From c3aff8b5b5c516eddc3929828be3e622bf5f6b1f Mon Sep 17 00:00:00 2001 From: "Shakes (WhiteOut)" Date: Sun, 17 Sep 2023 21:47:51 +1000 Subject: [PATCH 01/13] Added recognition branch and README for info. --- recognition/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 recognition/README.md diff --git a/recognition/README.md b/recognition/README.md new file mode 100644 index 0000000000..5c646231c2 --- /dev/null +++ b/recognition/README.md @@ -0,0 +1,10 @@ +# Recognition Tasks +Various recognition tasks solved in deep learning frameworks. + +Tasks may include: +* Image Segmentation +* Object detection +* Graph node classification +* Image super resolution +* Disease classification +* Generative modelling with StyleGAN and Stable Diffusion From 614aadc0285ff584ff750a30b8d62694a0a25bf3 Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Fri, 20 Oct 2023 23:29:01 +1000 Subject: [PATCH 02/13] 1st Pull Request --- .../Training Accuracy over Epochs.png | Bin 0 -> 25363 bytes .../Training Loss Over Epochs.png | Bin 0 -> 23567 bytes .../__pycache__/dataset.cpython-311.pyc | Bin 0 -> 4707 bytes .../__pycache__/modules.cpython-311.pyc | Bin 0 -> 5331 bytes recognition/PerceiverTransform/dataset.py | 64 +++++++++++++ recognition/PerceiverTransform/modules.py | 72 ++++++++++++++ recognition/PerceiverTransform/predict.py | 48 ++++++++++ recognition/PerceiverTransform/train.py | 90 ++++++++++++++++++ recognition/README.md | 35 +++++-- recognition/Training, Testing.png | Bin 0 -> 31141 bytes 10 files changed, 299 insertions(+), 10 deletions(-) create mode 100644 recognition/PerceiverTransform/Training Accuracy over Epochs.png create mode 100644 recognition/PerceiverTransform/Training Loss Over Epochs.png create mode 100644 recognition/PerceiverTransform/__pycache__/dataset.cpython-311.pyc create mode 100644 recognition/PerceiverTransform/__pycache__/modules.cpython-311.pyc create mode 100644 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transforms +from torch.utils.data import random_split +from PIL import Image + +class AlzheimerDataset(Dataset): + def __init__(self, root_dir, mode='train', transform=None): + """ + Args: + root_dir (string): Directory with all the images. + mode (string): Mode in which the dataset operates. Can be 'train' or 'test'. + transform (callable, optional): Optional transform to be applied on a sample. + """ + self.root_dir = os.path.join(root_dir, mode) # This appends 'train' or 'test' based on mode, effectively separating the datasets. + self.transform = transform + self.nc_images = [os.path.join(self.root_dir, 'NC', img) for img in os.listdir(os.path.join(self.root_dir, 'NC')) if img.endswith('.jpeg')] + self.ad_images = [os.path.join(self.root_dir, 'AD', img) for img in os.listdir(os.path.join(self.root_dir, 'AD')) if img.endswith('.jpeg')] + self.total_images = self.nc_images + self.ad_images + + def __len__(self): + return len(self.total_images) + + def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + + img_name = self.total_images[idx] + image = Image.open(img_name).convert('RGB') + + label = 0 if 'NC' in img_name else 1 # 0 for NC and 1 for AD + + if self.transform: + image = self.transform(image) + + return image, label + +def get_dataloaders(root_dir, batch_size): + """ + Returns train and test dataloaders. + """ + # Image preprocessing steps: resizing to a common size, converting to tensor, and normalizing. + # Adjust these transformations based on your specific needs. + transform = transforms.Compose([ + transforms.Resize((224, 224)), # Resizing to fit typical CNN input sizes. Adjust if necessary. + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + ]) + + # Here, the AlzheimerDataset class is instantiated twice, for training and testing purposes. + train_dataset = AlzheimerDataset(root_dir, mode='train', transform=transform) + test_dataset = AlzheimerDataset(root_dir, mode='test', transform=transform) + + # Splitting the training dataset into train and validation subsets + train_length = int(0.65 * len(train_dataset)) + valid_length = len(train_dataset) - train_length + train_subset, valid_subset = random_split(train_dataset, [train_length, valid_length]) + + train_loader = DataLoader(train_subset, batch_size=batch_size, shuffle=True) + valid_loader = DataLoader(valid_subset, batch_size=batch_size, shuffle=False) + test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + + return train_loader, valid_loader, test_loader \ No newline at end of file diff --git a/recognition/PerceiverTransform/modules.py b/recognition/PerceiverTransform/modules.py new file mode 100644 index 0000000000..4c220cd2c6 --- /dev/null +++ b/recognition/PerceiverTransform/modules.py @@ -0,0 +1,72 @@ +import torch +import torch.nn as nn + +class CrossAttention(nn.Module): + """Cross-Attention mechanism where the latent array attends to the input data.""" + + def __init__(self, embed_dim, num_heads): + super(CrossAttention, self).__init__() + self.attn = nn.MultiheadAttention(embed_dim, num_heads) + + def forward(self, x, latent): + # Ensure the sequence length of x and latent match + # by truncating or padding latent if necessary + seq_len_x = x.size(1) + seq_len_latent = latent.size(1) + if seq_len_latent < seq_len_x: + # Pad latent with zeros to match x's sequence length + padding = torch.zeros(latent.size(0), seq_len_x - seq_len_latent, latent.size(2)).to(latent.device) + latent = torch.cat([latent, padding], dim=1) + elif seq_len_latent > seq_len_x: + # Truncate latent to match x's sequence length + latent = latent[:, :seq_len_x, :] + + x = x.permute(1, 0, 2) + latent = latent.permute(1, 0, 2) + output, _ = self.attn(latent, x, x) + + output = output.permute(1, 0, 2) + + return output + latent + + +class LatentTransformer(nn.Module): + """Latent Transformer applies a series of self-attention and feed-forward networks on the latent array.""" + + def __init__(self, embed_dim, num_heads): + super(LatentTransformer, self).__init__() + self.self_attention = CrossAttention(embed_dim, num_heads) + self.feedforward = nn.Sequential( + nn.Linear(embed_dim, embed_dim), + nn.ReLU(), + nn.Linear(embed_dim, embed_dim) + ) + + def forward(self, latent): + latent = self.self_attention(latent, latent) + return self.feedforward(latent) + +class Perceiver(nn.Module): + """The Perceiver model that integrates all components.""" + + def __init__(self, input_dim, latent_dim, embed_dim, n_classes, num_heads): + super(Perceiver, self).__init__() + self.embed = nn.Linear(input_dim, embed_dim) + self.latent = nn.Parameter(torch.randn(1, latent_dim, embed_dim)) # Initialize latent array with batch dimension + self.cross_attention = CrossAttention(embed_dim, num_heads) + self.latent_transformer = LatentTransformer(embed_dim, num_heads) + self.classifier = nn.Linear(embed_dim, n_classes) + + def forward(self, x): + x = x.view(x.size(0), -1) + x = self.embed(x) + x = x.unsqueeze(1) + + # Repeat latent for each item in the batch + latent = self.latent.repeat(x.size(0), 1, 1) + + latent = self.cross_attention(x, latent) + latent = self.latent_transformer(latent) + latent_mean = latent.mean(dim=1) + + return self.classifier(latent_mean) diff --git a/recognition/PerceiverTransform/predict.py b/recognition/PerceiverTransform/predict.py new file mode 100644 index 0000000000..f8cb018be9 --- /dev/null +++ b/recognition/PerceiverTransform/predict.py @@ -0,0 +1,48 @@ +import torch +import torch.nn.functional as F +import torchvision.transforms as transforms +from PIL import Image +import matplotlib.pyplot as plt +from modules import Perceiver + +# Constants +DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +MODEL_PATH = './perceiver_model.pth' + +# Load the trained model +model = Perceiver( + input_dim=3 * 224 * 224, # Assuming image dimensions are (3, 224, 224) + latent_dim=256, + embed_dim=256, + n_classes=2, + num_heads=4 +).to(DEVICE) + +model.load_state_dict(torch.load(MODEL_PATH)) +model.eval() + +# Load and preprocess a sample image +def predict_image(img_path): + 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(img_path).convert('RGB') + image_tensor = transform(image).unsqueeze(0).to(DEVICE) + + # Predict + output = model(image_tensor) + probabilities = F.softmax(output, dim=1) + predicted_class = torch.argmax(output, dim=1).item() + + # Display the image and predictions + plt.imshow(image) + plt.title(f"Predicted Class: {'NC' if predicted_class == 0 else 'AD'}\nProbability: {probabilities[0][predicted_class]:.4f}") + plt.axis('off') + plt.show() + +if __name__ == "__main__": + img_path = input("Enter the path of the image: ") # Provide path to test images as input + predict_image(img_path) diff --git a/recognition/PerceiverTransform/train.py b/recognition/PerceiverTransform/train.py new file mode 100644 index 0000000000..9cd4dec347 --- /dev/null +++ b/recognition/PerceiverTransform/train.py @@ -0,0 +1,90 @@ +import torch +import torch.nn as nn +import torch.optim as optim +import matplotlib.pyplot as plt +from modules import Perceiver, CrossAttention +from dataset import get_dataloaders + +# Constants and hyperparameters +BATCH_SIZE = 32 +EPOCHS = 10 +LR = 0.005 +DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +MODEL_PATH = './perceiver_model.pth' + +# Model Initialization +model = Perceiver( + input_dim=3 * 224 * 224, + latent_dim=256, + embed_dim=256, + n_classes=2, + num_heads=4 +).to(DEVICE) + +loss_fn = nn.CrossEntropyLoss() +optimizer = optim.Adam(model.parameters(), lr=LR) + +# Data Loaders +train_loader, valid_loader, test_loader = get_dataloaders("C:\\Users\\AK\\Documents\\COMP3710\\AD_NC", batch_size=BATCH_SIZE) + +# Training +train_losses = [] +accuracies = [] + +for epoch in range(EPOCHS): + model.train() + running_loss = 0.0 + correct_train = 0 + total_train = 0 + for inputs, labels in train_loader: + inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) + + optimizer.zero_grad() + outputs = model(inputs) + loss = loss_fn(outputs, labels) + loss.backward() + optimizer.step() + + _, predicted = torch.max(outputs.data, 1) + total_train += labels.size(0) + correct_train += (predicted == labels).sum().item() + running_loss += loss.item() + + train_losses.append(running_loss / len(train_loader)) + accuracies.append(100 * correct_train / total_train) + + print(f"Epoch {epoch+1}/{EPOCHS}, Train Loss: {train_losses[-1]:.4f}, Train Accuracy: {accuracies[-1]:.2f}%") + +# After training, evaluate on the test set +model.eval() +correct_test = 0 +total_test = 0 +for inputs, labels in test_loader: + inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) + outputs = model(inputs) + _, predicted = torch.max(outputs.data, 1) + total_test += labels.size(0) + correct_test += (predicted == labels).sum().item() + +test_accuracy = 100 * correct_test / total_test +print(f"Test Accuracy: {test_accuracy:.2f}%") + +# Plotting +plt.figure(figsize=(10, 6)) +plt.plot(train_losses, label='Training Loss') +plt.xlabel('Epochs') +plt.ylabel('Loss') +plt.title('Training Loss over Epochs') +plt.legend() +plt.show() + +plt.figure(figsize=(10, 6)) +plt.plot(accuracies, label='Training Accuracy') +plt.xlabel('Epochs') +plt.ylabel('Accuracy') +plt.title('Training Accuracy over Epochs') +plt.legend() +plt.show() + +# Saving model +torch.save(model.state_dict(), MODEL_PATH) diff --git a/recognition/README.md b/recognition/README.md index 5c646231c2..1e5704ed0d 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -1,10 +1,25 @@ -# Recognition Tasks -Various recognition tasks solved in deep learning frameworks. - -Tasks may include: -* Image Segmentation -* Object detection -* Graph node classification -* Image super resolution -* Disease classification -* Generative modelling with StyleGAN and Stable Diffusion +# Perceiver Transformer +# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data (see Appendix for link) using a visual or perceiver transformer [8, 9] set having a minimum accuracy of 0.8 on the test set. +# Akshath Katyal +# 47144691 + + +## Task Description: + + + + +## Model/ALgorithm Description + + + +# List Dependencies + + + +# Provide examples of input, output and plots of algorithm + + + +# Describe any specific pre-processing you have used with references if any. 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5STei7M|Qnl8T(c7$moS_SlMf~Crc!4UppM0YKIm-M5h&) z5C=*TP-Se;i-5fww+FilVE1BOCx2>z0&xFyU=mALs->>qXq1riG68XgZ_99sbKSRY zZP6&<%u%aosr~Z;KcY;q?kJJbm!_xYi`~UW?VGP>Q6bQ_Z`11`)e=OOF}MO#9@1P=UT)EA9h@HC=pp z37q8w*3|B*Z&;Pt^$N_r1Kv@WR+cQBArn~HNGtH52fg4VT>9<8InF{ZCs4(jQ$IlL(4&kI$YPG}R7{gQ{&2ilMDYsT{E zRSOf-m^&$NL|LboPF0`Z`(F0TL+uH>ma$@6 zmUq~M;k?dfQ|k5xPOdkl15M;;XTs4*$(wSWr|GYuR(E4r0K!*zq9~jtfJ3>;9U@lg zg@;Zh_dFE^-SyX9H)Z>!p9nw}qRax8MGz-`&O)$|GX)B4wVMM;-&RO{rO(q?&cJCX|)ab zwe$t&S7JB+DqElwQ{Z8_a4XEDZHrRMs$z2a6{Dnq94q>@;v8v3_D#s>>66FftWpm7 zJ@T)D?+(jEzxdF!zUbVp(d%Y1l`I6`<)!VDKh{imdf%{fid)Na&zl|6ikxp`Q_Ntt z!Iso2`(|&l6T6$P=UI_novShM8qxB0Ba*~aJHA1i!^kMuP~p)Ff0+yzyOQLgNfsm@ z!fQx}lw>hexOBK#Y&LzlsEne4cb*NdAfFJI*OxLOTFBPGa8DoRc^LQAyL=q0#RB~V zFu#z@plqjk1G}r{ZPN6uwIR}jzyn?1ZSt2`)^RxlRvzom_QC$b)34)AyDYmtp#5dt zp`YHT79{WR3161>Gf0KFXL=Z{g^F~2ZjwNo`KgYKTLx=8QNX-oUC-ni(doF9D-!Vv z+fyXVEW(FWSktf54gVDnE8B(zzW|1S21&&dmGwo z19|KA-m!19;Y?W(c{Pe6pBlZnRI|Z-?;3^iH2W+49oU>4>c7`g*E>`A+xc7n1KNr7 AoB#j- literal 0 HcmV?d00001 From 246c85ed242df0436791c42f178e8629544784d8 Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 19:18:09 +1000 Subject: [PATCH 03/13] Fixed labelling problem with datasets. --- recognition/PerceiverTransform/dataset.py | 13 +++---------- recognition/PerceiverTransform/train.py | 6 +++--- 2 files changed, 6 insertions(+), 13 deletions(-) diff --git a/recognition/PerceiverTransform/dataset.py b/recognition/PerceiverTransform/dataset.py index 742ff1843f..5adc84e17f 100644 --- a/recognition/PerceiverTransform/dataset.py +++ b/recognition/PerceiverTransform/dataset.py @@ -7,12 +7,7 @@ class AlzheimerDataset(Dataset): def __init__(self, root_dir, mode='train', transform=None): - """ - Args: - root_dir (string): Directory with all the images. - mode (string): Mode in which the dataset operates. Can be 'train' or 'test'. - transform (callable, optional): Optional transform to be applied on a sample. - """ + self.root_dir = os.path.join(root_dir, mode) # This appends 'train' or 'test' based on mode, effectively separating the datasets. self.transform = transform self.nc_images = [os.path.join(self.root_dir, 'NC', img) for img in os.listdir(os.path.join(self.root_dir, 'NC')) if img.endswith('.jpeg')] @@ -29,7 +24,7 @@ def __getitem__(self, idx): img_name = self.total_images[idx] image = Image.open(img_name).convert('RGB') - label = 0 if 'NC' in img_name else 1 # 0 for NC and 1 for AD + label = 0 if img_name in self.nc_images else 1 # 0 for NC and 1 for AD if self.transform: image = self.transform(image) @@ -37,9 +32,7 @@ def __getitem__(self, idx): return image, label def get_dataloaders(root_dir, batch_size): - """ - Returns train and test dataloaders. - """ + # Image preprocessing steps: resizing to a common size, converting to tensor, and normalizing. # Adjust these transformations based on your specific needs. transform = transforms.Compose([ diff --git a/recognition/PerceiverTransform/train.py b/recognition/PerceiverTransform/train.py index 9cd4dec347..56ef1b069a 100644 --- a/recognition/PerceiverTransform/train.py +++ b/recognition/PerceiverTransform/train.py @@ -6,7 +6,7 @@ from dataset import get_dataloaders # Constants and hyperparameters -BATCH_SIZE = 32 +BATCH_SIZE = 5 EPOCHS = 10 LR = 0.005 DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -25,7 +25,7 @@ optimizer = optim.Adam(model.parameters(), lr=LR) # Data Loaders -train_loader, valid_loader, test_loader = get_dataloaders("C:\\Users\\AK\\Documents\\COMP3710\\AD_NC", batch_size=BATCH_SIZE) +train_loader, valid_loader, test_loader = get_dataloaders("C:\\Users\\AK\\Documents\\COMP3710\\AlzDataset", batch_size=BATCH_SIZE) # Training train_losses = [] @@ -87,4 +87,4 @@ plt.show() # Saving model -torch.save(model.state_dict(), MODEL_PATH) +torch.save(model.state_dict(), MODEL_PATH) \ No newline at end of file From 17aa1163d7d543a55904d280b94d70a2993a4edc Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 19:48:36 +1000 Subject: [PATCH 04/13] Changed the image input from RGB to Grayscale --- recognition/PerceiverTransform/dataset.py | 3 +-- recognition/PerceiverTransform/train.py | 10 +++++----- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/recognition/PerceiverTransform/dataset.py b/recognition/PerceiverTransform/dataset.py index 5adc84e17f..0259812606 100644 --- a/recognition/PerceiverTransform/dataset.py +++ b/recognition/PerceiverTransform/dataset.py @@ -22,7 +22,7 @@ def __getitem__(self, idx): idx = idx.tolist() img_name = self.total_images[idx] - image = Image.open(img_name).convert('RGB') + image = Image.open(img_name) label = 0 if img_name in self.nc_images else 1 # 0 for NC and 1 for AD @@ -38,7 +38,6 @@ def get_dataloaders(root_dir, batch_size): transform = transforms.Compose([ transforms.Resize((224, 224)), # Resizing to fit typical CNN input sizes. Adjust if necessary. transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # Here, the AlzheimerDataset class is instantiated twice, for training and testing purposes. diff --git a/recognition/PerceiverTransform/train.py b/recognition/PerceiverTransform/train.py index 56ef1b069a..b1a4d8d69c 100644 --- a/recognition/PerceiverTransform/train.py +++ b/recognition/PerceiverTransform/train.py @@ -14,11 +14,11 @@ # Model Initialization model = Perceiver( - input_dim=3 * 224 * 224, - latent_dim=256, - embed_dim=256, - n_classes=2, - num_heads=4 + input_dim = 224 * 224, + latent_dim = 256, + embed_dim = 256, + n_classes = 2, + num_heads = 4 ).to(DEVICE) loss_fn = nn.CrossEntropyLoss() From 86fadbd871d42a0fd13b4043e08fba4da155efeb Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 20:28:22 +1000 Subject: [PATCH 05/13] Added Validation for dataset after each epoch of training. --- recognition/PerceiverTransform/train.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/recognition/PerceiverTransform/train.py b/recognition/PerceiverTransform/train.py index b1a4d8d69c..3ea95d5854 100644 --- a/recognition/PerceiverTransform/train.py +++ b/recognition/PerceiverTransform/train.py @@ -55,6 +55,19 @@ print(f"Epoch {epoch+1}/{EPOCHS}, Train Loss: {train_losses[-1]:.4f}, Train Accuracy: {accuracies[-1]:.2f}%") +#Validation Stage + correct_val = 0 + total_val = 0 + for inputs, labels in valid_loader: + inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) + outputs = model(inputs) + _, predicted = torch.max(outputs.data, 1) + total_val += labels.size(0) + correct_val += (predicted == labels).sum().item() + + val_accuracy = 100 * correct_val / total_val + print(f"Validation Accuracy: {val_accuracy:.2f}%") + # After training, evaluate on the test set model.eval() correct_test = 0 From 9a5ffed26f3ee206176c9f59313890aa32b4da5e Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 20:41:29 +1000 Subject: [PATCH 06/13] fixed the input dimension of the model, changed tranformation of the predicted image --- recognition/PerceiverTransform/predict.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/recognition/PerceiverTransform/predict.py b/recognition/PerceiverTransform/predict.py index f8cb018be9..7c51479203 100644 --- a/recognition/PerceiverTransform/predict.py +++ b/recognition/PerceiverTransform/predict.py @@ -11,7 +11,7 @@ # Load the trained model model = Perceiver( - input_dim=3 * 224 * 224, # Assuming image dimensions are (3, 224, 224) + input_dim = 224 * 224, latent_dim=256, embed_dim=256, n_classes=2, @@ -26,10 +26,9 @@ def predict_image(img_path): 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(img_path).convert('RGB') + image = Image.open(img_path) image_tensor = transform(image).unsqueeze(0).to(DEVICE) # Predict @@ -45,4 +44,4 @@ def predict_image(img_path): if __name__ == "__main__": img_path = input("Enter the path of the image: ") # Provide path to test images as input - predict_image(img_path) + predict_image(img_path) \ No newline at end of file From 1f7ca4f807a0ac9114eb14098584678741f11e87 Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 20:53:00 +1000 Subject: [PATCH 07/13] removed redundant imports, code & old results visualization. --- .../Training Accuracy over Epochs.png | Bin 25363 -> 0 bytes .../Training Loss Over Epochs.png | Bin 23567 -> 0 bytes recognition/PerceiverTransform/train.py | 2 +- recognition/Training, Testing.png | Bin 31141 -> 0 bytes 4 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 recognition/PerceiverTransform/Training Accuracy over Epochs.png delete mode 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z>a?g|q;Mlwt7Dd4t9K_~ihn_pc)p8FOz}+rI;7Z=4HCow-Ed>=r5KR@>FfTxQz zK4Wgc(LGL|BteUjObN&rvowtmf4TR_(TbJ4l;Npqpl~&;L#eT%ei$QsQ;zeM31B84 zSn>_h;_bjJoy)62ndv*}O{beOkfa^$#ujyM`FTwlDv*rdN7O(2rwq9GVDSl0%f;FR zE0Bb3AdIYb1-{q|GzGYI9j()@otJ7^u~};SO;s9U-oS53S_-Tjf@$X>j+<=nQlG5L zJs7KSO^XI;=I#+|@y#@HI#{V^vrg(Vkxf)7So4nOqdg3HHmP2^KlDLk6 z3dHvQXLiB!SSkX+$)xTrK3ULMGng+wbh@;LK;w z!}fqs%`_x#RScDNY-JDpayLm8CEhS>9o!V^Y@t&aD{bXRh-TC(gz|m zV}wcdcBJ_TUL}3?$_HYhJ)Cj>_AaGOz;Rnq04=(a8cZnlr)#3Um`RJ zkZ1ZOLc=%#D5KcVf2q)zJxX@VdtR_Zd-x5kEsf#|DK3jwW##3XiY6i-j=CCUXZPSb z8YInx4Ca-tR*p4Pi$LMTbW>!8Pa)P)*?$Hbc|G;# z09@&nFlnMkD7xa`P3d1hk20GVUVGty%EmGLkGLcpx9J?5&tJK6URm<-;-3dSl2y82 IENvX{KgM1<0RR91 literal 0 HcmV?d00001 From 5e1195f05867796b247ba6887d466bb74b39a25f Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 22:30:58 +1000 Subject: [PATCH 10/13] Added README with Old documentation --- recognition/README.md | 32 +++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/recognition/README.md b/recognition/README.md index 1e5704ed0d..2100ec3f2e 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -5,21 +5,47 @@ ## Task Description: +The Perceiver is a cutting-edge deep learning architecture that has been making waves lately due to its exceptional performance across a diverse range of tasks. We decided to put it to the test ourselves and applied it to the problem of classifying Alzheimer's disease from brain images. With our implementation of the Perceiver model, we are confident in our ability to accurately classify Alzheimer's from non-invasive imaging methods, which can help with early detection and monitoring of the disease. +## Model/Algorithm Description +The Perceiver Transformer is a fresh deep learning structure that overcomes the usual constraint of Transformers requiring a fixed-input size. Its purpose is to tackle a vast range of input modes, from images to sound, without depending on domain-specific structures. The key perk of the Perceiver is its proficiency in handling inputs of any size by preserving a set of latent variables with a fixed size that interacts with the input data. The model was inspired by the tutorial from https://medium.com/@curttigges/the-annotated-perceiver-74752113eefb which discusses how "Perceiver IO, landmark multimodal neural architectures that solve many of the issues with classic transformers". -## Model/ALgorithm Description +# List Dependencies +* Numpy version 1.26.1 +* Pytorch 2.0 +* Matplotlib 3.8.0 -# List Dependencies +# Reproducibility +1. In `dataset.py`, edit the path of your `AD_NC` dataset. +2. With the dependencies installed, run python train.py file +3. Then run the predict.py to see results. + +# Results +This model was able to achieve really good results weirdly. It was no expected as the the number of epochs it was trained for was very small compared to larger numbers which often tend to get higher accuracy. The training time was pretty quick for 10 epochs and surprisingly showed 100% test set performance. This was definitely weird however when this model was run, it was not initially split into a validation set to compare it against. This could also be caused by data leakage and evaluation issues. +![Training/Testing]() +Below are some example plots of the results +* Training accuracy over epochs went up to 100. +* Training loss went to 0. +![1]() +![2]() -# Provide examples of input, output and plots of algorithm +I need to calculate the batch loss and run the test again with the validation set. # Describe any specific pre-processing you have used with references if any. Justify your training, validation and testing splits of the data. +* Preprocessing was done through Resizing of the images and normalizing the images using mean and standard deviation. + +* Justification of training and splitting of the data + +When working with data, it's important to divide it into three parts. The biggest portion (65%) is used for training, allowing the model to learn the underlying patterns and features. A smaller subset (15%) is used for validation to adjust hyperparameters and prevent overfitting. This helps make informed decisions about the training process, such as when to stop early. The remaining 20% is reserved for testing, ensuring that the model's final evaluation is done on unseen data, providing an unbiased estimate of its generalization capabilities. +# References +* [1]: https://medium.com/@curttigges/the-annotated-perceiver-74752113eefb +* [2]: https://medium.com/@curttigges/ From 82e479ad51556a0ca0f6704f78bd2583d369e2d4 Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 22:59:17 +1000 Subject: [PATCH 11/13] Finalized dataset.py and commenting --- recognition/PerceiverTransform/dataset.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/recognition/PerceiverTransform/dataset.py b/recognition/PerceiverTransform/dataset.py index 0259812606..cd644bf7ed 100644 --- a/recognition/PerceiverTransform/dataset.py +++ b/recognition/PerceiverTransform/dataset.py @@ -7,25 +7,31 @@ class AlzheimerDataset(Dataset): def __init__(self, root_dir, mode='train', transform=None): - + # Initialzing the dataset with parameters (root directory with entire dataset present) + self.root_dir = os.path.join(root_dir, mode) # This appends 'train' or 'test' based on mode, effectively separating the datasets. self.transform = transform + # Extracts the filepaths for each dataset type. self.nc_images = [os.path.join(self.root_dir, 'NC', img) for img in os.listdir(os.path.join(self.root_dir, 'NC')) if img.endswith('.jpeg')] self.ad_images = [os.path.join(self.root_dir, 'AD', img) for img in os.listdir(os.path.join(self.root_dir, 'AD')) if img.endswith('.jpeg')] self.total_images = self.nc_images + self.ad_images + def __len__(self): return len(self.total_images) + # Returns total number of images def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - + # Returns the images index img_name = self.total_images[idx] image = Image.open(img_name) - label = 0 if img_name in self.nc_images else 1 # 0 for NC and 1 for AD + # Assigning a label based on the image type. + label = 0 if img_name in self.nc_images else 1 #0 for NC and 1 for AD + # Applying tranformation if needed. if self.transform: image = self.transform(image) @@ -33,22 +39,22 @@ def __getitem__(self, idx): def get_dataloaders(root_dir, batch_size): - # Image preprocessing steps: resizing to a common size, converting to tensor, and normalizing. - # Adjust these transformations based on your specific needs. + # Defining image transformations for preprocessing purpose i.e resizing and converting to tensor. transform = transforms.Compose([ - transforms.Resize((224, 224)), # Resizing to fit typical CNN input sizes. Adjust if necessary. + transforms.Resize((224, 224)), # Resizing to fit typical CNN input sizes. transforms.ToTensor(), ]) - # Here, the AlzheimerDataset class is instantiated twice, for training and testing purposes. + # The AlzheimerDataset class is instantiated twice, dataset for training and testing purposes. train_dataset = AlzheimerDataset(root_dir, mode='train', transform=transform) test_dataset = AlzheimerDataset(root_dir, mode='test', transform=transform) - # Splitting the training dataset into train and validation subsets + # Splitting the training dataset into train and validation subsets using a 65-35 split. train_length = int(0.65 * len(train_dataset)) valid_length = len(train_dataset) - train_length train_subset, valid_subset = random_split(train_dataset, [train_length, valid_length]) + # Dataloaders created for the train, validation, and test datasets with specific batch sizes. train_loader = DataLoader(train_subset, batch_size=batch_size, shuffle=True) valid_loader = DataLoader(valid_subset, batch_size=batch_size, shuffle=False) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) From c9f1f103ba9bd537c8210b32c125a2ccacf71c50 Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 23:22:36 +1000 Subject: [PATCH 12/13] Updated changes and finished comments on the rest --- recognition/PerceiverTransform/modules.py | 19 +++++++++------ recognition/PerceiverTransform/predict.py | 11 +++++---- recognition/PerceiverTransform/train.py | 28 ++++++++++++----------- 3 files changed, 33 insertions(+), 25 deletions(-) diff --git a/recognition/PerceiverTransform/modules.py b/recognition/PerceiverTransform/modules.py index 4c220cd2c6..0372b75409 100644 --- a/recognition/PerceiverTransform/modules.py +++ b/recognition/PerceiverTransform/modules.py @@ -2,12 +2,13 @@ import torch.nn as nn class CrossAttention(nn.Module): - """Cross-Attention mechanism where the latent array attends to the input data.""" + #Cross-Attention mechanism where the latent array attends to the input data. def __init__(self, embed_dim, num_heads): super(CrossAttention, self).__init__() self.attn = nn.MultiheadAttention(embed_dim, num_heads) - + # Applies multi-head attention where the query is from latent space and the key-value pairs are from input data. + def forward(self, x, latent): # Ensure the sequence length of x and latent match # by truncating or padding latent if necessary @@ -21,17 +22,19 @@ def forward(self, x, latent): # Truncate latent to match x's sequence length latent = latent[:, :seq_len_x, :] + # Adjustes dimensions for attention mechanism x = x.permute(1, 0, 2) latent = latent.permute(1, 0, 2) + + # Applying attention output, _ = self.attn(latent, x, x) - output = output.permute(1, 0, 2) return output + latent class LatentTransformer(nn.Module): - """Latent Transformer applies a series of self-attention and feed-forward networks on the latent array.""" + #Latent Transformer applies a series of self-attention and feed-forward networks on the latent array. def __init__(self, embed_dim, num_heads): super(LatentTransformer, self).__init__() @@ -41,20 +44,22 @@ def __init__(self, embed_dim, num_heads): nn.ReLU(), nn.Linear(embed_dim, embed_dim) ) - + # Feedfoward network def forward(self, latent): latent = self.self_attention(latent, latent) return self.feedforward(latent) class Perceiver(nn.Module): - """The Perceiver model that integrates all components.""" + #The Perceiver model that integrates all components inlcuding embedding layer, crossattention mechanism latent transformer. def __init__(self, input_dim, latent_dim, embed_dim, n_classes, num_heads): super(Perceiver, self).__init__() + # Embedding layer to tranform self.embed = nn.Linear(input_dim, embed_dim) self.latent = nn.Parameter(torch.randn(1, latent_dim, embed_dim)) # Initialize latent array with batch dimension self.cross_attention = CrossAttention(embed_dim, num_heads) self.latent_transformer = LatentTransformer(embed_dim, num_heads) + # Final classification self.classifier = nn.Linear(embed_dim, n_classes) def forward(self, x): @@ -69,4 +74,4 @@ def forward(self, x): latent = self.latent_transformer(latent) latent_mean = latent.mean(dim=1) - return self.classifier(latent_mean) + return self.classifier(latent_mean) \ No newline at end of file diff --git a/recognition/PerceiverTransform/predict.py b/recognition/PerceiverTransform/predict.py index 7c51479203..5b36b7fe95 100644 --- a/recognition/PerceiverTransform/predict.py +++ b/recognition/PerceiverTransform/predict.py @@ -5,11 +5,11 @@ import matplotlib.pyplot as plt from modules import Perceiver -# Constants +# Constants and defining to use GPU if available DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") MODEL_PATH = './perceiver_model.pth' -# Load the trained model +# Load the trained model same as training model model = Perceiver( input_dim = 224 * 224, latent_dim=256, @@ -21,17 +21,18 @@ model.load_state_dict(torch.load(MODEL_PATH)) model.eval() -# Load and preprocess a sample image +# Load and preprocess a sample image (resizing and converting to tensor) def predict_image(img_path): transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), ]) - + + # Opening image to apply tranformations image = Image.open(img_path) image_tensor = transform(image).unsqueeze(0).to(DEVICE) - # Predict + # Predicting by passing image through model using probability output = model(image_tensor) probabilities = F.softmax(output, dim=1) predicted_class = torch.argmax(output, dim=1).item() diff --git a/recognition/PerceiverTransform/train.py b/recognition/PerceiverTransform/train.py index 022b1f3131..b5bf3827c2 100644 --- a/recognition/PerceiverTransform/train.py +++ b/recognition/PerceiverTransform/train.py @@ -12,24 +12,25 @@ DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") MODEL_PATH = './perceiver_model.pth' -# Model Initialization +# Model Initialization with specific parameters and dimensions model = Perceiver( input_dim = 224 * 224, latent_dim = 256, embed_dim = 256, - n_classes = 2, + n_classes = 2, # NC and AD (2 classes) num_heads = 4 -).to(DEVICE) +).to(DEVICE) # To utilize the GPU rather than the CPU +# Loss function and optimizer loss_fn = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=LR) -# Data Loaders +# Data Loaders for loading dataset train_loader, valid_loader, test_loader = get_dataloaders("C:\\Users\\AK\\Documents\\COMP3710\\AlzDataset", batch_size=BATCH_SIZE) -# Training -train_losses = [] -accuracies = [] +# Training loop +train_losses = [] # Training losses over epochs +accuracies = [] # Training accuracy for epoch in range(EPOCHS): model.train() @@ -41,7 +42,7 @@ optimizer.zero_grad() outputs = model(inputs) - loss = loss_fn(outputs, labels) + loss = loss_fn(outputs, labels) loss.backward() optimizer.step() @@ -58,7 +59,7 @@ #Validation Stage correct_val = 0 total_val = 0 - for inputs, labels in valid_loader: + for inputs, labels in valid_loader: # Interating through validation data inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) outputs = model(inputs) _, predicted = torch.max(outputs.data, 1) @@ -69,10 +70,10 @@ print(f"Validation Accuracy: {val_accuracy:.2f}%") # After training, evaluate on the test set -model.eval() +model.eval() # Setting model to evaluation mode correct_test = 0 total_test = 0 -for inputs, labels in test_loader: +for inputs, labels in test_loader: #Iterating through test data inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) outputs = model(inputs) _, predicted = torch.max(outputs.data, 1) @@ -82,7 +83,7 @@ test_accuracy = 100 * correct_test / total_test print(f"Test Accuracy: {test_accuracy:.2f}%") -# Plotting +# Plotting training loss over epochs plt.figure(figsize=(10, 6)) plt.plot(train_losses, label='Training Loss') plt.xlabel('Epochs') @@ -91,6 +92,7 @@ plt.legend() plt.show() +# Plotting accuracy over epochs plt.figure(figsize=(10, 6)) plt.plot(accuracies, label='Training Accuracy') plt.xlabel('Epochs') @@ -99,5 +101,5 @@ plt.legend() plt.show() -# Saving model +# Saving trained model torch.save(model.state_dict(), MODEL_PATH) \ No newline at end of file From 37db0c6f8496b7f386f95c4ffcc39167024c1b6e Mon Sep 17 00:00:00 2001 From: Akshath-K <110160158+Akshath-K@users.noreply.github.com> Date: Sun, 22 Oct 2023 23:42:38 +1000 Subject: [PATCH 13/13] Updated README based on modified model and results --- recognition/README.md | 45 ++++++++++++++++++++++--------------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/recognition/README.md b/recognition/README.md index 2100ec3f2e..ecf7e17c52 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -3,49 +3,50 @@ # Akshath Katyal # 47144691 - -## Task Description: -The Perceiver is a cutting-edge deep learning architecture that has been making waves lately due to its exceptional performance across a diverse range of tasks. We decided to put it to the test ourselves and applied it to the problem of classifying Alzheimer's disease from brain images. With our implementation of the Perceiver model, we are confident in our ability to accurately classify Alzheimer's from non-invasive imaging methods, which can help with early detection and monitoring of the disease. - +## Task Information: +The Perceiver is a cutting-edge deep learning architecture that has been making waves lately due to its exceptional performance across a diverse range of tasks. We decided to put it to the test ourselves and applied it to the problem of classifying Alzheimer's disease from brain images consisting of both (AD or Alzheimers disease and NC or Normal cognition). This implementation of the Perceiver model is used to accurately classify Alzheimer's from non-invasive imaging methods, which can help with early detection and monitoring of the disease.- ## Model/Algorithm Description -The Perceiver Transformer is a fresh deep learning structure that overcomes the usual constraint of Transformers requiring a fixed-input size. Its purpose is to tackle a vast range of input modes, from images to sound, without depending on domain-specific structures. The key perk of the Perceiver is its proficiency in handling inputs of any size by preserving a set of latent variables with a fixed size that interacts with the input data. The model was inspired by the tutorial from https://medium.com/@curttigges/the-annotated-perceiver-74752113eefb which discusses how "Perceiver IO, landmark multimodal neural architectures that solve many of the issues with classic transformers". - - +The Perceiver Transformer is a fresh deep learning structure that overcomes the usual constraint of Transformers requiring a fixed-input size. Its purpose is to tackle a vast range of input modes, from images to sound, without depending on domain-specific structures. The key perk of the Perceiver is its proficiency in handling inputs of any size by preserving a set of latent variables with a fixed size that interacts with the input data. The model was inspired by the tutorial from https://medium.com/@curttigges/the-annotated-perceiver-74752113eefb which discusses how "Perceiver IO, landmark multimodal neural architectures that solve many of the issues with classic transformers" but was modified accordingly for the ADNI dataset. # List Dependencies * Numpy version 1.26.1 * Pytorch 2.0 * Matplotlib 3.8.0 - +* Python 3.11.4 # Reproducibility -1. In `dataset.py`, edit the path of your `AD_NC` dataset. +1. In `train.py`, edit the path of your `AD_NC` dataset to be utilized by the loaders. 2. With the dependencies installed, run python train.py file -3. Then run the predict.py to see results. +3. This will show you the results based on epoch, once the training is done will display two visualizations of it. +4. Then run the `predict.py`. +5. Input the path of any image from the test dataset to check if it is able to predict the image correctly. # Results -This model was able to achieve really good results weirdly. It was no expected as the the number of epochs it was trained for was very small compared to larger numbers which often tend to get higher accuracy. The training time was pretty quick for 10 epochs and surprisingly showed 100% test set performance. This was definitely weird however when this model was run, it was not initially split into a validation set to compare it against. This could also be caused by data leakage and evaluation issues. +At first This model was able to achieve really good results weirdly. It was no expected as the the number of epochs it was trained for was very small compared to larger numbers which often tend to get higher accuracy. It was later discovered that the dataset was not being read correctly and the labelling of the images were wrong displaying 1 type of labels everytime hence showing 100% accuracy. The training time was pretty quick for 10 epochs and surprisingly showed 100% test set performance. However, after the model was fixed with correct paths, data split and labelling as well as changing images not from RGB but leaving it as grayscale. This was able to fix the issue however when this model was run, the test accuracy was around 50% (50.44% for 10 epochs and batch size of 5). This is equivalent to random guessing between two types (pretty much 50/50). -![Training/Testing]() +Here is an image of the training and accuracy of the data and test set. +![()]() -Below are some example plots of the results -* Training accuracy over epochs went up to 100. +Below are visualized plots of the loss and training accuracy over epochs. +* Previously before the model was modified, Training accuracy after 1 epochs went up to 100%. * Training loss went to 0. -![1]() -![2]() -I need to calculate the batch loss and run the test again with the validation set. +![1]![()]() +![2]![()]() +Based on these results, the loss and training accuracy it can be interpreted that the model is not properly trained. Increasing the number of epochs and decreasing the learning rate only changes the results by bare minimum. # Describe any specific pre-processing you have used with references if any. Justify your training, validation and testing splits of the data. -* Preprocessing was done through Resizing of the images and normalizing the images using mean and standard deviation. +* Preprocessing was done through Resizing of the images to fit typical neural network input sizes and then converting into tensors +* Justification of training and splitting of the data: -* Justification of training and splitting of the data +When working with data, it's important to divide it into three parts. The biggest portion (65%) is used for training, allowing the model to learn the underlying patterns and features. A smaller subset (35%) is used for validation to adjust hyperparameters and prevent overfitting. This helps make informed decisions about the training process, such as when to stop early, providing an unbiased estimate of its generalization capabilities. -When working with data, it's important to divide it into three parts. The biggest portion (65%) is used for training, allowing the model to learn the underlying patterns and features. A smaller subset (15%) is used for validation to adjust hyperparameters and prevent overfitting. This helps make informed decisions about the training process, such as when to stop early. The remaining 20% is reserved for testing, ensuring that the model's final evaluation is done on unseen data, providing an unbiased estimate of its generalization capabilities. +Potential change in the splitting can effect the model learning better however, the issue seems to be with the training. -# References +# References: * [1]: https://medium.com/@curttigges/the-annotated-perceiver-74752113eefb -* [2]: https://medium.com/@curttigges/ +* [2]: https://github.com/clint-kristopher-morris/Perceiver-Transformer/tree/main +