].tolist()],
+                "stability_score": mask_data["stability_score"][idx].item(),
+                "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
+            }
+            curr_anns.append(ann)
+
+        return curr_anns
+
+    def _generate_masks(self, image: np.ndarray) -> MaskData:
+        orig_size = image.shape[:2]
+        crop_boxes, layer_idxs = generate_crop_boxes(
+            orig_size, self.crop_n_layers, self.crop_overlap_ratio
+        )
+
+        # Iterate over image crops
+        data = MaskData()
+        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
+            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
+            data.cat(crop_data)
+
+        # Remove duplicate masks between crops
+        if len(crop_boxes) > 1:
+            # Prefer masks from smaller crops
+            scores = 1 / box_area(data["crop_boxes"])
+            scores = scores.to(data["boxes"].device)
+            keep_by_nms = batched_nms(
+                data["boxes"].float(),
+                scores,
+                torch.zeros_like(data["boxes"][:, 0]),  # categories
+                iou_threshold=self.crop_nms_thresh,
+            )
+            data.filter(keep_by_nms)
+
+        data.to_numpy()
+        return data
+
+    def _process_crop(
+        self,
+        image: np.ndarray,
+        crop_box: List[int],
+        crop_layer_idx: int,
+        orig_size: Tuple[int, ...],
+    ) -> MaskData:
+        # Crop the image and calculate embeddings
+        x0, y0, x1, y1 = crop_box
+        cropped_im = image[y0:y1, x0:x1, :]
+        cropped_im_size = cropped_im.shape[:2]
+        self.predictor.set_image(cropped_im)
+
+        # Get points for this crop
+        points_scale = np.array(cropped_im_size)[None, ::-1]
+        points_for_image = self.point_grids[crop_layer_idx] * points_scale
+
+        # Generate masks for this crop in batches
+        data = MaskData()
+        for (points,) in batch_iterator(self.points_per_batch, points_for_image):
+            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
+            data.cat(batch_data)
+            del batch_data
+        self.predictor.reset_image()
+
+        # Remove duplicates within this crop.
+        keep_by_nms = batched_nms(
+            data["boxes"].float(),
+            data["iou_preds"],
+            torch.zeros_like(data["boxes"][:, 0]),  # categories
+            iou_threshold=self.box_nms_thresh,
+        )
+        data.filter(keep_by_nms)
+
+        # Return to the original image frame
+        data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
+        data["points"] = uncrop_points(data["points"], crop_box)
+        data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
+
+        return data
+
+    def _process_batch(
+        self,
+        points: np.ndarray,
+        im_size: Tuple[int, ...],
+        crop_box: List[int],
+        orig_size: Tuple[int, ...],
+    ) -> MaskData:
+        orig_h, orig_w = orig_size
+
+        # Run model on this batch
+        transformed_points = self.predictor.transform.apply_coords(points, im_size)
+        in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
+        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
+        masks, iou_preds, _ = self.predictor.predict_torch(
+            in_points[:, None, :],
+            in_labels[:, None],
+            multimask_output=True,
+            return_logits=True,
+        )
+
+        # Serialize predictions and store in MaskData
+        data = MaskData(
+            masks=masks.flatten(0, 1),
+            iou_preds=iou_preds.flatten(0, 1),
+            points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
+        )
+        del masks
+
+        # Filter by predicted IoU
+        if self.pred_iou_thresh > 0.0:
+            keep_mask = data["iou_preds"] > self.pred_iou_thresh
+            data.filter(keep_mask)
+
+        # Calculate stability score
+        data["stability_score"] = calculate_stability_score(
+            data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
+        )
+        if self.stability_score_thresh > 0.0:
+            keep_mask = data["stability_score"] >= self.stability_score_thresh
+            data.filter(keep_mask)
+
+        # Threshold masks and calculate boxes
+        data["masks"] = data["masks"] > self.predictor.model.mask_threshold
+        data["boxes"] = batched_mask_to_box(data["masks"])
+
+        # Filter boxes that touch crop boundaries
+        keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
+        if not torch.all(keep_mask):
+            data.filter(keep_mask)
+
+        # Compress to RLE
+        data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
+        data["rles"] = mask_to_rle_pytorch(data["masks"])
+        del data["masks"]
+
+        return data
+
+    @staticmethod
+    def postprocess_small_regions(
+        mask_data: MaskData, min_area: int, nms_thresh: float
+    ) -> MaskData:
+        """
+        Removes small disconnected regions and holes in masks, then reruns
+        box NMS to remove any new duplicates.
+
+        Edits mask_data in place.
+
+        Requires open-cv as a dependency.
+        """
+        if len(mask_data["rles"]) == 0:
+            return mask_data
+
+        # Filter small disconnected regions and holes
+        new_masks = []
+        scores = []
+        for rle in mask_data["rles"]:
+            mask = rle_to_mask(rle)
+
+            mask, changed = remove_small_regions(mask, min_area, mode="holes")
+            unchanged = not changed
+            mask, changed = remove_small_regions(mask, min_area, mode="islands")
+            unchanged = unchanged and not changed
+
+            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
+            # Give score=0 to changed masks and score=1 to unchanged masks
+            # so NMS will prefer ones that didn't need postprocessing
+            scores.append(float(unchanged))
+
+        # Recalculate boxes and remove any new duplicates
+        masks = torch.cat(new_masks, dim=0)
+        boxes = batched_mask_to_box(masks)
+        keep_by_nms = batched_nms(
+            boxes.float(),
+            torch.as_tensor(scores),
+            torch.zeros_like(boxes[:, 0]),  # categories
+            iou_threshold=nms_thresh,
+        )
+
+        # Only recalculate RLEs for masks that have changed
+        for i_mask in keep_by_nms:
+            if scores[i_mask] == 0.0:
+                mask_torch = masks[i_mask].unsqueeze(0)
+                mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
+                mask_data["boxes"][i_mask] = boxes[i_mask]  # update res directly
+        mask_data.filter(keep_by_nms)
+
+        return mask_data
diff --git a/model/segment_anything_volumetric/build_sam.py b/model/segment_anything_volumetric/build_sam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0d4e98f55ed78cd551ed9a67a7a3de358c79374
--- /dev/null
+++ b/model/segment_anything_volumetric/build_sam.py
@@ -0,0 +1,111 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from functools import partial
+from pathlib import Path
+import urllib.request
+import torch
+
+from .modeling import (
+    ImageEncoderViT,
+    MaskDecoder,
+    PromptEncoder,
+    Sam,
+    TwoWayTransformer,
+)
+import numpy as np
+from .modeling.image_encoder_swin import SwinTransformer
+from monai.networks.nets import ViT
+from monai.networks.nets.swin_unetr import SwinTransformer as SwinViT
+
+from monai.utils import ensure_tuple_rep, optional_import
+
+
+"""
+Examples::
+            # for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48.
+            >>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48)
+            # for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage.
+            >>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2))
+            # for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing.
+            >>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2)
+"""
+
+def build_sam_vit_3d(checkpoint=None):
+    print('build_sam_vit_3d...')
+    return _build_sam(
+        image_encoder_type='vit',
+        embed_dim = 768,
+        patch_size=[4,16,16],
+        checkpoint=checkpoint,
+        image_size=[32,256,256],
+    )
+
+sam_model_registry = {
+    "vit": build_sam_vit_3d,
+}
+
+
+def _build_sam(
+    image_encoder_type,
+    embed_dim,
+    patch_size,
+    checkpoint,
+    image_size,
+):
+    mlp_dim = 3072
+    num_layers = 12
+    num_heads = 12
+    pos_embed = 'perceptron'
+    dropout_rate = 0.0
+    
+    image_encoder=ViT(
+        in_channels=1,
+        img_size=image_size,
+        patch_size=patch_size,
+        hidden_size=embed_dim,
+        mlp_dim=mlp_dim,
+        num_layers=num_layers,
+        num_heads=num_heads,
+        pos_embed=pos_embed,
+        classification=False,
+        dropout_rate=dropout_rate,
+    )
+    image_embedding_size = [int(item) for item in (np.array(image_size) / np.array(patch_size))]
+
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f, map_location='cpu')['state_dict']
+            encoder_dict = {k.replace('model.encoder.', ''): v for k, v in state_dict.items() if 'model.encoder.' in k}
+        image_encoder.load_state_dict(encoder_dict)
+        print(f'===> image_encoder.load_param: {checkpoint}')
+    sam = Sam(
+        image_encoder=image_encoder,
+        prompt_encoder=PromptEncoder(
+            embed_dim=embed_dim,
+            image_embedding_size=image_embedding_size,
+            input_image_size=image_size,
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            image_encoder_type=image_encoder_type,
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+            image_size=np.array(image_size),
+            patch_size=np.array(patch_size),
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    sam.eval()
+    return sam
diff --git a/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/image_encoder_swin-checkpoint.py b/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/image_encoder_swin-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0df343e823f87c2914b3940a60003e839a1d580
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/image_encoder_swin-checkpoint.py
@@ -0,0 +1,709 @@
+from typing import Sequence, Tuple, Type, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch.nn import LayerNorm
+
+from monai.networks.blocks import MLPBlock as Mlp
+from monai.networks.blocks import PatchEmbed, UnetOutBlock, UnetrBasicBlock, UnetrUpBlock
+from monai.networks.layers import DropPath, trunc_normal_
+from monai.utils import ensure_tuple_rep, optional_import
+
+rearrange, _ = optional_import("einops", name="rearrange")
+
+def window_partition(x, window_size):
+    """window partition operation based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        x: input tensor.
+        window_size: local window size.
+    """
+    x_shape = x.size()
+    if len(x_shape) == 5:
+        b, d, h, w, c = x_shape
+        x = x.view(
+            b,
+            d // window_size[0],
+            window_size[0],
+            h // window_size[1],
+            window_size[1],
+            w // window_size[2],
+            window_size[2],
+            c,
+        )
+        windows = (
+            x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, window_size[0] * window_size[1] * window_size[2], c)
+        )
+    elif len(x_shape) == 4:
+        b, h, w, c = x.shape
+        x = x.view(b, h // window_size[0], window_size[0], w // window_size[1], window_size[1], c)
+        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0] * window_size[1], c)
+    return windows
+
+
+def window_reverse(windows, window_size, dims):
+    """window reverse operation based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        windows: windows tensor.
+        window_size: local window size.
+        dims: dimension values.
+    """
+    if len(dims) == 4:
+        b, d, h, w = dims
+        x = windows.view(
+            b,
+            d // window_size[0],
+            h // window_size[1],
+            w // window_size[2],
+            window_size[0],
+            window_size[1],
+            window_size[2],
+            -1,
+        )
+        x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(b, d, h, w, -1)
+
+    elif len(dims) == 3:
+        b, h, w = dims
+        x = windows.view(b, h // window_size[0], w // window_size[0], window_size[0], window_size[1], -1)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
+    return x
+
+
+def get_window_size(x_size, window_size, shift_size=None):
+    """Computing window size based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        x_size: input size.
+        window_size: local window size.
+        shift_size: window shifting size.
+    """
+
+    use_window_size = list(window_size)
+    if shift_size is not None:
+        use_shift_size = list(shift_size)
+    for i in range(len(x_size)):
+        if x_size[i] <= window_size[i]:
+            use_window_size[i] = x_size[i]
+            if shift_size is not None:
+                use_shift_size[i] = 0
+
+    if shift_size is None:
+        return tuple(use_window_size)
+    else:
+        return tuple(use_window_size), tuple(use_shift_size)
+
+
+class WindowAttention(nn.Module):
+    """
+    Window based multi-head self attention module with relative position bias based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            qkv_bias: add a learnable bias to query, key, value.
+            attn_drop: attention dropout rate.
+            proj_drop: dropout rate of output.
+        """
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+        mesh_args = torch.meshgrid.__kwdefaults__
+
+        if len(self.window_size) == 3:
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(
+                    (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1),
+                    num_heads,
+                )
+            )
+            coords_d = torch.arange(self.window_size[0])
+            coords_h = torch.arange(self.window_size[1])
+            coords_w = torch.arange(self.window_size[2])
+            if mesh_args is not None:
+                coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w, indexing="ij"))
+            else:
+                coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))
+            coords_flatten = torch.flatten(coords, 1)
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+            relative_coords[:, :, 0] += self.window_size[0] - 1
+            relative_coords[:, :, 1] += self.window_size[1] - 1
+            relative_coords[:, :, 2] += self.window_size[2] - 1
+            relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
+            relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
+        elif len(self.window_size) == 2:
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+            )
+            coords_h = torch.arange(self.window_size[0])
+            coords_w = torch.arange(self.window_size[1])
+            if mesh_args is not None:
+                coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"))
+            else:
+                coords = torch.stack(torch.meshgrid(coords_h, coords_w))
+            coords_flatten = torch.flatten(coords, 1)
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+            relative_coords[:, :, 0] += self.window_size[0] - 1
+            relative_coords[:, :, 1] += self.window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+
+        relative_position_index = relative_coords.sum(-1)
+        self.register_buffer("relative_position_index", relative_position_index)
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask):
+        b, n, c = x.shape
+        qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.clone()[:n, :n].reshape(-1)
+        ].reshape(n, n, -1)
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
+        attn = attn + relative_position_bias.unsqueeze(0)
+        if mask is not None:
+            nw = mask.shape[0]
+            attn = attn.view(b // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, n, n)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+        x = (attn @ v).transpose(1, 2).reshape(b, n, c)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """
+    Swin Transformer block based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        shift_size: Sequence[int],
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        act_layer: str = "GELU",
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        use_checkpoint: bool = False,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            shift_size: window shift size.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop: dropout rate.
+            attn_drop: attention dropout rate.
+            drop_path: stochastic depth rate.
+            act_layer: activation layer.
+            norm_layer: normalization layer.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+        """
+
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=self.window_size,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(hidden_size=dim, mlp_dim=mlp_hidden_dim, act=act_layer, dropout_rate=drop, dropout_mode="swin")
+
+    def forward_part1(self, x, mask_matrix):
+        x_shape = x.size()
+        x = self.norm1(x)
+        if len(x_shape) == 5:
+            b, d, h, w, c = x.shape
+            window_size, shift_size = get_window_size((d, h, w), self.window_size, self.shift_size)
+            pad_l = pad_t = pad_d0 = 0
+            pad_d1 = (window_size[0] - d % window_size[0]) % window_size[0]
+            pad_b = (window_size[1] - h % window_size[1]) % window_size[1]
+            pad_r = (window_size[2] - w % window_size[2]) % window_size[2]
+            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
+            _, dp, hp, wp, _ = x.shape
+            dims = [b, dp, hp, wp]
+
+        elif len(x_shape) == 4:
+            b, h, w, c = x.shape
+            window_size, shift_size = get_window_size((h, w), self.window_size, self.shift_size)
+            pad_l = pad_t = 0
+            pad_r = (window_size[0] - h % window_size[0]) % window_size[0]
+            pad_b = (window_size[1] - w % window_size[1]) % window_size[1]
+            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+            _, hp, wp, _ = x.shape
+            dims = [b, hp, wp]
+
+        if any(i > 0 for i in shift_size):
+            if len(x_shape) == 5:
+                shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
+            elif len(x_shape) == 4:
+                shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+        x_windows = window_partition(shifted_x, window_size)
+        attn_windows = self.attn(x_windows, mask=attn_mask)
+        attn_windows = attn_windows.view(-1, *(window_size + (c,)))
+        shifted_x = window_reverse(attn_windows, window_size, dims)
+        if any(i > 0 for i in shift_size):
+            if len(x_shape) == 5:
+                x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
+            elif len(x_shape) == 4:
+                x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if len(x_shape) == 5:
+            if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
+                x = x[:, :d, :h, :w, :].contiguous()
+        elif len(x_shape) == 4:
+            if pad_r > 0 or pad_b > 0:
+                x = x[:, :h, :w, :].contiguous()
+
+        return x
+
+    def forward_part2(self, x):
+        return self.drop_path(self.mlp(self.norm2(x)))
+
+    def load_from(self, weights, n_block, layer):
+        root = f"module.{layer}.0.blocks.{n_block}."
+        block_names = [
+            "norm1.weight",
+            "norm1.bias",
+            "attn.relative_position_bias_table",
+            "attn.relative_position_index",
+            "attn.qkv.weight",
+            "attn.qkv.bias",
+            "attn.proj.weight",
+            "attn.proj.bias",
+            "norm2.weight",
+            "norm2.bias",
+            "mlp.fc1.weight",
+            "mlp.fc1.bias",
+            "mlp.fc2.weight",
+            "mlp.fc2.bias",
+        ]
+        with torch.no_grad():
+            self.norm1.weight.copy_(weights["state_dict"][root + block_names[0]])
+            self.norm1.bias.copy_(weights["state_dict"][root + block_names[1]])
+            self.attn.relative_position_bias_table.copy_(weights["state_dict"][root + block_names[2]])
+            self.attn.relative_position_index.copy_(weights["state_dict"][root + block_names[3]])
+            self.attn.qkv.weight.copy_(weights["state_dict"][root + block_names[4]])
+            self.attn.qkv.bias.copy_(weights["state_dict"][root + block_names[5]])
+            self.attn.proj.weight.copy_(weights["state_dict"][root + block_names[6]])
+            self.attn.proj.bias.copy_(weights["state_dict"][root + block_names[7]])
+            self.norm2.weight.copy_(weights["state_dict"][root + block_names[8]])
+            self.norm2.bias.copy_(weights["state_dict"][root + block_names[9]])
+            self.mlp.linear1.weight.copy_(weights["state_dict"][root + block_names[10]])
+            self.mlp.linear1.bias.copy_(weights["state_dict"][root + block_names[11]])
+            self.mlp.linear2.weight.copy_(weights["state_dict"][root + block_names[12]])
+            self.mlp.linear2.bias.copy_(weights["state_dict"][root + block_names[13]])
+
+    def forward(self, x, mask_matrix):
+        shortcut = x
+        if self.use_checkpoint:
+            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
+        else:
+            x = self.forward_part1(x, mask_matrix)
+        x = shortcut + self.drop_path(x)
+        if self.use_checkpoint:
+            x = x + checkpoint.checkpoint(self.forward_part2, x)
+        else:
+            x = x + self.forward_part2(x)
+        return x
+
+
+class PatchMerging(nn.Module):
+    """
+    Patch merging layer based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self, dim: int, norm_layer: Type[LayerNorm] = nn.LayerNorm, spatial_dims: int = 3
+    ) -> None:  # type: ignore
+        """
+        Args:
+            dim: number of feature channels.
+            norm_layer: normalization layer.
+            spatial_dims: number of spatial dims.
+        """
+
+        super().__init__()
+        self.dim = dim
+        if spatial_dims == 3:
+            self.reduction = nn.Linear(8 * dim, 2 * dim, bias=False)
+            self.norm = norm_layer(8 * dim)
+        elif spatial_dims == 2:
+            self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+            self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+
+        x_shape = x.size()
+        if len(x_shape) == 5:
+            b, d, h, w, c = x_shape
+            pad_input = (h % 2 == 1) or (w % 2 == 1) or (d % 2 == 1)
+            if pad_input:
+                x = F.pad(x, (0, 0, 0, d % 2, 0, w % 2, 0, h % 2))
+            x0 = x[:, 0::2, 0::2, 0::2, :]
+            x1 = x[:, 1::2, 0::2, 0::2, :]
+            x2 = x[:, 0::2, 1::2, 0::2, :]
+            x3 = x[:, 0::2, 0::2, 1::2, :]
+            x4 = x[:, 1::2, 0::2, 1::2, :]
+            x5 = x[:, 0::2, 1::2, 0::2, :]
+            x6 = x[:, 0::2, 0::2, 1::2, :]
+            x7 = x[:, 1::2, 1::2, 1::2, :]
+            x = torch.cat([x0, x1, x2, x3, x4, x5, x6, x7], -1)
+
+        elif len(x_shape) == 4:
+            b, h, w, c = x_shape
+            pad_input = (h % 2 == 1) or (w % 2 == 1)
+            if pad_input:
+                x = F.pad(x, (0, 0, 0, w % 2, 0, h % 2))
+            x0 = x[:, 0::2, 0::2, :]
+            x1 = x[:, 1::2, 0::2, :]
+            x2 = x[:, 0::2, 1::2, :]
+            x3 = x[:, 1::2, 1::2, :]
+            x = torch.cat([x0, x1, x2, x3], -1)
+
+        x = self.norm(x)
+        x = self.reduction(x)
+        return x
+
+
+def compute_mask(dims, window_size, shift_size, device):
+    """Computing region masks based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        dims: dimension values.
+        window_size: local window size.
+        shift_size: shift size.
+        device: device.
+    """
+
+    cnt = 0
+
+    if len(dims) == 3:
+        d, h, w = dims
+        img_mask = torch.zeros((1, d, h, w, 1), device=device)
+        for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
+            for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
+                for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2], None):
+                    img_mask[:, d, h, w, :] = cnt
+                    cnt += 1
+
+    elif len(dims) == 2:
+        h, w = dims
+        img_mask = torch.zeros((1, h, w, 1), device=device)
+        for h in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
+            for w in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+    mask_windows = window_partition(img_mask, window_size)
+    mask_windows = mask_windows.squeeze(-1)
+    attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+    return attn_mask
+
+
+class BasicLayer(nn.Module):
+    """
+    Basic Swin Transformer layer in one stage based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        drop_path: list,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        downsample: isinstance = None,  # type: ignore
+        use_checkpoint: bool = False,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            depths: number of layers in each stage.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            drop_path: stochastic depth rate.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop: dropout rate.
+            attn_drop: attention dropout rate.
+            norm_layer: normalization layer.
+            downsample: downsample layer at the end of the layer.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+        """
+
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = tuple(i // 2 for i in window_size)
+        self.no_shift = tuple(0 for i in window_size)
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=self.window_size,
+                    shift_size=self.no_shift if (i % 2 == 0) else self.shift_size,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                    use_checkpoint=use_checkpoint,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.downsample = downsample
+        if self.downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer, spatial_dims=len(self.window_size))
+
+    def forward(self, x):
+        x_shape = x.size()
+        if len(x_shape) == 5:
+            b, c, d, h, w = x_shape
+            window_size, shift_size = get_window_size((d, h, w), self.window_size, self.shift_size)
+            x = rearrange(x, "b c d h w -> b d h w c")
+            dp = int(np.ceil(d / window_size[0])) * window_size[0]
+            hp = int(np.ceil(h / window_size[1])) * window_size[1]
+            wp = int(np.ceil(w / window_size[2])) * window_size[2]
+            attn_mask = compute_mask([dp, hp, wp], window_size, shift_size, x.device)
+            for blk in self.blocks:
+                x = blk(x, attn_mask)
+            x = x.view(b, d, h, w, -1)
+            if self.downsample is not None:
+                x = self.downsample(x)
+            x = rearrange(x, "b d h w c -> b c d h w")
+
+        elif len(x_shape) == 4:
+            b, c, h, w = x_shape
+            window_size, shift_size = get_window_size((h, w), self.window_size, self.shift_size)
+            x = rearrange(x, "b c h w -> b h w c")
+            hp = int(np.ceil(h / window_size[0])) * window_size[0]
+            wp = int(np.ceil(w / window_size[1])) * window_size[1]
+            attn_mask = compute_mask([hp, wp], window_size, shift_size, x.device)
+            for blk in self.blocks:
+                x = blk(x, attn_mask)
+            x = x.view(b, h, w, -1)
+            if self.downsample is not None:
+                x = self.downsample(x)
+            x = rearrange(x, "b h w c -> b c h w")
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """
+    Swin Transformer based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        in_chans: int,
+        embed_dim: int,
+        window_size: Sequence[int],
+        patch_size: Sequence[int],
+        depths: Sequence[int],
+        num_heads: Sequence[int],
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        patch_norm: bool = False,
+        use_checkpoint: bool = False,
+        spatial_dims: int = 3,
+    ) -> None:
+        """
+        Args:
+            in_chans: dimension of input channels.
+            embed_dim: number of linear projection output channels.
+            window_size: local window size.
+            patch_size: patch size.
+            depths: number of layers in each stage.
+            num_heads: number of attention heads.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop_rate: dropout rate.
+            attn_drop_rate: attention dropout rate.
+            drop_path_rate: stochastic depth rate.
+            norm_layer: normalization layer.
+            patch_norm: add normalization after patch embedding.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+            spatial_dims: spatial dimension.
+        """
+
+        super().__init__()
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.window_size = window_size
+        self.patch_size = patch_size
+        self.patch_embed = PatchEmbed(
+            patch_size=self.patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,  # type: ignore
+            spatial_dims=spatial_dims,
+        )
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        # self.layers1 = nn.ModuleList()
+        # self.layers2 = nn.ModuleList()
+        # self.layers3 = nn.ModuleList()
+        # self.layers4 = nn.ModuleList()
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=self.window_size,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                norm_layer=norm_layer,
+                downsample=PatchMerging,
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+            # if i_layer == 0:
+            #     self.layers1.append(layer)
+            # elif i_layer == 1:
+            #     self.layers2.append(layer)
+            # elif i_layer == 2:
+            #     self.layers3.append(layer)
+            # elif i_layer == 3:
+            #     self.layers4.append(layer)
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+
+    def proj_out(self, x, normalize=False):
+        if normalize:
+            x_shape = x.size()
+            if len(x_shape) == 5:
+                n, ch, d, h, w = x_shape
+                x = rearrange(x, "n c d h w -> n d h w c")
+                x = F.layer_norm(x, [ch])
+                x = rearrange(x, "n d h w c -> n c d h w")
+            elif len(x_shape) == 4:
+                n, ch, h, w = x_shape
+                x = rearrange(x, "n c h w -> n h w c")
+                x = F.layer_norm(x, [ch])
+                x = rearrange(x, "n h w c -> n c h w")
+        return x
+
+    def forward(self, x, normalize=True):
+        # x input: [B*sample, C(1), H, W, D]
+        # x = rearrange(x, "b c h w d -> b c d h w")
+        # print('>> input: ', x.shape)
+        x = self.patch_embed(x)
+        # print('>> patch_embed: ', x.shape)
+        x = self.pos_drop(x)
+        for layer in self.layers:
+            x = layer(x.contiguous())
+            # print('>> layer: ', x.shape)
+        return x
+        # # x0_out = self.proj_out(x0, normalize)
+        # x1 = self.layers1[0](x0.contiguous())
+        # # x1_out = self.proj_out(x1, normalize)
+        # x2 = self.layers2[0](x1.contiguous())
+        # # x2_out = self.proj_out(x2, normalize)
+        # x3 = self.layers3[0](x2.contiguous())
+        # # x3_out = self.proj_out(x3, normalize)
+        # x4 = self.layers4[0](x3.contiguous())
+        # # x4_out = self.proj_out(x4, normalize)
+        # # return [x0_out, x1_out, x2_out, x3_out, x4_out]
+        
+    
\ No newline at end of file
diff --git a/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/prompt_encoder-checkpoint.py b/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/prompt_encoder-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc95b970ea18e427835a592046a3daf4fe2be567
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/.ipynb_checkpoints/prompt_encoder-checkpoint.py
@@ -0,0 +1,232 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch import nn
+
+from typing import Any, Optional, Tuple, Type
+
+from .common import LayerNorm2d
+import os
+
+class PromptEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int, int],
+        input_image_size: Tuple[int, int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1], 4 * image_embedding_size[2])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 3), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 3)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        mask_embedding = self.mask_downscaling(masks)
+        return mask_embedding
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+        text_embedding: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        elif text_embedding is not None:
+            return text_embedding.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+        text_embedding: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+          text: test prompt (B, 768)
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        # print('prompt encoder here...')
+        
+        bs = self._get_batch_size(points, boxes, masks, text_embedding)
+        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        # print('sparse_embeddings ', sparse_embeddings.shape)
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+        
+        if text_embedding is not None:
+            sparse_embeddings = torch.cat([sparse_embeddings, text_embedding.unsqueeze(dim=1)], dim=1)
+        
+        # print('box_embeddings ', box_embeddings.shape)
+        # print('sparse_embeddings after box/point/text', sparse_embeddings.shape)
+        
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1, 1).expand(
+                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1], self.image_embedding_size[2]
+            )
+        # print('dense_embeddings ', dense_embeddings.shape)
+        return sparse_embeddings, dense_embeddings
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            "positional_encoding_gaussian_matrix",
+            scale * torch.randn((3, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w, d = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w, d), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        z_embed = grid.cumsum(dim=2) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+        z_embed = z_embed / d
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
+        return pe.permute(3, 0, 1, 2)  # C x H x W x D
+
+    def forward_with_coords(
+        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        coords[:, :, 2] = coords[:, :, 2] / image_size[2]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C
diff --git a/model/segment_anything_volumetric/modeling/__init__.py b/model/segment_anything_volumetric/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..38e906243d898d7fc071c0fe218338c5cace3ea1
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .sam import Sam
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+from .transformer import TwoWayTransformer
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diff --git a/model/segment_anything_volumetric/modeling/common.py b/model/segment_anything_volumetric/modeling/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bf15236a3eb24d8526073bc4fa2b274cccb3f96
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/common.py
@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from typing import Type
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
diff --git a/model/segment_anything_volumetric/modeling/image_encoder.py b/model/segment_anything_volumetric/modeling/image_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e8436b55225c7b406849d4b2a69860998939ef4
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/image_encoder.py
@@ -0,0 +1,404 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Type
+
+from .common import LayerNorm2d, MLPBlock
+
+from monai.networks.blocks import PatchEmbed
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 1,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        # self.patch_embed = PatchEmbed(
+        #     kernel_size=(patch_size, patch_size),
+        #     stride=(patch_size, patch_size),
+        #     in_chans=in_chans,
+        #     embed_dim=embed_dim,
+        # )
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            spatial_dims=3,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        print('patch embedded shape: ', x.shape)    # embedded: [8, 768, 6, 6, 6]
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+# class PatchEmbed(nn.Module):
+#     """
+#     Image to Patch Embedding.
+#     """
+
+#     def __init__(
+#         self,
+#         kernel_size: Tuple[int, int] = (16, 16),
+#         stride: Tuple[int, int] = (16, 16),
+#         padding: Tuple[int, int] = (0, 0),
+#         in_chans: int = 3,
+#         embed_dim: int = 768,
+#     ) -> None:
+#         """
+#         Args:
+#             kernel_size (Tuple): kernel size of the projection layer.
+#             stride (Tuple): stride of the projection layer.
+#             padding (Tuple): padding size of the projection layer.
+#             in_chans (int): Number of input image channels.
+#             embed_dim (int): Patch embedding dimension.
+#         """
+#         super().__init__()
+
+#         self.proj = nn.Conv2d(
+#             in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+#         )
+
+#     def forward(self, x: torch.Tensor) -> torch.Tensor:
+#         x = self.proj(x)
+#         # B C H W -> B H W C
+#         x = x.permute(0, 2, 3, 1)
+#         return x
diff --git a/model/segment_anything_volumetric/modeling/image_encoder_swin.py b/model/segment_anything_volumetric/modeling/image_encoder_swin.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0df343e823f87c2914b3940a60003e839a1d580
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/image_encoder_swin.py
@@ -0,0 +1,709 @@
+from typing import Sequence, Tuple, Type, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch.nn import LayerNorm
+
+from monai.networks.blocks import MLPBlock as Mlp
+from monai.networks.blocks import PatchEmbed, UnetOutBlock, UnetrBasicBlock, UnetrUpBlock
+from monai.networks.layers import DropPath, trunc_normal_
+from monai.utils import ensure_tuple_rep, optional_import
+
+rearrange, _ = optional_import("einops", name="rearrange")
+
+def window_partition(x, window_size):
+    """window partition operation based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        x: input tensor.
+        window_size: local window size.
+    """
+    x_shape = x.size()
+    if len(x_shape) == 5:
+        b, d, h, w, c = x_shape
+        x = x.view(
+            b,
+            d // window_size[0],
+            window_size[0],
+            h // window_size[1],
+            window_size[1],
+            w // window_size[2],
+            window_size[2],
+            c,
+        )
+        windows = (
+            x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, window_size[0] * window_size[1] * window_size[2], c)
+        )
+    elif len(x_shape) == 4:
+        b, h, w, c = x.shape
+        x = x.view(b, h // window_size[0], window_size[0], w // window_size[1], window_size[1], c)
+        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0] * window_size[1], c)
+    return windows
+
+
+def window_reverse(windows, window_size, dims):
+    """window reverse operation based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        windows: windows tensor.
+        window_size: local window size.
+        dims: dimension values.
+    """
+    if len(dims) == 4:
+        b, d, h, w = dims
+        x = windows.view(
+            b,
+            d // window_size[0],
+            h // window_size[1],
+            w // window_size[2],
+            window_size[0],
+            window_size[1],
+            window_size[2],
+            -1,
+        )
+        x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(b, d, h, w, -1)
+
+    elif len(dims) == 3:
+        b, h, w = dims
+        x = windows.view(b, h // window_size[0], w // window_size[0], window_size[0], window_size[1], -1)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
+    return x
+
+
+def get_window_size(x_size, window_size, shift_size=None):
+    """Computing window size based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        x_size: input size.
+        window_size: local window size.
+        shift_size: window shifting size.
+    """
+
+    use_window_size = list(window_size)
+    if shift_size is not None:
+        use_shift_size = list(shift_size)
+    for i in range(len(x_size)):
+        if x_size[i] <= window_size[i]:
+            use_window_size[i] = x_size[i]
+            if shift_size is not None:
+                use_shift_size[i] = 0
+
+    if shift_size is None:
+        return tuple(use_window_size)
+    else:
+        return tuple(use_window_size), tuple(use_shift_size)
+
+
+class WindowAttention(nn.Module):
+    """
+    Window based multi-head self attention module with relative position bias based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            qkv_bias: add a learnable bias to query, key, value.
+            attn_drop: attention dropout rate.
+            proj_drop: dropout rate of output.
+        """
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+        mesh_args = torch.meshgrid.__kwdefaults__
+
+        if len(self.window_size) == 3:
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(
+                    (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1),
+                    num_heads,
+                )
+            )
+            coords_d = torch.arange(self.window_size[0])
+            coords_h = torch.arange(self.window_size[1])
+            coords_w = torch.arange(self.window_size[2])
+            if mesh_args is not None:
+                coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w, indexing="ij"))
+            else:
+                coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))
+            coords_flatten = torch.flatten(coords, 1)
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+            relative_coords[:, :, 0] += self.window_size[0] - 1
+            relative_coords[:, :, 1] += self.window_size[1] - 1
+            relative_coords[:, :, 2] += self.window_size[2] - 1
+            relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
+            relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
+        elif len(self.window_size) == 2:
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+            )
+            coords_h = torch.arange(self.window_size[0])
+            coords_w = torch.arange(self.window_size[1])
+            if mesh_args is not None:
+                coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"))
+            else:
+                coords = torch.stack(torch.meshgrid(coords_h, coords_w))
+            coords_flatten = torch.flatten(coords, 1)
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+            relative_coords[:, :, 0] += self.window_size[0] - 1
+            relative_coords[:, :, 1] += self.window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+
+        relative_position_index = relative_coords.sum(-1)
+        self.register_buffer("relative_position_index", relative_position_index)
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask):
+        b, n, c = x.shape
+        qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.clone()[:n, :n].reshape(-1)
+        ].reshape(n, n, -1)
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
+        attn = attn + relative_position_bias.unsqueeze(0)
+        if mask is not None:
+            nw = mask.shape[0]
+            attn = attn.view(b // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, n, n)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+        x = (attn @ v).transpose(1, 2).reshape(b, n, c)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """
+    Swin Transformer block based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        shift_size: Sequence[int],
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        act_layer: str = "GELU",
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        use_checkpoint: bool = False,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            shift_size: window shift size.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop: dropout rate.
+            attn_drop: attention dropout rate.
+            drop_path: stochastic depth rate.
+            act_layer: activation layer.
+            norm_layer: normalization layer.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+        """
+
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=self.window_size,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(hidden_size=dim, mlp_dim=mlp_hidden_dim, act=act_layer, dropout_rate=drop, dropout_mode="swin")
+
+    def forward_part1(self, x, mask_matrix):
+        x_shape = x.size()
+        x = self.norm1(x)
+        if len(x_shape) == 5:
+            b, d, h, w, c = x.shape
+            window_size, shift_size = get_window_size((d, h, w), self.window_size, self.shift_size)
+            pad_l = pad_t = pad_d0 = 0
+            pad_d1 = (window_size[0] - d % window_size[0]) % window_size[0]
+            pad_b = (window_size[1] - h % window_size[1]) % window_size[1]
+            pad_r = (window_size[2] - w % window_size[2]) % window_size[2]
+            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
+            _, dp, hp, wp, _ = x.shape
+            dims = [b, dp, hp, wp]
+
+        elif len(x_shape) == 4:
+            b, h, w, c = x.shape
+            window_size, shift_size = get_window_size((h, w), self.window_size, self.shift_size)
+            pad_l = pad_t = 0
+            pad_r = (window_size[0] - h % window_size[0]) % window_size[0]
+            pad_b = (window_size[1] - w % window_size[1]) % window_size[1]
+            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+            _, hp, wp, _ = x.shape
+            dims = [b, hp, wp]
+
+        if any(i > 0 for i in shift_size):
+            if len(x_shape) == 5:
+                shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
+            elif len(x_shape) == 4:
+                shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+        x_windows = window_partition(shifted_x, window_size)
+        attn_windows = self.attn(x_windows, mask=attn_mask)
+        attn_windows = attn_windows.view(-1, *(window_size + (c,)))
+        shifted_x = window_reverse(attn_windows, window_size, dims)
+        if any(i > 0 for i in shift_size):
+            if len(x_shape) == 5:
+                x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
+            elif len(x_shape) == 4:
+                x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if len(x_shape) == 5:
+            if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
+                x = x[:, :d, :h, :w, :].contiguous()
+        elif len(x_shape) == 4:
+            if pad_r > 0 or pad_b > 0:
+                x = x[:, :h, :w, :].contiguous()
+
+        return x
+
+    def forward_part2(self, x):
+        return self.drop_path(self.mlp(self.norm2(x)))
+
+    def load_from(self, weights, n_block, layer):
+        root = f"module.{layer}.0.blocks.{n_block}."
+        block_names = [
+            "norm1.weight",
+            "norm1.bias",
+            "attn.relative_position_bias_table",
+            "attn.relative_position_index",
+            "attn.qkv.weight",
+            "attn.qkv.bias",
+            "attn.proj.weight",
+            "attn.proj.bias",
+            "norm2.weight",
+            "norm2.bias",
+            "mlp.fc1.weight",
+            "mlp.fc1.bias",
+            "mlp.fc2.weight",
+            "mlp.fc2.bias",
+        ]
+        with torch.no_grad():
+            self.norm1.weight.copy_(weights["state_dict"][root + block_names[0]])
+            self.norm1.bias.copy_(weights["state_dict"][root + block_names[1]])
+            self.attn.relative_position_bias_table.copy_(weights["state_dict"][root + block_names[2]])
+            self.attn.relative_position_index.copy_(weights["state_dict"][root + block_names[3]])
+            self.attn.qkv.weight.copy_(weights["state_dict"][root + block_names[4]])
+            self.attn.qkv.bias.copy_(weights["state_dict"][root + block_names[5]])
+            self.attn.proj.weight.copy_(weights["state_dict"][root + block_names[6]])
+            self.attn.proj.bias.copy_(weights["state_dict"][root + block_names[7]])
+            self.norm2.weight.copy_(weights["state_dict"][root + block_names[8]])
+            self.norm2.bias.copy_(weights["state_dict"][root + block_names[9]])
+            self.mlp.linear1.weight.copy_(weights["state_dict"][root + block_names[10]])
+            self.mlp.linear1.bias.copy_(weights["state_dict"][root + block_names[11]])
+            self.mlp.linear2.weight.copy_(weights["state_dict"][root + block_names[12]])
+            self.mlp.linear2.bias.copy_(weights["state_dict"][root + block_names[13]])
+
+    def forward(self, x, mask_matrix):
+        shortcut = x
+        if self.use_checkpoint:
+            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
+        else:
+            x = self.forward_part1(x, mask_matrix)
+        x = shortcut + self.drop_path(x)
+        if self.use_checkpoint:
+            x = x + checkpoint.checkpoint(self.forward_part2, x)
+        else:
+            x = x + self.forward_part2(x)
+        return x
+
+
+class PatchMerging(nn.Module):
+    """
+    Patch merging layer based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self, dim: int, norm_layer: Type[LayerNorm] = nn.LayerNorm, spatial_dims: int = 3
+    ) -> None:  # type: ignore
+        """
+        Args:
+            dim: number of feature channels.
+            norm_layer: normalization layer.
+            spatial_dims: number of spatial dims.
+        """
+
+        super().__init__()
+        self.dim = dim
+        if spatial_dims == 3:
+            self.reduction = nn.Linear(8 * dim, 2 * dim, bias=False)
+            self.norm = norm_layer(8 * dim)
+        elif spatial_dims == 2:
+            self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+            self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+
+        x_shape = x.size()
+        if len(x_shape) == 5:
+            b, d, h, w, c = x_shape
+            pad_input = (h % 2 == 1) or (w % 2 == 1) or (d % 2 == 1)
+            if pad_input:
+                x = F.pad(x, (0, 0, 0, d % 2, 0, w % 2, 0, h % 2))
+            x0 = x[:, 0::2, 0::2, 0::2, :]
+            x1 = x[:, 1::2, 0::2, 0::2, :]
+            x2 = x[:, 0::2, 1::2, 0::2, :]
+            x3 = x[:, 0::2, 0::2, 1::2, :]
+            x4 = x[:, 1::2, 0::2, 1::2, :]
+            x5 = x[:, 0::2, 1::2, 0::2, :]
+            x6 = x[:, 0::2, 0::2, 1::2, :]
+            x7 = x[:, 1::2, 1::2, 1::2, :]
+            x = torch.cat([x0, x1, x2, x3, x4, x5, x6, x7], -1)
+
+        elif len(x_shape) == 4:
+            b, h, w, c = x_shape
+            pad_input = (h % 2 == 1) or (w % 2 == 1)
+            if pad_input:
+                x = F.pad(x, (0, 0, 0, w % 2, 0, h % 2))
+            x0 = x[:, 0::2, 0::2, :]
+            x1 = x[:, 1::2, 0::2, :]
+            x2 = x[:, 0::2, 1::2, :]
+            x3 = x[:, 1::2, 1::2, :]
+            x = torch.cat([x0, x1, x2, x3], -1)
+
+        x = self.norm(x)
+        x = self.reduction(x)
+        return x
+
+
+def compute_mask(dims, window_size, shift_size, device):
+    """Computing region masks based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+     Args:
+        dims: dimension values.
+        window_size: local window size.
+        shift_size: shift size.
+        device: device.
+    """
+
+    cnt = 0
+
+    if len(dims) == 3:
+        d, h, w = dims
+        img_mask = torch.zeros((1, d, h, w, 1), device=device)
+        for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
+            for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
+                for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2], None):
+                    img_mask[:, d, h, w, :] = cnt
+                    cnt += 1
+
+    elif len(dims) == 2:
+        h, w = dims
+        img_mask = torch.zeros((1, h, w, 1), device=device)
+        for h in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
+            for w in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+    mask_windows = window_partition(img_mask, window_size)
+    mask_windows = mask_windows.squeeze(-1)
+    attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+    return attn_mask
+
+
+class BasicLayer(nn.Module):
+    """
+    Basic Swin Transformer layer in one stage based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        num_heads: int,
+        window_size: Sequence[int],
+        drop_path: list,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        downsample: isinstance = None,  # type: ignore
+        use_checkpoint: bool = False,
+    ) -> None:
+        """
+        Args:
+            dim: number of feature channels.
+            depths: number of layers in each stage.
+            num_heads: number of attention heads.
+            window_size: local window size.
+            drop_path: stochastic depth rate.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop: dropout rate.
+            attn_drop: attention dropout rate.
+            norm_layer: normalization layer.
+            downsample: downsample layer at the end of the layer.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+        """
+
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = tuple(i // 2 for i in window_size)
+        self.no_shift = tuple(0 for i in window_size)
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=self.window_size,
+                    shift_size=self.no_shift if (i % 2 == 0) else self.shift_size,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                    use_checkpoint=use_checkpoint,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.downsample = downsample
+        if self.downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer, spatial_dims=len(self.window_size))
+
+    def forward(self, x):
+        x_shape = x.size()
+        if len(x_shape) == 5:
+            b, c, d, h, w = x_shape
+            window_size, shift_size = get_window_size((d, h, w), self.window_size, self.shift_size)
+            x = rearrange(x, "b c d h w -> b d h w c")
+            dp = int(np.ceil(d / window_size[0])) * window_size[0]
+            hp = int(np.ceil(h / window_size[1])) * window_size[1]
+            wp = int(np.ceil(w / window_size[2])) * window_size[2]
+            attn_mask = compute_mask([dp, hp, wp], window_size, shift_size, x.device)
+            for blk in self.blocks:
+                x = blk(x, attn_mask)
+            x = x.view(b, d, h, w, -1)
+            if self.downsample is not None:
+                x = self.downsample(x)
+            x = rearrange(x, "b d h w c -> b c d h w")
+
+        elif len(x_shape) == 4:
+            b, c, h, w = x_shape
+            window_size, shift_size = get_window_size((h, w), self.window_size, self.shift_size)
+            x = rearrange(x, "b c h w -> b h w c")
+            hp = int(np.ceil(h / window_size[0])) * window_size[0]
+            wp = int(np.ceil(w / window_size[1])) * window_size[1]
+            attn_mask = compute_mask([hp, wp], window_size, shift_size, x.device)
+            for blk in self.blocks:
+                x = blk(x, attn_mask)
+            x = x.view(b, h, w, -1)
+            if self.downsample is not None:
+                x = self.downsample(x)
+            x = rearrange(x, "b h w c -> b c h w")
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """
+    Swin Transformer based on: "Liu et al.,
+    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
+    <https://arxiv.org/abs/2103.14030>"
+    https://github.com/microsoft/Swin-Transformer
+    """
+
+    def __init__(
+        self,
+        in_chans: int,
+        embed_dim: int,
+        window_size: Sequence[int],
+        patch_size: Sequence[int],
+        depths: Sequence[int],
+        num_heads: Sequence[int],
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        norm_layer: Type[LayerNorm] = nn.LayerNorm,  # type: ignore
+        patch_norm: bool = False,
+        use_checkpoint: bool = False,
+        spatial_dims: int = 3,
+    ) -> None:
+        """
+        Args:
+            in_chans: dimension of input channels.
+            embed_dim: number of linear projection output channels.
+            window_size: local window size.
+            patch_size: patch size.
+            depths: number of layers in each stage.
+            num_heads: number of attention heads.
+            mlp_ratio: ratio of mlp hidden dim to embedding dim.
+            qkv_bias: add a learnable bias to query, key, value.
+            drop_rate: dropout rate.
+            attn_drop_rate: attention dropout rate.
+            drop_path_rate: stochastic depth rate.
+            norm_layer: normalization layer.
+            patch_norm: add normalization after patch embedding.
+            use_checkpoint: use gradient checkpointing for reduced memory usage.
+            spatial_dims: spatial dimension.
+        """
+
+        super().__init__()
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.window_size = window_size
+        self.patch_size = patch_size
+        self.patch_embed = PatchEmbed(
+            patch_size=self.patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,  # type: ignore
+            spatial_dims=spatial_dims,
+        )
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        # self.layers1 = nn.ModuleList()
+        # self.layers2 = nn.ModuleList()
+        # self.layers3 = nn.ModuleList()
+        # self.layers4 = nn.ModuleList()
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2**i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=self.window_size,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                norm_layer=norm_layer,
+                downsample=PatchMerging,
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+            # if i_layer == 0:
+            #     self.layers1.append(layer)
+            # elif i_layer == 1:
+            #     self.layers2.append(layer)
+            # elif i_layer == 2:
+            #     self.layers3.append(layer)
+            # elif i_layer == 3:
+            #     self.layers4.append(layer)
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+
+    def proj_out(self, x, normalize=False):
+        if normalize:
+            x_shape = x.size()
+            if len(x_shape) == 5:
+                n, ch, d, h, w = x_shape
+                x = rearrange(x, "n c d h w -> n d h w c")
+                x = F.layer_norm(x, [ch])
+                x = rearrange(x, "n d h w c -> n c d h w")
+            elif len(x_shape) == 4:
+                n, ch, h, w = x_shape
+                x = rearrange(x, "n c h w -> n h w c")
+                x = F.layer_norm(x, [ch])
+                x = rearrange(x, "n h w c -> n c h w")
+        return x
+
+    def forward(self, x, normalize=True):
+        # x input: [B*sample, C(1), H, W, D]
+        # x = rearrange(x, "b c h w d -> b c d h w")
+        # print('>> input: ', x.shape)
+        x = self.patch_embed(x)
+        # print('>> patch_embed: ', x.shape)
+        x = self.pos_drop(x)
+        for layer in self.layers:
+            x = layer(x.contiguous())
+            # print('>> layer: ', x.shape)
+        return x
+        # # x0_out = self.proj_out(x0, normalize)
+        # x1 = self.layers1[0](x0.contiguous())
+        # # x1_out = self.proj_out(x1, normalize)
+        # x2 = self.layers2[0](x1.contiguous())
+        # # x2_out = self.proj_out(x2, normalize)
+        # x3 = self.layers3[0](x2.contiguous())
+        # # x3_out = self.proj_out(x3, normalize)
+        # x4 = self.layers4[0](x3.contiguous())
+        # # x4_out = self.proj_out(x4, normalize)
+        # # return [x0_out, x1_out, x2_out, x3_out, x4_out]
+        
+    
\ No newline at end of file
diff --git a/model/segment_anything_volumetric/modeling/mask_decoder.py b/model/segment_anything_volumetric/modeling/mask_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9fc60905f1c51a83aff51a90df4fc4bf5d644d1
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/mask_decoder.py
@@ -0,0 +1,217 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import List, Tuple, Type, Optional
+
+from .common import LayerNorm2d
+
+class MaskDecoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        image_encoder_type: str,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: Type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+        image_size,
+        patch_size,
+    ) -> None:
+        """
+        Predicts masks given an image and prompt embeddings, using a
+        transformer architecture.
+
+        Arguments:
+          transformer_dim (int): the channel dimension of the transformer
+          transformer (nn.Module): the transformer used to predict masks
+          num_multimask_outputs (int): the number of masks to predict
+            when disambiguating masks
+          activation (nn.Module): the type of activation to use when
+            upscaling masks
+          iou_head_depth (int): the depth of the MLP used to predict
+            mask quality
+          iou_head_hidden_dim (int): the hidden dimension of the MLP
+            used to predict mask quality
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        if image_encoder_type == 'swin_vit':
+            self.feat_shape = image_size/patch_size
+            self.output_upscaling = nn.Sequential(
+                nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+                nn.LayerNorm((transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))),    # swin
+                activation(),
+                nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),        # swin
+                # nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),    # vit
+                activation(),
+            )
+        else:
+            self.feat_shape = image_size/patch_size * 2
+            self.output_upscaling = nn.Sequential(
+                nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+                nn.LayerNorm((transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))), # vit
+                activation(),
+                nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+                # nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),
+                activation(),
+            )
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [
+                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
+                for i in range(self.num_mask_tokens)
+            ]
+        )
+
+        self.iou_prediction_head = MLP(
+            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
+        )
+
+        self.txt_align_upscaled_embedding = nn.Linear(768, 96)
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        text_embedding: Optional[torch.Tensor],
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Arguments:
+          image_embeddings (torch.Tensor): the embeddings from the image encoder
+          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+          multimask_output (bool): Whether to return multiple masks or a single
+            mask.
+
+        Returns:
+          torch.Tensor: batched predicted masks
+          torch.Tensor: batched predictions of mask quality
+        """
+        # print('--------------decoder here--------------')
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            text_embedding=text_embedding,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for output
+        if multimask_output:
+            mask_slice = slice(1, None)
+        else:
+            mask_slice = slice(0, 1)
+        masks = masks[:, mask_slice, :, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        # Prepare output
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        text_embedding: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Predicts masks. See 'forward' for more details."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)    # [2, 7=(5+2), 256]
+        # Expand per-image data in batch direction to be per-mask
+        if image_embeddings.shape[0] != tokens.shape[0]:
+            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        else:
+            src = image_embeddings
+        src = src + dense_prompt_embeddings
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w, d = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, 0, :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w, d)
+        # print('src ', src.shape) # vit:[B, 768, 12, 12, 6], swin: [B, 6, 6, 3]
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: List[torch.Tensor] = []
+        for i in range(self.num_mask_tokens):
+            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w, d = upscaled_embedding.shape
+        # print('hyper_in ', hyper_in.shape)    # [2, 4, 96]
+        # print('upscaled_embedding ', upscaled_embedding.shape)    # [2, 96, 24, 24, 12]*
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w * d)).view(b, -1, h, w, d)
+        # print('masks here ', masks.shape) # [2, 4, 24, 24, 12]
+        
+        if text_embedding is not None:
+            # text_embedding: B x 768, upscaled_embedding: B x c x h x w x d => B x 1 x h x w x d
+            text_embedding_down = self.txt_align_upscaled_embedding(text_embedding).unsqueeze(dim=1)
+            upscaled_embedding = upscaled_embedding.view(b, c, h * w * d)
+            # print('text_embedding_down ', text_embedding_down.shape)  # [2, 1, 96]
+            # text_embedding_norm = F.normalize(text_embedding_down, dim=-1)
+            # upscaled_embedding_norm = F.normalize(upscaled_embedding, dim=1)
+            # sim = (text_embedding_norm @ upscaled_embedding_norm).view(b, -1, h, w, d)
+            # print(text_embedding_down.shape, upscaled_embedding.shape)
+            sim = (text_embedding_down @ upscaled_embedding).view(b, -1, h, w, d)
+            # print('sim ', sim.shape)  # [B, 1, 24, 24, 12]
+            sim = sim.repeat(1, masks.shape[1], 1, 1, 1)
+            # print('sim after', sim.shape) # [B, 4, 24, 24, 12]
+            masks = masks + sim
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+
+        return masks, iou_pred
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x
diff --git a/model/segment_anything_volumetric/modeling/prompt_encoder.py b/model/segment_anything_volumetric/modeling/prompt_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0acdfe8af845923b7ecf2a3f93e8f1b60a94f13
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/prompt_encoder.py
@@ -0,0 +1,232 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch import nn
+
+from typing import Any, Optional, Tuple, Type
+
+from .common import LayerNorm2d
+import os
+
+class PromptEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int, int],
+        input_image_size: Tuple[int, int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1], 4 * image_embedding_size[2])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 3), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 3)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        mask_embedding = self.mask_downscaling(masks)
+        return mask_embedding
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+        text_embedding: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        elif text_embedding is not None:
+            return text_embedding.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+        text_embedding: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+          text: test prompt (B, 768)
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        # print('prompt encoder here...')
+        
+        bs = self._get_batch_size(points, boxes, masks, text_embedding)
+        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        # print('sparse_embeddings ', sparse_embeddings.shape)
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+        
+        if text_embedding is not None:
+            sparse_embeddings = torch.cat([sparse_embeddings, text_embedding.unsqueeze(dim=1)], dim=1)
+        
+        # print('box_embeddings ', box_embeddings.shape)
+        # print('sparse_embeddings after box/point/text', sparse_embeddings.shape)
+        
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1, 1).expand(
+                bs, -1, int(self.image_embedding_size[0]), int(self.image_embedding_size[1]), int(self.image_embedding_size[2])
+            )
+        # print('dense_embeddings ', dense_embeddings.shape)
+        return sparse_embeddings, dense_embeddings
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            "positional_encoding_gaussian_matrix",
+            scale * torch.randn((3, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w, d = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w, d), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        z_embed = grid.cumsum(dim=2) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+        z_embed = z_embed / d
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
+        return pe.permute(3, 0, 1, 2)  # C x H x W x D
+
+    def forward_with_coords(
+        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        coords[:, :, 2] = coords[:, :, 2] / image_size[2]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C
diff --git a/model/segment_anything_volumetric/modeling/sam.py b/model/segment_anything_volumetric/modeling/sam.py
new file mode 100644
index 0000000000000000000000000000000000000000..acdc4e537f2332c490087c3775a0b9d3f85aa927
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/sam.py
@@ -0,0 +1,175 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import Any, Dict, List, Tuple
+
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+
+
+class Sam(nn.Module):
+    mask_threshold: float = 0.0
+    image_format: str = "RGB"
+
+    def __init__(
+        self,
+        image_encoder: ImageEncoderViT,
+        prompt_encoder: PromptEncoder,
+        mask_decoder: MaskDecoder,
+        pixel_mean: List[float] = [123.675, 116.28, 103.53],
+        pixel_std: List[float] = [58.395, 57.12, 57.375],
+    ) -> None:
+        """
+        SAM predicts object masks from an image and input prompts.
+
+        Arguments:
+          image_encoder (ImageEncoderViT): The backbone used to encode the
+            image into image embeddings that allow for efficient mask prediction.
+          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
+          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
+            and encoded prompts.
+          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
+          pixel_std (list(float)): Std values for normalizing pixels in the input image.
+        """
+        super().__init__()
+        self.image_encoder = image_encoder
+        self.prompt_encoder = prompt_encoder
+        self.mask_decoder = mask_decoder
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+    @property
+    def device(self) -> Any:
+        return self.pixel_mean.device
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batched_input: List[Dict[str, Any]],
+        multimask_output: bool,
+    ) -> List[Dict[str, torch.Tensor]]:
+        """
+        Predicts masks end-to-end from provided images and prompts.
+        If prompts are not known in advance, using SamPredictor is
+        recommended over calling the model directly.
+
+        Arguments:
+          batched_input (list(dict)): A list over input images, each a
+            dictionary with the following keys. A prompt key can be
+            excluded if it is not present.
+              'image': The image as a torch tensor in 3xHxW format,
+                already transformed for input to the model.
+              'original_size': (tuple(int, int)) The original size of
+                the image before transformation, as (H, W).
+              'point_coords': (torch.Tensor) Batched point prompts for
+                this image, with shape BxNx2. Already transformed to the
+                input frame of the model.
+              'point_labels': (torch.Tensor) Batched labels for point prompts,
+                with shape BxN.
+              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
+                Already transformed to the input frame of the model.
+              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
+                in the form Bx1xHxW.
+          multimask_output (bool): Whether the model should predict multiple
+            disambiguating masks, or return a single mask.
+
+        Returns:
+          (list(dict)): A list over input images, where each element is
+            as dictionary with the following keys.
+              'masks': (torch.Tensor) Batched binary mask predictions,
+                with shape BxCxHxW, where B is the number of input prompts,
+                C is determined by multimask_output, and (H, W) is the
+                original size of the image.
+              'iou_predictions': (torch.Tensor) The model's predictions
+                of mask quality, in shape BxC.
+              'low_res_logits': (torch.Tensor) Low resolution logits with
+                shape BxCxHxW, where H=W=256. Can be passed as mask input
+                to subsequent iterations of prediction.
+        """
+        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
+        image_embeddings = self.image_encoder(input_images)
+
+        outputs = []
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if "point_coords" in image_record:
+                points = (image_record["point_coords"], image_record["point_labels"])
+            else:
+                points = None
+            sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=points,
+                boxes=image_record.get("boxes", None),
+                masks=image_record.get("mask_inputs", None),
+            )
+            low_res_masks, iou_predictions = self.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.prompt_encoder.get_dense_pe(),
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+            )
+            masks = self.postprocess_masks(
+                low_res_masks,
+                input_size=image_record["image"].shape[-2:],
+                original_size=image_record["original_size"],
+            )
+            masks = masks > self.mask_threshold
+            outputs.append(
+                {
+                    "masks": masks,
+                    "iou_predictions": iou_predictions,
+                    "low_res_logits": low_res_masks,
+                }
+            )
+        return outputs
+
+    def postprocess_masks(
+        self,
+        masks: torch.Tensor,
+        input_size: Tuple[int, ...],
+        original_size: Tuple[int, ...],
+    ) -> torch.Tensor:
+        """
+        Remove padding and upscale masks to the original image size.
+
+        Arguments:
+          masks (torch.Tensor): Batched masks from the mask_decoder,
+            in BxCxHxW format.
+          input_size (tuple(int, int)): The size of the image input to the
+            model, in (H, W) format. Used to remove padding.
+          original_size (tuple(int, int)): The original size of the image
+            before resizing for input to the model, in (H, W) format.
+
+        Returns:
+          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
+            is given by original_size.
+        """
+        masks = F.interpolate(
+            masks,
+            (self.image_encoder.img_size, self.image_encoder.img_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+        masks = masks[..., : input_size[0], : input_size[1]]
+        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
+        return masks
+
+    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
+        """Normalize pixel values and pad to a square input."""
+        # Normalize colors
+        # TODO
+        x = (x - self.pixel_mean) / self.pixel_std
+
+        # Pad
+        h, w = x.shape[-2:]
+        padh = self.image_encoder.img_size - h
+        padw = self.image_encoder.img_size - w
+        x = F.pad(x, (0, padw, 0, padh))
+        return x
diff --git a/model/segment_anything_volumetric/modeling/transformer.py b/model/segment_anything_volumetric/modeling/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..692d04146dfc3bb215b252026e0a7be60d7c0c22
--- /dev/null
+++ b/model/segment_anything_volumetric/modeling/transformer.py
@@ -0,0 +1,240 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import Tensor, nn
+
+import math
+from typing import Tuple, Type
+
+from .common import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: Tensor,
+        image_pe: Tensor,
+        point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w, d = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attention layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out
diff --git a/model/segment_anything_volumetric/predictor.py b/model/segment_anything_volumetric/predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..821293589605b0aec9606c7882210c51f00d1aed
--- /dev/null
+++ b/model/segment_anything_volumetric/predictor.py
@@ -0,0 +1,269 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+from model.segment_anything_volumetric.modeling import Sam
+
+from typing import Optional, Tuple
+
+from .utils.transforms import ResizeLongestSide
+
+
+class SamPredictor:
+    def __init__(
+        self,
+        sam_model: Sam,
+    ) -> None:
+        """
+        Uses SAM to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model (Sam): The model to use for mask prediction.
+        """
+        super().__init__()
+        self.model = sam_model
+        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
+        self.reset_image()
+
+    def set_image(
+        self,
+        image: np.ndarray,
+        image_format: str = "RGB",
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray): The image for calculating masks. Expects an
+            image in HWC uint8 format, with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        assert image_format in [
+            "RGB",
+            "BGR",
+        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
+        if image_format != self.model.image_format:
+            image = image[..., ::-1]
+
+        # Transform the image to the form expected by the model
+        input_image = self.transform.apply_image(image)
+        input_image_torch = torch.as_tensor(input_image, device=self.device)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+
+        self.set_torch_image(input_image_torch, image.shape[:2])
+
+    @torch.no_grad()
+    def set_torch_image(
+        self,
+        transformed_image: torch.Tensor,
+        original_image_size: Tuple[int, ...],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method. Expects the input
+        image to be already transformed to the format expected by the model.
+
+        Arguments:
+          transformed_image (torch.Tensor): The input image, with shape
+            1x3xHxW, which has been transformed with ResizeLongestSide.
+          original_image_size (tuple(int, int)): The size of the image
+            before transformation, in (H, W) format.
+        """
+        assert (
+            len(transformed_image.shape) == 4
+            and transformed_image.shape[1] == 3
+            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
+        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
+        self.reset_image()
+
+        self.original_size = original_image_size
+        self.input_size = tuple(transformed_image.shape[-2:])
+        input_image = self.model.preprocess(transformed_image)
+        self.features = self.model.image_encoder(input_image)
+        self.is_image_set = True
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        # Transform input prompts
+        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
+        if point_coords is not None:
+            assert (
+                point_labels is not None
+            ), "point_labels must be supplied if point_coords is supplied."
+            point_coords = self.transform.apply_coords(point_coords, self.original_size)
+            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
+            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
+            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
+        if box is not None:
+            box = self.transform.apply_boxes(box, self.original_size)
+            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
+            box_torch = box_torch[None, :]
+        if mask_input is not None:
+            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
+            mask_input_torch = mask_input_torch[None, :, :, :]
+
+        masks, iou_predictions, low_res_masks = self.predict_torch(
+            coords_torch,
+            labels_torch,
+            box_torch,
+            mask_input_torch,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        masks_np = masks[0].detach().cpu().numpy()
+        iou_predictions_np = iou_predictions[0].detach().cpu().numpy()
+        low_res_masks_np = low_res_masks[0].detach().cpu().numpy()
+        return masks_np, iou_predictions_np, low_res_masks_np
+
+    @torch.no_grad()
+    def predict_torch(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using ResizeLongestSide.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          boxes (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        if point_coords is not None:
+            points = (point_coords, point_labels)
+        else:
+            points = None
+
+        # Embed prompts
+        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
+            points=points,
+            boxes=boxes,
+            masks=mask_input,
+        )
+
+        # Predict masks
+        low_res_masks, iou_predictions = self.model.mask_decoder(
+            image_embeddings=self.features,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+        )
+
+        # Upscale the masks to the original image resolution
+        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
+
+        if not return_logits:
+            masks = masks > self.model.mask_threshold
+
+        return masks, iou_predictions, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert self.features is not None, "Features must exist if an image has been set."
+        return self.features
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_image(self) -> None:
+        """Resets the currently set image."""
+        self.is_image_set = False
+        self.features = None
+        self.orig_h = None
+        self.orig_w = None
+        self.input_h = None
+        self.input_w = None
diff --git a/model/segment_anything_volumetric/utils/__init__.py b/model/segment_anything_volumetric/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/model/segment_anything_volumetric/utils/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
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diff --git a/model/segment_anything_volumetric/utils/amg.py b/model/segment_anything_volumetric/utils/amg.py
new file mode 100644
index 0000000000000000000000000000000000000000..be064071ef399fea96c673ad173689656c23534a
--- /dev/null
+++ b/model/segment_anything_volumetric/utils/amg.py
@@ -0,0 +1,346 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+import math
+from copy import deepcopy
+from itertools import product
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+
+class MaskData:
+    """
+    A structure for storing masks and their related data in batched format.
+    Implements basic filtering and concatenation.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        for v in kwargs.values():
+            assert isinstance(
+                v, (list, np.ndarray, torch.Tensor)
+            ), "MaskData only supports list, numpy arrays, and torch tensors."
+        self._stats = dict(**kwargs)
+
+    def __setitem__(self, key: str, item: Any) -> None:
+        assert isinstance(
+            item, (list, np.ndarray, torch.Tensor)
+        ), "MaskData only supports list, numpy arrays, and torch tensors."
+        self._stats[key] = item
+
+    def __delitem__(self, key: str) -> None:
+        del self._stats[key]
+
+    def __getitem__(self, key: str) -> Any:
+        return self._stats[key]
+
+    def items(self) -> ItemsView[str, Any]:
+        return self._stats.items()
+
+    def filter(self, keep: torch.Tensor) -> None:
+        for k, v in self._stats.items():
+            if v is None:
+                self._stats[k] = None
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = v[keep.detach().cpu().numpy()]
+            elif isinstance(v, list) and keep.dtype == torch.bool:
+                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
+            elif isinstance(v, list):
+                self._stats[k] = [v[i] for i in keep]
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def cat(self, new_stats: "MaskData") -> None:
+        for k, v in new_stats.items():
+            if k not in self._stats or self._stats[k] is None:
+                self._stats[k] = deepcopy(v)
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = torch.cat([self._stats[k], v], dim=0)
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
+            elif isinstance(v, list):
+                self._stats[k] = self._stats[k] + deepcopy(v)
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def to_numpy(self) -> None:
+        for k, v in self._stats.items():
+            if isinstance(v, torch.Tensor):
+                self._stats[k] = v.detach().cpu().numpy()
+
+
+def is_box_near_crop_edge(
+    boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
+) -> torch.Tensor:
+    """Filter masks at the edge of a crop, but not at the edge of the original image."""
+    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+    return torch.any(near_crop_edge, dim=1)
+
+
+def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
+    box_xywh = deepcopy(box_xyxy)
+    box_xywh[2] = box_xywh[2] - box_xywh[0]
+    box_xywh[3] = box_xywh[3] - box_xywh[1]
+    return box_xywh
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
+    assert len(args) > 0 and all(
+        len(a) == len(args[0]) for a in args
+    ), "Batched iteration must have inputs of all the same size."
+    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+    for b in range(n_batches):
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    """
+    Encodes masks to an uncompressed RLE, in the format expected by
+    pycoco tools.
+    """
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    change_indices = diff.nonzero()
+
+    # Encode run length
+    out = []
+    for i in range(b):
+        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+        cur_idxs = torch.cat(
+            [
+                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+                cur_idxs + 1,
+                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
+            ]
+        )
+        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+        counts = [] if tensor[i, 0] == 0 else [0]
+        counts.extend(btw_idxs.detach().cpu().tolist())
+        out.append({"size": [h, w], "counts": counts})
+    return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+    """Compute a binary mask from an uncompressed RLE."""
+    h, w = rle["size"]
+    mask = np.empty(h * w, dtype=bool)
+    idx = 0
+    parity = False
+    for count in rle["counts"]:
+        mask[idx : idx + count] = parity
+        idx += count
+        parity ^= True
+    mask = mask.reshape(w, h)
+    return mask.transpose()  # Put in C order
+
+
+def area_from_rle(rle: Dict[str, Any]) -> int:
+    return sum(rle["counts"][1::2])
+
+
+def calculate_stability_score(
+    masks: torch.Tensor, mask_threshold: float, threshold_offset: float
+) -> torch.Tensor:
+    """
+    Computes the stability score for a batch of masks. The stability
+    score is the IoU between the binary masks obtained by thresholding
+    the predicted mask logits at high and low values.
+    """
+    # One mask is always contained inside the other.
+    # Save memory by preventing unnecessary cast to torch.int64
+    intersections = (
+        (masks > (mask_threshold + threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    unions = (
+        (masks > (mask_threshold - threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+    """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
+    offset = 1 / (2 * n_per_side)
+    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+    points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+    return points
+
+
+def build_all_layer_point_grids(
+    n_per_side: int, n_layers: int, scale_per_layer: int
+) -> List[np.ndarray]:
+    """Generates point grids for all crop layers."""
+    points_by_layer = []
+    for i in range(n_layers + 1):
+        n_points = int(n_per_side / (scale_per_layer**i))
+        points_by_layer.append(build_point_grid(n_points))
+    return points_by_layer
+
+
+def generate_crop_boxes(
+    im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> Tuple[List[List[int]], List[int]]:
+    """
+    Generates a list of crop boxes of different sizes. Each layer
+    has (2**i)**2 boxes for the ith layer.
+    """
+    crop_boxes, layer_idxs = [], []
+    im_h, im_w = im_size
+    short_side = min(im_h, im_w)
+
+    # Original image
+    crop_boxes.append([0, 0, im_w, im_h])
+    layer_idxs.append(0)
+
+    def crop_len(orig_len, n_crops, overlap):
+        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+    for i_layer in range(n_layers):
+        n_crops_per_side = 2 ** (i_layer + 1)
+        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+        crop_w = crop_len(im_w, n_crops_per_side, overlap)
+        crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+        # Crops in XYWH format
+        for x0, y0 in product(crop_box_x0, crop_box_y0):
+            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+            crop_boxes.append(box)
+            layer_idxs.append(i_layer + 1)
+
+    return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
+    # Check if boxes has a channel dimension
+    if len(boxes.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0]], device=points.device)
+    # Check if points has a channel dimension
+    if len(points.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return points + offset
+
+
+def uncrop_masks(
+    masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
+) -> torch.Tensor:
+    x0, y0, x1, y1 = crop_box
+    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+        return masks
+    # Coordinate transform masks
+    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+    pad = (x0, pad_x - x0, y0, pad_y - y0)
+    return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(
+    mask: np.ndarray, area_thresh: float, mode: str
+) -> Tuple[np.ndarray, bool]:
+    """
+    Removes small disconnected regions and holes in a mask. Returns the
+    mask and an indicator of if the mask has been modified.
+    """
+    import cv2  # type: ignore
+
+    assert mode in ["holes", "islands"]
+    correct_holes = mode == "holes"
+    working_mask = (correct_holes ^ mask).astype(np.uint8)
+    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+    sizes = stats[:, -1][1:]  # Row 0 is background label
+    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+    if len(small_regions) == 0:
+        return mask, False
+    fill_labels = [0] + small_regions
+    if not correct_holes:
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
+        # If every region is below threshold, keep largest
+        if len(fill_labels) == 0:
+            fill_labels = [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+    from pycocotools import mask as mask_utils  # type: ignore
+
+    h, w = uncompressed_rle["size"]
+    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+    rle["counts"] = rle["counts"].decode("utf-8")  # Necessary to serialize with json
+    return rle
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
+    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
+    """
+    # torch.max below raises an error on empty inputs, just skip in this case
+    if torch.numel(masks) == 0:
+        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+    # Normalize shape to CxHxW
+    shape = masks.shape
+    h, w = shape[-2:]
+    if len(shape) > 2:
+        masks = masks.flatten(0, -3)
+    else:
+        masks = masks.unsqueeze(0)
+
+    # Get top and bottom edges
+    in_height, _ = torch.max(masks, dim=-1)
+    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+    in_height_coords = in_height_coords + h * (~in_height)
+    top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+    # Get left and right edges
+    in_width, _ = torch.max(masks, dim=-2)
+    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+    right_edges, _ = torch.max(in_width_coords, dim=-1)
+    in_width_coords = in_width_coords + w * (~in_width)
+    left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+    # If the mask is empty the right edge will be to the left of the left edge.
+    # Replace these boxes with [0, 0, 0, 0]
+    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+    out = out * (~empty_filter).unsqueeze(-1)
+
+    # Return to original shape
+    if len(shape) > 2:
+        out = out.reshape(*shape[:-2], 4)
+    else:
+        out = out[0]
+
+    return out
diff --git a/model/segment_anything_volumetric/utils/onnx.py b/model/segment_anything_volumetric/utils/onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3196bdf4b782e6eeb3da4ad66ef3c7b1741535fe
--- /dev/null
+++ b/model/segment_anything_volumetric/utils/onnx.py
@@ -0,0 +1,144 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from typing import Tuple
+
+from ..modeling import Sam
+from .amg import calculate_stability_score
+
+
+class SamOnnxModel(nn.Module):
+    """
+    This model should not be called directly, but is used in ONNX export.
+    It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
+    with some functions modified to enable model tracing. Also supports extra
+    options controlling what information. See the ONNX export script for details.
+    """
+
+    def __init__(
+        self,
+        model: Sam,
+        return_single_mask: bool,
+        use_stability_score: bool = False,
+        return_extra_metrics: bool = False,
+    ) -> None:
+        super().__init__()
+        self.mask_decoder = model.mask_decoder
+        self.model = model
+        self.img_size = model.image_encoder.img_size
+        self.return_single_mask = return_single_mask
+        self.use_stability_score = use_stability_score
+        self.stability_score_offset = 1.0
+        self.return_extra_metrics = return_extra_metrics
+
+    @staticmethod
+    def resize_longest_image_size(
+        input_image_size: torch.Tensor, longest_side: int
+    ) -> torch.Tensor:
+        input_image_size = input_image_size.to(torch.float32)
+        scale = longest_side / torch.max(input_image_size)
+        transformed_size = scale * input_image_size
+        transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
+        return transformed_size
+
+    def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
+        point_coords = point_coords + 0.5
+        point_coords = point_coords / self.img_size
+        point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
+        point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
+
+        point_embedding = point_embedding * (point_labels != -1)
+        point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
+            point_labels == -1
+        )
+
+        for i in range(self.model.prompt_encoder.num_point_embeddings):
+            point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
+                i
+            ].weight * (point_labels == i)
+
+        return point_embedding
+
+    def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
+        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
+        mask_embedding = mask_embedding + (
+            1 - has_mask_input
+        ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
+        return mask_embedding
+
+    def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
+        masks = F.interpolate(
+            masks,
+            size=(self.img_size, self.img_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)
+        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]  # type: ignore
+
+        orig_im_size = orig_im_size.to(torch.int64)
+        h, w = orig_im_size[0], orig_im_size[1]
+        masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
+        return masks
+
+    def select_masks(
+        self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Determine if we should return the multiclick mask or not from the number of points.
+        # The reweighting is used to avoid control flow.
+        score_reweight = torch.tensor(
+            [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
+        ).to(iou_preds.device)
+        score = iou_preds + (num_points - 2.5) * score_reweight
+        best_idx = torch.argmax(score, dim=1)
+        masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
+        iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
+
+        return masks, iou_preds
+
+    @torch.no_grad()
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        point_coords: torch.Tensor,
+        point_labels: torch.Tensor,
+        mask_input: torch.Tensor,
+        has_mask_input: torch.Tensor,
+        orig_im_size: torch.Tensor,
+    ):
+        sparse_embedding = self._embed_points(point_coords, point_labels)
+        dense_embedding = self._embed_masks(mask_input, has_mask_input)
+
+        masks, scores = self.model.mask_decoder.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embedding,
+            dense_prompt_embeddings=dense_embedding,
+        )
+
+        if self.use_stability_score:
+            scores = calculate_stability_score(
+                masks, self.model.mask_threshold, self.stability_score_offset
+            )
+
+        if self.return_single_mask:
+            masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
+
+        upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
+
+        if self.return_extra_metrics:
+            stability_scores = calculate_stability_score(
+                upscaled_masks, self.model.mask_threshold, self.stability_score_offset
+            )
+            areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
+            return upscaled_masks, scores, stability_scores, areas, masks
+
+        return upscaled_masks, scores, masks
diff --git a/model/segment_anything_volumetric/utils/transforms.py b/model/segment_anything_volumetric/utils/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..4738cc59439a4e83a22e93e1ae21d734feac9003
--- /dev/null
+++ b/model/segment_anything_volumetric/utils/transforms.py
@@ -0,0 +1,101 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
+
+from copy import deepcopy
+from typing import Tuple
+
+
+class ResizeLongestSide:
+    """
+    Resizes images to the longest side 'target_length', as well as provides
+    methods for resizing coordinates and boxes. Provides methods for
+    transforming both numpy array and batched torch tensors.
+    """
+
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
+        return np.array(resize(to_pil_image(image), target_size))
+    
+    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array of length 2 in the final dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(old_h, old_w, self.target_length)
+        new_coords = np.empty_like(coords)
+        new_coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        new_coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return new_coords
+
+
+    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array shape Bx4. Requires the original image size
+        in (H, W) format.
+        """
+        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Expects batched images with shape BxCxHxW and float format. This
+        transformation may not exactly match apply_image. apply_image is
+        the transformation expected by the model.
+        """
+        # Expects an image in BCHW format. May not exactly match apply_image.
+        target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
+        return F.interpolate(
+            image, target_size, mode="bilinear", align_corners=False, antialias=True
+        )
+
+    def apply_coords_torch(
+        self, coords: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with length 2 in the last dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(
+            original_size[0], original_size[1], self.target_length
+        )
+        coords = deepcopy(coords).to(torch.float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes_torch(
+        self, boxes: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with shape Bx4. Requires the original image
+        size in (H, W) format.
+        """
+        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    @staticmethod
+    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
+        """
+        Compute the output size given input size and target long side length.
+        """
+        scale = long_side_length * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        neww = int(neww + 0.5)
+        newh = int(newh + 0.5)
+        return (newh, neww)
diff --git a/model/utils/__pycache__/monai_inferers_utils.cpython-39.pyc b/model/utils/__pycache__/monai_inferers_utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9f6b0e58ed08765dc0817d0c9b5c9f2b4bd98d7e
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diff --git a/model/utils/__pycache__/visualize.cpython-39.pyc b/model/utils/__pycache__/visualize.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d4157de6948fcc450654e58fbb7ad6b4513df78
Binary files /dev/null and b/model/utils/__pycache__/visualize.cpython-39.pyc differ
diff --git a/model/utils/monai_inferers_utils.py b/model/utils/monai_inferers_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..075f40bdb35a55f9cb26c12245def1724e2ac7fd
--- /dev/null
+++ b/model/utils/monai_inferers_utils.py
@@ -0,0 +1,467 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import streamlit as st
+
+import warnings
+from typing import Any, Callable, Dict, List, Mapping, Sequence, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import random
+import time
+from monai.data.utils import compute_importance_map, dense_patch_slices, get_valid_patch_size
+from monai.transforms import Resize
+from monai.utils import (
+    BlendMode,
+    PytorchPadMode,
+    convert_data_type,
+    ensure_tuple,
+    fall_back_tuple,
+    look_up_option,
+    optional_import,
+)
+
+tqdm, _ = optional_import("tqdm", name="tqdm")
+
+__all__ = ["sliding_window_inference"]
+
+def logits2roi_coor(spatial_size, logits_global_single):
+    # crop predict
+    pred_global_single = torch.sigmoid(logits_global_single) > 0.5
+    ## get all pos idx
+    nonzero_indices = torch.nonzero(pred_global_single)
+    if nonzero_indices.shape[0] == 0:
+        return None, None, None, None, None, None
+    ## get boundary
+    min_d, max_d = nonzero_indices[:, 0].min(), nonzero_indices[:, 0].max()
+    min_h, max_h = nonzero_indices[:, 1].min(), nonzero_indices[:, 1].max()
+    min_w, max_w = nonzero_indices[:, 2].min(), nonzero_indices[:, 2].max()
+    ## padding
+    crop_d, crop_h, crop_w = max_d - min_d + 1, max_h - min_h + 1, max_w - min_w + 1,
+    window_d, window_h, window_w = spatial_size
+    padding_d, padding_h, padding_w = max(0, window_d-crop_d), max(0, window_h-crop_h), max(0, window_w-crop_w)
+    global_d, global_h, global_w = logits_global_single.shape
+    min_d = max(0, min_d - int(padding_d)//2)
+    min_h = max(0, min_h - int(padding_h)//2)
+    min_w = max(0, min_w - int(padding_w)//2)
+    max_d = min(global_d, max_d + int(padding_d)//2)
+    max_h = min(global_h, max_h + int(padding_h)//2)
+    max_w = min(global_w, max_w + int(padding_w)//2)
+    return min_d, min_h, min_w, max_d, max_h, max_w
+
+def build_binary_cube(bbox, binary_cube_shape):
+    min_coord = bbox[0][:3].int().tolist()
+    max_coord = bbox[0][3:].int().tolist()
+    binary_cube = torch.zeros(binary_cube_shape)
+    binary_cube[min_coord[0]:max_coord[0]+1, min_coord[1]:max_coord[1]+1, min_coord[2]:max_coord[2]+1] = 1
+    return binary_cube
+
+def build_binary_points(points, labels, shape):
+    binary_points = torch.zeros(shape, dtype=torch.int16)
+    print(shape, labels == 1)
+    binary_points[points[labels == 1, 0].long(), points[labels == 1, 1].long(), points[labels == 1, 2].long()] = 1
+    return binary_points
+
+def sliding_window_inference(
+    inputs: torch.Tensor,
+    prompt_reflection: Union[torch.Tensor, Tuple[torch.Tensor, ...]],
+    roi_size: Union[Sequence[int], int],
+    sw_batch_size: int,
+    predictor: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor], Dict[Any, torch.Tensor]]],
+    overlap: float = 0.25,
+    mode: Union[BlendMode, str] = BlendMode.CONSTANT,
+    sigma_scale: Union[Sequence[float], float] = 0.125,
+    padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,
+    cval: float = 0.0,
+    sw_device: Union[torch.device, str, None] = None,
+    device: Union[torch.device, str, None] = None,
+    progress: bool = False,
+    roi_weight_map: Union[torch.Tensor, None] = None,
+    *args: Any,
+    **kwargs: Any,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, ...], Dict[Any, torch.Tensor]]:
+    """
+    Sliding window inference on `inputs` with `predictor`.
+
+    The outputs of `predictor` could be a tensor, a tuple, or a dictionary of tensors.
+    Each output in the tuple or dict value is allowed to have different resolutions with respect to the input.
+    e.g., the input patch spatial size is [128,128,128], the output (a tuple of two patches) patch sizes
+    could be ([128,64,256], [64,32,128]).
+    In this case, the parameter `overlap` and `roi_size` need to be carefully chosen to ensure the output ROI is still
+    an integer. If the predictor's input and output spatial sizes are not equal, we recommend choosing the parameters
+    so that `overlap*roi_size*output_size/input_size` is an integer (for each spatial dimension).
+
+    When roi_size is larger than the inputs' spatial size, the input image are padded during inference.
+    To maintain the same spatial sizes, the output image will be cropped to the original input size.
+
+    Args:
+        inputs: input image to be processed (assuming NCHW[D])
+        roi_size: the spatial window size for inferences.
+            When its components have None or non-positives, the corresponding inputs dimension will be used.
+            if the components of the `roi_size` are non-positive values, the transform will use the
+            corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
+            to `(32, 64)` if the second spatial dimension size of img is `64`.
+        sw_batch_size: the batch size to run window slices.
+        predictor: given input tensor ``patch_data`` in shape NCHW[D],
+            The outputs of the function call ``predictor(patch_data)`` should be a tensor, a tuple, or a dictionary
+            with Tensor values. Each output in the tuple or dict value should have the same batch_size, i.e. NM'H'W'[D'];
+            where H'W'[D'] represents the output patch's spatial size, M is the number of output channels,
+            N is `sw_batch_size`, e.g., the input shape is (7, 1, 128,128,128),
+            the output could be a tuple of two tensors, with shapes: ((7, 5, 128, 64, 256), (7, 4, 64, 32, 128)).
+            In this case, the parameter `overlap` and `roi_size` need to be carefully chosen
+            to ensure the scaled output ROI sizes are still integers.
+            If the `predictor`'s input and output spatial sizes are different,
+            we recommend choosing the parameters so that ``overlap*roi_size*zoom_scale`` is an integer for each dimension.
+        overlap: Amount of overlap between scans.
+        mode: {``"constant"``, ``"gaussian"``}
+            How to blend output of overlapping windows. Defaults to ``"constant"``.
+
+            - ``"constant``": gives equal weight to all predictions.
+            - ``"gaussian``": gives less weight to predictions on edges of windows.
+
+        sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``"gaussian"``.
+            Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.
+            When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding
+            spatial dimensions.
+        padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
+            Padding mode for ``inputs``, when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
+            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
+        cval: fill value for 'constant' padding mode. Default: 0
+        sw_device: device for the window data.
+            By default the device (and accordingly the memory) of the `inputs` is used.
+            Normally `sw_device` should be consistent with the device where `predictor` is defined.
+        device: device for the stitched output prediction.
+            By default the device (and accordingly the memory) of the `inputs` is used. If for example
+            set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
+            `inputs` and `roi_size`. Output is on the `device`.
+        progress: whether to print a `tqdm` progress bar.
+        roi_weight_map: pre-computed (non-negative) weight map for each ROI.
+            If not given, and ``mode`` is not `constant`, this map will be computed on the fly.
+        args: optional args to be passed to ``predictor``.
+        kwargs: optional keyword args to be passed to ``predictor``.
+
+    Note:
+        - input must be channel-first and have a batch dim, supports N-D sliding window.
+
+    """
+    print('sliding window inference for ROI')
+    text = kwargs['text']
+    use_box = kwargs['use_box']
+    use_point = kwargs['use_point']
+    logits_global_single = kwargs['logits_global_single']
+    assert not (use_box and use_point)
+    compute_dtype = inputs.dtype
+    num_spatial_dims = len(inputs.shape) - 2
+    if overlap < 0 or overlap >= 1:
+        raise ValueError("overlap must be >= 0 and < 1.")
+
+    # determine image spatial size and batch size
+    # Note: all input images must have the same image size and batch size
+    batch_size, _, *image_size_ = inputs.shape
+
+    if device is None:
+        device = inputs.device
+    if sw_device is None:
+        sw_device = inputs.device
+
+    roi_size = fall_back_tuple(roi_size, image_size_)
+    # in case that image size is smaller than roi size
+    image_size = tuple(max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims))
+    pad_size = []
+    for k in range(len(inputs.shape) - 1, 1, -1):
+        diff = max(roi_size[k - 2] - inputs.shape[k], 0)
+        half = diff // 2
+        pad_size.extend([half, diff - half])
+    inputs = F.pad(inputs, pad=pad_size, mode=look_up_option(padding_mode, PytorchPadMode).value, value=cval)
+    #############
+    if use_point or use_box:
+        binary_prompt_map, global_preds = prompt_reflection
+        global_preds = F.pad(global_preds, pad=pad_size, mode=look_up_option(padding_mode, PytorchPadMode).value, value=cval)
+    #############
+    scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
+
+    # Store all slices in list
+    slices = dense_patch_slices(image_size, roi_size, scan_interval)
+    num_win = len(slices)  # number of windows per image
+    total_slices = num_win * batch_size  # total number of windows
+    if total_slices > 10:
+        return logits_global_single
+
+    # Create window-level importance map
+    valid_patch_size = get_valid_patch_size(image_size, roi_size)
+    if valid_patch_size == roi_size and (roi_weight_map is not None):
+        importance_map = roi_weight_map
+    else:
+        try:
+            importance_map = compute_importance_map(valid_patch_size, mode=mode, sigma_scale=sigma_scale, device=device)
+        except BaseException as e:
+            raise RuntimeError(
+                "Seems to be OOM. Please try smaller patch size or mode='constant' instead of mode='gaussian'."
+            ) from e
+    importance_map = convert_data_type(importance_map, torch.Tensor, device, compute_dtype)[0]  # type: ignore
+    # handle non-positive weights
+    min_non_zero = max(importance_map[importance_map != 0].min().item(), 1e-3)
+    importance_map = torch.clamp(importance_map.to(torch.float32), min=min_non_zero).to(compute_dtype)
+
+    # Perform predictions
+    dict_key, output_image_list, count_map_list = None, [], []
+    _initialized_ss = -1
+    is_tensor_output = True  # whether the predictor's output is a tensor (instead of dict/tuple)
+
+    # for each patch
+    
+    for slice_g in tqdm(range(0, total_slices, sw_batch_size)) if progress else range(0, total_slices, sw_batch_size):
+        # my_bar.progress(float(slice_g/total_slices), text=progress_text)
+        print(slice_g/total_slices)
+        slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))
+        unravel_slice = [
+            [slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)] + list(slices[idx % num_win])
+            for idx in slice_range
+        ]
+        window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(sw_device)
+        #############
+        
+        boxes = None
+        points = None
+        if use_point:
+            window_binary_prompt_map = torch.cat([binary_prompt_map[win_slice] for win_slice in unravel_slice]).to(sw_device)
+            point, point_label = select_points(window_binary_prompt_map.squeeze())
+            points = (point.unsqueeze(0).float(), point_label.unsqueeze(0).float())  
+            pseudo_label = torch.cat([global_preds[win_slice] for win_slice in unravel_slice]).to(sw_device)
+            boxes = generate_box(pseudo_label.squeeze()).unsqueeze(0).float()
+        if use_box:
+            if num_win == 1:
+                window_binary_prompt_map = torch.cat([binary_prompt_map[win_slice] for win_slice in unravel_slice]).to(sw_device)
+                boxes = generate_box(window_binary_prompt_map.squeeze()).unsqueeze(0).float()
+            else:
+                pseudo_label = torch.cat([global_preds[win_slice] for win_slice in unravel_slice]).to(sw_device)
+                boxes = generate_box(pseudo_label.squeeze()).unsqueeze(0).float()
+        seg_prob_out = predictor(window_data, text, boxes, points)  # batched patch segmentation
+        #############
+        # convert seg_prob_out to tuple seg_prob_tuple, this does not allocate new memory.
+        seg_prob_tuple: Tuple[torch.Tensor, ...]
+        if isinstance(seg_prob_out, torch.Tensor):
+            seg_prob_tuple = (seg_prob_out,)
+        elif isinstance(seg_prob_out, Mapping):
+            if dict_key is None:
+                dict_key = sorted(seg_prob_out.keys())  # track predictor's output keys
+            seg_prob_tuple = tuple(seg_prob_out[k] for k in dict_key)
+            is_tensor_output = False
+        else:
+            seg_prob_tuple = ensure_tuple(seg_prob_out)
+            is_tensor_output = False
+
+        # for each output in multi-output list
+        for ss, seg_prob in enumerate(seg_prob_tuple):
+            seg_prob = seg_prob.to(device)  # BxCxMxNxP or BxCxMxN
+
+            # compute zoom scale: out_roi_size/in_roi_size
+            zoom_scale = []
+            for axis, (img_s_i, out_w_i, in_w_i) in enumerate(
+                zip(image_size, seg_prob.shape[2:], window_data.shape[2:])
+            ):
+                _scale = out_w_i / float(in_w_i)
+                if not (img_s_i * _scale).is_integer():
+                    warnings.warn(
+                        f"For spatial axis: {axis}, output[{ss}] will have non-integer shape. Spatial "
+                        f"zoom_scale between output[{ss}] and input is {_scale}. Please pad inputs."
+                    )
+                zoom_scale.append(_scale)
+
+            if _initialized_ss < ss:  # init. the ss-th buffer at the first iteration
+                # construct multi-resolution outputs
+                output_classes = seg_prob.shape[1]
+                output_shape = [batch_size, output_classes] + [
+                    int(image_size_d * zoom_scale_d) for image_size_d, zoom_scale_d in zip(image_size, zoom_scale)
+                ]
+                # allocate memory to store the full output and the count for overlapping parts
+                output_image_list.append(torch.zeros(output_shape, dtype=compute_dtype, device=device))
+                count_map_list.append(torch.zeros([1, 1] + output_shape[2:], dtype=compute_dtype, device=device))
+                _initialized_ss += 1
+
+            # resizing the importance_map
+            resizer = Resize(spatial_size=seg_prob.shape[2:], mode="nearest", anti_aliasing=False)
+
+            # store the result in the proper location of the full output. Apply weights from importance map.
+            for idx, original_idx in zip(slice_range, unravel_slice):
+                # zoom roi
+                original_idx_zoom = list(original_idx)  # 4D for 2D image, 5D for 3D image
+                for axis in range(2, len(original_idx_zoom)):
+                    zoomed_start = original_idx[axis].start * zoom_scale[axis - 2]
+                    zoomed_end = original_idx[axis].stop * zoom_scale[axis - 2]
+                    if not zoomed_start.is_integer() or (not zoomed_end.is_integer()):
+                        warnings.warn(
+                            f"For axis-{axis-2} of output[{ss}], the output roi range is not int. "
+                            f"Input roi range is ({original_idx[axis].start}, {original_idx[axis].stop}). "
+                            f"Spatial zoom_scale between output[{ss}] and input is {zoom_scale[axis - 2]}. "
+                            f"Corresponding output roi range is ({zoomed_start}, {zoomed_end}).\n"
+                            f"Please change overlap ({overlap}) or roi_size ({roi_size[axis-2]}) for axis-{axis-2}. "
+                            "Tips: if overlap*roi_size*zoom_scale is an integer, it usually works."
+                        )
+                    original_idx_zoom[axis] = slice(int(zoomed_start), int(zoomed_end), None)
+                importance_map_zoom = resizer(importance_map.unsqueeze(0))[0].to(compute_dtype)
+                # store results and weights
+                output_image_list[ss][original_idx_zoom] += importance_map_zoom * seg_prob[idx - slice_g]
+                count_map_list[ss][original_idx_zoom] += (
+                    importance_map_zoom.unsqueeze(0).unsqueeze(0).expand(count_map_list[ss][original_idx_zoom].shape)
+                )
+    # account for any overlapping sections
+    for ss in range(len(output_image_list)):
+        output_image_list[ss] = (output_image_list[ss] / count_map_list.pop(0)).to(compute_dtype)
+
+    # remove padding if image_size smaller than roi_size
+    for ss, output_i in enumerate(output_image_list):
+        if torch.isnan(output_i).any() or torch.isinf(output_i).any():
+            warnings.warn("Sliding window inference results contain NaN or Inf.")
+
+        zoom_scale = [
+            seg_prob_map_shape_d / roi_size_d for seg_prob_map_shape_d, roi_size_d in zip(output_i.shape[2:], roi_size)
+        ]
+
+        final_slicing: List[slice] = []
+        for sp in range(num_spatial_dims):
+            slice_dim = slice(pad_size[sp * 2], image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2])
+            slice_dim = slice(
+                int(round(slice_dim.start * zoom_scale[num_spatial_dims - sp - 1])),
+                int(round(slice_dim.stop * zoom_scale[num_spatial_dims - sp - 1])),
+            )
+            final_slicing.insert(0, slice_dim)
+        while len(final_slicing) < len(output_i.shape):
+            final_slicing.insert(0, slice(None))
+        output_image_list[ss] = output_i[final_slicing]
+
+    if dict_key is not None:  # if output of predictor is a dict
+        final_output = dict(zip(dict_key, output_image_list))
+    else:
+        final_output = tuple(output_image_list)  # type: ignore
+    return final_output[0] if is_tensor_output else final_output  # type: ignore
+
+
+def _get_scan_interval(
+    image_size: Sequence[int], roi_size: Sequence[int], num_spatial_dims: int, overlap: float
+) -> Tuple[int, ...]:
+    """
+    Compute scan interval according to the image size, roi size and overlap.
+    Scan interval will be `int((1 - overlap) * roi_size)`, if interval is 0,
+    use 1 instead to make sure sliding window works.
+
+    """
+    if len(image_size) != num_spatial_dims:
+        raise ValueError("image coord different from spatial dims.")
+    if len(roi_size) != num_spatial_dims:
+        raise ValueError("roi coord different from spatial dims.")
+
+    scan_interval = []
+    for i in range(num_spatial_dims):
+        if roi_size[i] == image_size[i]:
+            scan_interval.append(int(roi_size[i]))
+        else:
+            interval = int(roi_size[i] * (1 - overlap))
+            scan_interval.append(interval if interval > 0 else 1)
+    return tuple(scan_interval)
+
+
+def generate_box(pred_pre, bbox_shift=None):
+    meaning_post_label = pred_pre # [h, w, d]
+    ones_idx = (meaning_post_label > 0).nonzero(as_tuple=True)
+    if all(tensor.nelement() == 0 for tensor in ones_idx):
+        bboxes = torch.tensor([-1,-1,-1,-1,-1,-1])
+        # print(bboxes, bboxes.shape)
+        return bboxes
+    min_coords = [dim.min() for dim in ones_idx]    # [x_min, y_min, z_min]
+    max_coords = [dim.max() for dim in ones_idx]    # [x_max, y_max, z_max]
+
+
+    if bbox_shift is None:
+        corner_min = []
+        corner_max = []
+        shape = meaning_post_label.shape
+        for coor in min_coords:
+            coor_ = max(0, coor)
+            corner_min.append(coor_)
+        for idx, coor in enumerate(max_coords):
+            coor_ = min(shape[idx], coor)
+            corner_max.append(coor_)
+        corner_min = torch.tensor(corner_min)
+        corner_max = torch.tensor(corner_max)
+        return torch.cat((corner_min, corner_max), dim=0)
+    else:
+        # add perturbation to bounding box coordinates
+        corner_min = []
+        corner_max = []
+        shape = meaning_post_label.shape
+        for coor in min_coords:
+            coor_ = max(0, coor + random.randint(-bbox_shift, bbox_shift))
+            corner_min.append(coor_)
+        for idx, coor in enumerate(max_coords):
+            coor_ = min(shape[idx], coor + random.randint(-bbox_shift, bbox_shift))
+            corner_max.append(coor_)
+        corner_min = torch.tensor(corner_min)
+        corner_max = torch.tensor(corner_max)
+        return torch.cat((corner_min, corner_max), dim=0)
+
+
+def select_points(preds, num_positive_extra=4, num_negative_extra=0, fix_extra_point_num=None):
+    spacial_dim = 3
+    points = torch.zeros((0, 3))
+    labels = torch.zeros((0))
+    pos_thred = 0.9
+    neg_thred = 0.1
+    
+    # get pos/net indices
+    positive_indices = torch.nonzero(preds > pos_thred, as_tuple=True) # ([pos x], [pos y], [pos z])
+    negative_indices = torch.nonzero(preds < neg_thred, as_tuple=True)
+
+    ones_idx = (preds > pos_thred).nonzero(as_tuple=True)
+    if all(tmp.nelement() == 0 for tmp in ones_idx):
+        # all neg
+        num_positive_extra = 0
+        selected_positive_point = torch.tensor([-1,-1,-1]).unsqueeze(dim=0)
+        points = torch.cat((points, selected_positive_point), dim=0)
+        labels = torch.cat((labels, torch.tensor([-1]).reshape(1)))
+    else:
+        # random select a pos point
+        random_idx = torch.randint(len(positive_indices[0]), (1,))
+        selected_positive_point = torch.tensor([positive_indices[i][random_idx] for i in range(spacial_dim)]).unsqueeze(dim=0)
+        points = torch.cat((points, selected_positive_point), dim=0)
+        labels = torch.cat((labels, torch.ones((1))))
+
+    if num_positive_extra > 0:
+        pos_idx_list = torch.randperm(len(positive_indices[0]))[:num_positive_extra]
+        extra_positive_points = []
+        for pos_idx in pos_idx_list:
+            extra_positive_points.append([positive_indices[i][pos_idx] for i in range(spacial_dim)])
+        extra_positive_points = torch.tensor(extra_positive_points).reshape(-1, 3)
+        points = torch.cat((points, extra_positive_points), dim=0)
+        labels = torch.cat((labels, torch.ones((extra_positive_points.shape[0]))))
+
+    if num_negative_extra > 0:
+        neg_idx_list = torch.randperm(len(negative_indices[0]))[:num_negative_extra]
+        extra_negative_points = []
+        for neg_idx in neg_idx_list:
+            extra_negative_points.append([negative_indices[i][neg_idx] for i in range(spacial_dim)])
+        extra_negative_points = torch.tensor(extra_negative_points).reshape(-1, 3)
+        points = torch.cat((points, extra_negative_points), dim=0)
+        labels = torch.cat((labels, torch.zeros((extra_negative_points.shape[0]))))
+        # print('extra_negative_points ', extra_negative_points, extra_negative_points.shape)
+        # print('==> points ', points.shape, labels)
+    
+    if fix_extra_point_num is None:
+        left_point_num = num_positive_extra + num_negative_extra + 1 - labels.shape[0]
+    else:
+        left_point_num = fix_extra_point_num  + 1 - labels.shape[0]
+
+    for _ in range(left_point_num):
+        ignore_point = torch.tensor([-1,-1,-1]).unsqueeze(dim=0)
+        points = torch.cat((points, ignore_point), dim=0)
+        labels = torch.cat((labels, torch.tensor([-1]).reshape(1)))
+
+    return (points, labels)
diff --git a/model/utils/visualize.py b/model/utils/visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4a6342665225e39886eaea5513a648273629420
--- /dev/null
+++ b/model/utils/visualize.py
@@ -0,0 +1,91 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image, ImageDraw
+import os
+import torch
+import monai.transforms as transforms
+
+def draw_result(category, image, bboxes, points, logits, gt3D):
+    zoom_out_transform = transforms.Compose([
+        transforms.AddChanneld(keys=["image", "label", "logits"]),
+        transforms.Resized(keys=["image", "label", "logits"], spatial_size=(32,256,256), mode='nearest-exact')
+    ])
+    print(image.shape, gt3D.shape, logits.shape)
+    image = image[0,0]
+    post_item = zoom_out_transform({
+        'image': image,
+        'label': gt3D,
+        'logits': logits
+    })
+    image, gt3D, logits = post_item['image'][0], post_item['label'][0], post_item['logits'][0]
+    preds = torch.sigmoid(logits)
+    preds = (preds > 0.5).int()
+
+    root_dir=os.path.join(f'./fig_examples/{category}/') 
+
+    if not os.path.exists(root_dir):
+        os.makedirs(root_dir)
+    if bboxes is not None:
+        x1, y1, z1, x2, y2, z2 = bboxes[0].cpu().numpy()
+    if points is not None:
+        points = (points[0].cpu().numpy(), points[1].cpu().numpy())
+        points_ax = points[0]   # [n, 3]
+        points_label = points[1] # [n]
+        # print(points_ax.shape, points_label.shape)
+
+    for j in range(image.shape[0]):
+        img_2d = image[j, :, :].detach().cpu().numpy()
+        preds_2d = preds[j, :, :].detach().cpu().numpy()
+        label_2d = gt3D[j, :, :].detach().cpu().numpy()
+        # if np.sum(label_2d) == 0 and np.sum(preds_2d) == 0:
+        #     continue
+
+        # orginal img
+        fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
+        ax1.imshow(img_2d, cmap='gray')
+        ax1.set_title('Image with prompt') 
+        ax1.axis('off')
+
+        # gt
+        ax2.imshow(img_2d, cmap='gray')
+        show_mask(label_2d, ax2)
+        ax2.set_title('Ground truth') 
+        ax2.axis('off')
+
+        # preds
+        ax3.imshow(img_2d, cmap='gray')
+        show_mask(preds_2d, ax3)
+        ax3.set_title('Prediction') 
+        ax3.axis('off')
+
+        # boxes
+        if bboxes is not None:
+            if j >= x1 and j <= x2:
+                show_box((z1, y1, z2, y2), ax1)
+        # points
+        if points is not None:
+            for point_idx in range(points_label.shape[0]):
+                point = points_ax[point_idx]
+                label = points_label[point_idx] # [1]
+                if j == point[0]:
+                    show_points(point, label, ax1)
+        
+        fig.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
+        plt.savefig(os.path.join(root_dir, f'{category}_{j}.png'), bbox_inches='tight')
+        plt.close()
+
+def show_mask(mask, ax):
+    color = np.array([251/255, 252/255, 30/255, 0.6])
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image, alpha=0.35)
+
+def show_box(box, ax):
+    x0, y0 = box[0], box[1]
+    w, h = box[2] - box[0], box[3] - box[1]
+    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='blue', facecolor=(0,0,0,0), lw=2))
+
+def show_points(points_ax, points_label, ax):
+    print('draw point')
+    color = 'red' if points_label == 0 else 'blue'
+    ax.scatter(points_ax[2], points_ax[1], c=color, marker='o', s=200)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a9788582e371e1dd38982e240079ed8f150910ee
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,144 @@
+absl-py==2.0.0
+alembic==1.12.1
+altair==5.2.0
+attrs==23.1.0
+beautifulsoup4==4.12.2
+blinker==1.7.0
+Brotli==1.0.9
+cachetools==5.3.2
+certifi==2023.11.17
+cffi==1.15.1
+charset-normalizer==2.0.4
+click==8.1.7
+cloudpickle==2.2.1
+contourpy==1.2.0
+cryptography==41.0.3
+cucim==23.10.0
+cycler==0.12.1
+databricks-cli==0.18.0
+docker==6.1.3
+einops==0.6.1
+entrypoints==0.4
+filelock==3.13.1
+fire==0.5.0
+Flask==3.0.0
+fonttools==4.45.1
+fsspec==2023.10.0
+gdown==4.7.1
+gitdb==4.0.11
+GitPython==3.1.40
+google-auth==2.23.4
+google-auth-oauthlib==1.1.0
+greenlet==3.0.1
+grpcio==1.59.3
+gunicorn==21.2.0
+huggingface-hub==0.19.4
+idna==3.4
+imagecodecs==2023.9.18
+imageio==2.33.0
+importlib-metadata==6.8.0
+importlib-resources==6.1.1
+itk==5.3.0
+itk-core==5.3.0
+itk-filtering==5.3.0
+itk-io==5.3.0
+itk-numerics==5.3.0
+itk-registration==5.3.0
+itk-segmentation==5.3.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.3.2
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.1
+kiwisolver==1.4.5
+lazy_loader==0.3
+lmdb==1.4.1
+Mako==1.3.0
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib==3.8.2
+mdurl==0.1.2
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mlflow==2.8.1
+monai==0.9.0
+networkx==3.2.1
+nibabel==5.1.0
+nptyping==2.5.0
+numpy==1.24.3
+oauthlib==3.2.2
+olefile==0.46
+openslide-python==1.1.2
+packaging==23.2
+pandas==2.1.3
+Pillow==10.1.0
+pip==23.3.1
+protobuf==4.23.4
+psutil==5.9.6
+pyarrow==14.0.1
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pycparser==2.21
+pydeck==0.8.1b0
+Pygments==2.17.2
+PyJWT==2.8.0
+pynrrd==1.0.0
+pyOpenSSL==23.2.0
+pyparsing==3.1.1
+PySocks==1.7.1
+python-dateutil==2.8.2
+pytorch-ignite==0.4.8
+pytz==2023.3.post1
+PyYAML==6.0.1
+querystring-parser==1.2.4
+referencing==0.31.1
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.13.2
+rsa==4.9
+sacremoses==0.1.1
+safetensors==0.4.1
+scikit-image==0.22.0
+scikit-learn==1.3.2
+scipy==1.11.4
+setuptools==68.0.0
+six==1.16.0
+smmap==5.0.1
+soupsieve==2.5
+SQLAlchemy==2.0.23
+sqlparse==0.4.4
+streamlit==1.28.2
+streamlit-cropper==0.2.2
+streamlit-drawable-canvas==0.9.3
+streamlit-image-coordinates==0.1.6
+tabulate==0.9.0
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tensorboardX==2.6.2.2
+termcolor==2.3.0
+threadpoolctl==3.2.0
+tifffile==2023.9.26
+tokenizers==0.12.1
+toml==0.10.2
+toolz==0.12.0
+torch==1.11.0
+torchaudio==0.11.0
+torchvision==0.12.0
+tornado==6.4
+tqdm==4.66.1
+transformers==4.18.0
+typing_extensions==4.7.1
+tzdata==2023.3
+tzlocal==5.2
+urllib3==1.26.18
+validators==0.22.0
+watchdog==3.0.0
+websocket-client==1.6.4
+Werkzeug==3.0.1
+wheel==0.41.2
+zipp==3.17.0
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d515339edb0b9ed4a62a9f896e719614e9a056
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,130 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image, ImageEnhance, ImageDraw
+import torch
+import streamlit as st
+from model.inference_cpu import inference_case
+
+initial_rectangle = {
+    "version": "4.4.0",
+    'objects': [
+        {
+            "type": "rect",
+            "version": "4.4.0",
+            "originX": "left",
+            "originY": "top",
+            "left": 50,
+            "top": 50,
+            "width": 100,
+            "height": 100,
+            'fill': 'rgba(255, 165, 0, 0.3)', 
+            'stroke': '#2909F1', 
+            'strokeWidth': 3, 
+            'strokeDashArray': None, 
+            'strokeLineCap': 'butt', 
+            'strokeDashOffset': 0, 
+            'strokeLineJoin': 'miter', 
+            'strokeUniform': True, 
+            'strokeMiterLimit': 4, 
+            'scaleX': 1, 
+            'scaleY': 1, 
+            'angle': 0, 
+            'flipX': False, 
+            'flipY': False, 
+            'opacity': 1, 
+            'shadow': None, 
+            'visible': True, 
+            'backgroundColor': '', 
+            'fillRule': 
+            'nonzero', 
+            'paintFirst': 
+            'fill', 
+            'globalCompositeOperation': 'source-over', 
+            'skewX': 0, 
+            'skewY': 0, 
+            'rx': 0, 
+            'ry': 0
+        }
+    ]
+}
+
+def run():
+    image = st.session_state.data_item["image"].float()
+    image_zoom_out = st.session_state.data_item["zoom_out_image"].float()
+    text_prompt = None
+    point_prompt = None
+    box_prompt = None
+    if st.session_state.use_text_prompt:
+        text_prompt = st.session_state.text_prompt
+    if st.session_state.use_point_prompt and len(st.session_state.points) > 0:
+        point_prompt = reflect_points_into_model(st.session_state.points)
+    if st.session_state.use_box_prompt:
+        box_prompt = reflect_box_into_model(st.session_state.rectangle_3Dbox)
+    inference_case.clear()
+    st.session_state.preds_3D = inference_case(image, image_zoom_out, 
+                                            text_prompt=text_prompt,
+                                            _point_prompt=point_prompt,
+                                            _box_prompt=box_prompt)
+
+def reflect_box_into_model(box_3d):
+    z1, y1, x1, z2, y2, x2 = box_3d
+    x1_prompt = int(x1 * 256.0 / 325.0)
+    y1_prompt = int(y1 * 256.0 / 325.0)
+    z1_prompt = int(z1 * 32.0 / 325.0)
+    x2_prompt = int(x2 * 256.0 / 325.0)
+    y2_prompt = int(y2 * 256.0 / 325.0)
+    z2_prompt = int(z2 * 32.0 / 325.0)
+    return torch.tensor(np.array([z1_prompt, y1_prompt, x1_prompt, z2_prompt, y2_prompt, x2_prompt]))
+
+def reflect_json_data_to_3D_box(json_data, view):
+    if view == 'xy':
+        st.session_state.rectangle_3Dbox[1] = json_data['objects'][0]['top']
+        st.session_state.rectangle_3Dbox[2] = json_data['objects'][0]['left']
+        st.session_state.rectangle_3Dbox[4] = json_data['objects'][0]['top'] + json_data['objects'][0]['height'] * json_data['objects'][0]['scaleY']
+        st.session_state.rectangle_3Dbox[5] = json_data['objects'][0]['left'] + json_data['objects'][0]['width'] * json_data['objects'][0]['scaleX']
+    print(st.session_state.rectangle_3Dbox)
+
+def reflect_points_into_model(points):
+    points_prompt_list = []
+    for point in points:
+        z, y, x = point
+        x_prompt = int(x * 256.0 / 325.0)
+        y_prompt = int(y * 256.0 / 325.0)
+        z_prompt = int(z * 32.0 / 325.0)
+        points_prompt_list.append([z_prompt, y_prompt, x_prompt])
+    points_prompt = np.array(points_prompt_list)
+    points_label = np.ones(points_prompt.shape[0])
+    print(points_prompt, points_label)
+    return (torch.tensor(points_prompt), torch.tensor(points_label))
+
+def show_points(points_ax, points_label, ax):
+    color = 'red' if points_label == 0 else 'blue'
+    ax.scatter(points_ax[0], points_ax[1], c=color, marker='o', s=200)
+
+def make_fig(image, preds, point_axs=None, current_idx=None, view=None):
+    # Convert A to an image
+    image = Image.fromarray((image * 255).astype(np.uint8)).convert("RGB")
+    enhancer = ImageEnhance.Contrast(image)
+    image = enhancer.enhance(2.0)
+
+    # Create a yellow mask from B
+    if preds is not None:
+        mask = np.where(preds == 1, 255, 0).astype(np.uint8)
+        mask = Image.merge("RGB", 
+                           (Image.fromarray(mask), 
+                            Image.fromarray(mask), 
+                            Image.fromarray(np.zeros_like(mask, dtype=np.uint8))))
+
+        # Overlay the mask on the image
+        image = Image.blend(image.convert("RGB"), mask, alpha=st.session_state.transparency)
+    
+    if point_axs is not None:
+        draw = ImageDraw.Draw(image)
+        radius = 5
+        for point in point_axs:
+            z, y, x = point
+            if view == 'xy' and z == current_idx:
+                draw.ellipse((x-radius, y-radius, x+radius, y+radius), fill="blue")
+            elif view == 'xz'and y == current_idx:
+                draw.ellipse((x-radius, z-radius, x+radius, z+radius), fill="blue")
+    return image
\ No newline at end of file