From d2af02ac357392ec93871f822076dc06fb3e3b4a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 19 Sep 2023 15:43:16 +1000 Subject: [PATCH 01/36] this is a test --- recognition/UNet3D/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/UNet3D/test.txt diff --git a/recognition/UNet3D/test.txt b/recognition/UNet3D/test.txt new file mode 100644 index 000000000..9680813ef --- /dev/null +++ b/recognition/UNet3D/test.txt @@ -0,0 +1 @@ +tese \ No newline at end of file From 89b328abf9a5c399bf89cca99c0cae004549620f Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 19 Sep 2023 15:46:38 +1000 Subject: [PATCH 02/36] Delete recognition/UNet3D/test.txt --- recognition/UNet3D/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/UNet3D/test.txt diff --git a/recognition/UNet3D/test.txt b/recognition/UNet3D/test.txt deleted file mode 100644 index 9680813ef..000000000 --- a/recognition/UNet3D/test.txt +++ /dev/null @@ -1 +0,0 @@ -tese \ No newline at end of file From 575606ff6e212590b3886107c3f1483bffa40978 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 19 Sep 2023 15:48:41 +1000 Subject: [PATCH 03/36] this is a test --- recognition/UNet3D/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/UNet3D/README.md diff --git a/recognition/UNet3D/README.md b/recognition/UNet3D/README.md new file mode 100644 index 000000000..30d74d258 --- /dev/null +++ b/recognition/UNet3D/README.md @@ -0,0 +1 @@ +test \ No newline at end of file From b7bc593beba9f5c4a5ad9e0aa624713f38860547 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 19 Sep 2023 15:50:51 +1000 Subject: [PATCH 04/36] Add task into README --- recognition/UNet3D/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recognition/UNet3D/README.md b/recognition/UNet3D/README.md index 30d74d258..dd25987f1 100644 --- a/recognition/UNet3D/README.md +++ b/recognition/UNet3D/README.md @@ -1 +1,2 @@ -test \ No newline at end of file +Task: +Segment the (downsampled) Prostate 3D data set with the UNet3D [5] with all labels having a minimum Dice similarity coefficient of 0.7 on the test set. See also CAN3D [6] for more details and use the data augmentation library here for TF or use the appropriate transforms in PyTorch. \ No newline at end of file From ddcd5c33f6ac4661864b822fc5b061cac43c6a5c Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Mon, 9 Oct 2023 20:49:32 +1000 Subject: [PATCH 05/36] create python files and write function to load data from given directory --- .vscode/settings.json | 3 ++ recognition/SiameseNetwork/README.md | 4 +++ recognition/SiameseNetwork/dataset.py | 47 +++++++++++++++++++++++++++ recognition/SiameseNetwork/modules.py | 1 + recognition/SiameseNetwork/predict.py | 0 recognition/SiameseNetwork/train.py | 3 ++ recognition/UNet3D/README.md | 2 -- 7 files changed, 58 insertions(+), 2 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 recognition/SiameseNetwork/README.md create mode 100644 recognition/SiameseNetwork/dataset.py create mode 100644 recognition/SiameseNetwork/modules.py create mode 100644 recognition/SiameseNetwork/predict.py create mode 100644 recognition/SiameseNetwork/train.py delete mode 100644 recognition/UNet3D/README.md diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..a6735e59a --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.analysis.typeCheckingMode": "off" +} \ No newline at end of file diff --git a/recognition/SiameseNetwork/README.md b/recognition/SiameseNetwork/README.md new file mode 100644 index 000000000..28e4f9e2c --- /dev/null +++ b/recognition/SiameseNetwork/README.md @@ -0,0 +1,4 @@ +Task: +Create a classifier based on Siamese network [10] to classify either Alzheimer’s disease (normal and AD) +of the ADNI brain data set or classify the ISIC 2020 Kaggle Challenge data set (normal and melanoma) +having an accuracy of around 0.8 on the test set (see Appendix for links). \ No newline at end of file diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py new file mode 100644 index 000000000..700e18676 --- /dev/null +++ b/recognition/SiameseNetwork/dataset.py @@ -0,0 +1,47 @@ +# containing the data loader for loading and preprocessing your data + +import os +import torch +import random +from torch.utils.data import DataLoader, Dataset +from PIL import Image + +class CustomDataset(Dataset): + def __init__(self, ad_dir, nc_dir, transform=None): + # get the file path + self.ad_folder = ad_dir + self.nc_folder = nc_dir + + # get the samples' name + self.ad_names = os.listdir(ad_dir) + self.nc_names = os.listdir(nc_dir) + + # define the transform + self.transform = transform + + def __len__(self): + return 2 * min(len(self.AD_names), len(self.NC_names)) + + def __getitem__(self, index): + + # path to the image, randomly select ad image and nc image + # chooce anchor image evently from ad image set and nc image set + if index % 2 == 0: + anchor_path = os.path.join(self.ad_folder, self.ad_names[index//2]) + else: + anchor_path = os.path.join(self.nc_folder, self.nc_names[index//2]) + ad_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + nc_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + + # open images + anchor = Image.open(anchor_path) + ad = Image.open(ad_path) + nc = Image.open(nc_path) + + # apply transformation + if self.transform: + anchor = self.transform(anchor) + ad = self.transform(ad) + nc = self.transform(nc) + + return anchor, ad, nc \ No newline at end of file diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/recognition/SiameseNetwork/modules.py @@ -0,0 +1 @@ + diff --git a/recognition/SiameseNetwork/predict.py b/recognition/SiameseNetwork/predict.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/SiameseNetwork/train.py b/recognition/SiameseNetwork/train.py new file mode 100644 index 000000000..48f1a5902 --- /dev/null +++ b/recognition/SiameseNetwork/train.py @@ -0,0 +1,3 @@ +# containing the source code for training, validating, testing and saving your model. +# The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. +# Make sure to plot the losses and metrics during training \ No newline at end of file diff --git a/recognition/UNet3D/README.md b/recognition/UNet3D/README.md deleted file mode 100644 index dd25987f1..000000000 --- a/recognition/UNet3D/README.md +++ /dev/null @@ -1,2 +0,0 @@ -Task: -Segment the (downsampled) Prostate 3D data set with the UNet3D [5] with all labels having a minimum Dice similarity coefficient of 0.7 on the test set. See also CAN3D [6] for more details and use the data augmentation library here for TF or use the appropriate transforms in PyTorch. \ No newline at end of file From 8de072fe6e2880c7a416bbf46acd7a530c206798 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Mon, 9 Oct 2023 20:52:49 +1000 Subject: [PATCH 06/36] delete some unneeded imports --- recognition/SiameseNetwork/dataset.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py index 700e18676..bf4fb801e 100644 --- a/recognition/SiameseNetwork/dataset.py +++ b/recognition/SiameseNetwork/dataset.py @@ -1,9 +1,8 @@ # containing the data loader for loading and preprocessing your data import os -import torch import random -from torch.utils.data import DataLoader, Dataset +from torch.utils.data import Dataset from PIL import Image class CustomDataset(Dataset): From 934fc85b8bfd850c4c9a679efa29ff4643387d07 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Mon, 9 Oct 2023 21:41:42 +1000 Subject: [PATCH 07/36] write a function to calculate mean and std --- recognition/SiameseNetwork/dataset.py | 44 ++++++++++++++++++++++++++- 1 file changed, 43 insertions(+), 1 deletion(-) diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py index bf4fb801e..59f0c2799 100644 --- a/recognition/SiameseNetwork/dataset.py +++ b/recognition/SiameseNetwork/dataset.py @@ -2,9 +2,13 @@ import os import random +import torch +import torchvision.transforms as transforms from torch.utils.data import Dataset from PIL import Image +device = torch.device('cuda') + class CustomDataset(Dataset): def __init__(self, ad_dir, nc_dir, transform=None): # get the file path @@ -43,4 +47,42 @@ def __getitem__(self, index): ad = self.transform(ad) nc = self.transform(nc) - return anchor, ad, nc \ No newline at end of file + return anchor, ad, nc + +# calculate the mean and std of the dataset +# input: The folder containing folders containing images +# outupt: mean and std of all images across all subfolders +def compute_mean_std(img_folder): + # get subfolders + subfolders = [dir for dir in os.listdir(img_folder) if os.path.isdir(os.path.join(img_folder, dir))] + + # transformer + transform = transforms.Compose([ + # transform image from numpy.ndarray to tensor + # and normalize pixels from 0~255 to 0~1 + transforms.ToTensor() + ]) + + num_px = 0 + sum_px = 0 + sum_px_sq = 0 + + for subfolder in subfolders: + subfolder_path = os.path.join(img_folder, subfolder) + img_names = os.listdir(subfolder_path) + + for img_name in img_names: + # open the image and put them into GPU + img_path = os.path.join(subfolder_path, img_name) + img = Image.open(img_path) + img_tensor = transform(img).to(device) + + num_px += img_tensor.numel() # get the # of px + sum_px += torch.sum(img_tensor) + sum_px_sq += torch.sum(img_tensor ** 2) + + # calculate mean and std for all images across all subfolders + mean = sum_px / num_px + std = torch.sqrt((sum_px_sq / num_px) - (mean ** 2)) + + return mean.item(), std.item() \ No newline at end of file From 7b4600bbda360e733aa29a7c5b1f2d651325ad80 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Mon, 9 Oct 2023 21:57:00 +1000 Subject: [PATCH 08/36] big fix --- recognition/SiameseNetwork/dataset.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py index 59f0c2799..36d6c087c 100644 --- a/recognition/SiameseNetwork/dataset.py +++ b/recognition/SiameseNetwork/dataset.py @@ -31,23 +31,25 @@ def __getitem__(self, index): # chooce anchor image evently from ad image set and nc image set if index % 2 == 0: anchor_path = os.path.join(self.ad_folder, self.ad_names[index//2]) + positive_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + negative_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) else: anchor_path = os.path.join(self.nc_folder, self.nc_names[index//2]) - ad_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) - nc_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + positive_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + negative_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) # open images anchor = Image.open(anchor_path) - ad = Image.open(ad_path) - nc = Image.open(nc_path) + positive = Image.open(positive_path) + negative = Image.open(negative_path) # apply transformation if self.transform: anchor = self.transform(anchor) - ad = self.transform(ad) - nc = self.transform(nc) + positive = self.transform(positive) + negative = self.transform(negative) - return anchor, ad, nc + return anchor, positive, negative # calculate the mean and std of the dataset # input: The folder containing folders containing images From bfa1e4533a875fffb5046c57067644ec3556947a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Mon, 9 Oct 2023 21:59:09 +1000 Subject: [PATCH 09/36] add file description --- recognition/SiameseNetwork/modules.py | 2 ++ recognition/SiameseNetwork/predict.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py index 8b1378917..711cdf71e 100644 --- a/recognition/SiameseNetwork/modules.py +++ b/recognition/SiameseNetwork/modules.py @@ -1 +1,3 @@ +# containing the source code of the components of your model. +# Each component must be implementated as a class or a function diff --git a/recognition/SiameseNetwork/predict.py b/recognition/SiameseNetwork/predict.py index e69de29bb..8d054b5b1 100644 --- a/recognition/SiameseNetwork/predict.py +++ b/recognition/SiameseNetwork/predict.py @@ -0,0 +1,2 @@ +# showing example usage of your trained model. +# Print out any results and / or provide visualisations where applicable \ No newline at end of file From 8d45f77d93e23c62d5eec9d601bbf4fa9a927aa7 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 10 Oct 2023 00:13:18 +1000 Subject: [PATCH 10/36] build a cnn net to provide embedding vector to siamese net --- recognition/SiameseNetwork/modules.py | 38 +++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py index 711cdf71e..c39dea376 100644 --- a/recognition/SiameseNetwork/modules.py +++ b/recognition/SiameseNetwork/modules.py @@ -1,3 +1,41 @@ # containing the source code of the components of your model. # Each component must be implementated as a class or a function +import torch.nn as nn + +# Build CNN network and get its embedding vector +class CNN(nn.Module): + def __init__(self, out_): + super(CNN, self).__init__() + self.conv = nn.Sequential( + + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 + + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 + + nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 + ) + + self.fc = nn.Sequential( + nn.Linear(128*32*30, 256), + nn.ReLU(inplace=True), + + nn.Linear(256, 128), + nn.ReLU(inplace=True), + + nn.Linear(128, 2), + ) + + def forward(self, x): + out = self.conv(x) + out = out.view(out.size()[0], -1) + out = self.fc(out) + return out + + \ No newline at end of file From 834990fc4254def71c8514a10cf7097f241e0497 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 10 Oct 2023 12:27:20 +1000 Subject: [PATCH 11/36] construct TripletLoss --- recognition/SiameseNetwork/modules.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py index c39dea376..2c00d0f9b 100644 --- a/recognition/SiameseNetwork/modules.py +++ b/recognition/SiameseNetwork/modules.py @@ -1,11 +1,12 @@ # containing the source code of the components of your model. # Each component must be implementated as a class or a function +import torch import torch.nn as nn # Build CNN network and get its embedding vector class CNN(nn.Module): - def __init__(self, out_): + def __init__(self): super(CNN, self).__init__() self.conv = nn.Sequential( @@ -37,5 +38,18 @@ def forward(self, x): out = out.view(out.size()[0], -1) out = self.fc(out) return out + + +# construct the triplet loss +class TripletLoss(nn.Module): + def __ini__(self, margin=1): + super(TripletLoss, self).__init__() - \ No newline at end of file + def forward(self, anchor, positive, negative): + # calculate euclidean distance from anchor to positive and negative + anchor_positive = (anchor - positive).pow(2).sum(1) + anchor_negative = (anchor - negative).pow(2).sum(1) + + # aclculate loss, use relu to ensure loss are non-negative + loss = torch.relu(anchor_positive - anchor_negative + self.margin) + return loss.mean() \ No newline at end of file From 0ccc1ed46fb76c064e056c746912d3d7fa25f77e Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 10 Oct 2023 12:48:20 +1000 Subject: [PATCH 12/36] constructing Siamese Network --- recognition/SiameseNetwork/modules.py | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py index 2c00d0f9b..66a4a2b91 100644 --- a/recognition/SiameseNetwork/modules.py +++ b/recognition/SiameseNetwork/modules.py @@ -5,9 +5,9 @@ import torch.nn as nn # Build CNN network and get its embedding vector -class CNN(nn.Module): +class Embedding(nn.Module): def __init__(self): - super(CNN, self).__init__() + super(Embedding, self).__init__() self.conv = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, padding=1), @@ -52,4 +52,18 @@ def forward(self, anchor, positive, negative): # aclculate loss, use relu to ensure loss are non-negative loss = torch.relu(anchor_positive - anchor_negative + self.margin) - return loss.mean() \ No newline at end of file + return loss.mean() + + +# construct the siamese network +class SiameseNet(nn.Module): + def __init__(self, embbeding): + super(SiameseNet, self).__init__() + self.embbeding = embbeding + + def forward(self, anchor, positive, negative): + # use the embbeding class to ge the embedding of anchor, positive and negative samples + embbeding_anchor = self.embbeding(anchor) + embbeding_positive = self.embbeding(positive) + embbeding_negative = self.embbeding(negative) + return embbeding_anchor, embbeding_positive, embbeding_negative \ No newline at end of file From d0c6fd31943f5899b259ce80161c2f22dc56825a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Tue, 10 Oct 2023 17:41:07 +1000 Subject: [PATCH 13/36] 1. update CustomDataset class, now it can split data to train set and validation set 2. working on train.py --- recognition/SiameseNetwork/dataset.py | 16 +++++++++++++++- recognition/SiameseNetwork/train.py | 21 ++++++++++++++++++++- 2 files changed, 35 insertions(+), 2 deletions(-) diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py index 36d6c087c..a1f4e4b50 100644 --- a/recognition/SiameseNetwork/dataset.py +++ b/recognition/SiameseNetwork/dataset.py @@ -10,7 +10,7 @@ device = torch.device('cuda') class CustomDataset(Dataset): - def __init__(self, ad_dir, nc_dir, transform=None): + def __init__(self, ad_dir, nc_dir, transform=None, validate=False, split_ratio=0.8): # get the file path self.ad_folder = ad_dir self.nc_folder = nc_dir @@ -22,6 +22,20 @@ def __init__(self, ad_dir, nc_dir, transform=None): # define the transform self.transform = transform + # splite data to train set and validation set + total_ad_samples = len(self.ad_names) + split_ad_samples = int(total_ad_samples * split_ratio) + total_nc_samples = len(self.nc_names) + split_nc_samples = int(total_nc_samples * split_ratio) + + if validate: + self.ad_names = self.ad_names[split_ad_samples:] + self.nc_names = self.nc_names[split_nc_samples:] + else: + self.ad_names = self.ad_names[:split_ad_samples] + self.nc_names = self.nc_names[:split_nc_samples] + + def __len__(self): return 2 * min(len(self.AD_names), len(self.NC_names)) diff --git a/recognition/SiameseNetwork/train.py b/recognition/SiameseNetwork/train.py index 48f1a5902..8555acf3d 100644 --- a/recognition/SiameseNetwork/train.py +++ b/recognition/SiameseNetwork/train.py @@ -1,3 +1,22 @@ # containing the source code for training, validating, testing and saving your model. # The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. -# Make sure to plot the losses and metrics during training \ No newline at end of file +# Make sure to plot the losses and metrics during training + +import dataset, modules +import torch +import torchvision.transforms as transforms + +train_folder_path = "D:/Study/MLDataSet/AD_NC/train" +AD_path = "D:/Study/MLDataSet/AD_NC/train/AD" +NC_path = "D:/Study/MLDataSet/AD_NC/train/NC" + +# calculate mean and std for train set +mean, std = dataset.compute_mean_std(train_folder_path) + +# define transform +transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((mean,), (std,)) + ]) + +# train_set = dataset.CustomDataset(ad_dir=AD_path, nc_dir=NC_path, transform=transform) From 58e7ed96b7758faaf0995c4365756ad566e3327a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Wed, 11 Oct 2023 12:08:51 +1000 Subject: [PATCH 14/36] 1. Fix some bug on modules.py 2. Pack data_loader as a function and move to dataset.py 3. Build training modules and validation modules --- .gitignore | 2 + recognition/SiameseNetwork/dataset.py | 29 +++++- recognition/SiameseNetwork/modules.py | 29 +++--- recognition/SiameseNetwork/train.py | 135 +++++++++++++++++++++++--- 4 files changed, 170 insertions(+), 25 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..b53fb2886 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +recognition/SiameseNetwork/__pycache__/dataset.cpython-311.pyc +recognition/SiameseNetwork/__pycache__/modules.cpython-311.pyc diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/SiameseNetwork/dataset.py index a1f4e4b50..ade19c1b6 100644 --- a/recognition/SiameseNetwork/dataset.py +++ b/recognition/SiameseNetwork/dataset.py @@ -4,7 +4,7 @@ import random import torch import torchvision.transforms as transforms -from torch.utils.data import Dataset +from torch.utils.data import Dataset, DataLoader from PIL import Image device = torch.device('cuda') @@ -37,7 +37,7 @@ def __init__(self, ad_dir, nc_dir, transform=None, validate=False, split_ratio=0 def __len__(self): - return 2 * min(len(self.AD_names), len(self.NC_names)) + return 2 * min(len(self.ad_names), len(self.nc_names)) def __getitem__(self, index): @@ -101,4 +101,27 @@ def compute_mean_std(img_folder): mean = sum_px / num_px std = torch.sqrt((sum_px_sq / num_px) - (mean ** 2)) - return mean.item(), std.item() \ No newline at end of file + return mean.item(), std.item() + +# a function to load all required data +def load_data(train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32): + # calculate mean and std for train set + mean, std = compute_mean_std(train_folder_path) + + # define transform + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((mean,), (std,)) + ]) + + # create dataset + train_set = CustomDataset(ad_dir=train_ad_path, nc_dir=train_nc_path, transform=transform, validate=False, split_ratio=0.8) + validation_set = CustomDataset(ad_dir=train_ad_path, nc_dir=train_nc_path, transform=transform, validate=True, split_ratio=0.8) + test_set = CustomDataset(ad_dir=test_ad_path, nc_dir=test_nc_path, transform=transform, validate=False, split_ratio=1) + + # create dataloader + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + validation_loader = DataLoader(validation_set, batch_size=batch_size, shuffle=False) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False) + + return train_loader, validation_loader, test_loader \ No newline at end of file diff --git a/recognition/SiameseNetwork/modules.py b/recognition/SiameseNetwork/modules.py index 66a4a2b91..ee1d43ef2 100644 --- a/recognition/SiameseNetwork/modules.py +++ b/recognition/SiameseNetwork/modules.py @@ -11,14 +11,17 @@ def __init__(self): self.conv = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 ) @@ -42,28 +45,32 @@ def forward(self, x): # construct the triplet loss class TripletLoss(nn.Module): - def __ini__(self, margin=1): + def __init__(self, margin=1): super(TripletLoss, self).__init__() + self.margin = margin def forward(self, anchor, positive, negative): # calculate euclidean distance from anchor to positive and negative - anchor_positive = (anchor - positive).pow(2).sum(1) - anchor_negative = (anchor - negative).pow(2).sum(1) + anchor_positive = (anchor - positive).pow(2).sum(1).sqrt() + anchor_negative = (anchor - negative).pow(2).sum(1).sqrt() - # aclculate loss, use relu to ensure loss are non-negative + # print("anchor_positive: ", anchor_positive.mean()) + # print("anchor_negative: ", anchor_negative.mean()) + + # calculate loss, use relu to ensure loss are non-negative loss = torch.relu(anchor_positive - anchor_negative + self.margin) return loss.mean() # construct the siamese network class SiameseNet(nn.Module): - def __init__(self, embbeding): + def __init__(self, embedding): super(SiameseNet, self).__init__() - self.embbeding = embbeding + self.embedding = embedding def forward(self, anchor, positive, negative): - # use the embbeding class to ge the embedding of anchor, positive and negative samples - embbeding_anchor = self.embbeding(anchor) - embbeding_positive = self.embbeding(positive) - embbeding_negative = self.embbeding(negative) - return embbeding_anchor, embbeding_positive, embbeding_negative \ No newline at end of file + # use the embedding class to ge the embedding of anchor, positive and negative samples + embedding_anchor = self.embedding(anchor) + embedding_positive = self.embedding(positive) + embedding_negative = self.embedding(negative) + return embedding_anchor, embedding_positive, embedding_negative \ No newline at end of file diff --git a/recognition/SiameseNetwork/train.py b/recognition/SiameseNetwork/train.py index 8555acf3d..5d46ed855 100644 --- a/recognition/SiameseNetwork/train.py +++ b/recognition/SiameseNetwork/train.py @@ -4,19 +4,132 @@ import dataset, modules import torch -import torchvision.transforms as transforms +import torch.optim as optim +device = torch.device('cuda') +# data path train_folder_path = "D:/Study/MLDataSet/AD_NC/train" -AD_path = "D:/Study/MLDataSet/AD_NC/train/AD" -NC_path = "D:/Study/MLDataSet/AD_NC/train/NC" +train_ad_path = "D:/Study/MLDataSet/AD_NC/train/AD" +train_nc_path = "D:/Study/MLDataSet/AD_NC/train/NC" +test_ad_path = "D:/Study/MLDataSet/AD_NC/test/AD" +test_nc_path = "D:/Study/MLDataSet/AD_NC/test/NC" -# calculate mean and std for train set -mean, std = dataset.compute_mean_std(train_folder_path) +# create data loader +train_loader, validation_loader, test_loader = dataset.load_data( + train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32) -# define transform -transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((mean,), (std,)) - ]) +# define models +embbeding = modules.Embedding() +model = modules.SiameseNet(embbeding) +model.to(device) -# train_set = dataset.CustomDataset(ad_dir=AD_path, nc_dir=NC_path, transform=transform) +# define loss function +criterion = modules.TripletLoss() +optimizer = optim.Adam(model.parameters(), lr=0.001) + + +# train +def train(train_loader, epoches): + + for epoch in range(epoches): + # set model to train mode + model.train() + + total_accuracy = 0 + total_samples = 0 + + for anchor, positive, negative in train_loader: + # move data to gpu + anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + + # front propagation + emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) + loss = criterion(emb_anchor, emb_positive, emb_negative) + + # calculate accuracy + batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) + total_accuracy += batch_accuracy * anchor.size(0) + total_samples += anchor.size(0) + + # back propagation + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # save the model after each epoch + torch.save(model.state_dict(), "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/SiameseNetwork/SiameseNet.pth") + + # calculate accuracy + avg_accuracy = total_accuracy / total_samples + + print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}") + +# validate +def validate(validation_loader): + # set model to evaluation mode + model.eval() + total_loss = 0 + total_accuracy = 0 + + with torch.no_grad(): + for anchor, positive, negative in validation_loader: + # move data to gpu + anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + + # front propagation + emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) + loss = criterion(emb_anchor, emb_positive, emb_negative) + + # get loss and accuracy + total_loss += loss.item() + total_accuracy += calculate_accuracy(emb_anchor, emb_positive, emb_negative) + + # calculate average loss and average accuracy + ave_loss = total_loss/len(validation_loader) + ave_accuracy = total_accuracy/len(validation_loader) + print(f"Average loss: {ave_loss:.4f} \n Average accuracy: {ave_accuracy:.4f}") + + return ave_loss, ave_accuracy + + +# test +def test(test_loader): + # set model to evaluation mode + model.eval() + # TODO + + +# calculate accuracy, +# if anchor_positive distance < threshold, these two samples will be considered similar +# if anchor_negative distance > threshold, these two samples will be considered unsimilar +def calculate_accuracy(anchor, positive, negative, threshold=0.5): + + # calculate the distance from anchor to positive and negative samples + # .sum(1) will reduce a dimension, so the shape of anchor_positive: [batch_size, 2] -> [batch_size] + anchor_positive = (anchor - positive).pow(2).sum(1).sqrt() + anchor_negative = (anchor - negative).pow(2).sum(1).sqrt() + + # correct predictions + true_positive = (anchor_positive < threshold).float() + true_negative = (anchor_negative > threshold).float() + + # total are true_positive + true_negative + false_positive + false_negative, + # they summing up equal to true_positive.size(0) + true_negative.size(0) + total = true_positive.size(0) + true_negative.size(0) + accuracy = (true_positive.sum().item() + true_negative.sum().item())/total + + return accuracy + +def main(): + # train the model + epoches = 10 + # print("Training") + # train(train_loader, epoches) + + # validate the model + print("Validating") + validate(validation_loader) + + +if __name__ == "__main__": + main() \ No newline at end of file From b650a0fce71d121ec158d3bb5f0573c87e946055 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Wed, 11 Oct 2023 12:46:56 +1000 Subject: [PATCH 15/36] change folder's name --- .gitignore | 2 ++ .../{SiameseNetwork => s4627382_SiameseNetwork}/README.md | 0 .../{SiameseNetwork => s4627382_SiameseNetwork}/dataset.py | 0 .../{SiameseNetwork => s4627382_SiameseNetwork}/modules.py | 0 .../{SiameseNetwork => s4627382_SiameseNetwork}/predict.py | 0 .../{SiameseNetwork => s4627382_SiameseNetwork}/train.py | 0 6 files changed, 2 insertions(+) rename recognition/{SiameseNetwork => s4627382_SiameseNetwork}/README.md (100%) rename recognition/{SiameseNetwork => s4627382_SiameseNetwork}/dataset.py (100%) rename recognition/{SiameseNetwork => s4627382_SiameseNetwork}/modules.py (100%) rename recognition/{SiameseNetwork => s4627382_SiameseNetwork}/predict.py (100%) rename recognition/{SiameseNetwork => s4627382_SiameseNetwork}/train.py (100%) diff --git a/.gitignore b/.gitignore index b53fb2886..f946d3d3c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,4 @@ recognition/SiameseNetwork/__pycache__/dataset.cpython-311.pyc recognition/SiameseNetwork/__pycache__/modules.cpython-311.pyc +recognition/s4627382_SiameseNetwork/__pycache__/dataset.cpython-311.pyc +recognition/s4627382_SiameseNetwork/__pycache__/modules.cpython-311.pyc diff --git a/recognition/SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md similarity index 100% rename from recognition/SiameseNetwork/README.md rename to recognition/s4627382_SiameseNetwork/README.md diff --git a/recognition/SiameseNetwork/dataset.py b/recognition/s4627382_SiameseNetwork/dataset.py similarity index 100% rename from recognition/SiameseNetwork/dataset.py rename to recognition/s4627382_SiameseNetwork/dataset.py diff --git a/recognition/SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py similarity index 100% rename from recognition/SiameseNetwork/modules.py rename to recognition/s4627382_SiameseNetwork/modules.py diff --git a/recognition/SiameseNetwork/predict.py b/recognition/s4627382_SiameseNetwork/predict.py similarity index 100% rename from recognition/SiameseNetwork/predict.py rename to recognition/s4627382_SiameseNetwork/predict.py diff --git a/recognition/SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py similarity index 100% rename from recognition/SiameseNetwork/train.py rename to recognition/s4627382_SiameseNetwork/train.py From eded51b368569e84c46141fc0f9bb8fc376b463d Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 12 Oct 2023 02:15:29 +1000 Subject: [PATCH 16/36] modify the model, and add semi-hard-sample-mining function --- recognition/s4627382_SiameseNetwork/README.md | 7 ++++- .../s4627382_SiameseNetwork/modules.py | 28 +++++++++++++++++-- recognition/s4627382_SiameseNetwork/train.py | 22 +++++++++++---- 3 files changed, 48 insertions(+), 9 deletions(-) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index 28e4f9e2c..a281b503e 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -1,4 +1,9 @@ Task: Create a classifier based on Siamese network [10] to classify either Alzheimer’s disease (normal and AD) of the ADNI brain data set or classify the ISIC 2020 Kaggle Challenge data set (normal and melanoma) -having an accuracy of around 0.8 on the test set (see Appendix for links). \ No newline at end of file +having an accuracy of around 0.8 on the test set (see Appendix for links). + +require libarary: +pytorch +cudatoolkit +annoy \ No newline at end of file diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index ee1d43ef2..b339a8c1f 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -30,10 +30,10 @@ def __init__(self): nn.Linear(128*32*30, 256), nn.ReLU(inplace=True), - nn.Linear(256, 128), + nn.Linear(256, 256), nn.ReLU(inplace=True), - nn.Linear(128, 2), + nn.Linear(256, 2), ) def forward(self, x): @@ -73,4 +73,26 @@ def forward(self, anchor, positive, negative): embedding_anchor = self.embedding(anchor) embedding_positive = self.embedding(positive) embedding_negative = self.embedding(negative) - return embedding_anchor, embedding_positive, embedding_negative \ No newline at end of file + return embedding_anchor, embedding_positive, embedding_negative + + +# The definition of semi_hard_triplet is the triplets such that the distance of +# anchor to negative smaller than anchor to positive, but the differences are still smaller than the margin +# This function returns a boolean tensor with shape [batch_size] +def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin): + # calculate distance from anchor to positive and negative samples + anchor_positive = (emb_anchor - emb_positive).pow(2).sum(1).sqrt() + anchor_negative = (emb_anchor - emb_negative).pow(2).sum(1).sqrt() + + # print("min posi: ", torch.min(anchor_positive).item()) + # print("max posi: ", torch.max(anchor_positive).item()) + # print("min nega: ", torch.min(anchor_negative).item()) + # print("max nega: ", torch.max(anchor_negative).item()) + + # find the semi_hard_triplets + hard_triplets = anchor_positive > anchor_negative + + semi_hard_triplets = (anchor_positive < anchor_negative) & (anchor_negative - anchor_positive < margin) + + triplet_mask = hard_triplets | semi_hard_triplets + return triplet_mask \ No newline at end of file diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index 5d46ed855..d4d6984e7 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -5,6 +5,7 @@ import dataset, modules import torch import torch.optim as optim +from annoy import AnnoyIndex device = torch.device('cuda') # data path @@ -24,7 +25,8 @@ model.to(device) # define loss function -criterion = modules.TripletLoss() +margin = 1 +criterion = modules.TripletLoss(margin) optimizer = optim.Adam(model.parameters(), lr=0.001) @@ -44,7 +46,16 @@ def train(train_loader, epoches): # front propagation emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - loss = criterion(emb_anchor, emb_positive, emb_negative) + mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin) + + masked_emb_anchor = emb_anchor[mask] + + if len(emb_anchor[mask]) == 0: + del emb_anchor, emb_positive, emb_negative + torch.cuda.empty_cache() + continue + + loss = criterion(masked_emb_anchor, emb_positive[mask], emb_negative[mask]) # calculate accuracy batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) @@ -57,7 +68,8 @@ def train(train_loader, epoches): optimizer.step() # save the model after each epoch - torch.save(model.state_dict(), "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/SiameseNetwork/SiameseNet.pth") + torch.save(model.state_dict(), + "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") # calculate accuracy avg_accuracy = total_accuracy / total_samples @@ -123,8 +135,8 @@ def calculate_accuracy(anchor, positive, negative, threshold=0.5): def main(): # train the model epoches = 10 - # print("Training") - # train(train_loader, epoches) + print("Training") + train(train_loader, epoches) # validate the model print("Validating") From ef2464fe5caa4cc50e20c666cb6a38f16b801aa0 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 12 Oct 2023 12:05:59 +1000 Subject: [PATCH 17/36] modify the model --- recognition/s4627382_SiameseNetwork/modules.py | 18 +++++++++--------- recognition/s4627382_SiameseNetwork/train.py | 4 +--- 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index b339a8c1f..12be34417 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -10,24 +10,24 @@ def __init__(self): super(Embedding, self).__init__() self.conv = nn.Sequential( - nn.Conv2d(1, 32, kernel_size=3, padding=1), - nn.BatchNorm2d(32), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 - - nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.Conv2d(1, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 + nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 + nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 + + # nn.Conv2d(64, 128, kernel_size=3, padding=1), + # nn.BatchNorm2d(128), + # nn.ReLU(inplace=True), + # nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 ) self.fc = nn.Sequential( - nn.Linear(128*32*30, 256), + nn.Linear(128*64*60, 256), nn.ReLU(inplace=True), nn.Linear(256, 256), diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index d4d6984e7..145b126a7 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -48,14 +48,12 @@ def train(train_loader, epoches): emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin) - masked_emb_anchor = emb_anchor[mask] - if len(emb_anchor[mask]) == 0: del emb_anchor, emb_positive, emb_negative torch.cuda.empty_cache() continue - loss = criterion(masked_emb_anchor, emb_positive[mask], emb_negative[mask]) + loss = criterion(emb_anchor[mask], emb_positive[mask], emb_negative[mask]) # calculate accuracy batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) From f417de139e7fa117c9a4d2e3ace64fc511991bbc Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 12 Oct 2023 21:25:41 +1000 Subject: [PATCH 18/36] adjust the model --- .gitignore | 1 + .../s4627382_SiameseNetwork/modules.py | 61 ++++++++----- recognition/s4627382_SiameseNetwork/train.py | 87 +++++++++++++------ 3 files changed, 102 insertions(+), 47 deletions(-) diff --git a/.gitignore b/.gitignore index f946d3d3c..184b6e564 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ recognition/SiameseNetwork/__pycache__/dataset.cpython-311.pyc recognition/SiameseNetwork/__pycache__/modules.cpython-311.pyc recognition/s4627382_SiameseNetwork/__pycache__/dataset.cpython-311.pyc recognition/s4627382_SiameseNetwork/__pycache__/modules.cpython-311.pyc +recognition/s4627382_SiameseNetwork/SiameseNet.pth diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index 12be34417..dac8fe663 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -3,6 +3,7 @@ import torch import torch.nn as nn +import random # Build CNN network and get its embedding vector class Embedding(nn.Module): @@ -10,30 +11,29 @@ def __init__(self): super(Embedding, self).__init__() self.conv = nn.Sequential( - nn.Conv2d(1, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 - nn.Conv2d(64, 128, kernel_size=3, padding=1), - nn.BatchNorm2d(128), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 - - # nn.Conv2d(64, 128, kernel_size=3, padding=1), - # nn.BatchNorm2d(128), - # nn.ReLU(inplace=True), - # nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 + nn.MaxPool2d(kernel_size=3, stride=2), # size: 128*120 -> 63*59 + nn.Dropout2d(0.2) ) self.fc = nn.Sequential( - nn.Linear(128*64*60, 256), + nn.Linear(64*63*59, 256), nn.ReLU(inplace=True), nn.Linear(256, 256), nn.ReLU(inplace=True), - nn.Linear(256, 2), + nn.Linear(256, 128), + nn.ReLU(inplace=True), + + nn.Linear(128, 2), ) def forward(self, x): @@ -79,20 +79,37 @@ def forward(self, anchor, positive, negative): # The definition of semi_hard_triplet is the triplets such that the distance of # anchor to negative smaller than anchor to positive, but the differences are still smaller than the margin # This function returns a boolean tensor with shape [batch_size] -def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin): - # calculate distance from anchor to positive and negative samples +# def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin): +def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, desire_sample_size, margin): + # Calculate distance from anchor to positive and negative samples anchor_positive = (emb_anchor - emb_positive).pow(2).sum(1).sqrt() anchor_negative = (emb_anchor - emb_negative).pow(2).sum(1).sqrt() - # print("min posi: ", torch.min(anchor_positive).item()) - # print("max posi: ", torch.max(anchor_positive).item()) - # print("min nega: ", torch.min(anchor_negative).item()) - # print("max nega: ", torch.max(anchor_negative).item()) - - # find the semi_hard_triplets + # Find the hard and semi-hard triplets hard_triplets = anchor_positive > anchor_negative - semi_hard_triplets = (anchor_positive < anchor_negative) & (anchor_negative - anchor_positive < margin) triplet_mask = hard_triplets | semi_hard_triplets - return triplet_mask \ No newline at end of file + + if triplet_mask.sum() < desire_sample_size: + needed_fill_count = desire_sample_size - triplet_mask.sum().item() + + # Compute distance difference for all samples + semi_hard_distances = anchor_negative - anchor_positive + semi_hard_distances[~semi_hard_triplets] = float('inf') # we are only interested in semi-hard samples + + # Retrieve distances for samples that are not yet selected + unselected_distances = semi_hard_distances[~triplet_mask] + + # Sort these distances and get the required number of samples + _, sorted_indices = torch.sort(unselected_distances, descending=False) + fill_indices = sorted_indices[:needed_fill_count].tolist() + + # Get the global indices for these selected samples + unselected_samples = torch.where(~triplet_mask)[0] + global_fill_indices = unselected_samples[fill_indices] + + # Update the triplet mask to include these newly selected samples + triplet_mask[global_fill_indices] = True + + return triplet_mask diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index 145b126a7..676384d4a 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -15,6 +15,10 @@ test_ad_path = "D:/Study/MLDataSet/AD_NC/test/AD" test_nc_path = "D:/Study/MLDataSet/AD_NC/test/NC" +# hyperparameters +margin = 0.5 +epoches = 8 + # create data loader train_loader, validation_loader, test_loader = dataset.load_data( train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32) @@ -25,20 +29,20 @@ model.to(device) # define loss function -margin = 1 + criterion = modules.TripletLoss(margin) optimizer = optim.Adam(model.parameters(), lr=0.001) # train def train(train_loader, epoches): - for epoch in range(epoches): # set model to train mode model.train() - + skip = 0 total_accuracy = 0 total_samples = 0 + highest_val_accuracy = 0 for anchor, positive, negative in train_loader: # move data to gpu @@ -46,15 +50,17 @@ def train(train_loader, epoches): # front propagation emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin) + mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, int(len(emb_anchor) * 0.5), margin) + # mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin) if len(emb_anchor[mask]) == 0: + skip += 1 del emb_anchor, emb_positive, emb_negative torch.cuda.empty_cache() continue - + loss = criterion(emb_anchor[mask], emb_positive[mask], emb_negative[mask]) - + # calculate accuracy batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) total_accuracy += batch_accuracy * anchor.size(0) @@ -65,14 +71,18 @@ def train(train_loader, epoches): loss.backward() optimizer.step() - # save the model after each epoch - torch.save(model.state_dict(), - "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") - # calculate accuracy avg_accuracy = total_accuracy / total_samples + + validate_loss, validate_accuracy = validate(validation_loader) + + print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}, validate loss: {validate_loss:.4f}, validate accuracy: {validate_accuracy:.4f}") - print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}") + # save the model + if validate_accuracy > highest_val_accuracy: + torch.save(model.state_dict(), + "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") + highest_val_accuracy = validate_accuracy # validate def validate(validation_loader): @@ -95,18 +105,39 @@ def validate(validation_loader): total_accuracy += calculate_accuracy(emb_anchor, emb_positive, emb_negative) # calculate average loss and average accuracy - ave_loss = total_loss/len(validation_loader) - ave_accuracy = total_accuracy/len(validation_loader) - print(f"Average loss: {ave_loss:.4f} \n Average accuracy: {ave_accuracy:.4f}") + validate_loss = total_loss/len(validation_loader) + validate_accuracy = total_accuracy/len(validation_loader) - return ave_loss, ave_accuracy + return validate_loss, validate_accuracy # test def test(test_loader): - # set model to evaluation mode + # set model to evaluation mode model.eval() - # TODO + total_loss = 0 + total_accuracy = 0 + + with torch.no_grad(): + for anchor, positive, negative in test_loader: + # move data to gpu + anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + + # front propagation + emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) + loss = criterion(emb_anchor, emb_positive, emb_negative) + + # get loss and accuracy + total_loss += loss.item() + total_accuracy += calculate_accuracy(emb_anchor, emb_positive, emb_negative) + + # calculate average loss and average accuracy + test_loss = total_loss/len(test_loader) + test_accuracy = total_accuracy/len(test_loader) + print(f"Test loss: {test_loss:.4f}, Test accuracy: {test_accuracy:.4f}") + + return test_loss, test_accuracy + # calculate accuracy, @@ -131,14 +162,20 @@ def calculate_accuracy(anchor, positive, negative, threshold=0.5): return accuracy def main(): - # train the model - epoches = 10 - print("Training") - train(train_loader, epoches) - - # validate the model - print("Validating") - validate(validation_loader) + mod = 1 + + if mod: + # train the model + print("Training") + train(train_loader, epoches) + + else: + print("Testing") + model.load_state_dict( + torch.load("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth")) + test(test_loader) + + print("Done") if __name__ == "__main__": From 89efa213b95612e7de6f2f1b96a97e2968cf07e5 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 12 Oct 2023 22:58:10 +1000 Subject: [PATCH 19/36] Use VGG16 to generate embedding instead of CNN --- .../s4627382_SiameseNetwork/dataset.py | 3 +- .../s4627382_SiameseNetwork/modules.py | 67 +++++++++++++------ recognition/s4627382_SiameseNetwork/train.py | 44 ++++++------ 3 files changed, 69 insertions(+), 45 deletions(-) diff --git a/recognition/s4627382_SiameseNetwork/dataset.py b/recognition/s4627382_SiameseNetwork/dataset.py index ade19c1b6..4861d0499 100644 --- a/recognition/s4627382_SiameseNetwork/dataset.py +++ b/recognition/s4627382_SiameseNetwork/dataset.py @@ -8,6 +8,7 @@ from PIL import Image device = torch.device('cuda') +batch_size = 32 class CustomDataset(Dataset): def __init__(self, ad_dir, nc_dir, transform=None, validate=False, split_ratio=0.8): @@ -104,7 +105,7 @@ def compute_mean_std(img_folder): return mean.item(), std.item() # a function to load all required data -def load_data(train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32): +def load_data(train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=batch_size): # calculate mean and std for train set mean, std = compute_mean_std(train_folder_path) diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index dac8fe663..605b481a9 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -3,42 +3,71 @@ import torch import torch.nn as nn -import random # Build CNN network and get its embedding vector -class Embedding(nn.Module): +class VGG16(nn.Module): def __init__(self): - super(Embedding, self).__init__() + super(VGG16, self).__init__() + + # convolution layers self.conv = nn.Sequential( - - nn.Conv2d(1, 32, kernel_size=3, padding=1), - nn.BatchNorm2d(32), + # Block 1 + nn.Conv2d(1, 64, kernel_size=3, padding=1), # size: 256*240 + nn.ReLU(inplace=True), + nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 + + # Block 2 + nn.Conv2d(64, 64, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(64, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 + + # Block 3 + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 + + # Block 4 + nn.Conv2d(256, 512, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), # size: 32*30 -> 16*15 - nn.Conv2d(32, 64, kernel_size=3, padding=1), - nn.BatchNorm2d(64), + # Block 5 + nn.Conv2d(512, 512, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=3, stride=2), # size: 128*120 -> 63*59 - nn.Dropout2d(0.2) + nn.MaxPool2d(kernel_size=2, stride=2), # size: 16*15 -> 8*7 ) + # fully connected layers self.fc = nn.Sequential( - nn.Linear(64*63*59, 256), + nn.Linear(512 * 8 * 7, 4096), nn.ReLU(inplace=True), - - nn.Linear(256, 256), - nn.ReLU(inplace=True), - - nn.Linear(256, 128), + nn.Dropout(), + nn.Linear(4096, 4096), nn.ReLU(inplace=True), - - nn.Linear(128, 2), + nn.Dropout(), + nn.Linear(4096, 16), ) + def forward(self, x): out = self.conv(x) - out = out.view(out.size()[0], -1) + out = out.view(out.size(0), -1) out = self.fc(out) return out diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index 676384d4a..afdba41bf 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -5,7 +5,6 @@ import dataset, modules import torch import torch.optim as optim -from annoy import AnnoyIndex device = torch.device('cuda') # data path @@ -23,13 +22,15 @@ train_loader, validation_loader, test_loader = dataset.load_data( train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32) + +accumulation_steps = 1 + # define models -embbeding = modules.Embedding() +embbeding = modules.VGG16() model = modules.SiameseNet(embbeding) model.to(device) # define loss function - criterion = modules.TripletLoss(margin) optimizer = optim.Adam(model.parameters(), lr=0.001) @@ -39,45 +40,37 @@ def train(train_loader, epoches): for epoch in range(epoches): # set model to train mode model.train() - skip = 0 total_accuracy = 0 total_samples = 0 highest_val_accuracy = 0 - for anchor, positive, negative in train_loader: + for batch_idx, (anchor, positive, negative) in enumerate(train_loader): # move data to gpu anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) # front propagation - emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, int(len(emb_anchor) * 0.5), margin) - # mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin) - - if len(emb_anchor[mask]) == 0: - skip += 1 - del emb_anchor, emb_positive, emb_negative - torch.cuda.empty_cache() - continue - - loss = criterion(emb_anchor[mask], emb_positive[mask], emb_negative[mask]) + emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) + loss = criterion(emb_anchor, emb_positive, emb_negative) # calculate accuracy batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) total_accuracy += batch_accuracy * anchor.size(0) total_samples += anchor.size(0) - # back propagation - optimizer.zero_grad() + # back propagation with accumulation + loss = loss / accumulation_steps # Normalize our loss (if averaged) loss.backward() - optimizer.step() - # calculate accuracy - avg_accuracy = total_accuracy / total_samples + if (batch_idx + 1) % accumulation_steps == 0: + optimizer.zero_grad() + optimizer.step() + # calculate accuracy, validation loss, and validation accuracy + avg_accuracy = total_accuracy / total_samples validate_loss, validate_accuracy = validate(validation_loader) print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}, validate loss: {validate_loss:.4f}, validate accuracy: {validate_accuracy:.4f}") - + # save the model if validate_accuracy > highest_val_accuracy: torch.save(model.state_dict(), @@ -113,7 +106,7 @@ def validate(validation_loader): # test def test(test_loader): - # set model to evaluation mode + # set model to evaluation mode model.eval() total_loss = 0 total_accuracy = 0 @@ -162,11 +155,12 @@ def calculate_accuracy(anchor, positive, negative, threshold=0.5): return accuracy def main(): - mod = 1 + training = 1 - if mod: + if training: # train the model print("Training") + optimizer.zero_grad() train(train_loader, epoches) else: From a0ca51b34901b2d047a3cf70d1f51c4d734aa248 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 12 Oct 2023 23:24:07 +1000 Subject: [PATCH 20/36] modify VGG16 --- recognition/s4627382_SiameseNetwork/modules.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index 605b481a9..79132708e 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -51,11 +51,12 @@ def __init__(self): nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # size: 16*15 -> 8*7 + nn.AdaptiveMaxPool2d((2, 2)) # size: 8*7 -> 2*2 ) # fully connected layers self.fc = nn.Sequential( - nn.Linear(512 * 8 * 7, 4096), + nn.Linear(512 * 2 * 2, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), From 65f75494a8b8892871f02086fa28c802570a08d0 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Sat, 14 Oct 2023 15:25:20 +1000 Subject: [PATCH 21/36] change back to original model --- recognition/README.md | 10 --- .../s4627382_SiameseNetwork/dataset.py | 27 ++++---- .../s4627382_SiameseNetwork/modules.py | 64 +++++-------------- recognition/s4627382_SiameseNetwork/train.py | 43 +++++++------ 4 files changed, 53 insertions(+), 91 deletions(-) delete mode 100644 recognition/README.md diff --git a/recognition/README.md b/recognition/README.md deleted file mode 100644 index 5c646231c..000000000 --- a/recognition/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# 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 diff --git a/recognition/s4627382_SiameseNetwork/dataset.py b/recognition/s4627382_SiameseNetwork/dataset.py index 4861d0499..2f7e14bc2 100644 --- a/recognition/s4627382_SiameseNetwork/dataset.py +++ b/recognition/s4627382_SiameseNetwork/dataset.py @@ -8,7 +8,7 @@ from PIL import Image device = torch.device('cuda') -batch_size = 32 +batch_size = 16 class CustomDataset(Dataset): def __init__(self, ad_dir, nc_dir, transform=None, validate=False, split_ratio=0.8): @@ -54,15 +54,12 @@ def __getitem__(self, index): negative_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) # open images - anchor = Image.open(anchor_path) - positive = Image.open(positive_path) - negative = Image.open(negative_path) - - # apply transformation - if self.transform: - anchor = self.transform(anchor) - positive = self.transform(positive) - negative = self.transform(negative) + with Image.open(anchor_path) as anchor, Image.open(positive_path) as positive, Image.open(negative_path) as negative: + # apply transformation + if self.transform: + anchor = self.transform(anchor) + positive = self.transform(positive) + negative = self.transform(negative) return anchor, positive, negative @@ -80,9 +77,9 @@ def compute_mean_std(img_folder): transforms.ToTensor() ]) - num_px = 0 - sum_px = 0 - sum_px_sq = 0 + num_px = torch.tensor(0, dtype=torch.float64) + sum_px = torch.tensor(0, dtype=torch.float64) + sum_px_sq = torch.tensor(0, dtype=torch.float64) for subfolder in subfolders: subfolder_path = os.path.join(img_folder, subfolder) @@ -92,7 +89,8 @@ def compute_mean_std(img_folder): # open the image and put them into GPU img_path = os.path.join(subfolder_path, img_name) img = Image.open(img_path) - img_tensor = transform(img).to(device) + img_tensor = transform(img) + img.close() num_px += img_tensor.numel() # get the # of px sum_px += torch.sum(img_tensor) @@ -102,6 +100,7 @@ def compute_mean_std(img_folder): mean = sum_px / num_px std = torch.sqrt((sum_px_sq / num_px) - (mean ** 2)) + print("mean: ", mean.item(), "std: ", std.item()) return mean.item(), std.item() # a function to load all required data diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index 79132708e..6eeb72618 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -3,72 +3,38 @@ import torch import torch.nn as nn +import random # Build CNN network and get its embedding vector -class VGG16(nn.Module): +class Embedding(nn.Module): def __init__(self): - super(VGG16, self).__init__() - - # convolution layers + super(Embedding, self).__init__() self.conv = nn.Sequential( - # Block 1 - nn.Conv2d(1, 64, kernel_size=3, padding=1), # size: 256*240 - nn.ReLU(inplace=True), - nn.Conv2d(64, 64, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 - - # Block 2 - nn.Conv2d(64, 64, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(64, 256, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 128*120 -> 64*60 - # Block 3 - nn.Conv2d(256, 256, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.Conv2d(256, 256, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 64*60 -> 32*30 - - # Block 4 - nn.Conv2d(256, 512, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(512, 512, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(512, 512, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 32*30 -> 16*15 + nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 - # Block 5 - nn.Conv2d(512, 512, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(512, 512, kernel_size=3, padding=1), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.Conv2d(512, 512, kernel_size=3, padding=1), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 16*15 -> 8*7 - nn.AdaptiveMaxPool2d((2, 2)) # size: 8*7 -> 2*2 + nn.MaxPool2d(kernel_size=3, stride=2), # size: 128*120 -> 63*59 ) - # fully connected layers self.fc = nn.Sequential( - nn.Linear(512 * 2 * 2, 4096), + nn.Linear(64*63*59, 256), nn.ReLU(inplace=True), - nn.Dropout(), - nn.Linear(4096, 4096), + + nn.Linear(256, 256), nn.ReLU(inplace=True), - nn.Dropout(), - nn.Linear(4096, 16), - ) + nn.Linear(256, 16), + ) def forward(self, x): out = self.conv(x) - out = out.view(out.size(0), -1) + out = out.view(out.size()[0], -1) out = self.fc(out) return out diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index afdba41bf..20cfc3b06 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -5,6 +5,7 @@ import dataset, modules import torch import torch.optim as optim +from annoy import AnnoyIndex device = torch.device('cuda') # data path @@ -22,11 +23,8 @@ train_loader, validation_loader, test_loader = dataset.load_data( train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=32) - -accumulation_steps = 1 - # define models -embbeding = modules.VGG16() +embbeding = modules.Embedding() model = modules.SiameseNet(embbeding) model.to(device) @@ -40,42 +38,52 @@ def train(train_loader, epoches): for epoch in range(epoches): # set model to train mode model.train() + skip = 0 total_accuracy = 0 total_samples = 0 highest_val_accuracy = 0 - for batch_idx, (anchor, positive, negative) in enumerate(train_loader): + for anchor, positive, negative in train_loader: # move data to gpu anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) # front propagation - emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - loss = criterion(emb_anchor, emb_positive, emb_negative) + emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) + + # mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, int(len(emb_anchor) * 0.5), margin) + mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, 0, margin) + if len(emb_anchor[mask]) == 0: + skip += 1 + del emb_anchor, emb_positive, emb_negative + torch.cuda.empty_cache() + continue + + loss = criterion(emb_anchor[mask], emb_positive[mask], emb_negative[mask]) + # loss = criterion(emb_anchor, emb_positive, emb_negative) # calculate accuracy batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) total_accuracy += batch_accuracy * anchor.size(0) total_samples += anchor.size(0) - # back propagation with accumulation - loss = loss / accumulation_steps # Normalize our loss (if averaged) + # back propagation + optimizer.zero_grad() loss.backward() + optimizer.step() - if (batch_idx + 1) % accumulation_steps == 0: - optimizer.zero_grad() - optimizer.step() - - # calculate accuracy, validation loss, and validation accuracy + # calculate accuracy avg_accuracy = total_accuracy / total_samples + validate_loss, validate_accuracy = validate(validation_loader) print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}, validate loss: {validate_loss:.4f}, validate accuracy: {validate_accuracy:.4f}") - + # save the model if validate_accuracy > highest_val_accuracy: torch.save(model.state_dict(), "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") highest_val_accuracy = validate_accuracy + print("Model saved") # validate def validate(validation_loader): @@ -155,12 +163,11 @@ def calculate_accuracy(anchor, positive, negative, threshold=0.5): return accuracy def main(): - training = 1 + train_mode = 1 - if training: + if train_mode: # train the model print("Training") - optimizer.zero_grad() train(train_loader, epoches) else: From 9aebbbf92778a515e4f0c97189b865752ece146f Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Wed, 18 Oct 2023 02:21:38 +1000 Subject: [PATCH 22/36] use constractive loss instead of triplet --- .gitignore | 3 + .../s4627382_SiameseNetwork/dataset.py | 47 ++--- .../s4627382_SiameseNetwork/modules.py | 98 +++------- recognition/s4627382_SiameseNetwork/train.py | 175 ++++++++---------- test/__pycache__/dataset.cpython-311.pyc | Bin 0 -> 7795 bytes test/__pycache__/modules.cpython-311.pyc | Bin 0 -> 4601 bytes 6 files changed, 139 insertions(+), 184 deletions(-) create mode 100644 test/__pycache__/dataset.cpython-311.pyc create mode 100644 test/__pycache__/modules.cpython-311.pyc diff --git a/.gitignore b/.gitignore index 184b6e564..c6ae77d4a 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,6 @@ recognition/SiameseNetwork/__pycache__/modules.cpython-311.pyc recognition/s4627382_SiameseNetwork/__pycache__/dataset.cpython-311.pyc recognition/s4627382_SiameseNetwork/__pycache__/modules.cpython-311.pyc recognition/s4627382_SiameseNetwork/SiameseNet.pth +test/dataset.py +test/modules.py +test/train.py diff --git a/recognition/s4627382_SiameseNetwork/dataset.py b/recognition/s4627382_SiameseNetwork/dataset.py index 2f7e14bc2..58517ed9f 100644 --- a/recognition/s4627382_SiameseNetwork/dataset.py +++ b/recognition/s4627382_SiameseNetwork/dataset.py @@ -1,5 +1,3 @@ -# containing the data loader for loading and preprocessing your data - import os import random import torch @@ -8,7 +6,7 @@ from PIL import Image device = torch.device('cuda') -batch_size = 16 +batch_size = 32 class CustomDataset(Dataset): def __init__(self, ad_dir, nc_dir, transform=None, validate=False, split_ratio=0.8): @@ -41,27 +39,35 @@ def __len__(self): return 2 * min(len(self.ad_names), len(self.nc_names)) def __getitem__(self, index): - - # path to the image, randomly select ad image and nc image - # chooce anchor image evently from ad image set and nc image set - if index % 2 == 0: - anchor_path = os.path.join(self.ad_folder, self.ad_names[index//2]) - positive_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) - negative_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) - else: - anchor_path = os.path.join(self.nc_folder, self.nc_names[index//2]) - positive_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) - negative_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + # Depending on the index, choose the type of pair + pair_type = index % 4 + + if pair_type == 0: # (ad, ad, 0) + img1_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + img2_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + label = 0 + elif pair_type == 1: # (nc, nc, 0) + img1_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + img2_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + label = 0 + elif pair_type == 2: # (ad, nc, 1) + img1_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + img2_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + label = 1 + else: # (nc, ad, 1) + img1_path = os.path.join(self.nc_folder, random.choice(self.nc_names)) + img2_path = os.path.join(self.ad_folder, random.choice(self.ad_names)) + label = 1 # open images - with Image.open(anchor_path) as anchor, Image.open(positive_path) as positive, Image.open(negative_path) as negative: + with Image.open(img1_path) as img1, Image.open(img2_path) as img2: # apply transformation if self.transform: - anchor = self.transform(anchor) - positive = self.transform(positive) - negative = self.transform(negative) - - return anchor, positive, negative + img1 = self.transform(img1) + img2 = self.transform(img2) + + return img1, img2, torch.tensor(label, dtype=torch.float32) + # calculate the mean and std of the dataset # input: The folder containing folders containing images @@ -103,7 +109,6 @@ def compute_mean_std(img_folder): print("mean: ", mean.item(), "std: ", std.item()) return mean.item(), std.item() -# a function to load all required data def load_data(train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=batch_size): # calculate mean and std for train set mean, std = compute_mean_std(train_folder_path) diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index 6eeb72618..f8dd2cdf4 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -1,16 +1,17 @@ -# containing the source code of the components of your model. + # containing the source code of the components of your model. # Each component must be implementated as a class or a function import torch import torch.nn as nn -import random +from sklearn.neighbors import KNeighborsClassifier +device = torch.device('cuda') # Build CNN network and get its embedding vector class Embedding(nn.Module): def __init__(self): super(Embedding, self).__init__() self.conv = nn.Sequential( - + nn.Conv2d(1, 32, kernel_size=3, padding=1), nn.BatchNorm2d(32), nn.ReLU(inplace=True), @@ -21,17 +22,14 @@ def __init__(self): nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), # size: 128*120 -> 63*59 ) - - self.fc = nn.Sequential( - nn.Linear(64*63*59, 256), - nn.ReLU(inplace=True), - nn.Linear(256, 256), + self.fc = nn.Sequential( + nn.Linear(64*63*59, 512), nn.ReLU(inplace=True), - nn.Linear(256, 16), + nn.Linear(512, 256), ) - + def forward(self, x): out = self.conv(x) out = out.view(out.size()[0], -1) @@ -39,24 +37,25 @@ def forward(self, x): return out -# construct the triplet loss -class TripletLoss(nn.Module): - def __init__(self, margin=1): - super(TripletLoss, self).__init__() +# construct the triplet loss +# formular: L = (1 - y) * 1/2 * D^2 + y * 1/2 * max(0, m - D)^2 +# where D = sample distance, m = margin, y = label, same: label = 0; diff, label = 1 +class ContrastiveLoss(nn.Module): + def __init__(self, margin=0.5): + super(ContrastiveLoss, self).__init__() self.margin = margin - - def forward(self, anchor, positive, negative): - # calculate euclidean distance from anchor to positive and negative - anchor_positive = (anchor - positive).pow(2).sum(1).sqrt() - anchor_negative = (anchor - negative).pow(2).sum(1).sqrt() - # print("anchor_positive: ", anchor_positive.mean()) - # print("anchor_negative: ", anchor_negative.mean()) + def forward(self, img1, img2, label): + # calculate euclidean distance + distance = (img1 - img2).pow(2).sum(1).sqrt() # calculate loss, use relu to ensure loss are non-negative - loss = torch.relu(anchor_positive - anchor_negative + self.margin) + loss_same = (1 - label) * 0.5 * (distance ** 2) + loss_diff = label * 0.5 * torch.relu(self.margin - distance).pow(2) + loss = loss_same + loss_diff + return loss.mean() - + # construct the siamese network class SiameseNet(nn.Module): @@ -64,48 +63,11 @@ def __init__(self, embedding): super(SiameseNet, self).__init__() self.embedding = embedding - def forward(self, anchor, positive, negative): - # use the embedding class to ge the embedding of anchor, positive and negative samples - embedding_anchor = self.embedding(anchor) - embedding_positive = self.embedding(positive) - embedding_negative = self.embedding(negative) - return embedding_anchor, embedding_positive, embedding_negative - - -# The definition of semi_hard_triplet is the triplets such that the distance of -# anchor to negative smaller than anchor to positive, but the differences are still smaller than the margin -# This function returns a boolean tensor with shape [batch_size] -# def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, margin): -def semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, desire_sample_size, margin): - # Calculate distance from anchor to positive and negative samples - anchor_positive = (emb_anchor - emb_positive).pow(2).sum(1).sqrt() - anchor_negative = (emb_anchor - emb_negative).pow(2).sum(1).sqrt() - - # Find the hard and semi-hard triplets - hard_triplets = anchor_positive > anchor_negative - semi_hard_triplets = (anchor_positive < anchor_negative) & (anchor_negative - anchor_positive < margin) - - triplet_mask = hard_triplets | semi_hard_triplets - - if triplet_mask.sum() < desire_sample_size: - needed_fill_count = desire_sample_size - triplet_mask.sum().item() - - # Compute distance difference for all samples - semi_hard_distances = anchor_negative - anchor_positive - semi_hard_distances[~semi_hard_triplets] = float('inf') # we are only interested in semi-hard samples - - # Retrieve distances for samples that are not yet selected - unselected_distances = semi_hard_distances[~triplet_mask] - - # Sort these distances and get the required number of samples - _, sorted_indices = torch.sort(unselected_distances, descending=False) - fill_indices = sorted_indices[:needed_fill_count].tolist() - - # Get the global indices for these selected samples - unselected_samples = torch.where(~triplet_mask)[0] - global_fill_indices = unselected_samples[fill_indices] - - # Update the triplet mask to include these newly selected samples - triplet_mask[global_fill_indices] = True - - return triplet_mask + def forward(self, img1, img2): + emb1 = self.embedding(img1) + emb2 = self.embedding(img2) + return emb1, emb2 + + def get_embedding(self, x): + return self.embedding(x) + diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index 20cfc3b06..d67761c68 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -1,11 +1,10 @@ -# containing the source code for training, validating, testing and saving your model. -# The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. -# Make sure to plot the losses and metrics during training - import dataset, modules import torch import torch.optim as optim -from annoy import AnnoyIndex +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +from sklearn.neighbors import KNeighborsClassifier + device = torch.device('cuda') # data path @@ -15,9 +14,8 @@ test_ad_path = "D:/Study/MLDataSet/AD_NC/test/AD" test_nc_path = "D:/Study/MLDataSet/AD_NC/test/NC" -# hyperparameters -margin = 0.5 -epoches = 8 +margin = 0.3 +epoches = 16 # create data loader train_loader, validation_loader, test_loader = dataset.load_data( @@ -29,63 +27,52 @@ model.to(device) # define loss function -criterion = modules.TripletLoss(margin) +criterion = modules.ContrastiveLoss(margin) optimizer = optim.Adam(model.parameters(), lr=0.001) - -# train def train(train_loader, epoches): for epoch in range(epoches): # set model to train mode model.train() - skip = 0 + total_loss = 0 total_accuracy = 0 total_samples = 0 - highest_val_accuracy = 0 - for anchor, positive, negative in train_loader: + for img1, img2, labels in train_loader: # move data to gpu - anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + img1, img2, labels = img1.to(device), img2.to(device), labels.to(device) # front propagation - emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - - # mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, int(len(emb_anchor) * 0.5), margin) - mask = modules.semi_hard_triplet_mining(emb_anchor, emb_positive, emb_negative, 0, margin) - if len(emb_anchor[mask]) == 0: - skip += 1 - del emb_anchor, emb_positive, emb_negative - torch.cuda.empty_cache() - continue - - loss = criterion(emb_anchor[mask], emb_positive[mask], emb_negative[mask]) - # loss = criterion(emb_anchor, emb_positive, emb_negative) + emb1, emb2 = model(img1, img2) + loss = criterion(emb1, emb2, labels) # calculate accuracy - batch_accuracy = calculate_accuracy(emb_anchor, emb_positive, emb_negative) - total_accuracy += batch_accuracy * anchor.size(0) - total_samples += anchor.size(0) + batch_accuracy = calculate_accuracy(emb1, emb2, labels) + total_loss += loss.item() * img1.size(0) + total_accuracy += batch_accuracy * img1.size(0) + total_samples += img1.size(0) # back propagation optimizer.zero_grad() loss.backward() optimizer.step() - # calculate accuracy + # calculate accuracy and loss avg_accuracy = total_accuracy / total_samples + avg_loss = total_loss / total_samples validate_loss, validate_accuracy = validate(validation_loader) - print(f"Epoch [{epoch+1}/{epoches}], Loss: {loss.item():.4f}, Accuracy: {avg_accuracy:.4f}, validate loss: {validate_loss:.4f}, validate accuracy: {validate_accuracy:.4f}") - + print(f"Epoch [{epoch+1}/{epoches}], Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.4f}, validate loss: {validate_loss:.4f}, validate accuracy: {validate_accuracy:.4f}") + + if epoch % 2 == 0: + visualize_embeddings(train_loader, model) + # save the model - if validate_accuracy > highest_val_accuracy: - torch.save(model.state_dict(), - "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") - highest_val_accuracy = validate_accuracy - print("Model saved") + torch.save(model.state_dict(), + "D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth") + print("Model saved") -# validate def validate(validation_loader): # set model to evaluation mode model.eval() @@ -93,17 +80,17 @@ def validate(validation_loader): total_accuracy = 0 with torch.no_grad(): - for anchor, positive, negative in validation_loader: + for img1, img2, labels in validation_loader: # move data to gpu - anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) + img1, img2, labels = img1.to(device), img2.to(device), labels.to(device) # front propagation - emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - loss = criterion(emb_anchor, emb_positive, emb_negative) + emb1, emb2 = model(img1, img2) + loss = criterion(emb1, emb2, labels) # get loss and accuracy total_loss += loss.item() - total_accuracy += calculate_accuracy(emb_anchor, emb_positive, emb_negative) + total_accuracy += calculate_accuracy(emb1, emb2, labels) # calculate average loss and average accuracy validate_loss = total_loss/len(validation_loader) @@ -112,72 +99,70 @@ def validate(validation_loader): return validate_loss, validate_accuracy -# test -def test(test_loader): - # set model to evaluation mode - model.eval() - total_loss = 0 - total_accuracy = 0 - - with torch.no_grad(): - for anchor, positive, negative in test_loader: - # move data to gpu - anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device) +# calculate accuracy, +# if distance < threshold, these two samples will be considered same +def calculate_accuracy(img1, img2, labels, threshold=0.5): + + # calculate the distance between two samples + distance = (img1 - img2).pow(2).sum(1).sqrt() - # front propagation - emb_anchor, emb_positive, emb_negative = model(anchor, positive, negative) - loss = criterion(emb_anchor, emb_positive, emb_negative) + # Predict similarity: 0 for same (distance < threshold), 1 for diff + predicts = (distance >= threshold).float() - # get loss and accuracy - total_loss += loss.item() - total_accuracy += calculate_accuracy(emb_anchor, emb_positive, emb_negative) + # Calculate accuracy by comparing predictions to labels + correct = (predicts == labels).float() + accuracy = correct.sum().item() / len(labels) - # calculate average loss and average accuracy - test_loss = total_loss/len(test_loader) - test_accuracy = total_accuracy/len(test_loader) - print(f"Test loss: {test_loss:.4f}, Test accuracy: {test_accuracy:.4f}") + return accuracy + +def visualize_embeddings(loader, model, num_samples=300): + model.eval() + embeddings = [] + labels_list = [] - return test_loss, test_accuracy + # Define label to color mapping + label_to_color = {0: 'red', 1: 'blue'} + + with torch.no_grad(): + for i, (img1, img2, labels) in enumerate(loader): + if i * loader.batch_size > num_samples: + break + + img1, img2 = img1.to(device), img2.to(device) + + emb1 = model.get_embedding(img1) + emb2 = model.get_embedding(img2) + + embeddings.append(emb1.cpu()) + embeddings.append(emb2.cpu()) + + labels_list.extend(labels.cpu().numpy()) + labels_list.extend(labels.cpu().numpy()) + # Convert labels to colors + color_labels = [label_to_color[label] for label in labels_list] + embeddings = torch.cat(embeddings, dim=0) + tsne = TSNE(n_components=2, random_state=42) + tsne_results = tsne.fit_transform(embeddings) -# calculate accuracy, -# if anchor_positive distance < threshold, these two samples will be considered similar -# if anchor_negative distance > threshold, these two samples will be considered unsimilar -def calculate_accuracy(anchor, positive, negative, threshold=0.5): - - # calculate the distance from anchor to positive and negative samples - # .sum(1) will reduce a dimension, so the shape of anchor_positive: [batch_size, 2] -> [batch_size] - anchor_positive = (anchor - positive).pow(2).sum(1).sqrt() - anchor_negative = (anchor - negative).pow(2).sum(1).sqrt() - - # correct predictions - true_positive = (anchor_positive < threshold).float() - true_negative = (anchor_negative > threshold).float() - - # total are true_positive + true_negative + false_positive + false_negative, - # they summing up equal to true_positive.size(0) + true_negative.size(0) - total = true_positive.size(0) + true_negative.size(0) - accuracy = (true_positive.sum().item() + true_negative.sum().item())/total - - return accuracy + plt.figure(figsize=(10, 7)) + plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=color_labels, s=50, alpha=0.6) + plt.title("t-SNE visualization of embeddings") + plt.show() def main(): - train_mode = 1 + + # hyperparameters - if train_mode: - # train the model + mode = 0 + if mode == 0: print("Training") train(train_loader, epoches) - else: + elif mode == 1: print("Testing") - model.load_state_dict( - torch.load("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth")) - test(test_loader) - print("Done") - if __name__ == "__main__": main() \ No newline at end of file diff --git a/test/__pycache__/dataset.cpython-311.pyc b/test/__pycache__/dataset.cpython-311.pyc new file mode 100644 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recognition/s4627382_SiameseNetwork/README.md | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index a281b503e..68f917295 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -1,9 +1 @@ -Task: -Create a classifier based on Siamese network [10] to classify either Alzheimer’s disease (normal and AD) -of the ADNI brain data set or classify the ISIC 2020 Kaggle Challenge data set (normal and melanoma) -having an accuracy of around 0.8 on the test set (see Appendix for links). - -require libarary: -pytorch -cudatoolkit -annoy \ No newline at end of file +# KNN classifier based on siamese network embedding From eecfac75710901ca8a9a6e0b4441bc784b6f4edd Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Wed, 18 Oct 2023 14:21:15 +1000 Subject: [PATCH 24/36] Final version --- .gitignore | 1 + .../s4627382_SiameseNetwork/modules.py | 61 ++++++++++++++++-- .../s4627382_SiameseNetwork/predict.py | 62 ++++++++++++++++++- recognition/s4627382_SiameseNetwork/train.py | 17 ++--- 4 files changed, 129 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index c6ae77d4a..15fef1351 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ recognition/s4627382_SiameseNetwork/SiameseNet.pth test/dataset.py test/modules.py test/train.py +recognition/s4627382_SiameseNetwork/knn.pkl diff --git a/recognition/s4627382_SiameseNetwork/modules.py b/recognition/s4627382_SiameseNetwork/modules.py index f8dd2cdf4..28d878571 100644 --- a/recognition/s4627382_SiameseNetwork/modules.py +++ b/recognition/s4627382_SiameseNetwork/modules.py @@ -1,9 +1,11 @@ - # containing the source code of the components of your model. +# containing the source code of the components of your model. # Each component must be implementated as a class or a function import torch import torch.nn as nn +import pickle from sklearn.neighbors import KNeighborsClassifier +from sklearn.metrics import accuracy_score device = torch.device('cuda') # Build CNN network and get its embedding vector @@ -15,16 +17,21 @@ def __init__(self): nn.Conv2d(1, 32, kernel_size=3, padding=1), nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=2, stride=2), # size: 256*240 -> 128*120 + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 256*240 -> 128*120 nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=3, stride=2), # size: 128*120 -> 63*59 + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 128*120 -> 64*60 + + nn.Conv2d(64, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 64*60 -> 32*30 ) self.fc = nn.Sequential( - nn.Linear(64*63*59, 512), + nn.Linear(64*32*30, 512), nn.ReLU(inplace=True), nn.Linear(512, 256), @@ -57,6 +64,29 @@ def forward(self, img1, img2, label): return loss.mean() +# get the trained embedding network +def extract_embeddings(loader, model): + model.eval() + embeddings = [] + labels_list = [] + + with torch.no_grad(): + for img1, img2, labels in loader: + img1, img2, labels = img1.to(device), img2.to(device), labels.to(device) + + emb1 = model.get_embedding(img1) + emb2 = model.get_embedding(img2) + + embeddings.append(emb1.cpu()) + embeddings.append(emb2.cpu()) + + labels_list.extend(labels.cpu().numpy()) + labels_list.extend(labels.cpu().numpy()) + + embeddings = torch.cat(embeddings, dim=0) + return embeddings, labels_list + + # construct the siamese network class SiameseNet(nn.Module): def __init__(self, embedding): @@ -71,3 +101,26 @@ def forward(self, img1, img2): def get_embedding(self, x): return self.embedding(x) + +# use embedding net to train knn clasifier +def knn(train_loader, val_loader, model, n_neighbors=5): + + # Extract embeddings from the train set + train_embeddings, train_labels = extract_embeddings(train_loader, model) + + # Train a KNN classifier + knn = KNeighborsClassifier(n_neighbors=n_neighbors) + knn.fit(train_embeddings, train_labels) + + # Extract embeddings from the validation set + val_embeddings, val_labels = extract_embeddings(val_loader, model) + + # Predict the labels of the validation set + test_preds = knn.predict(val_embeddings) + + # Calculate the accuracy + accuracy = accuracy_score(val_labels, test_preds) + print(f"KNN Accuracy: {accuracy:.4f}") + with open ("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/knn.pkl", "wb") as f: + pickle.dump(knn, f) + return accuracy \ No newline at end of file diff --git a/recognition/s4627382_SiameseNetwork/predict.py b/recognition/s4627382_SiameseNetwork/predict.py index 8d054b5b1..a630fea4b 100644 --- a/recognition/s4627382_SiameseNetwork/predict.py +++ b/recognition/s4627382_SiameseNetwork/predict.py @@ -1,2 +1,62 @@ # showing example usage of your trained model. -# Print out any results and / or provide visualisations where applicable \ No newline at end of file +# Print out any results and / or provide visualisations where applicable + +import modules +import torch +import pickle +import torchvision.transforms as transforms +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +from PIL import Image + +device = torch.device('cuda') + +def predict_image(image_path): + # load the image + image = Image.open(image_path) + + # trainsform the data + transform = transforms.Compose([ + transforms.ToTensor(), + ]) + + # move to gpu + image_tensor = transform(image).unsqueeze(0).to(device) + + # define and load model + embeddingNet = modules.Embedding() + model = modules.SiameseNet(embeddingNet) + model = model.to(device) + model.load_state_dict(torch.load("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth")) + with open("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/knn.pkl", "rb") as f: + knn = pickle.load(f) + + # extract embedding + embedding = model.get_embedding(image_tensor) + embedding_numpy = embedding.detach().cpu().numpy() + + # predict using KNN + prediction = knn.predict(embedding_numpy) + + # convert prediction to 'ad' or 'nc' + label_map = {0: 'ad', 1: 'nc'} + predicted_label = label_map[prediction[0]] + + return predicted_label + + +# display given image +def display_image(image_path): + # load image from given path + img = mpimg.imread(image_path) + + # plot img + plt.imshow(img) + plt.axis("off") + plt.show + +# Example usage: +image_path = "D:/Study/MLDataSet/AD_NC/test/AD/388206_78.jpeg" +predicted_label = predict_image(image_path) +display_image(image_path) +print(f"Predicted label: {predicted_label}, Ture label: AD") diff --git a/recognition/s4627382_SiameseNetwork/train.py b/recognition/s4627382_SiameseNetwork/train.py index d67761c68..418242923 100644 --- a/recognition/s4627382_SiameseNetwork/train.py +++ b/recognition/s4627382_SiameseNetwork/train.py @@ -3,7 +3,6 @@ import torch.optim as optim import matplotlib.pyplot as plt from sklearn.manifold import TSNE -from sklearn.neighbors import KNeighborsClassifier device = torch.device('cuda') @@ -14,8 +13,8 @@ test_ad_path = "D:/Study/MLDataSet/AD_NC/test/AD" test_nc_path = "D:/Study/MLDataSet/AD_NC/test/NC" -margin = 0.3 -epoches = 16 +margin = 1 +epoches = 10 # create data loader train_loader, validation_loader, test_loader = dataset.load_data( @@ -148,17 +147,21 @@ def visualize_embeddings(loader, model, num_samples=300): plt.figure(figsize=(10, 7)) plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=color_labels, s=50, alpha=0.6) - plt.title("t-SNE visualization of embeddings") + plt.title("Embedding visualization") plt.show() -def main(): - - # hyperparameters +def main(): mode = 0 if mode == 0: print("Training") train(train_loader, epoches) + modules.knn(train_loader, validation_loader, model, n_neighbors=5) + + if mode == 1: + print("Train classifier") + model.load_state_dict(torch.load("D:/Study/GitHubDTClone/COMP3710A3/PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/SiameseNet.pth")) + modules.knn(train_loader, validation_loader, model, n_neighbors=5) elif mode == 1: print("Testing") From a70d47e17d1a4c375cd51d57da4495a937e9ffc1 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 01:15:55 +1000 Subject: [PATCH 25/36] Update README.md --- recognition/s4627382_SiameseNetwork/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index 68f917295..f488b079a 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -1 +1,15 @@ # KNN classifier based on siamese network embedding +## Introduction +The purpose of this project is to construct a [Siamese network](#siamese-network) and use its embedding to train a [knn classifier](#k-nearest-neighbour) to classify the [Alzheimer's Disease Neuroimaging Initiative (ADNI)](#adni-dataset) brain dataset. + +### Siamese Network +A Siamese network is a distance-based neural network. It consists of two weight-shared subnetworks and a designated loss function. The network takes two images as inputs, and then pass through their corresponding subnetworks for feature extraction. These subnetworks produce two flattened layers, called embeddings, which are then fed into the loss function. +[Siamese Network Architecture](recognition\s4627382_SiameseNetwork\Images\SiameseNet.webp) +In here, contrastive loss will be used. This loss function will calculate + +### K Nearest Neighbour + +### ADNI Dataset + +## Reference +https://medium.com/swlh/one-shot-learning-with-siamese-network-1c7404c35fda From a89732e6264dbc296a98f51d9cbb3db9a7f00316 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 01:16:53 +1000 Subject: [PATCH 26/36] add images, delet test files --- README.md | 15 --------------- .../Images/SiameseNet.webp | Bin 0 -> 19848 bytes test/__pycache__/dataset.cpython-311.pyc | Bin 7795 -> 0 bytes test/__pycache__/modules.cpython-311.pyc | Bin 4601 -> 0 bytes 4 files changed, 15 deletions(-) delete mode 100644 README.md create mode 100644 recognition/s4627382_SiameseNetwork/Images/SiameseNet.webp delete mode 100644 test/__pycache__/dataset.cpython-311.pyc delete mode 100644 test/__pycache__/modules.cpython-311.pyc diff --git a/README.md b/README.md deleted file mode 100644 index 4a064f841..000000000 --- a/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Pattern Analysis -Pattern Analysis of various datasets by COMP3710 students at the University of Queensland. - -We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. - -This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. - -The library includes the following implemented in Tensorflow: -* fractals -* recognition problems - -In the recognition folder, you will find many recognition problems solved including: -* OASIS brain segmentation -* Classification -etc. diff --git a/recognition/s4627382_SiameseNetwork/Images/SiameseNet.webp b/recognition/s4627382_SiameseNetwork/Images/SiameseNet.webp new file mode 100644 index 0000000000000000000000000000000000000000..85c7ab03e6747e6fd78994ee2ecea5b615c8d4db GIT binary patch literal 19848 zcmZ5{W1MF(l;yu{+qUhhF59+k+qP|XnO(MRySi+f+p{}6J3Alp;pLaym*m}Z&q;Eh 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z{+^-t<>l7Q4EceN$4SYdwl@bp?43<1@@Sn0XiUZNWps1BEG;f&-|~}4nXg@%p{)U> z#68-fWvy;Gx)gIY5Pp1VjFp0M!@7zlVNOrj?Yfga*UTDxKF$y{GTzuPsn!Z<7n2dU zU;I5}V5t;}V3LSYV7cf5btodTm=yPlqA`;L0T8aKA?LB6TTL_Fu)gZ(nt{8bXChVO z8y^DOd_lWs)8${D=~!a2e|_#b)$|={gqj2AZc6vF@sFS4UwjoUy!R)7LT>L$BH?)G zVNyiwN)3KsQIlA1b40Id{%5C-;on;%{s$FP4b|q$r;4wWJpiDgA@vnv2*ASD2J zssDYrfYkp~<4ex{AGf&VBL8_PE;*Rue}1P(s(<{Kf&W1L^8P#L|LYY1An7WA>Qa%q r2ay6M|I2}w2>{%GIz&nei2FD3;sOAm!2O$OcWI&gk8k`Pd5Ql6xqNG+ From 9cfe2354dfe15085540cf3d4c67269c2f4ef0622 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 01:47:23 +1000 Subject: [PATCH 28/36] Update README.md --- recognition/s4627382_SiameseNetwork/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index f488b079a..59ab6b38c 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -4,7 +4,7 @@ The purpose of this project is to construct a [Siamese network](#siamese-network ### Siamese Network A Siamese network is a distance-based neural network. It consists of two weight-shared subnetworks and a designated loss function. The network takes two images as inputs, and then pass through their corresponding subnetworks for feature extraction. These subnetworks produce two flattened layers, called embeddings, which are then fed into the loss function. -[Siamese Network Architecture](recognition\s4627382_SiameseNetwork\Images\SiameseNet.webp) +![Siamese Network Architecture](PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/Images/SiameseNet.png) In here, contrastive loss will be used. 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Of these, 14,860 images are associated with Alzheimer’s disease (AD), while 15,660 images correspond to cognitively normal (NC) conditions. +![AD sample](Images/AD_sample.png), ![NC sample](Images/NC_sample.png). + ### Siamese Network A Siamese network is a distance-based neural network. It consists of two weight-shared subnetworks and a designated loss function. The network takes two images as inputs, and then pass through their corresponding subnetworks for feature extraction. These subnetworks produce two flattened layers, called embeddings, which are then fed into the loss function. -![Siamese Network Architecture](PatternAnalysis-2023/recognition/s4627382_SiameseNetwork/Images/SiameseNet.png) -In here, contrastive loss will be used. This loss function will calculate +![Siamese Network Architecture](Images/SiameseNet.png). + +In this project, contrastive loss will be used. The definition of contrastive loss is $L(x_1, x_2, y) = (1 - y) \times \frac{1} {2} D^2 + y \times \frac {1} {2} max(0, m - D)^2$ where $y$ is label, $D$ is distance and $m$ is margin. When the distance between two inputs are smaller than margin, they will be considered as similar (y = 0), dissimilar otherwise (y = 1). This loss function will pull similar samples closer to each other while push dissimilar samples away. + +### K Nearest Neighbour classifier +The knn classifier utilizes the embeddings from the Siamese network as its dataset. 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zTF{J^4^ol)VXE$Z`bB?fBvs$p5`-yI@xrOf1y&$!=#^WaZ3rrb_MY-qy!7UolsW3T?Ia4A z&CKlw$LvQA9m}_Wmyf9tDu|7&GnhRSsi;m>;@;3tloxOg-7uE<=4rz(xWlDaS5dzi zzXC{@Y9PNgSjVRq0v}X!7|YODSb%&tewYIPJW?#20fq~pEu6RB?aB29mjFl)Il!8J zTf)O-?~r76-Pq_GH{FpINGS zF(Y0|Oc(&AZTvg_;m5Q$FEfP``^uw0{dHqu+ZM}GBO_&3bU`CIlWITD(H{yID0-fX znJWC-s~IwPaPoU9Ffl)M* zZ9eQ+tH-?^5o(gR>NE4fi56KOe1O$%1-5T|MVkV#0vA!#;Ov5yZ&TNwHlNo<2+Pea z(`nh0RWeEj2e^)f=wA3k@YcRCX}ZDXu;6MNwcUwQd)0Za1T&Y1eS*Y)F{MQB?F{nw zd$UWwEK2lp_!^T3aX2ImM4&kwe!d{*vK$iRg~+o@kkBM2eEx6H|6W1Fz6+VOiy~_Z hOu?h)|6dc%#ACiaRcDb?(6~f?o~!Gr)d62d{148^PbmNZ literal 0 HcmV?d00001 From 96c7c3223ad6185647cf6c113b154b560388ec7a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 03:59:59 +1000 Subject: [PATCH 33/36] ajust some text error --- recognition/s4627382_SiameseNetwork/predict.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4627382_SiameseNetwork/predict.py b/recognition/s4627382_SiameseNetwork/predict.py index a630fea4b..34d389e6e 100644 --- a/recognition/s4627382_SiameseNetwork/predict.py +++ b/recognition/s4627382_SiameseNetwork/predict.py @@ -59,4 +59,4 @@ def display_image(image_path): image_path = "D:/Study/MLDataSet/AD_NC/test/AD/388206_78.jpeg" predicted_label = predict_image(image_path) display_image(image_path) -print(f"Predicted label: {predicted_label}, Ture label: AD") +print(f"Predicted label: {predicted_label}, Ture label: ad") From 5308a1b52fb222274a2065d3ce0515ab23bfc1c2 Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 04:27:31 +1000 Subject: [PATCH 34/36] Update README.md --- recognition/s4627382_SiameseNetwork/README.md | 139 +++++++++++++++++- 1 file changed, 135 insertions(+), 4 deletions(-) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index d21693c18..ab984d980 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -3,19 +3,150 @@ The purpose of this project is to construct a [Siamese network](#siamese-network) and use its embedding to train a [knn classifier](#k-nearest-neighbour) to classify the [Alzheimer's Disease Neuroimaging Initiative (ADNI)](#adni-dataset) brain dataset. ### ADNI Dataset -The ADNI dataset that use in here comprises 30,520 MRI brain slice in total. Of these, 14,860 images are associated with Alzheimer’s disease (AD), while 15,660 images correspond to cognitively normal (NC) conditions. -![AD sample](Images/AD_sample.png), ![NC sample](Images/NC_sample.png). +The ADNI dataset that use in here comprises 30,520 MRI brain slice in total. Of these, 14,860 images are associated with Alzheimer’s disease (AD), while 15,660 images correspond to cognitively normal (NC) conditions. +![AD sample](Images/AD_sample.jpeg) ![NC sample](Images/NC_sample.jpeg). +AD sample and NC sample ### Siamese Network A Siamese network is a distance-based neural network. It consists of two weight-shared subnetworks and a designated loss function. The network takes two images as inputs, and then pass through their corresponding subnetworks for feature extraction. These subnetworks produce two flattened layers, called embeddings, which are then fed into the loss function. ![Siamese Network Architecture](Images/SiameseNet.png). -In this project, contrastive loss will be used. The definition of contrastive loss is $L(x_1, x_2, y) = (1 - y) \times \frac{1} {2} D^2 + y \times \frac {1} {2} max(0, m - D)^2$ where $y$ is label, $D$ is distance and $m$ is margin. When the distance between two inputs are smaller than margin, they will be considered as similar (y = 0), dissimilar otherwise (y = 1). This loss function will pull similar samples closer to each other while push dissimilar samples away. +In this project, contrastive loss will be used. The definition of contrastive loss is $$L(x_1, x_2, y) = (1 - y) \times \frac{1} {2} D^2 + y \times \frac {1} {2} max(0, m - D)^2$$ where $y$ is label, $D$ is distance and $m$ is margin. +When the distance between two inputs are smaller than margin, they will be considered as similar (y = 0), dissimilar otherwise (y = 1). This loss function will pull similar samples closer to each other while push dissimilar samples away. ### K Nearest Neighbour classifier The knn classifier utilizes the embeddings from the Siamese network as its dataset. It predicts the label of new sample based on the majority vote from its k nearest neighbors. +## Training process +### Data loading +The data images should save in a folder with following stracture: +- AD_NC + - Train + - AD + - images + - NC + - images + - Test + - AD + - images + - NC + - images + +```python +def load_data(train_folder_path, train_ad_path, train_nc_path, test_ad_path, test_nc_path, batch_size=batch_size): + # calculate mean and std for train set + mean, std = compute_mean_std(train_folder_path) + + # define transform + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((mean,), (std,)) + ]) + + # create dataset + train_set = CustomDataset(ad_dir=train_ad_path, nc_dir=train_nc_path, transform=transform, validate=False, split_ratio=0.8) + validation_set = CustomDataset(ad_dir=train_ad_path, nc_dir=train_nc_path, transform=transform, validate=True, split_ratio=0.8) + test_set = CustomDataset(ad_dir=test_ad_path, nc_dir=test_nc_path, transform=transform, validate=False, split_ratio=1) + + # create dataloader + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + validation_loader = DataLoader(validation_set, batch_size=batch_size, shuffle=False) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False) + + return train_loader, validation_loader, test_loader +``` +The function above defines how to load the data. It uses **Train** folder to get all images inside and calculate the mean and standard deviation. Then it splits the training data into two parts, one part (80%) for train, and another part (20%) for validate. This will keep test data totally unknown to prevent overfitting. + +The data will be maked into 4 pairs, with label 0 when they are considered as similar and 1 otherwise. +(ad, ad, 0), (nc, nc, 0), (ad, nc, 1), (nc, ad, 1) +These four group of data will be evenly selected, but the image will be randomly choose from their belonging class. + +### Model training +The embedding model is a simple convolutional neural network, which accepts images in the size 256*240 as input and return a flatten layer with size in 256. +```python +class Embedding(nn.Module): + def __init__(self): + super(Embedding, self).__init__() + self.conv = nn.Sequential( + + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 256*240 -> 128*120 + + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 128*120 -> 64*60 + + nn.Conv2d(64, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # size: 64*60 -> 32*30 + ) + + self.fc = nn.Sequential( + nn.Linear(64*32*30, 512), + nn.ReLU(inplace=True), + + nn.Linear(512, 256), + ) + + def forward(self, x): + out = self.conv(x) + out = out.view(out.size()[0], -1) + out = self.fc(out) + return out +``` +After training, the siamese net and knn model will be saved to the given directory, but the trained model will not be uploaded to github. + +I trained the model in 10 epochs with the margin of 1, and draw the embedding visulization in every two epochs here are the accuracies and the losses. +Epoch [1/10], Loss: 0.8536, Accuracy: 0.5056, validate loss: 0.1353, validate accuracy: 0.5180 +![embedding visulization in epoch 1](Images/ep1.png). +Epoch [2/10], Loss: 0.2237, Accuracy: 0.5203, validate loss: 0.1375, validate accuracy: 0.4966 +Epoch [3/10], Loss: 0.1366, Accuracy: 0.5546, validate loss: 0.1245, validate accuracy: 0.5851 +![embedding visulization in epoch 3](Images/ep3.png). +Epoch [4/10], Loss: 0.1373, Accuracy: 0.6181, validate loss: 0.1189, validate accuracy: 0.6135 +Epoch [5/10], Loss: 0.1001, Accuracy: 0.7056, validate loss: 0.1202, validate accuracy: 0.6541 +![embedding visulization in epoch 5](Images/ep5.png). +Epoch [6/10], Loss: 0.1335, Accuracy: 0.6911, validate loss: 0.1147, validate accuracy: 0.6692 +Epoch [7/10], Loss: 0.0730, Accuracy: 0.7964, validate loss: 0.1131, validate accuracy: 0.6788 +![embedding visulization in epoch 7](Images/ep7.png). +Epoch [8/10], Loss: 0.0565, Accuracy: 0.8521, validate loss: 0.1001, validate accuracy: 0.7147 +Epoch [9/10], Loss: 0.0654, Accuracy: 0.8594, validate loss: 0.1076, validate accuracy: 0.6868 +![embedding visulization in epoch 9](Images/ep9.png). +Epoch [10/10], Loss: 0.0594, Accuracy: 0.8644, validate loss: 0.1047, validate accuracy: 0.7055 +KNN Accuracy: 0.4992 + +As can see, the model start overfitting in about epoch 7. The data tend to separate to two parts but finally become a squiggle. However, the two classes are still evenly mixed together and has no sign to seperate apart. I tried tons of different hyperparameters (different combinations of learing rate, margin and epochs), different model (tried many different way to construct embedding network), and different loss function (triplet loss, contrastive loss), but they all perform similarly. I also did normalize the data, and use hard sample mining, semi-hard sample mining, but they are all not work well. However, I have done everything I could to improve the model, but with little success, so this is my final version. + +### Prediction +```python +predict_image(image_path) +``` +This function takes a path of image as the input and it will return a predict value. +Here is the example usage: +```python +image_path = "D:/Study/MLDataSet/AD_NC/test/AD/388206_78.jpeg" +predicted_label = predict_image(image_path) +display_image(image_path) +print(f"Predicted label: {predicted_label}, Ture label: AD") +``` + +The return is: + +Predicted label: ad, Ture label: ad + +![Input image](Images/input_image.png). + +## Dependencies +- python 3.11.5 +- pytorch 2.1.0 py3.11_cuda11.8_cudnn8_0 pytorch +- torchvision 0.16.0 +- matplotlib 3.7.2 +- PIL 10.0.1 +- sklearn 1.2.2 ## Reference -https://medium.com/swlh/one-shot-learning-with-siamese-network-1c7404c35fda +Khandelwal, R. (2021, January 28). One Shot Learning with Siamese Network. The Startup. Medium. Retrieved from https://medium.com/swlh/one-shot-learning-with-siamese-network-1c7404c35fda From a33c53ea06d9230d2a8f14713364ac26c89ff72b Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 04:29:51 +1000 Subject: [PATCH 35/36] Update README.md --- recognition/s4627382_SiameseNetwork/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index ab984d980..817dd7c90 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -1,4 +1,5 @@ # KNN classifier based on siamese network embedding +Liang Kaige s4627382 ## Introduction The purpose of this project is to construct a [Siamese network](#siamese-network) and use its embedding to train a [knn classifier](#k-nearest-neighbour) to classify the [Alzheimer's Disease Neuroimaging Initiative (ADNI)](#adni-dataset) brain dataset. From 9da710ac1d711c4973076580e1a38fd51afa6b6a Mon Sep 17 00:00:00 2001 From: CMWL4501 <112795978+CMWL4501@users.noreply.github.com> Date: Thu, 19 Oct 2023 04:31:26 +1000 Subject: [PATCH 36/36] Update README.md --- recognition/s4627382_SiameseNetwork/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4627382_SiameseNetwork/README.md b/recognition/s4627382_SiameseNetwork/README.md index 817dd7c90..f1eaea9af 100644 --- a/recognition/s4627382_SiameseNetwork/README.md +++ b/recognition/s4627382_SiameseNetwork/README.md @@ -1,7 +1,7 @@ # KNN classifier based on siamese network embedding Liang Kaige s4627382 ## Introduction -The purpose of this project is to construct a [Siamese network](#siamese-network) and use its embedding to train a [knn classifier](#k-nearest-neighbour) to classify the [Alzheimer's Disease Neuroimaging Initiative (ADNI)](#adni-dataset) brain dataset. +The purpose of this project is to construct a [Siamese network](#siamese-network) and use its embedding to train a [knn classifier](#k-nearest-neighbour-classifier) to classify the [Alzheimer's Disease Neuroimaging Initiative (ADNI)](#adni-dataset) brain dataset. ### ADNI Dataset The ADNI dataset that use in here comprises 30,520 MRI brain slice in total. Of these, 14,860 images are associated with Alzheimer’s disease (AD), while 15,660 images correspond to cognitively normal (NC) conditions.

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