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README.MD

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+# Project Report: QPQS Ratio from Chest X-rays
+
+## Methodology
+### Data Augmentation
+The research employed data augmentation techniques, particularly CLAHE, to enhance the model's performance. However, after some experimets, augmentations such as CLAHE were found to degrade performance.
+
+### Model Modification and Training
+The last layers of pre-trained models from the TIMM (PyTorch Image Models) library were modified to suit the prediction task. The models were trained and evaluated through scripted procedures to minimize manual intervention. The dataset was split into training, validation, and testing sets with an 80-10-10 ratio.
+
+### Data Imbalance
+The dataset exhibited heavy variance, with a higher concentration of QPQS ratios within the range of 1 to 3. Beyond this range, the data points were sparse, leading to poorer performance for those QPQS values.
+
+## Models Tested
+The following models were tested:
+
+- **DenseNet**  
+    - `densenet`
+- **Inception_v3**  
+    - `inception_v3`
+
+- **ResNet**  
+    - `resnet18`
+    - `resnet34`
+    - `resnet50`
+
+- **VGG**  
+    - `vgg11`
+    - `vgg13`
+
+- **Vision Transformer (ViT)**  
+    - Base Variants:  
+        - `vit_base_8`
+        - `vit_base_16`
+        - `vit_base_32`
+    - Small Variants:  
+        - `vit_small_16`
+        - `vit_small_32`
+    - Large Variants:  
+        - `vit_large_16`
+        - `vit_large_32`
+    - Tiny Variant:  
+        - `vit_tiny`
+    - Huge Variant:  
+        - `vit_huge_14`
+
+## Results
+
+### Performance Analysis
+- **CLAHE Impact:**  
+Analysis revealed that using CLAHE reduced model performance across the board.
+
+- **Augmentation Impact:**  
+Models trained without any augmentations performed better than those trained with augmentations.
+
+- **Model Selection:**  
+Among all tested models, Vision Transformers (ViT) yielded the best performance. Further experimentation and parameter tuning with ViT models were undertaken to arrive at the best configuration.
+
+### Model Performance on Data Variance
+- **Lower QPQS Values (0-3.5):**  
+The models demonstrated decent performance for these values due to the abundance of data points.
+
+- **Higher QPQS Values:**  
+Models showed poorer performance due to the sparse representation of these values in the dataset, impacting the model's ability to generalize.
+
+## Conclusion
+The study concluded that Vision Transformer models performed the best in predicting the QPQS ratio from chest X-rays. Models without CLAHE and other augmentations delivered better performance, highlighting the importance of data preprocessing choices in model training. The data imbalance significantly impacted the model’s performance on higher QPQS values, emphasizing the need for a more balanced dataset for future work.
+
+
+## Best Model Details
+
+- **Model:** `vit_large_patch16`
+- **Training Data:** No Augmentation
+- **Validation Data:** No Augmentation
+- **Testing Data:** No Augmentation
+- **CLAHE:** Disabled
+- **Learning Rate:** `0.00005`
+- **Average Error:** `0.54`

app.py

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+import logging
+
+import torch
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+import numpy as np
+
+
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from scripts.trainer import XrayReg
+
+# Model selection dropdown options (extendable)
+MODEL_OPTIONS = {
+    "XrayReg (912yp4l6) [vit_large_patch16_224_in21k]": {
+        "ckpt": "xray_regression_noaug/912yp4l6/checkpoints/epoch=99-step=5900.ckpt",
+        "model_name": "vit_large_patch16_224_in21k"
+    },
+    "XrayReg (ie399gjr) [vit_small_patch16_224_in21k]": {
+        "ckpt": "xray_regression_noaug/ie399gjr/checkpoints/epoch=99-step=5900.ckpt",
+        "model_name": "vit_small_patch16_224_in21k"
+    },
+    "XrayReg (kcku20nx) [vit_large_patch16_224_in21k]": {
+        "ckpt": "xray_regression_noaug/kcku20nx/checkpoints/epoch=99-step=5900.ckpt",
+        "model_name": "vit_large_patch16_224_in21k"
+    },
+    "XrayReg (ohtmkj0i) [vit_base_patch16_224_in21k]": {
+        "ckpt": "xray_regression_noaug/ohtmkj0i/checkpoints/epoch=99-step=5900.ckpt",
+        "model_name": "vit_base_patch16_224_in21k"
+    },
+    "XrayReg (vlk8qrkx) [vit_large_patch16_224_in21k]": {
+        "ckpt": "xray_regression_noaug/vlk8qrkx/checkpoints/epoch=99-step=5900.ckpt",
+        "model_name": "vit_large_patch16_224_in21k"
+    },
+}
+
+
+def preprocess_image(inp):
+    """
+    Preprocess the input image.
+
+    Returns:
+        input_tensor: Tensor to be fed into the model.
+        rgb_img: NumPy array normalized to [0, 1] for GradCAM visualization.
+    """
+    try:
+        preprocess = transforms.Compose(
+            [
+                transforms.Resize((224, 224)),
+                transforms.ToTensor(),
+            ]
+        )
+        input_tensor = preprocess(inp).unsqueeze(0)
+        rgb_img = np.array(inp.resize((224, 224))).astype(np.float32) / 255.0
+        return input_tensor, rgb_img
+    except Exception as e:
+        logging.error("Error in image preprocessing: %s", e)
+        raise
+
+
+def load_custom_model(model_key):
+    model_info = MODEL_OPTIONS[model_key]
+    # Pass model_name to config for correct model instantiation
+    config = {"model": {"name": model_info["model_name"]}}
+    model = XrayReg.load_from_checkpoint(model_info["ckpt"])
+    model = model.model.cuda() if torch.cuda.is_available() else model.model
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad = True
+    return model
+
+
+def preprocess_image_custom(inp):
+    preprocess = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.Grayscale(num_output_channels=1),
+            transforms.ToTensor(),
+        ]
+    )
+    input_tensor = preprocess(inp).unsqueeze(0)
+    rgb_img = np.array(inp.resize((224, 224)).convert("RGB")).astype(np.float32) / 255.0
+    return input_tensor, rgb_img
+
+
+def predict_custom(model, input_tensor):
+    with torch.no_grad():
+        input_tensor = (
+            input_tensor.cuda() if torch.cuda.is_available() else input_tensor
+        )
+        pred = model(input_tensor)
+        pred = pred.cpu().numpy().flatten()[0]
+    return float(pred)
+
+
+def predict_and_cam_custom(inp, model):
+    input_tensor, rgb_img = preprocess_image_custom(inp)
+    value = predict_custom(model, input_tensor)
+    # GradCAM for regression: use last conv layer, target output
+    from pytorch_grad_cam import GradCAM
+    from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+
+    target_layers = [
+        layer
+        for name, layer in model.named_modules()
+        if isinstance(layer, torch.nn.Conv2d)
+    ][-1:]
+    gradcam = GradCAM(model=model, target_layers=target_layers)
+    targets = [ClassifierOutputTarget(0)]  # For regression, just use output
+    grayscale_cam = gradcam(input_tensor=input_tensor, targets=targets)[0]
+    cam_image = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
+    cam_pil = Image.fromarray(cam_image)
+    # Return as tuple (number, image) for Gradio
+    return value, cam_pil
+
+
+def create_interface_custom():
+    # Use stateful model cache to avoid reloading on every prediction
+    from functools import lru_cache
+
+    @lru_cache(maxsize=5)
+    def cached_load_model(model_key):
+        return load_custom_model(model_key)
+
+    def predict_wrapper(inp, model_key):
+        model = cached_load_model(model_key)
+        return predict_and_cam_custom(inp, model)
+
+    interface = gr.Interface(
+        fn=predict_wrapper,
+        inputs=[
+            gr.Image(type="pil"),
+            gr.Dropdown(list(MODEL_OPTIONS.keys()), label="Model"),
+        ],
+        outputs=[
+            gr.Number(label="Regression Output"),
+            gr.Image(type="pil", label="GradCAM Visualization"),
+        ],
+        examples=None,
+        title="Xray Regression Gradio App",
+        description="Upload an X-ray image and select a model to get regression output and GradCAM visualization.",
+        allow_flagging="never",
+        live=True,  # Ensures model reloads on dropdown change
+    )
+    return interface
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    interface = create_interface_custom()
+    interface.launch()
+
+
+if __name__ == "__main__":
+    main()

best_configs/vit_base_16.yaml

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+# Model configurations
+model:
+  name: "vit_base_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.001
+  optimizer: "adam"
+  max_epochs: 100
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 100
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

best_configs/vit_large_16 copy 2.yaml

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+# Model configurations
+model:
+  name: "vit_large_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.001
+  optimizer: "adam"
+  max_epochs: 100
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 100
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

best_configs/vit_large_16 copy.yaml

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+# Model configurations
+model:
+  name: "vit_large_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 100
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 100
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

best_configs/vit_large_16.yaml

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+# Model configurations
+model:
+  name: "vit_large_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 100
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 20
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

best_configs/vit_small_16.yaml

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+# Model configurations
+model:
+  name: "vit_small_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.001
+  optimizer: "adam"
+  max_epochs: 100
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 100
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/densenet.yaml

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+# Model configurations
+model:
+  name: "densenet121"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/densenet_clahe.yaml

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+# Model configurations
+model:
+  name: "densenet121"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/inception_v3.yaml

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+# Model configurations
+model:
+  name: "inception_v3"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/inception_v3_clahe.yaml

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+# Model configurations
+model:
+  name: "inception_v3"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet18.yaml

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+# Model configurations
+model:
+  name: "resnet18"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet18_clahe.yaml

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+# Model configurations
+model:
+  name: "resnet18"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet34.yaml

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+# Model configurations
+model:
+  name: "resnet34"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet34_clahe.yaml

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+# Model configurations
+model:
+  name: "resnet34"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet50.yaml

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+# Model configurations
+model:
+  name: "resnet50"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/resnet50_clahe.yaml

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+# Model configurations
+model:
+  name: "resnet50"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vgg11.yaml

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+# Model configurations
+model:
+  name: "vgg11"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vgg11_clahe.yaml

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+# Model configurations
+model:
+  name: "vgg11"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vgg13.yaml

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+# Model configurations
+model:
+  name: "vgg13"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vgg13_clahe.yaml

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+# Model configurations
+model:
+  name: "vgg13"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_16.yaml

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+# Model configurations
+model:
+  name: "vit_base_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_16_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_base_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_32.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_base_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_32_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_base_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_8.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_base_patch8_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_base_8_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_base_patch8_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_huge_14.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_huge_patch14_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_huge_14_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_huge_patch14_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_large_16.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_large_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_large_16_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_large_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_large_32.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_large_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_large_32_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_large_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_small_16.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_small_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_small_16_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_small_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_small_32.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_small_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_small_32_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_small_patch32_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_tiny.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_tiny_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: false
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

configs/vit_tiny_clahe.yaml

@@ -0,0 +1,27 @@
+# Model configurations
+model:
+  name: "vit_tiny_patch16_224_in21k"
+  pretrained: true
+  in_chans: 1
+  num_classes: 1
+
+# Training configurations
+training:
+  batch_size: 16
+  learning_rate: 0.00005
+  optimizer: "adam"
+  max_epochs: 50
+
+# Dataset configurations
+dataset:
+  root_dir: "/home/venom/Documents/x_ray/"
+  label_csv: "data/labels.csv"
+  val_split: 0.05
+  random_seed: 35
+  apply_equalization: true
+  batch_size: 16
+
+# Logging configurations
+logging:
+  log_dir: "./logs/"
+  log_every_n_steps: 1

data/labels.csv

@@ -0,0 +1,988 @@
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dataset/xray_loader.py

@@ -0,0 +1,147 @@
+import os
+import pandas as pd
+from PIL import Image
+import torch
+from torch.utils.data import Dataset, DataLoader, random_split
+from torchvision import transforms
+import lightning as L
+import kornia as K
+import numpy as np
+import random
+
+
+class XrayDataset(Dataset):
+
+    def __init__(self,
+                 data_frame,
+                 root_dir,
+                 transform=None,
+                 apply_equalization=False):
+        self.data_frame = data_frame
+        self.root_dir = root_dir
+        self.transform = transform
+        self.apply_equalization = apply_equalization
+
+    def __len__(self):
+        return len(self.data_frame)
+
+    def __getitem__(self, idx):
+        row = self.data_frame.iloc[idx]
+        img_path = os.path.join(self.root_dir, row["file_name"])
+        img = Image.open(img_path)
+
+        img = img.convert("L")
+
+        if self.transform:
+            img = self.transform(img)
+
+        # Apply CLAHE if flag is set
+        if self.apply_equalization:
+            #            img = transforms.ToTensor()(img)
+            img = K.enhance.equalize_clahe(img.unsqueeze(0)).squeeze(0)
+
+        label = torch.tensor(row["value"],
+                             dtype=torch.float)  # Ensure label is float
+        return img, label, row["file_name"]
+
+
+class XrayData(L.LightningDataModule):
+    common_seed = 42
+
+    @staticmethod
+    def seed_worker(worker_id):
+        worker_seed = torch.initial_seed() % 2**32
+        np.random.seed(worker_seed)
+        random.seed(worker_seed)
+
+    def __init__(
+        self,
+        root_dir,
+        label_csv,
+        batch_size=32,
+        val_split=0.2,
+        apply_equalization=False,
+    ):
+        super().__init__()
+        self.root_dir = root_dir
+        self.label_csv = label_csv
+        self.batch_size = batch_size
+        self.val_split = val_split
+        self.apply_equalization = apply_equalization
+
+        torch.manual_seed(self.common_seed)
+        torch.cuda.manual_seed_all(self.common_seed)
+        torch.backends.cudnn.deterministic = True
+
+        self.train_transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            # transforms.RandomHorizontalFlip(),
+            # transforms.RandomRotation(20),
+            transforms.ToTensor(),
+        ])
+
+        self.val_transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+        ])
+
+        self.full_dataset = None
+
+    def setup(self, stage=None):
+        data_frame = pd.read_csv(self.label_csv)
+        data_frame = data_frame.sample(
+            frac=1, random_state=self.common_seed).reset_index(drop=True)
+
+        dataset_size = len(data_frame)
+        val_size = int(dataset_size * self.val_split)
+        train_size = dataset_size - val_size
+
+        # Split the dataset using random_split
+        full_dataset = XrayDataset(
+            data_frame,
+            self.root_dir,
+            transform=None,  # We'll apply the correct transform later
+            apply_equalization=self.apply_equalization,
+        )
+
+        self.train_dataset, self.val_dataset = random_split(
+            full_dataset,
+            [train_size, val_size],
+            generator=torch.Generator().manual_seed(self.common_seed),
+        )
+
+        def train_transforms(x):
+            return self.train_transform(x) if self.train_transform else x
+
+        def val_transforms(x):
+            return self.val_transform(x) if self.val_transform else x
+
+        self.train_dataset.dataset.transform = train_transforms
+        self.val_dataset.dataset.transform = val_transforms
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=4,
+            worker_init_fn=self.seed_worker,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=4,
+            worker_init_fn=self.seed_worker,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=4,
+            worker_init_fn=self.seed_worker,
+        )

infer.ipynb

@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/venom/miniforge3/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/venom/miniforge3/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
+      "  @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from PIL import Image\n",
+    "import torch\n",
+    "from torch.utils.data import Dataset, DataLoader, random_split\n",
+    "from torchvision import transforms\n",
+    "import lightning as L\n",
+    "import kornia as K\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import sys\n",
+    "\n",
+    "PROJECT_ROOT = os.path.abspath(os.path.normpath(\"/home/venom/repo/xray-exp/\"))\n",
+    "sys.path.append(PROJECT_ROOT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from models.model_loader import create_model\n",
+    "from scripts.trainer import XrayReg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class XrayInferenceDataset(Dataset):\n",
+    "\n",
+    "    def __init__(self, root_dir, transform=None):\n",
+    "        self.root_dir = root_dir\n",
+    "        self.file_names = os.listdir(root_dir)\n",
+    "        self.transform = transform\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.file_names)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        file_name = self.file_names[idx]\n",
+    "        img_path = os.path.join(self.root_dir, file_name)\n",
+    "        img = Image.open(img_path)\n",
+    "\n",
+    "        img = img.convert(\"L\")\n",
+    "\n",
+    "        if self.transform:\n",
+    "            img = self.transform(img)\n",
+    "\n",
+    "        return img, file_name\n",
+    "\n",
+    "\n",
+    "class XrayDataInference(L.LightningDataModule):\n",
+    "    common_seed = 42\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def seed_worker(worker_id):\n",
+    "        worker_seed = torch.initial_seed() % 2**32\n",
+    "        np.random.seed(worker_seed)\n",
+    "        random.seed(worker_seed)\n",
+    "\n",
+    "    def __init__(self, root_dir, batch_size=32):\n",
+    "        super().__init__()\n",
+    "        self.root_dir = root_dir\n",
+    "        self.batch_size = batch_size\n",
+    "\n",
+    "        torch.manual_seed(self.common_seed)\n",
+    "        torch.cuda.manual_seed_all(self.common_seed)\n",
+    "        torch.backends.cudnn.deterministic = True\n",
+    "\n",
+    "        self.transform = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.Resize((224, 224)),\n",
+    "                transforms.ToTensor(),\n",
+    "            ]\n",
+    "        )\n",
+    "        self.inference_dataset = XrayInferenceDataset(\n",
+    "            self.root_dir, transform=self.transform\n",
+    "        )\n",
+    "\n",
+    "    def inference_dataloader(self):\n",
+    "        return DataLoader(\n",
+    "            self.inference_dataset,\n",
+    "            batch_size=self.batch_size,\n",
+    "            shuffle=False,\n",
+    "            num_workers=4,\n",
+    "            worker_init_fn=self.seed_worker,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/venom/miniforge3/lib/python3.10/site-packages/timm/models/_factory.py:117: UserWarning: Mapping deprecated model name vit_large_patch16_224_in21k to current vit_large_patch16_224.augreg_in21k.\n",
+      "  model = create_fn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer_config = XrayReg.load_from_checkpoint(\n",
+    "    \"/home/venom/repo/xray-exp/xray_regression_noaug/912yp4l6/checkpoints/epoch=99-step=5900.ckpt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infer_ds = XrayDataInference(\n",
+    "    \"/home/venom/Downloads/CXR AI PNG- FINAL 13-12/\", batch_size=16\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run inference against the infer_ds and log to a file (file name run_name)\n",
+    "\n",
+    "model = trainer_config.model\n",
+    "\n",
+    "model.eval()\n",
+    "model = model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run inference against the infer_ds and log to a file (file name run_ID)\n",
+    "RUN_ID = \"912yp4l6\"\n",
+    "\n",
+    "with open(f\"/home/venom/repo/xray-exp/inference_results/{RUN_ID}.csv\", \"w\") as f:\n",
+    "    f.write(\"file_name,predicted\\n\")\n",
+    "    for img, file_name in infer_ds.inference_dataloader():\n",
+    "        img = img.cuda()\n",
+    "        with torch.no_grad():\n",
+    "            pred = model(img)\n",
+    "            pred = pred.cpu().numpy()\n",
+    "            for i in range(len(pred)):\n",
+    "                f.write(f\"{file_name[i]},{pred[i][0]}\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

inference_results/912yp4l6.csv

@@ -0,0 +1,401 @@
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