added batching runner

.gitignore

@@ -20,5 +20,7 @@ modules/core/onnx_driver/
 *.so
 surrealml/rust_surrealml.cpython-310-darwin.so
 .surmlcache
-modules/core/model_stash/
-modules/pipelines/runners/integrated_training_runner/run_env/
+./modules/core/model_stash/
+./modules/pipelines/runners/integrated_training_runner/run_env/
+./modules/pipelines/runners/batch_training_runner/run_env/
+./modules/pipelines/data_access/target/

modules/pipelines/runners/batch_training_runner/README.md

@@ -0,0 +1,18 @@
+# Batch Runner
+
+This is where the image loading is still written in Rust, but there a python bindings meaning that the ML engineer
+can train the model in pure python importing and using the Rust code as a python library. This is done my merely
+pointing to the `data_access` library and performing a pip install. 
+
+This runner is specifically used to test if the model can handle batching. It uses both batches and epochs to train the model.
+
+# Running the runner
+
+We can run the runner by running the following command:
+
+```bash
+sh ./scripts/run.sh
+```
+
+This will import the Rust image loading code which will then load the image giving us the raw resized and flatterned
+image data in python. This can be directly fed into the ML model for training.

modules/pipelines/runners/batch_training_runner/scripts/run.sh

@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+# navigate to directory
+SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
+cd $SCRIPTPATH
+
+cd ..
+
+if [ -d run_env ]; then
+    rm -rf run_env
+fi
+
+python3 -m venv run_env
+source run_env/bin/activate
+
+pip install ../../data_access
+pip install -r ../../../../requirements.txt
+
+python src/main.py
+
+rm -rf run_env

modules/pipelines/runners/batch_training_runner/src/main.py

@@ -0,0 +1,91 @@
+from data_access_layer.data_access_layer import read_rgb_image
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+
+
+class SimpleCNN(nn.Module):
+
+    def __init__(self):
+        super(SimpleCNN, self).__init__()
+        # Input shape: (batch_size, 3, 480, 853)
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1) # Output: (batch_size, 16, 240, 427)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1) # Output: (batch_size, 32, 120, 214)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1) # Output: (batch_size, 64, 60, 107)
+        self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) # Output: (batch_size, 128, 30, 54)
+        self.flatten = nn.Flatten() # Flatten the output for the fully connected layer
+        self.fc1 = nn.Linear(128 * 30 * 54, 512) # First fully connected layer
+        self.fc2 = nn.Linear(512, 3) # Output layer with 3 disease classes
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = F.relu(self.conv4(x))
+        x = self.flatten(x)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+def main():
+    #  Define parameters
+    height = 480
+    width = 853
+    channels = 3
+    num_epochs = 100
+
+    # Create data
+    image = read_rgb_image("./assets/test.jpg", width, height)
+    image_array = np.array(image, dtype=np.float32).reshape((channels, height, width))
+    image_tensor = torch.from_numpy(image_array)
+
+    # Create batch data
+    batch_data = torch.stack([image_tensor for _ in range(3)], dim=0)
+    batch_data_2 = batch_data.clone()
+    all_batches = [batch_data, batch_data_2]
+
+    # Create tags and classes
+    disease_names = ['Colon Cancer', 'IBS', 'IBD']  # Adjust as necessary
+    num_classes = len(disease_names)
+
+    # Convert disease names to indices for use as tags
+    disease_to_index = {disease: index for index, disease in enumerate(disease_names)}
+    tags = torch.tensor([disease_to_index[disease] for disease in disease_names])
+
+    # Instantiate the model and adjust the final layer to match the number of classes/tags
+    model = SimpleCNN()
+    criterion = nn.CrossEntropyLoss()  # Loss function suitable for classification
+    optimizer = optim.Adam(model.parameters(), lr=0.001)  # Optimizer
+    model.train() # Sets the model to training mode
+
+    # This trains the model, using both batches and epochs
+    for epoch in range(num_epochs):
+        model.train()  # Set the model to training mode
+        # Iterate through the data in batches
+        for i, data in enumerate(all_batches):
+            # Forward pass
+            outputs = model(data)
+            loss = criterion(outputs, tags)
+
+            # Calculate accuracy
+            _, predicted = torch.max(outputs.data, 1)
+            correct = (predicted == tags).sum().item()
+            accuracy = correct / len(tags)
+
+            # Backward pass and optimization
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # Now, print out loss and accuracy for each batch
+            print(f"Batch [{i + 1}/{len(all_batches)}], Loss: {loss.item():.4f}, Accuracy: {accuracy:.4f}")
+
+            # After finishing all batches for the epoch, print a simple message
+        print(f"Epoch {epoch + 1} finished")
+
+
+if __name__ == "__main__":
+    main()