From 63800d3ff72c14895f8a4ec5552e13509c0451f0 Mon Sep 17 00:00:00 2001 From: Richard Chantra Date: Wed, 2 Oct 2024 01:26:22 +1000 Subject: [PATCH 01/31] Added GFNet Alzheimer's classification folder and README --- recognition/GFNet_ADNI_Classification_RichardChantra/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/GFNet_ADNI_Classification_RichardChantra/README.md diff --git a/recognition/GFNet_ADNI_Classification_RichardChantra/README.md b/recognition/GFNet_ADNI_Classification_RichardChantra/README.md new file mode 100644 index 000000000..e5ff6ea6a --- /dev/null +++ b/recognition/GFNet_ADNI_Classification_RichardChantra/README.md @@ -0,0 +1 @@ +ADNI brain data classification using GFNet From 93a1094891d68e5c90491a5a7297b27b1fe47c75 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Fri, 18 Oct 2024 23:27:50 +1100 Subject: [PATCH 02/31] Changed project to siim-isic_melanoma_siamese --- recognition/GFNet_ADNI_Classification_RichardChantra/README.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/GFNet_ADNI_Classification_RichardChantra/README.md diff --git a/recognition/GFNet_ADNI_Classification_RichardChantra/README.md b/recognition/GFNet_ADNI_Classification_RichardChantra/README.md deleted file mode 100644 index e5ff6ea6a..000000000 --- a/recognition/GFNet_ADNI_Classification_RichardChantra/README.md +++ /dev/null @@ -1 +0,0 @@ -ADNI brain data classification using GFNet From 0fbc8815af6a6f4956fbcc44e9baa16af67c59b3 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 21 Oct 2024 20:21:57 +1100 Subject: [PATCH 03/31] Initial preprocessing data pipeline with metadata and train-image folder --- recognition/LICENSE | 201 ++++++++++++++++++ recognition/README.md | 19 ++ recognition/siamese_richard_chantra/README.MD | 0 .../siamese_richard_chantra/dataset.py | 37 ++++ .../siamese_richard_chantra/modules.py | 0 .../siamese_richard_chantra/predict.py | 0 recognition/siamese_richard_chantra/train.py | 0 7 files changed, 257 insertions(+) create mode 100644 recognition/LICENSE create mode 100644 recognition/README.md create mode 100644 recognition/siamese_richard_chantra/README.MD create mode 100644 recognition/siamese_richard_chantra/dataset.py create mode 100644 recognition/siamese_richard_chantra/modules.py create mode 100644 recognition/siamese_richard_chantra/predict.py create mode 100644 recognition/siamese_richard_chantra/train.py diff --git a/recognition/LICENSE b/recognition/LICENSE new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/recognition/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/recognition/README.md b/recognition/README.md new file mode 100644 index 000000000..3a10f6515 --- /dev/null +++ b/recognition/README.md @@ -0,0 +1,19 @@ +# Pattern Analysis +Pattern Analysis of various datasets by COMP3710 students in 2024 at the University of Queensland. + +We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. + +This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. + +The library includes the following implemented in Tensorflow: +* fractals +* recognition problems + +In the recognition folder, you will find many recognition problems solved including: +* segmentation +* classification +* graph neural networks +* StyleGAN +* Stable diffusion +* transformers +etc. diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py new file mode 100644 index 000000000..93f11ce88 --- /dev/null +++ b/recognition/siamese_richard_chantra/dataset.py @@ -0,0 +1,37 @@ +import pandas as pd +from PIL import Image +import torch +from sklearn.model_selection import train_test_split + +# File paths +csv_path = 'archive/train-metadata.csv' +img_dir = 'archive/train-image/image/' + +# Load metadata +data = pd.read_csv(csv_path) + +# Statistics +print(f"Total images: {len(data)}") +print(f"Classes distribution: \n{data['target'].value_counts()}") + +# Head of metadata +print("\nFirst few rows of metadata:") +print(data.head()) + +# Open and return image +def load_image(image_id): + img_path = f'{img_dir}{image_id}.jpg' + image = Image.open(img_path) + return image + +# Train test split +train_data, test_data = train_test_split(data, test_size=0.2, random_state=42, stratify=data['target']) + +# Size of splits +print(f"\nTraining set size: {len(train_data)}") +print(f"Testing set size: {len(test_data)}") + +# Loading image +sample_image_id = train_data.iloc[0]['isic_id'] +sample_image = load_image(sample_image_id) +sample_image.show() diff --git a/recognition/siamese_richard_chantra/modules.py b/recognition/siamese_richard_chantra/modules.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py new file mode 100644 index 000000000..e69de29bb From d24a528b1a582c25ef398a054e1bb15f7be62905 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 21 Oct 2024 21:10:14 +1100 Subject: [PATCH 04/31] Updated README.MD with report structure. Filled out the problem section --- recognition/siamese_richard_chantra/README.MD | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index e69de29bb..92e02b153 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -0,0 +1,29 @@ +# Classification of Melanoma using the ISIC 2020 Kaggle Challenge dataset + +Richard Chantra s43032053 + +### Problem Statement + +- Melanomas are responsible for 75% of skin cancer deaths, it is estimated around 7,000 people die annually from the disease. +- Using computer vision techniques we can improve diagnosis of melanoma by assisting dermatologists thus improving diagnostic accuracy. +- The data we are using comes from International Skin Imaging Collaboration (ISIC) and is referred to as the ISIC-2020 Dataset. +- It is the largest publicly available collection of dematologically-QC skin lesions. +- The task is to classify melanomas with 0.8 accuracy for the test set. +- We will be implementing a Siamese Network + +### Preprocessing + +### Siamese Networks + +- First implemented by Taigman et al., 2014. in DeepFace: Closing the Gap to Human-Level Performance in Face Verification + + +### Siamese Network Architecture + +### Results + +plot the loss + +### Dependencies + +### References \ No newline at end of file From 0dd3fd1933a33dcfb23afd7ae70024457d20eb6f Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 01:49:14 +1100 Subject: [PATCH 05/31] Added Siamese Network with Contrastive Loss and training loop to train.py --- .../siamese_richard_chantra/dataset.py | 25 +++++- recognition/siamese_richard_chantra/train.py | 84 +++++++++++++++++++ 2 files changed, 108 insertions(+), 1 deletion(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 93f11ce88..69934944d 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -1,6 +1,7 @@ import pandas as pd from PIL import Image import torch +from torch.utils.data import DataLoader, Dataset from sklearn.model_selection import train_test_split # File paths @@ -31,7 +32,29 @@ def load_image(image_id): print(f"\nTraining set size: {len(train_data)}") print(f"Testing set size: {len(test_data)}") -# Loading image +# Dataset class for DataLoader +class ImageDataset(Dataset): + def __init__(self, data, img_dir): + self.data = data + self.img_dir = img_dir + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + row = self.data.iloc[idx] + image = load_image(row['isic_id']) + label = torch.tensor(row['target'], dtype=torch.float32) + return image, label + +# Create DataLoader +train_dataset = ImageDataset(train_data, img_dir) +test_dataset = ImageDataset(test_data, img_dir) + +train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) +test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) + +# Loading Image sample_image_id = train_data.iloc[0]['isic_id'] sample_image = load_image(sample_image_id) sample_image.show() diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index e69de29bb..c3cdd245c 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -0,0 +1,84 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from dataset import train_loader, test_loader +from tqdm import tqdm +import math + +# Siamese Network Architecture +class SiameseNetwork(nn.Module): + def __init__(self): + super(SiameseNetwork, self).__init__() + + # Convolutional Layer + self.cnn = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, padding=1), # First conv layer: 3 input channels, 64 output channels + nn.ReLU(), # Non-linearity + nn.MaxPool2d(2), # Reduce spatial dimensions + nn.Conv2d(64, 128, kernel_size=3, padding=1), # Second conv layer: 64 input channels, 128 output channels + nn.ReLU(), # Non-linearity + nn.MaxPool2d(2) # Reduce spatial dimensions + ) + + # Fully Connected Layer + self.fc = nn.Sequential( + nn.Linear(128 * 64 * 64, 256), # Flatten and reduce to 256 dimensions + nn.ReLU(), # Non-linearity + nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc + ) + + # Forward Pass + def forward(self, x1, x2): + # Process inputs through CNN + out1 = self.cnn(x1) + out2 = self.cnn(x2) + + # Flatten outputs + out1 = out1.view(out1.size(0), -1) + out2 = out2.view(out2.size(0), -1) + + # Generate embeddings + out1 = self.fc(out1) + out2 = self.fc(out2) + return out1, out2 + +# Contrastive Loss Function +def contrastive_loss(output1, output2, label, margin=1.0): + # Euclidean distance between the outputs + euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1)) + + # Contrastive loss function + # For similar pairs (label = 0), minimize distance + # For dissimilar pairs (label = 1), push distance to be greater than margin + loss = torch.mean((1 - label) * 0.5 * euclidean_distance ** 2 + + label * 0.5 * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) + return loss + +# Initialize model and optimizer +model = SiameseNetwork() +optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer + +# Training loop with contrastive loss +def train_siamese_network(model, train_loader, epochs=5, margin=1.0): + model.train() + + for epoch in range(epochs): + running_loss = 0.0 + model.train() + + for img1, img2, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}"): + # Convert data to float and reshape + img1, img2, labels = img1.float(), img2.float(), labels.unsqueeze(1).float() + + optimizer.zero_grad() # Reset gradients + output1, output2 = model(img1, img2) # Forward pass + loss = contrastive_loss(output1, output2, labels, margin=margin) # Compute loss + loss.backward() # Backprop + optimizer.step() # Update weights + + running_loss += loss.item() + + print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss / len(train_loader)}") + +# Train +train_siamese_network(model, train_loader, epochs=5, margin=1.0) From b1415d960f63caff807e6c19750cd78ac6cbb8a9 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 12:24:40 +1100 Subject: [PATCH 06/31] Added CUDA, checkpoint and logging support to predict.py as well as helper functions to predict in predict.py --- .../siamese_richard_chantra/predict.py | 26 ++++++++++++++ recognition/siamese_richard_chantra/train.py | 36 +++++++++++++++---- 2 files changed, 55 insertions(+), 7 deletions(-) diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index e69de29bb..dac222ed7 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -0,0 +1,26 @@ +import torch +from train import SiameseNetwork +from dataset import preprocess_image + +# Set device +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Initialize model +model = SiameseNetwork().to(device) + +# Load saved model +checkpoint = torch.load('best_model.pth') +model.load_state_dict(checkpoint['model_state_dict']) + +# Function to make a prediction +def predict_similarity(img1, img2, model): + model.eval() + with torch.no_grad(): + img1, img2 = img1.to(device), img2.to(device) + output1, output2 = model(img1, img2) + distance = torch.sqrt(torch.sum((output1 - output2) ** 2)) + return distance.item() + +# Example usage: +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index c3cdd245c..197c9eab3 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -15,7 +15,7 @@ def __init__(self): nn.Conv2d(3, 64, kernel_size=3, padding=1), # First conv layer: 3 input channels, 64 output channels nn.ReLU(), # Non-linearity nn.MaxPool2d(2), # Reduce spatial dimensions - nn.Conv2d(64, 128, kernel_size=3, padding=1), # Second conv layer: 64 input channels, 128 output channels + nn.Conv2d(64, 128, kernel_size=3, padding=1), # Second conv layer: 64 input channels, 128 output channels nn.ReLU(), # Non-linearity nn.MaxPool2d(2) # Reduce spatial dimensions ) @@ -54,13 +54,10 @@ def contrastive_loss(output1, output2, label, margin=1.0): label * 0.5 * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) return loss -# Initialize model and optimizer -model = SiameseNetwork() -optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer - # Training loop with contrastive loss def train_siamese_network(model, train_loader, epochs=5, margin=1.0): model.train() + best_loss = float('inf') for epoch in range(epochs): running_loss = 0.0 @@ -78,7 +75,32 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): running_loss += loss.item() + epoch_loss = running_loss / len(train_loader) + print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") + + # Save checkpoint if it's the best model so far + if epoch_loss < best_loss: + best_loss = epoch_loss + torch.save({ + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': best_loss, + }, 'best_model.pth') + + # Log epoch results + with open('siamese_training.txt', 'a') as f: + f.write(f"Epoch {epoch+1}, Loss: {epoch_loss}\n") + print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss / len(train_loader)}") -# Train -train_siamese_network(model, train_loader, epochs=5, margin=1.0) +if __name__ == "__main__": + # Set device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Initialize model and optimizer + model = SiameseNetwork().to(device) + optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer + + # Train model + train_siamese_network(model, train_loader, epochs=5, margin=1.0) From b3eb59b14b68add8af3511c9d05127823202b499 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 13:35:21 +1100 Subject: [PATCH 07/31] Replaced the convolutional layer with pretrained ResNet50 as a feature extractor --- recognition/siamese_richard_chantra/train.py | 55 +++++++++----------- 1 file changed, 25 insertions(+), 30 deletions(-) diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 197c9eab3..6ce39b631 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -4,34 +4,29 @@ from dataset import train_loader, test_loader from tqdm import tqdm import math +import torchvision.models as models # Siamese Network Architecture class SiameseNetwork(nn.Module): def __init__(self): super(SiameseNetwork, self).__init__() - # Convolutional Layer - self.cnn = nn.Sequential( - nn.Conv2d(3, 64, kernel_size=3, padding=1), # First conv layer: 3 input channels, 64 output channels - nn.ReLU(), # Non-linearity - nn.MaxPool2d(2), # Reduce spatial dimensions - nn.Conv2d(64, 128, kernel_size=3, padding=1), # Second conv layer: 64 input channels, 128 output channels - nn.ReLU(), # Non-linearity - nn.MaxPool2d(2) # Reduce spatial dimensions - ) + # ResNet50 Feature Extractor + resnet = models.resnet50(pretrained=True) + self.features = nn.Sequential(*list(resnet.children())[:-1]) # Fully Connected Layer self.fc = nn.Sequential( - nn.Linear(128 * 64 * 64, 256), # Flatten and reduce to 256 dimensions - nn.ReLU(), # Non-linearity + nn.Linear(2048, 256), # ResNet50 outputs 2048-dim feature vectors + nn.ReLU(), # Non-linearity nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc ) # Forward Pass def forward(self, x1, x2): - # Process inputs through CNN - out1 = self.cnn(x1) - out2 = self.cnn(x2) + # Process inputs through ResNet50 + out1 = self.features(x1) + out2 = self.features(x2) # Flatten outputs out1 = out1.view(out1.size(0), -1) @@ -75,24 +70,24 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): running_loss += loss.item() - epoch_loss = running_loss / len(train_loader) - print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") + epoch_loss = running_loss / len(train_loader) + print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") - # Save checkpoint if it's the best model so far - if epoch_loss < best_loss: - best_loss = epoch_loss - torch.save({ - 'epoch': epoch, - 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - 'loss': best_loss, - }, 'best_model.pth') + # Save checkpoint if it's the best model so far + if epoch_loss < best_loss: + best_loss = epoch_loss + torch.save({ + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': best_loss, + }, 'best_model.pth') - # Log epoch results - with open('siamese_training.txt', 'a') as f: - f.write(f"Epoch {epoch+1}, Loss: {epoch_loss}\n") + # Log epoch results + with open('siamese_training.txt', 'a') as f: + f.write(f"Epoch {epoch+1}, Loss: {epoch_loss}\n") - print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss / len(train_loader)}") + print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss / len(train_loader)}") if __name__ == "__main__": # Set device @@ -100,7 +95,7 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): # Initialize model and optimizer model = SiameseNetwork().to(device) - optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer + optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer # Train model train_siamese_network(model, train_loader, epochs=5, margin=1.0) From db2c954479a734aa974265941519652bec3793df Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 14:27:18 +1100 Subject: [PATCH 08/31] Preprocessing of images to support ResNet50 including resizing, convert to tensor and normalizing --- .../siamese_richard_chantra/dataset.py | 32 ++++++++++++++----- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 69934944d..5bff43d6c 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -3,6 +3,7 @@ import torch from torch.utils.data import DataLoader, Dataset from sklearn.model_selection import train_test_split +import torchvision.transforms as transforms # File paths csv_path = 'archive/train-metadata.csv' @@ -19,11 +20,22 @@ print("\nFirst few rows of metadata:") print(data.head()) -# Open and return image +# Define preprocessing transform for ResNet50 +def preprocess_image(image): + """Preprocess image for ResNet50 input""" + transform = transforms.Compose([ + transforms.Resize((224, 224)), # ResNet50 expected input size + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) # ImageNet normalization + ]) + return transform(image) + +# Open and preprocess image def load_image(image_id): img_path = f'{img_dir}{image_id}.jpg' - image = Image.open(img_path) - return image + image = Image.open(img_path).convert('RGB') # Ensure RGB format + return preprocess_image(image) # Train test split train_data, test_data = train_test_split(data, test_size=0.2, random_state=42, stratify=data['target']) @@ -43,7 +55,7 @@ def __len__(self): def __getitem__(self, idx): row = self.data.iloc[idx] - image = load_image(row['isic_id']) + image = load_image(row['isic_id']) # Now returns preprocessed tensor label = torch.tensor(row['target'], dtype=torch.float32) return image, label @@ -54,7 +66,11 @@ def __getitem__(self, idx): train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) -# Loading Image -sample_image_id = train_data.iloc[0]['isic_id'] -sample_image = load_image(sample_image_id) -sample_image.show() +# Test loading +if __name__ == "__main__": + # Load and display sample image info + sample_image_id = train_data.iloc[0]['isic_id'] + sample_tensor = load_image(sample_image_id) + print("\nSample image tensor shape:", sample_tensor.shape) + print("Sample image tensor range:", + f"min: {sample_tensor.min():.3f}, max: {sample_tensor.max():.3f}") \ No newline at end of file From ce4fafaf2d85e66285adcf2a62b310da8e23a3f5 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 15:23:21 +1100 Subject: [PATCH 09/31] Fixed dataloader to pick equal (and random) amount of pairs in dataset.py and enabled pin_memory for GPU data transfer support --- .../siamese_richard_chantra/dataset.py | 83 +++++++++++++++---- recognition/siamese_richard_chantra/train.py | 21 ++--- 2 files changed, 76 insertions(+), 28 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 5bff43d6c..e7f597595 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -4,11 +4,16 @@ from torch.utils.data import DataLoader, Dataset from sklearn.model_selection import train_test_split import torchvision.transforms as transforms +import numpy as np # File paths csv_path = 'archive/train-metadata.csv' img_dir = 'archive/train-image/image/' +# Set device +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print(f"Using device: {device}") + # Load metadata data = pd.read_csv(csv_path) @@ -44,33 +49,79 @@ def load_image(image_id): print(f"\nTraining set size: {len(train_data)}") print(f"Testing set size: {len(test_data)}") -# Dataset class for DataLoader -class ImageDataset(Dataset): +# Dataset class for Siamese Network +class SiameseDataset(Dataset): def __init__(self, data, img_dir): self.data = data self.img_dir = img_dir + self.labels = data['target'].values + self.image_ids = data['isic_id'].values def __len__(self): return len(self.data) def __getitem__(self, idx): - row = self.data.iloc[idx] - image = load_image(row['isic_id']) # Now returns preprocessed tensor - label = torch.tensor(row['target'], dtype=torch.float32) - return image, label + # Retrieve image + img1_id = self.image_ids[idx] + img1_label = self.labels[idx] + + # Randomly choice for: similar or dissimilar pair + should_get_same_class = np.random.random() > 0.5 + + if should_get_same_class: + # Second image from the same class + same_class_indices = np.where(self.labels == img1_label)[0] + second_idx = np.random.choice(same_class_indices) + while second_idx == idx: # Don't pick the same image + second_idx = np.random.choice(same_class_indices) + img2_id = self.image_ids[second_idx] + pair_label = torch.tensor(0.0) + else: + # Second image from the other class + other_class_indices = np.where(self.labels != img1_label)[0] + second_idx = np.random.choice(other_class_indices) + img2_id = self.image_ids[second_idx] + pair_label = torch.tensor(1.0) + + # Load and preprocess both images + img1 = load_image(img1_id) + img2 = load_image(img2_id) + + return img1, img2, pair_label # Create DataLoader -train_dataset = ImageDataset(train_data, img_dir) -test_dataset = ImageDataset(test_data, img_dir) +def get_dataloaders(batch_size=32): + train_dataset = SiameseDataset(train_data, img_dir) + test_dataset = SiameseDataset(test_data, img_dir) + + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + pin_memory=True + ) + + test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=False, + pin_memory=True + ) + + return train_loader, test_loader -train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) -test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) +# Create loaders +train_loader, test_loader = get_dataloaders(batch_size=32) # Test loading if __name__ == "__main__": - # Load and display sample image info - sample_image_id = train_data.iloc[0]['isic_id'] - sample_tensor = load_image(sample_image_id) - print("\nSample image tensor shape:", sample_tensor.shape) - print("Sample image tensor range:", - f"min: {sample_tensor.min():.3f}, max: {sample_tensor.max():.3f}") \ No newline at end of file + # Retrieving a batch from dataloader + batch = next(iter(train_loader)) + sample_img1, sample_img2, sample_label = batch + + print("\nSample batch data:") + print("Batch size:", sample_img1.shape[0]) + print("Image 1 shape:", sample_img1.shape) + print("Image 2 shape:", sample_img2.shape) + print("Labels shape:", sample_label.shape) + print("Sample labels:", sample_label[:5].tolist()) \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 6ce39b631..1c543d0c8 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -6,6 +6,10 @@ import math import torchvision.models as models +# Set device +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print(f"Using device: {device}") + # Siamese Network Architecture class SiameseNetwork(nn.Module): def __init__(self): @@ -43,8 +47,6 @@ def contrastive_loss(output1, output2, label, margin=1.0): euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1)) # Contrastive loss function - # For similar pairs (label = 0), minimize distance - # For dissimilar pairs (label = 1), push distance to be greater than margin loss = torch.mean((1 - label) * 0.5 * euclidean_distance ** 2 + label * 0.5 * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) return loss @@ -59,8 +61,8 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): model.train() for img1, img2, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}"): - # Convert data to float and reshape - img1, img2, labels = img1.float(), img2.float(), labels.unsqueeze(1).float() + img1, img2 = img1.to(device), img2.to(device) # Move tensors to device + labels = labels.to(device) optimizer.zero_grad() # Reset gradients output1, output2 = model(img1, img2) # Forward pass @@ -73,7 +75,7 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): epoch_loss = running_loss / len(train_loader) print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") - # Save checkpoint if it's the best model so far + # Save checkpoint for best model if epoch_loss < best_loss: best_loss = epoch_loss torch.save({ @@ -87,15 +89,10 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): with open('siamese_training.txt', 'a') as f: f.write(f"Epoch {epoch+1}, Loss: {epoch_loss}\n") - print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss / len(train_loader)}") - if __name__ == "__main__": - # Set device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - # Initialize model and optimizer + # Initialize model and move to device model = SiameseNetwork().to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer # Train model - train_siamese_network(model, train_loader, epochs=5, margin=1.0) + train_siamese_network(model, train_loader, epochs=5, margin=1.0) \ No newline at end of file From 55c1d299134c828675e49062e2694abb43ce6522 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 18:53:07 +1100 Subject: [PATCH 10/31] Added MLP Classifer feeding in the Siamese Network embeddings. Also added docstrings to functions. --- .../siamese_richard_chantra/dataset.py | 98 +++++------ recognition/siamese_richard_chantra/train.py | 156 ++++++++++++++---- 2 files changed, 171 insertions(+), 83 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index e7f597595..ed90a38aa 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -10,22 +10,18 @@ csv_path = 'archive/train-metadata.csv' img_dir = 'archive/train-image/image/' -# Set device -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -print(f"Using device: {device}") - # Load metadata data = pd.read_csv(csv_path) # Statistics print(f"Total images: {len(data)}") print(f"Classes distribution: \n{data['target'].value_counts()}") +print("\nNote: 0 = benign, 1 = malignant") # Head of metadata print("\nFirst few rows of metadata:") print(data.head()) -# Define preprocessing transform for ResNet50 def preprocess_image(image): """Preprocess image for ResNet50 input""" transform = transforms.Compose([ @@ -36,92 +32,84 @@ def preprocess_image(image): ]) return transform(image) -# Open and preprocess image def load_image(image_id): img_path = f'{img_dir}{image_id}.jpg' - image = Image.open(img_path).convert('RGB') # Ensure RGB format + image = Image.open(img_path).convert('RGB') return preprocess_image(image) # Train test split train_data, test_data = train_test_split(data, test_size=0.2, random_state=42, stratify=data['target']) -# Size of splits -print(f"\nTraining set size: {len(train_data)}") -print(f"Testing set size: {len(test_data)}") - -# Dataset class for Siamese Network class SiameseDataset(Dataset): + """Dataset for Siamese Network training and melanoma classification + + This dataset serves two purposes: + 1. Training the Siamese network: + - Creates pairs of images + - similarity_label: 0 if both images are from same class, 1 if different classes + 2. Melanoma classification: + - Each image has a diagnosis_label: 0 for benign, 1 for malignant + """ def __init__(self, data, img_dir): self.data = data self.img_dir = img_dir - self.labels = data['target'].values + self.diagnosis_labels = data['target'].values # 0 = benign, 1 = malignant self.image_ids = data['isic_id'].values def __len__(self): return len(self.data) def __getitem__(self, idx): - # Retrieve image + # Get the first image and associated diagnosis img1_id = self.image_ids[idx] - img1_label = self.labels[idx] + img1_diagnosis = self.diagnosis_labels[idx] - # Randomly choice for: similar or dissimilar pair + # Random choice of same-diagnosis or different-diagnosis pair should_get_same_class = np.random.random() > 0.5 if should_get_same_class: - # Second image from the same class - same_class_indices = np.where(self.labels == img1_label)[0] + # Get another image with same diagnosis (both benign or both malignant) + same_class_indices = np.where(self.diagnosis_labels == img1_diagnosis)[0] second_idx = np.random.choice(same_class_indices) - while second_idx == idx: # Don't pick the same image + while second_idx == idx: # Avoid picking the same image second_idx = np.random.choice(same_class_indices) img2_id = self.image_ids[second_idx] - pair_label = torch.tensor(0.0) + similarity_label = torch.tensor(0.0) # 0 = similar pair else: - # Second image from the other class - other_class_indices = np.where(self.labels != img1_label)[0] + # Get an image with different diagnosis + other_class_indices = np.where(self.diagnosis_labels != img1_diagnosis)[0] second_idx = np.random.choice(other_class_indices) img2_id = self.image_ids[second_idx] - pair_label = torch.tensor(1.0) + similarity_label = torch.tensor(1.0) # 1 = dissimilar pair + + # Get second image's diagnosis + img2_diagnosis = self.diagnosis_labels[second_idx] - # Load and preprocess both images + # Load and preprocess images img1 = load_image(img1_id) img2 = load_image(img2_id) - return img1, img2, pair_label + return { + 'img1': img1, + 'img2': img2, + 'similarity_label': similarity_label, + 'diagnosis1': torch.tensor(img1_diagnosis, dtype=torch.float32), + 'diagnosis2': torch.tensor(img2_diagnosis, dtype=torch.float32) + } # Create DataLoader -def get_dataloaders(batch_size=32): - train_dataset = SiameseDataset(train_data, img_dir) - test_dataset = SiameseDataset(test_data, img_dir) - - train_loader = DataLoader( - train_dataset, - batch_size=batch_size, - shuffle=True, - pin_memory=True - ) - - test_loader = DataLoader( - test_dataset, - batch_size=batch_size, - shuffle=False, - pin_memory=True - ) - - return train_loader, test_loader +train_dataset = SiameseDataset(train_data, img_dir) +test_dataset = SiameseDataset(test_data, img_dir) -# Create loaders -train_loader, test_loader = get_dataloaders(batch_size=32) +train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) +test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) -# Test loading if __name__ == "__main__": - # Retrieving a batch from dataloader + # Test the dataset batch = next(iter(train_loader)) - sample_img1, sample_img2, sample_label = batch - print("\nSample batch data:") - print("Batch size:", sample_img1.shape[0]) - print("Image 1 shape:", sample_img1.shape) - print("Image 2 shape:", sample_img2.shape) - print("Labels shape:", sample_label.shape) - print("Sample labels:", sample_label[:5].tolist()) \ No newline at end of file + print(f"Image 1 shape: {batch['img1'].shape}") + print(f"Image 2 shape: {batch['img2'].shape}") + print(f"Similarity labels: {batch['similarity_label'][:5]}") # Show first 5 + print(f"Diagnosis 1 (0=benign, 1=malignant): {batch['diagnosis1'][:5]}") # Show first 5 + print(f"Diagnosis 2 (0=benign, 1=malignant): {batch['diagnosis2'][:5]}") # Show first 5 \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 1c543d0c8..48f1fa3a7 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -10,8 +10,11 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") -# Siamese Network Architecture +# Original Siamese Network (unchanged) class SiameseNetwork(nn.Module): + """ + Siamese Network for learning image embeddings of benign and malignant melanomas. + """ def __init__(self): super(SiameseNetwork, self).__init__() @@ -21,13 +24,15 @@ def __init__(self): # Fully Connected Layer self.fc = nn.Sequential( - nn.Linear(2048, 256), # ResNet50 outputs 2048-dim feature vectors + nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector nn.ReLU(), # Non-linearity nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc ) - # Forward Pass def forward(self, x1, x2): + """ + The forward pass to compute embeddings for a pair of images + """ # Process inputs through ResNet50 out1 = self.features(x1) out2 = self.features(x2) @@ -40,59 +45,154 @@ def forward(self, x1, x2): out1 = self.fc(out1) out2 = self.fc(out2) return out1, out2 + + def get_embedding(self, x): + """ + Computing the embeddings for a single image + """ + features = self.features(x) + features = features.view(features.size(0), -1) + embedding = self.embedding_network(features) + return embedding + +# Separate MLP Classifier +class MLPClassifier(nn.Module): + """ + Using a Multi-layer Perceptron using Siamese Network embeddings to predict malignant melanoma + """ + def __init__(self, embedding_dim=128): + super(MelanomaClassifier, self).__init__() + self.classifier = nn.Sequential( + nn.Linear(embedding_dim, 64), + nn.ReLU(), + nn.Dropout(0.5), + nn.Linear(64, 32), + nn.ReLU(), + nn.Linear(32, 1), + nn.Sigmoid() # Squashes output into a probability of being malignant + ) + + def forward(self, embedding): + """ + Input: embedding from Siamese network + Output: probability of image being malignant (0 = benign, 1 = malignant) + """ + return self.classifier(embedding) # Contrastive Loss Function def contrastive_loss(output1, output2, label, margin=1.0): - # Euclidean distance between the outputs + """ + Contrastive loss used in the training of Siamese Network + """ euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1)) - - # Contrastive loss function loss = torch.mean((1 - label) * 0.5 * euclidean_distance ** 2 + label * 0.5 * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) return loss -# Training loop with contrastive loss +# Training Siamese Network def train_siamese_network(model, train_loader, epochs=5, margin=1.0): + """ + Train Siamese Network to learn embeddings from images + """ model.train() best_loss = float('inf') for epoch in range(epochs): running_loss = 0.0 - model.train() - for img1, img2, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}"): - img1, img2 = img1.to(device), img2.to(device) # Move tensors to device - labels = labels.to(device) + for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} - Siamese"): + # Get batch data + img1 = batch['img1'].to(device) + img2 = batch['img2'].to(device) + similarity_label = batch['similarity_label'].to(device) - optimizer.zero_grad() # Reset gradients - output1, output2 = model(img1, img2) # Forward pass - loss = contrastive_loss(output1, output2, labels, margin=margin) # Compute loss - loss.backward() # Backprop - optimizer.step() # Update weights + # Compute embeddings and loss + optimizer_siamese.zero_grad() + embedding1, embedding2 = model(img1, img2) + loss = contrastive_loss(embedding1, embedding2, similarity_label, margin) + loss.backward() + optimizer_siamese.step() running_loss += loss.item() epoch_loss = running_loss / len(train_loader) - print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") + print(f"Siamese Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") - # Save checkpoint for best model + # Save checkpoint for the best model if epoch_loss < best_loss: best_loss = epoch_loss torch.save({ 'epoch': epoch, 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), + 'optimizer_state_dict': optimizer_siamese.state_dict(), 'loss': best_loss, - }, 'best_model.pth') + }, 'best_siamese_model.pth') - # Log epoch results - with open('siamese_training.txt', 'a') as f: - f.write(f"Epoch {epoch+1}, Loss: {epoch_loss}\n") +# Train MLP Classifier using Siamese embeddings +def train_classifier(siamese_model, classifier, train_loader, epochs=5): + """ + Train MLP classifier to diagnose melanoma using learned embeddings + """ + classifier.train() + criterion = nn.BCELoss() + best_acc = 0.0 + + for epoch in range(epochs): + running_loss = 0.0 + correct = 0 + total = 0 + + for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} - Classifier"): + # Get first image and its diagnosis from batch + img1 = batch['img1'].to(device) + diagnosis_label = batch['diagnosis1'].to(device) # 0 = benign, 1 = malignant + + # Get embeddings from Siamese network + with torch.no_grad(): + embeddings = siamese_model.get_embedding(img1) + + # Classify embeddings + optimizer_classifier.zero_grad() + outputs = classifier(embeddings) + loss = criterion(outputs, binary_labels) + loss.backward() + optimizer_classifier.step() + + running_loss += loss.item() + + # Calculate accuracy + predicted = (outputs > 0.5).float() + total += binary_labels.size(0) + correct += (predicted == binary_labels).sum().item() + + epoch_loss = running_loss / len(train_loader) + accuracy = 100 * correct / total + print(f"Classifier Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}, Accuracy: {accuracy:.2f}%") + + # Save best model + if accuracy > best_acc: + best_acc = accuracy + torch.save({ + 'epoch': epoch, + 'model_state_dict': classifier.state_dict(), + 'optimizer_state_dict': optimizer_classifier.state_dict(), + 'accuracy': best_acc, + }, 'best_classifier_model.pth') if __name__ == "__main__": - # Initialize model and move to device - model = SiameseNetwork().to(device) - optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam Optimizer + # Initialize models + siamese_network = SiameseNetwork().to(device) + mlp_classifier = MLPClassifier().to(device) + + # Initialize optimizers + optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001) + optimizer_classifier = optim.Adam(mlp_classifier.parameters(), lr=0.001) - # Train model - train_siamese_network(model, train_loader, epochs=5, margin=1.0) \ No newline at end of file + # First train Siamese network + print("Training Siamese Network to learn embeddings from images:") + train_siamese_network(siamese_network, train_loader, epochs=5) + + # Then train classifier + print("\nTraining MLPClassifier using learned embeddings:") + siamese_network.eval() # Using the embeddings only + train_classifier(siamese_network, mlp_classifier, train_loader, epochs=5) \ No newline at end of file From be715114afe08c21e8a6329ae3464e11494b7cc5 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Tue, 22 Oct 2024 22:18:49 +1100 Subject: [PATCH 11/31] Updated predict.py to use the testset and added more evaluation metrics including accuracy, ROC-AUC and classification report --- .../siamese_richard_chantra/dataset.py | 20 +-- .../siamese_richard_chantra/predict.py | 162 ++++++++++++++++-- recognition/siamese_richard_chantra/train.py | 36 ++-- 3 files changed, 162 insertions(+), 56 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index ed90a38aa..cebce333f 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -18,10 +18,6 @@ print(f"Classes distribution: \n{data['target'].value_counts()}") print("\nNote: 0 = benign, 1 = malignant") -# Head of metadata -print("\nFirst few rows of metadata:") -print(data.head()) - def preprocess_image(image): """Preprocess image for ResNet50 input""" transform = transforms.Compose([ @@ -42,13 +38,6 @@ def load_image(image_id): class SiameseDataset(Dataset): """Dataset for Siamese Network training and melanoma classification - - This dataset serves two purposes: - 1. Training the Siamese network: - - Creates pairs of images - - similarity_label: 0 if both images are from same class, 1 if different classes - 2. Melanoma classification: - - Each image has a diagnosis_label: 0 for benign, 1 for malignant """ def __init__(self, data, img_dir): self.data = data @@ -105,11 +94,4 @@ def __getitem__(self, idx): test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) if __name__ == "__main__": - # Test the dataset - batch = next(iter(train_loader)) - print("\nSample batch data:") - print(f"Image 1 shape: {batch['img1'].shape}") - print(f"Image 2 shape: {batch['img2'].shape}") - print(f"Similarity labels: {batch['similarity_label'][:5]}") # Show first 5 - print(f"Diagnosis 1 (0=benign, 1=malignant): {batch['diagnosis1'][:5]}") # Show first 5 - print(f"Diagnosis 2 (0=benign, 1=malignant): {batch['diagnosis2'][:5]}") # Show first 5 \ No newline at end of file + pass \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index dac222ed7..d96caacd8 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -1,26 +1,150 @@ import torch -from train import SiameseNetwork -from dataset import preprocess_image +import numpy as np +from sklearn.metrics import roc_curve, auc, classification_report, confusion_matrix +import seaborn as sns +import matplotlib.pyplot as plt +from tqdm import tqdm +from train import SiameseNetwork, MLPClassifier +from dataset import test_loader -# Set device -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +# Precision set to 3dp +np.set_printoptions(precision=3, suppress=True) -# Initialize model -model = SiameseNetwork().to(device) +class Predict: + def __init__(self, siamese_network, mlp_classifier, device): + self.siamese_network = siamese_network + self.mlp_classifier = mlp_classifier + self.device = device -# Load saved model -checkpoint = torch.load('best_model.pth') -model.load_state_dict(checkpoint['model_state_dict']) + def predict(self, data_loader): + """Run predictions on the given data loader""" + # Set models to evaluation mode + self.siamese_network.eval() + self.mlp_classifier.eval() + + preds = [] + probs = [] + labels = [] + + with torch.no_grad(): # No need to track gradients for prediction + for batch in tqdm(data_loader, desc="Predicting"): + # Move batch to GPU if available + images = batch['img1'].to(self.device) # Get first image from pair + batch_labels = batch['diagnosis1'].to(self.device) # Get true label + + # Get embeddings from Siamese Network + embeddings = self.siamese_network.get_embedding(images) + + # Probability of being malignant + batch_probs = self.mlp_classifier(embeddings).squeeze() + batch_preds = (batch_probs > 0.5).float() + + # Store results using CPU + preds.extend(batch_preds.cpu().numpy()) + probs.extend(batch_probs.cpu().numpy()) + labels.extend(batch_labels.cpu().numpy()) + + # Convert lists to numpy arrays + return np.array(preds), np.array(probs), np.array(labels) -# Function to make a prediction -def predict_similarity(img1, img2, model): - model.eval() - with torch.no_grad(): - img1, img2 = img1.to(device), img2.to(device) - output1, output2 = model(img1, img2) - distance = torch.sqrt(torch.sum((output1 - output2) ** 2)) - return distance.item() +class Evaluate: + def __init__(self, preds, probs, labels): + self.preds = preds + self.probs = probs + self.labels = labels + + def evaluate(self): + """Evaluate predictions and return metrics""" + return { + 'basic_metrics': self._get_basic_metrics(), + 'roc_auc': self._get_roc_auc(), + 'class_report': classification_report(self.labels, self.preds, + target_names=['Benign', 'Malignant']) + } + + def _get_basic_metrics(self): + """Calculate accuracy metrics for both classes""" + accuracy = (self.preds == self.labels).mean() + malignant_mask = self.labels == 1 + benign_mask = self.labels == 0 + + return { + 'accuracy': accuracy, + 'malignant_accuracy': (self.preds[malignant_mask] == self.labels[malignant_mask]).mean(), + 'benign_accuracy': (self.preds[benign_mask] == self.labels[benign_mask]).mean() + } + + def _get_roc_auc(self): + """Calculate ROC curve and AUC score""" + fpr, tpr, _ = roc_curve(self.labels, self.probs) + return {'fpr': fpr, 'tpr': tpr, 'auc': auc(fpr, tpr)} + + def plot_results(self): + """Generate ROC curve and confusion matrix plots""" + # Plot ROC curve + roc_data = self._get_roc_auc() + plt.figure(figsize=(10, 8)) + sns.lineplot(x=roc_data['fpr'], y=roc_data['tpr']) + sns.lineplot(x=[0, 1], y=[0, 1], linestyle='--') + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title(f'ROC Curve (AUC = {roc_data["auc"]:.3f})') + plt.savefig('roc_curve.png') + plt.close() + + # Plot confusion matrix + cm = confusion_matrix(self.labels, self.preds) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='vlag', + xticklabels=['Benign', 'Malignant'], + yticklabels=['Benign', 'Malignant']) + plt.title('Confusion Matrix') + plt.savefig('confusion_matrix.png') + plt.close() + + def save_results(self, filename='evaluation_results.txt'): + """Save all evaluation metrics to file""" + results = self.evaluate() + metrics = results['basic_metrics'] + + with open(filename, 'w') as f: + f.write("Evaluation Results\n\n") + f.write(f"Overall Accuracy: {metrics['accuracy']}\n") + f.write(f"Malignant Accuracy: {metrics['malignant_accuracy']}\n") + f.write(f"Benign Accuracy: {metrics['benign_accuracy']}\n") + f.write(f"ROC-AUC Score: {results['roc_auc']['auc']}\n\n") + f.write(f"Classification Report:\n{results['class_report']}\n") -# Example usage: if __name__ == "__main__": - pass \ No newline at end of file + # Set device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + # Load models + siamese_network = SiameseNetwork().to(device) + mlp_classifier = MLPClassifier().to(device) + + # Load saved weights from training + siamese_network_checkpoint = torch.load('best_siamese_network.pth') + mlp_classifier_checkpoint = torch.load('best_mlp_classifier_model.pth') + siamese_network.load_state_dict(siamese_network_checkpoint['model_state_dict']) + mlp_classifier.load_state_dict(mlp_classifier_checkpoint['model_state_dict']) + + # Create Predict instance and run predictions + predictor = Predict(siamese_network, mlp_classifier, device) + preds, probs, labels = predictor.predict(test_loader) + + # Create Evaluate instance and run evaluation + evaluator = Evaluate(preds, probs, labels) + results = evaluator.evaluate() + + # Print evaluation results + print("Evaluation\n\n") + print(f"Overall Accuracy: {results['basic_metrics']['accuracy']}") + print(f"Malignant Accuracy: {results['basic_metrics']['malignant_accuracy']}") + print(f"ROC-AUC Score: {results['roc_auc']['auc']}\n") + print(results['class_report']) + + # Generate and save plots + evaluator.plot_results() + evaluator.save_results() diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 48f1fa3a7..77171e81f 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -24,9 +24,9 @@ def __init__(self): # Fully Connected Layer self.fc = nn.Sequential( - nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector - nn.ReLU(), # Non-linearity - nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc + nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector + nn.ReLU(), + nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc ) def forward(self, x1, x2): @@ -69,7 +69,7 @@ def __init__(self, embedding_dim=128): nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 1), - nn.Sigmoid() # Squashes output into a probability of being malignant + nn.Sigmoid() ) def forward(self, embedding): @@ -90,11 +90,11 @@ def contrastive_loss(output1, output2, label, margin=1.0): return loss # Training Siamese Network -def train_siamese_network(model, train_loader, epochs=5, margin=1.0): +def train_siamese_network(siamese_network, train_loader, epochs=5, margin=1.0): """ Train Siamese Network to learn embeddings from images """ - model.train() + siamese_network.train() best_loss = float('inf') for epoch in range(epochs): @@ -107,11 +107,11 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): similarity_label = batch['similarity_label'].to(device) # Compute embeddings and loss - optimizer_siamese.zero_grad() + siamese_network_optimizer.zero_grad() embedding1, embedding2 = model(img1, img2) loss = contrastive_loss(embedding1, embedding2, similarity_label, margin) loss.backward() - optimizer_siamese.step() + siamese_network_optimizer.step() running_loss += loss.item() @@ -123,13 +123,13 @@ def train_siamese_network(model, train_loader, epochs=5, margin=1.0): best_loss = epoch_loss torch.save({ 'epoch': epoch, - 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer_siamese.state_dict(), + 'model_state_dict': siamese_network.state_dict(), + 'optimizer_state_dict': siamese_network_optimizer.state_dict(), 'loss': best_loss, - }, 'best_siamese_model.pth') + }, 'best_siamese_network.pth') -# Train MLP Classifier using Siamese embeddings -def train_classifier(siamese_model, classifier, train_loader, epochs=5): +# Train MLP Classifier using Siamese Network embeddings +def train_mlp_classifier(siamese_network, mlp_classifier, train_loader, epochs=5): """ Train MLP classifier to diagnose melanoma using learned embeddings """ @@ -147,9 +147,9 @@ def train_classifier(siamese_model, classifier, train_loader, epochs=5): img1 = batch['img1'].to(device) diagnosis_label = batch['diagnosis1'].to(device) # 0 = benign, 1 = malignant - # Get embeddings from Siamese network + # Get embeddings from Siamese Network with torch.no_grad(): - embeddings = siamese_model.get_embedding(img1) + embeddings = siamese_network.get_embedding(img1) # Classify embeddings optimizer_classifier.zero_grad() @@ -177,7 +177,7 @@ def train_classifier(siamese_model, classifier, train_loader, epochs=5): 'model_state_dict': classifier.state_dict(), 'optimizer_state_dict': optimizer_classifier.state_dict(), 'accuracy': best_acc, - }, 'best_classifier_model.pth') + }, 'best_mlp_classifier.pth') if __name__ == "__main__": # Initialize models @@ -185,8 +185,8 @@ def train_classifier(siamese_model, classifier, train_loader, epochs=5): mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001) - optimizer_classifier = optim.Adam(mlp_classifier.parameters(), lr=0.001) + optimizer_siamese_network = optim.Adam(siamese_network.parameters(), lr=0.001) + optimizer_mlp_classifier = optim.Adam(mlp_classifier.parameters(), lr=0.001) # First train Siamese network print("Training Siamese Network to learn embeddings from images:") From a132ca5fbb91c75cd80de08ad127e075c81bb8c0 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Wed, 23 Oct 2024 19:50:25 +1100 Subject: [PATCH 12/31] Added Siamese Network checkpoint skip position and created a DataManager class to encapsulate data loading and configuration --- .../siamese_richard_chantra/dataset.py | 117 +++++++++---- .../siamese_richard_chantra/predict.py | 19 ++- recognition/siamese_richard_chantra/train.py | 157 +++++++++--------- 3 files changed, 178 insertions(+), 115 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index cebce333f..57c653bf0 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -6,38 +6,65 @@ import torchvision.transforms as transforms import numpy as np -# File paths -csv_path = 'archive/train-metadata.csv' -img_dir = 'archive/train-image/image/' +class DataManager: + """ + Managing the data flows and processing for the ISIC-2020 dataset + """ + def __init__(self, csv_path, img_dir): + self.csv_path = csv_path + self.img_dir = img_dir + self.data = None + self.train_loader = None + self.test_loader = None -# Load metadata -data = pd.read_csv(csv_path) + def load_data(self): + """ + Loading metadata from the CSV file. + """ + self.data = pd.read_csv(self.csv_path) -# Statistics -print(f"Total images: {len(data)}") -print(f"Classes distribution: \n{data['target'].value_counts()}") -print("\nNote: 0 = benign, 1 = malignant") + def split_data(self): + """ + Split the data into training and testing sets + """ + train_data, test_data = train_test_split(self.data, test_size=0.2, random_state=42, stratify=self.data['target']) + return train_data, test_data -def preprocess_image(image): - """Preprocess image for ResNet50 input""" - transform = transforms.Compose([ - transforms.Resize((224, 224)), # ResNet50 expected input size - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) # ImageNet normalization - ]) - return transform(image) + def create_dataloaders(self, batch_size=32): + """ + Creating torch DataLoader objects for training and testing + """ + train_data, test_data = self.split_data() + + # Create DataLoader + train_dataset = SiameseDataset(train_data, self.img_dir) + test_dataset = SiameseDataset(test_data, self.img_dir) -def load_image(image_id): - img_path = f'{img_dir}{image_id}.jpg' - image = Image.open(img_path).convert('RGB') - return preprocess_image(image) + self.train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + pin_memory=True, + num_workers=4 + ) + self.test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=False, + pin_memory=True, + num_workers=4 + ) + + def print_statistics(self): + # Statistics + print(f"Total images: {len(self.data)}") + print(f"Classes distribution: \n{self.data['target'].value_counts()}") + print("\nNote: 0 = benign, 1 = malignant") -# Train test split -train_data, test_data = train_test_split(data, test_size=0.2, random_state=42, stratify=data['target']) class SiameseDataset(Dataset): - """Dataset for Siamese Network training and melanoma classification + """ + Dataset for Siamese Network training and melanoma classification """ def __init__(self, data, img_dir): self.data = data @@ -46,6 +73,9 @@ def __init__(self, data, img_dir): self.image_ids = data['isic_id'].values def __len__(self): + """ + Returns the length of the dataset + """ return len(self.data) def __getitem__(self, idx): @@ -75,8 +105,8 @@ def __getitem__(self, idx): img2_diagnosis = self.diagnosis_labels[second_idx] # Load and preprocess images - img1 = load_image(img1_id) - img2 = load_image(img2_id) + img1 = load_image(img1_id, self.img_dir) + img2 = load_image(img2_id, self.img_dir) return { 'img1': img1, @@ -86,12 +116,33 @@ def __getitem__(self, idx): 'diagnosis2': torch.tensor(img2_diagnosis, dtype=torch.float32) } -# Create DataLoader -train_dataset = SiameseDataset(train_data, img_dir) -test_dataset = SiameseDataset(test_data, img_dir) +def preprocess_image(image): + """ + Preprocess image for ResNet50 input + """ + transform = transforms.Compose([ + transforms.Resize((224, 224)), # ResNet50 expected input size + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) # ImageNet normalization + ]) + return transform(image) + +def load_image(image_id, img_dir): + """ + Takes an image from the ISIC-2020 dataset and preprocesses it for the models + """ + img_path = f'{img_dir}{image_id}.jpg' + image = Image.open(img_path).convert('RGB') + return preprocess_image(image) -train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) -test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) if __name__ == "__main__": - pass \ No newline at end of file + # File paths + csv_path = 'archive/train-metadata.csv' + img_dir = 'archive/train-image/image/' + + data_manager = DataManager(csv_path, img_dir) + data_manager.load_data() + data_manager.create_dataloaders() + data_manager.print_statistics() \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index d96caacd8..c30ffe217 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -5,10 +5,7 @@ import matplotlib.pyplot as plt from tqdm import tqdm from train import SiameseNetwork, MLPClassifier -from dataset import test_loader - -# Precision set to 3dp -np.set_printoptions(precision=3, suppress=True) +from dataset import DataManager class Predict: def __init__(self, siamese_network, mlp_classifier, device): @@ -26,7 +23,7 @@ def predict(self, data_loader): probs = [] labels = [] - with torch.no_grad(): # No need to track gradients for prediction + with torch.no_grad(): for batch in tqdm(data_loader, desc="Predicting"): # Move batch to GPU if available images = batch['img1'].to(self.device) # Get first image from pair @@ -120,13 +117,19 @@ def save_results(self, filename='evaluation_results.txt'): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") + # Setup data + data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') + data_manager.load_data() + data_manager.create_dataloaders() + test_loader = data_manager.test_loader + # Load models siamese_network = SiameseNetwork().to(device) mlp_classifier = MLPClassifier().to(device) # Load saved weights from training siamese_network_checkpoint = torch.load('best_siamese_network.pth') - mlp_classifier_checkpoint = torch.load('best_mlp_classifier_model.pth') + mlp_classifier_checkpoint = torch.load('best_mlp_classifier.pth') siamese_network.load_state_dict(siamese_network_checkpoint['model_state_dict']) mlp_classifier.load_state_dict(mlp_classifier_checkpoint['model_state_dict']) @@ -139,7 +142,7 @@ def save_results(self, filename='evaluation_results.txt'): results = evaluator.evaluate() # Print evaluation results - print("Evaluation\n\n") + print("Evaluation\n") print(f"Overall Accuracy: {results['basic_metrics']['accuracy']}") print(f"Malignant Accuracy: {results['basic_metrics']['malignant_accuracy']}") print(f"ROC-AUC Score: {results['roc_auc']['auc']}\n") @@ -147,4 +150,4 @@ def save_results(self, filename='evaluation_results.txt'): # Generate and save plots evaluator.plot_results() - evaluator.save_results() + evaluator.save_results() \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 77171e81f..5c8235260 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -1,16 +1,11 @@ import torch import torch.nn as nn import torch.optim as optim -from dataset import train_loader, test_loader +from dataset import DataManager from tqdm import tqdm -import math import torchvision.models as models +from torchvision.models import ResNet50_Weights -# Set device -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -print(f"Using device: {device}") - -# Original Siamese Network (unchanged) class SiameseNetwork(nn.Module): """ Siamese Network for learning image embeddings of benign and malignant melanomas. @@ -19,49 +14,35 @@ def __init__(self): super(SiameseNetwork, self).__init__() # ResNet50 Feature Extractor - resnet = models.resnet50(pretrained=True) + resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) self.features = nn.Sequential(*list(resnet.children())[:-1]) # Fully Connected Layer self.fc = nn.Sequential( - nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector + nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector nn.ReLU(), - nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc + nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc ) def forward(self, x1, x2): - """ - The forward pass to compute embeddings for a pair of images - """ - # Process inputs through ResNet50 - out1 = self.features(x1) - out2 = self.features(x2) - - # Flatten outputs - out1 = out1.view(out1.size(0), -1) - out2 = out2.view(out2.size(0), -1) - - # Generate embeddings - out1 = self.fc(out1) - out2 = self.fc(out2) + """Forward pass to compute embeddings for a pair of images""" + # Get embeddings for both images + out1 = self.get_embedding(x1) + out2 = self.get_embedding(x2) return out1, out2 def get_embedding(self, x): - """ - Computing the embeddings for a single image - """ + """Computing embeddings for a single image""" features = self.features(x) features = features.view(features.size(0), -1) - embedding = self.embedding_network(features) - return embedding + return self.fc(features) -# Separate MLP Classifier class MLPClassifier(nn.Module): """ - Using a Multi-layer Perceptron using Siamese Network embeddings to predict malignant melanoma + MLP Classifier using Siamese Network embeddings to predict melanoma """ def __init__(self, embedding_dim=128): - super(MelanomaClassifier, self).__init__() + super(MLPClassifier, self).__init__() self.classifier = nn.Sequential( nn.Linear(embedding_dim, 64), nn.ReLU(), @@ -69,28 +50,30 @@ def __init__(self, embedding_dim=128): nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 1), - nn.Sigmoid() + nn.Sigmoid() ) def forward(self, embedding): """ Input: embedding from Siamese network - Output: probability of image being malignant (0 = benign, 1 = malignant) + Output: probability of being malignant (0 = benign, 1 = malignant) """ return self.classifier(embedding) -# Contrastive Loss Function def contrastive_loss(output1, output2, label, margin=1.0): """ - Contrastive loss used in the training of Siamese Network + Contrastive loss for Siamese Network training """ - euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1)) - loss = torch.mean((1 - label) * 0.5 * euclidean_distance ** 2 + - label * 0.5 * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) + # Calculate euclidean distance + euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1) + 1e-6) + + # Calculate contrastive loss + loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + + label * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) + return loss -# Training Siamese Network -def train_siamese_network(siamese_network, train_loader, epochs=5, margin=1.0): +def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, margin=1.0): """ Train Siamese Network to learn embeddings from images """ @@ -106,34 +89,39 @@ def train_siamese_network(siamese_network, train_loader, epochs=5, margin=1.0): img2 = batch['img2'].to(device) similarity_label = batch['similarity_label'].to(device) - # Compute embeddings and loss - siamese_network_optimizer.zero_grad() - embedding1, embedding2 = model(img1, img2) + # Forward pass + optimizer.zero_grad() + embedding1, embedding2 = siamese_network(img1, img2) + + # Calculate loss loss = contrastive_loss(embedding1, embedding2, similarity_label, margin) + + # Backward pass loss.backward() - siamese_network_optimizer.step() - + torch.nn.utils.clip_grad_norm_(siamese_network.parameters(), 1.0) # Gradient clipping + optimizer.step() + running_loss += loss.item() epoch_loss = running_loss / len(train_loader) - print(f"Siamese Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss}") + print(f"Siamese Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}") - # Save checkpoint for the best model + # Save best model if epoch_loss < best_loss: best_loss = epoch_loss torch.save({ 'epoch': epoch, 'model_state_dict': siamese_network.state_dict(), - 'optimizer_state_dict': siamese_network_optimizer.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), 'loss': best_loss, }, 'best_siamese_network.pth') -# Train MLP Classifier using Siamese Network embeddings -def train_mlp_classifier(siamese_network, mlp_classifier, train_loader, epochs=5): +def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loader, epochs=5): """ - Train MLP classifier to diagnose melanoma using learned embeddings + Train MLP classifier using Siamese Network embeddings """ - classifier.train() + mlp_classifier.train() + siamese_network.eval() # Freeze Siamese network criterion = nn.BCELoss() best_acc = 0.0 @@ -143,27 +131,29 @@ def train_mlp_classifier(siamese_network, mlp_classifier, train_loader, epochs=5 total = 0 for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} - Classifier"): - # Get first image and its diagnosis from batch + # Get image and its label img1 = batch['img1'].to(device) - diagnosis_label = batch['diagnosis1'].to(device) # 0 = benign, 1 = malignant + diagnosis_label = batch['diagnosis1'].to(device).unsqueeze(1) - # Get embeddings from Siamese Network + # Get embeddings without gradient tracking with torch.no_grad(): embeddings = siamese_network.get_embedding(img1) - # Classify embeddings - optimizer_classifier.zero_grad() - outputs = classifier(embeddings) - loss = criterion(outputs, binary_labels) + # Forward pass + optimizer.zero_grad() + outputs = mlp_classifier(embeddings) + loss = criterion(outputs, diagnosis_label) + + # Backward pass loss.backward() - optimizer_classifier.step() + optimizer.step() running_loss += loss.item() # Calculate accuracy predicted = (outputs > 0.5).float() - total += binary_labels.size(0) - correct += (predicted == binary_labels).sum().item() + total += diagnosis_label.size(0) + correct += (predicted == diagnosis_label).sum().item() epoch_loss = running_loss / len(train_loader) accuracy = 100 * correct / total @@ -174,25 +164,44 @@ def train_mlp_classifier(siamese_network, mlp_classifier, train_loader, epochs=5 best_acc = accuracy torch.save({ 'epoch': epoch, - 'model_state_dict': classifier.state_dict(), - 'optimizer_state_dict': optimizer_classifier.state_dict(), + 'model_state_dict': mlp_classifier.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), 'accuracy': best_acc, }, 'best_mlp_classifier.pth') if __name__ == "__main__": + # Set device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + SKIP_SIAMESE_TRAINING = 1 # 0: Begin training, 1: Skip training (use checkpoint) + + # Setup data + data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') + data_manager.load_data() + data_manager.create_dataloaders() + train_loader = data_manager.train_loader + test_loader = data_manager.test_loader + # Initialize models siamese_network = SiameseNetwork().to(device) mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese_network = optim.Adam(siamese_network.parameters(), lr=0.001) - optimizer_mlp_classifier = optim.Adam(mlp_classifier.parameters(), lr=0.001) - - # First train Siamese network - print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, train_loader, epochs=5) + optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001) + optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001) + + if SKIP_SIAMESE_TRAINING: + # Load Siamese Network checkpoint + print("Loading Siamese Network checkpoint...") + checkpoint = torch.load('best_siamese_network.pth') + siamese_network.load_state_dict(checkpoint['model_state_dict']) + print(f"Loaded Siamese Network checkpoint with loss: {checkpoint['loss']:.4f}") + else: + # Train Siamese network from scratch + print("Training Siamese Network to learn embeddings from images:") + train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=5) - # Then train classifier + # Train classifier print("\nTraining MLPClassifier using learned embeddings:") - siamese_network.eval() # Using the embeddings only - train_classifier(siamese_network, mlp_classifier, train_loader, epochs=5) \ No newline at end of file + train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=5) \ No newline at end of file From 25a46e27f40794c1c5fa94c8a271b04882881479 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Thu, 24 Oct 2024 03:35:55 +1100 Subject: [PATCH 13/31] Added a function to balance the dataset (undersampling) in DataManager --- .../siamese_richard_chantra/dataset.py | 38 ++++++++++++++----- 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 57c653bf0..fda2d23fe 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -19,16 +19,31 @@ def __init__(self, csv_path, img_dir): def load_data(self): """ - Loading metadata from the CSV file. + Loading metadata from the CSV file """ self.data = pd.read_csv(self.csv_path) + def balance_dataset(self, data): + """ + Balance dataset by undersampling the majority (benign) class + """ + # Separate majority and minority classes + malignant = data[data['target'] == 1] + benign = data[data['target'] == 0] + + n_samples = len(malignant) + benign_undersampled = benign.sample(n=n_samples, random_state=42) # random sample + balanced_data = pd.concat([malignant, benign_undersampled]) # combine + return balanced_data.sample(frac=1, random_state=42).reset_index(drop=True) # shuffle + def split_data(self): """ - Split the data into training and testing sets + Split the data into training and testing sets then balance """ train_data, test_data = train_test_split(self.data, test_size=0.2, random_state=42, stratify=self.data['target']) - return train_data, test_data + balanced_train_data = self.balance_dataset(train_data) + + return balanced_train_data, test_data def create_dataloaders(self, batch_size=32): """ @@ -56,12 +71,20 @@ def create_dataloaders(self, batch_size=32): ) def print_statistics(self): - # Statistics + """ + Print dataset statistics before and after balancing + """ + print("Original dataset statistics:") print(f"Total images: {len(self.data)}") print(f"Classes distribution: \n{self.data['target'].value_counts()}") + + train_data, test_data = self.split_data() + print("\nAfter balancing training data:") + print(f"Training set distribution: \n{train_data['target'].value_counts()}") + print(f"Test set distribution: \n{test_data['target'].value_counts()}") print("\nNote: 0 = benign, 1 = malignant") - +# Rest of the code remains unchanged class SiameseDataset(Dataset): """ Dataset for Siamese Network training and melanoma classification @@ -73,9 +96,6 @@ def __init__(self, data, img_dir): self.image_ids = data['isic_id'].values def __len__(self): - """ - Returns the length of the dataset - """ return len(self.data) def __getitem__(self, idx): @@ -87,7 +107,7 @@ def __getitem__(self, idx): should_get_same_class = np.random.random() > 0.5 if should_get_same_class: - # Get another image with same diagnosis (both benign or both malignant) + # Get another image with same diagnosis same_class_indices = np.where(self.diagnosis_labels == img1_diagnosis)[0] second_idx = np.random.choice(same_class_indices) while second_idx == idx: # Avoid picking the same image From 479cd95652f2d44fed5ad11678e271a729894024 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Thu, 24 Oct 2024 04:07:20 +1100 Subject: [PATCH 14/31] Enabled balance_dataset to also perform oversampling --- .../siamese_richard_chantra/dataset.py | 23 ++++++++++++++----- 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index fda2d23fe..499cb659b 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -23,18 +23,29 @@ def load_data(self): """ self.data = pd.read_csv(self.csv_path) - def balance_dataset(self, data): + def balance_dataset(self, data, data_augmentation='oversampling'): """ - Balance dataset by undersampling the majority (benign) class + Balance dataset using specified sampling strategy """ + if data_augmentation is None: + return data + # Separate majority and minority classes malignant = data[data['target'] == 1] benign = data[data['target'] == 0] - n_samples = len(malignant) - benign_undersampled = benign.sample(n=n_samples, random_state=42) # random sample - balanced_data = pd.concat([malignant, benign_undersampled]) # combine - return balanced_data.sample(frac=1, random_state=42).reset_index(drop=True) # shuffle + if data_augmentation == 'oversampling': + n_samples = len(benign) + malignant_oversampled = malignant.sample(n=n_samples, replace=True, random_state=42) + balanced_data = pd.concat([benign, malignant_oversampled]) + elif data_augmentation == 'undersampling': + n_samples = len(malignant) + benign_undersampled = benign.sample(n=n_samples, random_state=42) + balanced_data = pd.concat([malignant, benign_undersampled]) + else: + raise ValueError(f"Unknown data_augmentation parameter: {data_augmentation}") + + return balanced_data.sample(frac=1, random_state=42).reset_index(drop=True) def split_data(self): """ From dbb2b04f7ca5813399b05bb57cf3edc62a0a4391 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Thu, 24 Oct 2024 05:33:38 +1100 Subject: [PATCH 15/31] Added more dropout layers and weight decay as regularization to prevent overfitting of embeddings --- recognition/siamese_richard_chantra/train.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 5c8235260..9b52f09e6 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -21,6 +21,7 @@ def __init__(self): self.fc = nn.Sequential( nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector nn.ReLU(), + nn.Dropout(0.3), nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc ) @@ -46,9 +47,10 @@ def __init__(self, embedding_dim=128): self.classifier = nn.Sequential( nn.Linear(embedding_dim, 64), nn.ReLU(), - nn.Dropout(0.5), + nn.Dropout(0.7), nn.Linear(64, 32), nn.ReLU(), + nn.Dropout(0.5), nn.Linear(32, 1), nn.Sigmoid() ) @@ -188,8 +190,8 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001) - optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001) + optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001, weight_decay=1e-4) + optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001, weight_decay=1e-4) if SKIP_SIAMESE_TRAINING: # Load Siamese Network checkpoint @@ -200,8 +202,8 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade else: # Train Siamese network from scratch print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=5) + train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=10) # Train classifier print("\nTraining MLPClassifier using learned embeddings:") - train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=5) \ No newline at end of file + train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=10) \ No newline at end of file From c7b4e98646584c2ed94651c6f19bd3745796a9ac Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 02:24:39 +1100 Subject: [PATCH 16/31] Updated the training model parameters like learning rate and weight_decay in siamese network also fixed up image augmentations in dataset.py --- .../siamese_richard_chantra/dataset.py | 99 ++++++++++++------- recognition/siamese_richard_chantra/train.py | 10 +- 2 files changed, 71 insertions(+), 38 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 499cb659b..5dcd8faf0 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -23,9 +23,10 @@ def load_data(self): """ self.data = pd.read_csv(self.csv_path) - def balance_dataset(self, data, data_augmentation='oversampling'): + def balance_dataset(self, data, data_augmentation='oversampling', target_ratio=None): """ Balance dataset using specified sampling strategy + target_ratio is the ratio of benign desired in the balanced dataset """ if data_augmentation is None: return data @@ -42,17 +43,34 @@ def balance_dataset(self, data, data_augmentation='oversampling'): n_samples = len(malignant) benign_undersampled = benign.sample(n=n_samples, random_state=42) balanced_data = pd.concat([malignant, benign_undersampled]) + elif data_augmentation == 'ratio' and target_ratio is not None: + # Calculate target numbers for the specified ratio + total_samples = len(data) + target_benign_samples = int(total_samples * target_ratio) + target_malignant_samples = total_samples - target_benign_samples + + # Downsample benign or keep to maintain target + if len(benign) > target_benign_samples: + benign_sampled = benign.sample(n=target_benign_samples, random_state=42) + else: + benign_sampled = benign + + # Oversample malignant to reach target + malignant_oversampled = malignant.sample(n=target_malignant_samples, replace=True, random_state=42) + balanced_data = pd.concat([benign_sampled, malignant_oversampled]) else: - raise ValueError(f"Unknown data_augmentation parameter: {data_augmentation}") + raise ValueError(f"Invalid parameter for: data_augmentation or target_ratio") - return balanced_data.sample(frac=1, random_state=42).reset_index(drop=True) + return balanced_data.sample(frac=1, random_state=42).reset_index(drop=True) def split_data(self): """ - Split the data into training and testing sets then balance + Split the data into training and testing sets and balance """ + # Normal train test split train_data, test_data = train_test_split(self.data, test_size=0.2, random_state=42, stratify=self.data['target']) - balanced_train_data = self.balance_dataset(train_data) + # Balance dataset based on oversampling, undersampling or oversampling using a ratio + balanced_train_data = self.balance_dataset(train_data, data_augmentation='ratio', target_ratio=0.67) return balanced_train_data, test_data @@ -95,7 +113,6 @@ def print_statistics(self): print(f"Test set distribution: \n{test_data['target'].value_counts()}") print("\nNote: 0 = benign, 1 = malignant") -# Rest of the code remains unchanged class SiameseDataset(Dataset): """ Dataset for Siamese Network training and melanoma classification @@ -103,14 +120,27 @@ class SiameseDataset(Dataset): def __init__(self, data, img_dir): self.data = data self.img_dir = img_dir - self.diagnosis_labels = data['target'].values # 0 = benign, 1 = malignant + self.diagnosis_labels = data['target'].values self.image_ids = data['isic_id'].values - + + # resize and normalize + self.transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) +]) def __len__(self): + """ + Returns the length of the dataset + """ return len(self.data) def __getitem__(self, idx): - # Get the first image and associated diagnosis + """ + Takes an index and returns a pair of images with their labels + """ + # Get the first image and diagnosis label img1_id = self.image_ids[idx] img1_diagnosis = self.diagnosis_labels[idx] @@ -118,10 +148,10 @@ def __getitem__(self, idx): should_get_same_class = np.random.random() > 0.5 if should_get_same_class: - # Get another image with same diagnosis + # Get another image with same diagnosis label same_class_indices = np.where(self.diagnosis_labels == img1_diagnosis)[0] second_idx = np.random.choice(same_class_indices) - while second_idx == idx: # Avoid picking the same image + while second_idx == idx: # Don't pick same pair second_idx = np.random.choice(same_class_indices) img2_id = self.image_ids[second_idx] similarity_label = torch.tensor(0.0) # 0 = similar pair @@ -136,8 +166,8 @@ def __getitem__(self, idx): img2_diagnosis = self.diagnosis_labels[second_idx] # Load and preprocess images - img1 = load_image(img1_id, self.img_dir) - img2 = load_image(img2_id, self.img_dir) + img1 = self.load_and_transform(img1_id) + img2 = self.load_and_transform(img2_id) return { 'img1': img1, @@ -147,26 +177,29 @@ def __getitem__(self, idx): 'diagnosis2': torch.tensor(img2_diagnosis, dtype=torch.float32) } -def preprocess_image(image): - """ - Preprocess image for ResNet50 input - """ - transform = transforms.Compose([ - transforms.Resize((224, 224)), # ResNet50 expected input size - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) # ImageNet normalization - ]) - return transform(image) - -def load_image(image_id, img_dir): - """ - Takes an image from the ISIC-2020 dataset and preprocesses it for the models - """ - img_path = f'{img_dir}{image_id}.jpg' - image = Image.open(img_path).convert('RGB') - return preprocess_image(image) - + def load_and_transform(self, image_id, threshold=0.7): + """ + Load image and apply random augmentations if over theshold + """ + img_path = f'{self.img_dir}{image_id}.jpg' + image = Image.open(img_path).convert('RGB') + + # 30% chance of augmentation + if np.random.random() > threshold: + # List of possible augmentations: equal chance of a flip and a rotation + augmentations = [ + transforms.functional.hflip, + transforms.functional.vflip, + lambda img: transforms.functional.rotate(img, np.random.uniform(0, 360)), + lambda img: transforms.functional.rotate(img, np.random.uniform(0, 360)) + ] + + # Random augmentation of image + aug_func = np.random.choice(augmentations) + image = aug_func(image) + + # Apply standard preprocessing + return self.transform(image) if __name__ == "__main__": # File paths diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 9b52f09e6..21bed4656 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -62,7 +62,7 @@ def forward(self, embedding): """ return self.classifier(embedding) -def contrastive_loss(output1, output2, label, margin=1.0): +def contrastive_loss(output1, output2, label, margin=2.0): """ Contrastive loss for Siamese Network training """ @@ -176,7 +176,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - SKIP_SIAMESE_TRAINING = 1 # 0: Begin training, 1: Skip training (use checkpoint) + SKIP_SIAMESE_TRAINING = 0 # 0: Begin training, 1: Skip training (use checkpoint) # Setup data data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') @@ -190,7 +190,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001, weight_decay=1e-4) + optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.009, weight_decay=1.3e-3) optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001, weight_decay=1e-4) if SKIP_SIAMESE_TRAINING: @@ -202,8 +202,8 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade else: # Train Siamese network from scratch print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=10) + train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=18) # Train classifier print("\nTraining MLPClassifier using learned embeddings:") - train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=10) \ No newline at end of file + train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) \ No newline at end of file From 0761f88ca8534158b11bda1120270cb9f3ea5dc5 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 03:16:52 +1100 Subject: [PATCH 17/31] Added an extra dense layer to the siamese network and updated the batch size to be 256 because there is GPU capacity --- recognition/siamese_richard_chantra/dataset.py | 2 +- recognition/siamese_richard_chantra/train.py | 14 ++++++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 5dcd8faf0..b67fa0fb1 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -74,7 +74,7 @@ def split_data(self): return balanced_train_data, test_data - def create_dataloaders(self, batch_size=32): + def create_dataloaders(self, batch_size=256): """ Creating torch DataLoader objects for training and testing """ diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 21bed4656..1b67299d5 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -19,10 +19,12 @@ def __init__(self): # Fully Connected Layer self.fc = nn.Sequential( - nn.Linear(2048, 256), # Output of ResNet50 is a 2048 dim feature vector + nn.Linear(2048, 512), nn.ReLU(), nn.Dropout(0.3), - nn.Linear(256, 128) # Final embedding size of 128 for Euclidean distance calc + nn.Linear(512, 256), + nn.ReLU(), + nn.Linear(256, 128) ) def forward(self, x1, x2): @@ -62,7 +64,7 @@ def forward(self, embedding): """ return self.classifier(embedding) -def contrastive_loss(output1, output2, label, margin=2.0): +def contrastive_loss(output1, output2, label, margin=1.0): """ Contrastive loss for Siamese Network training """ @@ -123,7 +125,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade Train MLP classifier using Siamese Network embeddings """ mlp_classifier.train() - siamese_network.eval() # Freeze Siamese network + siamese_network.eval() # Freeze Siamese network criterion = nn.BCELoss() best_acc = 0.0 @@ -190,7 +192,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.009, weight_decay=1.3e-3) + optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001, weight_decay=5e-5) optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001, weight_decay=1e-4) if SKIP_SIAMESE_TRAINING: @@ -202,7 +204,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade else: # Train Siamese network from scratch print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=18) + train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=16) # Train classifier print("\nTraining MLPClassifier using learned embeddings:") From eee155187e57e778edfd0ac9f1cefc02c2441214 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 03:58:38 +1100 Subject: [PATCH 18/31] Refactored train.py and predict.py by adding components to modules.py --- .../siamese_richard_chantra/modules.py | 185 ++++++++++++++++++ .../siamese_richard_chantra/predict.py | 122 +----------- recognition/siamese_richard_chantra/train.py | 83 +------- 3 files changed, 201 insertions(+), 189 deletions(-) diff --git a/recognition/siamese_richard_chantra/modules.py b/recognition/siamese_richard_chantra/modules.py index e69de29bb..f0286bf0e 100644 --- a/recognition/siamese_richard_chantra/modules.py +++ b/recognition/siamese_richard_chantra/modules.py @@ -0,0 +1,185 @@ +import torch +import torch.nn as nn +import torchvision.models as models +from torchvision.models import ResNet50_Weights +from tqdm import tqdm +import numpy as np +import matplotlib.pyplot as plt +from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix, classification_report +import seaborn as sns + +class SiameseNetwork(nn.Module): + """ + Siamese Network for learning image embeddings of benign and malignant melanomas. + """ + def __init__(self): + super(SiameseNetwork, self).__init__() + + # ResNet50 Feature Extractor + resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) + self.features = nn.Sequential(*list(resnet.children())[:-1]) + + # Fully Connected Layer + self.fc = nn.Sequential( + nn.Linear(2048, 512), + nn.ReLU(), + nn.Dropout(0.3), + nn.Linear(512, 256), + nn.ReLU(), + nn.Linear(256, 128) + ) + + def forward(self, x1, x2): + """Forward pass to compute embeddings for a pair of images""" + # Get embeddings for both images + out1 = self.get_embedding(x1) + out2 = self.get_embedding(x2) + return out1, out2 + + def get_embedding(self, x): + """Computing embeddings for a single image""" + features = self.features(x) + features = features.view(features.size(0), -1) + return self.fc(features) + +class MLPClassifier(nn.Module): + """ + MLP Classifier using Siamese Network embeddings to predict melanoma + """ + def __init__(self, embedding_dim=128): + super(MLPClassifier, self).__init__() + self.classifier = nn.Sequential( + nn.Linear(embedding_dim, 64), + nn.ReLU(), + nn.Dropout(0.7), + nn.Linear(64, 32), + nn.ReLU(), + nn.Dropout(0.5), + nn.Linear(32, 1), + nn.Sigmoid() + ) + + def forward(self, embedding): + """ + Input: embedding from Siamese network + Output: probability of being malignant (0 = benign, 1 = malignant) + """ + return self.classifier(embedding) + +def contrastive_loss(output1, output2, label, margin=1.0): + """ + Contrastive loss for Siamese Network training + """ + # Calculate euclidean distance + euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1) + 1e-6) + + # Calculate contrastive loss + loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + + label * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) + + return loss + +class Predict: + def __init__(self, siamese_network, mlp_classifier, device): + self.siamese_network = siamese_network + self.mlp_classifier = mlp_classifier + self.device = device + + def predict(self, data_loader): + """Run predictions on the given data loader""" + # Set models to evaluation mode + self.siamese_network.eval() + self.mlp_classifier.eval() + + preds = [] + probs = [] + labels = [] + + with torch.no_grad(): + for batch in tqdm(data_loader, desc="Predicting"): + # Move batch to GPU if available + images = batch['img1'].to(self.device) # Get first image from pair + batch_labels = batch['diagnosis1'].to(self.device) # Get true label + + # Get embeddings from Siamese Network + embeddings = self.siamese_network.get_embedding(images) + + # Probability of being malignant + batch_probs = self.mlp_classifier(embeddings).squeeze() + batch_preds = (batch_probs > 0.5).float() + + # Store results using CPU + preds.extend(batch_preds.cpu().numpy()) + probs.extend(batch_probs.cpu().numpy()) + labels.extend(batch_labels.cpu().numpy()) + + # Convert lists to numpy arrays + return np.array(preds), np.array(probs), np.array(labels) + +class Evaluate: + def __init__(self, preds, probs, labels): + self.preds = preds + self.probs = probs + self.labels = labels + + def evaluate(self): + """Evaluate predictions and return metrics""" + return { + 'basic_metrics': self._get_basic_metrics(), + 'roc_auc': self._get_roc_auc(), + 'class_report': classification_report(self.labels, self.preds, + target_names=['Benign', 'Malignant']) + } + + def _get_basic_metrics(self): + """Calculate accuracy metrics for both classes""" + accuracy = (self.preds == self.labels).mean() + malignant_mask = self.labels == 1 + benign_mask = self.labels == 0 + + return { + 'accuracy': accuracy, + 'malignant_accuracy': (self.preds[malignant_mask] == self.labels[malignant_mask]).mean(), + 'benign_accuracy': (self.preds[benign_mask] == self.labels[benign_mask]).mean() + } + + def _get_roc_auc(self): + """Calculate ROC curve and AUC score""" + fpr, tpr, _ = roc_curve(self.labels, self.probs) + return {'fpr': fpr, 'tpr': tpr, 'auc': auc(fpr, tpr)} + + def plot_results(self): + """Generate ROC curve and confusion matrix plots""" + # Plot ROC curve + roc_data = self._get_roc_auc() + plt.figure(figsize=(10, 8)) + sns.lineplot(x=roc_data['fpr'], y=roc_data['tpr']) + sns.lineplot(x=[0, 1], y=[0, 1], linestyle='--') + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title(f'ROC Curve (AUC = {roc_data["auc"]:.3f})') + plt.savefig('roc_curve.png') + plt.close() + + # Plot confusion matrix + cm = confusion_matrix(self.labels, self.preds) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='vlag', + xticklabels=['Benign', 'Malignant'], + yticklabels=['Benign', 'Malignant']) + plt.title('Confusion Matrix') + plt.savefig('confusion_matrix.png') + plt.close() + + def save_results(self, filename='evaluation_results.txt'): + """Save all evaluation metrics to file""" + results = self.evaluate() + metrics = results['basic_metrics'] + + with open(filename, 'w') as f: + f.write("Evaluation Results\n\n") + f.write(f"Overall Accuracy: {metrics['accuracy']}\n") + f.write(f"Malignant Accuracy: {metrics['malignant_accuracy']}\n") + f.write(f"Benign Accuracy: {metrics['benign_accuracy']}\n") + f.write(f"ROC-AUC Score: {results['roc_auc']['auc']}\n\n") + f.write(f"Classification Report:\n{results['class_report']}\n") diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index c30ffe217..5f810f17e 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -1,119 +1,9 @@ import torch -import numpy as np -from sklearn.metrics import roc_curve, auc, classification_report, confusion_matrix -import seaborn as sns -import matplotlib.pyplot as plt -from tqdm import tqdm -from train import SiameseNetwork, MLPClassifier from dataset import DataManager +from modules import SiameseNetwork, MLPClassifier, Predict, Evaluate -class Predict: - def __init__(self, siamese_network, mlp_classifier, device): - self.siamese_network = siamese_network - self.mlp_classifier = mlp_classifier - self.device = device - - def predict(self, data_loader): - """Run predictions on the given data loader""" - # Set models to evaluation mode - self.siamese_network.eval() - self.mlp_classifier.eval() - - preds = [] - probs = [] - labels = [] - - with torch.no_grad(): - for batch in tqdm(data_loader, desc="Predicting"): - # Move batch to GPU if available - images = batch['img1'].to(self.device) # Get first image from pair - batch_labels = batch['diagnosis1'].to(self.device) # Get true label - - # Get embeddings from Siamese Network - embeddings = self.siamese_network.get_embedding(images) - - # Probability of being malignant - batch_probs = self.mlp_classifier(embeddings).squeeze() - batch_preds = (batch_probs > 0.5).float() - - # Store results using CPU - preds.extend(batch_preds.cpu().numpy()) - probs.extend(batch_probs.cpu().numpy()) - labels.extend(batch_labels.cpu().numpy()) - - # Convert lists to numpy arrays - return np.array(preds), np.array(probs), np.array(labels) - -class Evaluate: - def __init__(self, preds, probs, labels): - self.preds = preds - self.probs = probs - self.labels = labels - - def evaluate(self): - """Evaluate predictions and return metrics""" - return { - 'basic_metrics': self._get_basic_metrics(), - 'roc_auc': self._get_roc_auc(), - 'class_report': classification_report(self.labels, self.preds, - target_names=['Benign', 'Malignant']) - } - - def _get_basic_metrics(self): - """Calculate accuracy metrics for both classes""" - accuracy = (self.preds == self.labels).mean() - malignant_mask = self.labels == 1 - benign_mask = self.labels == 0 - - return { - 'accuracy': accuracy, - 'malignant_accuracy': (self.preds[malignant_mask] == self.labels[malignant_mask]).mean(), - 'benign_accuracy': (self.preds[benign_mask] == self.labels[benign_mask]).mean() - } - - def _get_roc_auc(self): - """Calculate ROC curve and AUC score""" - fpr, tpr, _ = roc_curve(self.labels, self.probs) - return {'fpr': fpr, 'tpr': tpr, 'auc': auc(fpr, tpr)} - - def plot_results(self): - """Generate ROC curve and confusion matrix plots""" - # Plot ROC curve - roc_data = self._get_roc_auc() - plt.figure(figsize=(10, 8)) - sns.lineplot(x=roc_data['fpr'], y=roc_data['tpr']) - sns.lineplot(x=[0, 1], y=[0, 1], linestyle='--') - plt.xlabel('False Positive Rate') - plt.ylabel('True Positive Rate') - plt.title(f'ROC Curve (AUC = {roc_data["auc"]:.3f})') - plt.savefig('roc_curve.png') - plt.close() - - # Plot confusion matrix - cm = confusion_matrix(self.labels, self.preds) - plt.figure(figsize=(10, 8)) - sns.heatmap(cm, annot=True, fmt='d', cmap='vlag', - xticklabels=['Benign', 'Malignant'], - yticklabels=['Benign', 'Malignant']) - plt.title('Confusion Matrix') - plt.savefig('confusion_matrix.png') - plt.close() - - def save_results(self, filename='evaluation_results.txt'): - """Save all evaluation metrics to file""" - results = self.evaluate() - metrics = results['basic_metrics'] - - with open(filename, 'w') as f: - f.write("Evaluation Results\n\n") - f.write(f"Overall Accuracy: {metrics['accuracy']}\n") - f.write(f"Malignant Accuracy: {metrics['malignant_accuracy']}\n") - f.write(f"Benign Accuracy: {metrics['benign_accuracy']}\n") - f.write(f"ROC-AUC Score: {results['roc_auc']['auc']}\n\n") - f.write(f"Classification Report:\n{results['class_report']}\n") - -if __name__ == "__main__": - # Set device +def main(): + # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") @@ -150,4 +40,8 @@ def save_results(self, filename='evaluation_results.txt'): # Generate and save plots evaluator.plot_results() - evaluator.save_results() \ No newline at end of file + evaluator.save_results() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 1b67299d5..cd679f82b 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -3,79 +3,7 @@ import torch.optim as optim from dataset import DataManager from tqdm import tqdm -import torchvision.models as models -from torchvision.models import ResNet50_Weights - -class SiameseNetwork(nn.Module): - """ - Siamese Network for learning image embeddings of benign and malignant melanomas. - """ - def __init__(self): - super(SiameseNetwork, self).__init__() - - # ResNet50 Feature Extractor - resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) - self.features = nn.Sequential(*list(resnet.children())[:-1]) - - # Fully Connected Layer - self.fc = nn.Sequential( - nn.Linear(2048, 512), - nn.ReLU(), - nn.Dropout(0.3), - nn.Linear(512, 256), - nn.ReLU(), - nn.Linear(256, 128) - ) - - def forward(self, x1, x2): - """Forward pass to compute embeddings for a pair of images""" - # Get embeddings for both images - out1 = self.get_embedding(x1) - out2 = self.get_embedding(x2) - return out1, out2 - - def get_embedding(self, x): - """Computing embeddings for a single image""" - features = self.features(x) - features = features.view(features.size(0), -1) - return self.fc(features) - -class MLPClassifier(nn.Module): - """ - MLP Classifier using Siamese Network embeddings to predict melanoma - """ - def __init__(self, embedding_dim=128): - super(MLPClassifier, self).__init__() - self.classifier = nn.Sequential( - nn.Linear(embedding_dim, 64), - nn.ReLU(), - nn.Dropout(0.7), - nn.Linear(64, 32), - nn.ReLU(), - nn.Dropout(0.5), - nn.Linear(32, 1), - nn.Sigmoid() - ) - - def forward(self, embedding): - """ - Input: embedding from Siamese network - Output: probability of being malignant (0 = benign, 1 = malignant) - """ - return self.classifier(embedding) - -def contrastive_loss(output1, output2, label, margin=1.0): - """ - Contrastive loss for Siamese Network training - """ - # Calculate euclidean distance - euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1) + 1e-6) - - # Calculate contrastive loss - loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + - label * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) - - return loss +from modules import SiameseNetwork, MLPClassifier, contrastive_loss def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, margin=1.0): """ @@ -173,7 +101,9 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade 'accuracy': best_acc, }, 'best_mlp_classifier.pth') -if __name__ == "__main__": + +def main(): + global device # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") @@ -208,4 +138,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade # Train classifier print("\nTraining MLPClassifier using learned embeddings:") - train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) \ No newline at end of file + train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) + +if __name__ == "__main__": + main() \ No newline at end of file From 6c34c6df96f597a513812b302d4a9b1640b94fea Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 04:06:23 +1100 Subject: [PATCH 19/31] Refactored train.py to perform prediction and evaluation of trained model what was previously performed in predict.py --- recognition/siamese_richard_chantra/train.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index cd679f82b..a77a1b236 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -3,7 +3,7 @@ import torch.optim as optim from dataset import DataManager from tqdm import tqdm -from modules import SiameseNetwork, MLPClassifier, contrastive_loss +from modules import SiameseNetwork, MLPClassifier, contrastive_loss, evaluate_model def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, margin=1.0): """ @@ -140,5 +140,9 @@ def main(): print("\nTraining MLPClassifier using learned embeddings:") train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) + # Evaluate trained model + print("\nEvaluating the model on test data:") + evaluate_model(siamese_network, mlp_classifier, test_loader, device) + if __name__ == "__main__": main() \ No newline at end of file From e23c96907e45f74395b8eaf6a00dadf8c976db63 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 05:49:10 +1100 Subject: [PATCH 20/31] Refactored predict.py to enable prediction of directory of fresh images using saved model --- .../siamese_richard_chantra/modules.py | 111 ++++++++++++++++-- .../siamese_richard_chantra/predict.py | 46 +++----- 2 files changed, 122 insertions(+), 35 deletions(-) diff --git a/recognition/siamese_richard_chantra/modules.py b/recognition/siamese_richard_chantra/modules.py index f0286bf0e..1b3958248 100644 --- a/recognition/siamese_richard_chantra/modules.py +++ b/recognition/siamese_richard_chantra/modules.py @@ -7,6 +7,10 @@ import matplotlib.pyplot as plt from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix, classification_report import seaborn as sns +from dataset import DataManager +from PIL import Image +import torchvision.transforms as transforms +import os class SiameseNetwork(nn.Module): """ @@ -80,13 +84,93 @@ def contrastive_loss(output1, output2, label, margin=1.0): return loss class Predict: + """ + Handling prediction for images and using a trained SiameseNetwork and MLPClassifier to do so + """ def __init__(self, siamese_network, mlp_classifier, device): self.siamese_network = siamese_network self.mlp_classifier = mlp_classifier self.device = device + @staticmethod + def load_image(image_path): + """ + Load and preprocess an image for prediction. + """ + # Apply transformations + transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + ]) + image = Image.open(image_path).convert('RGB') + return transform(image).unsqueeze(0) + + def predict_image(self, image_path): + """ + Predict whether a new image is benign or malignant. + """ + # Load and preprocess the image + image = self.load_image(image_path).to(self.device) + + # Eval mode + self.siamese_network.eval() + self.mlp_classifier.eval() + + with torch.no_grad(): + # Generate embedding for the image + embedding = self.siamese_network.get_embedding(image) + + # Classify the embedding + output = self.mlp_classifier(embedding) + prediction = (output > 0.5).float() + probability = output.item() + + return prediction.item(), probability + + def batch_predict(self, folder): + """ + Performs predictions on all images within a specified directory + """ + predictions = [] + probabilities = [] + image_names = [] + + for filename in tqdm(os.listdir(folder), desc="Predicting images"): + if filename.endswith(('.jpg')): + image_path = os.path.join(folder, filename) + prediction, probability = self.predict_image(image_path) + + predictions.append(prediction) + probabilities.append(probability) + image_names.append(filename) + + return predictions, probabilities, image_names + + def evaluate_predictions(self, predictions, probabilities): + """ + Evaluates a set of metrics for a batch of predictions + """ + benign_count = predictions.count(0) + malignant_count = predictions.count(1) + avg_probability = np.mean(probabilities) + + report = classification_report( + predictions, [1]*len(predictions), target_names=['Benign', 'Malignant'] + ) + + return { + 'benign_count': benign_count, + 'malignant_count': malignant_count, + 'avg_probability': avg_probability, + 'classification_report': report + } + def predict(self, data_loader): - """Run predictions on the given data loader""" + """ + Run predictions on the given data loader + """ # Set models to evaluation mode self.siamese_network.eval() self.mlp_classifier.eval() @@ -117,13 +201,18 @@ def predict(self, data_loader): return np.array(preds), np.array(probs), np.array(labels) class Evaluate: + """ + Evaluating the classifier using a number of metrics + """ def __init__(self, preds, probs, labels): self.preds = preds self.probs = probs self.labels = labels def evaluate(self): - """Evaluate predictions and return metrics""" + """ + Evaluate predictions and return metrics + """ return { 'basic_metrics': self._get_basic_metrics(), 'roc_auc': self._get_roc_auc(), @@ -132,7 +221,9 @@ def evaluate(self): } def _get_basic_metrics(self): - """Calculate accuracy metrics for both classes""" + """ + Calculate accuracy metrics for both classes + """ accuracy = (self.preds == self.labels).mean() malignant_mask = self.labels == 1 benign_mask = self.labels == 0 @@ -144,12 +235,16 @@ def _get_basic_metrics(self): } def _get_roc_auc(self): - """Calculate ROC curve and AUC score""" + """ + Calculate ROC curve and AUC score + """ fpr, tpr, _ = roc_curve(self.labels, self.probs) return {'fpr': fpr, 'tpr': tpr, 'auc': auc(fpr, tpr)} def plot_results(self): - """Generate ROC curve and confusion matrix plots""" + """ + Generate ROC curve and confusion matrix plots + """ # Plot ROC curve roc_data = self._get_roc_auc() plt.figure(figsize=(10, 8)) @@ -172,12 +267,14 @@ def plot_results(self): plt.close() def save_results(self, filename='evaluation_results.txt'): - """Save all evaluation metrics to file""" + """ + Save evaluation metrics to file + """ results = self.evaluate() metrics = results['basic_metrics'] with open(filename, 'w') as f: - f.write("Evaluation Results\n\n") + f.write("\nEvaluation Results:\n") f.write(f"Overall Accuracy: {metrics['accuracy']}\n") f.write(f"Malignant Accuracy: {metrics['malignant_accuracy']}\n") f.write(f"Benign Accuracy: {metrics['benign_accuracy']}\n") diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index 5f810f17e..d0cbfed9f 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -1,47 +1,37 @@ import torch -from dataset import DataManager -from modules import SiameseNetwork, MLPClassifier, Predict, Evaluate +from modules import SiameseNetwork, MLPClassifier, Predict def main(): - # Set device + # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - - # Setup data - data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') - data_manager.load_data() - data_manager.create_dataloaders() - test_loader = data_manager.test_loader - - # Load models + + # Load trained models siamese_network = SiameseNetwork().to(device) mlp_classifier = MLPClassifier().to(device) - # Load saved weights from training + # Load the saved model weights siamese_network_checkpoint = torch.load('best_siamese_network.pth') mlp_classifier_checkpoint = torch.load('best_mlp_classifier.pth') siamese_network.load_state_dict(siamese_network_checkpoint['model_state_dict']) mlp_classifier.load_state_dict(mlp_classifier_checkpoint['model_state_dict']) - # Create Predict instance and run predictions + # Create Predict instance predictor = Predict(siamese_network, mlp_classifier, device) - preds, probs, labels = predictor.predict(test_loader) - # Create Evaluate instance and run evaluation - evaluator = Evaluate(preds, probs, labels) - results = evaluator.evaluate() - - # Print evaluation results - print("Evaluation\n") - print(f"Overall Accuracy: {results['basic_metrics']['accuracy']}") - print(f"Malignant Accuracy: {results['basic_metrics']['malignant_accuracy']}") - print(f"ROC-AUC Score: {results['roc_auc']['auc']}\n") - print(results['class_report']) - - # Generate and save plots - evaluator.plot_results() - evaluator.save_results() + # Path to the folder with new images for prediction + folder_path = 'archive/test-image/image/' # Replace with your folder path + # Run predictions on the folder + predictions, probabilities, image_names = predictor.batch_predict(folder_path) + + # Evaluate and display results + results = predictor.evaluate_predictions(predictions, probabilities) + print(f"\nEvaluation Results: {folder_path}") + print(f"Benign Count: {results['benign_count']}") + print(f"Malignant Count: {results['malignant_count']}") + print(f"Average Probability of Malignant Melanoma: {results['avg_probability']:.2f}") + print("\nClassification Report:\n", results['classification_report']) if __name__ == "__main__": main() \ No newline at end of file From 3e4020d4af91835d81b8861f8d4c3591d3f06b41 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 06:15:36 +1100 Subject: [PATCH 21/31] Added header block for each of the 4 files and refactored train.py to use Evaluate from modules.py --- .../siamese_richard_chantra/dataset.py | 8 +++ .../siamese_richard_chantra/modules.py | 50 +++++++++++------- .../siamese_richard_chantra/predict.py | 14 +++-- recognition/siamese_richard_chantra/train.py | 52 ++++++++++++------- 4 files changed, 83 insertions(+), 41 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index b67fa0fb1..80356722f 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -1,3 +1,11 @@ +""" +- Manages and preprocesses melanoma dataset for model training +- Includes data loading, augmentation, and DataLoader generation + +@author: richardchantra +@student_number: 43032053 +""" + import pandas as pd from PIL import Image import torch diff --git a/recognition/siamese_richard_chantra/modules.py b/recognition/siamese_richard_chantra/modules.py index 1b3958248..65076c1ff 100644 --- a/recognition/siamese_richard_chantra/modules.py +++ b/recognition/siamese_richard_chantra/modules.py @@ -1,16 +1,24 @@ +""" +- The Siamese Network, MLP Classifier and Contrastive Loss are defined for use in train.py and predict.py +- The Predict class is defined for prediction using a saved model in predict.py +- The Evaluation class is defined for evaluating the performance post training in train.py + +@author: richardchantra +@student_number: 43032053 +""" + +import os import torch import torch.nn as nn import torchvision.models as models from torchvision.models import ResNet50_Weights +import torchvision.transforms as transforms from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt -from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix, classification_report +from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix import seaborn as sns -from dataset import DataManager from PIL import Image -import torchvision.transforms as transforms -import os class SiameseNetwork(nn.Module): """ @@ -34,17 +42,34 @@ def __init__(self): ) def forward(self, x1, x2): - """Forward pass to compute embeddings for a pair of images""" + """ + Forward pass to compute embeddings for a pair of images + """ # Get embeddings for both images out1 = self.get_embedding(x1) out2 = self.get_embedding(x2) return out1, out2 def get_embedding(self, x): - """Computing embeddings for a single image""" + """ + Computing embeddings for a single image + """ features = self.features(x) features = features.view(features.size(0), -1) return self.fc(features) + + def contrastive_loss(self, output1, output2, label, margin=1.0): + """ + Contrastive loss for Siamese Network training + """ + # Calculate euclidean distance + euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1) + 1e-6) + + # Calculate contrastive loss + loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + + label * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) + + return loss class MLPClassifier(nn.Module): """ @@ -70,18 +95,7 @@ def forward(self, embedding): """ return self.classifier(embedding) -def contrastive_loss(output1, output2, label, margin=1.0): - """ - Contrastive loss for Siamese Network training - """ - # Calculate euclidean distance - euclidean_distance = torch.sqrt(torch.sum((output1 - output2) ** 2, dim=1) + 1e-6) - - # Calculate contrastive loss - loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + - label * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2)) - - return loss + class Predict: """ diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index d0cbfed9f..55857def0 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -1,3 +1,11 @@ +""" +- Predicts melanoma classifications for images in a directory +- Provides evaluation metrics for batch predictions + +@author: richardchantra +@student_number: 43032053 +""" + import torch from modules import SiameseNetwork, MLPClassifier, Predict @@ -19,10 +27,10 @@ def main(): # Create Predict instance predictor = Predict(siamese_network, mlp_classifier, device) - # Path to the folder with new images for prediction - folder_path = 'archive/test-image/image/' # Replace with your folder path + # Path to the directory with new images for prediction + folder_path = 'archive/test-image/image/' # Replace with any directory - # Run predictions on the folder + # Run predictions on the directory predictions, probabilities, image_names = predictor.batch_predict(folder_path) # Evaluate and display results diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index a77a1b236..c58a95e34 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -1,9 +1,17 @@ +""" +- Trains a Siamese Network and MLP Classifier on melanoma images +- Evaluates model performance after training + +@author: richardchantra +@student_number: 43032053 +""" + import torch import torch.nn as nn import torch.optim as optim -from dataset import DataManager from tqdm import tqdm -from modules import SiameseNetwork, MLPClassifier, contrastive_loss, evaluate_model +from dataset import DataManager +from modules import SiameseNetwork, MLPClassifier, Evaluate, Predict def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, margin=1.0): """ @@ -26,11 +34,11 @@ def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, ma embedding1, embedding2 = siamese_network(img1, img2) # Calculate loss - loss = contrastive_loss(embedding1, embedding2, similarity_label, margin) + loss = siamese_network.contrastive_loss(embedding1, embedding2, similarity_label, margin) # Backward pass loss.backward() - torch.nn.utils.clip_grad_norm_(siamese_network.parameters(), 1.0) # Gradient clipping + torch.nn.utils.clip_grad_norm_(siamese_network.parameters(), 1.0) # Gradient clipping optimizer.step() running_loss += loss.item() @@ -103,13 +111,11 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade def main(): - global device # Set device + global device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - SKIP_SIAMESE_TRAINING = 0 # 0: Begin training, 1: Skip training (use checkpoint) - # Setup data data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') data_manager.load_data() @@ -125,24 +131,30 @@ def main(): optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001, weight_decay=5e-5) optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001, weight_decay=1e-4) - if SKIP_SIAMESE_TRAINING: - # Load Siamese Network checkpoint - print("Loading Siamese Network checkpoint...") - checkpoint = torch.load('best_siamese_network.pth') - siamese_network.load_state_dict(checkpoint['model_state_dict']) - print(f"Loaded Siamese Network checkpoint with loss: {checkpoint['loss']:.4f}") - else: - # Train Siamese network from scratch - print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=16) + print("Training Siamese Network to learn embeddings from images:") + train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=16) - # Train classifier + # Train the MLP classifier print("\nTraining MLPClassifier using learned embeddings:") train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) - # Evaluate trained model + # Evaluate the trained model print("\nEvaluating the model on test data:") - evaluate_model(siamese_network, mlp_classifier, test_loader, device) + predictor = Predict(siamese_network, mlp_classifier, device) + preds, probs, labels = predictor.predict(test_loader) + + evaluator = Evaluate(preds, probs, labels) + results = evaluator.evaluate() + + print("\nEvaluation Results:\n") + print(f"Overall Accuracy: {results['basic_metrics']['accuracy']}") + print(f"Malignant Accuracy: {results['basic_metrics']['malignant_accuracy']}") + print(f"ROC-AUC Score: {results['roc_auc']['auc']}\n") + print(results['class_report']) + + # Optionally save and plot results + evaluator.plot_results() + evaluator.save_results() if __name__ == "__main__": main() \ No newline at end of file From 523637bd0e680d9be8e850199c2301b57a3e4e94 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 06:40:15 +1100 Subject: [PATCH 22/31] Added function to track of loss for each epoch during siamese training and mlp training --- .../siamese_richard_chantra/dataset.py | 3 +- .../siamese_richard_chantra/modules.py | 18 +++++ .../siamese_richard_chantra/predict.py | 5 +- recognition/siamese_richard_chantra/train.py | 71 ++++++++++++++++--- 4 files changed, 83 insertions(+), 14 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 80356722f..c76e2bb55 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -217,4 +217,5 @@ def load_and_transform(self, image_id, threshold=0.7): data_manager = DataManager(csv_path, img_dir) data_manager.load_data() data_manager.create_dataloaders() - data_manager.print_statistics() \ No newline at end of file + data_manager.print_statistics() + \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/modules.py b/recognition/siamese_richard_chantra/modules.py index 65076c1ff..264a44b38 100644 --- a/recognition/siamese_richard_chantra/modules.py +++ b/recognition/siamese_richard_chantra/modules.py @@ -294,3 +294,21 @@ def save_results(self, filename='evaluation_results.txt'): f.write(f"Benign Accuracy: {metrics['benign_accuracy']}\n") f.write(f"ROC-AUC Score: {results['roc_auc']['auc']}\n\n") f.write(f"Classification Report:\n{results['class_report']}\n") + + @staticmethod + def plot_loss(self, data, title, xlabel, ylabel, save_path, color='b', marker='o'): + """ + Plots and saves a training loss graph + """ + plt.figure(figsize=(8, 6)) + plt.plot(data, label=title, marker=marker, color=color) + plt.title(title) + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.legend() + plt.grid(True) + plt.tight_layout() + plt.savefig(save_path) + plt.close() + print(f"Plot saved at: {save_path}") + \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index 55857def0..b117a562d 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -28,7 +28,7 @@ def main(): predictor = Predict(siamese_network, mlp_classifier, device) # Path to the directory with new images for prediction - folder_path = 'archive/test-image/image/' # Replace with any directory + folder_path = 'archive/test-image/image/' # Run predictions on the directory predictions, probabilities, image_names = predictor.batch_predict(folder_path) @@ -42,4 +42,5 @@ def main(): print("\nClassification Report:\n", results['classification_report']) if __name__ == "__main__": - main() \ No newline at end of file + main() + \ No newline at end of file diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index c58a95e34..254b8131a 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -6,6 +6,7 @@ @student_number: 43032053 """ +import os import torch import torch.nn as nn import torch.optim as optim @@ -19,6 +20,7 @@ def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, ma """ siamese_network.train() best_loss = float('inf') + epoch_losses = [] for epoch in range(epochs): running_loss = 0.0 @@ -38,12 +40,13 @@ def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, ma # Backward pass loss.backward() - torch.nn.utils.clip_grad_norm_(siamese_network.parameters(), 1.0) # Gradient clipping + torch.nn.utils.clip_grad_norm_(siamese_network.parameters(), 1.0) optimizer.step() running_loss += loss.item() epoch_loss = running_loss / len(train_loader) + epoch_losses.append(epoch_loss) print(f"Siamese Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}") # Save best model @@ -55,15 +58,17 @@ def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, ma 'optimizer_state_dict': optimizer.state_dict(), 'loss': best_loss, }, 'best_siamese_network.pth') + return epoch_losses def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loader, epochs=5): """ Train MLP classifier using Siamese Network embeddings """ mlp_classifier.train() - siamese_network.eval() # Freeze Siamese network + siamese_network.eval() criterion = nn.BCELoss() best_acc = 0.0 + epoch_losses = [] for epoch in range(epochs): running_loss = 0.0 @@ -96,6 +101,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade correct += (predicted == diagnosis_label).sum().item() epoch_loss = running_loss / len(train_loader) + epoch_losses.append(epoch_loss) accuracy = 100 * correct / total print(f"Classifier Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}, Accuracy: {accuracy:.2f}%") @@ -108,7 +114,7 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade 'optimizer_state_dict': optimizer.state_dict(), 'accuracy': best_acc, }, 'best_mlp_classifier.pth') - + return epoch_losses def main(): # Set device @@ -128,17 +134,60 @@ def main(): mlp_classifier = MLPClassifier().to(device) # Initialize optimizers - optimizer_siamese = optim.Adam(siamese_network.parameters(), lr=0.001, weight_decay=5e-5) - optimizer_mlp = optim.Adam(mlp_classifier.parameters(), lr=0.001, weight_decay=1e-4) + optimizer_siamese = optim.Adam( + siamese_network.parameters(), + lr=0.001, + weight_decay=5e-5 + ) + optimizer_mlp = optim.Adam( + mlp_classifier.parameters(), + lr=0.001, + weight_decay=1e-4 + ) + # Train the Siamese Network print("Training Siamese Network to learn embeddings from images:") - train_siamese_network(siamese_network, optimizer_siamese, train_loader, epochs=16) + siamese_losses = train_siamese_network( + siamese_network, + optimizer_siamese, + train_loader, + epochs=16 + ) - # Train the MLP classifier + # Train the MLP classifier print("\nTraining MLPClassifier using learned embeddings:") - train_mlp_classifier(siamese_network, mlp_classifier, optimizer_mlp, train_loader, epochs=8) + mlp_losses = train_mlp_classifier( + siamese_network, + mlp_classifier, + optimizer_mlp, + train_loader, + epochs=8 + ) + + # Plot and save training losses + save_dir = "plots" + os.makedirs(save_dir, exist_ok=True) + + Evaluate.plot_loss( + siamese_losses, + title="Siamese Network Training Loss per Epoch", + xlabel="Epoch", + ylabel="Loss", + save_path=os.path.join(save_dir, "siamese_network_loss.png"), + color='b', + marker='o' + ) + Evaluate.plot_loss( + mlp_losses, + title="MLP Classifier Training Loss per Epoch", + xlabel="Epoch", + ylabel="Loss", + save_path=os.path.join(save_dir, "mlp_classifier_loss.png"), + color='g', + marker='s' + ) - # Evaluate the trained model + # Evaluate the model after training print("\nEvaluating the model on test data:") predictor = Predict(siamese_network, mlp_classifier, device) preds, probs, labels = predictor.predict(test_loader) @@ -152,9 +201,9 @@ def main(): print(f"ROC-AUC Score: {results['roc_auc']['auc']}\n") print(results['class_report']) - # Optionally save and plot results + # Optionally save and plot evaluation results evaluator.plot_results() evaluator.save_results() if __name__ == "__main__": - main() \ No newline at end of file + main() From 848b1fa3677e72057ae5a8db963eb8be306bd7e8 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 07:03:07 +1100 Subject: [PATCH 23/31] Added argparsing functionality for dataset.py for csv_path, img_dir and batch_size --- .../siamese_richard_chantra/dataset.py | 43 ++++++++++++------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index c76e2bb55..78cb4bdee 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -13,6 +13,7 @@ from sklearn.model_selection import train_test_split import torchvision.transforms as transforms import numpy as np +import argparse class DataManager: """ @@ -111,15 +112,20 @@ def print_statistics(self): """ Print dataset statistics before and after balancing """ - print("Original dataset statistics:") - print(f"Total images: {len(self.data)}") - print(f"Classes distribution: \n{self.data['target'].value_counts()}") - + # Original dataset statistics + class_distribution = self.data['target'].value_counts() + print(f"Original dataset statistics:\n" + f"Total images: {len(self.data)}\n" + f"Classes distribution:\n{class_distribution}\n") + + # Split and display training/testing data statistics train_data, test_data = self.split_data() - print("\nAfter balancing training data:") - print(f"Training set distribution: \n{train_data['target'].value_counts()}") - print(f"Test set distribution: \n{test_data['target'].value_counts()}") - print("\nNote: 0 = benign, 1 = malignant") + train_distribution = train_data['target'].value_counts() + test_distribution = test_data['target'].value_counts() + print(f"After balancing training data:\n" + f"Training set distribution:\n{train_distribution}\n" + f"Test set distribution:\n{test_distribution}\n" + f"\nNote: 0 = benign, 1 = malignant") class SiameseDataset(Dataset): """ @@ -210,12 +216,17 @@ def load_and_transform(self, image_id, threshold=0.7): return self.transform(image) if __name__ == "__main__": - # File paths - csv_path = 'archive/train-metadata.csv' - img_dir = 'archive/train-image/image/' - - data_manager = DataManager(csv_path, img_dir) + parser = argparse.ArgumentParser(description="Dataset Manager for Melanoma Classification") + parser.add_argument('--csv_path', type=str, default='archive/train-metadata.csv', + help='Path to the CSV metadata file') + parser.add_argument('--img_dir', type=str, default='archive/train-image/image/', + help='Directory path to the image files') + parser.add_argument('--batch_size', type=int, default=256, + help='Batch size for DataLoader') + args = parser.parse_args() + + # Initialize DataManager with arguments + data_manager = DataManager(args.csv_path, args.img_dir) data_manager.load_data() - data_manager.create_dataloaders() - data_manager.print_statistics() - \ No newline at end of file + data_manager.create_dataloaders(batch_size=args.batch_size) + data_manager.print_statistics() \ No newline at end of file From ebaab2b4e13a5e20af0f78aaa06d2f842af807b3 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 07:10:04 +1100 Subject: [PATCH 24/31] Added argparsing functionality for train.py for csv_path, img_dir and batch_size, epochs_siamese, epochs_mlp and save_dir for plots --- recognition/siamese_richard_chantra/train.py | 25 ++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/recognition/siamese_richard_chantra/train.py b/recognition/siamese_richard_chantra/train.py index 254b8131a..862b66c9d 100644 --- a/recognition/siamese_richard_chantra/train.py +++ b/recognition/siamese_richard_chantra/train.py @@ -13,6 +13,7 @@ from tqdm import tqdm from dataset import DataManager from modules import SiameseNetwork, MLPClassifier, Evaluate, Predict +import argparse def train_siamese_network(siamese_network, optimizer, train_loader, epochs=5, margin=1.0): """ @@ -117,15 +118,31 @@ def train_mlp_classifier(siamese_network, mlp_classifier, optimizer, train_loade return epoch_losses def main(): + # Argument Parsing + parser = argparse.ArgumentParser(description="Training a Siamese Network and MLP Classifier on melanoma images") + parser.add_argument('--csv_path', type=str, default='archive/train-metadata.csv', + help='Path to the CSV metadata file') + parser.add_argument('--img_dir', type=str, default='archive/train-image/image/', + help='Directory path to the image files') + parser.add_argument('--batch_size', type=int, default=256, + help='Batch size for DataLoader') + parser.add_argument('--epochs_siamese', type=int, default=16, + help='Number of epochs for training the Siamese Network') + parser.add_argument('--epochs_mlp', type=int, default=8, + help='Number of epochs for training the MLP Classifier') + parser.add_argument('--save_dir', type=str, default="plots", + help='Directory to save training plots') + args = parser.parse_args() + # Set device global device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Setup data - data_manager = DataManager('archive/train-metadata.csv', 'archive/train-image/image/') + data_manager = DataManager(args.csv_path, args.img_dir) data_manager.load_data() - data_manager.create_dataloaders() + data_manager.create_dataloaders(batch_size=args.batch_size) train_loader = data_manager.train_loader test_loader = data_manager.test_loader @@ -151,7 +168,7 @@ def main(): siamese_network, optimizer_siamese, train_loader, - epochs=16 + epochs=args.epochs_siamese ) # Train the MLP classifier @@ -161,7 +178,7 @@ def main(): mlp_classifier, optimizer_mlp, train_loader, - epochs=8 + epochs=args.epochs_mlp ) # Plot and save training losses From ab40b72d76c881b7e23969f3068d7f85ea117984 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 07:16:33 +1100 Subject: [PATCH 25/31] Added argparsing functionality for predict.py for image folder_path, siamese_model_path and mlp_model_path --- .../siamese_richard_chantra/dataset.py | 1 + .../siamese_richard_chantra/predict.py | 25 ++++++++++++------- 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/recognition/siamese_richard_chantra/dataset.py b/recognition/siamese_richard_chantra/dataset.py index 78cb4bdee..45642767e 100644 --- a/recognition/siamese_richard_chantra/dataset.py +++ b/recognition/siamese_richard_chantra/dataset.py @@ -216,6 +216,7 @@ def load_and_transform(self, image_id, threshold=0.7): return self.transform(image) if __name__ == "__main__": + # Argument Parsing parser = argparse.ArgumentParser(description="Dataset Manager for Melanoma Classification") parser.add_argument('--csv_path', type=str, default='archive/train-metadata.csv', help='Path to the CSV metadata file') diff --git a/recognition/siamese_richard_chantra/predict.py b/recognition/siamese_richard_chantra/predict.py index b117a562d..357b091de 100644 --- a/recognition/siamese_richard_chantra/predict.py +++ b/recognition/siamese_richard_chantra/predict.py @@ -8,8 +8,19 @@ import torch from modules import SiameseNetwork, MLPClassifier, Predict +import argparse def main(): + # Argument Parsing + parser = argparse.ArgumentParser(description="Predicting melanomas using trained models on a directory of melanoma images") + parser.add_argument('--folder_path', type=str, default='archive/test-image/image/', + help='Directory with new images for prediction') + parser.add_argument('--siamese_model_path', type=str, default='best_siamese_network.pth', + help='Path to the saved Siamese Network model weights') + parser.add_argument('--mlp_model_path', type=str, default='best_mlp_classifier.pth', + help='Path to the saved MLP Classifier model weights') + args = parser.parse_args() + # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") @@ -19,23 +30,20 @@ def main(): mlp_classifier = MLPClassifier().to(device) # Load the saved model weights - siamese_network_checkpoint = torch.load('best_siamese_network.pth') - mlp_classifier_checkpoint = torch.load('best_mlp_classifier.pth') + siamese_network_checkpoint = torch.load(args.siamese_model_path) + mlp_classifier_checkpoint = torch.load(args.mlp_model_path) siamese_network.load_state_dict(siamese_network_checkpoint['model_state_dict']) mlp_classifier.load_state_dict(mlp_classifier_checkpoint['model_state_dict']) # Create Predict instance predictor = Predict(siamese_network, mlp_classifier, device) - - # Path to the directory with new images for prediction - folder_path = 'archive/test-image/image/' - # Run predictions on the directory - predictions, probabilities, image_names = predictor.batch_predict(folder_path) + # Run predictions on the specified directory + predictions, probabilities, image_names = predictor.batch_predict(args.folder_path) # Evaluate and display results results = predictor.evaluate_predictions(predictions, probabilities) - print(f"\nEvaluation Results: {folder_path}") + print(f"\nEvaluation Results for Directory: {args.folder_path}") print(f"Benign Count: {results['benign_count']}") print(f"Malignant Count: {results['malignant_count']}") print(f"Average Probability of Malignant Melanoma: {results['avg_probability']:.2f}") @@ -43,4 +51,3 @@ def main(): if __name__ == "__main__": main() - \ No newline at end of file From 8d15930f7f8c913dd4630c59bb703ae9a55f37cb Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 09:04:36 +1100 Subject: [PATCH 26/31] Added assets and updated README.md --- recognition/siamese_richard_chantra/README.MD | 196 ++++++++++++++++-- .../assets/confusion_matrix.png | Bin 0 -> 55150 bytes .../assets/embeddings_distribution.png | Bin 0 -> 61883 bytes .../assets/embeddings_tsne.png | Bin 0 -> 280217 bytes .../assets/mlp_loss.png | Bin 0 -> 81071 bytes .../assets/roc_curve.png | Bin 0 -> 109877 bytes .../assets/siamese_architecture.png | Bin 0 -> 220352 bytes .../assets/siamese_loss.png | Bin 0 -> 91202 bytes .../siamese_richard_chantra/modules.py | 12 +- recognition/siamese_richard_chantra/train.py | 6 +- 10 files changed, 188 insertions(+), 26 deletions(-) create mode 100644 recognition/siamese_richard_chantra/assets/confusion_matrix.png create mode 100644 recognition/siamese_richard_chantra/assets/embeddings_distribution.png create mode 100644 recognition/siamese_richard_chantra/assets/embeddings_tsne.png create mode 100644 recognition/siamese_richard_chantra/assets/mlp_loss.png create mode 100644 recognition/siamese_richard_chantra/assets/roc_curve.png create mode 100644 recognition/siamese_richard_chantra/assets/siamese_architecture.png create mode 100644 recognition/siamese_richard_chantra/assets/siamese_loss.png diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index 92e02b153..45b404e31 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -1,29 +1,191 @@ -# Classification of Melanoma using the ISIC 2020 Kaggle Challenge dataset +# Classification of Melanoma using the ISIC 2020 Kaggle Challenge Dataset with Siamese Networks -Richard Chantra s43032053 +Author: Richard Chantra +Student Number: s43032053 -### Problem Statement +## Problem Statement -- Melanomas are responsible for 75% of skin cancer deaths, it is estimated around 7,000 people die annually from the disease. -- Using computer vision techniques we can improve diagnosis of melanoma by assisting dermatologists thus improving diagnostic accuracy. -- The data we are using comes from International Skin Imaging Collaboration (ISIC) and is referred to as the ISIC-2020 Dataset. -- It is the largest publicly available collection of dematologically-QC skin lesions. -- The task is to classify melanomas with 0.8 accuracy for the test set. -- We will be implementing a Siamese Network +Melanomas are responsible for 75% of skin cancer deaths, with an estimated 7,000 annual fatalities. This project aims to assist dermatologists by developing a computer vision system to assist in classifying melanomas. We utilize the International Skin Imaging Collaboration (ISIC) 2020 Dataset, the largest publicly available collection of dermatologically-QC skin lesions, with the goal of achieving 0.8 accuracy on the test set. -### Preprocessing +## Structure of Dataset -### Siamese Networks +The dataset comes from the ISIC 2020 Challenge and presents a significant class imbalance challenge: -- First implemented by Taigman et al., 2014. in DeepFace: Closing the Gap to Human-Level Performance in Face Verification +- Total Images: 33,126 + - Benign (non-melanoma): 98% (32,626 images) + - Malignant (melanoma): 2% (584 images) +- Image Format: 256x256 pixel JPG files +- Source: https://www.kaggle.com/datasets/nischaydnk/isic-2020-jpg-256x256-resized/data +## Preprocessing -### Siamese Network Architecture +Our preprocessing pipeline addresses several key challenges: -### Results +**Adjusting Class Imbalance:** +The extreme 98:2 ratio of benign to malignant cases required careful handling. We implemented an oversampling strategy targeting a 67:33 (benign:malignant) distribution in the training set. This ratio was chosen after extensive experimentation, as it provided optimal balance between benign case accuracy and melanoma detection sensitivity. -plot the loss +**Data Augmentation:** +To improve model robustness, we implemented a controlled augmentation strategy: +- Random horizontal and vertical flips +- Random rotations between 0-360 degrees +- Augmentations applied with 30% probability to maintain dataset characteristics +- Ratio of transformations: horizontal flips : vertical flips : rotations = 1:1:2 -### Dependencies +**Image Normalization:** +Images are normalized using ResNet50's pretrained requirements: +- Mean values: [0.485, 0.456, 0.406] +- Standard deviation: [0.229, 0.224, 0.225] +This normalization ensures optimal feature extraction from the pretrained network. -### References \ No newline at end of file +**Dataset Organization:** +- 80:20 train-test split for model evaluation +- Siamese pair creation with 50% similar and 50% dissimilar pairs +- Careful control to prevent self-pairing of images + +## Architecture + +We used a Siamese network with ResNet50 to learn features from skin lesion images. ResNet50 creates 2048-length vectors from each image. These vectors contain the key patterns that help identify melanomas. + +The Siamese setup compares images in pairs using two ResNet50s that share the same weights. We used contrastive loss because we only need to separate two classes: benign and malignant. This was simpler than triplet loss which would add unnecessary complexity. + +![Siamese Network Architecture](assets/siamese_architecture.png) + +The MLP classifier takes these features and makes the final decision. It reduces the 2048 features through three layers (128→64→32→1). We added high dropout rates (0.7, 0.5) because we had few malignant samples and needed to prevent overfitting. + +## Training Observations + +Key insights from the training process: + +1. Class imbalance significantly impacts model performance: + - Initial experiments showed bias toward benign prediction + - 67:33 ratio provided best balance of sensitivity and specificity + +2. Model Stability: + - Embeddings showed overfitting beyond 8 epochs + - Addressed through learning rate adjustment and weight decay + - Upsampling outperformed downsampling in maintaining data characteristics + +3. Class Distribution Effects: + - 50:50 split showed high malignant recall but poor benign performance + - Final 67:33 ratio achieved better overall balance + +## Results + + +### Performance Metrics +- Overall Accuracy: 94% +- Malignant Detection Rate: 32% +- Benign Accuracy: 95% +- ROC-AUC Score: 0.792 + +### Detailed Classification Performance +``` + Precision Recall F1-score Support +Benign 0.99 0.95 0.97 6509 +Malignant 0.10 0.32 0.15 117 +``` + +### Confusion Matrix +``` +Predicted: Benign Malignant +Actual Benign: 6162 347 +Actual Malignant: 79 38 +``` + +### Training and Evaluation Plots + +![Embedding Distribution](assets/embedding_distribution.png) +*Distribution of embedding distances. It shows a clear separation between similar and dissimilar pairs* + +![t-SNE Visualization](assets/tsne_visualization.png) +*t-SNE visualization of learned embeddings showing clusters* + +![MLP Loss](assets/mlp_loss.png) +*MLP Classifier training loss showing consistent convergence* + +![Siamese Loss](assets/siamese_loss.png) +*Siamese Network training loss demonstrating stable learning* + +![ROC Curve](assets/roc_curve.png) +*ROC curve with AUC = 0.792 indicating good discriminative ability* + +### Discussion +The model achieved mixed results across different metrics. The overall accuracy was 94% but only 32% of melanomas were detected. The benign detection rate was strong at 95% accuracy. + +Looking at the confusion matrix: +6162 Benign were correctly identified +347 Benign were mistakenly flagged as melanomas +79 Malignant were missed +38 Malignant were caught + +The training graphs show steady improvement. Siamese network loss dropped from 0.23 to 0.08 over 15 epochs, while the MLP classifier stabilized at 0.16 loss after 10 epochs. +The t-SNE visualization displays clear grouping of similar cases, though some overlap exists between benign and malignant clusters. The ROC curve analysis produced an AUC score of 0.792, indicating decent separation between classes despite the severe data imbalance. + +## Conclusions + +The results highlight a key issue in melanoma detection: getting high overall accuracy doesn't mean the system works well enough for practical use. Missing 68% of melanomas is a critical problem that needs addressing. The data suggests the model can spot general patterns separating benign from malignant cases, but lacks the precision needed for clinical applications. More melanoma samples and targeted architectural changes could improve detection rates while maintaining the current strong performance on benign cases. + +## Instructions + +1. **Data Preparation:** + - Download the ISIC 2020 dataset from Kaggle + - Extract images to a designated folder + - Ensure CSV metadata file is present + +2. **Environment Setup:** + - Install Python 3.8 or higher + - Install required dependencies + - Set up appropriate paths in configuration + +3. **Model Training:** + - `dataset.py` only needs to be run to get an overview of the data otherwise all data preparation happens in `train.py` + - Run `train.py` with any additional parameters + - Monitor training progress (Using the current set up should take around 30 minutes using a NVIDIA L40s) + - Review generated metrics + +4. **Making Predictions:** + - Ensure images are in a designated directory + - Run `predict.py` with any additional parameters + - Review classification results + +## Files + +- `modules.py`: Defines Siamese network, MLP classifier, loss functions, and evaluation +- `dataset.py`: Manages data loading, augmentation, and data balancing +- `train.py`: Trains the Siamese Network and MLP classifier +- `predict.py`: Performs predictions and evaluation on new image data +- `README.md`: Project documentation + +## Dependencies + +matplotlib==3.8.2 +numpy==2.1.2 +pandas==2.2.3 +Pillow==11.0.0 +scikit_learn==1.3.2 +seaborn==0.13.2 +torch==2.2.1+cu121 +torchvision==0.17.1+cu121 +tqdm==4.66.5 + +## References + +1. Becoming Human. (n.d.). *Siamese networks: Algorithm, applications and PyTorch implementation*. Retrieved from https://becominghuman.ai/siamese-networks-algorithm-applications-and-pytorch-implementation-4ffa3304c18 + +2. Song, T. (n.d.). *PyTorch implementation of Siamese network*. Retrieved from https://tianyusong.com/projects/pytorch-implementation%E2%80%8B-siamese-network/ + +3. Challenge Enthusiast. (n.d.). *Training a Siamese model with a triplet loss function on MNIST dataset using PyTorch*. Retrieved from https://challengeenthusiast.com/training-a-siamese-model-with-a-triplet-loss-function-on-mnist-dataset-using-pytorch-225908e59bda + +4. Analytics Vidhya. (n.d.). *A friendly introduction to Siamese networks*. Retrieved from https://medium.com/analytics-vidhya/a-friendly-introduction-to-siamese-networks-283f31bf38cd + +5. Hackernoon. (n.d.). *One-shot learning with Siamese networks in PyTorch*. Retrieved from https://hackernoon.com/one-shot-learning-with-siamese-networks-in-pytorch-8ddaab10340e?source=post_page-----283f31bf38cd-------------------------------- + +6. PyTorch. (n.d.). *Siamese network main code example*. GitHub. Retrieved from https://github.com/pytorch/examples/blob/main/siamese_network/main.py + +7. Analytics India Magazine. (n.d.). *A beginner's guide to Scikit-learn’s MLPClassifier*. Retrieved from https://analyticsindiamag.com/ai-mysteries/a-beginners-guide-to-scikit-learns-mlpclassifier/ + +8. GeeksforGeeks. (n.d.). *How to normalize images in PyTorch*. Retrieved from https://www.geeksforgeeks.org/how-to-normalize-images-in-pytorch/ + +9. Abdallah, A. (2022). *Oversampling for better machine learning with imbalanced data*. Medium. Retrieved from https://medium.com/@abdallahashraf90x/oversampling-for-better-machine-learning-with-imbalanced-data-68f9b5ac2696 + +10. Metaor AI. (2023). *Solving the class imbalance problem*. Medium. 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It shows a clear separation between similar and dissimilar pairs* -![t-SNE Visualization](assets/tsne_visualization.png) +t-SNE Visualization *t-SNE visualization of learned embeddings showing clusters* -![MLP Loss](assets/mlp_loss.png) +MLP Loss *MLP Classifier training loss showing consistent convergence* -![Siamese Loss](assets/siamese_loss.png) +Siamese Loss *Siamese Network training loss demonstrating stable learning* -![ROC Curve](assets/roc_curve.png) +ROC Curve *ROC curve with AUC = 0.792 indicating good discriminative ability* ### Discussion From aa0607c99d2c31fa6b3c49a2204faae8cab1bc7d Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 09:10:13 +1100 Subject: [PATCH 28/31] Fixed formatting in README.md --- recognition/siamese_richard_chantra/README.MD | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index eda5c46f6..c75385395 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -156,17 +156,20 @@ The results highlight a key issue in melanoma detection: getting high overall ac - `predict.py`: Performs predictions and evaluation on new image data - `README.md`: Project documentation + +```markdown ## Dependencies -matplotlib==3.8.2 -numpy==2.1.2 -pandas==2.2.3 -Pillow==11.0.0 -scikit_learn==1.3.2 -seaborn==0.13.2 -torch==2.2.1+cu121 -torchvision==0.17.1+cu121 -tqdm==4.66.5 +matplotlib==3.8.2 +numpy==2.1.2 +pandas==2.2.3 +Pillow==11.0.0 +scikit_learn==1.3.2 +seaborn==0.13.2 +torch==2.2.1+cu121 +torchvision==0.17.1+cu121 +tqdm==4.66.5 +``` ## References From f3b7aa049d25dc65ba4bcd02e806f3a9d0fbf7b8 Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 09:10:43 +1100 Subject: [PATCH 29/31] Fixed formatting in README.md --- recognition/siamese_richard_chantra/README.MD | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index c75385395..89b425e82 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -157,9 +157,8 @@ The results highlight a key issue in melanoma detection: getting high overall ac - `README.md`: Project documentation -```markdown ## Dependencies - +```markdown matplotlib==3.8.2 numpy==2.1.2 pandas==2.2.3 From 23a177821783609a2443783a7ff5b49924b9b60b Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 09:17:02 +1100 Subject: [PATCH 30/31] Fixed formatting in README.md --- recognition/siamese_richard_chantra/README.MD | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index 89b425e82..c4d1b9338 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -37,14 +37,14 @@ Images are normalized using ResNet50's pretrained requirements: - Standard deviation: [0.229, 0.224, 0.225] This normalization ensures optimal feature extraction from the pretrained network. -**Dataset Organization:** +**Dataset Handling:** - 80:20 train-test split for model evaluation - Siamese pair creation with 50% similar and 50% dissimilar pairs - Careful control to prevent self-pairing of images ## Architecture -We used a Siamese network with ResNet50 to learn features from skin lesion images. ResNet50 creates 2048-length vectors from each image. These vectors contain the key patterns that help identify melanomas. +A Siamese network was used with ResNet50 to learn features from melanoma images. ResNet50 creates 2048-length vectors from each image and these embeddings help identify melanomas. The Siamese setup compares images in pairs using two ResNet50s that share the same weights. We used contrastive loss because we only need to separate two classes: benign and malignant. This was simpler than triplet loss which would add unnecessary complexity. @@ -114,7 +114,7 @@ The model achieved mixed results across different metrics. The overall accuracy Looking at the confusion matrix: 6162 Benign were correctly identified -347 Benign were mistakenly flagged as melanomas +347 Benign were mistakenly flagged as malignant 79 Malignant were missed 38 Malignant were caught @@ -123,7 +123,7 @@ The t-SNE visualization displays clear grouping of similar cases, though some ov ## Conclusions -The results highlight a key issue in melanoma detection: getting high overall accuracy doesn't mean the system works well enough for practical use. Missing 68% of melanomas is a critical problem that needs addressing. The data suggests the model can spot general patterns separating benign from malignant cases, but lacks the precision needed for clinical applications. More melanoma samples and targeted architectural changes could improve detection rates while maintaining the current strong performance on benign cases. +The results highlight a key issue in melanoma detection that getting high overall accuracy doesn't mean the system works well enough for practical use. The results suggests the model can spot general patterns separating benign from malignant cases but struggles obtaining a high accuracy. More melanoma samples and targeted architectural changes could improve detection rates. ## Instructions From 345502e855d60a277d46c698b6e7c83f84b3553c Mon Sep 17 00:00:00 2001 From: richardchantra Date: Mon, 28 Oct 2024 09:20:21 +1100 Subject: [PATCH 31/31] Fixed formatting in README.md --- recognition/siamese_richard_chantra/README.MD | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recognition/siamese_richard_chantra/README.MD b/recognition/siamese_richard_chantra/README.MD index c4d1b9338..de17c0258 100644 --- a/recognition/siamese_richard_chantra/README.MD +++ b/recognition/siamese_richard_chantra/README.MD @@ -95,19 +95,19 @@ Actual Malignant: 79 38 ### Training and Evaluation Plots Embedding Distribution -*Distribution of embedding distances. It shows a clear separation between similar and dissimilar pairs* +This plot shows the distribution of embedding distances. There is a clear separation between similar and dissimilar pairs. t-SNE Visualization -*t-SNE visualization of learned embeddings showing clusters* +This is a t-SNE visualization of learned embeddings and shows the ability of the Siamese Network to create useful clusters. MLP Loss -*MLP Classifier training loss showing consistent convergence* +This is a plot of the MLP Classifier training loss and shows consistent convergence. Siamese Loss -*Siamese Network training loss demonstrating stable learning* +This is a plot of the Siamese Network training loss and it demonstrates stable learning. ROC Curve -*ROC curve with AUC = 0.792 indicating good discriminative ability* +This is the ROC curve with AUC = 0.792. This indicates good discriminative ability. ### Discussion The model achieved mixed results across different metrics. The overall accuracy was 94% but only 32% of melanomas were detected. The benign detection rate was strong at 95% accuracy.

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