From 9fca4c35b82e28a7f955ce13b0f2dfa8ca1befa5 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 22 Sep 2023 14:44:16 +1000
Subject: [PATCH 01/79] Set up basic project skeleton

---
 recognition/Problem_47452752/README.md  | 1 +
 recognition/Problem_47452752/dataset.py | 1 +
 recognition/Problem_47452752/modules.py | 1 +
 recognition/Problem_47452752/predict.py | 1 +
 recognition/Problem_47452752/train.py   | 8 ++++++++
 5 files changed, 12 insertions(+)
 create mode 100644 recognition/Problem_47452752/README.md
 create mode 100644 recognition/Problem_47452752/dataset.py
 create mode 100644 recognition/Problem_47452752/modules.py
 create mode 100644 recognition/Problem_47452752/predict.py
 create mode 100644 recognition/Problem_47452752/train.py

diff --git a/recognition/Problem_47452752/README.md b/recognition/Problem_47452752/README.md
new file mode 100644
index 000000000..2cd4adb2b
--- /dev/null
+++ b/recognition/Problem_47452752/README.md
@@ -0,0 +1 @@
+# README.md file that describes your report.
\ No newline at end of file
diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
new file mode 100644
index 000000000..947b41bd1
--- /dev/null
+++ b/recognition/Problem_47452752/dataset.py
@@ -0,0 +1 @@
+# containing the data loader for loading and preprocessing your data
\ No newline at end of file
diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
new file mode 100644
index 000000000..713287117
--- /dev/null
+++ b/recognition/Problem_47452752/modules.py
@@ -0,0 +1 @@
+# containing the source code of the components of your model. Each component must be implementated as a class or a function
\ No newline at end of file
diff --git a/recognition/Problem_47452752/predict.py b/recognition/Problem_47452752/predict.py
new file mode 100644
index 000000000..309350c73
--- /dev/null
+++ b/recognition/Problem_47452752/predict.py
@@ -0,0 +1 @@
+# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
\ No newline at end of file
diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
new file mode 100644
index 000000000..974881b93
--- /dev/null
+++ b/recognition/Problem_47452752/train.py
@@ -0,0 +1,8 @@
+# containing the source code for training, validating, testing and saving your model. 
+# The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
+# Make sure to plot the losses and metrics during training
+
+import modules
+import dataset
+
+

From fe2533578a1769675ffe4f706b4ca1ff08b5f9f4 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 22 Sep 2023 15:06:39 +1000
Subject: [PATCH 02/79] created .gitignore to store data and model files

---
 recognition/Problem_47452752/.gitignore | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 recognition/Problem_47452752/.gitignore

diff --git a/recognition/Problem_47452752/.gitignore b/recognition/Problem_47452752/.gitignore
new file mode 100644
index 000000000..28966cc73
--- /dev/null
+++ b/recognition/Problem_47452752/.gitignore
@@ -0,0 +1,3 @@
+# Ignore datasets and models
+data/
+models/

From 82802d563520f342d909d21247eb36d2e5cec32d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sat, 21 Oct 2023 04:41:25 +1000
Subject: [PATCH 03/79] Setup the UNet modules according to the paper

---
 recognition/Problem_47452752/modules.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 713287117..4c26a237b 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -1 +1,19 @@
-# containing the source code of the components of your model. Each component must be implementated as a class or a function
\ No newline at end of file
+"""
+Contains the source code of the components in my model. Each component is implemented as a class or function. 
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ContextModule(nn.Module):
+    pass
+
+
+class LocalizationModule(nn.Module):
+    pass
+
+
+class UNet(nn.Module):
+    pass

From 9becad1369e0ed3424a6ad62be51fabd348da2b8 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sat, 21 Oct 2023 05:41:57 +1000
Subject: [PATCH 04/79] Attempted to build the UNet according to paper...

---
 recognition/Problem_47452752/modules.py | 57 ++++++++++++++++++++++---
 1 file changed, 52 insertions(+), 5 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 4c26a237b..7f504e930 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -7,13 +7,60 @@
 import torch.nn.functional as F
 
 
-class ContextModule(nn.Module):
-    pass
+class Encoder(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        # leakyReLU, convolution, normalization
+        pass
 
+    def forward(self, x):
+        # element-wise summation of pre and post processed features
+        pass
 
-class LocalizationModule(nn.Module):
-    pass
+
+class Decoder(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        pass
+
+    def forward(self, x, skip_connection):
+        # halve the in_features, concativate with skip features, then halve again
+        pass
 
 
 class UNet(nn.Module):
-    pass
+    def __init__(self, in_channels, out_channels):
+        super(UNet, self).__init__()
+
+        # Context Pathway (encoders)
+        self.enc1 = Encoder(in_channels, 16)
+        self.enc2 = Encoder(32, 32)
+        self.enc3 = Encoder(64, 64)
+        self.enc4 = Encoder(128, 128)
+        self.enc5 = Encoder(256, 256)
+
+        # Convolutions for downsampling
+        self.down1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
+        self.down2 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
+        self.down3 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.down4 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
+        self.down5 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        # Localization Pathway
+        self.up1 = Decoder(256, 64)
+        self.up2 = Decoder(64, 64)
+        self.up3 = Decoder(128, 128)
+
+        # Final output layer
+        self.out_conv = nn.Conv3d(16, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        # Context Pathway
+        enc1 = self.enc1(x)
+        enc2 = self.enc2(self.down1(enc1))
+        enc3 = self.enc3(self.down2(enc2))
+
+        # Localization Pathway with skip connections (decoding)
+        dec1 = self.up1(enc3, enc2)
+        dec2 = self.up2(dec1, enc1)
+
+        # Final output
+        return self.out_conv(dec2)

From 597515be53cc71d8d6f390c326f6d92d695d39cc Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 13:36:43 +1000
Subject: [PATCH 05/79] Implemented Encoder, changed UNet accordingly

---
 recognition/Problem_47452752/modules.py | 56 +++++++++++++++++--------
 1 file changed, 39 insertions(+), 17 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 7f504e930..0640ac108 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -8,13 +8,32 @@
 
 
 class Encoder(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        # leakyReLU, convolution, normalization
-        pass
+    """
+    Encoder that computes the activations in the context pathway. This class behaves as the 'context module' from the paper.
+    Each Encoder module is a pre-activation residual block with two 3x3x3 convolutional layers and a dropout layer (p = 0.3) in between.
+    Instance normalization and leaky ReLU is used throughout the network.
+    """
+
+    def __init__(self, in_channels):
+        # We use leakyReLU with negative slope 1e-2
+        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
+        # 3x3x3 convolutional layer that preserves the input size
+        self.conv = nn.Conv3d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+        # Dropout layer with p_dropout = 0.3
+        self.dropout = nn.Dropout(p=0.3)
+        # Normalize the batch with instance normalization
+        self.norm = nn.InstanceNorm3d()
 
     def forward(self, x):
-        # element-wise summation of pre and post processed features
-        pass
+        shortcut = x
+
+        residual = self.relu(self.norm(x))
+        residual = self.dropout(residual)
+        residual = self.relu(self.norm(x))
+
+        return residual + shortcut
 
 
 class Decoder(nn.Module):
@@ -22,7 +41,7 @@ def __init__(self, in_channels, out_channels):
         pass
 
     def forward(self, x, skip_connection):
-        # halve the in_features, concativate with skip features, then halve again
+        # halve the in_features, concativate with skip features
         pass
 
 
@@ -31,11 +50,11 @@ def __init__(self, in_channels, out_channels):
         super(UNet, self).__init__()
 
         # Context Pathway (encoders)
-        self.enc1 = Encoder(in_channels, 16)
-        self.enc2 = Encoder(32, 32)
-        self.enc3 = Encoder(64, 64)
-        self.enc4 = Encoder(128, 128)
-        self.enc5 = Encoder(256, 256)
+        self.enc1 = Encoder(in_channels)
+        self.enc2 = Encoder(32)
+        self.enc3 = Encoder(64)
+        self.enc4 = Encoder(128)
+        self.enc5 = Encoder(256)
 
         # Convolutions for downsampling
         self.down1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
@@ -45,18 +64,21 @@ def __init__(self, in_channels, out_channels):
         self.down5 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
 
         # Localization Pathway
-        self.up1 = Decoder(256, 64)
-        self.up2 = Decoder(64, 64)
-        self.up3 = Decoder(128, 128)
+        self.up1 = Decoder(256, 128)
+        self.up2 = Decoder(128, 64)
+        self.up3 = Decoder(64, 32)
+        self.up4 = Decoder(32, 16)
 
         # Final output layer
         self.out_conv = nn.Conv3d(16, out_channels, kernel_size=1)
 
     def forward(self, x):
         # Context Pathway
-        enc1 = self.enc1(x)
-        enc2 = self.enc2(self.down1(enc1))
-        enc3 = self.enc3(self.down2(enc2))
+        enc1 = self.enc1(self.down1(x))
+        enc2 = self.enc2(self.down2(enc1))
+        enc3 = self.enc3(self.down3(enc2))
+        enc4 = self.enc4(self.down1(enc3))
+        enc5 = self.enc5(self.down2(enc4))
 
         # Localization Pathway with skip connections (decoding)
         dec1 = self.up1(enc3, enc2)

From da0ebe4eb28396c10789e980f4a82d79ee6c9053 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 13:40:15 +1000
Subject: [PATCH 06/79] Changed UNet Encoder input args

---
 recognition/Problem_47452752/modules.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 0640ac108..05fd7f34a 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -50,13 +50,13 @@ def __init__(self, in_channels, out_channels):
         super(UNet, self).__init__()
 
         # Context Pathway (encoders)
-        self.enc1 = Encoder(in_channels)
+        self.enc1 = Encoder(16)
         self.enc2 = Encoder(32)
         self.enc3 = Encoder(64)
         self.enc4 = Encoder(128)
         self.enc5 = Encoder(256)
 
-        # Convolutions for downsampling
+        # Convolutions that connect context modules, used for downsampling
         self.down1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
         self.down2 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
         self.down3 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)

From 241cbb7fb92ff08c03c04707d0267bd1d323dcf6 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 15:26:11 +1000
Subject: [PATCH 07/79] Implemented upsampling module

---
 recognition/Problem_47452752/modules.py | 57 ++++++++++++++++++++++++-
 1 file changed, 55 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 05fd7f34a..9d7e4768d 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -15,6 +15,7 @@ class Encoder(nn.Module):
     """
 
     def __init__(self, in_channels):
+        super(Encoder, self).__init__()
         # We use leakyReLU with negative slope 1e-2
         self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
         # 3x3x3 convolutional layer that preserves the input size
@@ -24,7 +25,7 @@ def __init__(self, in_channels):
         # Dropout layer with p_dropout = 0.3
         self.dropout = nn.Dropout(p=0.3)
         # Normalize the batch with instance normalization
-        self.norm = nn.InstanceNorm3d()
+        self.norm = nn.InstanceNorm3d(in_channels)
 
     def forward(self, x):
         shortcut = x
@@ -36,11 +37,63 @@ def forward(self, x):
         return residual + shortcut
 
 
+class Up(nn.Module):
+    """
+    Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
+    We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
+    that halves the number of feature maps.
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(Up, self).__init__()
+        self.upsample = nn.Sequential(
+            # Upscale the feature voxels
+            nn.Upsample(scale_factor=2, mode="nearest"),
+            # Normalize the batch useing instance normalization
+            nn.InstanceNorm3d(in_channels),
+            # Introduce non-linearity using leakyReLU with negative slope 1e-2
+            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
+            # Apply 3x3x3 convolution that havles number of feature maps
+            nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
+        )
+
+    def forward(self, x):
+        return self.conv(self.relu(self.norm(x)))
+
+
 class Decoder(nn.Module):
     def __init__(self, in_channels, out_channels):
-        pass
+        super(Decoder, self).__init__()
+        # We use leakyReLU with negative slope 1e-2
+        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
+
+        # Upsampling module:
+
+        self.upsample = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode="nearest"),
+            nn.InstanceNorm3d(in_channels),
+            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
+            nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
+        )
+
+        # Localization module:
+
+        self.norm1 = nn.InstanceNorm3d(2 * out_channels)
+        #
+        self.conv1 = nn.Conv3d(
+            2 * out_channels, 2 * out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        self.norm2 = nn.InstanceNorm3d(out_channels)
+        self.conv2 = nn.Conv3d(
+            in_channels // 2, out_channels, kernel_size=3, stride=1, padding=1
+        )
 
     def forward(self, x, skip_connection):
+        upsampled = self.upsample(x)
+        upsampled = self.relu(self.norm(upsampled))
+        upsampled = self.conv(upsampled)
+
         # halve the in_features, concativate with skip features
         pass
 

From c70f54a4b29d8eac9f53741cf3651efbfc775f8c Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 15:44:33 +1000
Subject: [PATCH 08/79] Implemented Localisation module

---
 recognition/Problem_47452752/modules.py | 60 ++++++++++++-------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 9d7e4768d..7b6160cbc 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -7,15 +7,15 @@
 import torch.nn.functional as F
 
 
-class Encoder(nn.Module):
+class Context(nn.Module):
     """
     Encoder that computes the activations in the context pathway. This class behaves as the 'context module' from the paper.
-    Each Encoder module is a pre-activation residual block with two 3x3x3 convolutional layers and a dropout layer (p = 0.3) in between.
+    Each Context module is a pre-activation residual block with two 3x3x3 convolutional layers and a dropout layer (p = 0.3) in between.
     Instance normalization and leaky ReLU is used throughout the network.
     """
 
     def __init__(self, in_channels):
-        super(Encoder, self).__init__()
+        super(Context, self).__init__()
         # We use leakyReLU with negative slope 1e-2
         self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
         # 3x3x3 convolutional layer that preserves the input size
@@ -30,11 +30,11 @@ def __init__(self, in_channels):
     def forward(self, x):
         shortcut = x
 
-        residual = self.relu(self.norm(x))
-        residual = self.dropout(residual)
-        residual = self.relu(self.norm(x))
+        x = self.relu(self.norm(x))
+        x = self.dropout(x)
+        x = self.relu(self.norm(x))
 
-        return residual + shortcut
+        return x + shortcut
 
 
 class Up(nn.Module):
@@ -57,43 +57,43 @@ def __init__(self, in_channels, out_channels):
             nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
         )
 
-    def forward(self, x):
-        return self.conv(self.relu(self.norm(x)))
+    def forward(self, x, skip_channels):
+        x = self.conv(self.relu(self.norm(x)))
+        return torch.cat([x, skip_channels], dim=1)
 
 
-class Decoder(nn.Module):
+class Localisation(nn.Module):
     def __init__(self, in_channels, out_channels):
-        super(Decoder, self).__init__()
+        super(Localisation, self).__init__()
         # We use leakyReLU with negative slope 1e-2
         self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
 
-        # Upsampling module:
+        # The Localisation module involves two rounds of normalizations and convoltions
 
-        self.upsample = nn.Sequential(
-            nn.Upsample(scale_factor=2, mode="nearest"),
-            nn.InstanceNorm3d(in_channels),
-            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
-            nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
-        )
-
-        # Localization module:
-
-        self.norm1 = nn.InstanceNorm3d(2 * out_channels)
-        #
+        # Round 1, we apply a 3x3x3 convolution
+        self.norm1 = nn.InstanceNorm3d(in_channels)
         self.conv1 = nn.Conv3d(
-            2 * out_channels, 2 * out_channels, kernel_size=3, stride=1, padding=1
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
-
+        # Round 2, we halve the number of feature maps using a 1x1x1 convolution
         self.norm2 = nn.InstanceNorm3d(out_channels)
         self.conv2 = nn.Conv3d(
             in_channels // 2, out_channels, kernel_size=3, stride=1, padding=1
         )
 
+        def forward(self, x):
+            x = self.conv1(self.relu(self.norm1(x)))
+            x = self.conv2(self.relu(self.norm2(x)))
+            return x
+
     def forward(self, x, skip_connection):
         upsampled = self.upsample(x)
         upsampled = self.relu(self.norm(upsampled))
         upsampled = self.conv(upsampled)
 
+        # Concatenate with skip features from the context pathway
+        x = torch.cat([x, skip_features], dim=1)
+
         # halve the in_features, concativate with skip features
         pass
 
@@ -103,11 +103,11 @@ def __init__(self, in_channels, out_channels):
         super(UNet, self).__init__()
 
         # Context Pathway (encoders)
-        self.enc1 = Encoder(16)
-        self.enc2 = Encoder(32)
-        self.enc3 = Encoder(64)
-        self.enc4 = Encoder(128)
-        self.enc5 = Encoder(256)
+        self.enc1 = Context(16)
+        self.enc2 = Context(32)
+        self.enc3 = Context(64)
+        self.enc4 = Context(128)
+        self.enc5 = Context(256)
 
         # Convolutions that connect context modules, used for downsampling
         self.down1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)

From 5edc8b19f9843c8f9086f3f94ff00330e58cc813 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 15:48:43 +1000
Subject: [PATCH 09/79] Fixed afew bugs

---
 recognition/Problem_47452752/modules.py | 22 ++++++----------------
 1 file changed, 6 insertions(+), 16 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 7b6160cbc..35ce83c68 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -58,7 +58,7 @@ def __init__(self, in_channels, out_channels):
         )
 
     def forward(self, x, skip_channels):
-        x = self.conv(self.relu(self.norm(x)))
+        x = self.upsample(x)
         return torch.cat([x, skip_channels], dim=1)
 
 
@@ -78,24 +78,14 @@ def __init__(self, in_channels, out_channels):
         # Round 2, we halve the number of feature maps using a 1x1x1 convolution
         self.norm2 = nn.InstanceNorm3d(out_channels)
         self.conv2 = nn.Conv3d(
-            in_channels // 2, out_channels, kernel_size=3, stride=1, padding=1
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
         )
 
-        def forward(self, x):
-            x = self.conv1(self.relu(self.norm1(x)))
-            x = self.conv2(self.relu(self.norm2(x)))
-            return x
-
-    def forward(self, x, skip_connection):
-        upsampled = self.upsample(x)
-        upsampled = self.relu(self.norm(upsampled))
-        upsampled = self.conv(upsampled)
-
-        # Concatenate with skip features from the context pathway
-        x = torch.cat([x, skip_features], dim=1)
+    def forward(self, x):
+        x = self.conv1(self.relu(self.norm1(x)))
+        x = self.conv2(self.relu(self.norm2(x)))
+        return x
 
-        # halve the in_features, concativate with skip features
-        pass
 
 
 class UNet(nn.Module):

From 6f6ff64568731b46780e12b9d8ae010041393c64 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 18:01:28 +1000
Subject: [PATCH 10/79] Worked on UNet + created Segmentation class

---
 recognition/Problem_47452752/modules.py | 102 ++++++++++++++----------
 1 file changed, 62 insertions(+), 40 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 35ce83c68..8449ac5bd 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -17,7 +17,7 @@ class Context(nn.Module):
     def __init__(self, in_channels):
         super(Context, self).__init__()
         # We use leakyReLU with negative slope 1e-2
-        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
+        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=False)
         # 3x3x3 convolutional layer that preserves the input size
         self.conv = nn.Conv3d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
@@ -37,15 +37,15 @@ def forward(self, x):
         return x + shortcut
 
 
-class Up(nn.Module):
+class Upsample(nn.Module):
     """
     Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
     We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
     that halves the number of feature maps.
     """
 
-    def __init__(self, in_channels, out_channels):
-        super(Up, self).__init__()
+    def __init__(self, in_channels):
+        super(Upsample, self).__init__()
         self.upsample = nn.Sequential(
             # Upscale the feature voxels
             nn.Upsample(scale_factor=2, mode="nearest"),
@@ -54,7 +54,7 @@ def __init__(self, in_channels, out_channels):
             # Introduce non-linearity using leakyReLU with negative slope 1e-2
             nn.LeakyReLU(negative_slope=1e-2, inplace=True),
             # Apply 3x3x3 convolution that havles number of feature maps
-            nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
+            nn.Conv3d(in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1),
         )
 
     def forward(self, x, skip_channels):
@@ -63,22 +63,19 @@ def forward(self, x, skip_channels):
 
 
 class Localisation(nn.Module):
-    def __init__(self, in_channels, out_channels):
+    def __init__(self, in_channels):
         super(Localisation, self).__init__()
         # We use leakyReLU with negative slope 1e-2
         self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
-
-        # The Localisation module involves two rounds of normalizations and convoltions
-
-        # Round 1, we apply a 3x3x3 convolution
+        # We use a 3x3x3 convolution first
         self.norm1 = nn.InstanceNorm3d(in_channels)
         self.conv1 = nn.Conv3d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
-        # Round 2, we halve the number of feature maps using a 1x1x1 convolution
-        self.norm2 = nn.InstanceNorm3d(out_channels)
+        # We halve the number of feature maps by using a 1x1x1 convolution
+        self.norm2 = nn.InstanceNorm3d(in_channels // 2)
         self.conv2 = nn.Conv3d(
-            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
     def forward(self, x):
@@ -87,45 +84,70 @@ def forward(self, x):
         return x
 
 
+class Segmentation(nn.Module):
+    pass
+
 
 class UNet(nn.Module):
-    def __init__(self, in_channels, out_channels):
+    def __init__(self, in_channels, out_channels, num_classes):
         super(UNet, self).__init__()
 
-        # Context Pathway (encoders)
-        self.enc1 = Context(16)
-        self.enc2 = Context(32)
-        self.enc3 = Context(64)
-        self.enc4 = Context(128)
-        self.enc5 = Context(256)
+        # Context modules (encoders)
+        self.context1 = Context(16)
+        self.context2 = Context(32)
+        self.context3 = Context(64)
+        self.context4 = Context(128)
+        self.context5 = Context(256)
+
+        # Upsampling modules
+        self.up1 = Upsample(256)
+        self.up2 = Upsample(128)
+        self.up3 = Upsample(64)
+        self.up4 = Upsample(32)
+
+        # Localisation modules
+        self.local1 = Localisation(128)
+        self.local2 = Localisation(64)
+        self.local3 = Localisation(32)
 
         # Convolutions that connect context modules, used for downsampling
-        self.down1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
-        self.down2 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
-        self.down3 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
-        self.down4 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
-        self.down5 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
-
-        # Localization Pathway
-        self.up1 = Decoder(256, 128)
-        self.up2 = Decoder(128, 64)
-        self.up3 = Decoder(64, 32)
-        self.up4 = Decoder(32, 16)
+        self.conv1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
+        self.conv3 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.conv4 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
+        self.conv5 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
 
         # Final output layer
         self.out_conv = nn.Conv3d(16, out_channels, kernel_size=1)
 
+        # To upscale the segmentaion layers 
+        self.upscale = nn.Upsample(scale_factor=2, mode="trilinear", align_corners=True)
+
+        # Segmentation layers 
+        self.segment1 = nn.Conv3d(64, num_classes, kernel_size=1, stride=1)
+        self.segment2 = nn.Conv3d(32, num_classes, kernel_size=1, stride=1)
+        self.segment3 = nn.Conv3d(16, num_classes, kernel_size=1, stride=1)
+        
+
     def forward(self, x):
+
         # Context Pathway
-        enc1 = self.enc1(self.down1(x))
-        enc2 = self.enc2(self.down2(enc1))
-        enc3 = self.enc3(self.down3(enc2))
-        enc4 = self.enc4(self.down1(enc3))
-        enc5 = self.enc5(self.down2(enc4))
-
-        # Localization Pathway with skip connections (decoding)
-        dec1 = self.up1(enc3, enc2)
-        dec2 = self.up2(dec1, enc1)
+        c1 = self.context1(self.conv1(x))
+        c2 = self.context2(self.conv2(c1))
+        c3 = self.context3(self.conv3(c2))
+        c4 = self.context4(self.conv4(c3))
+        x = self.context5(self.conv5(c4))
+
+        # Decoding Pathway (upsample + localisation)
+        l1 = self.local1(self.up1(x, c4))
+        l2 = self.local2(self.up2(l1, c3)) # TODO
+        l3 = self.local3(self.up3(l2, c2)) # TODO
+        x = self.out_conv(self.up4(l3, c1))
+
+        # Deep Supervision 
+        s1 = self.segment1() 
+
+
 
         # Final output
         return self.out_conv(dec2)

From 82d0e90be08bcfe81fc181b14507e64c041a8f12 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 18:35:52 +1000
Subject: [PATCH 11/79] Finished Unet + Segmentation

---
 recognition/Problem_47452752/modules.py | 73 ++++++++++++++-----------
 1 file changed, 42 insertions(+), 31 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 8449ac5bd..e8b85bee7 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -85,11 +85,21 @@ def forward(self, x):
 
 
 class Segmentation(nn.Module):
-    pass
+    def __init__(self, in_channels, num_classes):
+        super(Segmentation, self).__init__()
+        self.conv = nn.Conv3d(in_channels, num_classes, kernel_size=1, stride=1)
+        self.upscale = nn.Upsample(scale_factor=2, mode="nearest")
+    
+    def forward(self, x, other_layer=None):
+        x = self.conv(x)
+        if other_layer is not None:
+            x += other_layer
+        return self.upscale(x)
+        
 
 
 class UNet(nn.Module):
-    def __init__(self, in_channels, out_channels, num_classes):
+    def __init__(self, in_channels, num_classes):
         super(UNet, self).__init__()
 
         # Context modules (encoders)
@@ -110,44 +120,45 @@ def __init__(self, in_channels, out_channels, num_classes):
         self.local2 = Localisation(64)
         self.local3 = Localisation(32)
 
+        # Convolution used on input channel
+        self.in_conv = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
+
         # Convolutions that connect context modules, used for downsampling
-        self.conv1 = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
-        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
-        self.conv3 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
-        self.conv4 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
-        self.conv5 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
-
-        # Final output layer
-        self.out_conv = nn.Conv3d(16, out_channels, kernel_size=1)
-
-        # To upscale the segmentaion layers 
-        self.upscale = nn.Upsample(scale_factor=2, mode="trilinear", align_corners=True)
-
-        # Segmentation layers 
-        self.segment1 = nn.Conv3d(64, num_classes, kernel_size=1, stride=1)
-        self.segment2 = nn.Conv3d(32, num_classes, kernel_size=1, stride=1)
-        self.segment3 = nn.Conv3d(16, num_classes, kernel_size=1, stride=1)
+        self.conv1 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
+        self.conv2 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.conv3 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
+        self.conv4 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        # Convolution used at the end of the localization pathway
+        self.end_conv = nn.Conv3d(32, 32, kernel_size=1)
+
+        # Segmentation layers
+        self.segment1 = Segmentation(64, num_classes)
+        self.segment2 = Segmentation(32, num_classes)
+        self.segment3 = Segmentation(16, num_classes)
         
 
     def forward(self, x):
 
         # Context Pathway
-        c1 = self.context1(self.conv1(x))
-        c2 = self.context2(self.conv2(c1))
-        c3 = self.context3(self.conv3(c2))
-        c4 = self.context4(self.conv4(c3))
-        x = self.context5(self.conv5(c4))
+        c1 = self.context1(self.in_conv(x))
+        c2 = self.context2(self.conv1(c1))
+        c3 = self.context3(self.conv2(c2))
+        c4 = self.context4(self.conv3(c3))
+        x = self.context5(self.conv4(c4))
 
-        # Decoding Pathway (upsample + localisation)
+        # Localization Pathway (upsample + localisation)
         l1 = self.local1(self.up1(x, c4))
-        l2 = self.local2(self.up2(l1, c3)) # TODO
-        l3 = self.local3(self.up3(l2, c2)) # TODO
-        x = self.out_conv(self.up4(l3, c1))
+        l2 = self.local2(self.up2(l1, c3))
+        l3 = self.local3(self.up3(l2, c2))
+        end = self.end_conv(self.up4(l3, c1))
 
         # Deep Supervision 
-        s1 = self.segment1() 
-
+        s1 = self.segment1(l2)
+        s2 = self.segment2(l3, s1)
+        s3 = self.segment3(s2, end)
 
+        # Apply softmax
+        out = F.softmax(s3, dim=1)
 
-        # Final output
-        return self.out_conv(dec2)
+        return out

From 3cb04a7761cd91f38f520b9a4fb8239aa949f446 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 19:26:27 +1000
Subject: [PATCH 12/79] Cleaned up afew things

---
 recognition/Problem_47452752/modules.py | 52 +++++++++++++------------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index e8b85bee7..927fc88bd 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -16,24 +16,27 @@ class Context(nn.Module):
 
     def __init__(self, in_channels):
         super(Context, self).__init__()
-        # We use leakyReLU with negative slope 1e-2
-        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=False)
         # 3x3x3 convolutional layer that preserves the input size
         self.conv = nn.Conv3d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
-        # Dropout layer with p_dropout = 0.3
+        # Dropout layer with p_drop = 0.3
         self.dropout = nn.Dropout(p=0.3)
-        # Normalize the batch with instance normalization
+        # Instance normalization of the input is used
         self.norm = nn.InstanceNorm3d(in_channels)
 
     def forward(self, x):
+        # Keep track of the initial input
         shortcut = x
-
-        x = self.relu(self.norm(x))
+        # First convolutional layer
+        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
+        x = self.conv(x)
+        # Dropout layer
         x = self.dropout(x)
-        x = self.relu(self.norm(x))
-
+        # Second convolution layer
+        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
+        x = self.conv(x)
+        # Return the residual output
         return x + shortcut
 
 
@@ -41,24 +44,26 @@ class Upsample(nn.Module):
     """
     Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
     We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
-    that halves the number of feature maps.
+    that halves the number of feature maps. Instance normalization and leaky ReLU is used throughout the network.
     """
 
     def __init__(self, in_channels):
         super(Upsample, self).__init__()
-        self.upsample = nn.Sequential(
-            # Upscale the feature voxels
-            nn.Upsample(scale_factor=2, mode="nearest"),
-            # Normalize the batch useing instance normalization
-            nn.InstanceNorm3d(in_channels),
-            # Introduce non-linearity using leakyReLU with negative slope 1e-2
-            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
-            # Apply 3x3x3 convolution that havles number of feature maps
-            nn.Conv3d(in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1),
+        # Instance normalization of the input is used
+        self.norm = nn.InstanceNorm3d(in_channels)
+        # 3x3x3 convolution layer with half number of feature maps
+        self.conv = nn.Conv3d(
+            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
     def forward(self, x, skip_channels):
-        x = self.upsample(x)
+        # Repeat feature voxels twice in each spatial dimension
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        # Normalize and introduce non-linearlity
+        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
+        # Apply a 3x3x3 convolution to halve the number of feature maps
+        x = self.conv(x)
+
         return torch.cat([x, skip_channels], dim=1)
 
 
@@ -66,7 +71,7 @@ class Localisation(nn.Module):
     def __init__(self, in_channels):
         super(Localisation, self).__init__()
         # We use leakyReLU with negative slope 1e-2
-        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=True)
+        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=False)
         # We use a 3x3x3 convolution first
         self.norm1 = nn.InstanceNorm3d(in_channels)
         self.conv1 = nn.Conv3d(
@@ -89,13 +94,12 @@ def __init__(self, in_channels, num_classes):
         super(Segmentation, self).__init__()
         self.conv = nn.Conv3d(in_channels, num_classes, kernel_size=1, stride=1)
         self.upscale = nn.Upsample(scale_factor=2, mode="nearest")
-    
+
     def forward(self, x, other_layer=None):
         x = self.conv(x)
         if other_layer is not None:
             x += other_layer
         return self.upscale(x)
-        
 
 
 class UNet(nn.Module):
@@ -136,10 +140,8 @@ def __init__(self, in_channels, num_classes):
         self.segment1 = Segmentation(64, num_classes)
         self.segment2 = Segmentation(32, num_classes)
         self.segment3 = Segmentation(16, num_classes)
-        
 
     def forward(self, x):
-
         # Context Pathway
         c1 = self.context1(self.in_conv(x))
         c2 = self.context2(self.conv1(c1))
@@ -153,7 +155,7 @@ def forward(self, x):
         l3 = self.local3(self.up3(l2, c2))
         end = self.end_conv(self.up4(l3, c1))
 
-        # Deep Supervision 
+        # Deep Supervision
         s1 = self.segment1(l2)
         s2 = self.segment2(l3, s1)
         s3 = self.segment3(s2, end)

From d9f60b00c5c5dab698fee79d36f2bba45aee2e46 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Sun, 22 Oct 2023 20:26:46 +1000
Subject: [PATCH 13/79] Cleaned up the modules

---
 recognition/Problem_47452752/modules.py | 136 ++++++++++++------------
 1 file changed, 70 insertions(+), 66 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 927fc88bd..da762aeec 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -40,7 +40,7 @@ def forward(self, x):
         return x + shortcut
 
 
-class Upsample(nn.Module):
+class Upsampling(nn.Module):
     """
     Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
     We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
@@ -48,65 +48,76 @@ class Upsample(nn.Module):
     """
 
     def __init__(self, in_channels):
-        super(Upsample, self).__init__()
-        # Instance normalization of the input is used
-        self.norm = nn.InstanceNorm3d(in_channels)
-        # 3x3x3 convolution layer with half number of feature maps
-        self.conv = nn.Conv3d(
+        super(Upsampling, self).__init__()
+        # Upsamping components:
+        self.up_norm = nn.InstanceNorm3d(in_channels)
+        self.up_conv = nn.Conv3d(
             in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
-    def forward(self, x, skip_channels):
-        # Repeat feature voxels twice in each spatial dimension
-        x = F.interpolate(x, scale_factor=2, mode="nearest")
-        # Normalize and introduce non-linearlity
-        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
-        # Apply a 3x3x3 convolution to halve the number of feature maps
-        x = self.conv(x)
-
-        return torch.cat([x, skip_channels], dim=1)
-
-
-class Localisation(nn.Module):
-    def __init__(self, in_channels):
-        super(Localisation, self).__init__()
-        # We use leakyReLU with negative slope 1e-2
-        self.relu = nn.LeakyReLU(negative_slope=1e-2, inplace=False)
-        # We use a 3x3x3 convolution first
-        self.norm1 = nn.InstanceNorm3d(in_channels)
-        self.conv1 = nn.Conv3d(
+        # Localisation components:
+        self.merged_norm = nn.InstanceNorm3d(in_channels)
+        self.merged_conv = nn.Conv3d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
-        # We halve the number of feature maps by using a 1x1x1 convolution
-        self.norm2 = nn.InstanceNorm3d(in_channels // 2)
-        self.conv2 = nn.Conv3d(
+        self.half_norm = nn.InstanceNorm3d(in_channels // 2)
+        self.half_conv = nn.Conv3d(
             in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
-    def forward(self, x):
-        x = self.conv1(self.relu(self.norm1(x)))
-        x = self.conv2(self.relu(self.norm2(x)))
-        return x
+    def forward(self, x, skip_features, local: bool = True):
+        """
+        Forward pass of the Upsampling module, with an optional localisation step.
+
+        Parameters:
+        - x (torch.Tensor): Input tensor, representing the activations from a deeper layer.
+        - skip_features (torch.Tensor): Activations from the corresponding encoder layer, for the skip connection.
+        - local (bool, optional): If True, the localisation operations are applied after upsampling. Default is True.
+
+        Returns:
+        - torch.Tensor: Upsampled (and optionally localised) feature map.
+
+        The function first upsamples the input tensor 'x' and then concatenates the result with the 'skip_features'.
+        If 'local' is True, it subsequently applies the localisation steps to refine the feature maps further.
+        """
+        # Upsampling Module
+        upsampled = F.interpolate(x, scale_factor=2, mode="nearest")
+        upsampled = F.leaky_relu(self.up_norm(upsampled), negative_slope=1e-2)
+        upsampled = self.up_conv(upsampled)
+
+        # Concatenate upsampled features with context features
+        merged = torch.cat([upsampled, skip_features], dim=1)
+
+        if local is False:
+            return merged
+
+        # Localisation Module
+        localised = F.leaky_relu(self.merged_norm(merged), negative_slope=1e-2)
+        localised = self.merged_conv(localised)  # First convolutional layer
+
+        localised = F.leaky_relu(self.half_norm(localised), negative_slope=1e-2)
+        localised = self.half_conv(localised)  # Second convolutional layer
+
+        return localised
 
 
 class Segmentation(nn.Module):
     def __init__(self, in_channels, num_classes):
         super(Segmentation, self).__init__()
         self.conv = nn.Conv3d(in_channels, num_classes, kernel_size=1, stride=1)
-        self.upscale = nn.Upsample(scale_factor=2, mode="nearest")
 
     def forward(self, x, other_layer=None):
         x = self.conv(x)
         if other_layer is not None:
             x += other_layer
-        return self.upscale(x)
+        return F.interpolate(x, scale_factor=2, mode="nearest")
 
 
 class UNet(nn.Module):
     def __init__(self, in_channels, num_classes):
         super(UNet, self).__init__()
 
-        # Context modules (encoders)
+        # Context modules
         self.context1 = Context(16)
         self.context2 = Context(32)
         self.context3 = Context(64)
@@ -114,26 +125,21 @@ def __init__(self, in_channels, num_classes):
         self.context5 = Context(256)
 
         # Upsampling modules
-        self.up1 = Upsample(256)
-        self.up2 = Upsample(128)
-        self.up3 = Upsample(64)
-        self.up4 = Upsample(32)
+        self.up1 = Upsampling(256)
+        self.up2 = Upsampling(128)
+        self.up3 = Upsampling(64)
+        self.up4 = Upsampling(32)
 
-        # Localisation modules
-        self.local1 = Localisation(128)
-        self.local2 = Localisation(64)
-        self.local3 = Localisation(32)
+        # Convolutional layer used on input channel
+        self.input_conv = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
 
-        # Convolution used on input channel
-        self.in_conv = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
-
-        # Convolutions that connect context modules, used for downsampling
+        # Convolutional layers that connect context modules
         self.conv1 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
         self.conv2 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
         self.conv3 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
         self.conv4 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
 
-        # Convolution used at the end of the localization pathway
+        # Convolutional layer for the localisation pathway output
         self.end_conv = nn.Conv3d(32, 32, kernel_size=1)
 
         # Segmentation layers
@@ -143,24 +149,22 @@ def __init__(self, in_channels, num_classes):
 
     def forward(self, x):
         # Context Pathway
-        c1 = self.context1(self.in_conv(x))
-        c2 = self.context2(self.conv1(c1))
-        c3 = self.context3(self.conv2(c2))
-        c4 = self.context4(self.conv3(c3))
-        x = self.context5(self.conv4(c4))
-
-        # Localization Pathway (upsample + localisation)
-        l1 = self.local1(self.up1(x, c4))
-        l2 = self.local2(self.up2(l1, c3))
-        l3 = self.local3(self.up3(l2, c2))
-        end = self.end_conv(self.up4(l3, c1))
+        cntx_1 = self.context1(self.input_conv(x))
+        cntx_2 = self.context2(self.conv1(cntx_1))
+        cntx_3 = self.context3(self.conv2(cntx_2))
+        cntx_4 = self.context4(self.conv3(cntx_3))
+        cntx_5 = self.context5(self.conv4(cntx_4))
+
+        # Localization Pathway
+        local_1 = self.up1(cntx_5, cntx_4)
+        local_2 = self.up2(local_1, cntx_3)
+        local_3 = self.up3(local_2, cntx_2)
+        local_out = self.end_conv(self.up4(local_3, cntx_1, False))
 
         # Deep Supervision
-        s1 = self.segment1(l2)
-        s2 = self.segment2(l3, s1)
-        s3 = self.segment3(s2, end)
-
-        # Apply softmax
-        out = F.softmax(s3, dim=1)
+        seg_1 = self.segment1(local_2, None)
+        seg_2 = self.segment2(local_3, seg_1)
+        seg_3 = self.segment3(seg_2, local_out)
 
-        return out
+        # Apply softmax and return
+        return F.softmax(seg_3, dim=1)

From 171396a1cb0e84952c9d6dc91da32ffdb4a36b66 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 02:07:26 +1000
Subject: [PATCH 14/79] Made UNet 2D, setup Dataloader, added utils

---
 recognition/Problem_47452752/dataset.py | 83 ++++++++++++++++++++++++-
 recognition/Problem_47452752/modules.py | 34 +++++-----
 recognition/Problem_47452752/utils.py   | 55 ++++++++++++++++
 3 files changed, 155 insertions(+), 17 deletions(-)
 create mode 100644 recognition/Problem_47452752/utils.py

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 947b41bd1..96fc67bfd 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -1 +1,82 @@
-# containing the data loader for loading and preprocessing your data
\ No newline at end of file
+"""
+File containing the data loaders used for loading and preprocessing the data.
+
+"""
+
+import os
+import torch
+from utils import RandomCenterCrop, RandomRotate90, DictTransform
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.dataset import random_split
+from torchvision import transforms
+import torchvision.transforms.functional as TF
+from PIL import Image
+import numpy as np
+
+
+class ISICDataset(Dataset):
+    def __init__(
+        self,
+        image_dir,
+        mask_dir,
+        transform=None,
+    ):
+        self.image_dir = image_dir
+        self.mask_dir = mask_dir
+        self.image_ids = [img.split(".")[0] for img in os.listdir(image_dir)]
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.image_ids)
+
+    def __getitem__(self, idx):
+        img_name = os.path.join(self.image_dir, self.image_ids[idx] + ".jpg")
+        mask_name = os.path.join(
+            self.mask_dir, self.image_ids[idx] + "_Segmentation.png"
+        )
+
+        if not os.path.exists(img_name) or not os.path.exists(mask_name):
+            raise FileNotFoundError(
+                f"Image or mask not found for ID {self.image_ids[idx]}"
+            )
+
+        with image.open(img_name) as image, Image.open(mask_name) as mask:
+            image = image.convert("RGB")
+            mask = mask.convert("L")
+
+            sample = {"image": image, "mask": mask}
+
+            if self.transform:
+                sample = self.transform(sample)
+
+        # Convert mask to binary 0/1 tensor
+        sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 127.5).float()
+
+        return sample["image"], sample["mask"]
+
+
+# Transformation pipeline to augment the dataset
+transform = transforms.Compose(
+    [
+        RandomRotate90(),
+        RandomCenterCrop(),
+        DictTransform(transforms.RandomHorizontalFlip()),
+        DictTransform(transforms.RandomVerticalFlip()),
+        DictTransform(transforms.Resize((256, 256))),
+        DictTransform(transforms.ToTensor()),
+        DictTransform(transforms.Lambda(lambda img_tensor: torch.cat([img_tensor, TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV"))], dim=0)), False),
+        DictTransform(transforms.Normalize([0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), False),
+    ]
+)
+
+image_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
+mask_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
+
+dataset = ISICDataset(image_dir, mask_dir, transform)
+test_size = int(0.20 * len(dataset))
+train_size = len(dataset) - test_size
+
+train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
+
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index da762aeec..c3e68ce22 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -16,14 +16,14 @@ class Context(nn.Module):
 
     def __init__(self, in_channels):
         super(Context, self).__init__()
-        # 3x3x3 convolutional layer that preserves the input size
-        self.conv = nn.Conv3d(
+        # 3x3 convolutional layer that preserves the input size
+        self.conv = nn.Conv2d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
         # Dropout layer with p_drop = 0.3
         self.dropout = nn.Dropout(p=0.3)
         # Instance normalization of the input is used
-        self.norm = nn.InstanceNorm3d(in_channels)
+        self.norm = nn.InstanceNorm2d(in_channels)
 
     def forward(self, x):
         # Keep track of the initial input
@@ -50,18 +50,18 @@ class Upsampling(nn.Module):
     def __init__(self, in_channels):
         super(Upsampling, self).__init__()
         # Upsamping components:
-        self.up_norm = nn.InstanceNorm3d(in_channels)
-        self.up_conv = nn.Conv3d(
+        self.up_norm = nn.InstanceNorm2d(in_channels)
+        self.up_conv = nn.Conv2d(
             in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
         # Localisation components:
-        self.merged_norm = nn.InstanceNorm3d(in_channels)
-        self.merged_conv = nn.Conv3d(
+        self.merged_norm = nn.InstanceNorm2d(in_channels)
+        self.merged_conv = nn.Conv2d(
             in_channels, in_channels, kernel_size=3, stride=1, padding=1
         )
-        self.half_norm = nn.InstanceNorm3d(in_channels // 2)
-        self.half_conv = nn.Conv3d(
+        self.half_norm = nn.InstanceNorm2d(in_channels // 2)
+        self.half_conv = nn.Conv2d(
             in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
         )
 
@@ -104,7 +104,7 @@ def forward(self, x, skip_features, local: bool = True):
 class Segmentation(nn.Module):
     def __init__(self, in_channels, num_classes):
         super(Segmentation, self).__init__()
-        self.conv = nn.Conv3d(in_channels, num_classes, kernel_size=1, stride=1)
+        self.conv = nn.Conv2d(in_channels, num_classes, kernel_size=1, stride=1)
 
     def forward(self, x, other_layer=None):
         x = self.conv(x)
@@ -131,16 +131,16 @@ def __init__(self, in_channels, num_classes):
         self.up4 = Upsampling(32)
 
         # Convolutional layer used on input channel
-        self.input_conv = nn.Conv3d(in_channels, 16, kernel_size=3, stride=1, padding=1)
+        self.input_conv = nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1)
 
         # Convolutional layers that connect context modules
-        self.conv1 = nn.Conv3d(16, 32, kernel_size=3, stride=2, padding=1)
-        self.conv2 = nn.Conv3d(32, 64, kernel_size=3, stride=2, padding=1)
-        self.conv3 = nn.Conv3d(64, 128, kernel_size=3, stride=2, padding=1)
-        self.conv4 = nn.Conv3d(128, 256, kernel_size=3, stride=2, padding=1)
+        self.conv1 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
 
         # Convolutional layer for the localisation pathway output
-        self.end_conv = nn.Conv3d(32, 32, kernel_size=1)
+        self.end_conv = nn.Conv2d(32, 32, kernel_size=1)
 
         # Segmentation layers
         self.segment1 = Segmentation(64, num_classes)
@@ -168,3 +168,5 @@ def forward(self, x):
 
         # Apply softmax and return
         return F.softmax(seg_3, dim=1)
+
+
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
new file mode 100644
index 000000000..bb7ee5fc1
--- /dev/null
+++ b/recognition/Problem_47452752/utils.py
@@ -0,0 +1,55 @@
+import torch
+from random import choice
+import torchvision.transforms.functional as TF
+
+class RandomRotate90:
+    """Randomly rotates the image by 90, 180, or 270 degrees."""
+
+    def __init__(self, p=1.0):
+        self.p = p
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if torch.rand(1) < self.p:
+            degrees = [90, 180, 270]
+            angle = choice(degrees)
+            image = TF.rotate(image, angle)
+            mask = TF.rotate(mask, angle)
+        return {"image": image, "mask": mask}
+
+
+class RandomCenterCrop:
+    """Randomly crops the center of the image by 80% or 70%."""
+
+    def __init__(self, scales=[0.8, 0.7], p=1.0):
+        self.scales = scales
+        self.p = p
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if torch.rand(1) < self.p:
+            scale = choice(self.scales)
+            image = TF.center_crop(
+                image, (int(image.height * scale), int(image.width * scale))
+            )
+            mask = TF.center_crop(
+                mask, (int(mask.height * scale), int(mask.width * scale))
+            )
+        return {"image": image, "mask": mask}
+
+
+class DictTransform:
+    def __init__(self, transform, transform_mask=True):
+        self.transform = transform
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if self.transform_mask:
+            return {
+                "image": self.transform(image),
+                "mask": self.transform(mask),
+            }
+        return {
+            "image": self.transform(image),
+            "mask": mask,
+        }
\ No newline at end of file

From 3b364fed310914745d4fdb6b8d554f2550c16dba Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 20:16:12 +1000
Subject: [PATCH 15/79] Did some debugging on modules

---
 recognition/Problem_47452752/modules.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index c3e68ce22..f094cba21 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -106,10 +106,12 @@ def __init__(self, in_channels, num_classes):
         super(Segmentation, self).__init__()
         self.conv = nn.Conv2d(in_channels, num_classes, kernel_size=1, stride=1)
 
-    def forward(self, x, other_layer=None):
+    def forward(self, x, other_layer, upscale=True):
         x = self.conv(x)
         if other_layer is not None:
             x += other_layer
+        if not upscale:
+            return x
         return F.interpolate(x, scale_factor=2, mode="nearest")
 
 
@@ -145,7 +147,7 @@ def __init__(self, in_channels, num_classes):
         # Segmentation layers
         self.segment1 = Segmentation(64, num_classes)
         self.segment2 = Segmentation(32, num_classes)
-        self.segment3 = Segmentation(16, num_classes)
+        self.segment3 = Segmentation(32, num_classes)
 
     def forward(self, x):
         # Context Pathway
@@ -164,9 +166,8 @@ def forward(self, x):
         # Deep Supervision
         seg_1 = self.segment1(local_2, None)
         seg_2 = self.segment2(local_3, seg_1)
-        seg_3 = self.segment3(seg_2, local_out)
+        seg_3 = self.segment3(local_out, seg_2, upscale=False)
 
         # Apply softmax and return
         return F.softmax(seg_3, dim=1)
 
-

From a0ff43aeae10059b35d17c6e88ba9f9515de761d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 20:31:04 +1000
Subject: [PATCH 16/79] Started implementing the training loop etc

---
 recognition/Problem_47452752/predict.py | 29 ++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/predict.py b/recognition/Problem_47452752/predict.py
index 309350c73..2e6d091f5 100644
--- a/recognition/Problem_47452752/predict.py
+++ b/recognition/Problem_47452752/predict.py
@@ -1 +1,28 @@
-# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
\ No newline at end of file
+# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
+
+from dataset import ISICDataset, transform
+from modules import UNet
+import torch
+import torch.optim as optim
+
+
+# Hyper-parameters
+num_epochs = 100
+
+def dice_loss(predicted, target, epsilon=1e-5):
+    pass
+
+model = UNet(6, 3)
+optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
+scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
+
+for epoch in range(num_epochs):
+    model.train() # Switch to training mode
+    for images, masks in train_loader:
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = dice_loss(outputs, masks)
+        loss.backward()
+        optimizer.step()
+        
+    scheduler.step() # Adjust learning rate

From 1b3e31db59319d105f0e55e38bdff331d1211a0b Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 21:42:20 +1000
Subject: [PATCH 17/79] Realised this was the wrong spot...

---
 recognition/Problem_47452752/predict.py | 27 -------------------------
 1 file changed, 27 deletions(-)

diff --git a/recognition/Problem_47452752/predict.py b/recognition/Problem_47452752/predict.py
index 2e6d091f5..f1732adff 100644
--- a/recognition/Problem_47452752/predict.py
+++ b/recognition/Problem_47452752/predict.py
@@ -1,28 +1 @@
 # showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
-
-from dataset import ISICDataset, transform
-from modules import UNet
-import torch
-import torch.optim as optim
-
-
-# Hyper-parameters
-num_epochs = 100
-
-def dice_loss(predicted, target, epsilon=1e-5):
-    pass
-
-model = UNet(6, 3)
-optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
-scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
-
-for epoch in range(num_epochs):
-    model.train() # Switch to training mode
-    for images, masks in train_loader:
-        optimizer.zero_grad()
-        outputs = model(images)
-        loss = dice_loss(outputs, masks)
-        loss.backward()
-        optimizer.step()
-        
-    scheduler.step() # Adjust learning rate

From e465710b1fc69024a13f0614b52d0ed0ed5c7693 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 21:42:37 +1000
Subject: [PATCH 18/79] Switched from softmax to sigmoid UNet output

---
 recognition/Problem_47452752/modules.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index f094cba21..71db2609d 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -116,7 +116,7 @@ def forward(self, x, other_layer, upscale=True):
 
 
 class UNet(nn.Module):
-    def __init__(self, in_channels, num_classes):
+    def __init__(self, in_channels, num_classes=2):
         super(UNet, self).__init__()
 
         # Context modules
@@ -168,6 +168,12 @@ def forward(self, x):
         seg_2 = self.segment2(local_3, seg_1)
         seg_3 = self.segment3(local_out, seg_2, upscale=False)
 
-        # Apply softmax and return
-        return F.softmax(seg_3, dim=1)
+        # Apply sigmoid (paper used softmax, but this is binary) and return
+        return torch.sigmoid(seg_3)
+        # if num_classes > 2: return F.softmax(seg_3, dim=1)
 
+# Lil testing
+model = UNet(6)
+test = torch.randn(1, 6, 32, 32)
+output = model(test)
+print(output.size())
\ No newline at end of file

From 66f66527de2b406c5278d7e0dbbcff6354a7ab10 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 21:42:50 +1000
Subject: [PATCH 19/79] Implemented loader methods

---
 recognition/Problem_47452752/dataset.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 96fc67bfd..44550beb0 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -17,8 +17,8 @@
 class ISICDataset(Dataset):
     def __init__(
         self,
-        image_dir,
-        mask_dir,
+        image_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
+        mask_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
         transform=None,
     ):
         self.image_dir = image_dir
@@ -69,14 +69,15 @@ def __getitem__(self, idx):
     ]
 )
 
-image_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
-mask_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
 
-dataset = ISICDataset(image_dir, mask_dir, transform)
+def train_loader(train_dataset, batch_size, shuffle=True):
+    return DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle)
+
+def test_loader(test_dataset, batch_size, shuffle=False):
+    return DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle)
+
+dataset = ISICDataset(transform)
 test_size = int(0.20 * len(dataset))
 train_size = len(dataset) - test_size
-
 train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
 
-train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

From ea20f0c001b8e02c777a7bcd3f01a4b3ed077217 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 23:01:30 +1000
Subject: [PATCH 20/79] Fixed afew things

---
 recognition/Problem_47452752/dataset.py | 11 +++++-----
 recognition/Problem_47452752/modules.py |  8 +++----
 recognition/Problem_47452752/utils.py   | 29 +++++++++++++++++++++++--
 3 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 44550beb0..35b95156c 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -23,7 +23,7 @@ def __init__(
     ):
         self.image_dir = image_dir
         self.mask_dir = mask_dir
-        self.image_ids = [img.split(".")[0] for img in os.listdir(image_dir)]
+        self.image_ids = [img.split(".")[0] for img in os.listdir(image_dir) if img.endswith('.jpg')]
         self.transform = transform
 
     def __len__(self):
@@ -40,7 +40,7 @@ def __getitem__(self, idx):
                 f"Image or mask not found for ID {self.image_ids[idx]}"
             )
 
-        with image.open(img_name) as image, Image.open(mask_name) as mask:
+        with Image.open(img_name) as image, Image.open(mask_name) as mask:
             image = image.convert("RGB")
             mask = mask.convert("L")
 
@@ -69,6 +69,8 @@ def __getitem__(self, idx):
     ]
 )
 
+def split_data(dataset, train_size, test_size):
+    return random_split(dataset, [train_size, test_size])
 
 def train_loader(train_dataset, batch_size, shuffle=True):
     return DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle)
@@ -76,8 +78,5 @@ def train_loader(train_dataset, batch_size, shuffle=True):
 def test_loader(test_dataset, batch_size, shuffle=False):
     return DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle)
 
-dataset = ISICDataset(transform)
-test_size = int(0.20 * len(dataset))
-train_size = len(dataset) - test_size
-train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
+
 
diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 71db2609d..1682cd3cd 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -173,7 +173,7 @@ def forward(self, x):
         # if num_classes > 2: return F.softmax(seg_3, dim=1)
 
 # Lil testing
-model = UNet(6)
-test = torch.randn(1, 6, 32, 32)
-output = model(test)
-print(output.size())
\ No newline at end of file
+# model = UNet(6)
+# test = torch.randn(1, 6, 32, 32)
+# output = model(test)
+# print(output.size())
\ No newline at end of file
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index bb7ee5fc1..c97e06be8 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -2,6 +2,7 @@
 from random import choice
 import torchvision.transforms.functional as TF
 
+
 class RandomRotate90:
     """Randomly rotates the image by 90, 180, or 270 degrees."""
 
@@ -41,7 +42,7 @@ def __call__(self, sample):
 class DictTransform:
     def __init__(self, transform, transform_mask=True):
         self.transform = transform
-
+        self.transform_mask = transform_mask
     def __call__(self, sample):
         image, mask = sample["image"], sample["mask"]
         if self.transform_mask:
@@ -52,4 +53,28 @@ def __call__(self, sample):
         return {
             "image": self.transform(image),
             "mask": mask,
-        }
\ No newline at end of file
+        }
+
+
+def dice_loss(predicted, target, epsilon=1e-5):
+    intersection = (predicted * target).sum()
+    union = predicted.sum() + target.sum()
+
+    dice_loss = -1 * (intersection + epsilon) / (union + epsilon)
+    return dice_loss
+
+
+def general_dice_loss(predicted, target):
+    # One-hot encode the target segmentation map
+    target_one_hot = torch.zeros_like(predicted)
+    for k in range(target_one_hot.shape[1]):
+        target_one_hot[:, k] = target == k
+
+    # Compute the Dice loss for each class, then average
+    intersection = (predicted * target_one_hot).sum(dim=(2, 3))
+    union = (predicted + target_one_hot).sum(dim=(2, 3))
+
+    dice_scores = 2 * intersection / union
+    loss = 1 - dice_scores.mean()
+
+    return loss

From b431fced9e3e541ecebf4a67cd39838ccb360952 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 23:02:01 +1000
Subject: [PATCH 21/79] Improved training loop

---
 recognition/Problem_47452752/train.py | 52 +++++++++++++++++++++++++--
 1 file changed, 49 insertions(+), 3 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 974881b93..58d10f1ac 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -1,8 +1,54 @@
-# containing the source code for training, validating, testing and saving your model. 
+# containing the source code for training, validating, testing and saving your model.
 # The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
 # Make sure to plot the losses and metrics during training
 
-import modules
-import dataset
+from dataset import ISICDataset, transform, train_loader, test_loader, split_data
+from modules import UNet
+from utils import dice_loss
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
 
 
+# Hyper-parameters
+num_epochs = 50
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Loading up the dataset and applying custom augmentations
+dataset = ISICDataset(transform)
+
+# Splitting into testing and training sets
+test_size = int(0.2 * len(dataset))
+train_size = len(dataset) - test_size
+train_dataset, test_dataset = split_data(dataset, train_size, test_size)
+
+#
+train_loader = train_loader(train_dataset, 100)
+test_loader = test_loader(test_dataset, 100)
+
+# Creating an instance of my UNet to be trained
+model = UNet(in_channels=6, num_classes=2)
+model = model.to(device)
+
+# Setup the optimizer
+optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
+scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
+
+for epoch in range(num_epochs):
+    model.train()  # Switch to training mode
+    for images, masks in train_loader:
+        # Move the data onto the device
+        images, masks = images.to(device), masks.to(device)
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+
+        # Forward pass
+        outputs = model(images)
+        loss = dice_loss(outputs, masks)
+
+        # Backward pass + optimization
+        loss.backward()
+        optimizer.step()
+
+    scheduler.step()  # Adjust learning rate

From 78e3bde28ef84139b2b16928761a60b9b45b9c43 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 23:35:21 +1000
Subject: [PATCH 22/79] Implemented a dice score method

---
 recognition/Problem_47452752/utils.py | 20 +++++++++++++++++---
 1 file changed, 17 insertions(+), 3 deletions(-)

diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index c97e06be8..7a6c4d47f 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -56,12 +56,16 @@ def __call__(self, sample):
         }
 
 
-def dice_loss(predicted, target, epsilon=1e-5):
+def dice_loss(predicted, target, epsilon=1e-7):
+    # Flatten the tensors 
+    predicted = predicted.view(-1)
+    target = target.view(-1)
+
     intersection = (predicted * target).sum()
     union = predicted.sum() + target.sum()
 
-    dice_loss = -1 * (intersection + epsilon) / (union + epsilon)
-    return dice_loss
+    dice_loss = 2 * (intersection + epsilon) / (union + epsilon)
+    return 1 - dice_loss
 
 
 def general_dice_loss(predicted, target):
@@ -78,3 +82,13 @@ def general_dice_loss(predicted, target):
     loss = 1 - dice_scores.mean()
 
     return loss
+
+def dice_coefficient(predicted, target):
+    """ Compute Dice Coefficient for single sample. """
+    intersection = (predicted * target).sum().float()
+    union = (predicted + target).sum().float()
+    
+    if union == 0:
+        return 1.0
+
+    return 2 * intersection / union
\ No newline at end of file

From 0e993465189042fb0d6fd5e663d2395e5e613ed1 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 23:35:39 +1000
Subject: [PATCH 23/79] set up my model testing

---
 recognition/Problem_47452752/train.py | 45 ++++++++++++++++++++++++---
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 58d10f1ac..0a9a26427 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -1,10 +1,13 @@
-# containing the source code for training, validating, testing and saving your model.
-# The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
-# Make sure to plot the losses and metrics during training
+"""
+Contains the source code for training, validating, testing and saving my model.
+The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
+Make sure to plot the losses and metrics during training
+"""
+
 
 from dataset import ISICDataset, transform, train_loader, test_loader, split_data
 from modules import UNet
-from utils import dice_loss
+from utils import dice_loss, dice_coefficient
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
@@ -14,6 +17,7 @@
 num_epochs = 50
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 # Loading up the dataset and applying custom augmentations
 dataset = ISICDataset(transform)
 
@@ -22,7 +26,7 @@
 train_size = len(dataset) - test_size
 train_dataset, test_dataset = split_data(dataset, train_size, test_size)
 
-#
+# Data loaders for training and testing
 train_loader = train_loader(train_dataset, 100)
 test_loader = test_loader(test_dataset, 100)
 
@@ -34,6 +38,7 @@
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
 
+# Training loop
 for epoch in range(num_epochs):
     model.train()  # Switch to training mode
     for images, masks in train_loader:
@@ -52,3 +57,33 @@
         optimizer.step()
 
     scheduler.step()  # Adjust learning rate
+
+# Save the model
+torch.save(model.state_dict(), "recognition/Problem_47452752/model.pth")
+
+# Switch to evaluation mode
+model.eval()
+
+# Metrics storage
+dice_scores = []
+
+# No gradient computation during evaluation
+with torch.no_grad():
+    for inputs, masks in test_loader:
+        inputs, masks = inputs.to(device), masks.to(device)
+
+        # Compute predictions
+        outputs = model(inputs)
+
+        # Convert outputs to binary mask
+        # If sigmoid activation is used in the last layer
+        predicted_masks = (outputs > 0.5).float()
+
+        # Compute Dice coefficient
+        for pred, true in zip(predicted_masks, masks):
+            dice_scores.append(dice_coefficient(pred, true).item())
+
+# Calculate average Dice score
+avg_dice_score = sum(dice_scores) / len(dice_scores)
+
+print(f"Average Dice Coefficient on Test Set: {avg_dice_score:.4f}")

From c791103fec4020b5a5f927d0408dd30f32641e04 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Tue, 24 Oct 2023 23:35:54 +1000
Subject: [PATCH 24/79] started my predict.py file

---
 recognition/Problem_47452752/predict.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/recognition/Problem_47452752/predict.py b/recognition/Problem_47452752/predict.py
index f1732adff..83b2855cb 100644
--- a/recognition/Problem_47452752/predict.py
+++ b/recognition/Problem_47452752/predict.py
@@ -1 +1,9 @@
 # showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
+
+from modules import UNet
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = UNet(in_channels=6, num_classes=2)  # Adjust parameters if they've changed
+model.load_state_dict(torch.load("recognition/Problem_47452752/model.pth"))
+model = model.to(device)
\ No newline at end of file

From d444b7ad6a2d8fc45edb6c25b278fa8d4b6af389 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 00:12:08 +1000
Subject: [PATCH 25/79] committing a slurm script

---
 recognition/Problem_47452752/slurm.sh | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 recognition/Problem_47452752/slurm.sh

diff --git a/recognition/Problem_47452752/slurm.sh b/recognition/Problem_47452752/slurm.sh
new file mode 100644
index 000000000..08f4d8854
--- /dev/null
+++ b/recognition/Problem_47452752/slurm.sh
@@ -0,0 +1,13 @@
+#!/bin/bash 
+#SBATCH --nodes=1 
+#SBATCH --ntasks=1 
+#SBATCH --gres=gpu:1 
+#SBATCH --partition=vgpu 
+#SBATCH --job-name="COMP3710 Report"
+#SBATCH --mail-user=s4745275@student.uq.edu.au
+#SBATCH --mail-type=ALL 
+#SBATCH -e test_err.txt
+#SBATCH -o test_out.txt
+
+source /home/Student/s4745275/miniconda/bin/activate /home/Student/s4745275/my_demo_environment
+python train.py

From 0d0f302edcb1c4ca983b2cde31d6479481431b57 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 00:30:47 +1000
Subject: [PATCH 26/79] fixed bug in dataset

---
 recognition/Problem_47452752/dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 35b95156c..bedf60f23 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -17,8 +17,8 @@
 class ISICDataset(Dataset):
     def __init__(
         self,
-        image_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
-        mask_dir = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
+        image_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
+        mask_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
         transform=None,
     ):
         self.image_dir = image_dir

From efd6aab6c66dedcc577fe136bb2a303c3c425c45 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 00:41:03 +1000
Subject: [PATCH 27/79] modified ISICDataset __init__ method

---
 recognition/Problem_47452752/dataset.py | 32 +++++++++++++++++++------
 1 file changed, 25 insertions(+), 7 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index bedf60f23..e5abce472 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -17,13 +17,15 @@
 class ISICDataset(Dataset):
     def __init__(
         self,
+        transform,
         image_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
         mask_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
-        transform=None,
     ):
         self.image_dir = image_dir
         self.mask_dir = mask_dir
-        self.image_ids = [img.split(".")[0] for img in os.listdir(image_dir) if img.endswith('.jpg')]
+        self.image_ids = [
+            img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
+        ]
         self.transform = transform
 
     def __len__(self):
@@ -64,19 +66,35 @@ def __getitem__(self, idx):
         DictTransform(transforms.RandomVerticalFlip()),
         DictTransform(transforms.Resize((256, 256))),
         DictTransform(transforms.ToTensor()),
-        DictTransform(transforms.Lambda(lambda img_tensor: torch.cat([img_tensor, TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV"))], dim=0)), False),
-        DictTransform(transforms.Normalize([0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), False),
+        DictTransform(
+            transforms.Lambda(
+                lambda img_tensor: torch.cat(
+                    [
+                        img_tensor,
+                        TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV")),
+                    ],
+                    dim=0,
+                )
+            ),
+            False,
+        ),
+        DictTransform(
+            transforms.Normalize(
+                [0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+            ),
+            False,
+        ),
     ]
 )
 
+
 def split_data(dataset, train_size, test_size):
     return random_split(dataset, [train_size, test_size])
 
+
 def train_loader(train_dataset, batch_size, shuffle=True):
     return DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle)
 
+
 def test_loader(test_dataset, batch_size, shuffle=False):
     return DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle)
-
-
-

From 6f8b2f995b300bc330d07acbcd97301fa9eaffdc Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 01:04:08 +1000
Subject: [PATCH 28/79] added a check for inconsistencies in the DB

---
 recognition/Problem_47452752/dataset.py | 29 ++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index e5abce472..039713a9e 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -13,6 +13,31 @@
 from PIL import Image
 import numpy as np
 
+excluded_ids = ["ISIC_0013499"]
+
+# def filter_consistent_data(
+#     image_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
+#     mask_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
+# ):
+#     image_ids = [
+#         img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
+#     ]
+
+#     consistent_ids = [
+#         img_id
+#         for img_id in image_ids
+#         if os.path.exists(os.path.join(mask_dir, img_id + "_segmentation.jpg"))
+#     ]
+
+#     inconsistent_ids = set(image_ids) - set(consistent_ids)
+
+#     if inconsistent_ids:
+#         print(
+#             f"Found {len(inconsistent_ids)} inconsistent image IDs. They will be excluded from the dataset."
+#         )
+
+#     return consistent_ids
+
 
 class ISICDataset(Dataset):
     def __init__(
@@ -24,7 +49,9 @@ def __init__(
         self.image_dir = image_dir
         self.mask_dir = mask_dir
         self.image_ids = [
-            img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
+            img.split(".")[0]
+            for img in os.listdir(image_dir)
+            if img.endswith(".jpg") and img.split(".")[0] not in excluded_ids
         ]
         self.transform = transform
 

From 6308bde1835a5f5954f86e76d907d173e6151b00 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 01:39:58 +1000
Subject: [PATCH 29/79] Put some inconsistency fixes and check in

---
 recognition/Problem_47452752/dataset.py       | 86 +++++++++++--------
 .../Problem_47452752/inconsistent_ids.txt     |  0
 recognition/Problem_47452752/train.py         |  4 +-
 3 files changed, 54 insertions(+), 36 deletions(-)
 create mode 100644 recognition/Problem_47452752/inconsistent_ids.txt

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 039713a9e..ba548b300 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -1,6 +1,5 @@
 """
 File containing the data loaders used for loading and preprocessing the data.
-
 """
 
 import os
@@ -13,39 +12,45 @@
 from PIL import Image
 import numpy as np
 
-excluded_ids = ["ISIC_0013499"]
+image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
+mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
 
-# def filter_consistent_data(
-#     image_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
-#     mask_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
-# ):
-#     image_ids = [
-#         img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
-#     ]
 
-#     consistent_ids = [
-#         img_id
-#         for img_id in image_ids
-#         if os.path.exists(os.path.join(mask_dir, img_id + "_segmentation.jpg"))
-#     ]
+def check_consistency(image_dir=image_path, mask_dir=mask_path):
+    image_ids = {
+        img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
+    }
+    mask_ids = {
+        mask.split("_segmentation.")[0]
+        for mask in os.listdir(mask_dir)
+        if mask.endswith("_segmentation.jpg")
+    }
 
-#     inconsistent_ids = set(image_ids) - set(consistent_ids)
+    # Using list differences to find inconsistencies
+    images_without_masks = image_ids - mask_ids
+    masks_without_images = mask_ids - image_ids
 
-#     if inconsistent_ids:
-#         print(
-#             f"Found {len(inconsistent_ids)} inconsistent image IDs. They will be excluded from the dataset."
-#         )
+    if images_without_masks or masks_without_images:
+        inconsistent_ids = images_without_masks.union(masks_without_images)
+        # Save to a file
+        with open("inconsistent_ids.txt", "w") as file:
+            for ID in inconsistent_ids:
+                file.write(f"{ID}\n")
 
-#     return consistent_ids
+        print(f"Detected {len(inconsistent_ids)} inconsistencies, fixed em tho")
 
 
 class ISICDataset(Dataset):
     def __init__(
         self,
         transform,
-        image_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2",
-        mask_dir="/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2",
+        image_dir=image_path,
+        mask_dir=mask_path,
     ):
+        # Load the inconsistent IDs
+        with open("inconsistent_ids.txt", "r") as file:
+            excluded_ids = set(line.strip() for line in file)
+
         self.image_dir = image_dir
         self.mask_dir = mask_dir
         self.image_ids = [
@@ -58,30 +63,41 @@ def __init__(
     def __len__(self):
         return len(self.image_ids)
 
+    def handle_inconsistency(self):
+        images_without_masks, masks_without_images = check_consistency(
+            self.image_dir, self.mask_dir
+        )
+        inconsistent_ids = images_without_masks.union(masks_without_images)
+
+        # Save to a file
+        with open("inconsistent_ids.txt", "a") as file:  # 'a' mode for appending
+            for ID in inconsistent_ids:
+                file.write(f"{ID}\n")
+
     def __getitem__(self, idx):
         img_name = os.path.join(self.image_dir, self.image_ids[idx] + ".jpg")
         mask_name = os.path.join(
             self.mask_dir, self.image_ids[idx] + "_Segmentation.png"
         )
 
-        if not os.path.exists(img_name) or not os.path.exists(mask_name):
-            raise FileNotFoundError(
-                f"Image or mask not found for ID {self.image_ids[idx]}"
-            )
+        try:
+            with Image.open(img_name) as image, Image.open(mask_name) as mask:
+                image = image.convert("RGB")
+                mask = mask.convert("L")
 
-        with Image.open(img_name) as image, Image.open(mask_name) as mask:
-            image = image.convert("RGB")
-            mask = mask.convert("L")
+                sample = {"image": image, "mask": mask}
 
-            sample = {"image": image, "mask": mask}
+                if self.transform:
+                    sample = self.transform(sample)
 
-            if self.transform:
-                sample = self.transform(sample)
+            # Convert mask to binary 0/1 tensor
+            sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 127.5).float()
 
-        # Convert mask to binary 0/1 tensor
-        sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 127.5).float()
+            return sample["image"], sample["mask"]
 
-        return sample["image"], sample["mask"]
+        except FileNotFoundError:
+            self.handle_inconsistency()
+            return self.__getitem__(idx)
 
 
 # Transformation pipeline to augment the dataset
diff --git a/recognition/Problem_47452752/inconsistent_ids.txt b/recognition/Problem_47452752/inconsistent_ids.txt
new file mode 100644
index 000000000..e69de29bb
diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 0a9a26427..f3e81ce95 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -5,7 +5,7 @@
 """
 
 
-from dataset import ISICDataset, transform, train_loader, test_loader, split_data
+from dataset import ISICDataset, transform, train_loader, test_loader, split_data, check_consistency
 from modules import UNet
 from utils import dice_loss, dice_coefficient
 import torch
@@ -17,6 +17,8 @@
 num_epochs = 50
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# Check if the dataset is consistent
+check_consistency()
 
 # Loading up the dataset and applying custom augmentations
 dataset = ISICDataset(transform)

From 662f1b3eeea03fc3ead2392146c35afcb12ec9ec Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 01:45:44 +1000
Subject: [PATCH 30/79] fixed check_consistency bug

---
 recognition/Problem_47452752/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index ba548b300..bc7e7d2e6 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -23,7 +23,7 @@ def check_consistency(image_dir=image_path, mask_dir=mask_path):
     mask_ids = {
         mask.split("_segmentation.")[0]
         for mask in os.listdir(mask_dir)
-        if mask.endswith("_segmentation.jpg")
+        if mask.endswith("_segmentation.png")
     }
 
     # Using list differences to find inconsistencies

From 8e3db88b5cb2d16fbb5d035bf6d947d3ca0b2925 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 01:50:51 +1000
Subject: [PATCH 31/79] fixed consistency logic further

---
 recognition/Problem_47452752/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index bc7e7d2e6..79100f9af 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -77,7 +77,7 @@ def handle_inconsistency(self):
     def __getitem__(self, idx):
         img_name = os.path.join(self.image_dir, self.image_ids[idx] + ".jpg")
         mask_name = os.path.join(
-            self.mask_dir, self.image_ids[idx] + "_Segmentation.png"
+            self.mask_dir, self.image_ids[idx] + "_segmentation.png"
         )
 
         try:

From 82b143b5073676c05698859b1285978aa31d85d1 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 01:58:28 +1000
Subject: [PATCH 32/79] put some debugging print statements

---
 recognition/Problem_47452752/train.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index f3e81ce95..1d03ec66b 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -52,6 +52,9 @@
 
         # Forward pass
         outputs = model(images)
+        print(outputs.size())
+        print(masks.size())
+
         loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization

From c9453bf88f6f8ebbc3cd182a04ea144ca537b202 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 02:09:14 +1000
Subject: [PATCH 33/79] Hopefully fixed the incompatibility  for dice_loss

---
 recognition/Problem_47452752/train.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 1d03ec66b..66f20581e 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -51,7 +51,9 @@
         optimizer.zero_grad()
 
         # Forward pass
-        outputs = model(images)
+        _, outputs = torch.max(model(images), 1)
+        outputs = outputs.unsqueeze(1)
+        # outputs = model(images)
         print(outputs.size())
         print(masks.size())
 

From 01b2f19b1ec4b6ff75cb3672ce7d08536bf82667 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 02:14:23 +1000
Subject: [PATCH 34/79] the previous 'fix' failed, trying something else

---
 recognition/Problem_47452752/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 66f20581e..fed900041 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -51,12 +51,12 @@
         optimizer.zero_grad()
 
         # Forward pass
-        _, outputs = torch.max(model(images), 1)
-        outputs = outputs.unsqueeze(1)
-        # outputs = model(images)
+        outputs = model(images)
+        outputs = (outputs > 0.5).float()
         print(outputs.size())
         print(masks.size())
-
+        # _, outputs = torch.max(model(images), 1)
+        # outputs = outputs.unsqueeze(1)
         loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization

From c761df7e613bc294662b6377f53b5afff89c4d1f Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 02:20:08 +1000
Subject: [PATCH 35/79] Trying out expanding masks channels

---
 recognition/Problem_47452752/train.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index fed900041..0d794558b 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -52,12 +52,8 @@
 
         # Forward pass
         outputs = model(images)
-        outputs = (outputs > 0.5).float()
-        print(outputs.size())
-        print(masks.size())
-        # _, outputs = torch.max(model(images), 1)
-        # outputs = outputs.unsqueeze(1)
-        loss = dice_loss(outputs, masks)
+        masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+        loss = dice_loss(outputs, masks_expanded)
 
         # Backward pass + optimization
         loss.backward()

From f1f20dcafab3b60a86b640b43669589a0656e771 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 21:35:58 +1000
Subject: [PATCH 36/79] added print statements for debugging

---
 recognition/Problem_47452752/train.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 0d794558b..b2388a209 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -14,7 +14,7 @@
 
 
 # Hyper-parameters
-num_epochs = 50
+num_epochs = 5
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Check if the dataset is consistent
@@ -53,6 +53,8 @@
         # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+        print(outputs.shape)  # Should be [100, 2, 256, 256]
+        print(masks.shape)    # Should be [100, 2, 256, 256]
         loss = dice_loss(outputs, masks_expanded)
 
         # Backward pass + optimization
@@ -61,6 +63,7 @@
 
     scheduler.step()  # Adjust learning rate
 
+print("training complete") # TODO
 # Save the model
 torch.save(model.state_dict(), "recognition/Problem_47452752/model.pth")
 

From 2fa3f4ceeb7bf696c0347c6ae0808f588bf387d9 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 22:13:26 +1000
Subject: [PATCH 37/79] testing the save method

---
 recognition/Problem_47452752/train.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index b2388a209..d2706e566 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -14,7 +14,7 @@
 
 
 # Hyper-parameters
-num_epochs = 5
+num_epochs = 1
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Check if the dataset is consistent
@@ -35,7 +35,9 @@
 # Creating an instance of my UNet to be trained
 model = UNet(in_channels=6, num_classes=2)
 model = model.to(device)
-
+torch.save(model.state_dict(), "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth")
+print("saved")
+exit()
 # Setup the optimizer
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
@@ -53,8 +55,8 @@
         # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-        print(outputs.shape)  # Should be [100, 2, 256, 256]
-        print(masks.shape)    # Should be [100, 2, 256, 256]
+        print(outputs.shape)
+        print(masks.shape)  
         loss = dice_loss(outputs, masks_expanded)
 
         # Backward pass + optimization

From 940a21e9f99cc6abc72989b9e903124e2d655a3b Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 22:24:36 +1000
Subject: [PATCH 38/79] Reduced the model a lil for debugging efficiency

---
 recognition/Problem_47452752/train.py | 48 ++++++++++++++++++---------
 1 file changed, 33 insertions(+), 15 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index d2706e566..7b5f8277a 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -11,6 +11,7 @@
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
+from torch.utils.data import Subset # for testing only TODO
 
 
 # Hyper-parameters
@@ -18,26 +19,42 @@
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Check if the dataset is consistent
-check_consistency()
+# check_consistency()
+
+# debug data
 
-# Loading up the dataset and applying custom augmentations
 dataset = ISICDataset(transform)
+subset_indices = list(range(200))  # debugging on first 200 samples
+subset = Subset(dataset, subset_indices)
+
+test_size = int(0.5 * len(subset))
+train_size = len(subset) - test_size
+train_dataset, test_dataset = split_data(subset, train_size, test_size)
+
+train_loader = train_loader(train_dataset, 50)
+test_loader = test_loader(test_dataset, 50)
+
+
+
+
 
-# Splitting into testing and training sets
-test_size = int(0.2 * len(dataset))
-train_size = len(dataset) - test_size
-train_dataset, test_dataset = split_data(dataset, train_size, test_size)
 
-# Data loaders for training and testing
-train_loader = train_loader(train_dataset, 100)
-test_loader = test_loader(test_dataset, 100)
+# # Loading up the dataset and applying custom augmentations
+# dataset = ISICDataset(transform)
+
+# # Splitting into testing and training sets
+# test_size = int(0.2 * len(dataset))
+# train_size = len(dataset) - test_size
+# train_dataset, test_dataset = split_data(dataset, train_size, test_size)
+
+# # Data loaders for training and testing
+# train_loader = train_loader(train_dataset, 100)
+# test_loader = test_loader(test_dataset, 100)
 
 # Creating an instance of my UNet to be trained
 model = UNet(in_channels=6, num_classes=2)
 model = model.to(device)
-torch.save(model.state_dict(), "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth")
-print("saved")
-exit()
+
 # Setup the optimizer
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
@@ -55,8 +72,8 @@
         # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-        print(outputs.shape)
-        print(masks.shape)  
+        # print(outputs.shape)
+        # print(masks.shape)  
         loss = dice_loss(outputs, masks_expanded)
 
         # Backward pass + optimization
@@ -66,8 +83,9 @@
     scheduler.step()  # Adjust learning rate
 
 print("training complete") # TODO
+
 # Save the model
-torch.save(model.state_dict(), "recognition/Problem_47452752/model.pth")
+torch.save(model.state_dict(), "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth")
 
 # Switch to evaluation mode
 model.eval()

From 0ca5655a269288e334b5bbb6573bace6bc57e954 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 22:34:13 +1000
Subject: [PATCH 39/79] Training and testing passed debugging!

---
 recognition/Problem_47452752/train.py | 67 ++++++++++++++-------------
 1 file changed, 36 insertions(+), 31 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 7b5f8277a..26adb2024 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -5,51 +5,54 @@
 """
 
 
-from dataset import ISICDataset, transform, train_loader, test_loader, split_data, check_consistency
+from sympy import false
+from dataset import (
+    ISICDataset,
+    transform,
+    train_loader,
+    test_loader,
+    split_data,
+    check_consistency,
+)
 from modules import UNet
 from utils import dice_loss, dice_coefficient
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
-from torch.utils.data import Subset # for testing only TODO
+from torch.utils.data import Subset  # for testing only TODO
 
 
 # Hyper-parameters
-num_epochs = 1
+num_epochs = 3
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Check if the dataset is consistent
 # check_consistency()
+debugging = False
 
-# debug data
-
-dataset = ISICDataset(transform)
-subset_indices = list(range(200))  # debugging on first 200 samples
-subset = Subset(dataset, subset_indices)
-
-test_size = int(0.5 * len(subset))
-train_size = len(subset) - test_size
-train_dataset, test_dataset = split_data(subset, train_size, test_size)
-
-train_loader = train_loader(train_dataset, 50)
-test_loader = test_loader(test_dataset, 50)
-
+if debugging:
+    dataset = ISICDataset(transform)
+    subset_indices = list(range(200))  # debugging on first 200 samples
+    subset = Subset(dataset, subset_indices)
 
+    test_size = int(0.5 * len(subset))
+    train_size = len(subset) - test_size
+    train_dataset, test_dataset = split_data(subset, train_size, test_size)
 
+    train_loader = train_loader(train_dataset, 50)
+    test_loader = test_loader(test_dataset, 50)
 
+# Loading up the dataset and applying custom augmentations
+dataset = ISICDataset(transform)
 
+# Splitting into testing and training sets
+test_size = int(0.2 * len(dataset))
+train_size = len(dataset) - test_size
+train_dataset, test_dataset = split_data(dataset, train_size, test_size)
 
-# # Loading up the dataset and applying custom augmentations
-# dataset = ISICDataset(transform)
-
-# # Splitting into testing and training sets
-# test_size = int(0.2 * len(dataset))
-# train_size = len(dataset) - test_size
-# train_dataset, test_dataset = split_data(dataset, train_size, test_size)
-
-# # Data loaders for training and testing
-# train_loader = train_loader(train_dataset, 100)
-# test_loader = test_loader(test_dataset, 100)
+# Data loaders for training and testing
+train_loader = train_loader(train_dataset, 100)
+test_loader = test_loader(test_dataset, 100)
 
 # Creating an instance of my UNet to be trained
 model = UNet(in_channels=6, num_classes=2)
@@ -72,8 +75,7 @@
         # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-        # print(outputs.shape)
-        # print(masks.shape)  
+
         loss = dice_loss(outputs, masks_expanded)
 
         # Backward pass + optimization
@@ -82,10 +84,13 @@
 
     scheduler.step()  # Adjust learning rate
 
-print("training complete") # TODO
+print("training complete")  # TODO
 
 # Save the model
-torch.save(model.state_dict(), "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth")
+torch.save(
+    model.state_dict(),
+    "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
+)
 
 # Switch to evaluation mode
 model.eval()

From 5c3701db7f90f895d4e083fe03da879c76bd85fb Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Wed, 25 Oct 2023 23:12:40 +1000
Subject: [PATCH 40/79] Added training feedback to loop

---
 recognition/Problem_47452752/train.py | 52 ++++++++++++++++++---------
 1 file changed, 36 insertions(+), 16 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 26adb2024..cbc5f5551 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -62,35 +62,55 @@
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
 
-# Training loop
+running_loss = 0.0
+print_every = 10  # Print every 10 batches.
+
 for epoch in range(num_epochs):
-    model.train()  # Switch to training mode
-    for images, masks in train_loader:
-        # Move the data onto the device
+    model.train()
+    for i, (images, masks) in enumerate(train_loader, 1):
         images, masks = images.to(device), masks.to(device)
-
-        # Zero the parameter gradients
         optimizer.zero_grad()
-
-        # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-
         loss = dice_loss(outputs, masks_expanded)
-
-        # Backward pass + optimization
         loss.backward()
         optimizer.step()
+        
+        running_loss += loss.item()
+        if i % print_every == 0:  # Print every `print_every` batches
+            print(f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}")
+            running_loss = 0.0
+
 
-    scheduler.step()  # Adjust learning rate
+# # Training loop
+# for epoch in range(num_epochs):
+#     model.train()  # Switch to training mode
+#     for images, masks in train_loader:
+#         # Move the data onto the device
+#         images, masks = images.to(device), masks.to(device)
+
+#         # Zero the parameter gradients
+#         optimizer.zero_grad()
+
+#         # Forward pass
+#         outputs = model(images)
+#         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+
+#         loss = dice_loss(outputs, masks_expanded)
+
+#         # Backward pass + optimization
+#         loss.backward()
+#         optimizer.step()
+
+#     scheduler.step()  # Adjust learning rate
 
 print("training complete")  # TODO
 
 # Save the model
-torch.save(
-    model.state_dict(),
-    "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
-)
+# torch.save(
+#     model.state_dict(),
+#     "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
+# )
 
 # Switch to evaluation mode
 model.eval()

From 204afb6de5ca84312be76a33d4afb6f05aa63d3d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 00:15:55 +1000
Subject: [PATCH 41/79] removed redundent methods

---
 recognition/Problem_47452752/dataset.py | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index 79100f9af..e085c70c2 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -5,8 +5,7 @@
 import os
 import torch
 from utils import RandomCenterCrop, RandomRotate90, DictTransform
-from torch.utils.data import Dataset, DataLoader
-from torch.utils.data.dataset import random_split
+from torch.utils.data import Dataset
 from torchvision import transforms
 import torchvision.transforms.functional as TF
 from PIL import Image
@@ -129,15 +128,3 @@ def __getitem__(self, idx):
         ),
     ]
 )
-
-
-def split_data(dataset, train_size, test_size):
-    return random_split(dataset, [train_size, test_size])
-
-
-def train_loader(train_dataset, batch_size, shuffle=True):
-    return DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle)
-
-
-def test_loader(test_dataset, batch_size, shuffle=False):
-    return DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle)

From 5470f3bc1e5faa9a5739ad98ea01890b7a9e8bb9 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 00:16:08 +1000
Subject: [PATCH 42/79] Implemented validation set stuff

---
 recognition/Problem_47452752/train.py | 100 ++++++++++++++++----------
 1 file changed, 62 insertions(+), 38 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index cbc5f5551..d3b13f93d 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -5,16 +5,14 @@
 """
 
 
-from sympy import false
 from dataset import (
     ISICDataset,
     transform,
-    train_loader,
-    test_loader,
-    split_data,
     check_consistency,
 )
 from modules import UNet
+from torch.utils.data import DataLoader
+from torch.utils.data.dataset import random_split
 from utils import dice_loss, dice_coefficient
 import torch
 import torch.optim as optim
@@ -23,7 +21,7 @@
 
 
 # Hyper-parameters
-num_epochs = 3
+num_epochs = 5
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Check if the dataset is consistent
@@ -45,14 +43,21 @@
 # Loading up the dataset and applying custom augmentations
 dataset = ISICDataset(transform)
 
-# Splitting into testing and training sets
-test_size = int(0.2 * len(dataset))
-train_size = len(dataset) - test_size
-train_dataset, test_dataset = split_data(dataset, train_size, test_size)
+total_size = len(dataset)
 
-# Data loaders for training and testing
-train_loader = train_loader(train_dataset, 100)
-test_loader = test_loader(test_dataset, 100)
+# Splitting into training, validation and testing sets
+train_size = int(total_size * 0.7)
+val_size = int(total_size * 0.2)
+test_size = total_size - train_size - val_size
+
+train_dataset, val_dataset, test_dataset = random_split(
+    dataset, [train_size, val_size, test_size]
+)
+
+# Data loaders for training, validation and testing
+train_loader = DataLoader(train_dataset, 32, True)
+validation_loader = DataLoader(val_dataset, 100, False)
+test_loader = DataLoader(test_dataset, 100, False)
 
 # Creating an instance of my UNet to be trained
 model = UNet(in_channels=6, num_classes=2)
@@ -62,55 +67,75 @@
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
 
+print("Training loop:")
+
+# Variables used for training feedback:
 running_loss = 0.0
 print_every = 10  # Print every 10 batches.
+best_val_loss = float('inf')
+patience = 3 # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
     model.train()
     for i, (images, masks) in enumerate(train_loader, 1):
+        # Move the data onto the device
         images, masks = images.to(device), masks.to(device)
+
+        # Zero the parameter gradients
         optimizer.zero_grad()
+
+        # Forward pass
         outputs = model(images)
         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+
         loss = dice_loss(outputs, masks_expanded)
+
+        # Backward pass + optimization
         loss.backward()
         optimizer.step()
-        
+
         running_loss += loss.item()
         if i % print_every == 0:  # Print every `print_every` batches
-            print(f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}")
+            print(
+                f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}"
+            )
             running_loss = 0.0
+    
+    # Evaluate the model using the validation set
+    model.eval()
+    val_loss = 0
 
+    with torch.no_grad():
+        for images, masks in validation_loader:
+            # Load the validation data onto the device
+            images, masks = images.to(device), masks.to(device)
+            
+            # Evaluate the loss
+            outputs = model(images)
+            loss = dice_loss(outputs, masks)
+            val_loss += loss.item()
 
-# # Training loop
-# for epoch in range(num_epochs):
-#     model.train()  # Switch to training mode
-#     for images, masks in train_loader:
-#         # Move the data onto the device
-#         images, masks = images.to(device), masks.to(device)
+    val_loss /= len(validation_loader)
+    print(f"Epoch {epoch}, Validation Loss = {val_loss:.4f}")
 
-#         # Zero the parameter gradients
-#         optimizer.zero_grad()
+    # Model checkpointing
+    if val_loss < best_val_loss:
+        best_val_loss = val_loss
+        torch.save(model.state_dict(), "best_model.pth")
+        no_improvement = 0
+    else:
+        no_improvement += 1
 
-#         # Forward pass
-#         outputs = model(images)
-#         masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+    scheduler.step()  # Adjust learning rate
 
-#         loss = dice_loss(outputs, masks_expanded)
 
-#         # Backward pass + optimization
-#         loss.backward()
-#         optimizer.step()
-
-#     scheduler.step()  # Adjust learning rate
-
-print("training complete")  # TODO
+print("training complete")
 
 # Save the model
-# torch.save(
-#     model.state_dict(),
-#     "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
-# )
+torch.save(
+    model.state_dict(),
+    "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
+)
 
 # Switch to evaluation mode
 model.eval()
@@ -127,7 +152,6 @@
         outputs = model(inputs)
 
         # Convert outputs to binary mask
-        # If sigmoid activation is used in the last layer
         predicted_masks = (outputs > 0.5).float()
 
         # Compute Dice coefficient

From 1eae8060a07396b5764e25866ff68eb3fc8e03ed Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 00:21:40 +1000
Subject: [PATCH 43/79] Included mask channel expansion to validation

---
 recognition/Problem_47452752/train.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index d3b13f93d..898be5010 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -17,7 +17,7 @@
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
-from torch.utils.data import Subset  # for testing only TODO
+from torch.utils.data import Subset  # for debugging only TODO
 
 
 # Hyper-parameters
@@ -35,10 +35,10 @@
 
     test_size = int(0.5 * len(subset))
     train_size = len(subset) - test_size
-    train_dataset, test_dataset = split_data(subset, train_size, test_size)
+    train_dataset, test_dataset = random_split(subset, train_size, test_size)
 
-    train_loader = train_loader(train_dataset, 50)
-    test_loader = test_loader(test_dataset, 50)
+    train_loader = DataLoader(train_dataset, 50)
+    test_loader = DataLoader(test_dataset, 50)
 
 # Loading up the dataset and applying custom augmentations
 dataset = ISICDataset(transform)
@@ -112,7 +112,8 @@
             
             # Evaluate the loss
             outputs = model(images)
-            loss = dice_loss(outputs, masks)
+            masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+            loss = dice_loss(outputs, masks_expanded)
             val_loss += loss.item()
 
     val_loss /= len(validation_loader)

From 04501911144f044347007efd0571976c5b3f94b3 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 18:36:20 +1000
Subject: [PATCH 44/79] Changed num_classes to 1 and implemented thresholding
 on output. dice_loss modified accordingly

---
 recognition/Problem_47452752/train.py | 111 +++++++++++++++-----------
 recognition/Problem_47452752/utils.py |  14 ++--
 2 files changed, 71 insertions(+), 54 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 898be5010..f5a492750 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -24,11 +24,19 @@
 num_epochs = 5
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# Conditional parameters used for debugging
+check = False
+debugging = True
+saving = False
+validating = False
+
 # Check if the dataset is consistent
-# check_consistency()
-debugging = False
+if check:
+    check_consistency()
 
+# Construct smaller debugging datasets
 if debugging:
+    num_epochs = 1
     dataset = ISICDataset(transform)
     subset_indices = list(range(200))  # debugging on first 200 samples
     subset = Subset(dataset, subset_indices)
@@ -40,27 +48,29 @@
     train_loader = DataLoader(train_dataset, 50)
     test_loader = DataLoader(test_dataset, 50)
 
-# Loading up the dataset and applying custom augmentations
-dataset = ISICDataset(transform)
+# Construct full datasets
+if not debugging:
+    # Loading up the dataset and applying custom augmentations
+    dataset = ISICDataset(transform)
 
-total_size = len(dataset)
+    total_size = len(dataset)
 
-# Splitting into training, validation and testing sets
-train_size = int(total_size * 0.7)
-val_size = int(total_size * 0.2)
-test_size = total_size - train_size - val_size
+    # Splitting into training, validation and testing sets
+    train_size = int(total_size * 0.7)
+    val_size = int(total_size * 0.2)
+    test_size = total_size - train_size - val_size
 
-train_dataset, val_dataset, test_dataset = random_split(
-    dataset, [train_size, val_size, test_size]
-)
+    train_dataset, val_dataset, test_dataset = random_split(
+        dataset, [train_size, val_size, test_size]
+    )
 
-# Data loaders for training, validation and testing
-train_loader = DataLoader(train_dataset, 32, True)
-validation_loader = DataLoader(val_dataset, 100, False)
-test_loader = DataLoader(test_dataset, 100, False)
+    # Data loaders for training, validation and testing
+    train_loader = DataLoader(train_dataset, 32, True)
+    validation_loader = DataLoader(val_dataset, 100, False)
+    test_loader = DataLoader(test_dataset, 100, False)
 
 # Creating an instance of my UNet to be trained
-model = UNet(in_channels=6, num_classes=2)
+model = UNet(in_channels=6, num_classes=1)
 model = model.to(device)
 
 # Setup the optimizer
@@ -86,46 +96,50 @@
 
         # Forward pass
         outputs = model(images)
-        masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+        print(outputs.size())
+        thresholded_output = (outputs > 0.5).float()
+        # masks_expanded = torch.cat((masks, 1 - masks), dim=1)
 
-        loss = dice_loss(outputs, masks_expanded)
+        loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization
         loss.backward()
         optimizer.step()
 
+    if validating:
+
         running_loss += loss.item()
         if i % print_every == 0:  # Print every `print_every` batches
             print(
                 f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}"
             )
             running_loss = 0.0
-    
-    # Evaluate the model using the validation set
-    model.eval()
-    val_loss = 0
-
-    with torch.no_grad():
-        for images, masks in validation_loader:
-            # Load the validation data onto the device
-            images, masks = images.to(device), masks.to(device)
             
-            # Evaluate the loss
-            outputs = model(images)
-            masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-            loss = dice_loss(outputs, masks_expanded)
-            val_loss += loss.item()
-
-    val_loss /= len(validation_loader)
-    print(f"Epoch {epoch}, Validation Loss = {val_loss:.4f}")
-
-    # Model checkpointing
-    if val_loss < best_val_loss:
-        best_val_loss = val_loss
-        torch.save(model.state_dict(), "best_model.pth")
-        no_improvement = 0
-    else:
-        no_improvement += 1
+        # Evaluate the model using the validation set
+        model.eval()
+        val_loss = 0
+
+        with torch.no_grad():
+            for images, masks in validation_loader:
+                # Load the validation data onto the device
+                images, masks = images.to(device), masks.to(device)
+                
+                # Evaluate the loss
+                outputs = model(images)
+                masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+                loss = dice_loss(outputs, masks_expanded)
+                val_loss += loss.item()
+
+        val_loss /= len(validation_loader)
+        print(f"Epoch {epoch + 1}, Validation Loss = {val_loss:.4f}")
+
+        # Model checkpointing
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            torch.save(model.state_dict(), "best_model.pth")
+            no_improvement = 0
+        else:
+            no_improvement += 1
 
     scheduler.step()  # Adjust learning rate
 
@@ -133,10 +147,11 @@
 print("training complete")
 
 # Save the model
-torch.save(
-    model.state_dict(),
-    "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
-)
+if saving:
+    torch.save(
+        model.state_dict(),
+        "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
+    )
 
 # Switch to evaluation mode
 model.eval()
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index 7a6c4d47f..ba7a745df 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -58,14 +58,16 @@ def __call__(self, sample):
 
 def dice_loss(predicted, target, epsilon=1e-7):
     # Flatten the tensors 
-    predicted = predicted.view(-1)
-    target = target.view(-1)
+    # predicted = predicted.view(-1)
+    # target = target.view(-1)
+    dice_losses = [1 - dice_coefficient(pred, target) for pred, target in zip(predicted, target)]
+    penalized_losses = [loss * 2 if (1 - loss) < 0.8 else loss for loss in dice_losses]
+    average_penalized_loss = sum(penalized_losses) / len(penalized_losses)
 
-    intersection = (predicted * target).sum()
-    union = predicted.sum() + target.sum()
+    # max_dice_loss = max(dice_losses) # Penalize the worst performance
+    # dice_loss = sum(dice_losses) / len(dice_losses)
 
-    dice_loss = 2 * (intersection + epsilon) / (union + epsilon)
-    return 1 - dice_loss
+    return average_penalized_loss
 
 
 def general_dice_loss(predicted, target):

From 075b9ba082cac16ff085d57cab94555e0ce938f9 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 18:38:58 +1000
Subject: [PATCH 45/79] fixed bug in random_split

---
 recognition/Problem_47452752/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index f5a492750..68ce7a885 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -43,7 +43,7 @@
 
     test_size = int(0.5 * len(subset))
     train_size = len(subset) - test_size
-    train_dataset, test_dataset = random_split(subset, train_size, test_size)
+    train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
     train_loader = DataLoader(train_dataset, 50)
     test_loader = DataLoader(test_dataset, 50)
@@ -114,7 +114,7 @@
                 f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}"
             )
             running_loss = 0.0
-            
+
         # Evaluate the model using the validation set
         model.eval()
         val_loss = 0

From 1d6848c7ee1835d957ecb155548a2004f74c7244 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 19:00:40 +1000
Subject: [PATCH 46/79] Downsized the debugging dataset, trying to lower gpu
 memory

---
 recognition/Problem_47452752/modules.py |  4 ++--
 recognition/Problem_47452752/train.py   | 25 +++++++++++++------------
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
index 1682cd3cd..9fab0bf37 100644
--- a/recognition/Problem_47452752/modules.py
+++ b/recognition/Problem_47452752/modules.py
@@ -116,7 +116,7 @@ def forward(self, x, other_layer, upscale=True):
 
 
 class UNet(nn.Module):
-    def __init__(self, in_channels, num_classes=2):
+    def __init__(self, in_channels, num_classes=1):
         super(UNet, self).__init__()
 
         # Context modules
@@ -173,7 +173,7 @@ def forward(self, x):
         # if num_classes > 2: return F.softmax(seg_3, dim=1)
 
 # Lil testing
-# model = UNet(6)
+# model = UNet(6, 1)
 # test = torch.randn(1, 6, 32, 32)
 # output = model(test)
 # print(output.size())
\ No newline at end of file
diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 68ce7a885..5467e126e 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -38,15 +38,15 @@
 if debugging:
     num_epochs = 1
     dataset = ISICDataset(transform)
-    subset_indices = list(range(200))  # debugging on first 200 samples
+    subset_indices = list(range(40))  # debugging on first 40 samples
     subset = Subset(dataset, subset_indices)
 
     test_size = int(0.5 * len(subset))
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
-    train_loader = DataLoader(train_dataset, 50)
-    test_loader = DataLoader(test_dataset, 50)
+    train_loader = DataLoader(train_dataset, 10)
+    test_loader = DataLoader(test_dataset, 10)
 
 # Construct full datasets
 if not debugging:
@@ -80,27 +80,28 @@
 print("Training loop:")
 
 # Variables used for training feedback:
-running_loss = 0.0
-print_every = 10  # Print every 10 batches.
-best_val_loss = float('inf')
-patience = 3 # Number of epochs to wait before stopping
+if validating:
+    running_loss = 0.0
+    print_every = 10  # Print every 10 batches.
+    best_val_loss = float('inf')
+    patience = 3 # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
     model.train()
     for i, (images, masks) in enumerate(train_loader, 1):
         # Move the data onto the device
         images, masks = images.to(device), masks.to(device)
-
+        print(f"masks = {masks.size()}")
         # Zero the parameter gradients
         optimizer.zero_grad()
 
         # Forward pass
         outputs = model(images)
-        print(outputs.size())
-        thresholded_output = (outputs > 0.5).float()
-        # masks_expanded = torch.cat((masks, 1 - masks), dim=1)
+        print(f"outputs = {outputs.size()}")
+        
+        binary_output = (outputs > 0.5).float()
 
-        loss = dice_loss(outputs, masks)
+        loss = dice_loss(binary_output, masks)
 
         # Backward pass + optimization
         loss.backward()

From 48bc775698b1014a5cef24eef97fd228469fda0e Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:35:55 +1000
Subject: [PATCH 47/79] Using batched operations in dice_loss and
 dice_coefficient methods to improve efficiency

---
 recognition/Problem_47452752/utils.py | 62 ++++++++++++++++++++-------
 1 file changed, 46 insertions(+), 16 deletions(-)

diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index ba7a745df..7e08d9945 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -43,6 +43,7 @@ class DictTransform:
     def __init__(self, transform, transform_mask=True):
         self.transform = transform
         self.transform_mask = transform_mask
+
     def __call__(self, sample):
         image, mask = sample["image"], sample["mask"]
         if self.transform_mask:
@@ -56,20 +57,51 @@ def __call__(self, sample):
         }
 
 
+# max_dice_loss = max(dice_losses) # Penalize the worst performance
+# dice_loss = sum(dice_losses) / len(dice_losses)
 def dice_loss(predicted, target, epsilon=1e-7):
-    # Flatten the tensors 
-    # predicted = predicted.view(-1)
-    # target = target.view(-1)
-    dice_losses = [1 - dice_coefficient(pred, target) for pred, target in zip(predicted, target)]
-    penalized_losses = [loss * 2 if (1 - loss) < 0.8 else loss for loss in dice_losses]
-    average_penalized_loss = sum(penalized_losses) / len(penalized_losses)
+    # Compute dice coefficient for all images in the batch
+    intersect = (predicted * target).sum(dim=[2, 3])
+    union = (predicted + target).sum(dim=[2, 3])
+    dice_scores = (2.0 * intersect + epsilon) / (union + epsilon)
+
+    # Penalize losses
+    penalties = torch.where(dice_scores < 0.8, 2.0, 1.0)
+    losses = 1.0 - dice_scores
+    penalized_losses = penalties * losses
+
+    # Average penalized loss
+    average_penalized_loss = penalized_losses.mean()
+
+    return average_penalized_loss
 
-    # max_dice_loss = max(dice_losses) # Penalize the worst performance
-    # dice_loss = sum(dice_losses) / len(dice_losses)
 
+def dice_loss(predicted, target, epsilon=1e-7):
+    # Compute dice coefficient for each image in the batch
+    dice_scores = dice_coefficient(predicted, target)
+    # Compute dice loss for each image in the batch
+    dice_losses =  1.0 - dice_scores
+    # Penalize any images with dice score less than 0.8
+    penalized_losses = torch.where(dice_scores < 0.8, dice_losses * 2, dice_losses)
+    # Return the average loss
+    average_penalized_loss = penalized_losses.mean()
     return average_penalized_loss
 
 
+def dice_coefficient(
+    predicted: torch.Tensor, target: torch.Tensor, epsilon=1e-7
+) -> torch.Tensor:
+    """Compute dice coefficient for each image in the batch"""
+    predicted = predicted.contiguous().view(predicted.shape[0], -1)
+    target = target.contiguous().view(target.shape[0], -1)
+
+    intersection = (predicted * target).sum(dim=1)
+    return (2.0 * intersection + epsilon) / (
+        predicted.sum(dim=1) + target.sum(dim=1) + epsilon
+    )
+
+
+
 def general_dice_loss(predicted, target):
     # One-hot encode the target segmentation map
     target_one_hot = torch.zeros_like(predicted)
@@ -85,12 +117,10 @@ def general_dice_loss(predicted, target):
 
     return loss
 
-def dice_coefficient(predicted, target):
-    """ Compute Dice Coefficient for single sample. """
-    intersection = (predicted * target).sum().float()
-    union = (predicted + target).sum().float()
-    
-    if union == 0:
-        return 1.0
 
-    return 2 * intersection / union
\ No newline at end of file
+# lil testing
+# pred = torch.randn(3, 6, 32, 32)
+# tar = torch.randn(3, 6, 32, 32)
+# x = dice_coefficient(pred, tar)
+# y = dice_loss(pred, tar)
+# 

From bec7d667bb854a30d66220b98b38a9d94d77270f Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:37:02 +1000
Subject: [PATCH 48/79] Removed non-differentiable binary conversion from
 training loop, also created an evauation helper

---
 recognition/Problem_47452752/train.py | 94 +++++++++++++--------------
 1 file changed, 47 insertions(+), 47 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 5467e126e..dfd7fb923 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -10,6 +10,7 @@
     transform,
     check_consistency,
 )
+import numpy as np
 from modules import UNet
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import random_split
@@ -30,6 +31,35 @@
 saving = False
 validating = False
 
+
+def evaluate_model(model, data_loader, device):
+    # set the model to evaluation mode
+    model.eval()
+
+    # List to store individual dice scores for each sample
+    all_dice_scores = []
+
+    # No gradient computation during evaluation
+    with torch.no_grad():
+        for images, masks in data_loader:
+            images, masks = images.to(device), masks.to(device)
+
+            # Compute predictions
+            outputs = model(images)
+
+            # Convert predictions to binary using threshold
+            outputs = (outputs > 0.5).float()
+
+            # Compute and store the dice coefficients
+            batch_dice_scores = dice_coefficient(outputs, masks)
+            all_dice_scores.extend(batch_dice_scores.cpu().numpy())
+
+    avg_dice_score = np.mean(all_dice_scores)
+    min_dice_score = np.min(all_dice_scores)
+
+    return avg_dice_score, min_dice_score
+
+
 # Check if the dataset is consistent
 if check:
     check_consistency()
@@ -83,32 +113,33 @@
 if validating:
     running_loss = 0.0
     print_every = 10  # Print every 10 batches.
-    best_val_loss = float('inf')
-    patience = 3 # Number of epochs to wait before stopping
+    best_val_score = 0.0
+    patience = 3  # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
+    # Set the model to training mode
     model.train()
+
     for i, (images, masks) in enumerate(train_loader, 1):
         # Move the data onto the device
         images, masks = images.to(device), masks.to(device)
-        print(f"masks = {masks.size()}")
+
         # Zero the parameter gradients
         optimizer.zero_grad()
 
         # Forward pass
         outputs = model(images)
         print(f"outputs = {outputs.size()}")
-        
-        binary_output = (outputs > 0.5).float()
+        print(f"masks = {masks.size()}")
 
-        loss = dice_loss(binary_output, masks)
+        loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization
         loss.backward()
         optimizer.step()
 
     if validating:
-
+        # Keep track of the running loss for testing feedback
         running_loss += loss.item()
         if i % print_every == 0:  # Print every `print_every` batches
             print(
@@ -117,25 +148,15 @@
             running_loss = 0.0
 
         # Evaluate the model using the validation set
-        model.eval()
-        val_loss = 0
-
-        with torch.no_grad():
-            for images, masks in validation_loader:
-                # Load the validation data onto the device
-                images, masks = images.to(device), masks.to(device)
-                
-                # Evaluate the loss
-                outputs = model(images)
-                masks_expanded = torch.cat((masks, 1 - masks), dim=1)
-                loss = dice_loss(outputs, masks_expanded)
-                val_loss += loss.item()
-
-        val_loss /= len(validation_loader)
-        print(f"Epoch {epoch + 1}, Validation Loss = {val_loss:.4f}")
+        dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
+        val_loss = 1 - dice_similarity
+        # Print out the validation metrics
+        print(
+            f"Validation metrics during epoch {epoch + 1}, loss = {val_loss:.4f}, similarity = {dice_similarity:.4f}, minimum = {dice_minimum:.4f}"
+        )
 
         # Model checkpointing
-        if val_loss < best_val_loss:
+        if dice_similarity > best_val_score and dice_minimum > 0.7:
             best_val_loss = val_loss
             torch.save(model.state_dict(), "best_model.pth")
             no_improvement = 0
@@ -154,28 +175,7 @@
         "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
     )
 
-# Switch to evaluation mode
-model.eval()
-
-# Metrics storage
-dice_scores = []
-
-# No gradient computation during evaluation
-with torch.no_grad():
-    for inputs, masks in test_loader:
-        inputs, masks = inputs.to(device), masks.to(device)
-
-        # Compute predictions
-        outputs = model(inputs)
-
-        # Convert outputs to binary mask
-        predicted_masks = (outputs > 0.5).float()
-
-        # Compute Dice coefficient
-        for pred, true in zip(predicted_masks, masks):
-            dice_scores.append(dice_coefficient(pred, true).item())
-
-# Calculate average Dice score
-avg_dice_score = sum(dice_scores) / len(dice_scores)
+avg_dice_score, min_dice_score = evaluate_model(model, test_loader, device)
 
 print(f"Average Dice Coefficient on Test Set: {avg_dice_score:.4f}")
+print(f"Minimum Dice Coefficient on Test Set: {min_dice_score:.4f}")

From 64ca95d61e6a7d6230654703d9e6f4bb677b8a78 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:40:19 +1000
Subject: [PATCH 49/79] including testing feedback for debugging

---
 recognition/Problem_47452752/train.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index dfd7fb923..4db412d7b 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -109,12 +109,11 @@ def evaluate_model(model, data_loader, device):
 
 print("Training loop:")
 
-# Variables used for training feedback:
-if validating:
-    running_loss = 0.0
-    print_every = 10  # Print every 10 batches.
-    best_val_score = 0.0
-    patience = 3  # Number of epochs to wait before stopping
+# Variables used for training feedback and validation:
+running_loss = 0.0
+print_every = 1  # Print every 10 batches.
+best_val_score = 0.0
+patience = 3  # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
     # Set the model to training mode
@@ -138,7 +137,6 @@ def evaluate_model(model, data_loader, device):
         loss.backward()
         optimizer.step()
 
-    if validating:
         # Keep track of the running loss for testing feedback
         running_loss += loss.item()
         if i % print_every == 0:  # Print every `print_every` batches
@@ -147,6 +145,7 @@ def evaluate_model(model, data_loader, device):
             )
             running_loss = 0.0
 
+    if validating:
         # Evaluate the model using the validation set
         dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
         val_loss = 1 - dice_similarity

From 97dad359eb628ff1e6dea2fb410e9aeab355874d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:43:04 +1000
Subject: [PATCH 50/79] Gonna try 3 epochs in debugging instead of 1

---
 recognition/Problem_47452752/train.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 4db412d7b..f86b71eb4 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -66,7 +66,7 @@ def evaluate_model(model, data_loader, device):
 
 # Construct smaller debugging datasets
 if debugging:
-    num_epochs = 1
+    num_epochs = 3
     dataset = ISICDataset(transform)
     subset_indices = list(range(40))  # debugging on first 40 samples
     subset = Subset(dataset, subset_indices)
@@ -128,8 +128,8 @@ def evaluate_model(model, data_loader, device):
 
         # Forward pass
         outputs = model(images)
-        print(f"outputs = {outputs.size()}")
-        print(f"masks = {masks.size()}")
+        # print(f"outputs = {outputs.size()}")
+        # print(f"masks = {masks.size()}")
 
         loss = dice_loss(outputs, masks)
 

From 091e4d924da266458a190731ec6aff8cbe48bc07 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:45:55 +1000
Subject: [PATCH 51/79] using 15 epochs for debugging

---
 recognition/Problem_47452752/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index f86b71eb4..171ae67f7 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -66,7 +66,7 @@ def evaluate_model(model, data_loader, device):
 
 # Construct smaller debugging datasets
 if debugging:
-    num_epochs = 3
+    num_epochs = 15
     dataset = ISICDataset(transform)
     subset_indices = list(range(40))  # debugging on first 40 samples
     subset = Subset(dataset, subset_indices)

From 90627dd780a635d7fb75d0967b7cd7ea7c5a62ed Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 20:53:26 +1000
Subject: [PATCH 52/79] Debugging with epochs = 3, samples = 200, batches = 50

---
 recognition/Problem_47452752/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 171ae67f7..1ea0504f6 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -66,17 +66,17 @@ def evaluate_model(model, data_loader, device):
 
 # Construct smaller debugging datasets
 if debugging:
-    num_epochs = 15
+    num_epochs = 3
     dataset = ISICDataset(transform)
-    subset_indices = list(range(40))  # debugging on first 40 samples
+    subset_indices = list(range(200))  # debugging on first 40 samples
     subset = Subset(dataset, subset_indices)
 
     test_size = int(0.5 * len(subset))
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
-    train_loader = DataLoader(train_dataset, 10)
-    test_loader = DataLoader(test_dataset, 10)
+    train_loader = DataLoader(train_dataset, 50)
+    test_loader = DataLoader(test_dataset, 50)
 
 # Construct full datasets
 if not debugging:

From 365f642203516db8d210fd21e96ceb362df70f5d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 21:14:09 +1000
Subject: [PATCH 53/79] Trying a ChatGPT generated DiceLoss module as criterion
 for debugging purposes

---
 recognition/Problem_47452752/train.py | 14 ++++++++------
 recognition/Problem_47452752/utils.py | 13 ++++++++++++-
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 1ea0504f6..fd7087a79 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -14,7 +14,7 @@
 from modules import UNet
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import random_split
-from utils import dice_loss, dice_coefficient
+from utils import dice_loss, dice_coefficient, DiceLoss
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
@@ -66,17 +66,17 @@ def evaluate_model(model, data_loader, device):
 
 # Construct smaller debugging datasets
 if debugging:
-    num_epochs = 3
+    num_epochs = 5
     dataset = ISICDataset(transform)
-    subset_indices = list(range(200))  # debugging on first 40 samples
+    subset_indices = list(range(120))  # debugging on first 40 samples
     subset = Subset(dataset, subset_indices)
 
     test_size = int(0.5 * len(subset))
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
-    train_loader = DataLoader(train_dataset, 50)
-    test_loader = DataLoader(test_dataset, 50)
+    train_loader = DataLoader(train_dataset, 20)
+    test_loader = DataLoader(test_dataset, 20)
 
 # Construct full datasets
 if not debugging:
@@ -104,6 +104,7 @@ def evaluate_model(model, data_loader, device):
 model = model.to(device)
 
 # Setup the optimizer
+criterion = DiceLoss() # for debugging
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
 
@@ -131,7 +132,8 @@ def evaluate_model(model, data_loader, device):
         # print(f"outputs = {outputs.size()}")
         # print(f"masks = {masks.size()}")
 
-        loss = dice_loss(outputs, masks)
+        loss = criterion(outputs, masks)
+        # loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization
         loss.backward()
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index 7e08d9945..fb8f4f7e9 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -1,6 +1,7 @@
 import torch
 from random import choice
 import torchvision.transforms.functional as TF
+import torch.nn as nn
 
 
 class RandomRotate90:
@@ -57,6 +58,17 @@ def __call__(self, sample):
         }
 
 
+class DiceLoss(nn.Module):
+    def __init__(self, eps=1e-7):
+        super(DiceLoss, self).__init__()
+        self.eps = eps
+
+    def forward(self, output, target):
+        numerator = 2. * torch.sum(output * target)
+        denominator = torch.sum(output + target)
+        return 1 - (numerator + self.eps) / (denominator + self.eps)
+
+
 # max_dice_loss = max(dice_losses) # Penalize the worst performance
 # dice_loss = sum(dice_losses) / len(dice_losses)
 def dice_loss(predicted, target, epsilon=1e-7):
@@ -101,7 +113,6 @@ def dice_coefficient(
     )
 
 
-
 def general_dice_loss(predicted, target):
     # One-hot encode the target segmentation map
     target_one_hot = torch.zeros_like(predicted)

From 7cfc99a41294d149e9568a2011e9014917c12d93 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 21:32:48 +1000
Subject: [PATCH 54/79] removed some dice loss debugging methods

---
 recognition/Problem_47452752/train.py |  6 ++---
 recognition/Problem_47452752/utils.py | 33 +++------------------------
 2 files changed, 5 insertions(+), 34 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index fd7087a79..de037b639 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -14,7 +14,7 @@
 from modules import UNet
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import random_split
-from utils import dice_loss, dice_coefficient, DiceLoss
+from utils import dice_loss, dice_coefficient
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
@@ -104,7 +104,6 @@ def evaluate_model(model, data_loader, device):
 model = model.to(device)
 
 # Setup the optimizer
-criterion = DiceLoss() # for debugging
 optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
 scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
 
@@ -132,8 +131,7 @@ def evaluate_model(model, data_loader, device):
         # print(f"outputs = {outputs.size()}")
         # print(f"masks = {masks.size()}")
 
-        loss = criterion(outputs, masks)
-        # loss = dice_loss(outputs, masks)
+        loss = dice_loss(outputs, masks)
 
         # Backward pass + optimization
         loss.backward()
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
index fb8f4f7e9..068989fe3 100644
--- a/recognition/Problem_47452752/utils.py
+++ b/recognition/Problem_47452752/utils.py
@@ -58,45 +58,18 @@ def __call__(self, sample):
         }
 
 
-class DiceLoss(nn.Module):
-    def __init__(self, eps=1e-7):
-        super(DiceLoss, self).__init__()
-        self.eps = eps
-
-    def forward(self, output, target):
-        numerator = 2. * torch.sum(output * target)
-        denominator = torch.sum(output + target)
-        return 1 - (numerator + self.eps) / (denominator + self.eps)
-
-
 # max_dice_loss = max(dice_losses) # Penalize the worst performance
 # dice_loss = sum(dice_losses) / len(dice_losses)
-def dice_loss(predicted, target, epsilon=1e-7):
-    # Compute dice coefficient for all images in the batch
-    intersect = (predicted * target).sum(dim=[2, 3])
-    union = (predicted + target).sum(dim=[2, 3])
-    dice_scores = (2.0 * intersect + epsilon) / (union + epsilon)
-
-    # Penalize losses
-    penalties = torch.where(dice_scores < 0.8, 2.0, 1.0)
-    losses = 1.0 - dice_scores
-    penalized_losses = penalties * losses
-
-    # Average penalized loss
-    average_penalized_loss = penalized_losses.mean()
-
-    return average_penalized_loss
-
-
 def dice_loss(predicted, target, epsilon=1e-7):
     # Compute dice coefficient for each image in the batch
     dice_scores = dice_coefficient(predicted, target)
     # Compute dice loss for each image in the batch
-    dice_losses =  1.0 - dice_scores
+    dice_losses = 1.0 - dice_scores
     # Penalize any images with dice score less than 0.8
     penalized_losses = torch.where(dice_scores < 0.8, dice_losses * 2, dice_losses)
     # Return the average loss
     average_penalized_loss = penalized_losses.mean()
+
     return average_penalized_loss
 
 
@@ -134,4 +107,4 @@ def general_dice_loss(predicted, target):
 # tar = torch.randn(3, 6, 32, 32)
 # x = dice_coefficient(pred, tar)
 # y = dice_loss(pred, tar)
-# 
+#

From 6561a0d7770ab0be90229ff64bde207ed115ee72 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 21:51:50 +1000
Subject: [PATCH 55/79] experimenting with different batch sizes

---
 recognition/Problem_47452752/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index de037b639..872c26fe3 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -66,17 +66,17 @@ def evaluate_model(model, data_loader, device):
 
 # Construct smaller debugging datasets
 if debugging:
-    num_epochs = 5
+    num_epochs = 3
     dataset = ISICDataset(transform)
-    subset_indices = list(range(120))  # debugging on first 40 samples
+    subset_indices = list(range(500))  # debugging on first 500 samples
     subset = Subset(dataset, subset_indices)
 
-    test_size = int(0.5 * len(subset))
+    test_size = int(0.2 * len(subset))
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
     train_loader = DataLoader(train_dataset, 20)
-    test_loader = DataLoader(test_dataset, 20)
+    test_loader = DataLoader(test_dataset, 50)
 
 # Construct full datasets
 if not debugging:

From 035a0d505ab5cdaa81e4b5076cdb4e9a6ba3cf78 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 21:55:29 +1000
Subject: [PATCH 56/79] trying training batch size of 50, been problematic
 previously

---
 recognition/Problem_47452752/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 872c26fe3..d49ea099c 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -75,7 +75,7 @@ def evaluate_model(model, data_loader, device):
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
-    train_loader = DataLoader(train_dataset, 20)
+    train_loader = DataLoader(train_dataset, 50)
     test_loader = DataLoader(test_dataset, 50)
 
 # Construct full datasets

From f40097f4fd23810f4bbfcf0e358a3458197f79a1 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 21:57:56 +1000
Subject: [PATCH 57/79] trying batch size of 40

---
 recognition/Problem_47452752/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index d49ea099c..ec68dcd95 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -75,7 +75,7 @@ def evaluate_model(model, data_loader, device):
     train_size = len(subset) - test_size
     train_dataset, test_dataset = random_split(subset, [train_size, test_size])
 
-    train_loader = DataLoader(train_dataset, 50)
+    train_loader = DataLoader(train_dataset, 40)
     test_loader = DataLoader(test_dataset, 50)
 
 # Construct full datasets

From ca6b1147aac063e82484d541597837d09f9ecff6 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 23:22:44 +1000
Subject: [PATCH 58/79] Changed the threshold from 127.5 to 0.5 for binarizing
 the mask

---
 recognition/Problem_47452752/dataset.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
index e085c70c2..bfa655535 100644
--- a/recognition/Problem_47452752/dataset.py
+++ b/recognition/Problem_47452752/dataset.py
@@ -13,9 +13,9 @@
 
 image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
 mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
+inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/inconsistent_ids.txt"
 
-
-def check_consistency(image_dir=image_path, mask_dir=mask_path):
+def check_consistency(image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path):
     image_ids = {
         img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
     }
@@ -32,7 +32,7 @@ def check_consistency(image_dir=image_path, mask_dir=mask_path):
     if images_without_masks or masks_without_images:
         inconsistent_ids = images_without_masks.union(masks_without_images)
         # Save to a file
-        with open("inconsistent_ids.txt", "w") as file:
+        with open(inconsistent_path, "w") as file:
             for ID in inconsistent_ids:
                 file.write(f"{ID}\n")
 
@@ -45,9 +45,10 @@ def __init__(
         transform,
         image_dir=image_path,
         mask_dir=mask_path,
+        inconsistent_path=inconsistent_path
     ):
         # Load the inconsistent IDs
-        with open("inconsistent_ids.txt", "r") as file:
+        with open(inconsistent_path, "r") as file:
             excluded_ids = set(line.strip() for line in file)
 
         self.image_dir = image_dir
@@ -69,7 +70,7 @@ def handle_inconsistency(self):
         inconsistent_ids = images_without_masks.union(masks_without_images)
 
         # Save to a file
-        with open("inconsistent_ids.txt", "a") as file:  # 'a' mode for appending
+        with open(inconsistent_path, "a") as file:  # 'a' mode for appending
             for ID in inconsistent_ids:
                 file.write(f"{ID}\n")
 
@@ -83,14 +84,13 @@ def __getitem__(self, idx):
             with Image.open(img_name) as image, Image.open(mask_name) as mask:
                 image = image.convert("RGB")
                 mask = mask.convert("L")
-
                 sample = {"image": image, "mask": mask}
 
                 if self.transform:
                     sample = self.transform(sample)
-
+                
             # Convert mask to binary 0/1 tensor
-            sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 127.5).float()
+            sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 0.5).float()
 
             return sample["image"], sample["mask"]
 

From c0f75e7ed0a923a74e56a9d288ac721c8db09657 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 23:28:27 +1000
Subject: [PATCH 59/79] The model is finally working, going to try a real
 training cycle

---
 recognition/Problem_47452752/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index ec68dcd95..0b782112e 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -27,9 +27,9 @@
 
 # Conditional parameters used for debugging
 check = False
-debugging = True
+debugging = False
 saving = False
-validating = False
+validating = True
 
 
 def evaluate_model(model, data_loader, device):

From 7a171a802fd0bf5636f4bc80632a8e6711363cbb Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 23:54:09 +1000
Subject: [PATCH 60/79] forgot to declare 'no_improvement', fixed

---
 recognition/Problem_47452752/train.py | 34 ++++++++++++++++++---------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 0b782112e..37a1333f7 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -27,7 +27,7 @@
 
 # Conditional parameters used for debugging
 check = False
-debugging = False
+debugging = True
 saving = False
 validating = True
 
@@ -64,19 +64,26 @@ def evaluate_model(model, data_loader, device):
 if check:
     check_consistency()
 
-# Construct smaller debugging datasets
+# Construct debugging datasets (small subset for fast tests)
 if debugging:
-    num_epochs = 3
+    num_epochs = 2
+    subset_size = 200
+
     dataset = ISICDataset(transform)
-    subset_indices = list(range(500))  # debugging on first 500 samples
+    subset_indices = list(range(subset_size))
     subset = Subset(dataset, subset_indices)
 
-    test_size = int(0.2 * len(subset))
-    train_size = len(subset) - test_size
-    train_dataset, test_dataset = random_split(subset, [train_size, test_size])
+    train_size = int(subset_size * 0.6)
+    val_size = int(subset_size * 0.2)
+    test_size = subset_size - train_size - val_size
 
-    train_loader = DataLoader(train_dataset, 40)
-    test_loader = DataLoader(test_dataset, 50)
+    train_dataset, val_dataset, test_dataset = random_split(
+        dataset, [train_size, val_size, test_size]
+    )
+
+    train_loader = DataLoader(train_dataset, 32, True)
+    validation_loader = DataLoader(val_dataset, 20, False)
+    test_loader = DataLoader(test_dataset, 20, False)
 
 # Construct full datasets
 if not debugging:
@@ -111,9 +118,10 @@ def evaluate_model(model, data_loader, device):
 
 # Variables used for training feedback and validation:
 running_loss = 0.0
+no_improvement = 0
 print_every = 1  # Print every 10 batches.
 best_val_score = 0.0
-patience = 3  # Number of epochs to wait before stopping
+patience = 8  # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
     # Set the model to training mode
@@ -155,13 +163,17 @@ def evaluate_model(model, data_loader, device):
         )
 
         # Model checkpointing
-        if dice_similarity > best_val_score and dice_minimum > 0.7:
+        if dice_similarity > best_val_score and dice_minimum > 0.75:
             best_val_loss = val_loss
             torch.save(model.state_dict(), "best_model.pth")
             no_improvement = 0
         else:
             no_improvement += 1
 
+    # Early stoppage if the model hasn't improved in `patience` epochs
+    if no_improvement > patience:
+        break
+
     scheduler.step()  # Adjust learning rate
 
 

From 296a12b8cb605b0b9411534aaa7800ab1af1523d Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Thu, 26 Oct 2023 23:56:05 +1000
Subject: [PATCH 61/79] fixed the error in the debugging random_split

---
 recognition/Problem_47452752/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 37a1333f7..cd41a0925 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -78,7 +78,7 @@ def evaluate_model(model, data_loader, device):
     test_size = subset_size - train_size - val_size
 
     train_dataset, val_dataset, test_dataset = random_split(
-        dataset, [train_size, val_size, test_size]
+        subset, [train_size, val_size, test_size]
     )
 
     train_loader = DataLoader(train_dataset, 32, True)

From 8cc942718a9e41fa03e23c68cbd00f80c2af17b7 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 00:21:45 +1000
Subject: [PATCH 62/79] Using test partition in slurm script

---
 recognition/Problem_47452752/slurm.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/slurm.sh b/recognition/Problem_47452752/slurm.sh
index 08f4d8854..d5d8eb462 100644
--- a/recognition/Problem_47452752/slurm.sh
+++ b/recognition/Problem_47452752/slurm.sh
@@ -2,7 +2,7 @@
 #SBATCH --nodes=1 
 #SBATCH --ntasks=1 
 #SBATCH --gres=gpu:1 
-#SBATCH --partition=vgpu 
+#SBATCH --partition=test 
 #SBATCH --job-name="COMP3710 Report"
 #SBATCH --mail-user=s4745275@student.uq.edu.au
 #SBATCH --mail-type=ALL 

From 498f92c4b11b9f69da21ffa5291ecff9c85acb46 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 00:29:52 +1000
Subject: [PATCH 63/79] Testing out validation and test batch size = 100

---
 recognition/Problem_47452752/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index cd41a0925..4912ef779 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -22,7 +22,7 @@
 
 
 # Hyper-parameters
-num_epochs = 5
+num_epochs = 50
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Conditional parameters used for debugging
@@ -67,7 +67,7 @@ def evaluate_model(model, data_loader, device):
 # Construct debugging datasets (small subset for fast tests)
 if debugging:
     num_epochs = 2
-    subset_size = 200
+    subset_size = 500
 
     dataset = ISICDataset(transform)
     subset_indices = list(range(subset_size))
@@ -82,8 +82,8 @@ def evaluate_model(model, data_loader, device):
     )
 
     train_loader = DataLoader(train_dataset, 32, True)
-    validation_loader = DataLoader(val_dataset, 20, False)
-    test_loader = DataLoader(test_dataset, 20, False)
+    validation_loader = DataLoader(val_dataset, 100, False)
+    test_loader = DataLoader(test_dataset, 100, False)
 
 # Construct full datasets
 if not debugging:

From e82203c2317e8d571434a51517c8689f680ad286 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 00:32:34 +1000
Subject: [PATCH 64/79] The model works, time to train properly!

---
 recognition/Problem_47452752/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
index 4912ef779..48dae1aee 100644
--- a/recognition/Problem_47452752/train.py
+++ b/recognition/Problem_47452752/train.py
@@ -27,8 +27,8 @@
 
 # Conditional parameters used for debugging
 check = False
-debugging = True
-saving = False
+debugging = False
+saving = True
 validating = True
 
 

From 051f27db14f2ddab7d1f8b7bbacccd7f1854de0c Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 00:33:54 +1000
Subject: [PATCH 65/79] Switched back to vgpu partition

---
 recognition/Problem_47452752/slurm.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recognition/Problem_47452752/slurm.sh b/recognition/Problem_47452752/slurm.sh
index d5d8eb462..08f4d8854 100644
--- a/recognition/Problem_47452752/slurm.sh
+++ b/recognition/Problem_47452752/slurm.sh
@@ -2,7 +2,7 @@
 #SBATCH --nodes=1 
 #SBATCH --ntasks=1 
 #SBATCH --gres=gpu:1 
-#SBATCH --partition=test 
+#SBATCH --partition=vgpu 
 #SBATCH --job-name="COMP3710 Report"
 #SBATCH --mail-user=s4745275@student.uq.edu.au
 #SBATCH --mail-type=ALL 

From a0d80de9b808b4ed7125b69e48861514ef98bb7f Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 00:56:15 +1000
Subject: [PATCH 66/79] Gave the problem directory a meaningful name

---
 .../UNet_Segmentation_s4745275/README.md      |   1 +
 .../UNet_Segmentation_s4745275/dataset.py     | 130 ++++++++++++
 .../UNet_Segmentation_s4745275/modules.py     | 179 ++++++++++++++++
 .../UNet_Segmentation_s4745275/predict.py     |   9 +
 .../UNet_Segmentation_s4745275/slurm.sh       |  13 ++
 .../UNet_Segmentation_s4745275/train.py       | 192 ++++++++++++++++++
 .../UNet_Segmentation_s4745275/utils.py       | 110 ++++++++++
 7 files changed, 634 insertions(+)
 create mode 100644 recognition/UNet_Segmentation_s4745275/README.md
 create mode 100644 recognition/UNet_Segmentation_s4745275/dataset.py
 create mode 100644 recognition/UNet_Segmentation_s4745275/modules.py
 create mode 100644 recognition/UNet_Segmentation_s4745275/predict.py
 create mode 100644 recognition/UNet_Segmentation_s4745275/slurm.sh
 create mode 100644 recognition/UNet_Segmentation_s4745275/train.py
 create mode 100644 recognition/UNet_Segmentation_s4745275/utils.py

diff --git a/recognition/UNet_Segmentation_s4745275/README.md b/recognition/UNet_Segmentation_s4745275/README.md
new file mode 100644
index 000000000..2cd4adb2b
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/README.md
@@ -0,0 +1 @@
+# README.md file that describes your report.
\ No newline at end of file
diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
new file mode 100644
index 000000000..bfa655535
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -0,0 +1,130 @@
+"""
+File containing the data loaders used for loading and preprocessing the data.
+"""
+
+import os
+import torch
+from utils import RandomCenterCrop, RandomRotate90, DictTransform
+from torch.utils.data import Dataset
+from torchvision import transforms
+import torchvision.transforms.functional as TF
+from PIL import Image
+import numpy as np
+
+image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
+mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
+inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/inconsistent_ids.txt"
+
+def check_consistency(image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path):
+    image_ids = {
+        img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
+    }
+    mask_ids = {
+        mask.split("_segmentation.")[0]
+        for mask in os.listdir(mask_dir)
+        if mask.endswith("_segmentation.png")
+    }
+
+    # Using list differences to find inconsistencies
+    images_without_masks = image_ids - mask_ids
+    masks_without_images = mask_ids - image_ids
+
+    if images_without_masks or masks_without_images:
+        inconsistent_ids = images_without_masks.union(masks_without_images)
+        # Save to a file
+        with open(inconsistent_path, "w") as file:
+            for ID in inconsistent_ids:
+                file.write(f"{ID}\n")
+
+        print(f"Detected {len(inconsistent_ids)} inconsistencies, fixed em tho")
+
+
+class ISICDataset(Dataset):
+    def __init__(
+        self,
+        transform,
+        image_dir=image_path,
+        mask_dir=mask_path,
+        inconsistent_path=inconsistent_path
+    ):
+        # Load the inconsistent IDs
+        with open(inconsistent_path, "r") as file:
+            excluded_ids = set(line.strip() for line in file)
+
+        self.image_dir = image_dir
+        self.mask_dir = mask_dir
+        self.image_ids = [
+            img.split(".")[0]
+            for img in os.listdir(image_dir)
+            if img.endswith(".jpg") and img.split(".")[0] not in excluded_ids
+        ]
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.image_ids)
+
+    def handle_inconsistency(self):
+        images_without_masks, masks_without_images = check_consistency(
+            self.image_dir, self.mask_dir
+        )
+        inconsistent_ids = images_without_masks.union(masks_without_images)
+
+        # Save to a file
+        with open(inconsistent_path, "a") as file:  # 'a' mode for appending
+            for ID in inconsistent_ids:
+                file.write(f"{ID}\n")
+
+    def __getitem__(self, idx):
+        img_name = os.path.join(self.image_dir, self.image_ids[idx] + ".jpg")
+        mask_name = os.path.join(
+            self.mask_dir, self.image_ids[idx] + "_segmentation.png"
+        )
+
+        try:
+            with Image.open(img_name) as image, Image.open(mask_name) as mask:
+                image = image.convert("RGB")
+                mask = mask.convert("L")
+                sample = {"image": image, "mask": mask}
+
+                if self.transform:
+                    sample = self.transform(sample)
+                
+            # Convert mask to binary 0/1 tensor
+            sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 0.5).float()
+
+            return sample["image"], sample["mask"]
+
+        except FileNotFoundError:
+            self.handle_inconsistency()
+            return self.__getitem__(idx)
+
+
+# Transformation pipeline to augment the dataset
+transform = transforms.Compose(
+    [
+        RandomRotate90(),
+        RandomCenterCrop(),
+        DictTransform(transforms.RandomHorizontalFlip()),
+        DictTransform(transforms.RandomVerticalFlip()),
+        DictTransform(transforms.Resize((256, 256))),
+        DictTransform(transforms.ToTensor()),
+        DictTransform(
+            transforms.Lambda(
+                lambda img_tensor: torch.cat(
+                    [
+                        img_tensor,
+                        TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV")),
+                    ],
+                    dim=0,
+                )
+            ),
+            False,
+        ),
+        DictTransform(
+            transforms.Normalize(
+                [0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+            ),
+            False,
+        ),
+    ]
+)
diff --git a/recognition/UNet_Segmentation_s4745275/modules.py b/recognition/UNet_Segmentation_s4745275/modules.py
new file mode 100644
index 000000000..9fab0bf37
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/modules.py
@@ -0,0 +1,179 @@
+"""
+Contains the source code of the components in my model. Each component is implemented as a class or function. 
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Context(nn.Module):
+    """
+    Encoder that computes the activations in the context pathway. This class behaves as the 'context module' from the paper.
+    Each Context module is a pre-activation residual block with two 3x3x3 convolutional layers and a dropout layer (p = 0.3) in between.
+    Instance normalization and leaky ReLU is used throughout the network.
+    """
+
+    def __init__(self, in_channels):
+        super(Context, self).__init__()
+        # 3x3 convolutional layer that preserves the input size
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+        # Dropout layer with p_drop = 0.3
+        self.dropout = nn.Dropout(p=0.3)
+        # Instance normalization of the input is used
+        self.norm = nn.InstanceNorm2d(in_channels)
+
+    def forward(self, x):
+        # Keep track of the initial input
+        shortcut = x
+        # First convolutional layer
+        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
+        x = self.conv(x)
+        # Dropout layer
+        x = self.dropout(x)
+        # Second convolution layer
+        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
+        x = self.conv(x)
+        # Return the residual output
+        return x + shortcut
+
+
+class Upsampling(nn.Module):
+    """
+    Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
+    We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
+    that halves the number of feature maps. Instance normalization and leaky ReLU is used throughout the network.
+    """
+
+    def __init__(self, in_channels):
+        super(Upsampling, self).__init__()
+        # Upsamping components:
+        self.up_norm = nn.InstanceNorm2d(in_channels)
+        self.up_conv = nn.Conv2d(
+            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
+        )
+
+        # Localisation components:
+        self.merged_norm = nn.InstanceNorm2d(in_channels)
+        self.merged_conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.half_norm = nn.InstanceNorm2d(in_channels // 2)
+        self.half_conv = nn.Conv2d(
+            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x, skip_features, local: bool = True):
+        """
+        Forward pass of the Upsampling module, with an optional localisation step.
+
+        Parameters:
+        - x (torch.Tensor): Input tensor, representing the activations from a deeper layer.
+        - skip_features (torch.Tensor): Activations from the corresponding encoder layer, for the skip connection.
+        - local (bool, optional): If True, the localisation operations are applied after upsampling. Default is True.
+
+        Returns:
+        - torch.Tensor: Upsampled (and optionally localised) feature map.
+
+        The function first upsamples the input tensor 'x' and then concatenates the result with the 'skip_features'.
+        If 'local' is True, it subsequently applies the localisation steps to refine the feature maps further.
+        """
+        # Upsampling Module
+        upsampled = F.interpolate(x, scale_factor=2, mode="nearest")
+        upsampled = F.leaky_relu(self.up_norm(upsampled), negative_slope=1e-2)
+        upsampled = self.up_conv(upsampled)
+
+        # Concatenate upsampled features with context features
+        merged = torch.cat([upsampled, skip_features], dim=1)
+
+        if local is False:
+            return merged
+
+        # Localisation Module
+        localised = F.leaky_relu(self.merged_norm(merged), negative_slope=1e-2)
+        localised = self.merged_conv(localised)  # First convolutional layer
+
+        localised = F.leaky_relu(self.half_norm(localised), negative_slope=1e-2)
+        localised = self.half_conv(localised)  # Second convolutional layer
+
+        return localised
+
+
+class Segmentation(nn.Module):
+    def __init__(self, in_channels, num_classes):
+        super(Segmentation, self).__init__()
+        self.conv = nn.Conv2d(in_channels, num_classes, kernel_size=1, stride=1)
+
+    def forward(self, x, other_layer, upscale=True):
+        x = self.conv(x)
+        if other_layer is not None:
+            x += other_layer
+        if not upscale:
+            return x
+        return F.interpolate(x, scale_factor=2, mode="nearest")
+
+
+class UNet(nn.Module):
+    def __init__(self, in_channels, num_classes=1):
+        super(UNet, self).__init__()
+
+        # Context modules
+        self.context1 = Context(16)
+        self.context2 = Context(32)
+        self.context3 = Context(64)
+        self.context4 = Context(128)
+        self.context5 = Context(256)
+
+        # Upsampling modules
+        self.up1 = Upsampling(256)
+        self.up2 = Upsampling(128)
+        self.up3 = Upsampling(64)
+        self.up4 = Upsampling(32)
+
+        # Convolutional layer used on input channel
+        self.input_conv = nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1)
+
+        # Convolutional layers that connect context modules
+        self.conv1 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        # Convolutional layer for the localisation pathway output
+        self.end_conv = nn.Conv2d(32, 32, kernel_size=1)
+
+        # Segmentation layers
+        self.segment1 = Segmentation(64, num_classes)
+        self.segment2 = Segmentation(32, num_classes)
+        self.segment3 = Segmentation(32, num_classes)
+
+    def forward(self, x):
+        # Context Pathway
+        cntx_1 = self.context1(self.input_conv(x))
+        cntx_2 = self.context2(self.conv1(cntx_1))
+        cntx_3 = self.context3(self.conv2(cntx_2))
+        cntx_4 = self.context4(self.conv3(cntx_3))
+        cntx_5 = self.context5(self.conv4(cntx_4))
+
+        # Localization Pathway
+        local_1 = self.up1(cntx_5, cntx_4)
+        local_2 = self.up2(local_1, cntx_3)
+        local_3 = self.up3(local_2, cntx_2)
+        local_out = self.end_conv(self.up4(local_3, cntx_1, False))
+
+        # Deep Supervision
+        seg_1 = self.segment1(local_2, None)
+        seg_2 = self.segment2(local_3, seg_1)
+        seg_3 = self.segment3(local_out, seg_2, upscale=False)
+
+        # Apply sigmoid (paper used softmax, but this is binary) and return
+        return torch.sigmoid(seg_3)
+        # if num_classes > 2: return F.softmax(seg_3, dim=1)
+
+# Lil testing
+# model = UNet(6, 1)
+# test = torch.randn(1, 6, 32, 32)
+# output = model(test)
+# print(output.size())
\ No newline at end of file
diff --git a/recognition/UNet_Segmentation_s4745275/predict.py b/recognition/UNet_Segmentation_s4745275/predict.py
new file mode 100644
index 000000000..83b2855cb
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/predict.py
@@ -0,0 +1,9 @@
+# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
+
+from modules import UNet
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = UNet(in_channels=6, num_classes=2)  # Adjust parameters if they've changed
+model.load_state_dict(torch.load("recognition/Problem_47452752/model.pth"))
+model = model.to(device)
\ No newline at end of file
diff --git a/recognition/UNet_Segmentation_s4745275/slurm.sh b/recognition/UNet_Segmentation_s4745275/slurm.sh
new file mode 100644
index 000000000..08f4d8854
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/slurm.sh
@@ -0,0 +1,13 @@
+#!/bin/bash 
+#SBATCH --nodes=1 
+#SBATCH --ntasks=1 
+#SBATCH --gres=gpu:1 
+#SBATCH --partition=vgpu 
+#SBATCH --job-name="COMP3710 Report"
+#SBATCH --mail-user=s4745275@student.uq.edu.au
+#SBATCH --mail-type=ALL 
+#SBATCH -e test_err.txt
+#SBATCH -o test_out.txt
+
+source /home/Student/s4745275/miniconda/bin/activate /home/Student/s4745275/my_demo_environment
+python train.py
diff --git a/recognition/UNet_Segmentation_s4745275/train.py b/recognition/UNet_Segmentation_s4745275/train.py
new file mode 100644
index 000000000..48dae1aee
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/train.py
@@ -0,0 +1,192 @@
+"""
+Contains the source code for training, validating, testing and saving my model.
+The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
+Make sure to plot the losses and metrics during training
+"""
+
+
+from dataset import (
+    ISICDataset,
+    transform,
+    check_consistency,
+)
+import numpy as np
+from modules import UNet
+from torch.utils.data import DataLoader
+from torch.utils.data.dataset import random_split
+from utils import dice_loss, dice_coefficient
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import Subset  # for debugging only TODO
+
+
+# Hyper-parameters
+num_epochs = 50
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Conditional parameters used for debugging
+check = False
+debugging = False
+saving = True
+validating = True
+
+
+def evaluate_model(model, data_loader, device):
+    # set the model to evaluation mode
+    model.eval()
+
+    # List to store individual dice scores for each sample
+    all_dice_scores = []
+
+    # No gradient computation during evaluation
+    with torch.no_grad():
+        for images, masks in data_loader:
+            images, masks = images.to(device), masks.to(device)
+
+            # Compute predictions
+            outputs = model(images)
+
+            # Convert predictions to binary using threshold
+            outputs = (outputs > 0.5).float()
+
+            # Compute and store the dice coefficients
+            batch_dice_scores = dice_coefficient(outputs, masks)
+            all_dice_scores.extend(batch_dice_scores.cpu().numpy())
+
+    avg_dice_score = np.mean(all_dice_scores)
+    min_dice_score = np.min(all_dice_scores)
+
+    return avg_dice_score, min_dice_score
+
+
+# Check if the dataset is consistent
+if check:
+    check_consistency()
+
+# Construct debugging datasets (small subset for fast tests)
+if debugging:
+    num_epochs = 2
+    subset_size = 500
+
+    dataset = ISICDataset(transform)
+    subset_indices = list(range(subset_size))
+    subset = Subset(dataset, subset_indices)
+
+    train_size = int(subset_size * 0.6)
+    val_size = int(subset_size * 0.2)
+    test_size = subset_size - train_size - val_size
+
+    train_dataset, val_dataset, test_dataset = random_split(
+        subset, [train_size, val_size, test_size]
+    )
+
+    train_loader = DataLoader(train_dataset, 32, True)
+    validation_loader = DataLoader(val_dataset, 100, False)
+    test_loader = DataLoader(test_dataset, 100, False)
+
+# Construct full datasets
+if not debugging:
+    # Loading up the dataset and applying custom augmentations
+    dataset = ISICDataset(transform)
+
+    total_size = len(dataset)
+
+    # Splitting into training, validation and testing sets
+    train_size = int(total_size * 0.7)
+    val_size = int(total_size * 0.2)
+    test_size = total_size - train_size - val_size
+
+    train_dataset, val_dataset, test_dataset = random_split(
+        dataset, [train_size, val_size, test_size]
+    )
+
+    # Data loaders for training, validation and testing
+    train_loader = DataLoader(train_dataset, 32, True)
+    validation_loader = DataLoader(val_dataset, 100, False)
+    test_loader = DataLoader(test_dataset, 100, False)
+
+# Creating an instance of my UNet to be trained
+model = UNet(in_channels=6, num_classes=1)
+model = model.to(device)
+
+# Setup the optimizer
+optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
+scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
+
+print("Training loop:")
+
+# Variables used for training feedback and validation:
+running_loss = 0.0
+no_improvement = 0
+print_every = 1  # Print every 10 batches.
+best_val_score = 0.0
+patience = 8  # Number of epochs to wait before stopping
+
+for epoch in range(num_epochs):
+    # Set the model to training mode
+    model.train()
+
+    for i, (images, masks) in enumerate(train_loader, 1):
+        # Move the data onto the device
+        images, masks = images.to(device), masks.to(device)
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+
+        # Forward pass
+        outputs = model(images)
+        # print(f"outputs = {outputs.size()}")
+        # print(f"masks = {masks.size()}")
+
+        loss = dice_loss(outputs, masks)
+
+        # Backward pass + optimization
+        loss.backward()
+        optimizer.step()
+
+        # Keep track of the running loss for testing feedback
+        running_loss += loss.item()
+        if i % print_every == 0:  # Print every `print_every` batches
+            print(
+                f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}"
+            )
+            running_loss = 0.0
+
+    if validating:
+        # Evaluate the model using the validation set
+        dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
+        val_loss = 1 - dice_similarity
+        # Print out the validation metrics
+        print(
+            f"Validation metrics during epoch {epoch + 1}, loss = {val_loss:.4f}, similarity = {dice_similarity:.4f}, minimum = {dice_minimum:.4f}"
+        )
+
+        # Model checkpointing
+        if dice_similarity > best_val_score and dice_minimum > 0.75:
+            best_val_loss = val_loss
+            torch.save(model.state_dict(), "best_model.pth")
+            no_improvement = 0
+        else:
+            no_improvement += 1
+
+    # Early stoppage if the model hasn't improved in `patience` epochs
+    if no_improvement > patience:
+        break
+
+    scheduler.step()  # Adjust learning rate
+
+
+print("training complete")
+
+# Save the model
+if saving:
+    torch.save(
+        model.state_dict(),
+        "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
+    )
+
+avg_dice_score, min_dice_score = evaluate_model(model, test_loader, device)
+
+print(f"Average Dice Coefficient on Test Set: {avg_dice_score:.4f}")
+print(f"Minimum Dice Coefficient on Test Set: {min_dice_score:.4f}")
diff --git a/recognition/UNet_Segmentation_s4745275/utils.py b/recognition/UNet_Segmentation_s4745275/utils.py
new file mode 100644
index 000000000..068989fe3
--- /dev/null
+++ b/recognition/UNet_Segmentation_s4745275/utils.py
@@ -0,0 +1,110 @@
+import torch
+from random import choice
+import torchvision.transforms.functional as TF
+import torch.nn as nn
+
+
+class RandomRotate90:
+    """Randomly rotates the image by 90, 180, or 270 degrees."""
+
+    def __init__(self, p=1.0):
+        self.p = p
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if torch.rand(1) < self.p:
+            degrees = [90, 180, 270]
+            angle = choice(degrees)
+            image = TF.rotate(image, angle)
+            mask = TF.rotate(mask, angle)
+        return {"image": image, "mask": mask}
+
+
+class RandomCenterCrop:
+    """Randomly crops the center of the image by 80% or 70%."""
+
+    def __init__(self, scales=[0.8, 0.7], p=1.0):
+        self.scales = scales
+        self.p = p
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if torch.rand(1) < self.p:
+            scale = choice(self.scales)
+            image = TF.center_crop(
+                image, (int(image.height * scale), int(image.width * scale))
+            )
+            mask = TF.center_crop(
+                mask, (int(mask.height * scale), int(mask.width * scale))
+            )
+        return {"image": image, "mask": mask}
+
+
+class DictTransform:
+    def __init__(self, transform, transform_mask=True):
+        self.transform = transform
+        self.transform_mask = transform_mask
+
+    def __call__(self, sample):
+        image, mask = sample["image"], sample["mask"]
+        if self.transform_mask:
+            return {
+                "image": self.transform(image),
+                "mask": self.transform(mask),
+            }
+        return {
+            "image": self.transform(image),
+            "mask": mask,
+        }
+
+
+# max_dice_loss = max(dice_losses) # Penalize the worst performance
+# dice_loss = sum(dice_losses) / len(dice_losses)
+def dice_loss(predicted, target, epsilon=1e-7):
+    # Compute dice coefficient for each image in the batch
+    dice_scores = dice_coefficient(predicted, target)
+    # Compute dice loss for each image in the batch
+    dice_losses = 1.0 - dice_scores
+    # Penalize any images with dice score less than 0.8
+    penalized_losses = torch.where(dice_scores < 0.8, dice_losses * 2, dice_losses)
+    # Return the average loss
+    average_penalized_loss = penalized_losses.mean()
+
+    return average_penalized_loss
+
+
+def dice_coefficient(
+    predicted: torch.Tensor, target: torch.Tensor, epsilon=1e-7
+) -> torch.Tensor:
+    """Compute dice coefficient for each image in the batch"""
+    predicted = predicted.contiguous().view(predicted.shape[0], -1)
+    target = target.contiguous().view(target.shape[0], -1)
+
+    intersection = (predicted * target).sum(dim=1)
+    return (2.0 * intersection + epsilon) / (
+        predicted.sum(dim=1) + target.sum(dim=1) + epsilon
+    )
+
+
+def general_dice_loss(predicted, target):
+    # One-hot encode the target segmentation map
+    target_one_hot = torch.zeros_like(predicted)
+    for k in range(target_one_hot.shape[1]):
+        target_one_hot[:, k] = target == k
+
+    # Compute the Dice loss for each class, then average
+    intersection = (predicted * target_one_hot).sum(dim=(2, 3))
+    union = (predicted + target_one_hot).sum(dim=(2, 3))
+
+    dice_scores = 2 * intersection / union
+    loss = 1 - dice_scores.mean()
+
+    return loss
+
+
+# lil testing
+# pred = torch.randn(3, 6, 32, 32)
+# tar = torch.randn(3, 6, 32, 32)
+# x = dice_coefficient(pred, tar)
+# y = dice_loss(pred, tar)
+#

From 1dda16af201a79c653594f0b084c1df21621bbfe Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 02:17:05 +1000
Subject: [PATCH 67/79] Made the stoppage criteria less strict in training

---
 recognition/UNet_Segmentation_s4745275/dataset.py | 2 +-
 recognition/UNet_Segmentation_s4745275/train.py   | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
index bfa655535..12b2cc3a7 100644
--- a/recognition/UNet_Segmentation_s4745275/dataset.py
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -13,7 +13,7 @@
 
 image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
 mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
-inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/inconsistent_ids.txt"
+inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/UNet_Segmentation_s4745275/inconsistent_ids.txt"
 
 def check_consistency(image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path):
     image_ids = {
diff --git a/recognition/UNet_Segmentation_s4745275/train.py b/recognition/UNet_Segmentation_s4745275/train.py
index 48dae1aee..fc247e540 100644
--- a/recognition/UNet_Segmentation_s4745275/train.py
+++ b/recognition/UNet_Segmentation_s4745275/train.py
@@ -120,7 +120,8 @@ def evaluate_model(model, data_loader, device):
 running_loss = 0.0
 no_improvement = 0
 print_every = 1  # Print every 10 batches.
-best_val_score = 0.0
+best_val_similarity = 0.0
+best_val_minimum = 0.0
 patience = 8  # Number of epochs to wait before stopping
 
 for epoch in range(num_epochs):
@@ -163,7 +164,7 @@ def evaluate_model(model, data_loader, device):
         )
 
         # Model checkpointing
-        if dice_similarity > best_val_score and dice_minimum > 0.75:
+        if dice_similarity > best_val_similarity and dice_minimum >= best_val_minimum:
             best_val_loss = val_loss
             torch.save(model.state_dict(), "best_model.pth")
             no_improvement = 0

From d951222e3abdd0ffafecda5109f5ad64ff7d9568 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 02:45:39 +1000
Subject: [PATCH 68/79] deleted the old directory

---
 recognition/Problem_47452752/.gitignore       |   3 -
 recognition/Problem_47452752/README.md        |   1 -
 recognition/Problem_47452752/dataset.py       | 130 ------------
 .../Problem_47452752/inconsistent_ids.txt     |   0
 recognition/Problem_47452752/modules.py       | 179 ----------------
 recognition/Problem_47452752/predict.py       |   9 -
 recognition/Problem_47452752/slurm.sh         |  13 --
 recognition/Problem_47452752/train.py         | 192 ------------------
 recognition/Problem_47452752/utils.py         | 110 ----------
 9 files changed, 637 deletions(-)
 delete mode 100644 recognition/Problem_47452752/.gitignore
 delete mode 100644 recognition/Problem_47452752/README.md
 delete mode 100644 recognition/Problem_47452752/dataset.py
 delete mode 100644 recognition/Problem_47452752/inconsistent_ids.txt
 delete mode 100644 recognition/Problem_47452752/modules.py
 delete mode 100644 recognition/Problem_47452752/predict.py
 delete mode 100644 recognition/Problem_47452752/slurm.sh
 delete mode 100644 recognition/Problem_47452752/train.py
 delete mode 100644 recognition/Problem_47452752/utils.py

diff --git a/recognition/Problem_47452752/.gitignore b/recognition/Problem_47452752/.gitignore
deleted file mode 100644
index 28966cc73..000000000
--- a/recognition/Problem_47452752/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Ignore datasets and models
-data/
-models/
diff --git a/recognition/Problem_47452752/README.md b/recognition/Problem_47452752/README.md
deleted file mode 100644
index 2cd4adb2b..000000000
--- a/recognition/Problem_47452752/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# README.md file that describes your report.
\ No newline at end of file
diff --git a/recognition/Problem_47452752/dataset.py b/recognition/Problem_47452752/dataset.py
deleted file mode 100644
index bfa655535..000000000
--- a/recognition/Problem_47452752/dataset.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""
-File containing the data loaders used for loading and preprocessing the data.
-"""
-
-import os
-import torch
-from utils import RandomCenterCrop, RandomRotate90, DictTransform
-from torch.utils.data import Dataset
-from torchvision import transforms
-import torchvision.transforms.functional as TF
-from PIL import Image
-import numpy as np
-
-image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
-mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
-inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/inconsistent_ids.txt"
-
-def check_consistency(image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path):
-    image_ids = {
-        img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
-    }
-    mask_ids = {
-        mask.split("_segmentation.")[0]
-        for mask in os.listdir(mask_dir)
-        if mask.endswith("_segmentation.png")
-    }
-
-    # Using list differences to find inconsistencies
-    images_without_masks = image_ids - mask_ids
-    masks_without_images = mask_ids - image_ids
-
-    if images_without_masks or masks_without_images:
-        inconsistent_ids = images_without_masks.union(masks_without_images)
-        # Save to a file
-        with open(inconsistent_path, "w") as file:
-            for ID in inconsistent_ids:
-                file.write(f"{ID}\n")
-
-        print(f"Detected {len(inconsistent_ids)} inconsistencies, fixed em tho")
-
-
-class ISICDataset(Dataset):
-    def __init__(
-        self,
-        transform,
-        image_dir=image_path,
-        mask_dir=mask_path,
-        inconsistent_path=inconsistent_path
-    ):
-        # Load the inconsistent IDs
-        with open(inconsistent_path, "r") as file:
-            excluded_ids = set(line.strip() for line in file)
-
-        self.image_dir = image_dir
-        self.mask_dir = mask_dir
-        self.image_ids = [
-            img.split(".")[0]
-            for img in os.listdir(image_dir)
-            if img.endswith(".jpg") and img.split(".")[0] not in excluded_ids
-        ]
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.image_ids)
-
-    def handle_inconsistency(self):
-        images_without_masks, masks_without_images = check_consistency(
-            self.image_dir, self.mask_dir
-        )
-        inconsistent_ids = images_without_masks.union(masks_without_images)
-
-        # Save to a file
-        with open(inconsistent_path, "a") as file:  # 'a' mode for appending
-            for ID in inconsistent_ids:
-                file.write(f"{ID}\n")
-
-    def __getitem__(self, idx):
-        img_name = os.path.join(self.image_dir, self.image_ids[idx] + ".jpg")
-        mask_name = os.path.join(
-            self.mask_dir, self.image_ids[idx] + "_segmentation.png"
-        )
-
-        try:
-            with Image.open(img_name) as image, Image.open(mask_name) as mask:
-                image = image.convert("RGB")
-                mask = mask.convert("L")
-                sample = {"image": image, "mask": mask}
-
-                if self.transform:
-                    sample = self.transform(sample)
-                
-            # Convert mask to binary 0/1 tensor
-            sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 0.5).float()
-
-            return sample["image"], sample["mask"]
-
-        except FileNotFoundError:
-            self.handle_inconsistency()
-            return self.__getitem__(idx)
-
-
-# Transformation pipeline to augment the dataset
-transform = transforms.Compose(
-    [
-        RandomRotate90(),
-        RandomCenterCrop(),
-        DictTransform(transforms.RandomHorizontalFlip()),
-        DictTransform(transforms.RandomVerticalFlip()),
-        DictTransform(transforms.Resize((256, 256))),
-        DictTransform(transforms.ToTensor()),
-        DictTransform(
-            transforms.Lambda(
-                lambda img_tensor: torch.cat(
-                    [
-                        img_tensor,
-                        TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV")),
-                    ],
-                    dim=0,
-                )
-            ),
-            False,
-        ),
-        DictTransform(
-            transforms.Normalize(
-                [0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
-            ),
-            False,
-        ),
-    ]
-)
diff --git a/recognition/Problem_47452752/inconsistent_ids.txt b/recognition/Problem_47452752/inconsistent_ids.txt
deleted file mode 100644
index e69de29bb..000000000
diff --git a/recognition/Problem_47452752/modules.py b/recognition/Problem_47452752/modules.py
deleted file mode 100644
index 9fab0bf37..000000000
--- a/recognition/Problem_47452752/modules.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""
-Contains the source code of the components in my model. Each component is implemented as a class or function. 
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Context(nn.Module):
-    """
-    Encoder that computes the activations in the context pathway. This class behaves as the 'context module' from the paper.
-    Each Context module is a pre-activation residual block with two 3x3x3 convolutional layers and a dropout layer (p = 0.3) in between.
-    Instance normalization and leaky ReLU is used throughout the network.
-    """
-
-    def __init__(self, in_channels):
-        super(Context, self).__init__()
-        # 3x3 convolutional layer that preserves the input size
-        self.conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-        # Dropout layer with p_drop = 0.3
-        self.dropout = nn.Dropout(p=0.3)
-        # Instance normalization of the input is used
-        self.norm = nn.InstanceNorm2d(in_channels)
-
-    def forward(self, x):
-        # Keep track of the initial input
-        shortcut = x
-        # First convolutional layer
-        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
-        x = self.conv(x)
-        # Dropout layer
-        x = self.dropout(x)
-        # Second convolution layer
-        x = F.leaky_relu(self.norm(x), negative_slope=1e-2)
-        x = self.conv(x)
-        # Return the residual output
-        return x + shortcut
-
-
-class Upsampling(nn.Module):
-    """
-    Upsampling module used to tranfer information from low resolution feature maps into high resolution fearure maps.
-    We use a simple upscale that repeats the feature voxels twice in each spatial dimension, followed by a 3x3x3 convolution
-    that halves the number of feature maps. Instance normalization and leaky ReLU is used throughout the network.
-    """
-
-    def __init__(self, in_channels):
-        super(Upsampling, self).__init__()
-        # Upsamping components:
-        self.up_norm = nn.InstanceNorm2d(in_channels)
-        self.up_conv = nn.Conv2d(
-            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
-        )
-
-        # Localisation components:
-        self.merged_norm = nn.InstanceNorm2d(in_channels)
-        self.merged_conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-        self.half_norm = nn.InstanceNorm2d(in_channels // 2)
-        self.half_conv = nn.Conv2d(
-            in_channels, in_channels // 2, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, x, skip_features, local: bool = True):
-        """
-        Forward pass of the Upsampling module, with an optional localisation step.
-
-        Parameters:
-        - x (torch.Tensor): Input tensor, representing the activations from a deeper layer.
-        - skip_features (torch.Tensor): Activations from the corresponding encoder layer, for the skip connection.
-        - local (bool, optional): If True, the localisation operations are applied after upsampling. Default is True.
-
-        Returns:
-        - torch.Tensor: Upsampled (and optionally localised) feature map.
-
-        The function first upsamples the input tensor 'x' and then concatenates the result with the 'skip_features'.
-        If 'local' is True, it subsequently applies the localisation steps to refine the feature maps further.
-        """
-        # Upsampling Module
-        upsampled = F.interpolate(x, scale_factor=2, mode="nearest")
-        upsampled = F.leaky_relu(self.up_norm(upsampled), negative_slope=1e-2)
-        upsampled = self.up_conv(upsampled)
-
-        # Concatenate upsampled features with context features
-        merged = torch.cat([upsampled, skip_features], dim=1)
-
-        if local is False:
-            return merged
-
-        # Localisation Module
-        localised = F.leaky_relu(self.merged_norm(merged), negative_slope=1e-2)
-        localised = self.merged_conv(localised)  # First convolutional layer
-
-        localised = F.leaky_relu(self.half_norm(localised), negative_slope=1e-2)
-        localised = self.half_conv(localised)  # Second convolutional layer
-
-        return localised
-
-
-class Segmentation(nn.Module):
-    def __init__(self, in_channels, num_classes):
-        super(Segmentation, self).__init__()
-        self.conv = nn.Conv2d(in_channels, num_classes, kernel_size=1, stride=1)
-
-    def forward(self, x, other_layer, upscale=True):
-        x = self.conv(x)
-        if other_layer is not None:
-            x += other_layer
-        if not upscale:
-            return x
-        return F.interpolate(x, scale_factor=2, mode="nearest")
-
-
-class UNet(nn.Module):
-    def __init__(self, in_channels, num_classes=1):
-        super(UNet, self).__init__()
-
-        # Context modules
-        self.context1 = Context(16)
-        self.context2 = Context(32)
-        self.context3 = Context(64)
-        self.context4 = Context(128)
-        self.context5 = Context(256)
-
-        # Upsampling modules
-        self.up1 = Upsampling(256)
-        self.up2 = Upsampling(128)
-        self.up3 = Upsampling(64)
-        self.up4 = Upsampling(32)
-
-        # Convolutional layer used on input channel
-        self.input_conv = nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1)
-
-        # Convolutional layers that connect context modules
-        self.conv1 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
-        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
-        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
-        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
-
-        # Convolutional layer for the localisation pathway output
-        self.end_conv = nn.Conv2d(32, 32, kernel_size=1)
-
-        # Segmentation layers
-        self.segment1 = Segmentation(64, num_classes)
-        self.segment2 = Segmentation(32, num_classes)
-        self.segment3 = Segmentation(32, num_classes)
-
-    def forward(self, x):
-        # Context Pathway
-        cntx_1 = self.context1(self.input_conv(x))
-        cntx_2 = self.context2(self.conv1(cntx_1))
-        cntx_3 = self.context3(self.conv2(cntx_2))
-        cntx_4 = self.context4(self.conv3(cntx_3))
-        cntx_5 = self.context5(self.conv4(cntx_4))
-
-        # Localization Pathway
-        local_1 = self.up1(cntx_5, cntx_4)
-        local_2 = self.up2(local_1, cntx_3)
-        local_3 = self.up3(local_2, cntx_2)
-        local_out = self.end_conv(self.up4(local_3, cntx_1, False))
-
-        # Deep Supervision
-        seg_1 = self.segment1(local_2, None)
-        seg_2 = self.segment2(local_3, seg_1)
-        seg_3 = self.segment3(local_out, seg_2, upscale=False)
-
-        # Apply sigmoid (paper used softmax, but this is binary) and return
-        return torch.sigmoid(seg_3)
-        # if num_classes > 2: return F.softmax(seg_3, dim=1)
-
-# Lil testing
-# model = UNet(6, 1)
-# test = torch.randn(1, 6, 32, 32)
-# output = model(test)
-# print(output.size())
\ No newline at end of file
diff --git a/recognition/Problem_47452752/predict.py b/recognition/Problem_47452752/predict.py
deleted file mode 100644
index 83b2855cb..000000000
--- a/recognition/Problem_47452752/predict.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
-
-from modules import UNet
-import torch
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = UNet(in_channels=6, num_classes=2)  # Adjust parameters if they've changed
-model.load_state_dict(torch.load("recognition/Problem_47452752/model.pth"))
-model = model.to(device)
\ No newline at end of file
diff --git a/recognition/Problem_47452752/slurm.sh b/recognition/Problem_47452752/slurm.sh
deleted file mode 100644
index 08f4d8854..000000000
--- a/recognition/Problem_47452752/slurm.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash 
-#SBATCH --nodes=1 
-#SBATCH --ntasks=1 
-#SBATCH --gres=gpu:1 
-#SBATCH --partition=vgpu 
-#SBATCH --job-name="COMP3710 Report"
-#SBATCH --mail-user=s4745275@student.uq.edu.au
-#SBATCH --mail-type=ALL 
-#SBATCH -e test_err.txt
-#SBATCH -o test_out.txt
-
-source /home/Student/s4745275/miniconda/bin/activate /home/Student/s4745275/my_demo_environment
-python train.py
diff --git a/recognition/Problem_47452752/train.py b/recognition/Problem_47452752/train.py
deleted file mode 100644
index 48dae1aee..000000000
--- a/recognition/Problem_47452752/train.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""
-Contains the source code for training, validating, testing and saving my model.
-The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”.
-Make sure to plot the losses and metrics during training
-"""
-
-
-from dataset import (
-    ISICDataset,
-    transform,
-    check_consistency,
-)
-import numpy as np
-from modules import UNet
-from torch.utils.data import DataLoader
-from torch.utils.data.dataset import random_split
-from utils import dice_loss, dice_coefficient
-import torch
-import torch.optim as optim
-import torch.nn.functional as F
-from torch.utils.data import Subset  # for debugging only TODO
-
-
-# Hyper-parameters
-num_epochs = 50
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Conditional parameters used for debugging
-check = False
-debugging = False
-saving = True
-validating = True
-
-
-def evaluate_model(model, data_loader, device):
-    # set the model to evaluation mode
-    model.eval()
-
-    # List to store individual dice scores for each sample
-    all_dice_scores = []
-
-    # No gradient computation during evaluation
-    with torch.no_grad():
-        for images, masks in data_loader:
-            images, masks = images.to(device), masks.to(device)
-
-            # Compute predictions
-            outputs = model(images)
-
-            # Convert predictions to binary using threshold
-            outputs = (outputs > 0.5).float()
-
-            # Compute and store the dice coefficients
-            batch_dice_scores = dice_coefficient(outputs, masks)
-            all_dice_scores.extend(batch_dice_scores.cpu().numpy())
-
-    avg_dice_score = np.mean(all_dice_scores)
-    min_dice_score = np.min(all_dice_scores)
-
-    return avg_dice_score, min_dice_score
-
-
-# Check if the dataset is consistent
-if check:
-    check_consistency()
-
-# Construct debugging datasets (small subset for fast tests)
-if debugging:
-    num_epochs = 2
-    subset_size = 500
-
-    dataset = ISICDataset(transform)
-    subset_indices = list(range(subset_size))
-    subset = Subset(dataset, subset_indices)
-
-    train_size = int(subset_size * 0.6)
-    val_size = int(subset_size * 0.2)
-    test_size = subset_size - train_size - val_size
-
-    train_dataset, val_dataset, test_dataset = random_split(
-        subset, [train_size, val_size, test_size]
-    )
-
-    train_loader = DataLoader(train_dataset, 32, True)
-    validation_loader = DataLoader(val_dataset, 100, False)
-    test_loader = DataLoader(test_dataset, 100, False)
-
-# Construct full datasets
-if not debugging:
-    # Loading up the dataset and applying custom augmentations
-    dataset = ISICDataset(transform)
-
-    total_size = len(dataset)
-
-    # Splitting into training, validation and testing sets
-    train_size = int(total_size * 0.7)
-    val_size = int(total_size * 0.2)
-    test_size = total_size - train_size - val_size
-
-    train_dataset, val_dataset, test_dataset = random_split(
-        dataset, [train_size, val_size, test_size]
-    )
-
-    # Data loaders for training, validation and testing
-    train_loader = DataLoader(train_dataset, 32, True)
-    validation_loader = DataLoader(val_dataset, 100, False)
-    test_loader = DataLoader(test_dataset, 100, False)
-
-# Creating an instance of my UNet to be trained
-model = UNet(in_channels=6, num_classes=1)
-model = model.to(device)
-
-# Setup the optimizer
-optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-5)
-scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.985)
-
-print("Training loop:")
-
-# Variables used for training feedback and validation:
-running_loss = 0.0
-no_improvement = 0
-print_every = 1  # Print every 10 batches.
-best_val_score = 0.0
-patience = 8  # Number of epochs to wait before stopping
-
-for epoch in range(num_epochs):
-    # Set the model to training mode
-    model.train()
-
-    for i, (images, masks) in enumerate(train_loader, 1):
-        # Move the data onto the device
-        images, masks = images.to(device), masks.to(device)
-
-        # Zero the parameter gradients
-        optimizer.zero_grad()
-
-        # Forward pass
-        outputs = model(images)
-        # print(f"outputs = {outputs.size()}")
-        # print(f"masks = {masks.size()}")
-
-        loss = dice_loss(outputs, masks)
-
-        # Backward pass + optimization
-        loss.backward()
-        optimizer.step()
-
-        # Keep track of the running loss for testing feedback
-        running_loss += loss.item()
-        if i % print_every == 0:  # Print every `print_every` batches
-            print(
-                f"Epoch {epoch + 1}, Batch {i}: Loss = {running_loss / print_every:.4f}"
-            )
-            running_loss = 0.0
-
-    if validating:
-        # Evaluate the model using the validation set
-        dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
-        val_loss = 1 - dice_similarity
-        # Print out the validation metrics
-        print(
-            f"Validation metrics during epoch {epoch + 1}, loss = {val_loss:.4f}, similarity = {dice_similarity:.4f}, minimum = {dice_minimum:.4f}"
-        )
-
-        # Model checkpointing
-        if dice_similarity > best_val_score and dice_minimum > 0.75:
-            best_val_loss = val_loss
-            torch.save(model.state_dict(), "best_model.pth")
-            no_improvement = 0
-        else:
-            no_improvement += 1
-
-    # Early stoppage if the model hasn't improved in `patience` epochs
-    if no_improvement > patience:
-        break
-
-    scheduler.step()  # Adjust learning rate
-
-
-print("training complete")
-
-# Save the model
-if saving:
-    torch.save(
-        model.state_dict(),
-        "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
-    )
-
-avg_dice_score, min_dice_score = evaluate_model(model, test_loader, device)
-
-print(f"Average Dice Coefficient on Test Set: {avg_dice_score:.4f}")
-print(f"Minimum Dice Coefficient on Test Set: {min_dice_score:.4f}")
diff --git a/recognition/Problem_47452752/utils.py b/recognition/Problem_47452752/utils.py
deleted file mode 100644
index 068989fe3..000000000
--- a/recognition/Problem_47452752/utils.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import torch
-from random import choice
-import torchvision.transforms.functional as TF
-import torch.nn as nn
-
-
-class RandomRotate90:
-    """Randomly rotates the image by 90, 180, or 270 degrees."""
-
-    def __init__(self, p=1.0):
-        self.p = p
-
-    def __call__(self, sample):
-        image, mask = sample["image"], sample["mask"]
-        if torch.rand(1) < self.p:
-            degrees = [90, 180, 270]
-            angle = choice(degrees)
-            image = TF.rotate(image, angle)
-            mask = TF.rotate(mask, angle)
-        return {"image": image, "mask": mask}
-
-
-class RandomCenterCrop:
-    """Randomly crops the center of the image by 80% or 70%."""
-
-    def __init__(self, scales=[0.8, 0.7], p=1.0):
-        self.scales = scales
-        self.p = p
-
-    def __call__(self, sample):
-        image, mask = sample["image"], sample["mask"]
-        if torch.rand(1) < self.p:
-            scale = choice(self.scales)
-            image = TF.center_crop(
-                image, (int(image.height * scale), int(image.width * scale))
-            )
-            mask = TF.center_crop(
-                mask, (int(mask.height * scale), int(mask.width * scale))
-            )
-        return {"image": image, "mask": mask}
-
-
-class DictTransform:
-    def __init__(self, transform, transform_mask=True):
-        self.transform = transform
-        self.transform_mask = transform_mask
-
-    def __call__(self, sample):
-        image, mask = sample["image"], sample["mask"]
-        if self.transform_mask:
-            return {
-                "image": self.transform(image),
-                "mask": self.transform(mask),
-            }
-        return {
-            "image": self.transform(image),
-            "mask": mask,
-        }
-
-
-# max_dice_loss = max(dice_losses) # Penalize the worst performance
-# dice_loss = sum(dice_losses) / len(dice_losses)
-def dice_loss(predicted, target, epsilon=1e-7):
-    # Compute dice coefficient for each image in the batch
-    dice_scores = dice_coefficient(predicted, target)
-    # Compute dice loss for each image in the batch
-    dice_losses = 1.0 - dice_scores
-    # Penalize any images with dice score less than 0.8
-    penalized_losses = torch.where(dice_scores < 0.8, dice_losses * 2, dice_losses)
-    # Return the average loss
-    average_penalized_loss = penalized_losses.mean()
-
-    return average_penalized_loss
-
-
-def dice_coefficient(
-    predicted: torch.Tensor, target: torch.Tensor, epsilon=1e-7
-) -> torch.Tensor:
-    """Compute dice coefficient for each image in the batch"""
-    predicted = predicted.contiguous().view(predicted.shape[0], -1)
-    target = target.contiguous().view(target.shape[0], -1)
-
-    intersection = (predicted * target).sum(dim=1)
-    return (2.0 * intersection + epsilon) / (
-        predicted.sum(dim=1) + target.sum(dim=1) + epsilon
-    )
-
-
-def general_dice_loss(predicted, target):
-    # One-hot encode the target segmentation map
-    target_one_hot = torch.zeros_like(predicted)
-    for k in range(target_one_hot.shape[1]):
-        target_one_hot[:, k] = target == k
-
-    # Compute the Dice loss for each class, then average
-    intersection = (predicted * target_one_hot).sum(dim=(2, 3))
-    union = (predicted + target_one_hot).sum(dim=(2, 3))
-
-    dice_scores = 2 * intersection / union
-    loss = 1 - dice_scores.mean()
-
-    return loss
-
-
-# lil testing
-# pred = torch.randn(3, 6, 32, 32)
-# tar = torch.randn(3, 6, 32, 32)
-# x = dice_coefficient(pred, tar)
-# y = dice_loss(pred, tar)
-#

From 856d8b87de478f438199805d789e9a499bdc8120 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 03:56:37 +1000
Subject: [PATCH 69/79] Worked on my README file

---
 recognition/README.md | 56 +++++++++++++++++++++++++++++++++++--------
 1 file changed, 46 insertions(+), 10 deletions(-)

diff --git a/recognition/README.md b/recognition/README.md
index 5c646231c..d28a0f3a8 100644
--- a/recognition/README.md
+++ b/recognition/README.md
@@ -1,10 +1,46 @@
-# Recognition Tasks
-Various recognition tasks solved in deep learning frameworks.
-
-Tasks may include:
-* Image Segmentation
-* Object detection
-* Graph node classification
-* Image super resolution
-* Disease classification
-* Generative modelling with StyleGAN and Stable Diffusion
+# ISIC Lesion Segmentation Algorithm
+
+## Description
+The ISIC Lesion Segmentation Algorithm was designed to automatically segment skin lesion boundaries from dermatocopic images. Early detection of skin lesions is crucial in improving the prognosis of skin cancers such as melanoma. The algorithm operates by analyzing input images and leverages a convolutional neural network (CNN) to identify and segment potential skin lesions, distinguishing them from the healthy skin. The dice similarity coefficient is used to compare the algorithm output to the ground truth reference mask, which essentially measures the proportion of output pixels that match the true image. 
+
+The model is a modified UNet and is comprised of several CNN layers, skip connections and uses deep supervision facilitated by segmentation layers connecting different levels of the network to the final output. The architecture of the model was inspired by the [improved UNet](https://arxiv.org/abs/1802.10508v1) (Figure 1) which proved to be an effective 3D brain tumor segmentation model during the BRATS 2017 challenge. The network is trained using the 2018 ISIC (International Skin Imaging Collaboration) dataset, which contains annotated images of various skin lesions.
+
+![Image of the improved UNet architecture](./UNet_Segmentation_s4745275/images/Improved_UNet_architecture.png)
+Figure 1: Improved UNet architecture. Designed by F. Isensee et al.
+
+## Dependencies
+
+To run the ISIC Lesion Segmentation Algorithm, you'll need the following libraries:
+
+- Python (only verified for 3.7+)
+- numpy: For numerical computations and some tensor operations
+- PyTorch: For building and training the neural network
+- matplotlib: For plotting and visualisation
+
+To install any dependencies you can use `pip install`
+
+## Usage
+
+### Input
+
+
+### Output
+
+### Plots
+
+## Pre-processing
+
+# Data Splits
+
+The data was divided as follows:
+
+- Training: 70%
+- Validation: 20%
+- Testing: 10%
+
+With this split the bulk of the data (70%) is dedicated to training. It takes a substantial amount of data to effectively train a deep learning model like my UNet. By training with a larger proportion of the dataset the model is exposed to a broad range of data samples, which is favourable for learning the underlying patterns. Since the dataset was quite large (2500+ samples) I decided 70% was an ample amount.
+
+The validation set is used to evaluate the model thoughout its training. The models performance on the validation set is used as a criteria for early stopping. This is a strategy to prevent overfitting. Thus, having a sufficiently large validation set is crucial as it ensures the decision to stop training is made on a reliable performance metric, rather than the noise of a smaller sample. 
+
+Once the model is fully trained the test set provides a final, unbiased performance assessment.
+While 10% is a fairly proportion of the data, it still provides an adequate number of samples considering the size of the dataset. Thus, the test set provides a reliable estimate of the model's real-world performance.

From e2f9ba6d67d272a4f4641b96f424150e0e4683b6 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 05:08:38 +1000
Subject: [PATCH 70/79] Training again, lost a lot of data ):

---
 recognition/UNet_Segmentation_s4745275/train.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/recognition/UNet_Segmentation_s4745275/train.py b/recognition/UNet_Segmentation_s4745275/train.py
index fc247e540..b98b5e319 100644
--- a/recognition/UNet_Segmentation_s4745275/train.py
+++ b/recognition/UNet_Segmentation_s4745275/train.py
@@ -7,7 +7,7 @@
 
 from dataset import (
     ISICDataset,
-    transform,
+    process_and_augment,
     check_consistency,
 )
 import numpy as np
@@ -22,7 +22,7 @@
 
 
 # Hyper-parameters
-num_epochs = 50
+num_epochs = 25
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Conditional parameters used for debugging
@@ -69,7 +69,7 @@ def evaluate_model(model, data_loader, device):
     num_epochs = 2
     subset_size = 500
 
-    dataset = ISICDataset(transform)
+    dataset = ISICDataset(process_and_augment)
     subset_indices = list(range(subset_size))
     subset = Subset(dataset, subset_indices)
 
@@ -88,7 +88,7 @@ def evaluate_model(model, data_loader, device):
 # Construct full datasets
 if not debugging:
     # Loading up the dataset and applying custom augmentations
-    dataset = ISICDataset(transform)
+    dataset = ISICDataset(process_and_augment)
 
     total_size = len(dataset)
 
@@ -116,10 +116,10 @@ def evaluate_model(model, data_loader, device):
 
 print("Training loop:")
 
-# Variables used for training feedback and validation:
+# Variables used for training and validation:
 running_loss = 0.0
 no_improvement = 0
-print_every = 1  # Print every 10 batches.
+print_every = 20  # Print training feedback every 20 batches
 best_val_similarity = 0.0
 best_val_minimum = 0.0
 patience = 8  # Number of epochs to wait before stopping

From 72b48cb5c5f97ea69b0fb728fd6bb275c05f16cd Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 05:42:19 +1000
Subject: [PATCH 71/79] created individual image and mask pre-processing

---
 .../UNet_Segmentation_s4745275/dataset.py     | 37 ++++++++++++++++---
 1 file changed, 32 insertions(+), 5 deletions(-)

diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
index 12b2cc3a7..b91d2101c 100644
--- a/recognition/UNet_Segmentation_s4745275/dataset.py
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -15,7 +15,10 @@
 mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
 inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/UNet_Segmentation_s4745275/inconsistent_ids.txt"
 
-def check_consistency(image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path):
+
+def check_consistency(
+    image_dir=image_path, mask_dir=mask_path, inconsistent_path=inconsistent_path
+):
     image_ids = {
         img.split(".")[0] for img in os.listdir(image_dir) if img.endswith(".jpg")
     }
@@ -45,7 +48,7 @@ def __init__(
         transform,
         image_dir=image_path,
         mask_dir=mask_path,
-        inconsistent_path=inconsistent_path
+        inconsistent_path=inconsistent_path,
     ):
         # Load the inconsistent IDs
         with open(inconsistent_path, "r") as file:
@@ -88,7 +91,7 @@ def __getitem__(self, idx):
 
                 if self.transform:
                     sample = self.transform(sample)
-                
+
             # Convert mask to binary 0/1 tensor
             sample["mask"] = (torch.tensor(np.array(sample["mask"])) > 0.5).float()
 
@@ -99,8 +102,32 @@ def __getitem__(self, idx):
             return self.__getitem__(idx)
 
 
-# Transformation pipeline to augment the dataset
-transform = transforms.Compose(
+pre_process_image = transforms.Compose(
+    [
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Lambda(
+            lambda img_tensor: torch.cat(
+                [
+                    img_tensor,
+                    TF.to_tensor(TF.to_pil_image(img_tensor).convert("HSV")),
+                ],
+                dim=0,
+            )
+        ),
+        transforms.Normalize(
+            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+        ),
+    ]
+)
+
+pre_process_mask = transforms.Compose(
+    [transforms.Resize((256, 256)), transforms.ToTensor()]
+)
+
+
+# Transformation pipeline to pre-process and augment the dataset
+process_and_augment = transforms.Compose(
     [
         RandomRotate90(),
         RandomCenterCrop(),

From dc5ac4d2debca729f4e16ee9c2ccfe5024939282 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 05:48:54 +1000
Subject: [PATCH 72/79] Added images

---
 .../images/ISIC_0000000.jpg                    | Bin 0 -> 13796 bytes
 .../images/ISIC_0000000_segmentation.png       | Bin 0 -> 5251 bytes
 .../images/Improved_UNet_architecture.png      | Bin 0 -> 339486 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/ISIC_0000000.jpg
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/ISIC_0000000_segmentation.png
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/Improved_UNet_architecture.png

diff --git a/recognition/UNet_Segmentation_s4745275/images/ISIC_0000000.jpg b/recognition/UNet_Segmentation_s4745275/images/ISIC_0000000.jpg
new file mode 100644
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From 4b321fa23416bdbc93157f7b82b764622693a64a Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 05:49:33 +1000
Subject: [PATCH 73/79] Added best_model.pth

---
 .../UNet_Segmentation_s4745275/best_model.pth |  0
 .../UNet_Segmentation_s4745275/dataset.py     |  2 +-
 .../UNet_Segmentation_s4745275/predict.py     | 77 ++++++++++++++++++-
 3 files changed, 74 insertions(+), 5 deletions(-)
 create mode 100644 recognition/UNet_Segmentation_s4745275/best_model.pth

diff --git a/recognition/UNet_Segmentation_s4745275/best_model.pth b/recognition/UNet_Segmentation_s4745275/best_model.pth
new file mode 100644
index 000000000..e69de29bb
diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
index b91d2101c..b775f500d 100644
--- a/recognition/UNet_Segmentation_s4745275/dataset.py
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -4,7 +4,7 @@
 
 import os
 import torch
-from utils import RandomCenterCrop, RandomRotate90, DictTransform
+from recognition.UNet_Segmentation_s4745275.utils import RandomCenterCrop, RandomRotate90, DictTransform
 from torch.utils.data import Dataset
 from torchvision import transforms
 import torchvision.transforms.functional as TF
diff --git a/recognition/UNet_Segmentation_s4745275/predict.py b/recognition/UNet_Segmentation_s4745275/predict.py
index 83b2855cb..2d62a4d9d 100644
--- a/recognition/UNet_Segmentation_s4745275/predict.py
+++ b/recognition/UNet_Segmentation_s4745275/predict.py
@@ -1,9 +1,78 @@
-# showing example usage of your trained model. Print out any results and / or provide visualisations where applicable
+"""Showing an example useage of the trained model using an image in the images folder. We also compare the prediction to the true mask"""
 
+import matplotlib as plt
 from modules import UNet
+from dataset import pre_process_image, pre_process_mask
+from PIL import Image
 import torch
 
+# If available, its favourable to use the model on a GPU device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = UNet(in_channels=6, num_classes=2)  # Adjust parameters if they've changed
-model.load_state_dict(torch.load("recognition/Problem_47452752/model.pth"))
-model = model.to(device)
\ No newline at end of file
+
+# Example image and the corrsponding mask is located in the images directory
+example_path = "recognition/UNet_Segmentation_s4745275/images/ISIC_0000000.jpg"
+mask_path = (
+    "recognition/UNet_Segmentation_s4745275/images/ISIC_0000000_segmentation.png"
+)
+
+# Trained model weights and parameters are stored in best_model.pth
+model_path = "recognition/UNet_Segmentation_s4745275/best_model.pth"
+
+# Load an instance of the trained model
+model = UNet(in_channels=6, num_classes=1)
+# model.load_state_dict(torch.load(model_path))
+model = model.to(device)
+
+# Load the input image in RGB mode
+image = Image.open(example_path).convert("RGB")
+# Pre-process the input image
+image = (
+    pre_process_image(image).unsqueeze(0).to(device)
+)  # Add a batch dimension and send to device
+
+with torch.no_grad():
+    prediction = model(image)  # This is the prediction of the algorithm
+    prediction = (prediction > 0.5).float()  # Binarize the output
+
+
+# Visual comparison of the predicted segment to the true segment
+
+# Convert output tensor to numpy array for visualization
+predicted_np = prediction.squeeze().cpu().numpy()
+fix, ax = plt.sub
+fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+
+# Correct Mask
+mask = Image.open(mask_path).convert("L")
+mask = pre_process_mask(mask)
+print(mask.size())
+exit()
+ax[0].imshow(mask.numpy().transpose(1, 2, 0))
+ax[0].set_title("True Mask")
+# Original Image
+ax[0].imshow(image.squeeze().cpu().numpy().transpose(1, 2, 0))
+ax[0].set_title("Original Image")
+
+# Predicted Mask
+ax[1].imshow(predicted_np, cmap="gray")
+ax[1].set_title("Predicted Mask")
+
+plt.show()
+
+exit()
+
+
+mask = Image.open(mask)
+
+
+# Assuming `predicted_masks` contains the model's outputs
+predicted_mask = predicted_masks[0].detach().numpy()
+
+fig, ax = plt.subplots(1, 3, figsize=(15, 5))
+ax[0].imshow(img_rgb)
+ax[0].set_title("RGB Image")
+ax[1].imshow(mask, cmap="gray")
+ax[1].set_title("Ground Truth Mask")
+ax[2].imshow(predicted_mask, cmap="gray")
+ax[2].set_title("Predicted Mask")
+plt.show()

From 1ae739830772aa5f19d9f26553e96c978c38e8bf Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 06:37:44 +1000
Subject: [PATCH 74/79] Wrote the README file, finished the predict.py file

---
 recognition/README.md                         | 16 ++++++++--
 .../UNet_Segmentation_s4745275/dataset.py     |  2 +-
 .../UNet_Segmentation_s4745275/modules.py     |  8 ++---
 .../UNet_Segmentation_s4745275/predict.py     | 32 ++++---------------
 .../UNet_Segmentation_s4745275/utils.py       |  1 +
 5 files changed, 25 insertions(+), 34 deletions(-)

diff --git a/recognition/README.md b/recognition/README.md
index d28a0f3a8..9eb8eb406 100644
--- a/recognition/README.md
+++ b/recognition/README.md
@@ -20,15 +20,25 @@ To run the ISIC Lesion Segmentation Algorithm, you'll need the following librari
 To install any dependencies you can use `pip install`
 
 ## Usage
-
+#### See predict.py for a full usage demonstration of the model.
 ### Input
-
+torch.Tensor with shape [batch_size, 6, 256, 256] 
+- The batch_size denotes the number of inputted images, this is the only argument that varies
+- 6 channels (3 for RGB and 3 for HSV)
+- The image has dimensions 256x256
 
 ### Output
 
-### Plots
+torch.Tensor was shape [batch_size, 1, 256, 256] 
+- The batch_size denotes the number of inputted images, this is the only argument that varies
+- 1 channel containing probabilities of being 
+- The image has dimensions 256x256
 
+##
+![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/incredibly_good_prediction.png)
+Figure 2: An example output from a random sample (not an amazing depiction of the efficacy...)
 ## Pre-processing
+Various transformation pipelines where implemented for pre-processing and data augmentation. These can be found in the dataset.py file. These are used to convert inputted images or masks into tensors compatible with the model (see Input and Output), as well as normalizing the inputs. During training the process_and_augment pipeline was used to perform several random scalings, flips, rotations etc to improve the generalizability of the model during learning.
 
 # Data Splits
 
diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
index b775f500d..b91d2101c 100644
--- a/recognition/UNet_Segmentation_s4745275/dataset.py
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -4,7 +4,7 @@
 
 import os
 import torch
-from recognition.UNet_Segmentation_s4745275.utils import RandomCenterCrop, RandomRotate90, DictTransform
+from utils import RandomCenterCrop, RandomRotate90, DictTransform
 from torch.utils.data import Dataset
 from torchvision import transforms
 import torchvision.transforms.functional as TF
diff --git a/recognition/UNet_Segmentation_s4745275/modules.py b/recognition/UNet_Segmentation_s4745275/modules.py
index 9fab0bf37..6c1ff6968 100644
--- a/recognition/UNet_Segmentation_s4745275/modules.py
+++ b/recognition/UNet_Segmentation_s4745275/modules.py
@@ -173,7 +173,7 @@ def forward(self, x):
         # if num_classes > 2: return F.softmax(seg_3, dim=1)
 
 # Lil testing
-# model = UNet(6, 1)
-# test = torch.randn(1, 6, 32, 32)
-# output = model(test)
-# print(output.size())
\ No newline at end of file
+model = UNet(6, 1)
+test = torch.randn(1, 6, 32, 32)
+output = model(test)
+print(output.size())
\ No newline at end of file
diff --git a/recognition/UNet_Segmentation_s4745275/predict.py b/recognition/UNet_Segmentation_s4745275/predict.py
index 2d62a4d9d..7a767f6e5 100644
--- a/recognition/UNet_Segmentation_s4745275/predict.py
+++ b/recognition/UNet_Segmentation_s4745275/predict.py
@@ -1,6 +1,7 @@
 """Showing an example useage of the trained model using an image in the images folder. We also compare the prediction to the true mask"""
 
 import matplotlib as plt
+from matplotlib import pyplot
 from modules import UNet
 from dataset import pre_process_image, pre_process_mask
 from PIL import Image
@@ -35,23 +36,19 @@
     prediction = (prediction > 0.5).float()  # Binarize the output
 
 
-# Visual comparison of the predicted segment to the true segment
+# Visual comparison of the predicted segment to the true segment:
 
 # Convert output tensor to numpy array for visualization
 predicted_np = prediction.squeeze().cpu().numpy()
-fix, ax = plt.sub
-fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+fig, ax = pyplot.subplots(1, 2, figsize=(10, 5))
 
-# Correct Mask
+# Open and process the correct mask for visualization
 mask = Image.open(mask_path).convert("L")
 mask = pre_process_mask(mask)
-print(mask.size())
-exit()
+
+# True Mask
 ax[0].imshow(mask.numpy().transpose(1, 2, 0))
 ax[0].set_title("True Mask")
-# Original Image
-ax[0].imshow(image.squeeze().cpu().numpy().transpose(1, 2, 0))
-ax[0].set_title("Original Image")
 
 # Predicted Mask
 ax[1].imshow(predicted_np, cmap="gray")
@@ -59,20 +56,3 @@
 
 plt.show()
 
-exit()
-
-
-mask = Image.open(mask)
-
-
-# Assuming `predicted_masks` contains the model's outputs
-predicted_mask = predicted_masks[0].detach().numpy()
-
-fig, ax = plt.subplots(1, 3, figsize=(15, 5))
-ax[0].imshow(img_rgb)
-ax[0].set_title("RGB Image")
-ax[1].imshow(mask, cmap="gray")
-ax[1].set_title("Ground Truth Mask")
-ax[2].imshow(predicted_mask, cmap="gray")
-ax[2].set_title("Predicted Mask")
-plt.show()
diff --git a/recognition/UNet_Segmentation_s4745275/utils.py b/recognition/UNet_Segmentation_s4745275/utils.py
index 068989fe3..46c4beb47 100644
--- a/recognition/UNet_Segmentation_s4745275/utils.py
+++ b/recognition/UNet_Segmentation_s4745275/utils.py
@@ -1,3 +1,4 @@
+"""Contains any helper methods or classes"""
 import torch
 from random import choice
 import torchvision.transforms.functional as TF

From 056988db0c95972da603115eec3ad5e62a670287 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 08:27:46 +1000
Subject: [PATCH 75/79] Removed debugging stuff

---
 recognition/README.md                         |  15 ++-
 .../UNet_Segmentation_s4745275/README.md      |   1 -
 .../UNet_Segmentation_s4745275/dataset.py     |   1 +
 .../UNet_Segmentation_s4745275/predict.py     |   8 +-
 .../UNet_Segmentation_s4745275/train.py       | 105 ++++++------------
 5 files changed, 57 insertions(+), 73 deletions(-)
 delete mode 100644 recognition/UNet_Segmentation_s4745275/README.md

diff --git a/recognition/README.md b/recognition/README.md
index 9eb8eb406..f30751e6f 100644
--- a/recognition/README.md
+++ b/recognition/README.md
@@ -16,9 +16,14 @@ To run the ISIC Lesion Segmentation Algorithm, you'll need the following librari
 - numpy: For numerical computations and some tensor operations
 - PyTorch: For building and training the neural network
 - matplotlib: For plotting and visualisation
+- PIL: For loading the dataset and visualisation
 
 To install any dependencies you can use `pip install`
 
+## Reproducability
+
+To run the algorithm and reproduce the outcomes I've produced you may need to modify the directory paths used when initialising ISICDataset. Also, the directory where the model state dictionary is saved / loaded may also need to be modified. 
+
 ## Usage
 #### See predict.py for a full usage demonstration of the model.
 ### Input
@@ -34,9 +39,13 @@ torch.Tensor was shape [batch_size, 1, 256, 256]
 - 1 channel containing probabilities of being 
 - The image has dimensions 256x256
 
-##
-![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/incredibly_good_prediction.png)
-Figure 2: An example output from a random sample (not an amazing depiction of the efficacy...)
+## Results
+In the end the model achieved an average dice similarity of 0.7364 on the test set. This was after substantial training (50 epochs) which suggests there is room for improvement within the model. If I had time I'd have employed techniques like hyperparameter tuning, and perhaps explored other optimisers.
+
+![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/figure_2.png)
+Figure 2: An example output from a random sample. Black indicates non-lesion, white indicates lesion. (25 epochs)
+
+With that being said the model does show some ability in segmenting the image. This is demonstrated in Figure 2: the output mask somewhat resembles the true mask, espectially around the edge. 
 ## Pre-processing
 Various transformation pipelines where implemented for pre-processing and data augmentation. These can be found in the dataset.py file. These are used to convert inputted images or masks into tensors compatible with the model (see Input and Output), as well as normalizing the inputs. During training the process_and_augment pipeline was used to perform several random scalings, flips, rotations etc to improve the generalizability of the model during learning.
 
diff --git a/recognition/UNet_Segmentation_s4745275/README.md b/recognition/UNet_Segmentation_s4745275/README.md
deleted file mode 100644
index 2cd4adb2b..000000000
--- a/recognition/UNet_Segmentation_s4745275/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# README.md file that describes your report.
\ No newline at end of file
diff --git a/recognition/UNet_Segmentation_s4745275/dataset.py b/recognition/UNet_Segmentation_s4745275/dataset.py
index b91d2101c..8526847b5 100644
--- a/recognition/UNet_Segmentation_s4745275/dataset.py
+++ b/recognition/UNet_Segmentation_s4745275/dataset.py
@@ -11,6 +11,7 @@
 from PIL import Image
 import numpy as np
 
+# These are the default paths for me, they may not apply to you. Modify as required
 image_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1-2_Training_Input_x2"
 mask_path = "/home/groups/comp3710/ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2"
 inconsistent_path = "/home/Student/s4745275/PatternAnalysis-2023/recognition/UNet_Segmentation_s4745275/inconsistent_ids.txt"
diff --git a/recognition/UNet_Segmentation_s4745275/predict.py b/recognition/UNet_Segmentation_s4745275/predict.py
index 7a767f6e5..50b27983f 100644
--- a/recognition/UNet_Segmentation_s4745275/predict.py
+++ b/recognition/UNet_Segmentation_s4745275/predict.py
@@ -1,8 +1,10 @@
 """Showing an example useage of the trained model using an image in the images folder. We also compare the prediction to the true mask"""
 
+import os
 import matplotlib as plt
 from matplotlib import pyplot
 from modules import UNet
+# import train
 from dataset import pre_process_image, pre_process_mask
 from PIL import Image
 import torch
@@ -17,7 +19,11 @@
 )
 
 # Trained model weights and parameters are stored in best_model.pth
-model_path = "recognition/UNet_Segmentation_s4745275/best_model.pth"
+if os.path.exists("recognition/UNet_Segmentation_s4745275/best_model.pth"):
+    model_path = "recognition/UNet_Segmentation_s4745275/best_model.pth"
+else:
+    # Execute the train.py file, which will create the file
+    pass #os.system("python train.py")
 
 # Load an instance of the trained model
 model = UNet(in_channels=6, num_classes=1)
diff --git a/recognition/UNet_Segmentation_s4745275/train.py b/recognition/UNet_Segmentation_s4745275/train.py
index b98b5e319..86f567531 100644
--- a/recognition/UNet_Segmentation_s4745275/train.py
+++ b/recognition/UNet_Segmentation_s4745275/train.py
@@ -18,20 +18,15 @@
 import torch
 import torch.optim as optim
 import torch.nn.functional as F
-from torch.utils.data import Subset  # for debugging only TODO
 
+# Set to True if you encounter dataset inconsistencies
+check = False
 
 # Hyper-parameters
 num_epochs = 25
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Conditional parameters used for debugging
-check = False
-debugging = False
-saving = True
-validating = True
-
-
+# Method used during validation and testing to evaulate the model
 def evaluate_model(model, data_loader, device):
     # set the model to evaluation mode
     model.eval()
@@ -64,49 +59,27 @@ def evaluate_model(model, data_loader, device):
 if check:
     check_consistency()
 
-# Construct debugging datasets (small subset for fast tests)
-if debugging:
-    num_epochs = 2
-    subset_size = 500
-
-    dataset = ISICDataset(process_and_augment)
-    subset_indices = list(range(subset_size))
-    subset = Subset(dataset, subset_indices)
-
-    train_size = int(subset_size * 0.6)
-    val_size = int(subset_size * 0.2)
-    test_size = subset_size - train_size - val_size
-
-    train_dataset, val_dataset, test_dataset = random_split(
-        subset, [train_size, val_size, test_size]
-    )
 
-    train_loader = DataLoader(train_dataset, 32, True)
-    validation_loader = DataLoader(val_dataset, 100, False)
-    test_loader = DataLoader(test_dataset, 100, False)
+# Loading up the dataset and applying custom augmentations
+dataset = ISICDataset(process_and_augment)
 
-# Construct full datasets
-if not debugging:
-    # Loading up the dataset and applying custom augmentations
-    dataset = ISICDataset(process_and_augment)
+total_size = len(dataset)
 
-    total_size = len(dataset)
+# Splitting into training, validation and testing sets
+train_size = int(total_size * 0.7)
+val_size = int(total_size * 0.2)
+test_size = total_size - train_size - val_size
 
-    # Splitting into training, validation and testing sets
-    train_size = int(total_size * 0.7)
-    val_size = int(total_size * 0.2)
-    test_size = total_size - train_size - val_size
-
-    train_dataset, val_dataset, test_dataset = random_split(
-        dataset, [train_size, val_size, test_size]
-    )
+train_dataset, val_dataset, test_dataset = random_split(
+    dataset, [train_size, val_size, test_size]
+)
 
-    # Data loaders for training, validation and testing
-    train_loader = DataLoader(train_dataset, 32, True)
-    validation_loader = DataLoader(val_dataset, 100, False)
-    test_loader = DataLoader(test_dataset, 100, False)
+# Data loaders for training, validation and testing
+train_loader = DataLoader(train_dataset, 32, True)
+validation_loader = DataLoader(val_dataset, 100, False)
+test_loader = DataLoader(test_dataset, 100, False)
 
-# Creating an instance of my UNet to be trained
+# Creating an instance of the UNet to be trained
 model = UNet(in_channels=6, num_classes=1)
 model = model.to(device)
 
@@ -137,8 +110,6 @@ def evaluate_model(model, data_loader, device):
 
         # Forward pass
         outputs = model(images)
-        # print(f"outputs = {outputs.size()}")
-        # print(f"masks = {masks.size()}")
 
         loss = dice_loss(outputs, masks)
 
@@ -154,22 +125,21 @@ def evaluate_model(model, data_loader, device):
             )
             running_loss = 0.0
 
-    if validating:
-        # Evaluate the model using the validation set
-        dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
-        val_loss = 1 - dice_similarity
-        # Print out the validation metrics
-        print(
-            f"Validation metrics during epoch {epoch + 1}, loss = {val_loss:.4f}, similarity = {dice_similarity:.4f}, minimum = {dice_minimum:.4f}"
-        )
-
-        # Model checkpointing
-        if dice_similarity > best_val_similarity and dice_minimum >= best_val_minimum:
-            best_val_loss = val_loss
-            torch.save(model.state_dict(), "best_model.pth")
-            no_improvement = 0
-        else:
-            no_improvement += 1
+    # Evaluate the model using the validation set
+    dice_similarity, dice_minimum = evaluate_model(model, validation_loader, device)
+    val_loss = 1 - dice_similarity
+    # Print out the validation metrics
+    print(
+        f"Validation metrics during epoch {epoch + 1}, loss = {val_loss:.4f}, similarity = {dice_similarity:.4f}, minimum = {dice_minimum:.4f}"
+    )
+
+    # Model checkpointing
+    if dice_similarity > best_val_similarity and dice_minimum >= best_val_minimum:
+        best_val_loss = val_loss
+        torch.save(model.state_dict(), "best_model.pth")
+        no_improvement = 0
+    else:
+        no_improvement += 1
 
     # Early stoppage if the model hasn't improved in `patience` epochs
     if no_improvement > patience:
@@ -181,11 +151,10 @@ def evaluate_model(model, data_loader, device):
 print("training complete")
 
 # Save the model
-if saving:
-    torch.save(
-        model.state_dict(),
-        "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/model.pth",
-    )
+torch.save(
+    model.state_dict(),
+    "/home/Student/s4745275/PatternAnalysis-2023/recognition/Problem_47452752/best_model.pth",
+)
 
 avg_dice_score, min_dice_score = evaluate_model(model, test_loader, device)
 

From 7f30cbf9edbf576debee6ee131b01e8ebd6e2396 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 08:38:53 +1000
Subject: [PATCH 76/79] Improved the README file

---
 recognition/README.md | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/recognition/README.md b/recognition/README.md
index f30751e6f..7ab283fdd 100644
--- a/recognition/README.md
+++ b/recognition/README.md
@@ -1,9 +1,9 @@
 # ISIC Lesion Segmentation Algorithm
 
 ## Description
-The ISIC Lesion Segmentation Algorithm was designed to automatically segment skin lesion boundaries from dermatocopic images. Early detection of skin lesions is crucial in improving the prognosis of skin cancers such as melanoma. The algorithm operates by analyzing input images and leverages a convolutional neural network (CNN) to identify and segment potential skin lesions, distinguishing them from the healthy skin. The dice similarity coefficient is used to compare the algorithm output to the ground truth reference mask, which essentially measures the proportion of output pixels that match the true image. 
+The ISIC Lesion Segmentation Algorithm was designed to automatically segment skin lesion boundaries from dermatoscopic images. Early detection of malignant skin lesions is crucial in improving the prognosis of skin cancers such as melanoma. The algorithm operates by analyzing input images and leverages a convolutional neural network (CNN) to identify and segment potential skin lesions, distinguishing them from healthy skin. The Dice similarity coefficient is used to compare the algorithm output to the ground truth reference mask, which essentially measures the proportion of output pixels that match the true image.
 
-The model is a modified UNet and is comprised of several CNN layers, skip connections and uses deep supervision facilitated by segmentation layers connecting different levels of the network to the final output. The architecture of the model was inspired by the [improved UNet](https://arxiv.org/abs/1802.10508v1) (Figure 1) which proved to be an effective 3D brain tumor segmentation model during the BRATS 2017 challenge. The network is trained using the 2018 ISIC (International Skin Imaging Collaboration) dataset, which contains annotated images of various skin lesions.
+The model is a modified UNet and is composed of several CNN layers, skip connections, and uses deep supervision facilitated by segmentation layers that connect different levels of the network to the final output. The architecture of the model was inspired by the [improved UNet](https://arxiv.org/abs/1802.10508v1) (Figure 1), which proved to be an effective 3D brain tumor segmentation model during the BRATS 2017 challenge. The network is trained using the 2018 ISIC (International Skin Imaging Collaboration) dataset, which contains annotated images of various skin lesions.
 
 ![Image of the improved UNet architecture](./UNet_Segmentation_s4745275/images/Improved_UNet_architecture.png)
 Figure 1: Improved UNet architecture. Designed by F. Isensee et al.
@@ -16,13 +16,19 @@ To run the ISIC Lesion Segmentation Algorithm, you'll need the following librari
 - numpy: For numerical computations and some tensor operations
 - PyTorch: For building and training the neural network
 - matplotlib: For plotting and visualisation
-- PIL: For loading the dataset and visualisation
+- PIL (Pillow): For loading the dataset and visualisation
 
 To install any dependencies you can use `pip install`
 
 ## Reproducability
 
-To run the algorithm and reproduce the outcomes I've produced you may need to modify the directory paths used when initialising ISICDataset. Also, the directory where the model state dictionary is saved / loaded may also need to be modified. 
+To run the algorithm and reproduce the results I've obtained, please be aware of the following considerations:
+
+1. Directory Paths for ISICDataset: The paths specified when initializing ISICDataset may need to be modified to match the directory structure on your machine. Ensure that you point it to the correct location where your dataset resides.
+
+2. Model State Dictionary Directory: The directory where the model state dictionary is saved/loaded may differ based on your setup. Adjust the path accordingly to ensure the algorithm can access the model or save it correctly.
+
+Always ensure that you have the necessary permissions to read/write in the specified directories and that the paths are correctly formatted.
 
 ## Usage
 #### See predict.py for a full usage demonstration of the model.
@@ -40,26 +46,28 @@ torch.Tensor was shape [batch_size, 1, 256, 256]
 - The image has dimensions 256x256
 
 ## Results
-In the end the model achieved an average dice similarity of 0.7364 on the test set. This was after substantial training (50 epochs) which suggests there is room for improvement within the model. If I had time I'd have employed techniques like hyperparameter tuning, and perhaps explored other optimisers.
+Ultimately, after extensive training over 50 epochs, the model attained an average Dice similarity coefficient of 0.7364 on the test set. This performance indicates potential areas for improvement. Given more time, I would delve into techniques like hyperparameter tuning and possibly experiment with alternative optimizers.
 
 ![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/figure_2.png)
 Figure 2: An example output from a random sample. Black indicates non-lesion, white indicates lesion. (25 epochs)
 
-With that being said the model does show some ability in segmenting the image. This is demonstrated in Figure 2: the output mask somewhat resembles the true mask, espectially around the edge. 
+That said, the model does exhibit proficiency in segmenting the image. This is evident in Figure 2, where the output mask closely mirrors the true mask, especially around the edges.
+
 ## Pre-processing
-Various transformation pipelines where implemented for pre-processing and data augmentation. These can be found in the dataset.py file. These are used to convert inputted images or masks into tensors compatible with the model (see Input and Output), as well as normalizing the inputs. During training the process_and_augment pipeline was used to perform several random scalings, flips, rotations etc to improve the generalizability of the model during learning.
+Various transformation pipelines were implemented for both pre-processing and data augmentation. You can find these in the dataset.py file. They serve to convert the provided images or masks into tensors compatible with the model (refer to the Input and Output section), as well as to normalize the inputs. During training, the process_and_augment pipeline was employed, performing random scalings, flips, rotations, and more to enhance the model's generalizability during learning.
+
 
 # Data Splits
 
-The data was divided as follows:
+The data was partitioned as follows:
 
 - Training: 70%
 - Validation: 20%
 - Testing: 10%
 
-With this split the bulk of the data (70%) is dedicated to training. It takes a substantial amount of data to effectively train a deep learning model like my UNet. By training with a larger proportion of the dataset the model is exposed to a broad range of data samples, which is favourable for learning the underlying patterns. Since the dataset was quite large (2500+ samples) I decided 70% was an ample amount.
+With this configuration, a significant majority (70%) of the data is allocated for training. Deep learning models, like the UNet I implemented, require a robust volume of data for effective training. By dedicating a larger segment of the dataset to training, the model can encounter a more diverse array of samples, which is essential for discerning and internalizing underlying patterns. Given the dataset's substantial size (over 2500 samples), allocating 70% to training felt appropriate.
 
-The validation set is used to evaluate the model thoughout its training. The models performance on the validation set is used as a criteria for early stopping. This is a strategy to prevent overfitting. Thus, having a sufficiently large validation set is crucial as it ensures the decision to stop training is made on a reliable performance metric, rather than the noise of a smaller sample. 
+The validation set serves a dual purpose: it allows for ongoing evaluation during training and aids in determining when to cease training — a tactic known as early stopping — to mitigate overfitting. A generous validation set is imperative to ensure that the decision to halt training is anchored in a trustworthy performance metric rather than the inconsistencies of a smaller subset.
 
-Once the model is fully trained the test set provides a final, unbiased performance assessment.
-While 10% is a fairly proportion of the data, it still provides an adequate number of samples considering the size of the dataset. Thus, the test set provides a reliable estimate of the model's real-world performance.
+Finally, the test set offers an objective assessment of the model's performance post-training. While 10% might seem modest, given the dataset's magnitude, it still yields a significant number of samples. Consequently, the test set furnishes a dependable measure of how the model is likely to perform in real-world scenarios.
+The data was divided as follows:

From 76d6260e11739a2fed40802ed70d114acc97532a Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 08:39:36 +1000
Subject: [PATCH 77/79] If the best_model.pth DNE execute train.py

---
 recognition/UNet_Segmentation_s4745275/predict.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/recognition/UNet_Segmentation_s4745275/predict.py b/recognition/UNet_Segmentation_s4745275/predict.py
index 50b27983f..f2da760b0 100644
--- a/recognition/UNet_Segmentation_s4745275/predict.py
+++ b/recognition/UNet_Segmentation_s4745275/predict.py
@@ -4,7 +4,6 @@
 import matplotlib as plt
 from matplotlib import pyplot
 from modules import UNet
-# import train
 from dataset import pre_process_image, pre_process_mask
 from PIL import Image
 import torch
@@ -22,8 +21,8 @@
 if os.path.exists("recognition/UNet_Segmentation_s4745275/best_model.pth"):
     model_path = "recognition/UNet_Segmentation_s4745275/best_model.pth"
 else:
-    # Execute the train.py file, which will create the file
-    pass #os.system("python train.py")
+    # Execute the train.py file, which will create best_model.pth
+    os.system("python train.py")
 
 # Load an instance of the trained model
 model = UNet(in_channels=6, num_classes=1)
@@ -61,4 +60,3 @@
 ax[1].set_title("Predicted Mask")
 
 plt.show()
-

From fa0eea2ded86845b18a1620c9ad9d4d85e996492 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 08:40:18 +1000
Subject: [PATCH 78/79] performance images

---
 .../images/Figure_1.png                        | Bin 0 -> 37943 bytes
 .../images/Figure_2.png                        | Bin 0 -> 165030 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/Figure_1.png
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/Figure_2.png

diff --git a/recognition/UNet_Segmentation_s4745275/images/Figure_1.png b/recognition/UNet_Segmentation_s4745275/images/Figure_1.png
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From dd276594e52bfbad46d1c4f4f6089dc4e9ed1299 Mon Sep 17 00:00:00 2001
From: Noah Smyth <noah.smyth@hotmail.com>
Date: Fri, 27 Oct 2023 09:01:29 +1000
Subject: [PATCH 79/79] Fixed afew README image naming bugs

---
 recognition/README.md                         |   4 ++--
 .../images/Figure_1.png                       | Bin 37943 -> 339486 bytes
 .../images/Improved_UNet_architecture.png     | Bin 339486 -> 0 bytes
 .../images/Training_evolution.png             | Bin 0 -> 37943 bytes
 4 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 recognition/UNet_Segmentation_s4745275/images/Improved_UNet_architecture.png
 create mode 100644 recognition/UNet_Segmentation_s4745275/images/Training_evolution.png

diff --git a/recognition/README.md b/recognition/README.md
index 7ab283fdd..a52c3600a 100644
--- a/recognition/README.md
+++ b/recognition/README.md
@@ -5,7 +5,7 @@ The ISIC Lesion Segmentation Algorithm was designed to automatically segment ski
 
 The model is a modified UNet and is composed of several CNN layers, skip connections, and uses deep supervision facilitated by segmentation layers that connect different levels of the network to the final output. The architecture of the model was inspired by the [improved UNet](https://arxiv.org/abs/1802.10508v1) (Figure 1), which proved to be an effective 3D brain tumor segmentation model during the BRATS 2017 challenge. The network is trained using the 2018 ISIC (International Skin Imaging Collaboration) dataset, which contains annotated images of various skin lesions.
 
-![Image of the improved UNet architecture](./UNet_Segmentation_s4745275/images/Improved_UNet_architecture.png)
+![Image of the improved UNet architecture](./UNet_Segmentation_s4745275/images/Figure_1.png)
 Figure 1: Improved UNet architecture. Designed by F. Isensee et al.
 
 ## Dependencies
@@ -48,7 +48,7 @@ torch.Tensor was shape [batch_size, 1, 256, 256]
 ## Results
 Ultimately, after extensive training over 50 epochs, the model attained an average Dice similarity coefficient of 0.7364 on the test set. This performance indicates potential areas for improvement. Given more time, I would delve into techniques like hyperparameter tuning and possibly experiment with alternative optimizers.
 
-![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/figure_2.png)
+![Beautiful demonstation of the model efficacy](./UNet_Segmentation_s4745275/images/Figure_2.png)
 Figure 2: An example output from a random sample. Black indicates non-lesion, white indicates lesion. (25 epochs)
 
 That said, the model does exhibit proficiency in segmenting the image. This is evident in Figure 2, where the output mask closely mirrors the true mask, especially around the edges.
diff --git a/recognition/UNet_Segmentation_s4745275/images/Figure_1.png b/recognition/UNet_Segmentation_s4745275/images/Figure_1.png
index 0d34533c2cff560e065146cafd6626c764f08be5..3bca31e8a620d5244e6ee5cf7774647b7bc240ad 100644
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