Upload cxr_image_synthesis_latent_diffusion_model version 1.0.1

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/figure_1.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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configs/inference.json

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+{
+    "imports": [
+        "$import torch",
+        "$from datetime import datetime",
+        "$from pathlib import Path",
+        "$from transformers import CLIPTextModel",
+        "$from transformers import CLIPTokenizer"
+    ],
+    "bundle_root": ".",
+    "dataset_dir": "",
+    "dataset": "",
+    "evaluator": "",
+    "inferer": "",
+    "load_old": 1,
+    "model_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/output'",
+    "create_output_dir": "$Path(@output_dir).mkdir(exist_ok=True)",
+    "prompt": "Big right-sided pleural effusion",
+    "prompt_list": "$['', @prompt]",
+    "guidance_scale": 7.0,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "tokenizer": "$CLIPTokenizer.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\", subfolder=\"tokenizer\")",
+    "text_encoder": "$CLIPTextModel.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\", subfolder=\"text_encoder\")",
+    "tokenized_prompt": "$@tokenizer(@prompt_list, padding=\"max_length\", [email protected]_max_length, truncation=True,return_tensors=\"pt\")",
+    "prompt_embeds": "$@text_encoder(@tokenized_prompt.input_ids.squeeze(1))[0].to(@device)",
+    "out_file": "$datetime.now().strftime('sample_%H%M%S_%d%m%Y')",
+    "autoencoder_def": {
+        "_target_": "monai.networks.nets.AutoencoderKL",
+        "spatial_dims": 2,
+        "in_channels": 1,
+        "out_channels": 1,
+        "latent_channels": 3,
+        "channels": [
+            64,
+            128,
+            128,
+            128
+        ],
+        "num_res_blocks": 2,
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "attention_levels": [
+            false,
+            false,
+            false,
+            false
+        ],
+        "with_encoder_nonlocal_attn": false,
+        "with_decoder_nonlocal_attn": false
+    },
+    "network_def": "@diffusion_def",
+    "load_autoencoder_path": "$@model_dir + '/autoencoder.pt'",
+    "load_autoencoder_func": "$@autoencoder_def.load_old_state_dict if bool(@load_old) else @autoencoder_def.load_state_dict",
+    "load_autoencoder": "$@load_autoencoder_func(torch.load(@load_autoencoder_path))",
+    "autoencoder": "$@autoencoder_def.to(@device)",
+    "diffusion_def": {
+        "_target_": "monai.networks.nets.DiffusionModelUNet",
+        "spatial_dims": 2,
+        "in_channels": 3,
+        "out_channels": 3,
+        "channels": [
+            256,
+            512,
+            768
+        ],
+        "num_res_blocks": 2,
+        "attention_levels": [
+            false,
+            true,
+            true
+        ],
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "resblock_updown": false,
+        "num_head_channels": [
+            0,
+            512,
+            768
+        ],
+        "with_conditioning": true,
+        "transformer_num_layers": 1,
+        "cross_attention_dim": 1024
+    },
+    "load_diffusion_path": "$@model_dir + '/model.pt'",
+    "load_diffusion_func": "$@diffusion_def.load_old_state_dict if bool(@load_old) else @diffusion_def.load_state_dict",
+    "load_diffusion": "$@load_diffusion_func(torch.load(@load_diffusion_path))",
+    "diffusion": "$@diffusion_def.to(@device)",
+    "scheduler": {
+        "_target_": "monai.networks.schedulers.DDIMScheduler",
+        "_requires_": [
+            "@load_diffusion",
+            "@load_autoencoder"
+        ],
+        "beta_start": 0.0015,
+        "beta_end": 0.0205,
+        "num_train_timesteps": 1000,
+        "schedule": "scaled_linear_beta",
+        "prediction_type": "v_prediction",
+        "clip_sample": false
+    },
+    "noise": "$torch.randn((1, 3, 64, 64)).to(@device)",
+    "set_timesteps": "[email protected]_timesteps(num_inference_steps=50)",
+    "sampler": {
+        "_target_": "scripts.sampler.Sampler",
+        "_requires_": "@set_timesteps"
+    },
+    "sample": "[email protected]_fn(@noise, @autoencoder, @diffusion, @scheduler, @prompt_embeds)",
+    "saver": {
+        "_target_": "scripts.saver.JPGSaver",
+        "_requires_": "@create_output_dir",
+        "output_dir": "@output_dir"
+    },
+    "run": "[email protected](@sample, @out_file)",
+    "save": "$torch.save(@sample, @output_dir + '/' + @out_file + '.pt')"
+}

configs/logging.conf

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

configs/metadata.json

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+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
+    "version": "1.0.1",
+    "changelog": {
+        "1.0.1": "update to huggingface hosting",
+        "1.0.0": "Initial release"
+    },
+    "monai_version": "1.4.0",
+    "pytorch_version": "2.5.1",
+    "numpy_version": "1.26.4",
+    "required_packages_version": {
+        "transformers": "4.46.3"
+    },
+    "task": "Chest X-ray image synthesis",
+    "description": "A generative model for creating high-resolution chest X-ray based on MIMIC dataset",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "authors": "Walter Hugo Lopez Pinaya, Mark Graham, Eric Kerfoot, Virginia Fernandez",
+    "data_source": "https://physionet.org/content/mimic-cxr-jpg/2.0.0/",
+    "data_type": "image",
+    "image_classes": "Radiography (X-ray) with 512 x 512 pixels",
+    "intended_use": "This is a research tool/prototype and not to be used clinically",
+    "network_data_format": {
+        "inputs": {
+            "latent_representation": {
+                "type": "image",
+                "format": "magnitude",
+                "modality": "CXR",
+                "num_channels": 3,
+                "spatial_shape": [
+                    77,
+                    64,
+                    64
+                ],
+                "dtype": "float32",
+                "value_range": [],
+                "is_patch_data": false
+            },
+            "timesteps": {
+                "format": "magnitude",
+                "num_channels": 1,
+                "spatial_shape": [
+                    1
+                ],
+                "type": "vector",
+                "value_range": [
+                    0,
+                    1000
+                ],
+                "dtype": "long"
+            },
+            "context": {
+                "format": "magnitude",
+                "num_channels": 1024,
+                "spatial_shape": [
+                    1
+                ],
+                "type": "vector",
+                "value_range": [],
+                "dtype": "float32"
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "magnitude",
+                "modality": "CXR",
+                "num_channels": 1,
+                "spatial_shape": [
+                    512,
+                    512
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "X-ray"
+                }
+            }
+        }
+    }
+}

docs/README.md

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+# Description
+
+A diffusion model to synthetise X-Ray images based on radiological report impressions.
+
+# Model Overview
+This model is trained from scratch using the Latent Diffusion Model architecture [1] and is used for the synthesis of
+2D Chest X-ray conditioned on Radiological reports. The model is divided into two parts: an autoencoder with a
+KL-regularisation model that compresses data into a latent space and a diffusion model that learns to generate
+conditioned synthetic latent representations. This model is conditioned on Findings and Impressions from radiological
+reports. The original repository can be found [here](https://github.com/Warvito/generative_chestxray)
+
+![](./figure_1.png) <br>
+<p align="center">
+Figure 1 - Synthetic images from the model. </p>
+
+# Data
+The model was trained on brain data from 90,000 participants from the MIMIC dataset [2] [3]. We downsampled the
+original images to have a format of 512 x 512 pixels.
+
+## Preprocessing
+We resized the original images to make the smallest sides have 512 pixels. When inputting it to the network, we center
+cropped the images to 512 x 512. The pixel intensity was normalised to be between [0, 1]. The text data was obtained
+from associated radiological reports. We randoomly extracted sentences from the findings and impressions sections of the
+reports, having a maximum of 5 sentences and 77 tokens. The text was tokenised using the CLIPTokenizer from
+transformers package (https://github.com/huggingface/transformers) (pretrained model
+"stabilityai/stable-diffusion-2-1-base") and then encoded using CLIPTextModel from the same package and pretrained
+model.
+
+# Examples of inference
+
+Here we included a few examples of commands to sample images from the model and save them as .jpg files. The available
+arguments for this task are: "--prompt" (str) text prompt to condition the model on; "--guidance_scale" (float), the
+parameter that controls how much the image generation process follows the text prompt. The higher the value, the more
+the image sticks to a given text input (the common range is between 1-21).
+
+Examples:
+
+```shell
+$ python -m monai.bundle run --config_file configs/inference.json --prompt "Big right-sided pleural effusion" --guidance_scale 7.0
+```
+
+```shell
+$ python -m monai.bundle run --config_file configs/inference.json --prompt "Small right-sided pleural effusion" --guidance_scale 7.0
+```
+
+```shell
+$ python -m monai.bundle run --config_file configs/inference.json --prompt "Bilateral pleural effusion" --guidance_scale 7.0
+```
+
+```shell
+$ python -m monai.bundle run --config_file configs/inference.json --prompt "Cardiomegaly" --guidance_scale 7.0
+```
+
+## Using a new version of the model
+
+If you want to use the checkpoints from a newly fine-tuned model, you need to set parameter load_old to 0 when you run inference,
+to avoid the function load_old_state_dict being called instead of load_state_dict to be called, currently default, as it is
+required to load the checkpoint from the original GenerativeModels repository.
+
+```shell
+$ python -m monai.bundle run --config_file configs/inference.json --prompt "Pleural effusion." --guidance_scale 7.0 --load_old 0
+```
+
+## References
+
+
+[1] Pinaya, Walter HL, et al. "Brain imaging generation with latent diffusion models." MICCAI Workshop on Deep Generative Models. Springer, Cham, 2022.
+
+[2] Johnson, A., Lungren, M., Peng, Y., Lu, Z., Mark, R., Berkowitz, S., & Horng, S. (2019). MIMIC-CXR-JPG - chest radiographs with structured labels (version 2.0.0). PhysioNet. https://doi.org/10.13026/8360-t248.
+
+[3] Johnson AE, Pollard TJ, Berkowitz S, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. MIMIC-CXR: A large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042. 2019 Jan 21.

models/autoencoder.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:17a704d96984ce07423d98695949f19f91ebca58c562067603995a22368cb116
+size 18491405

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d3a16c4798847614fd262e4132c44414aa30b0aa04044ab1b2c4e23e73d03c3
+size 1054758129

scripts/sampler.py

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+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from monai.utils import optional_import
+from torch.cuda.amp import autocast
+
+tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
+
+
+class Sampler:
+    def __init__(self) -> None:
+        super().__init__()
+
+    @torch.no_grad()
+    def sampling_fn(
+        self,
+        noise: torch.Tensor,
+        autoencoder_model: nn.Module,
+        diffusion_model: nn.Module,
+        scheduler: nn.Module,
+        prompt_embeds: torch.Tensor,
+        guidance_scale: float = 7.0,
+        scale_factor: float = 0.3,
+    ) -> torch.Tensor:
+        if has_tqdm:
+            progress_bar = tqdm(scheduler.timesteps)
+        else:
+            progress_bar = iter(scheduler.timesteps)
+
+        for t in progress_bar:
+            noise_input = torch.cat([noise] * 2)
+            model_output = diffusion_model(
+                noise_input, timesteps=torch.Tensor((t,)).to(noise.device).long(), context=prompt_embeds
+            )
+            noise_pred_uncond, noise_pred_text = model_output.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+            noise, _ = scheduler.step(noise_pred, t, noise)
+
+        with autocast():
+            sample = autoencoder_model.decode_stage_2_outputs(noise / scale_factor)
+
+        return sample

scripts/saver.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import numpy as np
+import torch
+from PIL import Image
+
+
+class JPGSaver:
+    def __init__(self, output_dir: str) -> None:
+        super().__init__()
+        self.output_dir = output_dir
+
+    def save(self, image_data: torch.Tensor, file_name: str) -> None:
+        image_data = np.clip(image_data.cpu().numpy(), 0, 1)
+        image_data = (image_data * 255).astype(np.uint8)
+        im = Image.fromarray(image_data[0, 0])
+        im.save(self.output_dir + "/" + file_name + ".jpg")