From 55fc66791e01f066bc8842ae5ff6dc5b000cec05 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Mon, 20 Oct 2025 18:32:43 +1000 Subject: [PATCH 01/16] Created skeleton files for dataset.py, train.py, predict.py, and module.py. Implemented very basic UNet model in module.py, including a test script for a forward pass without any data to ensure that the input will pass through all layers without error Updated README.md to list all the current files used in the project --- recognition/2D_OASIS_s43971125/README.md | 8 ++ recognition/2D_OASIS_s43971125/dataset.py | 43 ++++++++++ recognition/2D_OASIS_s43971125/modules.py | 99 +++++++++++++++++++++++ recognition/2D_OASIS_s43971125/predict.py | 27 +++++++ recognition/2D_OASIS_s43971125/train.py | 44 ++++++++++ 5 files changed, 221 insertions(+) create mode 100644 recognition/2D_OASIS_s43971125/README.md create mode 100644 recognition/2D_OASIS_s43971125/dataset.py create mode 100644 recognition/2D_OASIS_s43971125/modules.py create mode 100644 recognition/2D_OASIS_s43971125/predict.py create mode 100644 recognition/2D_OASIS_s43971125/train.py diff --git a/recognition/2D_OASIS_s43971125/README.md b/recognition/2D_OASIS_s43971125/README.md new file mode 100644 index 000000000..230cb44cf --- /dev/null +++ b/recognition/2D_OASIS_s43971125/README.md @@ -0,0 +1,8 @@ + +# Files + +- **dataset.py**: contains the dataloader for loading the 2D OASIS dataset +- **modules.py**: contains the 2D UNet model +- **train.py**: contains the code for training, testing, validating, and saving the UNet model +- **predict.py**: loads the trained model and visualises examples of real implementation + diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py new file mode 100644 index 000000000..5e31bca0d --- /dev/null +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -0,0 +1,43 @@ +# Contains the data loader + +import os +import torch +import torch.utils.data import Dataset, Dataloader +import nibabel as nib +import numpy as np + + +class OASISDataset(Dataset): + def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train"): + self.root_dir = root_dir + self.split=split + # filepaths go here + self.image_paths=[] + self.label_paths=[] + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + # load images and labels + image = nib.load(self.image_paths[idx]).get_fdata() + label = nib.load(self.label_paths[idx]).get_fdata() + + # Convert to tensors + image = torch.tensor(image, dtype=torch.float32).unsqueeze(0) # Add channel dim + label = torch.tensor(label, dtype=torch.long) + + return image, label + + +def get_dataloaders(batch_size=4): + # Returns the pytorch dataloaders for our 3 datasets + train_dataset = OASISDataset(split="train") + val_dataset = OASISDataset(split="val") + test_dataset = OASISDataset(split="test") + + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=batch_size) + test_loader = DataLoader(test_dataset, batch_size=batch_size) + + return train_loader, val_loader, test_loader diff --git a/recognition/2D_OASIS_s43971125/modules.py b/recognition/2D_OASIS_s43971125/modules.py new file mode 100644 index 000000000..be7528de1 --- /dev/null +++ b/recognition/2D_OASIS_s43971125/modules.py @@ -0,0 +1,99 @@ +# Contains the source code of the model. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels): + #Very basic ConvBlock, based on lecture example + super(ConvBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.bn1 = nn.BatchNorm2d(out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.bn2 = nn.BatchNorm2d(out_channels) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x))) + x = F.relu(self.bn2(self.conv2(x))) + return x + +class DownBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(DownBlock, self).__init__() + self.conv = ConvBlock(in_channels, out_channels) + self.pool = nn.MaxPool2d(2) + + def forward(self, x): + x = self.conv(x) + p = self.pool(x) + return x, p # return features before pooling for skip connection + +class UpBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(UpBlock, self).__init__() + self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2) + self.conv = ConvBlock(in_channels, out_channels) # in_channels includes skip connection + + def forward(self, x, skip): + x = self.up(x) + x = torch.cat([x, skip], dim=1) # concatenate along channel dimension + x = self.conv(x) + return x + + + +class UNet(nn.Module): + + def __init__(self, in_channels=1, out_channels=3, base_filters=64): + super(UNet, self).__init__() + # Encoder + self.down1 = DownBlock(in_channels, base_filters) + self.down2 = DownBlock(base_filters, base_filters*2) + self.down3 = DownBlock(base_filters*2, base_filters*4) + self.down4 = DownBlock(base_filters*4, base_filters*8) + + # Bottleneck + self.bottleneck = ConvBlock(base_filters*8, base_filters*16) + + # Decoder + self.up4 = UpBlock(base_filters*16, base_filters*8) + self.up3 = UpBlock(base_filters*8, base_filters*4) + self.up2 = UpBlock(base_filters*4, base_filters*2) + self.up1 = UpBlock(base_filters*2, base_filters) + + # Final conv + self.final_conv = nn.Conv2d(base_filters, out_channels, kernel_size=1) + pass + + def forward(self, x): + # Encoder + s1, p1 = self.down1(x) + s2, p2 = self.down2(p1) + s3, p3 = self.down3(p2) + s4, p4 = self.down4(p3) + + # Bottleneck + b = self.bottleneck(p4) + + # Decoder + d4 = self.up4(b, s4) + d3 = self.up3(d4, s3) + d2 = self.up2(d3, s2) + d1 = self.up1(d2, s1) + + out = self.final_conv(d1) + return out + + +def build_unet(in_channels=1, out_channels=3): + return UNet(in_channels, out_channels) + + +#Forward pass test +if __name__ == "__main__": + x = torch.randn(1, 1, 256, 256) # batch_size=1, channels=1, H=W=256 + model = build_unet() + y = model(x) + print("Input shape:", x.shape) + print("Output shape:", y.shape) # should be [1, out_channels, 256, 256] diff --git a/recognition/2D_OASIS_s43971125/predict.py b/recognition/2D_OASIS_s43971125/predict.py new file mode 100644 index 000000000..65dfeabe4 --- /dev/null +++ b/recognition/2D_OASIS_s43971125/predict.py @@ -0,0 +1,27 @@ +# Contains example usage of the model + +import torch +from model import build_unet +from dataset import OASISDataset +import matplotlib.pyplot as plt + + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Load trained model +model = build_unet(in_channels=1, out_channels=3).to(device) +model.load_state_dict(torch.load("../checkpoints/unet_final.pth")) +model.eval() + +# Load example +dataset = OASISDataset(split="test") +image, label = dataset[0] +image = image.to(device).unsqueeze(0) # Add batch dimension + +# Predict segmentation +with torch.no_grad(): + output = model(image) + predicted = torch.argmax(output, dim=1).squeeze(0).cpu().numpy() + + +#TODO: Visualisation diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py new file mode 100644 index 000000000..d7aee15ca --- /dev/null +++ b/recognition/2D_OASIS_s43971125/train.py @@ -0,0 +1,44 @@ +# Contains the source code for training and validation + +import torch +import torch.nn as nn +import torch.optim as optim +from model import build_unet +from dataset import get_dataloaders +import os + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Load data +train_loader, val_loader, test_loader = get_dataloaders(batch_size=4) + +# Build model +model = build_unet(in_channels=1, out_channels=3).to(device) + +# Loss & optimizer +criterion = nn.CrossEntropyLoss() +optimizer = optim.Adam(model.parameters(), lr=1e-4) + +# Training loop +num_epochs = 50 +checkpoint_dir = "../checkpoints" +os.makedirs(checkpoint_dir, exist_ok=True) + +for epoch in range(num_epochs): + model.train() + for images, labels in train_loader: + images = images.to(device) + labels = labels.to(device) + + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + # TODO: Validation step, DICE coefficient computation + + # Save checkpoint + torch.save(model.state_dict(), os.path.join(checkpoint_dir, f"unet_epoch_{epoch}.pth")) + +# TODO: add test evaluation for Dice From 572a4ca1b730e452d59ff138e510941371e9067e Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 24 Oct 2025 10:52:19 +1000 Subject: [PATCH 02/16] Updated dataset.py to be functional with the OASIS dataset. Added functions for loading 2D data provided by assignment - Realised after that they aren't necessary for the OASIS dataset, but as I hope to tackle the HipMRI dataset if I can complete this task, I have left them in for now. Will remove before submission if they are still unused. --- recognition/2D_OASIS_s43971125/dataset.py | 85 +++++++++++++++++------ 1 file changed, 65 insertions(+), 20 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index 5e31bca0d..1631523db 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -1,43 +1,88 @@ # Contains the data loader + import os import torch import torch.utils.data import Dataset, Dataloader import nibabel as nib import numpy as np - +from PIL import Image +from tqdm import tqdm +from glob import glob class OASISDataset(Dataset): - def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train"): - self.root_dir = root_dir - self.split=split - # filepaths go here - self.image_paths=[] - self.label_paths=[] + def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train", categorical=false): + + image_dir = os.path.join(root_dir, f"keras_png_slices_{split}") + label_dir = os.path.join(root_dir, f"keras_png_slices_seg_{split}") + + self.image_paths = sorted(glob.glob(os.path.join(image_dir, "*.png"))) + self.label_paths = sorted(glob.glob(os.path.join(label_dir, "*.png"))) + + self.transform = transform def __len__(self): return len(self.image_paths) def __getitem__(self, idx): # load images and labels - image = nib.load(self.image_paths[idx]).get_fdata() - label = nib.load(self.label_paths[idx]).get_fdata() + image = Image.open(self.image_paths[idx]).convert("L") + label = Image.open(self.label_paths[idx]).convert("L") # Convert to tensors - image = torch.tensor(image, dtype=torch.float32).unsqueeze(0) # Add channel dim - label = torch.tensor(label, dtype=torch.long) + image = torch.tensor(np.array(image), dtype=torch.float32).unsqueeze(0) / 255.0 + label = torch.tensor(np.array(label), dtype=torch.long) + + if self.transform: + image = self.transform(image) return image, label + #Functions copied from Assignment - will be used if switch from easy to normal task + #Unnecessary for OASIS dataset as it is already in PNG format + def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: + channels = np.unique(arr) + res = np.zeros(arr.shape + (len(channels),),dtype=dtype) + for c in channels: + c = int(c) + res[..., c:c+1][arr == c] = 1 + + return res -def get_dataloaders(batch_size=4): - # Returns the pytorch dataloaders for our 3 datasets - train_dataset = OASISDataset(split="train") - val_dataset = OASISDataset(split="val") - test_dataset = OASISDataset(split="test") + def load_data_2D(imageNames, normImage=False, categorical=False, + dtype=np.float32, getAffines=False, early_stop=False): + affines = [] - train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=batch_size) - test_loader = DataLoader(test_dataset, batch_size=batch_size) + num = len(imageNames) + first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged') + if len(first_case.shape) == 3: + first_case = first_case[:,:,0] #remove extra dimension + if categorical: + first_case = to_channels(first_case, dtype=dtype) + rows, cols, channels = first_case.shape + images = np.zeros((num, rows, cols, channels), dtype=dtype) + else: + rows, cols = first_case.shape + images = np.zeros((num, rows, cols), dtype=dtype) - return train_loader, val_loader, test_loader + for i, inName in enumerate(tqdm(imageNames)): + niftiImage = nib.load(inName) + inImage = niftiImage.get_fdata(caching='unchanged') + affine = niftiImage.affine + if len(inImage.shape) == 3: + inImage = inImage[:,:,0] + inImage = inImage.astype(dtype) + if normImage: + inImage = (inImage - inImage.mean()) / inImage.std() + if categorical: + inImage = utils.to_channels(inImage, dtype=dtype) + images[i,:,:,:] = inImage + else: + images[i,:,:] = inIMage + affines.append(affine) + if i > 20 and early_stop: + break + if getAffines: + return images, affines + else: + return images From 010fc12777a2436451413c60bef6a23dac07067d Mon Sep 17 00:00:00 2001 From: s4397112 Date: Mon, 3 Nov 2025 16:34:47 +1000 Subject: [PATCH 03/16] Filled out train.py with the basic training functionality. Adds some comments - more needed. Added get_dataloaders function to dataset.py. Added DiceLoss coefficient calculation functionality to train.py - currently only works for binary segmentation - may need to be changed. --- recognition/2D_OASIS_s43971125/dataset.py | 61 +++---------- recognition/2D_OASIS_s43971125/train.py | 101 ++++++++++++++++++---- 2 files changed, 96 insertions(+), 66 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index 1631523db..c0ac709a4 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -38,51 +38,18 @@ def __getitem__(self, idx): return image, label - #Functions copied from Assignment - will be used if switch from easy to normal task - #Unnecessary for OASIS dataset as it is already in PNG format - def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: - channels = np.unique(arr) - res = np.zeros(arr.shape + (len(channels),),dtype=dtype) - for c in channels: - c = int(c) - res[..., c:c+1][arr == c] = 1 +# Helper function that is called by train.py to get all 3 dataloaders +def get_dataloaders(root_dir="/home/groups/comp3710/OASIS", batch_size=4): + # define the 3 datasets + train_dataset = OASISDataset(root_dir=root_dir, split="train") + val_dataset = OASISDataset(root_dir=root_dir, split="validate") + test_dataset = OASISDataset(root_dir=root_dir, split="test") + + #Construct the 3 dataloaders + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) + test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + + #Return the 3 dataloaders + return train_loader, val_loader, test_loader - return res - - def load_data_2D(imageNames, normImage=False, categorical=False, - dtype=np.float32, getAffines=False, early_stop=False): - affines = [] - - num = len(imageNames) - first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged') - if len(first_case.shape) == 3: - first_case = first_case[:,:,0] #remove extra dimension - if categorical: - first_case = to_channels(first_case, dtype=dtype) - rows, cols, channels = first_case.shape - images = np.zeros((num, rows, cols, channels), dtype=dtype) - else: - rows, cols = first_case.shape - images = np.zeros((num, rows, cols), dtype=dtype) - - for i, inName in enumerate(tqdm(imageNames)): - niftiImage = nib.load(inName) - inImage = niftiImage.get_fdata(caching='unchanged') - affine = niftiImage.affine - if len(inImage.shape) == 3: - inImage = inImage[:,:,0] - inImage = inImage.astype(dtype) - if normImage: - inImage = (inImage - inImage.mean()) / inImage.std() - if categorical: - inImage = utils.to_channels(inImage, dtype=dtype) - images[i,:,:,:] = inImage - else: - images[i,:,:] = inIMage - affines.append(affine) - if i > 20 and early_stop: - break - if getAffines: - return images, affines - else: - return images diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index d7aee15ca..1c5bb0533 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -3,42 +3,105 @@ import torch import torch.nn as nn import torch.optim as optim -from model import build_unet -from dataset import get_dataloaders import os +from modules import UNet +from dataset import get_dataloaders + +#Create the DiceLoss functionality +class DiceLoss(nn.Module): + #Subclass behaves like other Pytorch Loss functions + #smooth is a small constant - used in the calculation step to avoid division by zero + def __init__(self, smooth=1e-6): + super(DiceLoss, self).__init__() + self.smooth = smooth + + def forward(self, preds, targets): + #torch.softmax turns the UNet output scores into probabilities, which sum to 1 + preds = torch.softmax(preds, dim=1) + #flatten the predictions and labels into 1D tensors + preds_flat = preds.contiguous().view(-1) + targets_flat = targets.contiguous().view(-1) + #find the intersection between predicted probabilities and true values + intersection = (preds_flat * targets_flat).sum() + #Calculate the Dice Coefficient and subtract it from 1 to find the Dice Loss + return 1 - ((2. * intersection + self.smooth) / + (preds_flat.sum() + targets_flat.sum() + self.smooth)) + +def dice_coefficient(preds, targets, smooth=1e-6): + preds = torch.softmax(preds, dim=1) + preds = torch.argmax(preds, dim=1) + intersection = (preds * targets).sum() + return (2. * intersection + smooth) / (preds.sum() + targets.sum() + smooth) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -# Load data -train_loader, val_loader, test_loader = get_dataloaders(batch_size=4) +# Set the training paremeters +num_epochs = 1 +batch_size = 4 +learning_rate = 1e-4 +save_path = "best.pth" + +# Load in the datasets +train_load, val_load, test_load = get_dataloaders(batch_size=batch_size) -# Build model -model = build_unet(in_channels=1, out_channels=3).to(device) +# Define the model, loss (DiceLoss) and optimiser (Adam) -# Loss & optimizer -criterion = nn.CrossEntropyLoss() -optimizer = optim.Adam(model.parameters(), lr=1e-4) +model = UNet(in_channels=1, out_channels=3).to(device) +criterion = DiceLoss() +optimiser = optim.Adam(model.parameters(), lr=learning_rate) -# Training loop -num_epochs = 50 -checkpoint_dir = "../checkpoints" + +best_val_dice = 50 os.makedirs(checkpoint_dir, exist_ok=True) +# Training loop for epoch in range(num_epochs): model.train() - for images, labels in train_loader: + running_loss = 0.0 + + for images, labels in train_load: images = images.to(device) labels = labels.to(device) - optimizer.zero_grad() + optimiser.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() - optimizer.step() + optimiser.step() - # TODO: Validation step, DICE coefficient computation + running_loss += loss.item() - # Save checkpoint - torch.save(model.state_dict(), os.path.join(checkpoint_dir, f"unet_epoch_{epoch}.pth")) + avg_loss = running_loss / len(train_load) + + #Validation Step + model.eval() + val_dice = 0.0 + with torch.no_grad(): + for images, labels in val_load: + images = images.to(device) + labels = labels.to(device) + outputs = model(images) + val_dice += dice_coefficient(outputs, labels) + + val_dice /= len(val_load) + + # Save the best model found so far + if val_dice > best_val_dice: + torch.save(model.state_dict, save_path) + best_val_dice = val_dice + + +# Final test evaluation to find Dice Coefficient + +model.load_state_dict(torch.load(save_path)) +model.eval() +test_dice = 0.0 +with torch.no_grad(): + for images, labels in test_load: + images = images.to(device) + labels = labels.to(device) + outputs = model(images) + test_dice += dice_coefficient(outputs, labels) + test_dice /= len(test_load) -# TODO: add test evaluation for Dice From b073220e2224405b11b241dbe416890eba8b8d6d Mon Sep 17 00:00:00 2001 From: s4397112 Date: Wed, 5 Nov 2025 13:06:59 +1000 Subject: [PATCH 04/16] Fixed numerous small errors in dataset and train (mostly typos, misdirected functions, missing variable initialisations). Tested to find that 2D_OASIS data has 4 segmentation classes, so updated DiceLoss functionality accordingly Ran a quick test with one epoch, and confirmed that it created a "best.pth" file with no errors - now ready to test with multiple epochs, adjusted parameters, etc --- recognition/2D_OASIS_s43971125/dataset.py | 24 +++++++++----- recognition/2D_OASIS_s43971125/train.py | 38 ++++++++++++++++------- 2 files changed, 43 insertions(+), 19 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index c0ac709a4..6342cee7a 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -3,15 +3,13 @@ import os import torch -import torch.utils.data import Dataset, Dataloader -import nibabel as nib +from torch.utils.data import Dataset, DataLoader import numpy as np from PIL import Image -from tqdm import tqdm -from glob import glob +import glob class OASISDataset(Dataset): - def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train", categorical=false): + def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train", categorical=False, transform=None): image_dir = os.path.join(root_dir, f"keras_png_slices_{split}") label_dir = os.path.join(root_dir, f"keras_png_slices_seg_{split}") @@ -29,10 +27,22 @@ def __getitem__(self, idx): image = Image.open(self.image_paths[idx]).convert("L") label = Image.open(self.label_paths[idx]).convert("L") + # Convert to np arrays + image_np = np.array(image) + label_np = np.array(label, dtype=np.uint8) + + + # Convert to tensors - image = torch.tensor(np.array(image), dtype=torch.float32).unsqueeze(0) / 255.0 - label = torch.tensor(np.array(label), dtype=torch.long) + image = torch.tensor(image_np, dtype=torch.float32).unsqueeze(0) / 255.0 + label = torch.tensor(label_np, dtype=torch.long) + + #remap labels from [0, 85, 170, 255] to [0, 1, 2, 3], so that one-hot encoding of + #targets (in the diceloss function) works correctly. We do this by just dividing every label + #by 85, and rounding down. + label = torch.div(label, 85, rounding_mode=floor) + #optional transformation step - only used if transform is set in __init__ if self.transform: image = self.transform(image) diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index 1c5bb0533..996f135b9 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -17,16 +17,28 @@ def __init__(self, smooth=1e-6): self.smooth = smooth def forward(self, preds, targets): + #preds = predictions, in form [BatchSize, Classes, Height, Width] = [B, C, H, W] + #targets are in form [B, H, W] + #torch.softmax turns the UNet output scores into probabilities, which sum to 1 preds = torch.softmax(preds, dim=1) - #flatten the predictions and labels into 1D tensors - preds_flat = preds.contiguous().view(-1) - targets_flat = targets.contiguous().view(-1) - #find the intersection between predicted probabilities and true values - intersection = (preds_flat * targets_flat).sum() - #Calculate the Dice Coefficient and subtract it from 1 to find the Dice Loss - return 1 - ((2. * intersection + self.smooth) / - (preds_flat.sum() + targets_flat.sum() + self.smooth)) + + #Convert targets to one-hot encoding. This ensures that when we flatten it into + # a 1D tensor, it's the same size as the predictions tensor, so we can calculate + #the intersection. + targets_onehot = torch.nn.functional.one_hot(targets, num_classes=preds.shape[1]) + # This line swaps the order around, so it matches preds order (which is the just + # the raw output of UNet, which gives [BatchSize, NumClasses, ImageHeight, ImageWidth] + targets_onehot = targets_onehot.permute(0, 3, 1, 2).float() + + #Calculate the Dice score for each class + intersection = (preds * targets_onehot).sum(dim=(2, 3)) + union = preds.sum(dim=(2, 3)) + targets_onehot.sum(dim=(2,3)) + dice_score = (2.0 * intersection + self.smooth) / (union + self.smooth) + + #Subtract the mean dice score across all classes from 1 to find the average DiceLoss + loss = 1 - dice_score.mean() + return loss def dice_coefficient(preds, targets, smooth=1e-6): preds = torch.softmax(preds, dim=1) @@ -34,7 +46,8 @@ def dice_coefficient(preds, targets, smooth=1e-6): intersection = (preds * targets).sum() return (2. * intersection + smooth) / (preds.sum() + targets.sum() + smooth) -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +#device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +device = torch.device("cpu") # Set the training paremeters num_epochs = 1 @@ -47,13 +60,14 @@ def dice_coefficient(preds, targets, smooth=1e-6): # Define the model, loss (DiceLoss) and optimiser (Adam) -model = UNet(in_channels=1, out_channels=3).to(device) +model = UNet(in_channels=1, out_channels=4).to(device) criterion = DiceLoss() optimiser = optim.Adam(model.parameters(), lr=learning_rate) best_val_dice = 50 -os.makedirs(checkpoint_dir, exist_ok=True) +# Don't need below currently, only saving best model. May be used if I want all models. +#os.makedirs(checkpoint_dir, exist_ok=True) # Training loop for epoch in range(num_epochs): @@ -88,7 +102,7 @@ def dice_coefficient(preds, targets, smooth=1e-6): # Save the best model found so far if val_dice > best_val_dice: - torch.save(model.state_dict, save_path) + torch.save(model.state_dict(), save_path) best_val_dice = val_dice From a5f22f760f28d8b63db97a3181807eadd03d8ac8 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Wed, 5 Nov 2025 21:03:50 +1000 Subject: [PATCH 05/16] A few more changes to dataset and train. Established that training loop works Added functionality to print out results of each epoch to log file. Fixed a few minor errors. Making this commit before running a longer test to see if results improve between training loops. --- recognition/2D_OASIS_s43971125/dataset.py | 2 +- recognition/2D_OASIS_s43971125/train.py | 21 ++++++++++++++------- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index 6342cee7a..c7d323e6e 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -40,7 +40,7 @@ def __getitem__(self, idx): #remap labels from [0, 85, 170, 255] to [0, 1, 2, 3], so that one-hot encoding of #targets (in the diceloss function) works correctly. We do this by just dividing every label #by 85, and rounding down. - label = torch.div(label, 85, rounding_mode=floor) + label = torch.div(label, 85, rounding_mode='floor') #optional transformation step - only used if transform is set in __init__ if self.transform: diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index 996f135b9..c3d486867 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -4,10 +4,14 @@ import torch.nn as nn import torch.optim as optim import os +import sys + from modules import UNet from dataset import get_dataloaders +sys.stdout.reconfigure(line_buffering=True) + #Create the DiceLoss functionality class DiceLoss(nn.Module): #Subclass behaves like other Pytorch Loss functions @@ -46,12 +50,12 @@ def dice_coefficient(preds, targets, smooth=1e-6): intersection = (preds * targets).sum() return (2. * intersection + smooth) / (preds.sum() + targets.sum() + smooth) -#device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -device = torch.device("cpu") +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +#device = torch.device("cpu") # Set the training paremeters -num_epochs = 1 -batch_size = 4 +num_epochs = 10 +batch_size = 6 learning_rate = 1e-4 save_path = "best.pth" @@ -65,7 +69,7 @@ def dice_coefficient(preds, targets, smooth=1e-6): optimiser = optim.Adam(model.parameters(), lr=learning_rate) -best_val_dice = 50 +best_val_dice = 0.0 # Don't need below currently, only saving best model. May be used if I want all models. #os.makedirs(checkpoint_dir, exist_ok=True) @@ -74,7 +78,7 @@ def dice_coefficient(preds, targets, smooth=1e-6): model.train() running_loss = 0.0 - for images, labels in train_load: + for i, (images, labels) in enumerate(train_load): images = images.to(device) labels = labels.to(device) @@ -85,8 +89,11 @@ def dice_coefficient(preds, targets, smooth=1e-6): optimiser.step() running_loss += loss.item() + if i % 5 == 0: + print(f"Epoch {epoch+1}, Batch {i}, Loss: {loss.item():.4f}") avg_loss = running_loss / len(train_load) + print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") #Validation Step model.eval() @@ -118,4 +125,4 @@ def dice_coefficient(preds, targets, smooth=1e-6): outputs = model(images) test_dice += dice_coefficient(outputs, labels) test_dice /= len(test_load) - +print(f"Final Dice Coefficient: {test_dice:.4f}") From a638d8352d3d80b5caad6e15d3c42ec31b58bdd8 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Wed, 5 Nov 2025 21:47:33 +1000 Subject: [PATCH 06/16] Updates the dice_coefficient function to match the DiceLoss function Previously it was calibrated for Binary segmentation, so it gave a totally incorrect answer. Should be working correctly now. --- recognition/2D_OASIS_s43971125/train.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index c3d486867..f9bbe0811 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -44,11 +44,17 @@ def forward(self, preds, targets): loss = 1 - dice_score.mean() return loss +#Same as above, but this is used during the evaluation step, while the DiceLoss is used for training def dice_coefficient(preds, targets, smooth=1e-6): preds = torch.softmax(preds, dim=1) - preds = torch.argmax(preds, dim=1) - intersection = (preds * targets).sum() - return (2. * intersection + smooth) / (preds.sum() + targets.sum() + smooth) + num_classes = preds.shape[1] + targets_onehot = torch.nn.functional.one_hot(targets, num_classes=num_classes) + targets_onehot = targets_onehot.permute(0, 3, 1, 2).float() + + intersection = (preds * targets_onehot).sum(dim=(2, 3)) + union = preds.sum(dim=(2, 3)) + targets_onehot.sum(dim=(2, 3)) + dice_score = (2.0 * intersection + smooth) / (union + smooth) + return dice.mean() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") #device = torch.device("cpu") @@ -78,7 +84,7 @@ def dice_coefficient(preds, targets, smooth=1e-6): model.train() running_loss = 0.0 - for i, (images, labels) in enumerate(train_load): + for images, labels in train_load: images = images.to(device) labels = labels.to(device) @@ -89,8 +95,6 @@ def dice_coefficient(preds, targets, smooth=1e-6): optimiser.step() running_loss += loss.item() - if i % 5 == 0: - print(f"Epoch {epoch+1}, Batch {i}, Loss: {loss.item():.4f}") avg_loss = running_loss / len(train_load) print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") From 2fefa9454af44df428b6a63dd3752c939bf549bf Mon Sep 17 00:00:00 2001 From: s4397112 Date: Thu, 6 Nov 2025 17:24:52 +1000 Subject: [PATCH 07/16] Adds Slurm Script to commit, this is the Script that has been used to run train.py Existed beforehand, forgot to commit it earlier. Not many changes here - attempted to get HIPMri working but was having issues loading the data, eventually just reverted to the 2D_OASIS, so eventually just reverted back to what I had before, with a couple of improvements made to check progress during training --- recognition/2D_OASIS_s43971125/dataset.py | 1 + recognition/2D_OASIS_s43971125/runner.sh | 19 +++++++ recognition/2D_OASIS_s43971125/train.py | 66 ++++++++++++++++------- 3 files changed, 67 insertions(+), 19 deletions(-) create mode 100644 recognition/2D_OASIS_s43971125/runner.sh diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index c7d323e6e..60c8634be 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -8,6 +8,7 @@ from PIL import Image import glob + class OASISDataset(Dataset): def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train", categorical=False, transform=None): diff --git a/recognition/2D_OASIS_s43971125/runner.sh b/recognition/2D_OASIS_s43971125/runner.sh new file mode 100644 index 000000000..ed4af9ec5 --- /dev/null +++ b/recognition/2D_OASIS_s43971125/runner.sh @@ -0,0 +1,19 @@ +#!/bin/bash +#SBATCH --partition=a100 +#SBATCH --job-name=TrainingTest +#SBATCH --output=slurm_logs/ModuleTest_%j.log +#SBATCH --error=slurm_logs/ModuleTest_%j.err +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=1 +#SBATCH --time=01:00:00 +#SBATCH --gres=gpu:a100:1 + + +# Source conda setup script +source ~/miniconda3/etc/profile.d/conda.sh + +# Activate your environment +conda activate torch # replace with your environment name + +# Run the Python script +python train.py diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index f9bbe0811..9e98dfddb 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -45,23 +45,30 @@ def forward(self, preds, targets): return loss #Same as above, but this is used during the evaluation step, while the DiceLoss is used for training -def dice_coefficient(preds, targets, smooth=1e-6): +#Calculates dice_coefficient per class +def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): preds = torch.softmax(preds, dim=1) - num_classes = preds.shape[1] - targets_onehot = torch.nn.functional.one_hot(targets, num_classes=num_classes) - targets_onehot = targets_onehot.permute(0, 3, 1, 2).float() + preds = torch.argmax(preds, dim=1) - intersection = (preds * targets_onehot).sum(dim=(2, 3)) - union = preds.sum(dim=(2, 3)) + targets_onehot.sum(dim=(2, 3)) - dice_score = (2.0 * intersection + smooth) / (union + smooth) - return dice.mean() + dice_scores = [] + for i in range(num_classes): + pred_i = (preds == i).float() + target_i = (targets == i).float() + + intersection = (pred_i * target_i).sum() + union = pred_i.sum() + target_i.sum() + + dice = (2.0 * intersection + smooth) / (union + smooth) + dice_scores.append(dice.item()) + + return torch.tensor(dice_scores, device=preds.device) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") #device = torch.device("cpu") # Set the training paremeters num_epochs = 10 -batch_size = 6 +batch_size = 4 learning_rate = 1e-4 save_path = "best.pth" @@ -101,32 +108,53 @@ def dice_coefficient(preds, targets, smooth=1e-6): #Validation Step model.eval() - val_dice = 0.0 + dice_scores = [] with torch.no_grad(): for images, labels in val_load: images = images.to(device) labels = labels.to(device) outputs = model(images) - val_dice += dice_coefficient(outputs, labels) + dice_batch = dice_coefficient(outputs, labels, num_classes=4) + dice_scores.append(dice_batch) + + dice_scores = torch.stack(dice_scores) + mean_dice = dice_scores.mean(dim=0) + + # Compute overall mean Dice score + mean_val_dice = mean_dice.mean().item() - val_dice /= len(val_load) + # Print per-class results and average results + print(f"Validation Dice per class: {mean_dice.cpu().numpy()}") + print(f"Mean Validation Dice: {mean_val_dice:.4f}") - # Save the best model found so far - if val_dice > best_val_dice: + # Mean probability for each class + probs = torch.softmax(outputs, dim=1) + mean_probs = probs.mean(dim=(0, 2, 3)) + print(f"Mean predicted probabilities per class: {mean_probs.cpu().numpy()}") + + + # Save the best model so far, based on the average Dice score + if mean_val_dice > best_val_dice: torch.save(model.state_dict(), save_path) - best_val_dice = val_dice + best_val_dice = mean_val_dice + best_per_class = mean_dice.cpu().numpy() + print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") + # Final test evaluation to find Dice Coefficient model.load_state_dict(torch.load(save_path)) model.eval() -test_dice = 0.0 +dice_totals = torch.zeros(4) with torch.no_grad(): for images, labels in test_load: images = images.to(device) labels = labels.to(device) outputs = model(images) - test_dice += dice_coefficient(outputs, labels) - test_dice /= len(test_load) -print(f"Final Dice Coefficient: {test_dice:.4f}") + dice_scores = dice_coefficient(outputs, labels, num_classes=4) + dice_totals += torch.tensor(dice_scores, device=device) +dice_averages = dice_totals / len(test_load) + +for i, dice in enumerate(dice_averages): + print(f"Class {i} Dice: {dice:.4f}") From 9d4577da63f9ea05c355c44274ab42e54f523a15 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 08:41:10 +1000 Subject: [PATCH 08/16] Filled out predict.py to do a very simple prediction Currently loads model and one image, does a prediction for that image, and visualises results May update again batch over all test images. Only changes to train.py are in the parameters (batch size and epoch number) --- recognition/2D_OASIS_s43971125/predict.py | 42 +++++++++++++++++------ recognition/2D_OASIS_s43971125/train.py | 11 +++--- 2 files changed, 38 insertions(+), 15 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/predict.py b/recognition/2D_OASIS_s43971125/predict.py index 65dfeabe4..a2df28cd2 100644 --- a/recognition/2D_OASIS_s43971125/predict.py +++ b/recognition/2D_OASIS_s43971125/predict.py @@ -1,27 +1,49 @@ # Contains example usage of the model import torch -from model import build_unet +from modules import UNet from dataset import OASISDataset import matplotlib.pyplot as plt - +from train import dice_coefficient device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load trained model -model = build_unet(in_channels=1, out_channels=3).to(device) -model.load_state_dict(torch.load("../checkpoints/unet_final.pth")) +model = UNet(in_channels=1, out_channels=4).to(device) +#Note that in this instance, the trained model is named "best.pth" and is located in the same +#directory as predict.py - by default, this is what train.py will produce. +model.load_state_dict(torch.load("best.pth")) model.eval() -# Load example -dataset = OASISDataset(split="test") -image, label = dataset[0] +# Load a single test example +test_dataset = OASISDataset(split="test") +image, label = test_dataset[0] + +#Add batch dimension and send image and label to the GPU image = image.to(device).unsqueeze(0) # Add batch dimension +label = label.to(device).unsqueeze(0) -# Predict segmentation +# Forward pass with torch.no_grad(): output = model(image) - predicted = torch.argmax(output, dim=1).squeeze(0).cpu().numpy() + probs = torch.softmax(output, dim=1) + predicted = torch.argmax(output, dim=1) +#Compute Dice per class +dice_scores = dice_coefficient(output, label, num_classes=4) +print("Dice per Class:", dice_scores) + +#Visualise results +import matplotlib.pyplot as plt +plt.figure(figsize=(12,4)) +plt.subplot(1,3,1) +plt.title("Input") +plt.imshow(image[0,0].cpu(), cmap='gray') +plt.subplot(1,3,2) +plt.title("Ground Truth") +plt.imshow(label.cpu(), cmap='jet') +plt.subplot(1,3,3) +plt.title("Prediction") +plt.imshow(predicted[0].cpu(), cmap='jet') +plt.show() -#TODO: Visualisation diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index 9e98dfddb..388f60851 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -67,8 +67,8 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): #device = torch.device("cpu") # Set the training paremeters -num_epochs = 10 -batch_size = 4 +num_epochs = 15 +batch_size = 8 learning_rate = 1e-4 save_path = "best.pth" @@ -140,20 +140,21 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): best_per_class = mean_dice.cpu().numpy() print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") - + #blank line to separate each epoch + print() # Final test evaluation to find Dice Coefficient model.load_state_dict(torch.load(save_path)) model.eval() -dice_totals = torch.zeros(4) +dice_totals = torch.zeros(4, device=device) with torch.no_grad(): for images, labels in test_load: images = images.to(device) labels = labels.to(device) outputs = model(images) dice_scores = dice_coefficient(outputs, labels, num_classes=4) - dice_totals += torch.tensor(dice_scores, device=device) + dice_totals += dice_scores.to(device) dice_averages = dice_totals / len(test_load) for i, dice in enumerate(dice_averages): From b886db65edf44cd7a082f2c668085e9aad928e8d Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 11:21:43 +1000 Subject: [PATCH 09/16] Adds Improved UNet model to modules.py Adds some comments to modules.py explaining improved UNet implementation - more to come Adjusted predict.py functionality to export visualisation to png Adjusted parameters in train.py --- recognition/2D_OASIS_s43971125/modules.py | 172 +++++++++++++++++++++- recognition/2D_OASIS_s43971125/predict.py | 10 +- recognition/2D_OASIS_s43971125/train.py | 158 ++++++++++---------- 3 files changed, 255 insertions(+), 85 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/modules.py b/recognition/2D_OASIS_s43971125/modules.py index be7528de1..53ada3ddd 100644 --- a/recognition/2D_OASIS_s43971125/modules.py +++ b/recognition/2D_OASIS_s43971125/modules.py @@ -44,8 +44,7 @@ def forward(self, x, skip): class UNet(nn.Module): - - def __init__(self, in_channels=1, out_channels=3, base_filters=64): + def __init__(self, in_channels=1, out_channels=4, base_filters=64): super(UNet, self).__init__() # Encoder self.down1 = DownBlock(in_channels, base_filters) @@ -85,10 +84,177 @@ def forward(self, x): out = self.final_conv(d1) return out +#Pre-activation residual block +class PreActResBlock(nn.Module): + #dropout_prob is optional tuning metric, used to regularise the block (prevents overfitting) + def __init__(self, in_ch, out_ch, dropout_prob=0.0): + super().__init__() + self.in_ch = in_ch + self.out_ch = out_ch + self.dropout_prob = dropout_prob -def build_unet(in_channels=1, out_channels=3): + #First Normalisation + convolution + self.norm1 = nn.InstanceNorm2d(in_ch, affine=False) + self.conv1 = nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1, bias=False) + + #Second normalisation + convolution + self.norm2 = nn.InstanceNorm2d(out_ch, affine=False) + self.conv2 = nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1, bias=False) + + self.dropout = nn.Dropout2d(p=dropout_prob) if dropout_prob > 0 else nn.Identity() + + #check that input can be added to output + self.skip = nn.Conv2d(in_ch, out_ch, kernel_size=1) if in_ch != out_ch else nn.Identity() + + def forward(self, x): + out = self.conv1(F.relu(self.norm1(x))) + out = self.dropout(out) + out = self.conv2(F.relu(self.norm2(out))) + # residual add + return out + self.skip(x) + +class DownResBlock(nn.Module): + def __init__(self, in_ch, out_ch, dropout_prob=0.0): + super().__init__() + self.res = PreActResBlock(in_ch, out_ch, dropout_prob=dropout_prob) + self.pool = nn.MaxPool2d(kernel_size=2) + + def forward(self, x): + features = self.res(x) + pooled = self.pool(features) + return features, pooled + +class UpResBlock(nn.Module): + def __init__(self, in_ch, skip_ch, out_ch, dropout_prob=0.0): + """ + in_ch = channels of decoder input (from previous layer) + skip_ch = channels from encoder skip connection + out_ch = desired output channels after block + First upsample (in_ch -> out_ch), then concatenate with skip (skip_ch), + then use a PreActResBlock with in_ch = out_ch + skip_ch, out_ch = out_ch + """ + super().__init__() + self.up = nn.ConvTranspose2d(in_ch, out_ch, kernel_size=2, stride=2) + self.conv = PreActResBlock(out_ch + skip_ch, out_ch, dropout_prob=dropout_prob) + + def forward(self, x, skip): + x = self.up(x) + # If shapes differ by one pixel due to odd sizes, center-crop or pad (common safe approach) + if x.shape[-2:] != skip.shape[-2:]: + # simple center crop/pad to match skip spatial dims + target_h, target_w = skip.shape[-2], skip.shape[-1] + x = F.interpolate(x, size=(target_h, target_w), mode='bilinear', align_corners=False) + x = torch.cat([x, skip], dim=1) + x = self.conv(x) + return x + +""" +Improved UNet: +- PreAct residual blocks +- InstanceNorm +- Dropout2d +- Deep supervision +Parameters: +in_channels: input channels (e.g. 1) +out_channels: number of segmentation classes (4 for 2D_OASIS) +base_filters: number of filters at first level (commonly 32 or 64) +dropout_prob: probability for spatial dropout inside blocks +deep_supervision: whether to include deep supervision +""" +class ImprovedUNet(nn.Module): + def __init__(self, in_channels=1, out_channels=4, base_filters=64, + dropout_prob=0.1, deep_supervision=True): + super().__init__() + f = base_filters + self.deep_supervision = deep_supervision + + # Encoder + self.enc1 = DownResBlock(in_channels, f, dropout_prob=dropout_prob) + self.enc2 = DownResBlock(f, f*2, dropout_prob=dropout_prob) + self.enc3 = DownResBlock(f*2, f*4, dropout_prob=dropout_prob) + self.enc4 = DownResBlock(f*4, f*8, dropout_prob=dropout_prob) + + # Bottleneck + self.bottleneck = PreActResBlock(f*8, f*16, dropout_prob=dropout_prob) + + # Decoder (note channel bookkeeping) + self.up4 = UpResBlock(f*16, skip_ch=f*8, out_ch=f*8, dropout_prob=dropout_prob) + self.up3 = UpResBlock(f*8, skip_ch=f*4, out_ch=f*4, dropout_prob=dropout_prob) + self.up2 = UpResBlock(f*4, skip_ch=f*2, out_ch=f*2, dropout_prob=dropout_prob) + self.up1 = UpResBlock(f*2, skip_ch=f, out_ch=f, dropout_prob=dropout_prob) + + # Final 1x1 conv to logits + self.final_conv = nn.Conv2d(f, out_channels, kernel_size=1) + + # Auxiliary heads for deep supervision - map intermediate decoder features to logits + if self.deep_supervision: + self.aux4 = nn.Conv2d(f*8, out_channels, kernel_size=1) # from d4 + self.aux3 = nn.Conv2d(f*4, out_channels, kernel_size=1) # from d3 + self.aux2 = nn.Conv2d(f*2, out_channels, kernel_size=1) # from d2 + # We do not need aux for d1 since final_conv handles it + + def forward(self, x): + # Encoder Step + # each call of self.enc(1-4) is an instance of DownResBlock + #s1-4 are saved so they can be used for skip connections + #p1-4 are the progressively downsampled features of the input + s1, p1 = self.enc1(x) # s1: f + s2, p2 = self.enc2(p1) # s2: f*2 + s3, p3 = self.enc3(p2) # s3: f*4 + s4, p4 = self.enc4(p3) # s4: f*8 + + # Bottleneck + # This is the deepest layer of the network (i.e we encode down to here, do feature extraction + # at the lowest level (most abstracted features), then decode back up to higher level. + b = self.bottleneck(p4) # f*16 + + # Decoder + #Each call of up(1-4) is an instance of upResBlock + #it takes a feature map and a skip connection, upsamples the feature map, concatenates + #it with the skip connection, then merges the features. + # each call utilises the previous feature map that has been upsampled + d4 = self.up4(b, s4) # f*8 + d3 = self.up3(d4, s3) # f*4 + d2 = self.up2(d3, s2) # f*2 + d1 = self.up1(d2, s1) # f + + # Final convolution + # We take our final feature map from the decode, and map it to the number of output classes + # we can then feed this result into DiceLoss + out_final = self.final_conv(d1) # [B, out_channels, H, W] + + if not self.deep_supervision: + return out_final + + # Deep supervision + # create extra predictions at lower decoder levels (i.e not d1) + # These are essentially "early" predictions, at lower resolutions + aux4 = self.aux4(d4) + aux3 = self.aux3(d3) + aux2 = self.aux2(d2) + + # Upsample Aux + # because each decoder layer has a different spatial size, have to upsample all of them so they match + # out_final's size + target_size = out_final.shape[-2:] + aux4_up = F.interpolate(aux4, size=target_size, mode='bilinear', align_corners=False) + aux3_up = F.interpolate(aux3, size=target_size, mode='bilinear', align_corners=False) + aux2_up = F.interpolate(aux2, size=target_size, mode='bilinear', align_corners=False) + + # Sum elementwise to combine them. + # By doing this, gives stronger supervision to early layers + combined = out_final + aux2_up + aux3_up + aux4_up + return combined + + + +def build_unet(in_channels=1, out_channels=4): return UNet(in_channels, out_channels) +def build_improved_unet(in_channels=1, out_channels=4, base_filters=32, dropout_prob=0.1, deep_supervision=True): + return ImprovedUNet(in_channels=in_channels, out_channels=out_channels, + base_filters=base_filters, dropout_prob=dropout_prob, + deep_supervision=deep_supervision) #Forward pass test if __name__ == "__main__": diff --git a/recognition/2D_OASIS_s43971125/predict.py b/recognition/2D_OASIS_s43971125/predict.py index a2df28cd2..7c5ba2e8c 100644 --- a/recognition/2D_OASIS_s43971125/predict.py +++ b/recognition/2D_OASIS_s43971125/predict.py @@ -34,16 +34,20 @@ print("Dice per Class:", dice_scores) #Visualise results -import matplotlib.pyplot as plt plt.figure(figsize=(12,4)) + plt.subplot(1,3,1) plt.title("Input") plt.imshow(image[0,0].cpu(), cmap='gray') + plt.subplot(1,3,2) plt.title("Ground Truth") -plt.imshow(label.cpu(), cmap='jet') +plt.imshow(label.squeeze().cpu(), cmap='jet') + plt.subplot(1,3,3) plt.title("Prediction") plt.imshow(predicted[0].cpu(), cmap='jet') -plt.show() +plt.tight_layout() +plt.savefig("prediction_example.png", dpi=300, bbox_inches='tight') +print("Saved visualisation") diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index 388f60851..a93cf2370 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -6,12 +6,10 @@ import os import sys - from modules import UNet +from modules import ImprovedUNet from dataset import get_dataloaders -sys.stdout.reconfigure(line_buffering=True) - #Create the DiceLoss functionality class DiceLoss(nn.Module): #Subclass behaves like other Pytorch Loss functions @@ -63,99 +61,101 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): return torch.tensor(dice_scores, device=preds.device) -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -#device = torch.device("cpu") +#Note: wrap the training loop so it's only run when train.py is called, not when it's imported +if __name__ == "__main__": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + #device = torch.device("cpu") -# Set the training paremeters -num_epochs = 15 -batch_size = 8 -learning_rate = 1e-4 -save_path = "best.pth" + # Set the training paremeters + num_epochs = 5 + batch_size = 4 + learning_rate = 1e-4 + save_path = "best.pth" -# Load in the datasets -train_load, val_load, test_load = get_dataloaders(batch_size=batch_size) + # Load in the datasets + train_load, val_load, test_load = get_dataloaders(batch_size=batch_size) -# Define the model, loss (DiceLoss) and optimiser (Adam) + # Define the model, loss (DiceLoss) and optimiser (Adam) -model = UNet(in_channels=1, out_channels=4).to(device) -criterion = DiceLoss() -optimiser = optim.Adam(model.parameters(), lr=learning_rate) + model = ImprovedUNet(in_channels=1, out_channels=4).to(device) + criterion = DiceLoss() + optimiser = optim.Adam(model.parameters(), lr=learning_rate) -best_val_dice = 0.0 + best_val_dice = 0.0 # Don't need below currently, only saving best model. May be used if I want all models. #os.makedirs(checkpoint_dir, exist_ok=True) -# Training loop -for epoch in range(num_epochs): - model.train() - running_loss = 0.0 + # Training loop + for epoch in range(num_epochs): + model.train() + running_loss = 0.0 + + for images, labels in train_load: + images = images.to(device) + labels = labels.to(device) + + optimiser.zero_grad() + outputs = model(images) + loss = criterion(outputs, labels) + loss.backward() + optimiser.step() + + running_loss += loss.item() + + avg_loss = running_loss / len(train_load) + print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") + + #Validation Step + model.eval() + dice_scores = [] + with torch.no_grad(): + for images, labels in val_load: + images = images.to(device) + labels = labels.to(device) + outputs = model(images) + dice_batch = dice_coefficient(outputs, labels, num_classes=4) + dice_scores.append(dice_batch) + + dice_scores = torch.stack(dice_scores) + mean_dice = dice_scores.mean(dim=0) + + # Compute overall mean Dice score + mean_val_dice = mean_dice.mean().item() + + # Print per-class results and average results + print(f"Validation Dice per class: {mean_dice.cpu().numpy()}") + print(f"Mean Validation Dice: {mean_val_dice:.4f}") - for images, labels in train_load: - images = images.to(device) - labels = labels.to(device) + # Mean probability for each class + probs = torch.softmax(outputs, dim=1) + mean_probs = probs.mean(dim=(0, 2, 3)) + print(f"Mean predicted probabilities per class: {mean_probs.cpu().numpy()}") - optimiser.zero_grad() - outputs = model(images) - loss = criterion(outputs, labels) - loss.backward() - optimiser.step() - running_loss += loss.item() + # Save the best model so far, based on the average Dice score + if mean_val_dice > best_val_dice: + torch.save(model.state_dict(), save_path) + best_val_dice = mean_val_dice + best_per_class = mean_dice.cpu().numpy() + print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") - avg_loss = running_loss / len(train_load) - print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") + #blank line to separate each epoch + print() - #Validation Step + # Final test evaluation to find Dice Coefficient + + model.load_state_dict(torch.load(save_path)) model.eval() - dice_scores = [] + dice_totals = torch.zeros(4, device=device) with torch.no_grad(): - for images, labels in val_load: + for images, labels in test_load: images = images.to(device) labels = labels.to(device) outputs = model(images) - dice_batch = dice_coefficient(outputs, labels, num_classes=4) - dice_scores.append(dice_batch) + dice_scores = dice_coefficient(outputs, labels, num_classes=4) + dice_totals += dice_scores.to(device) + dice_averages = dice_totals / len(test_load) - dice_scores = torch.stack(dice_scores) - mean_dice = dice_scores.mean(dim=0) - - # Compute overall mean Dice score - mean_val_dice = mean_dice.mean().item() - - # Print per-class results and average results - print(f"Validation Dice per class: {mean_dice.cpu().numpy()}") - print(f"Mean Validation Dice: {mean_val_dice:.4f}") - - # Mean probability for each class - probs = torch.softmax(outputs, dim=1) - mean_probs = probs.mean(dim=(0, 2, 3)) - print(f"Mean predicted probabilities per class: {mean_probs.cpu().numpy()}") - - - # Save the best model so far, based on the average Dice score - if mean_val_dice > best_val_dice: - torch.save(model.state_dict(), save_path) - best_val_dice = mean_val_dice - best_per_class = mean_dice.cpu().numpy() - print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") - - #blank line to separate each epoch - print() - -# Final test evaluation to find Dice Coefficient - -model.load_state_dict(torch.load(save_path)) -model.eval() -dice_totals = torch.zeros(4, device=device) -with torch.no_grad(): - for images, labels in test_load: - images = images.to(device) - labels = labels.to(device) - outputs = model(images) - dice_scores = dice_coefficient(outputs, labels, num_classes=4) - dice_totals += dice_scores.to(device) -dice_averages = dice_totals / len(test_load) - -for i, dice in enumerate(dice_averages): - print(f"Class {i} Dice: {dice:.4f}") + for i, dice in enumerate(dice_averages): + print(f"Class {i} Dice: {dice:.4f}") From 2a46226bbb735c520f2238b77196b7aa5287f62f Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 12:35:22 +1000 Subject: [PATCH 10/16] Adds comments to modules, train, dataset. Adds skeleton to README.md that includes all potential Headers and hyperlinks --- recognition/2D_OASIS_s43971125/README.md | 45 ++++++++++++++++++++--- recognition/2D_OASIS_s43971125/dataset.py | 19 +++++++++- recognition/2D_OASIS_s43971125/modules.py | 2 + recognition/2D_OASIS_s43971125/train.py | 24 ++++++------ 4 files changed, 71 insertions(+), 19 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/README.md b/recognition/2D_OASIS_s43971125/README.md index 230cb44cf..7b29a35ea 100644 --- a/recognition/2D_OASIS_s43971125/README.md +++ b/recognition/2D_OASIS_s43971125/README.md @@ -1,8 +1,41 @@ +# Table of Contents +- [Improved 2D UNet](#improved-2d-unet) + - [Problem](#problem) + - [Model](#model) +- [Loading Data](#loading-data) +- [Training](#training) + - [Loss](#loss) + - [Optimiser](#optimiser) +- [Testing](#testing) +- [Result](#result) +- [Discussion](#discussion) +- [Conclusion](#conclusion) +- [References](#references) +- [Dependencies](#dependencies) -# Files +# Improved 2D UNet -- **dataset.py**: contains the dataloader for loading the 2D OASIS dataset -- **modules.py**: contains the 2D UNet model -- **train.py**: contains the code for training, testing, validating, and saving the UNet model -- **predict.py**: loads the trained model and visualises examples of real implementation - +## Problem + + +## Model + +# Loading Data + +# Training + +## Loss + +## Optimiser + +# Testing + +# Result + +# Discussion + +# Conclusion + +# References + +# Dependencies diff --git a/recognition/2D_OASIS_s43971125/dataset.py b/recognition/2D_OASIS_s43971125/dataset.py index 60c8634be..f2f3fd277 100644 --- a/recognition/2D_OASIS_s43971125/dataset.py +++ b/recognition/2D_OASIS_s43971125/dataset.py @@ -8,8 +8,25 @@ from PIL import Image import glob - +''' +OASISDataset class. +Loads the 2D_OASIS_dataset, converts the images and labels to pytorch tensors, +Remaps the labels to one-hot encoding, so that they are compatible with the DiceLoss function +Optionally transforms the image if 'transform' is set, then returns the images and labels +''' class OASISDataset(Dataset): + '''Current setup assumes that 2D_OASIS dataset is located in the specified root_dir + with the images and labels already split into train, test, and validation directory + Also assumes that all labels and images are .png files + /Oasis- + -keras_png_slices_train + -keras_png_slices_test + -keras_png_slices_val + -keras_png_slices_seg_train + -keras_png_slices_seg_test + -keras-png_slices_seg_val + If there is a different location, need to adjust root_dir, image_dir and label_dir accordingly + ''' def __init__(self, root_dir="/home/groups/comp3710/OASIS", split="train", categorical=False, transform=None): image_dir = os.path.join(root_dir, f"keras_png_slices_{split}") diff --git a/recognition/2D_OASIS_s43971125/modules.py b/recognition/2D_OASIS_s43971125/modules.py index 53ada3ddd..d9f21c61e 100644 --- a/recognition/2D_OASIS_s43971125/modules.py +++ b/recognition/2D_OASIS_s43971125/modules.py @@ -243,6 +243,8 @@ def forward(self, x): # Sum elementwise to combine them. # By doing this, gives stronger supervision to early layers + # also means we can swap out standard UNet and Improved UNet in train.py with no changes + # since they both return a single output this way combined = out_final + aux2_up + aux3_up + aux4_up return combined diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index a93cf2370..f215fa0fc 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -7,13 +7,12 @@ import sys from modules import UNet -from modules import ImprovedUNet from dataset import get_dataloaders -#Create the DiceLoss functionality +#Create the DiceLoss functionality, as explained in the lectures class DiceLoss(nn.Module): #Subclass behaves like other Pytorch Loss functions - #smooth is a small constant - used in the calculation step to avoid division by zero + #smooth is a small constant - used in the calculation step to avoid potential division by zero def __init__(self, smooth=1e-6): super(DiceLoss, self).__init__() self.smooth = smooth @@ -49,6 +48,7 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): preds = torch.argmax(preds, dim=1) dice_scores = [] + # for each class, compute the dice score, then add them to a tensor. for i in range(num_classes): pred_i = (preds == i).float() target_i = (targets == i).float() @@ -58,7 +58,7 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): dice = (2.0 * intersection + smooth) / (union + smooth) dice_scores.append(dice.item()) - + #return the tensor that contains the dice score for each class return torch.tensor(dice_scores, device=preds.device) #Note: wrap the training loop so it's only run when train.py is called, not when it's imported @@ -67,10 +67,10 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): #device = torch.device("cpu") # Set the training paremeters - num_epochs = 5 - batch_size = 4 + num_epochs = 25 + batch_size = 4 learning_rate = 1e-4 - save_path = "best.pth" + save_path = "best_improved.pth" # Load in the datasets train_load, val_load, test_load = get_dataloaders(batch_size=batch_size) @@ -82,11 +82,12 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): optimiser = optim.Adam(model.parameters(), lr=learning_rate) + # set the initial best dice score to 0. Gets updated each time the model + # finds a better score best_val_dice = 0.0 -# Don't need below currently, only saving best model. May be used if I want all models. -#os.makedirs(checkpoint_dir, exist_ok=True) # Training loop + # Note all print statements have no impact on training, are optional to track progress for epoch in range(num_epochs): model.train() running_loss = 0.0 @@ -140,11 +141,10 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): best_per_class = mean_dice.cpu().numpy() print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") - #blank line to separate each epoch + #blank line to separate each epoch printout in the log print() - # Final test evaluation to find Dice Coefficient - + # Final test evaluation to find Dice Coefficient for each class model.load_state_dict(torch.load(save_path)) model.eval() dice_totals = torch.zeros(4, device=device) From 930c9d61bd02765f3879a8a1a48ac3d0731ba5e5 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 13:44:16 +1000 Subject: [PATCH 11/16] Add documentation folder. This contains all the relevant images that will be linked to in the README file. Adds information about the problem and model to the READme file. Adjusts the parameters of train.py. --- recognition/2D_OASIS_s43971125/README.md | 33 +++++++++++++++++- .../documentation/Improved_UNet.png | Bin 0 -> 83432 bytes .../2D_OASIS_s43971125/documentation/UNet.png | Bin 0 -> 56560 bytes .../documentation/prediction_example.png | Bin 0 -> 132221 bytes recognition/2D_OASIS_s43971125/train.py | 1 + 5 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 recognition/2D_OASIS_s43971125/documentation/Improved_UNet.png create mode 100644 recognition/2D_OASIS_s43971125/documentation/UNet.png create mode 100644 recognition/2D_OASIS_s43971125/documentation/prediction_example.png diff --git a/recognition/2D_OASIS_s43971125/README.md b/recognition/2D_OASIS_s43971125/README.md index 7b29a35ea..51fdc969a 100644 --- a/recognition/2D_OASIS_s43971125/README.md +++ b/recognition/2D_OASIS_s43971125/README.md @@ -14,13 +14,44 @@ - [Dependencies](#dependencies) # Improved 2D UNet +A UNet is a convolutional neural network that is designed for image segmentation. This means it +classifies every pixel of an image into one of several different categories. This is particularly useful +for medical images, where you might want to classify which parts of an image correspond to certain types of tissue. ## Problem - +The problem being solved is the segmentation of 2D mri images of brains, into 4 different types of brain tissue. +The goal is for all 4 labels to have a minimum Dice similarity coefficient of 0.9 - so once the model is done training, +it could be used on any 2D mri image of a brain to accurately identify which parts of the image correspond to each type +of tissue. This could be useful for identifying potential abnormalities when scanning for illness. ## Model +[modules.py](modules.py) + +An Improved 2D UNet is similar in basic structure to a UNet. It follows the same U-Shaped architecture, where an image +is gradually reduced in spatial size while the number of channels are increased (essentially +zooming in on the image, in a way) in the encoder step. Then the feature maps are upscaled back to the original size +of the image while maintaining the learned feature, until eventually you end up with a segmented image of the same size +as the original. UNet also utilise skip connections, where it can take the outputs from earlier levels of the encoder +and hand them directly to the decoder of the same level - this helps to reconstruct boundaries, because reducing spatial +dimensions leads to information loss. + +![UNet Architecture](documentation/UNet.png) + +An Improved 2D UNet has some enhancements however. Instead of the normal convolutional blocks, it use residual blocks +with pre-activation (activation before convolution). It has optional dropout layers to avoid overfitting (unused here), +instance normalisation instead of batch normalisation (better stability with small batch sizes), and deep supervision. +Deep supervision essentially combines the output from all the decoder layers to give the final output. This provides +better gradient flow (because it provides the gradient from all levels of the decoder, faster convergence (training +stabilises faster which speeds it up) and improved accuracy (because we are taking outputs from all levels of the encoder, +intermediate features are more prominent, which will lead to better final predictions + +![Improed UNet Architecture](documentation/Improved_UNet.png) # Loading Data +[dataset.py](dataset.py) +For this problem, loading the data is relatively simple. No transforms need to be applied, and the data is already +separated into images and labels, and training/testing/validation groups. 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b/recognition/2D_OASIS_s43971125/train.py @@ -7,6 +7,7 @@ import sys from modules import UNet +from modules import ImprovedUNet from dataset import get_dataloaders #Create the DiceLoss functionality, as explained in the lectures From 2e55dbcf4deed0fad5326176248757f22f1d3a39 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 14:16:13 +1000 Subject: [PATCH 12/16] Adds some comments to modules.py Updates README with information about training the model --- recognition/2D_OASIS_s43971125/README.md | 27 ++++++++++++++++++----- recognition/2D_OASIS_s43971125/modules.py | 9 ++++++-- 2 files changed, 28 insertions(+), 8 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/README.md b/recognition/2D_OASIS_s43971125/README.md index 51fdc969a..1fb63a323 100644 --- a/recognition/2D_OASIS_s43971125/README.md +++ b/recognition/2D_OASIS_s43971125/README.md @@ -8,8 +8,6 @@ - [Optimiser](#optimiser) - [Testing](#testing) - [Result](#result) -- [Discussion](#discussion) -- [Conclusion](#conclusion) - [References](#references) - [Dependencies](#dependencies) @@ -54,19 +52,36 @@ separated into images and labels, and training/testing/validation groups. All th each image and label to a pytorch tensor so that they can be processed by the Improved UNet. # Training +Parameters used were as follows (these can be adjusted within train.py +Batch size: 4 +Number of Epochs: 50 +Learning Rate: 1e-4 + ## Loss +This model uses Dice similarity coefficient loss (DiceLoss). It computes the simarilty of our segmented images and +manually delineated images, based on how much they overlap. Specifically, we use Multiclass Dice loss (where we compute +the dice score for each class separately, then take the average). +DiceLoss function was generated by AI, then adjusted based on the information from +the lecture slides. ## Optimiser +This model uses the Adam (Adaptive Moment Estimation) optimiser, which is built-in to pytorch. It works by +adjusting the learning rate for each parameter during training, by keeping track of how gradients are changing +for each weight and adjusting how much each weight is updated. It's a very common choice for deep segmentation models +such as UNet. # Testing -# Result - -# Discussion -# Conclusion +# Result # References +Lecture Slides for PNGs # Dependencies +pytorch=2.6.0 +torchvision=0.21.0 +numpy=1.25.0 +pillow-9.5.0 +matplotlib=3.8.0 diff --git a/recognition/2D_OASIS_s43971125/modules.py b/recognition/2D_OASIS_s43971125/modules.py index d9f21c61e..bd3f4fdfd 100644 --- a/recognition/2D_OASIS_s43971125/modules.py +++ b/recognition/2D_OASIS_s43971125/modules.py @@ -41,7 +41,9 @@ def forward(self, x, skip): x = self.conv(x) return x - +''' +Basic UNet - this code was adapted from the Lectures +''' class UNet(nn.Module): def __init__(self, in_channels=1, out_channels=4, base_filters=64): @@ -84,7 +86,10 @@ def forward(self, x): out = self.final_conv(d1) return out -#Pre-activation residual block + +#Below are all the requirements for the improved UNet + + class PreActResBlock(nn.Module): #dropout_prob is optional tuning metric, used to regularise the block (prevents overfitting) def __init__(self, in_ch, out_ch, dropout_prob=0.0): From faadaab2cb8da25706eb12aa912465b84e82ffd0 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 14:55:25 +1000 Subject: [PATCH 13/16] Updates predict.py to be modular (separates the function that loads the model and the fuction that provides visualisation. Adds a second visualisation function that shows the per-class visualisation --- recognition/2D_OASIS_s43971125/predict.py | 136 ++++++++++++++++------ 1 file changed, 101 insertions(+), 35 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/predict.py b/recognition/2D_OASIS_s43971125/predict.py index 7c5ba2e8c..2dcf44d81 100644 --- a/recognition/2D_OASIS_s43971125/predict.py +++ b/recognition/2D_OASIS_s43971125/predict.py @@ -2,52 +2,118 @@ import torch from modules import UNet +from modules import ImprovedUNet from dataset import OASISDataset import matplotlib.pyplot as plt from train import dice_coefficient -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +def load_model(model_path="best_improved.pth", in_channels=1, out_channels=4, device=None): + """ + Loads a trained UNet model from the specified checkpoint path. + """ + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -# Load trained model -model = UNet(in_channels=1, out_channels=4).to(device) -#Note that in this instance, the trained model is named "best.pth" and is located in the same -#directory as predict.py - by default, this is what train.py will produce. -model.load_state_dict(torch.load("best.pth")) -model.eval() + model = ImprovedUNet(in_channels=in_channels, out_channels=out_channels).to(device) + model.load_state_dict(torch.load(model_path, map_location=device)) + model.eval() + return model, device -# Load a single test example -test_dataset = OASISDataset(split="test") -image, label = test_dataset[0] -#Add batch dimension and send image and label to the GPU -image = image.to(device).unsqueeze(0) # Add batch dimension -label = label.to(device).unsqueeze(0) +def predict_and_visualise(model, dataset_split="test", sample_index=0, save_path="prediction_example.png", num_classes=4, device=None): + """ + Runs inference on one sample from the test dataset and visualises input, ground truth, and output (prediction). Saves the + generated image to save_path. + """ + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -# Forward pass -with torch.no_grad(): - output = model(image) - probs = torch.softmax(output, dim=1) - predicted = torch.argmax(output, dim=1) + # Load one sample from dataset + dataset = OASISDataset(split=dataset_split) + image, label = dataset[sample_index] -#Compute Dice per class -dice_scores = dice_coefficient(output, label, num_classes=4) -print("Dice per Class:", dice_scores) + # Add batch dimension and move to device + image = image.unsqueeze(0).to(device) + label = label.unsqueeze(0).to(device) -#Visualise results -plt.figure(figsize=(12,4)) + # Forward pass + with torch.no_grad(): + output = model(image) + probs = torch.softmax(output, dim=1) + predicted = torch.argmax(output, dim=1) + + # Compute Dice per class + dice_scores = dice_coefficient(output, label, num_classes=num_classes) + print(f"Dice per Class (sample {sample_index}):", dice_scores) + + # Visualise results + plt.figure(figsize=(12, 4)) + + plt.subplot(1, 3, 1) + plt.title("Input") + plt.imshow(image[0, 0].cpu(), cmap='gray') + + plt.subplot(1, 3, 2) + plt.title("Ground Truth") + plt.imshow(label.squeeze().cpu(), cmap='jet') + + plt.subplot(1, 3, 3) + plt.title("Prediction") + plt.imshow(predicted[0].cpu(), cmap='jet') + + plt.tight_layout() + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.close() + print(f"Saved prediction visualisation to {save_path}") + +def predict_and_visualise_per_class(model, dataset_split="test", sample_index=0, save_path="prediction_per_class.png", num_classes=4, device=None): + """ + Runs inference on one sample from the dataset and visualises input, ground truth, + final predicted segmentation, and per-class probability maps. + """ + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Load one sample from dataset + dataset = OASISDataset(split=dataset_split) + image, label = dataset[sample_index] + + # Add batch dimension and move to device + image = image.unsqueeze(0).to(device) + label = label.unsqueeze(0).to(device) + + # Forward pass + with torch.no_grad(): + output = model(image) + probs = torch.softmax(output, dim=1) # [B, C, H, W] + predicted = torch.argmax(output, dim=1) + + # Compute Dice per class + dice_scores = dice_coefficient(output, label, num_classes=num_classes) + print(f"Dice per Class (sample {sample_index}):", dice_scores) + + # Visualise results + n_cols = 3 + num_classes # input + GT + overall prediction + per-class + plt.figure(figsize=(4 * n_cols, 4)) + + # Each Class Probability Map + for c in range(num_classes): + plt.subplot(1, n_cols, 4 + c) + plt.title(f"Class {c} Prob") + plt.imshow(probs[0, c].cpu(), cmap='inferno') + plt.axis('off') + + plt.tight_layout() + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.close() + print(f"Saved visualisation to {save_path}") + + +if __name__ == "__main__": + model, device = load_model() + predict_and_visualise(model, sample_index=5, save_path="prediction_example.png", device=device) + predict_and_visualise_per_class(model, sample_index=5, save_path="prediction_per_class.png", device=device) -plt.subplot(1,3,1) -plt.title("Input") -plt.imshow(image[0,0].cpu(), cmap='gray') -plt.subplot(1,3,2) -plt.title("Ground Truth") -plt.imshow(label.squeeze().cpu(), cmap='jet') -plt.subplot(1,3,3) -plt.title("Prediction") -plt.imshow(predicted[0].cpu(), cmap='jet') -plt.tight_layout() -plt.savefig("prediction_example.png", dpi=300, bbox_inches='tight') -print("Saved visualisation") From 0c7e3d0196834ac8a10913ca67594eb29e43b822 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 15:04:04 +1000 Subject: [PATCH 14/16] Add a third visualisation function to predict.py that plots the dice scores --- recognition/2D_OASIS_s43971125/predict.py | 29 ++++++++++++++++++----- 1 file changed, 23 insertions(+), 6 deletions(-) diff --git a/recognition/2D_OASIS_s43971125/predict.py b/recognition/2D_OASIS_s43971125/predict.py index 2dcf44d81..9ddc7cdbb 100644 --- a/recognition/2D_OASIS_s43971125/predict.py +++ b/recognition/2D_OASIS_s43971125/predict.py @@ -64,12 +64,12 @@ def predict_and_visualise(model, dataset_split="test", sample_index=0, save_path plt.tight_layout() plt.savefig(save_path, dpi=300, bbox_inches='tight') plt.close() - print(f"Saved prediction visualisation to {save_path}") + #Note: return dice scores so that we can plot them using a different function + return dice_scores def predict_and_visualise_per_class(model, dataset_split="test", sample_index=0, save_path="prediction_per_class.png", num_classes=4, device=None): """ - Runs inference on one sample from the dataset and visualises input, ground truth, - final predicted segmentation, and per-class probability maps. + Runs inference on one sample from the dataset and visualises per-class probability maps. """ if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -106,14 +106,31 @@ def predict_and_visualise_per_class(model, dataset_split="test", sample_index=0, plt.tight_layout() plt.savefig(save_path, dpi=300, bbox_inches='tight') plt.close() - print(f"Saved visualisation to {save_path}") +def plot_dice_scores(dice_scores, save_path="dice_scores.png"): + """ + Plots a bar chart of Dice scores for each class. + """ + classes = [f"Class {i}" for i in range(len(dice_scores))] + plt.figure(figsize=(6, 4)) + plt.bar(classes, dice_scores, color="skyblue", edgecolor="black") + plt.ylim(0, 1.05) + plt.ylabel("Dice Score") + plt.title("Dice Score per Class") + plt.grid(axis='y', linestyle='--', alpha=0.6) + plt.tight_layout() + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.close() + print(f"Saved Dice score plot to {save_path}") + if __name__ == "__main__": model, device = load_model() - predict_and_visualise(model, sample_index=5, save_path="prediction_example.png", device=device) + #Note: plot_dice_scores requires dice_scores as input + #for simplicity,we just return them when we are plotting the example input-output + dice_scores = predict_and_visualise(model, sample_index=5, save_path="prediction_example.png", device=device) predict_and_visualise_per_class(model, sample_index=5, save_path="prediction_per_class.png", device=device) - + plot_dice_scores(dice_scores, save_path="dice_scores.png") From 0c5b5ec43dea35fbd7e93efe94331986da55b61e Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 15:15:20 +1000 Subject: [PATCH 15/16] Completion of README file, adds testing, results, and documentation section Final adjustment to train.py so default parameters match those in README Fixed the function in predict.py to properly graph the dice scores Adds the image of the dice scores to the documentation --- recognition/2D_OASIS_s43971125/README.md | 20 +++++++++++++++--- .../documentation/dice_scores.png | Bin 0 -> 54271 bytes .../documentation/prediction_example.png | Bin 132221 -> 133359 bytes .../documentation/prediction_per_class.png | Bin 0 -> 58688 bytes recognition/2D_OASIS_s43971125/predict.py | 9 +++----- recognition/2D_OASIS_s43971125/train.py | 18 ++++++++-------- 6 files changed, 29 insertions(+), 18 deletions(-) create mode 100644 recognition/2D_OASIS_s43971125/documentation/dice_scores.png create mode 100644 recognition/2D_OASIS_s43971125/documentation/prediction_per_class.png diff --git a/recognition/2D_OASIS_s43971125/README.md b/recognition/2D_OASIS_s43971125/README.md index 1fb63a323..66c1311e2 100644 --- a/recognition/2D_OASIS_s43971125/README.md +++ b/recognition/2D_OASIS_s43971125/README.md @@ -43,7 +43,7 @@ better gradient flow (because it provides the gradient from all levels of the de stabilises faster which speeds it up) and improved accuracy (because we are taking outputs from all levels of the encoder, intermediate features are more prominent, which will lead to better final predictions -![Improed UNet Architecture](documentation/Improved_UNet.png) +![Improved UNet Architecture](documentation/Improved_UNet.png) # Loading Data [dataset.py](dataset.py) @@ -72,12 +72,26 @@ for each weight and adjusting how much each weight is updated. It's a very commo such as UNet. # Testing - +The model was tested by measuring the Dice Scores for each class. Each Dice score is calculated independently to +avoid imbalanced class errors. See results for an example of the input and output, the probability maps for +each class, and the dice scores per class. # Result +Dice scores per class +Class 0: 0.9993 +Class 1: 0.9585 +Class 2: 0.9653 +Class 3: 0.9783 +![Dice Scores](documentation/dice_dcores.png) + +Example input-output +![Visualisation](documentation/prediction_example.png) + +Example per-class visualisation +![Per-class visualisation](documentation/prediction_per_class.png) # References -Lecture Slides for PNGs +Lecture Slides for model PNGs # Dependencies pytorch=2.6.0 diff --git a/recognition/2D_OASIS_s43971125/documentation/dice_scores.png b/recognition/2D_OASIS_s43971125/documentation/dice_scores.png new file mode 100644 index 0000000000000000000000000000000000000000..bace36e080dd23abd0b8ed3d8c98defde223f5d0 GIT binary patch literal 54271 zcmeFa2~?D4)-6mrttK&Qhywxw2ImQa$~?suA;c)6fXt|fC?#Nt$P9Lj5g8+jD5C+R z%*Z^015g42L8&TajFf;<3<5Gw|2{?1Uw`-Z_x#qOawQjRkcTlOS_kEw|IcJ}} z_c>fUbWmr>XJVfT2?;IH{Z{L+kkGO9C18#!R7dQ 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per-class plt.figure(figsize=(4 * n_cols, 4)) @@ -112,9 +108,10 @@ def plot_dice_scores(dice_scores, save_path="dice_scores.png"): """ Plots a bar chart of Dice scores for each class. """ - classes = [f"Class {i}" for i in range(len(dice_scores))] + dice_scores_np = dice_scores.cpu().numpy() + classes = [f"Class {i}" for i in range(len(dice_scores_np))] plt.figure(figsize=(6, 4)) - plt.bar(classes, dice_scores, color="skyblue", edgecolor="black") + plt.bar(classes, dice_scores_np, color="skyblue", edgecolor="black") plt.ylim(0, 1.05) plt.ylabel("Dice Score") plt.title("Dice Score per Class") diff --git a/recognition/2D_OASIS_s43971125/train.py b/recognition/2D_OASIS_s43971125/train.py index 8f69b943d..4a0d19f16 100644 --- a/recognition/2D_OASIS_s43971125/train.py +++ b/recognition/2D_OASIS_s43971125/train.py @@ -68,7 +68,7 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): #device = torch.device("cpu") # Set the training paremeters - num_epochs = 25 + num_epochs = 50 batch_size = 4 learning_rate = 1e-4 save_path = "best_improved.pth" @@ -106,7 +106,7 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): running_loss += loss.item() avg_loss = running_loss / len(train_load) - print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") + #print(f"Epoch {epoch+1} finished, Avg Loss: {avg_loss:.4f}") #Validation Step model.eval() @@ -126,13 +126,13 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): mean_val_dice = mean_dice.mean().item() # Print per-class results and average results - print(f"Validation Dice per class: {mean_dice.cpu().numpy()}") - print(f"Mean Validation Dice: {mean_val_dice:.4f}") + #print(f"Validation Dice per class: {mean_dice.cpu().numpy()}") + #print(f"Mean Validation Dice: {mean_val_dice:.4f}") # Mean probability for each class probs = torch.softmax(outputs, dim=1) mean_probs = probs.mean(dim=(0, 2, 3)) - print(f"Mean predicted probabilities per class: {mean_probs.cpu().numpy()}") + #print(f"Mean predicted probabilities per class: {mean_probs.cpu().numpy()}") # Save the best model so far, based on the average Dice score @@ -140,10 +140,10 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): torch.save(model.state_dict(), save_path) best_val_dice = mean_val_dice best_per_class = mean_dice.cpu().numpy() - print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") + #print(f"✅ New best model saved (Avg Dice: {mean_val_dice:.4f}, per class: {best_per_class})") #blank line to separate each epoch printout in the log - print() + #print() # Final test evaluation to find Dice Coefficient for each class model.load_state_dict(torch.load(save_path)) @@ -158,5 +158,5 @@ def dice_coefficient(preds, targets, num_classes=4, smooth=1e-6): dice_totals += dice_scores.to(device) dice_averages = dice_totals / len(test_load) - for i, dice in enumerate(dice_averages): - print(f"Class {i} Dice: {dice:.4f}") + #for i, dice in enumerate(dice_averages): + # print(f"Class {i} Dice: {dice:.4f}") From 3b5998e5147772cd3cee864c07ce6a76fbd26a77 Mon Sep 17 00:00:00 2001 From: s4397112 Date: Fri, 7 Nov 2025 15:22:25 +1000 Subject: [PATCH 16/16] Removed slurm script from Repo --- recognition/2D_OASIS_s43971125/runner.sh | 19 ------------------- 1 file changed, 19 deletions(-) delete mode 100644 recognition/2D_OASIS_s43971125/runner.sh diff --git a/recognition/2D_OASIS_s43971125/runner.sh b/recognition/2D_OASIS_s43971125/runner.sh deleted file mode 100644 index ed4af9ec5..000000000 --- a/recognition/2D_OASIS_s43971125/runner.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash -#SBATCH --partition=a100 -#SBATCH --job-name=TrainingTest -#SBATCH --output=slurm_logs/ModuleTest_%j.log -#SBATCH --error=slurm_logs/ModuleTest_%j.err -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=1 -#SBATCH --time=01:00:00 -#SBATCH --gres=gpu:a100:1 - - -# Source conda setup script -source ~/miniconda3/etc/profile.d/conda.sh - -# Activate your environment -conda activate torch # replace with your environment name - -# Run the Python script -python train.py