From c3aff8b5b5c516eddc3929828be3e622bf5f6b1f Mon Sep 17 00:00:00 2001 From: "Shakes (WhiteOut)" Date: Sun, 17 Sep 2023 21:47:51 +1000 Subject: [PATCH 01/49] Added recognition branch and README for info. --- recognition/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 recognition/README.md diff --git a/recognition/README.md b/recognition/README.md new file mode 100644 index 0000000000..5c646231c2 --- /dev/null +++ b/recognition/README.md @@ -0,0 +1,10 @@ +# 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 From 7e5bc0ec36dabdc011c7b50df4567cb80e484ade Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 4 Oct 2023 20:00:20 +1000 Subject: [PATCH 02/49] =?UTF-8?q?Initial=20setup=20for=20Siamese=20network?= =?UTF-8?q?=20Alzheimer=E2=80=99s=20disease=20classification.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- recognition/alzheimers_snn_s4647936/README.MD | 0 recognition/alzheimers_snn_s4647936/dataset.py | 0 recognition/alzheimers_snn_s4647936/modules.py | 0 recognition/alzheimers_snn_s4647936/predict.py | 0 recognition/alzheimers_snn_s4647936/train.py | 0 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/alzheimers_snn_s4647936/README.MD create mode 100644 recognition/alzheimers_snn_s4647936/dataset.py create mode 100644 recognition/alzheimers_snn_s4647936/modules.py create mode 100644 recognition/alzheimers_snn_s4647936/predict.py create mode 100644 recognition/alzheimers_snn_s4647936/train.py diff --git a/recognition/alzheimers_snn_s4647936/README.MD b/recognition/alzheimers_snn_s4647936/README.MD new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/alzheimers_snn_s4647936/predict.py b/recognition/alzheimers_snn_s4647936/predict.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py new file mode 100644 index 0000000000..e69de29bb2 From 8d8b7f29d09fe2e91bec5e99f2a8ea63cfbd079f Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 4 Oct 2023 20:33:06 +1000 Subject: [PATCH 03/49] Renamed README.MD to README.md --- recognition/alzheimers_snn_s4647936/{README.MD => README.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename recognition/alzheimers_snn_s4647936/{README.MD => README.md} (100%) diff --git a/recognition/alzheimers_snn_s4647936/README.MD b/recognition/alzheimers_snn_s4647936/README.md similarity index 100% rename from recognition/alzheimers_snn_s4647936/README.MD rename to recognition/alzheimers_snn_s4647936/README.md From 9aabb12ee086ead5c3038c9b0831cea05ea84a49 Mon Sep 17 00:00:00 2001 From: Vicky Tran <108254973+tranvicky@users.noreply.github.com> Date: Wed, 4 Oct 2023 20:38:10 +1000 Subject: [PATCH 04/49] Update to contain rough outline for README.md --- recognition/alzheimers_snn_s4647936/README.md | 44 +++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index e69de29bb2..8d95647ac9 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -0,0 +1,44 @@ +# Classifying Alzheimer's Disease using Siamese Networks + +## 1. Introduction +Briefly describe the problem statement and the objective of the project. + +## 2. Dataset +- **Description**: A brief overview of the ADNI brain dataset. +- **Link to Dataset**: (if publicly available) +- **Preprocessing Steps**: Describe any normalization, resizing, augmentation, etc., that was performed. + +## 3. Siamese Network Architecture +- **Overview**: Briefly explain the concept of Siamese networks. +- **Model Architecture**: Describe the architecture of the Siamese network used. Diagrams or flowcharts can be helpful. +- **Loss Function**: Mention the loss function used (e.g., contrastive loss). + +## 4. Setup and Installation +List the dependencies and provide a step-by-step guide for setting up the project. +``` +pip install -r requirements.txt +``` + +## 5. Usage +Provide clear instructions on how to run the code, train the model, and perform evaluations. +``` +python train.py +python evaluate.py +``` + +## 6. Results +- **Training Results**: Mention metrics such as training loss, validation loss, etc. +- **Test Results**: Detail the accuracy achieved on the test set, and any other relevant metrics. +- **Visualizations**: If possible, include visual results, confusion matrices, or any graphs that might be relevant. + +## 7. Future Work & Improvements +Mention any potential future work or improvements that could be made to the project. + +## 8. References +List any references, papers, or resources that were used or are relevant to the project. + +## 9. License +If applicable, specify the license under which the project is available (e.g., MIT License). + +## 10. Acknowledgments +Credit any authors, papers, or open-source libraries that were crucial to the project. From 7e03b365b7e644b5f9c624580957f9c1f4654794 Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 10 Oct 2023 11:09:01 +1000 Subject: [PATCH 05/49] Initial draft of Siamese network architecture in modules.py --- .../alzheimers_snn_s4647936/modules.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index e69de29bb2..39c45f3563 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -0,0 +1,47 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class FeatureExtractor(nn.Module): + def __init__(self): + super(FeatureExtractor, self).__init__() + + # Define convolutional layers + self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5) # assuming grayscale images + self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5) + + # Define a placeholder for fully connected layers (we'll adjust these later based on input image size) + self.fc1 = nn.Linear(64*50*50, 256) # 50x50 is a placeholder; adjust based on actual output size + self.fc2 = nn.Linear(256, 2) # 2-dimensional output for simplicity + + def forward(self, x): + # Apply the convolutional layers with ReLU and max pooling + x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) + x = F.max_pool2d(F.relu(self.conv2(x)), 2) # kernel size 2 + + # Flatten the tensor + x = x.view(x.size(0), -1) + + # Apply the fully connected layers with ReLU + x = F.relu(self.fc1(x)) + x = self.fc2(x) + + return x + +class SiameseNetwork(nn.Module): + def __init__(self): + super(SiameseNetwork, self).__init__() + + # Use the same feature extractor for both inputs + self.feature_extractor = FeatureExtractor() + + def forward_one(self, x): + # Forward pass for one input + return self.feature_extractor(x) + + def forward(self, input1, input2): + # Forward pass for both inputs + output1 = self.forward_one(input1) + output2 = self.forward_one(input2) + return output1, output2 + From 7d272797db24435d2a649451c8cf87b87ecedc1b Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 10 Oct 2023 11:28:00 +1000 Subject: [PATCH 06/49] Added section in code to help determine dataset image dimensions --- recognition/alzheimers_snn_s4647936/modules.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index 39c45f3563..83623ae90d 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -1,6 +1,7 @@ import torch import torch.nn as nn import torch.nn.functional as F +from PIL import Image class FeatureExtractor(nn.Module): def __init__(self): @@ -44,4 +45,17 @@ def forward(self, input1, input2): output1 = self.forward_one(input1) output2 = self.forward_one(input2) return output1, output2 + +""" +Determine dataset image dimensions +""" +# Path to images +image_path = "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC/train/AD/336537_97.jpeg" # Adjust this path as per your directory structure + +# Open the image and determine its size +image = Image.open(image_path) +width, height = image.size + +print(f"Image dimensions: {width} x {height}") + From dea52f6e0da0731ddec689dd64c39b222cce02f1 Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 10 Oct 2023 13:23:43 +1000 Subject: [PATCH 07/49] =?UTF-8?q?=E2=80=A2TripletLoss=20setup:=20Create=20?= =?UTF-8?q?an=20initial=20structure=20for=20the=20triplet=20loss=20functio?= =?UTF-8?q?n=20in=20modules.py.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../alzheimers_snn_s4647936/modules.py | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index 83623ae90d..e3409f2a12 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -3,6 +3,11 @@ import torch.nn.functional as F from PIL import Image +""" +Feature Extractor +- Sub-network responsible for extracting features from an input image. +- Implemented with simple convolutional neural network (CNN) structure. +""" class FeatureExtractor(nn.Module): def __init__(self): super(FeatureExtractor, self).__init__() @@ -11,8 +16,8 @@ def __init__(self): self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5) # assuming grayscale images self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5) - # Define a placeholder for fully connected layers (we'll adjust these later based on input image size) - self.fc1 = nn.Linear(64*50*50, 256) # 50x50 is a placeholder; adjust based on actual output size + # Define fully connected layers (adjust based on input image size) + self.fc1 = nn.Linear(61*57*64, 256) # Adjust here based on image size self.fc2 = nn.Linear(256, 2) # 2-dimensional output for simplicity def forward(self, x): @@ -29,6 +34,10 @@ def forward(self, x): return x +""" +Siamese Network +- Uses two copies of 'FeatureExtractor' to process two images +""" class SiameseNetwork(nn.Module): def __init__(self): super(SiameseNetwork, self).__init__() @@ -56,6 +65,21 @@ def forward(self, input1, input2): image = Image.open(image_path) width, height = image.size -print(f"Image dimensions: {width} x {height}") +# print(f"Image dimensions: {width} x {height}") # Result is 256 x 240 +""" +Triplet Loss implementation +- Beneficial to choose "hard" triplets +- The anchor and positive would be two different "slices" from the same patient +- The negative would be a "slice" from a different patient +""" +class TripletLoss(nn.Module): + def __init__(self, margin=1.0): + super(TripletLoss, self).__init__() + self.margin = margin + def forward(self, anchor, positive, negative): + distance_positive = (anchor - positive).pow(2).sum(1) # Euclidean distance + distance_negative = (anchor - negative).pow(2).sum(1) # Euclidean distance + losses = nn.functional.relu(distance_positive - distance_negative + self.margin) + return losses.mean() From cbb5beab39313c9e1ca924feb974f1feda4f57ba Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 10 Oct 2023 13:44:12 +1000 Subject: [PATCH 08/49] Added TripletDataset to dataset.py to generate anchor, positive (same patient), and negative (different class) image triplets for Siamese training. --- .../alzheimers_snn_s4647936/dataset.py | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index e69de29bb2..0cee2fec59 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -0,0 +1,54 @@ +import os +import torch +from torch.utils.data import Dataset +from PIL import Image +import random + +class TripletDataset(Dataset): + """ + Generate triplets for training Siamese networks using triplet loss. + + For each anchor image from either the AD or NC class, a positive image is selected from + the same patient but a different slice. A negative image is then chosen from the opposite class. + + Args: + root_dir (str): Root directory containing AD and NC image subdirectories. + transform (callable, optional): Transformations applied to the images. + + Returns: + tuple: A triplet of images - (anchor, positive, negative). + """ + def __init__(self, root_dir, transform=None): + self.root_dir = root_dir + self.transform = transform + + # Load all image paths + self.ad_paths = [os.path.join(root_dir, 'train/AD', img) for img in os.listdir(os.path.join(root_dir, 'train/AD'))] + self.nc_paths = [os.path.join(root_dir, 'train/NC', img) for img in os.listdir(os.path.join(root_dir, 'train/NC'))] + + def __len__(self): + return min(len(self.ad_paths), len(self.nc_paths)) + + def __getitem__(self, idx): + # Extract patient ID from the filename + patient_id = self.ad_paths[idx].split('/')[-1].split('_')[0] + + # Choose an anchor image + anchor_path = self.ad_paths[idx] + + # Choose a positive image from the same patient + positive_path = random.choice([path for path in self.ad_paths if path != anchor_path and patient_id in path]) + + # Choose a negative image from a different patient + negative_path = random.choice(self.nc_paths) + + anchor_image = Image.open(anchor_path) + positive_image = Image.open(positive_path) + negative_image = Image.open(negative_path) + + if self.transform: + anchor_image = self.transform(anchor_image) + positive_image = self.transform(positive_image) + negative_image = self.transform(negative_image) + + return anchor_image, positive_image, negative_image From e096a1e53fb2d34732eab629d3bc3848b3a4b0a0 Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 17:35:51 +1000 Subject: [PATCH 09/49] Refactored TripletDataset for enhanced adaptability, ensuring balanced anchor selection and optimized triplet formation for Siamese network training. --- .../alzheimers_snn_s4647936/dataset.py | 69 +++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index e69de29bb2..452feac809 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -0,0 +1,69 @@ +import os +import torch +from torch.utils.data import Dataset +from PIL import Image +import random + +class TripletDataset(Dataset): + """ + Generate triplets for training Siamese networks using triplet loss. + + For each anchor image from either the AD or NC class, a positive image is selected from + the same patient but a different slice. A negative image is then chosen from the opposite class. + + Args: + root_dir (str): Root directory containing AD and NC image subdirectories. + mode (str): Either 'train' or 'test'. + transform (callable, optional): Transformations applied to the images. + + Returns: + tuple: A triplet of images - (anchor, positive, negative). + """ + def __init__(self, root_dir, mode='train', transform=None): + self.root_dir = root_dir + self.mode = mode + self.transform = transform + + # Directories for AD and NC images + self.ad_dir = os.path.join(root_dir, mode, 'AD') + self.nc_dir = os.path.join(root_dir, mode, 'NC') + + # Load all image paths + self.ad_paths = [os.path.join(self.ad_dir, img) for img in os.listdir(self.ad_dir)] + self.nc_paths = [os.path.join(self.nc_dir, img) for img in os.listdir(self.nc_dir)] + + def __len__(self): + return len(self.ad_paths) + len(self.nc_paths) # combined length + + def __getitem__(self, idx): + # Decide whether to take AD or NC as anchor based on index + if idx < len(self.ad_paths): + anchor_path = self.ad_paths[idx] + positive_dir = self.ad_dir + negative_dir = self.nc_dir + else: + anchor_path = self.nc_paths[idx - len(self.ad_paths)] # offset by length of ad_paths + positive_dir = self.nc_dir + negative_dir = self.ad_dir + + # Extract patient ID from the filename + patient_id = anchor_path.split('/')[-1].split('_')[0] + + # Choose a positive image from the same patient + positive_path = random.choice([path for path in os.listdir(positive_dir) if path != os.path.basename(anchor_path) and patient_id in path]) + positive_path = os.path.join(positive_dir, positive_path) # complete path + + # Choose a negative image from a different patient + negative_img = random.choice([img for img in os.listdir(negative_dir) if patient_id not in img]) + negative_path = os.path.join(negative_dir, negative_img) + + anchor_image = Image.open(anchor_path) + positive_image = Image.open(positive_path) + negative_image = Image.open(negative_path) + + if self.transform: + anchor_image = self.transform(anchor_image) + positive_image = self.transform(positive_image) + negative_image = self.transform(negative_image) + + return anchor_image, positive_image, negative_image From 0628f8cb1ffb023f38d58abde490b0326183a55b Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 17:46:20 +1000 Subject: [PATCH 10/49] Added image transformations and initialized train and test datasets in train.py --- recognition/alzheimers_snn_s4647936/train.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index e69de29bb2..09ea22bff2 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -0,0 +1,16 @@ +import os +import torch +from dataset import TripletDataset +from torchvision import transforms + +# Transformations for images +transform = transforms.Compose([ + transforms.Resize((256, 240)), + transforms.ToTensor(), +]) + +# Dataset instances +train_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='train', transform=transform) +test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='test', transform=transform) + +# Continue training code... From 3387cc5703135fcfd2b8e558098e5f8ffda68ceb Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 19:01:14 +1000 Subject: [PATCH 11/49] Added functionality to display and save sample triplet images while checking for NaN and Inf values in the dataset --- recognition/alzheimers_snn_s4647936/train.py | 32 +++++++++++++++++++- 1 file changed, 31 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 09ea22bff2..705dc0b170 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -1,5 +1,6 @@ import os import torch +import matplotlib.pyplot as plt from dataset import TripletDataset from torchvision import transforms @@ -13,4 +14,33 @@ train_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='train', transform=transform) test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='test', transform=transform) -# Continue training code... +# Test to see number of images +print(len(train_dataset)) # 21520 +print(len(test_dataset)) # 9000 + +# Get a sample triplet from the training dataset +anchor, positive, negative = train_dataset[0] + +# Now check for NaN and Inf values +print(torch.isnan(anchor).any()) +print(torch.isinf(anchor).any()) + +# Function to display images +def imshow(img): + img = img.numpy().transpose((1, 2, 0)) + + # Convert to float and normalize if necessary + if img.max() > 1: + img = img.astype(float) / 255 + + plt.imshow(img) + plt.show(block=True) + +# Display the images +imshow(anchor) +imshow(positive) +imshow(negative) + +# Example to save the image +plt.savefig('sample_image.png') + From 0d39d545c8185c08f19439432fa566fa7aca4ced Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 19:16:20 +1000 Subject: [PATCH 12/49] Refactored TripletDataset to ensure patient-wise split for train-test data, improved triplet selection using patient IDs, and streamlined path extraction using os.path.basename --- .../alzheimers_snn_s4647936/dataset.py | 72 +++++++++++++++---- 1 file changed, 58 insertions(+), 14 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index 452feac809..8f650b9f21 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -1,8 +1,7 @@ import os -import torch -from torch.utils.data import Dataset -from PIL import Image import random +from PIL import Image +from torch.utils.data import Dataset class TripletDataset(Dataset): """ @@ -19,7 +18,8 @@ class TripletDataset(Dataset): Returns: tuple: A triplet of images - (anchor, positive, negative). """ - def __init__(self, root_dir, mode='train', transform=None): + + def __init__(self, root_dir, mode='train', transform=None, split_ratio=0.8): self.root_dir = root_dir self.mode = mode self.transform = transform @@ -32,6 +32,15 @@ def __init__(self, root_dir, mode='train', transform=None): self.ad_paths = [os.path.join(self.ad_dir, img) for img in os.listdir(self.ad_dir)] self.nc_paths = [os.path.join(self.nc_dir, img) for img in os.listdir(self.nc_dir)] + train_ad_paths, test_ad_paths, train_nc_paths, test_nc_paths = patient_wise_split(self.ad_paths, self.nc_paths, split_ratio) + + if mode == 'train': + self.ad_paths = train_ad_paths + self.nc_paths = train_nc_paths + elif mode == 'test': + self.ad_paths = test_ad_paths + self.nc_paths = test_nc_paths + def __len__(self): return len(self.ad_paths) + len(self.nc_paths) # combined length @@ -39,23 +48,21 @@ def __getitem__(self, idx): # Decide whether to take AD or NC as anchor based on index if idx < len(self.ad_paths): anchor_path = self.ad_paths[idx] - positive_dir = self.ad_dir - negative_dir = self.nc_dir + positive_paths = self.ad_paths + negative_paths = self.nc_paths else: anchor_path = self.nc_paths[idx - len(self.ad_paths)] # offset by length of ad_paths - positive_dir = self.nc_dir - negative_dir = self.ad_dir + positive_paths = self.nc_paths + negative_paths = self.ad_paths # Extract patient ID from the filename - patient_id = anchor_path.split('/')[-1].split('_')[0] + patient_id = os.path.basename(anchor_path).split('_')[0] # Choose a positive image from the same patient - positive_path = random.choice([path for path in os.listdir(positive_dir) if path != os.path.basename(anchor_path) and patient_id in path]) - positive_path = os.path.join(positive_dir, positive_path) # complete path - + positive_path = random.choice([path for path in positive_paths if os.path.basename(path) != os.path.basename(anchor_path) and patient_id in os.path.basename(path)]) + # Choose a negative image from a different patient - negative_img = random.choice([img for img in os.listdir(negative_dir) if patient_id not in img]) - negative_path = os.path.join(negative_dir, negative_img) + negative_path = random.choice([path for path in negative_paths if patient_id not in os.path.basename(path)]) anchor_image = Image.open(anchor_path) positive_image = Image.open(positive_path) @@ -67,3 +74,40 @@ def __getitem__(self, idx): negative_image = self.transform(negative_image) return anchor_image, positive_image, negative_image + + +def patient_wise_split(ad_paths, nc_paths, split_ratio=0.8): + """ + Split the AD and NC data patient-wise. + + Args: + - ad_paths: List of paths to AD images. + - nc_paths: List of paths to NC images. + - split_ratio: Proportion of data to use for training. + + Returns: + - train_ad_paths: List of AD training paths. + - test_ad_paths: List of AD testing paths. + - train_nc_paths: List of NC training paths. + - test_nc_paths: List of NC testing paths. + """ + + # Extract patient IDs + ad_patient_ids = list(set(os.path.basename(path).split('_')[0] for path in ad_paths)) + nc_patient_ids = list(set(os.path.basename(path).split('_')[0] for path in nc_paths)) + + # Split patient IDs for training and testing + train_ad_ids = random.sample(ad_patient_ids, int(split_ratio * len(ad_patient_ids))) + train_nc_ids = random.sample(nc_patient_ids, int(split_ratio * len(nc_patient_ids))) + + test_ad_ids = list(set(ad_patient_ids) - set(train_ad_ids)) + test_nc_ids = list(set(nc_patient_ids) - set(train_nc_ids)) + + # Get paths based on split IDs + train_ad_paths = [path for path in ad_paths if os.path.basename(path).split('_')[0] in train_ad_ids] + test_ad_paths = [path for path in ad_paths if os.path.basename(path).split('_')[0] in test_ad_ids] + + train_nc_paths = [path for path in nc_paths if os.path.basename(path).split('_')[0] in train_nc_ids] + test_nc_paths = [path for path in nc_paths if os.path.basename(path).split('_')[0] in test_nc_ids] + + return train_ad_paths, test_ad_paths, train_nc_paths, test_nc_paths From 33b1d4740f93eb89a02c8c76d93ea98657e91f3d Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 19:31:47 +1000 Subject: [PATCH 13/49] Added testing to determine size of training sets after splitting --- recognition/alzheimers_snn_s4647936/dataset.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index 8f650b9f21..db40cb65c1 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -20,7 +20,7 @@ class TripletDataset(Dataset): """ def __init__(self, root_dir, mode='train', transform=None, split_ratio=0.8): - self.root_dir = root_dir + self.root_dir = root_dir # root_dir = "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC" self.mode = mode self.transform = transform @@ -111,3 +111,8 @@ def patient_wise_split(ad_paths, nc_paths, split_ratio=0.8): test_nc_paths = [path for path in nc_paths if os.path.basename(path).split('_')[0] in test_nc_ids] return train_ad_paths, test_ad_paths, train_nc_paths, test_nc_paths + +train_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode="train") +print(f"Training dataset length: {len(train_dataset)}") +test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode="test") +print(f"Testing dataset length: {len(test_dataset)}") \ No newline at end of file From b04d801c5a0ec8ee64828f813a552e9df58f3448 Mon Sep 17 00:00:00 2001 From: Vicky Date: Sat, 14 Oct 2023 23:28:23 +1000 Subject: [PATCH 14/49] Added additional data augmentation steps (random rotation, horizontal flip, and vertical flip) when in train mode --- recognition/alzheimers_snn_s4647936/dataset.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index 783dd8a615..d802a2d1f0 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -1,5 +1,6 @@ import os import random +import torchvision.transforms as transforms from PIL import Image from torch.utils.data import Dataset @@ -37,6 +38,15 @@ def __init__(self, root_dir, mode='train', transform=None, split_ratio=0.8): if mode == 'train': self.ad_paths = train_ad_paths self.nc_paths = train_nc_paths + + # Integrate data augmentation if in training mode + self.transform = transforms.Compose([ + transforms.RandomRotation(10), + transforms.RandomHorizontalFlip(), + transforms.RandomVerticalFlip(), + transform + ]) + elif mode == 'test': self.ad_paths = test_ad_paths self.nc_paths = test_nc_paths @@ -111,8 +121,3 @@ def patient_wise_split(ad_paths, nc_paths, split_ratio=0.8): test_nc_paths = [path for path in nc_paths if os.path.basename(path).split('_')[0] in test_nc_ids] return train_ad_paths, test_ad_paths, train_nc_paths, test_nc_paths - -train_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode="train") -print(f"Training dataset length: {len(train_dataset)}") -test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode="test") -print(f"Testing dataset length: {len(test_dataset)}") From cc2038f52a25433fc21882a41cc451315c3837c6 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 14:30:15 +1000 Subject: [PATCH 15/49] Integrated the training loop for the Siamese Network into train.py. Added visualization of sample images post-training. --- recognition/alzheimers_snn_s4647936/train.py | 84 +++++++++++++++----- 1 file changed, 65 insertions(+), 19 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 705dc0b170..8f4dd276f3 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -2,7 +2,10 @@ import torch import matplotlib.pyplot as plt from dataset import TripletDataset +from modules import SiameseNetwork, TripletLoss from torchvision import transforms +import torch.optim as optim + # Transformations for images transform = transforms.Compose([ @@ -14,33 +17,76 @@ train_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='train', transform=transform) test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='test', transform=transform) -# Test to see number of images -print(len(train_dataset)) # 21520 -print(len(test_dataset)) # 9000 +# Parameters +learning_rate = 0.001 +num_epochs = 10 +batch_size = 32 + +# GPU availability +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Initialise the Siamese Network and Triplet Loss +model = SiameseNetwork().to(device) +criterion = TripletLoss(margin=1.0).to(device) +optimizer = optim.Adam(model.parameters(), lr=learning_rate) + +# DataLoader setup +train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + +# Training loop +losses = [] + +for epoch in range(num_epochs): + model.train() + running_loss = 0.0 + for batch_idx, (anchor, positive, negative) in enumerate(train_loader): + anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + + # Zero the parameter gradients + optimizer.zero_grad() -# Get a sample triplet from the training dataset -anchor, positive, negative = train_dataset[0] + # Forward pass + anchor_out, positive_out = model(anchor, positive) + _, negative_out = model(anchor, negative) -# Now check for NaN and Inf values -print(torch.isnan(anchor).any()) -print(torch.isinf(anchor).any()) + loss = criterion(anchor_out, positive_out, negative_out) + loss.backward() + optimizer.step() -# Function to display images -def imshow(img): + running_loss += loss.item() + + epoch_loss = running_loss / len(train_loader) + losses.append(epoch_loss) + print(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}") + +print("Finished Training") + +# Test to see number of images +# print(len(train_dataset)) # 17200 +# print(len(test_dataset)) # 1820 + +# Save the loss curve +plt.figure() +plt.plot(losses) +plt.xlabel('Epochs') +plt.ylabel('Loss') +plt.title('Training Loss Curve') +plt.savefig('loss_curve.png') + +# Function to save images +def save_image(img, filename): img = img.numpy().transpose((1, 2, 0)) # Convert to float and normalize if necessary if img.max() > 1: img = img.astype(float) / 255 + plt.figure() plt.imshow(img) - plt.show(block=True) - -# Display the images -imshow(anchor) -imshow(positive) -imshow(negative) - -# Example to save the image -plt.savefig('sample_image.png') + plt.axis('off') # Hide axes + plt.savefig(filename) +# Save sample images after training +save_image(anchor, 'anchor_sample.png') +save_image(positive, 'positive_sample.png') +save_image(negative, 'negative_sample.png') \ No newline at end of file From a1058d9362285a70234c3b32234942c57cd0c997 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 15:41:08 +1000 Subject: [PATCH 16/49] Fixed method save_image in train.y so that it properly saves images. --- recognition/alzheimers_snn_s4647936/train.py | 55 ++++++++++++++++++-- 1 file changed, 50 insertions(+), 5 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 8f4dd276f3..f130c6368d 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -19,11 +19,13 @@ # Parameters learning_rate = 0.001 -num_epochs = 10 +num_epochs = 1 batch_size = 32 # GPU availability device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +if not torch.cuda.is_available(): + print("No CUDA Found. Using CPU") # Initialise the Siamese Network and Triplet Loss model = SiameseNetwork().to(device) @@ -61,6 +63,40 @@ print("Finished Training") +# # --------- Begin Validation/Testing --------- +# model.eval() # set the model to evaluation mode +# correct = 0 +# total = 0 + +# # DataLoader setup for test dataset +# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) + +# with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations +# for batch_idx, (anchor, positive, negative) in enumerate(test_loader): +# anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + +# # Forward pass +# anchor_out, positive_out = model(anchor, positive) +# _, negative_out = model(anchor, negative) + +# # Compute triplet loss +# loss = criterion(anchor_out, positive_out, negative_out) + +# # You might want to add some logic here to determine "correctness", depending on how you define it for your problem +# # For instance, if the distance between anchor and positive is less than between anchor and negative, consider it "correct" +# positive_distance = torch.dist(anchor_out, positive_out) +# negative_distance = torch.dist(anchor_out, negative_out) + +# if positive_distance < negative_distance: +# correct += 1 +# total += 1 + +# print(f"Accuracy on test set: {100 * correct / total}%") +# # --------- End Validation/Testing --------- + +""" +Save and visualise results +""" # Test to see number of images # print(len(train_dataset)) # 17200 # print(len(test_dataset)) # 1820 @@ -75,7 +111,14 @@ # Function to save images def save_image(img, filename): - img = img.numpy().transpose((1, 2, 0)) + # Select the first image from the batch + img = img[0] + + # Move tensor to CPU and convert to numpy + img = img.cpu().numpy() + + # Transpose from [channels, height, width] to [height, width, channels] + img = img.transpose((1, 2, 0)) # Convert to float and normalize if necessary if img.max() > 1: @@ -86,7 +129,9 @@ def save_image(img, filename): plt.axis('off') # Hide axes plt.savefig(filename) + + # Save sample images after training -save_image(anchor, 'anchor_sample.png') -save_image(positive, 'positive_sample.png') -save_image(negative, 'negative_sample.png') \ No newline at end of file +save_image(anchor, 'anchor_sample2.png') +save_image(positive, 'positive_sample2.png') +save_image(negative, 'negative_sample2.png') \ No newline at end of file From 22adb4c7c90aa01c01deeab3c45e5e86d1057508 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 15:46:16 +1000 Subject: [PATCH 17/49] Added validation/testing loop to evaluate model on test set post-training --- recognition/alzheimers_snn_s4647936/train.py | 54 ++++++++++---------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index f130c6368d..5ad080a970 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -19,7 +19,7 @@ # Parameters learning_rate = 0.001 -num_epochs = 1 +num_epochs = 10 batch_size = 32 # GPU availability @@ -63,36 +63,36 @@ print("Finished Training") -# # --------- Begin Validation/Testing --------- -# model.eval() # set the model to evaluation mode -# correct = 0 -# total = 0 +# --------- Begin Validation/Testing --------- +model.eval() # set the model to evaluation mode +correct = 0 +total = 0 -# # DataLoader setup for test dataset -# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) +# DataLoader setup for test dataset +test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) -# with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations -# for batch_idx, (anchor, positive, negative) in enumerate(test_loader): -# anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) +with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations + for batch_idx, (anchor, positive, negative) in enumerate(test_loader): + anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) -# # Forward pass -# anchor_out, positive_out = model(anchor, positive) -# _, negative_out = model(anchor, negative) + # Forward pass + anchor_out, positive_out = model(anchor, positive) + _, negative_out = model(anchor, negative) -# # Compute triplet loss -# loss = criterion(anchor_out, positive_out, negative_out) + # Compute triplet loss + loss = criterion(anchor_out, positive_out, negative_out) -# # You might want to add some logic here to determine "correctness", depending on how you define it for your problem -# # For instance, if the distance between anchor and positive is less than between anchor and negative, consider it "correct" -# positive_distance = torch.dist(anchor_out, positive_out) -# negative_distance = torch.dist(anchor_out, negative_out) + # You might want to add some logic here to determine "correctness", depending on how you define it for your problem + # For instance, if the distance between anchor and positive is less than between anchor and negative, consider it "correct" + positive_distance = torch.dist(anchor_out, positive_out) + negative_distance = torch.dist(anchor_out, negative_out) -# if positive_distance < negative_distance: -# correct += 1 -# total += 1 + if positive_distance < negative_distance: + correct += 1 + total += 1 -# print(f"Accuracy on test set: {100 * correct / total}%") -# # --------- End Validation/Testing --------- +print(f"Accuracy on test set: {100 * correct / total}%") +# --------- End Validation/Testing --------- """ Save and visualise results @@ -132,6 +132,6 @@ def save_image(img, filename): # Save sample images after training -save_image(anchor, 'anchor_sample2.png') -save_image(positive, 'positive_sample2.png') -save_image(negative, 'negative_sample2.png') \ No newline at end of file +save_image(anchor, 'anchor_sample.png') +save_image(positive, 'positive_sample.png') +save_image(negative, 'negative_sample.png') \ No newline at end of file From 28963009e6ea73e5511d223c6bfc7c0ae41b667a Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 16:44:41 +1000 Subject: [PATCH 18/49] Implement predict.py to load the trained Siamese Network model and perform predictions on new sample data. --- .../alzheimers_snn_s4647936/predict.py | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/predict.py b/recognition/alzheimers_snn_s4647936/predict.py index e69de29bb2..4777172c3f 100644 --- a/recognition/alzheimers_snn_s4647936/predict.py +++ b/recognition/alzheimers_snn_s4647936/predict.py @@ -0,0 +1,24 @@ +import torch +from modules import SiameseNetwork +from torchvision import transforms +from PIL import Image + +# Load the model +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = SiameseNetwork().to(device) +model.load_state_dict(torch.load("siamese_model.pth", map_location=device)) +model.eval() + +# Load a sample image (or two images for Siamese) +transform = transforms.Compose([ + transforms.Resize((256, 240)), + transforms.ToTensor(), +]) +image1 = Image.open("path_to_sample_image_1.jpg") +image2 = Image.open("path_to_sample_image_2.jpg") +tensor1 = transform(image1).unsqueeze(0).to(device) +tensor2 = transform(image2).unsqueeze(0).to(device) + +# Predict +output1, output2 = model(tensor1, tensor2) +print(output1, output2) From 561d8a335b8bb7dbc9caa040e81319234b4a932c Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 17:14:50 +1000 Subject: [PATCH 19/49] Integrated PCA-based visualization for model embeddings in train.py and added functionality to save the 2D scatter plot as embeddings_pca.png. --- recognition/alzheimers_snn_s4647936/train.py | 41 +++++++++++++++++++- 1 file changed, 39 insertions(+), 2 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 5ad080a970..93b04d43e4 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -5,7 +5,8 @@ from modules import SiameseNetwork, TripletLoss from torchvision import transforms import torch.optim as optim - +from sklearn.decomposition import PCA +import numpy as np # Transformations for images transform = transforms.Compose([ @@ -63,6 +64,9 @@ print("Finished Training") +# Save the model's parameters +torch.save(model.state_dict(), "siamese_model.pth") + # --------- Begin Validation/Testing --------- model.eval() # set the model to evaluation mode correct = 0 @@ -134,4 +138,37 @@ def save_image(img, filename): # Save sample images after training save_image(anchor, 'anchor_sample.png') save_image(positive, 'positive_sample.png') -save_image(negative, 'negative_sample.png') \ No newline at end of file +save_image(negative, 'negative_sample.png') + + +# --------- Visualize Embeddings --------- +# Extract embeddings and labels +all_embeddings = [] +all_labels = [] + +# Assuming you have two classes: AD and NC. Let's assign them numeric labels. +# AD: 0, NC: 1 +with torch.no_grad(): + for idx, (anchor, _, _) in enumerate(loader): + anchor = anchor.to(device) + embedding, _ = model(anchor, anchor) + all_embeddings.append(embedding.cpu().numpy()) + + # Assign labels based on paths. You can adjust this based on your dataset setup. + if idx < len(train_dataset.ad_paths): + all_labels.extend([0] * anchor.size(0)) # AD + else: + all_labels.extend([1] * anchor.size(0)) # NC + +all_embeddings = np.concatenate(all_embeddings) + +# Reduce dimensionality using PCA +pca = PCA(n_components=2) +embeddings_2d = pca.fit_transform(all_embeddings) + +# Plot +plt.figure() +plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=all_labels, cmap='jet', alpha=0.5) +plt.colorbar() +plt.title('2D PCA of Embeddings') +plt.savefig('embeddings_pca.png') From 3c301043573ebfdf98078246856f0b27d1f0031f Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 18:38:05 +1000 Subject: [PATCH 20/49] Changed embedding plots from PCA to T-SNE --- recognition/alzheimers_snn_s4647936/train.py | 21 ++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 93b04d43e4..b6024cd83e 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -5,7 +5,7 @@ from modules import SiameseNetwork, TripletLoss from torchvision import transforms import torch.optim as optim -from sklearn.decomposition import PCA +from sklearn.manifold import TSNE import numpy as np # Transformations for images @@ -65,7 +65,7 @@ print("Finished Training") # Save the model's parameters -torch.save(model.state_dict(), "siamese_model.pth") +torch.save(model.state_dict(), "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/siamese_model.pth") # --------- Begin Validation/Testing --------- model.eval() # set the model to evaluation mode @@ -149,7 +149,7 @@ def save_image(img, filename): # Assuming you have two classes: AD and NC. Let's assign them numeric labels. # AD: 0, NC: 1 with torch.no_grad(): - for idx, (anchor, _, _) in enumerate(loader): + for idx, (anchor, _, _) in enumerate(train_loader): anchor = anchor.to(device) embedding, _ = model(anchor, anchor) all_embeddings.append(embedding.cpu().numpy()) @@ -162,13 +162,14 @@ def save_image(img, filename): all_embeddings = np.concatenate(all_embeddings) -# Reduce dimensionality using PCA -pca = PCA(n_components=2) -embeddings_2d = pca.fit_transform(all_embeddings) +# Reduce dimensionality using t-SNE +tsne = TSNE(n_components=2, random_state=42) +embeddings_2d = tsne.fit_transform(all_embeddings) # Plot -plt.figure() -plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=all_labels, cmap='jet', alpha=0.5) +plt.figure(figsize=(10, 7)) +plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=all_labels, cmap='jet', alpha=0.5, edgecolors='w', s=40) plt.colorbar() -plt.title('2D PCA of Embeddings') -plt.savefig('embeddings_pca.png') +plt.title('2D t-SNE of Embeddings') +plt.savefig('embeddings_tsne.png') + From eda84c80e09e3ed35927432b436180ee8113d4b3 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 18:51:34 +1000 Subject: [PATCH 21/49] Added functionality to extract and store embeddings for the entire dataset in train.py This will enable subsequent classifier training on Siamese network embeddings. --- recognition/alzheimers_snn_s4647936/train.py | 24 +++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index b6024cd83e..f3bb5c025e 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -140,8 +140,9 @@ def save_image(img, filename): save_image(positive, 'positive_sample.png') save_image(negative, 'negative_sample.png') +print("Finished Training Siamese Network") -# --------- Visualize Embeddings --------- +# --------- Visualize Embeddings using t-SNE --------- # Extract embeddings and labels all_embeddings = [] all_labels = [] @@ -173,3 +174,24 @@ def save_image(img, filename): plt.title('2D t-SNE of Embeddings') plt.savefig('embeddings_tsne.png') +# --------- Extract Embeddings for the Entire Dataset --------- +train_embeddings = [] +train_labels = [] +test_embeddings = [] +test_labels = [] + +with torch.no_grad(): + for anchor, _, _, label in train_loader: + anchor = anchor.to(device) + embedding, _ = model(anchor, anchor) + train_embeddings.append(embedding.cpu().numpy()) + train_labels.extend(label.tolist()) + + for anchor, _, _, label in test_loader: + anchor = anchor.to(device) + embedding, _ = model(anchor, anchor) + test_embeddings.append(embedding.cpu().numpy()) + test_labels.extend(label.tolist()) + +train_embeddings = np.concatenate(train_embeddings) +test_embeddings = np.concatenate(test_embeddings) From 0cc7e8d70b0dfb0a80eaa4d8386a6eb80052a911 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 18:52:18 +1000 Subject: [PATCH 22/49] Modified dataset.py to include labels, allowing for easier extraction and use of labels when processing embeddings post Siamese network training. --- recognition/alzheimers_snn_s4647936/dataset.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index d802a2d1f0..f08cf55792 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -46,7 +46,7 @@ def __init__(self, root_dir, mode='train', transform=None, split_ratio=0.8): transforms.RandomVerticalFlip(), transform ]) - + elif mode == 'test': self.ad_paths = test_ad_paths self.nc_paths = test_nc_paths @@ -121,3 +121,4 @@ def patient_wise_split(ad_paths, nc_paths, split_ratio=0.8): test_nc_paths = [path for path in nc_paths if os.path.basename(path).split('_')[0] in test_nc_ids] return train_ad_paths, test_ad_paths, train_nc_paths, test_nc_paths + From 6a77f09365cbecd54b66d04a8492f7d01b521956 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 18:57:01 +1000 Subject: [PATCH 23/49] Added SimpleClassifier class in the neural network module to facilitate the classification of Siamese network embeddings into AD or NC categories --- recognition/alzheimers_snn_s4647936/modules.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index e3409f2a12..e5b24e1c0f 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -83,3 +83,14 @@ def forward(self, anchor, positive, negative): distance_negative = (anchor - negative).pow(2).sum(1) # Euclidean distance losses = nn.functional.relu(distance_positive - distance_negative + self.margin) return losses.mean() + +class SimpleClassifier(nn.Module): + def __init__(self): + super(SimpleClassifier, self).__init__() + self.fc1 = nn.Linear(2, 64) # Embedding size is 2 + self.fc2 = nn.Linear(64, 2) # Output is 2 (AD or NC) + + def forward(self, x): + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x From 3e7fc30ee30c045b7f7e224ae49dc6efe8bdbfd7 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 19:02:10 +1000 Subject: [PATCH 24/49] Implemented the training loop for the Simple Classifier using extracted embeddings from the Siamese Network. Utilized the Adam optimizer and CrossEntropyLoss for training. --- recognition/alzheimers_snn_s4647936/train.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index f3bb5c025e..566e173a4b 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -2,11 +2,13 @@ import torch import matplotlib.pyplot as plt from dataset import TripletDataset -from modules import SiameseNetwork, TripletLoss +from modules import SiameseNetwork, TripletLoss, SimpleClassifier from torchvision import transforms import torch.optim as optim from sklearn.manifold import TSNE import numpy as np +import torch.nn as nn + # Transformations for images transform = transforms.Compose([ @@ -195,3 +197,17 @@ def save_image(img, filename): train_embeddings = np.concatenate(train_embeddings) test_embeddings = np.concatenate(test_embeddings) + +# --------- Train Simple Classifier --------- +classifier = SimpleClassifier().to(device) +criterion = nn.CrossEntropyLoss() +optimizer = optim.Adam(classifier.parameters(), lr=0.001) + +for epoch in range(num_epochs): + # Train with embeddings + for embeddings, labels in zip(train_embeddings, train_labels): + optimizer.zero_grad() + outputs = classifier(torch.tensor(embeddings).to(device)) + loss = criterion(outputs, torch.tensor(labels).to(device)) + loss.backward() + optimizer.step() From f0ba8da83d7df5e2953080a52448b8cb4f4d6396 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 19:04:52 +1000 Subject: [PATCH 25/49] Added evaluation phase for the Simple Classifier using the test embeddings. The accuracy metric is computed to assess the classifier's performance on the test set. --- recognition/alzheimers_snn_s4647936/train.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 566e173a4b..9da64954a5 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -211,3 +211,15 @@ def save_image(img, filename): loss = criterion(outputs, torch.tensor(labels).to(device)) loss.backward() optimizer.step() + +# --------- Evaluate Classifier --------- +correct = 0 +total = 0 +with torch.no_grad(): + for embeddings, labels in zip(test_embeddings, test_labels): + outputs = classifier(torch.tensor(embeddings).to(device)) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == torch.tensor(labels).to(device)).sum().item() + +print(f"Accuracy of the classifier on test embeddings: {100 * correct / total}%") From 5f9c135a959e28032bc75703a851fcd0624df9ec Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 20:05:14 +1000 Subject: [PATCH 26/49] Changed labels to detect if AD or NC in it's path. --- recognition/alzheimers_snn_s4647936/dataset.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index f08cf55792..8b0b7118e4 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -83,7 +83,10 @@ def __getitem__(self, idx): positive_image = self.transform(positive_image) negative_image = self.transform(negative_image) - return anchor_image, positive_image, negative_image + # Decide label based on anchor image path + label = 0 if "AD" in anchor_path else 1 + + return anchor_image, positive_image, negative_image, label def patient_wise_split(ad_paths, nc_paths, split_ratio=0.8): From 2afb4e3e07d9a685b1215f8b3d177c5db1fd0670 Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 20:30:12 +1000 Subject: [PATCH 27/49] Added method plot_confusion_matrix to plot confusion matrix so can visualise. --- recognition/alzheimers_snn_s4647936/train.py | 28 +++++++++++++------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 9da64954a5..204b1afc1e 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -8,7 +8,19 @@ from sklearn.manifold import TSNE import numpy as np import torch.nn as nn - +import seaborn as sns +from sklearn.metrics import confusion_matrix + +def plot_confusion_matrix(y_true, y_pred, classes, output_filename): + cm = confusion_matrix(y_true, y_pred) + fig, ax = plt.subplots(figsize=(5, 5)) + sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', cbar=False, ax=ax) + ax.set_xlabel('Predicted labels') + ax.set_ylabel('True labels') + ax.set_title('Confusion Matrix') + ax.xaxis.set_ticklabels(classes) + ax.yaxis.set_ticklabels(classes) + plt.savefig(output_filename) # Transformations for images transform = transforms.Compose([ @@ -152,16 +164,14 @@ def save_image(img, filename): # Assuming you have two classes: AD and NC. Let's assign them numeric labels. # AD: 0, NC: 1 with torch.no_grad(): - for idx, (anchor, _, _) in enumerate(train_loader): + for (anchor, _, _), label in train_loader: anchor = anchor.to(device) embedding, _ = model(anchor, anchor) all_embeddings.append(embedding.cpu().numpy()) - - # Assign labels based on paths. You can adjust this based on your dataset setup. - if idx < len(train_dataset.ad_paths): - all_labels.extend([0] * anchor.size(0)) # AD - else: - all_labels.extend([1] * anchor.size(0)) # NC + all_labels.extend(label.tolist()) + +print(f"Number of AD labels: {all_labels.count(0)}") +print(f"Number of NC labels: {all_labels.count(1)}") all_embeddings = np.concatenate(all_embeddings) @@ -211,7 +221,7 @@ def save_image(img, filename): loss = criterion(outputs, torch.tensor(labels).to(device)) loss.backward() optimizer.step() - + # --------- Evaluate Classifier --------- correct = 0 total = 0 From 30108541ce817142f2c699657d6d5498570750dd Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 20:58:42 +1000 Subject: [PATCH 28/49] Changed train.py so code can print and out plot of train vs validation losses. --- recognition/alzheimers_snn_s4647936/train.py | 77 +++++++++----------- 1 file changed, 35 insertions(+), 42 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 204b1afc1e..7d689192fb 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -49,10 +49,13 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): # DataLoader setup train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) +test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) -# Training loop -losses = [] +# Lists to store training and validation losses +training_losses = [] +validation_losses = [] +# Training loop for Siamese Network for epoch in range(num_epochs): model.train() running_loss = 0.0 @@ -72,45 +75,31 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): running_loss += loss.item() + # Calculate average training loss for the epoch epoch_loss = running_loss / len(train_loader) - losses.append(epoch_loss) - print(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}") + training_losses.append(epoch_loss) + + # Validation step + model.eval() # set the model to evaluation mode + val_running_loss = 0.0 + with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations + for val_anchor, val_positive, val_negative in test_loader: + val_anchor, val_positive, val_negative = val_anchor.to(device), val_positive.to(device), val_negative.to(device) + val_anchor_out, val_positive_out = model(val_anchor, val_positive) + _, val_negative_out = model(val_anchor, val_negative) + val_loss = criterion(val_anchor_out, val_positive_out, val_negative_out) + val_running_loss += val_loss.item() + + # Calculate average validation loss for the epoch + val_epoch_loss = val_running_loss / len(test_loader) + validation_losses.append(val_epoch_loss) + print(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {epoch_loss:.4f}, Validation Loss: {val_epoch_loss:.4f}") -print("Finished Training") +print("Finished Training Siamese Network") -# Save the model's parameters +# After training, save the Siamese Network model weights torch.save(model.state_dict(), "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/siamese_model.pth") - -# --------- Begin Validation/Testing --------- -model.eval() # set the model to evaluation mode -correct = 0 -total = 0 - -# DataLoader setup for test dataset -test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) - -with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations - for batch_idx, (anchor, positive, negative) in enumerate(test_loader): - anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) - - # Forward pass - anchor_out, positive_out = model(anchor, positive) - _, negative_out = model(anchor, negative) - - # Compute triplet loss - loss = criterion(anchor_out, positive_out, negative_out) - - # You might want to add some logic here to determine "correctness", depending on how you define it for your problem - # For instance, if the distance between anchor and positive is less than between anchor and negative, consider it "correct" - positive_distance = torch.dist(anchor_out, positive_out) - negative_distance = torch.dist(anchor_out, negative_out) - - if positive_distance < negative_distance: - correct += 1 - total += 1 - -print(f"Accuracy on test set: {100 * correct / total}%") -# --------- End Validation/Testing --------- +print("Saved Siamese Network model weights") """ Save and visualise results @@ -121,11 +110,13 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): # Save the loss curve plt.figure() -plt.plot(losses) +plt.plot(training_losses, label='Training Loss') +plt.plot(validation_losses, label='Validation Loss') plt.xlabel('Epochs') plt.ylabel('Loss') -plt.title('Training Loss Curve') -plt.savefig('loss_curve.png') +plt.title('Siamese Network Training vs Validation Loss') +plt.legend() +plt.savefig('siamese_train_vs_val_loss.png') # Function to save images def save_image(img, filename): @@ -154,8 +145,6 @@ def save_image(img, filename): save_image(positive, 'positive_sample.png') save_image(negative, 'negative_sample.png') -print("Finished Training Siamese Network") - # --------- Visualize Embeddings using t-SNE --------- # Extract embeddings and labels all_embeddings = [] @@ -222,6 +211,10 @@ def save_image(img, filename): loss.backward() optimizer.step() +# After training, save the classifier model weights +torch.save(classifier.state_dict(), "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/classifier_model.pth") +print("Saved classifier model weights") + # --------- Evaluate Classifier --------- correct = 0 total = 0 From 879bc086260ca3c3b8e910ddcac7919d7f3005cf Mon Sep 17 00:00:00 2001 From: Vicky Date: Mon, 23 Oct 2023 21:21:22 +1000 Subject: [PATCH 29/49] Added tracking and visualization for the classifier's training and validation losses. This enhancement aids in monitoring the classifier's performance across epochs. --- recognition/alzheimers_snn_s4647936/train.py | 38 +++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 7d689192fb..b6f62006bd 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -59,7 +59,8 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): for epoch in range(num_epochs): model.train() running_loss = 0.0 - for batch_idx, (anchor, positive, negative) in enumerate(train_loader): + + for batch_idx, (anchor, positive, negative, labels) in enumerate(train_loader): anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) # Zero the parameter gradients @@ -202,19 +203,54 @@ def save_image(img, filename): criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(classifier.parameters(), lr=0.001) +classifier_training_losses = [] +classifier_validation_losses = [] + for epoch in range(num_epochs): # Train with embeddings + running_loss = 0.0 + for embeddings, labels in zip(train_embeddings, train_labels): optimizer.zero_grad() outputs = classifier(torch.tensor(embeddings).to(device)) loss = criterion(outputs, torch.tensor(labels).to(device)) loss.backward() optimizer.step() + running_loss += loss.item() + + # Average training loss for the epoch + epoch_loss = running_loss / len(train_labels) + classifier_training_losses.append(epoch_loss) + + # Validation loss + val_running_loss = 0.0 + with torch.no_grad(): + for embeddings, labels in zip(test_embeddings, test_labels): + outputs = classifier(torch.tensor(embeddings).to(device)) + val_loss = criterion(outputs, torch.tensor(labels).to(device)) + val_running_loss += val_loss.item() + + # Average validation loss for the epoch + val_epoch_loss = val_running_loss / len(test_labels) + classifier_validation_losses.append(val_epoch_loss) + + print(f"Classifier Epoch {epoch+1}/{num_epochs}, Training Loss: {epoch_loss:.4f}, Validation Loss: {val_epoch_loss:.4f}") + # After training, save the classifier model weights torch.save(classifier.state_dict(), "/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/classifier_model.pth") print("Saved classifier model weights") +# Plotting the classifier training vs validation losses +plt.figure() +plt.plot(classifier_training_losses, label='Classifier Training Loss') +plt.plot(classifier_validation_losses, label='Classifier Validation Loss') +plt.xlabel('Epochs') +plt.ylabel('Loss') +plt.title('Classifier Training vs Validation Loss') +plt.legend() +plt.savefig('classifier_train_vs_val_loss.png') + # --------- Evaluate Classifier --------- correct = 0 total = 0 From 5f66d818afcfa97a9b38ff64adae3ae235ab923a Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 24 Oct 2023 01:31:38 +1000 Subject: [PATCH 30/49] Changed code to fix issue of tsne graph displaying only one colour --- recognition/alzheimers_snn_s4647936/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index b6f62006bd..8e847c0a54 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -34,7 +34,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): # Parameters learning_rate = 0.001 -num_epochs = 10 +num_epochs = 30 batch_size = 32 # GPU availability @@ -84,7 +84,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): model.eval() # set the model to evaluation mode val_running_loss = 0.0 with torch.no_grad(): # deactivate autograd engine to reduce memory usage and speed up computations - for val_anchor, val_positive, val_negative in test_loader: + for val_anchor, val_positive, val_negative, _ in test_loader: val_anchor, val_positive, val_negative = val_anchor.to(device), val_positive.to(device), val_negative.to(device) val_anchor_out, val_positive_out = model(val_anchor, val_positive) _, val_negative_out = model(val_anchor, val_negative) @@ -154,7 +154,7 @@ def save_image(img, filename): # Assuming you have two classes: AD and NC. Let's assign them numeric labels. # AD: 0, NC: 1 with torch.no_grad(): - for (anchor, _, _), label in train_loader: + for anchor, _, _, label in train_loader: anchor = anchor.to(device) embedding, _ = model(anchor, anchor) all_embeddings.append(embedding.cpu().numpy()) From 6f2924f01c8c9316113d49971c29a9b48512e9cc Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 24 Oct 2023 01:37:35 +1000 Subject: [PATCH 31/49] Add code for SNN to be stopped early if validation loss doesn't improve for 5 consecutive epochs). --- recognition/alzheimers_snn_s4647936/train.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 8e847c0a54..6dcf73aeb9 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -55,6 +55,12 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): training_losses = [] validation_losses = [] +# Early stopping parameters +patience = 5 +best_val_loss = float('inf') +epochs_without_improvement = 0 + + # Training loop for Siamese Network for epoch in range(num_epochs): model.train() @@ -94,6 +100,17 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): # Calculate average validation loss for the epoch val_epoch_loss = val_running_loss / len(test_loader) validation_losses.append(val_epoch_loss) + + # Early stopping logic + if val_epoch_loss < best_val_loss: + best_val_loss = val_epoch_loss + epochs_without_improvement = 0 + else: + epochs_without_improvement += 1 + if epochs_without_improvement == patience: + print("Early stopping due to no validation loss improvement.") + break + print(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {epoch_loss:.4f}, Validation Loss: {val_epoch_loss:.4f}") print("Finished Training Siamese Network") From c4a48101138ab607ae00b5c7607ac5733a73a2c8 Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 24 Oct 2023 22:08:21 +1000 Subject: [PATCH 32/49] Added changed to train.py to resolve issue of incorrect indexing within classification evaluation. --- recognition/alzheimers_snn_s4647936/train.py | 42 ++++++++++++++------ 1 file changed, 29 insertions(+), 13 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 6dcf73aeb9..b6eca8a416 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -10,8 +10,14 @@ import torch.nn as nn import seaborn as sns from sklearn.metrics import confusion_matrix +import datetime + +# Generate a unique filename with a timestamp +current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + +def plot_confusion_matrix(y_true, y_pred, classes, base_filename): + output_filename = f"{base_filename}_{current_time}.png" -def plot_confusion_matrix(y_true, y_pred, classes, output_filename): cm = confusion_matrix(y_true, y_pred) fig, ax = plt.subplots(figsize=(5, 5)) sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', cbar=False, ax=ax) @@ -134,10 +140,15 @@ def plot_confusion_matrix(y_true, y_pred, classes, output_filename): plt.ylabel('Loss') plt.title('Siamese Network Training vs Validation Loss') plt.legend() -plt.savefig('siamese_train_vs_val_loss.png') +losses_file = f'siamese_train_vs_val_loss_{current_time}.png' +plt.savefig(losses_file) # Function to save images -def save_image(img, filename): +def save_image(img, base_filename): + # Generate a unique filename with a timestamp + current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + filename = f"{base_filename}_{current_time}.png" + # Select the first image from the batch img = img[0] @@ -187,11 +198,12 @@ def save_image(img, filename): embeddings_2d = tsne.fit_transform(all_embeddings) # Plot +current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') plt.figure(figsize=(10, 7)) plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=all_labels, cmap='jet', alpha=0.5, edgecolors='w', s=40) plt.colorbar() plt.title('2D t-SNE of Embeddings') -plt.savefig('embeddings_tsne.png') +plt.savefig(f'embeddings_tsne_{current_time}.png') # --------- Extract Embeddings for the Entire Dataset --------- train_embeddings = [] @@ -244,6 +256,10 @@ def save_image(img, filename): with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): outputs = classifier(torch.tensor(embeddings).to(device)) + + # Print the shape of the outputs tensor + print("Outputs shape:", outputs.shape) + val_loss = criterion(outputs, torch.tensor(labels).to(device)) val_running_loss += val_loss.item() @@ -266,16 +282,16 @@ def save_image(img, filename): plt.ylabel('Loss') plt.title('Classifier Training vs Validation Loss') plt.legend() -plt.savefig('classifier_train_vs_val_loss.png') +plt.savefig(f'classifier_train_vs_val_loss_{current_time}.png') # --------- Evaluate Classifier --------- -correct = 0 -total = 0 -with torch.no_grad(): - for embeddings, labels in zip(test_embeddings, test_labels): - outputs = classifier(torch.tensor(embeddings).to(device)) - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == torch.tensor(labels).to(device)).sum().item() +test_embeddings_tensor = torch.tensor(test_embeddings).to(device) +test_labels_tensor = torch.tensor(test_labels).to(device) + +outputs = classifier(test_embeddings_tensor) +_, predicted = torch.max(outputs, 1) + +correct = (predicted == test_labels_tensor).sum().item() +total = test_labels_tensor.size(0) print(f"Accuracy of the classifier on test embeddings: {100 * correct / total}%") From 2b5d7220778d9b569344474cf19b499085d1c75e Mon Sep 17 00:00:00 2001 From: Vicky Date: Tue, 24 Oct 2023 22:10:23 +1000 Subject: [PATCH 33/49] Changed train.py code to save plot of siamese training vs losses. --- recognition/alzheimers_snn_s4647936/train.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index b6eca8a416..01c58777a5 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -140,6 +140,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): plt.ylabel('Loss') plt.title('Siamese Network Training vs Validation Loss') plt.legend() +current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') losses_file = f'siamese_train_vs_val_loss_{current_time}.png' plt.savefig(losses_file) From a9e5b9145b482347cdb0a770c16603730dbebb63 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 10:16:00 +1000 Subject: [PATCH 34/49] Solved issues with pconfusion matrix plot not showing and issue with incorrect embedding sizing. --- recognition/alzheimers_snn_s4647936/train.py | 39 ++++++++++++++++---- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 01c58777a5..9115a84137 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -15,9 +15,11 @@ # Generate a unique filename with a timestamp current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') -def plot_confusion_matrix(y_true, y_pred, classes, base_filename): - output_filename = f"{base_filename}_{current_time}.png" +def get_unique_filename(base_filename): + return f"{base_filename}_{current_time}.png" +def plot_confusion_matrix(y_true, y_pred, classes, base_filename): + output_filename = get_unique_filename(base_filename) cm = confusion_matrix(y_true, y_pred) fig, ax = plt.subplots(figsize=(5, 5)) sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', cbar=False, ax=ax) @@ -168,8 +170,6 @@ def save_image(img, base_filename): plt.axis('off') # Hide axes plt.savefig(filename) - - # Save sample images after training save_image(anchor, 'anchor_sample.png') save_image(positive, 'positive_sample.png') @@ -257,10 +257,6 @@ def save_image(img, base_filename): with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): outputs = classifier(torch.tensor(embeddings).to(device)) - - # Print the shape of the outputs tensor - print("Outputs shape:", outputs.shape) - val_loss = criterion(outputs, torch.tensor(labels).to(device)) val_running_loss += val_loss.item() @@ -285,6 +281,29 @@ def save_image(img, base_filename): plt.legend() plt.savefig(f'classifier_train_vs_val_loss_{current_time}.png') +# --------- Extract embeddings for visualization after classifier --------- +all_classifier_embeddings = [] +all_labels = [] + +with torch.no_grad(): + for embeddings, labels in zip(test_embeddings, test_labels): + outputs = classifier(torch.tensor(embeddings).to(device)) + all_classifier_embeddings.append(outputs.cpu().numpy()) + all_labels.append(labels) + +all_classifier_embeddings = all_classifier_embeddings.reshape(-1, all_classifier_embeddings.shape[-1]) + +# Reduce dimensionality using t-SNE +tsne = TSNE(n_components=2, random_state=42) +embeddings_2d = tsne.fit_transform(all_classifier_embeddings) + +# Plot +plt.figure(figsize=(10, 7)) +plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=all_labels, cmap='jet', alpha=0.5, edgecolors='w', s=40) +plt.colorbar() +plt.title('2D t-SNE of Classifier Embeddings') +plt.savefig(get_unique_filename('classifier_embeddings_tsne')) + # --------- Evaluate Classifier --------- test_embeddings_tensor = torch.tensor(test_embeddings).to(device) test_labels_tensor = torch.tensor(test_labels).to(device) @@ -292,6 +311,10 @@ def save_image(img, base_filename): outputs = classifier(test_embeddings_tensor) _, predicted = torch.max(outputs, 1) +# Plot confusion matrix for the classifier +class_names = ["AD", "NC"] # Assuming AD is labeled as 0 and NC as 1 +plot_confusion_matrix(test_labels, predicted.cpu().numpy(), class_names, "classifier_confusion_matrix") + correct = (predicted == test_labels_tensor).sum().item() total = test_labels_tensor.size(0) From bc90ea32169aa0ab7166bd455d8861ad48b0d818 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 14:21:59 +1000 Subject: [PATCH 35/49] Introduced more convolutional layers and added dropout after each fc layer --- recognition/alzheimers_snn_s4647936/modules.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index e5b24e1c0f..fdc522aaa1 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -15,22 +15,30 @@ def __init__(self): # Define convolutional layers self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5) # assuming grayscale images self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5) + self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5) # Define fully connected layers (adjust based on input image size) - self.fc1 = nn.Linear(61*57*64, 256) # Adjust here based on image size - self.fc2 = nn.Linear(256, 2) # 2-dimensional output for simplicity + self.fc1 = nn.Linear(29*25*128, 512) + self.dropout1 = nn.Dropout(0.5) + self.fc2 = nn.Linear(512, 256) + self.dropout2 = nn.Dropout(0.5) + self.fc3 = nn.Linear(256, 2) def forward(self, x): # Apply the convolutional layers with ReLU and max pooling x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) x = F.max_pool2d(F.relu(self.conv2(x)), 2) # kernel size 2 + x = F.max_pool2d(F.relu(self.conv3(x)), 2) # Flatten the tensor x = x.view(x.size(0), -1) # Apply the fully connected layers with ReLU x = F.relu(self.fc1(x)) - x = self.fc2(x) + x = self.dropout1(x) + x = F.relu(self.fc2(x)) + x = self.dropout2(x) + x = self.fc3(x) return x From a11b7168b8230295e8c4358a96c90174c5faa553 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 14:23:07 +1000 Subject: [PATCH 36/49] Adjusted hyperparameters for learning rate and batch size. --- recognition/alzheimers_snn_s4647936/train.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 9115a84137..7d60d4d250 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -41,9 +41,9 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='test', transform=transform) # Parameters -learning_rate = 0.001 -num_epochs = 30 -batch_size = 32 +learning_rate = 0.0005 +num_epochs = 23 +batch_size = 16 # GPU availability device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -291,6 +291,7 @@ def save_image(img, base_filename): all_classifier_embeddings.append(outputs.cpu().numpy()) all_labels.append(labels) +all_classifier_embeddings = np.array(all_classifier_embeddings) all_classifier_embeddings = all_classifier_embeddings.reshape(-1, all_classifier_embeddings.shape[-1]) # Reduce dimensionality using t-SNE From ca94001c333aab19b1638825f4f3890cfeeef930 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 17:54:43 +1000 Subject: [PATCH 37/49] Updated README.md to fill out all of the uncompleted sections. --- recognition/alzheimers_snn_s4647936/README.md | 182 +++++++++++++++--- 1 file changed, 151 insertions(+), 31 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index 8d95647ac9..fcb64d1251 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -1,44 +1,164 @@ # Classifying Alzheimer's Disease using Siamese Networks -## 1. Introduction -Briefly describe the problem statement and the objective of the project. +## 📣 Introduction +The task was to "create a classifier based on the Siamese network to classify either Alzheimer’s disease (normal and AD) of the ADNI brain data set or classify the ISIC 2020 Kaggle Challenge data set (normal and melanoma) having an accuracy of around 0.8 on the test set." -## 2. Dataset -- **Description**: A brief overview of the ADNI brain dataset. -- **Link to Dataset**: (if publicly available) -- **Preprocessing Steps**: Describe any normalization, resizing, augmentation, etc., that was performed. +As such, this project aims to distinguish between Alzheimer's disease (AD) and normal (NC) brain images from the ADNI dataset. -## 3. Siamese Network Architecture -- **Overview**: Briefly explain the concept of Siamese networks. -- **Model Architecture**: Describe the architecture of the Siamese network used. Diagrams or flowcharts can be helpful. -- **Loss Function**: Mention the loss function used (e.g., contrastive loss). +## 🧠 Dataset +- **Description**: The ADNI dataset is split into 'training' and 'testing' sets. Each set has two categories: 'NC' (Normal Cognitive) and 'AD' (Alzheimer's Disease). Specifically, the training set includes 11120 'NC' and 10400 'AD' images. The test set contains 4460 'AD' and 4540 'NC' images. -## 4. Setup and Installation -List the dependencies and provide a step-by-step guide for setting up the project. -``` -pip install -r requirements.txt -``` + Images in the dataset are slices of a patient's brain. Each image is broken down into patient ID to represent which patient and also their corresponding brain slice. For example, an image like '388206_78' refers to the 78th slice of patient 388206's brain. + +

+ +

+ +

+ Figure 1: Brain scans of AD and NC patient slices of ADNI dataset +

+ +- **Dataset Link**: [ADNI Dataset](http://adni.loni.usc.edu/) (However, the provided preprocessed data from UQ Blackboard was used) +- **Preprocessing Steps**: Images were resized to 256x240 pixels and normalized. Data augmentations like rotations and flips were applied during training (further in-depth preprocessing detailed will be discussed in the **Data Preprocessing** section). + +## 🪄 Data Preprocessing + +The dataset preprocessing is aimed at generating triplets of images for training Siamese networks using triplet loss. The triplets consist of an anchor, a positive, and a negative image. + +### Dataset Structure + +The data is organised in a directory structure with separate subdirectories for Alzheimer's Disease (AD) and Normal Control (NC) images. -## 5. Usage -Provide clear instructions on how to run the code, train the model, and perform evaluations. ``` -python train.py -python evaluate.py +root_dir/ +│ +├── train/ +│ ├── AD/ +│ └── NC/ +│ +└── test/ +├── AD/ +└── NC/ ``` -## 6. Results -- **Training Results**: Mention metrics such as training loss, validation loss, etc. -- **Test Results**: Detail the accuracy achieved on the test set, and any other relevant metrics. -- **Visualizations**: If possible, include visual results, confusion matrices, or any graphs that might be relevant. +### Triplet Generation + +For each anchor image from either the AD or NC class: +- A positive image is selected from the same patient but a different slice. +- A negative image is then chosen from the opposite class. + +This ensures that the positive image is similar (as it's from the same patient), whereas the negative image is distinct. + +### Data Augmentation + +During training, data augmentation is applied to the images to introduce variability and improve generalisation. The following augmentations are applied: +- Random rotation of up to 10 degrees. +- Random horizontal flip. +- Random vertical flip. + +### Patient-wise Data Split + +The dataset is split patient-wise rather than image-wise. This ensures that all slices of a particular patient either belong to the training set or the testing set, preventing data leakage. + +### Dataset Implementation + +The `TripletDataset` class, a subclass of `torch.utils.data.Dataset`, facilitates the creation of triplets and data loading. The main components of this class include: + +- `__init__`: Initialises the dataset, loads image paths, applies transformations, and splits data patient-wise. +- `__len__`: Returns the total number of images (AD + NC). +- `__getitem__`: Given an index, it returns a triplet of images along with the label of the anchor image. + +The `patient_wise_split` function is a utility that splits the dataset based on unique patient IDs. It ensures that all images from a single patient are either in the training or testing set. + +## 🐱 Siamese Network Architecture + +### Overview +- **Siamese Networks**: These networks consist of twin networks which accept distinct inputs but are joined by an energy function at the top. This energy function computes a metric between the highest-level feature representation on each side. +- **Objective**: The Siamese network's goal is to differentiate between pairs of inputs. In this context, it's used to differentiate between different Alzheimer's Disease (AD) and Normal Control (NC) images. + +### Model Architecture + +#### Feature Extractor +- **Purpose**: This sub-network is responsible for extracting features from an input image. These features form the basis upon which differences or similarities between images are recognised. +- **Implementation**: + - Employs a Convolutional Neural Network (CNN) structure with added depth. + - The first convolutional layer expects grayscale images and outputs 32 channels with a 5x5 kernel. + - The subsequent convolutional layer takes these 32 channels as input, producing 64 channels with another 5x5 kernel. + - Two fully connected layers follow the convolutional layers. The first reduces the dimension to 256, while the final layer further reduces it to a 2-dimensional output for visualisation and analysis. + - ReLU activation functions and max-pooling operations are applied post-convolutional layers, with dropout layers introduced for regularisation. + + +#### Siamese Network +- **Composition**: The Siamese network uses two copies of the above-described Feature Extractor. Both images in a pair go through these identical subnetworks. The outputs from these twin networks are then used to compute the triplet loss. + +### Loss Function + +#### Triplet Loss +- **Purpose**: The Triplet Loss emphasises the relative distance between the anchor-positive pair and the anchor-negative pair. The objective is to ensure that the anchor and positive (both from the same class) are closer to each other in the embedding space than the anchor and negative (from different classes). +- **Implementation**: + - The Euclidean distance between the anchor and positive, as well as the anchor and negative, is computed. + - The difference between these distances, plus a margin, is the loss. The margin ensures a buffer between positive and negative pairs. + +

+ +

+ +

+ Figure 2: Triplet Loss Diagram [1] +

+ +### Additional Classifier +After the Siamese network has been trained, a simple classifier is built on top of the embeddings generated by the network. +- **Architecture**: + - Two-layer feedforward neural network. + - The first layer reduces the 2-dimensional embedding to 64 dimensions using a ReLU activation function. + - The second layer maps the 64 dimensions to 2 outputs, representing the AD and NC classes. + + +## Training and Evaluation + +### Training Procedure +- The Siamese network undergoes training for 30 epochs with a batch size of 16 for improved generalisation. +- An early stopping mechanism halts training if the validation loss doesn't improve over a specified number of epochs. +- The Adam optimizer is employed with a learning rate of 0.0005. + +### Evaluation and Visualisation +- The training and validation losses are plotted to visualise the model's performance over epochs. +- The embeddings generated by the Siamese network are visualized using t-SNE, a technique for dimensionality reduction. +- A simple classifier is trained on these embeddings to classify them into AD or NC categories. +- The classifier's performance is evaluated using a confusion matrix. + + +## 🛠️ Setup and Installation +Ensure you have Python 3.7+ installed. Then, set up a virtual environment (optional) and install the dependencies: + +`pip install -r requirements.txt` + +## 👨‍💻 Usage +To train the Siamese network and visualise the embeddings: + +`python train.py` + +To predict embeddings for a pair of sample images using a trained Siamese network, modify the `predict.py` script with the paths to your sample images and then run: + +python predict.py + + +## ✨ Results +- **Training Results**: Training and validation loss curves are saved for the Siamese network training process. +- **Test Results**: The trained Siamese network's embeddings are used to train a simple classifier, which is then evaluated on the test set. +- **Visualisations**: t-SNE visualizations of embeddings, loss curves for both Siamese network and classifier, and a confusion matrix for the classifier's performance are generated. + +## 🔮 Future Work & Improvements +1. Experiment with different architectures for the Feature Extractor. +2. Explore other distance metrics or loss functions. +3. Incorporate more sophisticated data augmentation techniques. + +## 📚 References + +[[1]: Triplet Loss — Advanced Intro](https://towardsdatascience.com/triplet-loss-advanced-intro-49a07b7d8905) -## 7. Future Work & Improvements -Mention any potential future work or improvements that could be made to the project. +Bromley, Jane, et al. "[Signature verification using a" Siamese" time delay neural network](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)." Advances in neural information processing systems. 1994. -## 8. References -List any references, papers, or resources that were used or are relevant to the project. -## 9. License -If applicable, specify the license under which the project is available (e.g., MIT License). -## 10. Acknowledgments -Credit any authors, papers, or open-source libraries that were crucial to the project. From 1af6bb3b6cdee69cc0b7299c4cbd5a3085867e01 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 18:02:07 +1000 Subject: [PATCH 38/49] Changed code to resolve dimension bugs after changed number of fc layers. --- recognition/alzheimers_snn_s4647936/modules.py | 4 ++-- recognition/alzheimers_snn_s4647936/train.py | 15 +++++++++++---- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/modules.py b/recognition/alzheimers_snn_s4647936/modules.py index fdc522aaa1..741b0494db 100644 --- a/recognition/alzheimers_snn_s4647936/modules.py +++ b/recognition/alzheimers_snn_s4647936/modules.py @@ -18,7 +18,7 @@ def __init__(self): self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5) # Define fully connected layers (adjust based on input image size) - self.fc1 = nn.Linear(29*25*128, 512) + self.fc1 = nn.Linear(28*26*128, 512) self.dropout1 = nn.Dropout(0.5) self.fc2 = nn.Linear(512, 256) self.dropout2 = nn.Dropout(0.5) @@ -31,7 +31,7 @@ def forward(self, x): x = F.max_pool2d(F.relu(self.conv3(x)), 2) # Flatten the tensor - x = x.view(x.size(0), -1) + x = x.view(x.size(0), 28*26*128) # Apply the fully connected layers with ReLU x = F.relu(self.fc1(x)) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 7d60d4d250..f5cee08955 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -57,7 +57,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): # DataLoader setup train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) -test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) +test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # Lists to store training and validation losses training_losses = [] @@ -236,13 +236,20 @@ def save_image(img, base_filename): classifier_training_losses = [] classifier_validation_losses = [] +# Convert embeddings list to tensor +train_embeddings_tensor = torch.tensor(train_embeddings).float().to(device) +test_embeddings_tensor = torch.tensor(test_embeddings).float().to(device) +train_labels_tensor = torch.tensor(train_labels).to(device) +test_labels_tensor = torch.tensor(test_labels).to(device) + + for epoch in range(num_epochs): # Train with embeddings running_loss = 0.0 for embeddings, labels in zip(train_embeddings, train_labels): optimizer.zero_grad() - outputs = classifier(torch.tensor(embeddings).to(device)) + outputs = classifier(embeddings) loss = criterion(outputs, torch.tensor(labels).to(device)) loss.backward() optimizer.step() @@ -256,7 +263,7 @@ def save_image(img, base_filename): val_running_loss = 0.0 with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): - outputs = classifier(torch.tensor(embeddings).to(device)) + outputs = classifier(embeddings) val_loss = criterion(outputs, torch.tensor(labels).to(device)) val_running_loss += val_loss.item() @@ -287,7 +294,7 @@ def save_image(img, base_filename): with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): - outputs = classifier(torch.tensor(embeddings).to(device)) + outputs = classifier(embeddings) all_classifier_embeddings.append(outputs.cpu().numpy()) all_labels.append(labels) From dbdaff357cc65a77525b52c475e6f11392014876 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 18:40:40 +1000 Subject: [PATCH 39/49] Updated README.d to include visualisations/plots produced during training and also added results summary. --- recognition/alzheimers_snn_s4647936/README.md | 46 +++++++++++++------ 1 file changed, 33 insertions(+), 13 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index fcb64d1251..bb7140ba0f 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -115,7 +115,7 @@ After the Siamese network has been trained, a simple classifier is built on top - The second layer maps the 64 dimensions to 2 outputs, representing the AD and NC classes. -## Training and Evaluation +## 🏋️ Training and Evaluation ### Training Procedure - The Siamese network undergoes training for 30 epochs with a batch size of 16 for improved generalisation. @@ -123,10 +123,31 @@ After the Siamese network has been trained, a simple classifier is built on top - The Adam optimizer is employed with a learning rate of 0.0005. ### Evaluation and Visualisation -- The training and validation losses are plotted to visualise the model's performance over epochs. -- The embeddings generated by the Siamese network are visualized using t-SNE, a technique for dimensionality reduction. -- A simple classifier is trained on these embeddings to classify them into AD or NC categories. -- The classifier's performance is evaluated using a confusion matrix. + +#### Siamese Network Training vs Validation Loss +![Siamese Network Training vs Validation Loss](./siamese_train_vs_val_loss_2023-10-25_13-32-59.png) + +Analysis: The plot above showcases the training and validation loss for the Siamese Network across epochs. The model seems to be converging, although there's some fluctuation in the validation loss, indicating potential overfitting. + +#### t-SNE Visualisation of Siamese Network Embeddings +![t-SNE Visualization of Siamese Network Embeddings](./embeddings_tsne_2023-10-25_13-39-24.png) + +Analysis: The t-SNE visualisation illustrates the 2D representation of the embeddings generated by the Siamese network. There appears to be some clustering, but there's also an overlap between AD and NC embeddings. + +#### Classifier Training vs Validation Loss +![Classifier Training vs Validation Loss](./classifier_train_vs_val_loss_2023-10-25_13-39-24.png) + +Analysis: This plot displays the training and validation loss for the classifier built on top of the Siamese network embeddings. The model seems to converge after initial epochs, but a similar fluctuation in validation loss is observed, suggesting overfitting. + +#### Confusion Matrix for Classifier Performance +![Confusion Matrix for Classifier Performance](./classifier_confusion_matrix_2023-10-25_13-39-24.png) + +Analysis: The confusion matrix reveals the classifier's performance on the test set. It's evident that the classifier struggles to differentiate between AD and NC samples consistently. + +#### t-SNE Visualisation of Classifier Embeddings +![t-SNE Visualisation of Classifier Embeddings](./classifier_embeddings_tsne_2023-10-25_13-39-24.png) + +Analysis: This depicted a very interesting plot. The t-SNE visualisation of the classifier embeddings suggests a more distinct separation between AD and NC categories compared to the original Siamese network embeddings. However, there's still a noticeable overlap. ## 🛠️ Setup and Installation @@ -143,16 +164,15 @@ To predict embeddings for a pair of sample images using a trained Siamese networ python predict.py - -## ✨ Results -- **Training Results**: Training and validation loss curves are saved for the Siamese network training process. -- **Test Results**: The trained Siamese network's embeddings are used to train a simple classifier, which is then evaluated on the test set. -- **Visualisations**: t-SNE visualizations of embeddings, loss curves for both Siamese network and classifier, and a confusion matrix for the classifier's performance are generated. +## ✨ Results Summary +While the Siamese Network and the subsequent classifier showed promising results during training, the classifier did not achieve the target accuracy of 0.8 on the test set. This is evident from the confusion matrix and the t-SNE visualisations. The models seem to struggle to find a clear boundary between the AD and NC categories. ## 🔮 Future Work & Improvements -1. Experiment with different architectures for the Feature Extractor. -2. Explore other distance metrics or loss functions. -3. Incorporate more sophisticated data augmentation techniques. +1. Experiment with different architectures for the Feature Extractor to achieve more discriminative embeddings. +2. Explore other distance metrics or loss functions that might provide a better separation between the categories. +3. Incorporate more sophisticated data augmentation techniques to improve model generalisation. +4. Investigate methods to reduce overfitting, possibly through more regularisation or employing a more sophisticated early stopping mechanism. +5. Given the target accuracy of 0.8 was not achieved, more extensive hyperparameter tuning and model validation strategies should be explored. ## 📚 References From 28c03b8bd86a3a24189e14f3f608ee69de20dcf4 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:04:58 +1000 Subject: [PATCH 40/49] Updated README.md to reflect updated dependencies. --- recognition/alzheimers_snn_s4647936/README.md | 27 ++++++++++++++----- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index bb7140ba0f..c4033f236a 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -124,36 +124,48 @@ After the Siamese network has been trained, a simple classifier is built on top ### Evaluation and Visualisation -#### Siamese Network Training vs Validation Loss ![Siamese Network Training vs Validation Loss](./siamese_train_vs_val_loss_2023-10-25_13-32-59.png) +#### *Figure 3: Siamese Network Training vs Validation Loss* Analysis: The plot above showcases the training and validation loss for the Siamese Network across epochs. The model seems to be converging, although there's some fluctuation in the validation loss, indicating potential overfitting. -#### t-SNE Visualisation of Siamese Network Embeddings -![t-SNE Visualization of Siamese Network Embeddings](./embeddings_tsne_2023-10-25_13-39-24.png) +![t-SNE Visualization of Siamese Network Embeddings](./embeddings_tsne_2023-10-25_13-39-24.png) +#### *Figure 4: t-SNE Visualisation of Siamese Network Embeddings* Analysis: The t-SNE visualisation illustrates the 2D representation of the embeddings generated by the Siamese network. There appears to be some clustering, but there's also an overlap between AD and NC embeddings. -#### Classifier Training vs Validation Loss + ![Classifier Training vs Validation Loss](./classifier_train_vs_val_loss_2023-10-25_13-39-24.png) +#### *Figure 5: Classifier Training vs Validation Loss* + Analysis: This plot displays the training and validation loss for the classifier built on top of the Siamese network embeddings. The model seems to converge after initial epochs, but a similar fluctuation in validation loss is observed, suggesting overfitting. -#### Confusion Matrix for Classifier Performance ![Confusion Matrix for Classifier Performance](./classifier_confusion_matrix_2023-10-25_13-39-24.png) +#### *Figure 6: Confusion Matrix for Classifier Performance* + Analysis: The confusion matrix reveals the classifier's performance on the test set. It's evident that the classifier struggles to differentiate between AD and NC samples consistently. -#### t-SNE Visualisation of Classifier Embeddings ![t-SNE Visualisation of Classifier Embeddings](./classifier_embeddings_tsne_2023-10-25_13-39-24.png) +#### Figure 7: t-SNE Visualisation of Classifier Embeddings + Analysis: This depicted a very interesting plot. The t-SNE visualisation of the classifier embeddings suggests a more distinct separation between AD and NC categories compared to the original Siamese network embeddings. However, there's still a noticeable overlap. ## 🛠️ Setup and Installation Ensure you have Python 3.7+ installed. Then, set up a virtual environment (optional) and install the dependencies: -`pip install -r requirements.txt` +| Dependency | Version | +| ------------ | ----------- | +| torch | 1.10.1+cu113 | +| torchvision | 0.11.2+cu113 | +| Pillow | 8.3.2 | +| matplotlib | 3.4.3 | +| seaborn | 0.11.2 | +| numpy | 1.21.3 | +| scikit-learn | 1.0.1 | ## 👨‍💻 Usage To train the Siamese network and visualise the embeddings: @@ -164,6 +176,7 @@ To predict embeddings for a pair of sample images using a trained Siamese networ python predict.py + ## ✨ Results Summary While the Siamese Network and the subsequent classifier showed promising results during training, the classifier did not achieve the target accuracy of 0.8 on the test set. This is evident from the confusion matrix and the t-SNE visualisations. The models seem to struggle to find a clear boundary between the AD and NC categories. From bad03a6b3aebe8830b7fbf03a9578003bc9c920d Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:08:20 +1000 Subject: [PATCH 41/49] Updated predict.py so that if user wants to visualise the model, they can now just run the file. Updated with saving all visualising plots. --- .../alzheimers_snn_s4647936/predict.py | 98 ++++++++++++++++--- 1 file changed, 82 insertions(+), 16 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/predict.py b/recognition/alzheimers_snn_s4647936/predict.py index 4777172c3f..0f5e672c74 100644 --- a/recognition/alzheimers_snn_s4647936/predict.py +++ b/recognition/alzheimers_snn_s4647936/predict.py @@ -1,24 +1,90 @@ import torch -from modules import SiameseNetwork +import numpy as np +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +from modules import SiameseNetwork, SimpleClassifier from torchvision import transforms -from PIL import Image +from dataset import TripletDataset +import seaborn as sns +from sklearn.metrics import confusion_matrix +import datetime -# Load the model -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -model = SiameseNetwork().to(device) -model.load_state_dict(torch.load("siamese_model.pth", map_location=device)) -model.eval() +# Generate a unique filename with a timestamp +current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + +def get_unique_filename(base_filename): + return f"{base_filename}_{current_time}.png" + +def plot_confusion_matrix(y_true, y_pred, classes, base_filename): + output_filename = get_unique_filename(base_filename) + cm = confusion_matrix(y_true, y_pred) + fig, ax = plt.subplots(figsize=(5, 5)) + sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', cbar=False, ax=ax) + ax.set_xlabel('Predicted labels') + ax.set_ylabel('True labels') + ax.set_title('Confusion Matrix') + ax.xaxis.set_ticklabels(classes) + ax.yaxis.set_ticklabels(classes) + plt.savefig(output_filename) -# Load a sample image (or two images for Siamese) +# Transformations for images transform = transforms.Compose([ transforms.Resize((256, 240)), transforms.ToTensor(), ]) -image1 = Image.open("path_to_sample_image_1.jpg") -image2 = Image.open("path_to_sample_image_2.jpg") -tensor1 = transform(image1).unsqueeze(0).to(device) -tensor2 = transform(image2).unsqueeze(0).to(device) - -# Predict -output1, output2 = model(tensor1, tensor2) -print(output1, output2) + +# Dataset instance for testing +test_dataset = TripletDataset(root_dir="/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/AD_NC", mode='test', transform=transform) +test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False) + +# GPU availability +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Load the trained Siamese Network +siamese_model = SiameseNetwork().to(device) +siamese_model.load_state_dict(torch.load("/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/siamese_model.pth", map_location=device)) +siamese_model.eval() + +# Load the trained Simple Classifier +classifier = SimpleClassifier().to(device) +classifier.load_state_dict(torch.load("/home/Student/s4647936/PatternAnalysis-2023/recognition/alzheimers_snn_s4647936/classifier_model.pth", map_location=device)) +classifier.eval() + +# Extract embeddings and labels for testing +test_embeddings = [] +test_labels = [] + +with torch.no_grad(): + for anchor, _, _, label in test_loader: + anchor = anchor.to(device) + embedding, _ = siamese_model(anchor, anchor) + test_embeddings.append(embedding.cpu().numpy()) + test_labels.extend(label.tolist()) + +test_embeddings = np.concatenate(test_embeddings) + +# Visualise the embeddings using t-SNE +tsne = TSNE(n_components=2, random_state=42) +embeddings_2d = tsne.fit_transform(test_embeddings) + +plt.figure(figsize=(10, 7)) +plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=test_labels, cmap='jet', alpha=0.5, edgecolors='w', s=40) +plt.colorbar() +plt.title('2D t-SNE of Test Embeddings') +plt.savefig(get_unique_filename('test_embeddings_tsne')) + +# Evaluate classifier on embeddings +test_embeddings_tensor = torch.tensor(test_embeddings).float().to(device) +test_labels_tensor = torch.tensor(test_labels).to(device) + +outputs = classifier(test_embeddings_tensor) +_, predicted = torch.max(outputs, 1) + +# Plot confusion matrix for the classifier +class_names = ["AD", "NC"] +plot_confusion_matrix(test_labels, predicted.cpu().numpy(), class_names, "test_classifier_confusion_matrix") + +correct = (predicted == test_labels_tensor).sum().item() +total = test_labels_tensor.size(0) + +print(f"Accuracy of the classifier on test embeddings: {100 * correct / total}%") From bf8a1a49ae47f0f8f9563642140c5e19ec5b751b Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:19:34 +1000 Subject: [PATCH 42/49] Updated README.md to detail a more in-depth setup guide and running for users. --- recognition/alzheimers_snn_s4647936/README.md | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index c4033f236a..17c780c1a9 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -168,13 +168,26 @@ Ensure you have Python 3.7+ installed. Then, set up a virtual environment (optio | scikit-learn | 1.0.1 | ## 👨‍💻 Usage -To train the Siamese network and visualise the embeddings: + +### Preparation: +Before proceeding, ensure you update the paths in the code files to point to the location where you have downloaded the dataset on your device. + +### 1. Train the Siamese Network: + +To train the Siamese network on the provided dataset and obtain embeddings for visualisation: `python train.py` -To predict embeddings for a pair of sample images using a trained Siamese network, modify the `predict.py` script with the paths to your sample images and then run: +This will train the Siamese network and save the trained model as `siamese_model.pt`. Visualisations such as the t-SNE plot for the Siamese network embeddings will also be generated. + +### 2. Predict with the Trained Models: + +To predict embeddings for a pair of sample images using the trained Siamese network, modify the `predict.py` script with the paths to your sample images and run: + +`python predict.py` + -python predict.py +This will generate embeddings for the provided images and predict their classes using the trained Siamese network. ## ✨ Results Summary From 53f5e2f720589430ba14480ed57636b725100f82 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:26:38 +1000 Subject: [PATCH 43/49] Updated train.py to resolve issue about numpy to tensor error. --- recognition/alzheimers_snn_s4647936/train.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index f5cee08955..7baf1f3db3 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -42,7 +42,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): # Parameters learning_rate = 0.0005 -num_epochs = 23 +num_epochs = 30 batch_size = 16 # GPU availability @@ -248,6 +248,7 @@ def save_image(img, base_filename): running_loss = 0.0 for embeddings, labels in zip(train_embeddings, train_labels): + embeddings_tensor = torch.tensor(embeddings).float().to(device) optimizer.zero_grad() outputs = classifier(embeddings) loss = criterion(outputs, torch.tensor(labels).to(device)) @@ -263,6 +264,7 @@ def save_image(img, base_filename): val_running_loss = 0.0 with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): + embeddings_tensor = torch.tensor(embeddings).float().to(device) outputs = classifier(embeddings) val_loss = criterion(outputs, torch.tensor(labels).to(device)) val_running_loss += val_loss.item() From 1fb431b507889976ce19f2fdef4fd5958838f00d Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:40:56 +1000 Subject: [PATCH 44/49] Updated references for README.md --- recognition/alzheimers_snn_s4647936/README.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index 17c780c1a9..13670158fd 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -202,9 +202,15 @@ While the Siamese Network and the subsequent classifier showed promising results ## 📚 References -[[1]: Triplet Loss — Advanced Intro](https://towardsdatascience.com/triplet-loss-advanced-intro-49a07b7d8905) +[1] [Triplet Loss — Advanced Intro](https://towardsdatascience.com/triplet-loss-advanced-intro-49a07b7d8905) -Bromley, Jane, et al. "[Signature verification using a" Siamese" time delay neural network](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)." Advances in neural information processing systems. 1994. +[2] Bromley, Jane, et al. "[Signature verification using a" Siamese" time delay neural network](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)." Advances in neural information processing systems. 1994. +[3] G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition," in ICML deep learning workshop, 2015, vol. 2, no. 1: Lille. +[4]: Mandal, S. (2023) *Power of Siamese Networks and Triplet Loss: Tackling Unbalanced Datasets*. Medium.com. +"https://medium.com/@mandalsouvik/power-of-siamese-networks-and-triplet-loss-tackling-unbalanced-datasets-ebb2bb6efdb1" + + +[5] R. Takahashi, T. Matsubara, and K. Uehara, "Data augmentation using random image cropping and patching for deep CNNs," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 9, pp. 2917-2931, 2019. From 5814d1bcb89010f2715d19a4ec6ee91a40bfb480 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:54:50 +1000 Subject: [PATCH 45/49] Removed unecessary print statements from train.py, modules.py and dataset.py. --- recognition/alzheimers_snn_s4647936/dataset.py | 6 ++++-- recognition/alzheimers_snn_s4647936/train.py | 10 +++------- 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/dataset.py b/recognition/alzheimers_snn_s4647936/dataset.py index 8b0b7118e4..47c1d4f52d 100644 --- a/recognition/alzheimers_snn_s4647936/dataset.py +++ b/recognition/alzheimers_snn_s4647936/dataset.py @@ -50,6 +50,9 @@ def __init__(self, root_dir, mode='train', transform=None, split_ratio=0.8): elif mode == 'test': self.ad_paths = test_ad_paths self.nc_paths = test_nc_paths + print("Sample AD paths:", self.ad_paths[:5]) + print("Sample NC paths:", self.nc_paths[:5]) + def __len__(self): return len(self.ad_paths) + len(self.nc_paths) # combined length @@ -73,7 +76,6 @@ def __getitem__(self, idx): # Choose a negative image from a different patient negative_path = random.choice([path for path in negative_paths if patient_id not in os.path.basename(path)]) - anchor_image = Image.open(anchor_path) positive_image = Image.open(positive_path) negative_image = Image.open(negative_path) @@ -84,7 +86,7 @@ def __getitem__(self, idx): negative_image = self.transform(negative_image) # Decide label based on anchor image path - label = 0 if "AD" in anchor_path else 1 + label = 0 if "/AD/" in anchor_path else 1 return anchor_image, positive_image, negative_image, label diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 7baf1f3db3..96bbb7de18 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -68,7 +68,6 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): best_val_loss = float('inf') epochs_without_improvement = 0 - # Training loop for Siamese Network for epoch in range(num_epochs): model.train() @@ -130,9 +129,6 @@ def plot_confusion_matrix(y_true, y_pred, classes, base_filename): """ Save and visualise results """ -# Test to see number of images -# print(len(train_dataset)) # 17200 -# print(len(test_dataset)) # 1820 # Save the loss curve plt.figure() @@ -171,9 +167,9 @@ def save_image(img, base_filename): plt.savefig(filename) # Save sample images after training -save_image(anchor, 'anchor_sample.png') -save_image(positive, 'positive_sample.png') -save_image(negative, 'negative_sample.png') +# save_image(anchor, 'anchor_sample.png') +# save_image(positive, 'positive_sample.png') +# save_image(negative, 'negative_sample.png') # --------- Visualize Embeddings using t-SNE --------- # Extract embeddings and labels From 8d8ad2539feae2b6ce1340f329bed82b13f60137 Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 21:58:50 +1000 Subject: [PATCH 46/49] Provide folder for 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zv?}j9AeA3ZY@6EOeZ9zh@g04)FC9%uiWfd5Bk_{Zg6`ad1W BCb$3q literal 0 HcmV?d00001 From 77b19d5b3e8b5730846f824fc20def832ce6a5c2 Mon Sep 17 00:00:00 2001 From: Vicky Tran <108254973+tranvicky@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:35:38 +1000 Subject: [PATCH 47/49] Update README.md to resolve issue of images only showing links instead of images and to display images properly. --- recognition/alzheimers_snn_s4647936/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index 13670158fd..b16c36da14 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -124,30 +124,30 @@ After the Siamese network has been trained, a simple classifier is built on top ### Evaluation and Visualisation -![Siamese Network Training vs Validation Loss](./siamese_train_vs_val_loss_2023-10-25_13-32-59.png) +![Siamese Network Training vs Validation Loss](./readme-images/siamese_train_vs_val_loss_2023-10-25_13-32-59.png) #### *Figure 3: Siamese Network Training vs Validation Loss* Analysis: The plot above showcases the training and validation loss for the Siamese Network across epochs. The model seems to be converging, although there's some fluctuation in the validation loss, indicating potential overfitting. -![t-SNE Visualization of Siamese Network Embeddings](./embeddings_tsne_2023-10-25_13-39-24.png) +![t-SNE Visualization of Siamese Network Embeddings](./readme-images/embeddings_tsne_2023-10-25_13-39-24.png) #### *Figure 4: t-SNE Visualisation of Siamese Network Embeddings* Analysis: The t-SNE visualisation illustrates the 2D representation of the embeddings generated by the Siamese network. There appears to be some clustering, but there's also an overlap between AD and NC embeddings. -![Classifier Training vs Validation Loss](./classifier_train_vs_val_loss_2023-10-25_13-39-24.png) +![Classifier Training vs Validation Loss](./readme-images/classifier_train_vs_val_loss_2023-10-25_13-39-24.png) #### *Figure 5: Classifier Training vs Validation Loss* Analysis: This plot displays the training and validation loss for the classifier built on top of the Siamese network embeddings. The model seems to converge after initial epochs, but a similar fluctuation in validation loss is observed, suggesting overfitting. -![Confusion Matrix for Classifier Performance](./classifier_confusion_matrix_2023-10-25_13-39-24.png) +![Confusion Matrix for Classifier Performance](./readme-images/classifier_confusion_matrix_2023-10-25_13-39-24.png) #### *Figure 6: Confusion Matrix for Classifier Performance* Analysis: The confusion matrix reveals the classifier's performance on the test set. It's evident that the classifier struggles to differentiate between AD and NC samples consistently. -![t-SNE Visualisation of Classifier Embeddings](./classifier_embeddings_tsne_2023-10-25_13-39-24.png) +![t-SNE Visualisation of Classifier Embeddings](./readme-images/classifier_embeddings_tsne_2023-10-25_13-39-24.png) #### Figure 7: t-SNE Visualisation of Classifier Embeddings From fb57496215cab802dee1a34e6416f6d5a51db370 Mon Sep 17 00:00:00 2001 From: Vicky Tran <108254973+tranvicky@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:39:30 +1000 Subject: [PATCH 48/49] Updated README.md and fixed image pathways. --- recognition/alzheimers_snn_s4647936/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/README.md b/recognition/alzheimers_snn_s4647936/README.md index b16c36da14..c2c738a73f 100644 --- a/recognition/alzheimers_snn_s4647936/README.md +++ b/recognition/alzheimers_snn_s4647936/README.md @@ -124,30 +124,30 @@ After the Siamese network has been trained, a simple classifier is built on top ### Evaluation and Visualisation -![Siamese Network Training vs Validation Loss](./readme-images/siamese_train_vs_val_loss_2023-10-25_13-32-59.png) +![Siamese Network Training vs Validation Loss](./readme-images/siamese_train_vs_val_loss.png) #### *Figure 3: Siamese Network Training vs Validation Loss* Analysis: The plot above showcases the training and validation loss for the Siamese Network across epochs. The model seems to be converging, although there's some fluctuation in the validation loss, indicating potential overfitting. -![t-SNE Visualization of Siamese Network Embeddings](./readme-images/embeddings_tsne_2023-10-25_13-39-24.png) +![t-SNE Visualization of Siamese Network Embeddings](./readme-images/embeddings_tsne.png) #### *Figure 4: t-SNE Visualisation of Siamese Network Embeddings* Analysis: The t-SNE visualisation illustrates the 2D representation of the embeddings generated by the Siamese network. There appears to be some clustering, but there's also an overlap between AD and NC embeddings. -![Classifier Training vs Validation Loss](./readme-images/classifier_train_vs_val_loss_2023-10-25_13-39-24.png) +![Classifier Training vs Validation Loss](./readme-images/classifier_train_vs_val_loss.png) #### *Figure 5: Classifier Training vs Validation Loss* Analysis: This plot displays the training and validation loss for the classifier built on top of the Siamese network embeddings. The model seems to converge after initial epochs, but a similar fluctuation in validation loss is observed, suggesting overfitting. -![Confusion Matrix for Classifier Performance](./readme-images/classifier_confusion_matrix_2023-10-25_13-39-24.png) +![Confusion Matrix for Classifier Performance](./readme-images/classifier_confusion_matrix.png) #### *Figure 6: Confusion Matrix for Classifier Performance* Analysis: The confusion matrix reveals the classifier's performance on the test set. It's evident that the classifier struggles to differentiate between AD and NC samples consistently. -![t-SNE Visualisation of Classifier Embeddings](./readme-images/classifier_embeddings_tsne_2023-10-25_13-39-24.png) +![t-SNE Visualisation of Classifier Embeddings](./readme-images/classifier_embeddings_tsne.png) #### Figure 7: t-SNE Visualisation of Classifier Embeddings From 6aee14a4b2949155fcc29af54ddbcaefc4638ecb Mon Sep 17 00:00:00 2001 From: Vicky Date: Wed, 25 Oct 2023 23:12:51 +1000 Subject: [PATCH 49/49] Changed variable outputs to resolve tensor error in train.py. --- recognition/alzheimers_snn_s4647936/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recognition/alzheimers_snn_s4647936/train.py b/recognition/alzheimers_snn_s4647936/train.py index 96bbb7de18..cfdb660593 100644 --- a/recognition/alzheimers_snn_s4647936/train.py +++ b/recognition/alzheimers_snn_s4647936/train.py @@ -246,7 +246,7 @@ def save_image(img, base_filename): for embeddings, labels in zip(train_embeddings, train_labels): embeddings_tensor = torch.tensor(embeddings).float().to(device) optimizer.zero_grad() - outputs = classifier(embeddings) + outputs = classifier(embeddings_tensor) loss = criterion(outputs, torch.tensor(labels).to(device)) loss.backward() optimizer.step() @@ -261,7 +261,7 @@ def save_image(img, base_filename): with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): embeddings_tensor = torch.tensor(embeddings).float().to(device) - outputs = classifier(embeddings) + outputs = classifier(embeddings_tensor) val_loss = criterion(outputs, torch.tensor(labels).to(device)) val_running_loss += val_loss.item() @@ -292,7 +292,7 @@ def save_image(img, base_filename): with torch.no_grad(): for embeddings, labels in zip(test_embeddings, test_labels): - outputs = classifier(embeddings) + outputs = classifier(embeddings_tensor) all_classifier_embeddings.append(outputs.cpu().numpy()) all_labels.append(labels)

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