From 1f52e3993867095a2266fad6ed44da25406d2e04 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 11:33:49 +1000 Subject: [PATCH 01/37] set up & model --- .gitignore | 4 ++ README.md | 18 +---- load.py | 135 +++++++++++++++++++++++++++++++++++ model.py | 206 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 346 insertions(+), 17 deletions(-) create mode 100644 .gitignore create mode 100644 load.py create mode 100644 model.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..c81a2c99a --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ + +HipMRI_study_complete_release_v1/.DS_Store +.DS_Store +/HipMRI_study_complete_release_v1 diff --git a/README.md b/README.md index 3a10f6515..5a1a60f58 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,3 @@ -# Pattern Analysis -Pattern Analysis of various datasets by COMP3710 students in 2024 at the University of Queensland. +# Generative VQVAE model for MR images of the male pelvis -We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. -This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. - -The library includes the following implemented in Tensorflow: -* fractals -* recognition problems - -In the recognition folder, you will find many recognition problems solved including: -* segmentation -* classification -* graph neural networks -* StyleGAN -* Stable diffusion -* transformers -etc. diff --git a/load.py b/load.py new file mode 100644 index 000000000..fa15e0cd6 --- /dev/null +++ b/load.py @@ -0,0 +1,135 @@ +import numpy as np +import nibabel as nib +from tqdm import tqdm + +def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: + channels = np.unique(arr) + res = np.zeros(arr.shape + (len(channels),), dtype=dtype) + for c in channels: + c = int(c) + res[..., c:c+1][arr == c] = 1 + return res + +def load_data_2D(imageNames, normImage=False, categorical=False, dtype=np.float32, + getAffines=False, early_stop=False): + ''' + Load medical image data from names, cases list provided into a list for each. + + This function pre-allocates 4D arrays for conv2d to avoid excessive memory usage. + + normImage: bool (normalise the image 0.0-1.0) + early_stop: Stop loading pre-maturely, leaves arrays mostly empty, for quick loading and testing scripts. + ''' + affines = [] + + # get fixed size + num = len(imageNames) + first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged') + if len(first_case.shape) == 3: + first_case = first_case[:,:,0] # sometimes extra dims, remove + + if categorical: + first_case = to_channels(first_case, dtype=dtype) + rows, cols, channels = first_case.shape + images = np.zeros((num, rows, cols, channels), dtype=dtype) + else: + rows, cols = first_case.shape + images = np.zeros((num, rows, cols), dtype=dtype) + + for i, inName in enumerate(tqdm(imageNames)): + niftiImage = nib.load(inName) + inImage = niftiImage.get_fdata(caching='unchanged') # read disk only + affine = niftiImage.affine + + if len(inImage.shape) == 3: + inImage = inImage[:,:,0] # sometimes extra dims in HipMRI_study data + + inImage = inImage.astype(dtype) + + if normImage: + inImage = (inImage - inImage.mean()) / inImage.std() + + if categorical: + inImage = utils.to_channels(inImage, dtype=dtype) + images[i,:,:,:] = inImage + else: + images[i,:,:] = inImage + + affines.append(affine) + + if i > 20 and early_stop: + break + + if getAffines: + return images, affines + else: + return images + +def load_data_3D(imageNames, normImage=False, categorical=False, dtype=np.float32, + getAffines=False, orient=False, early_stop=False): + ''' + Load medical image data from names, cases list provided into a list for each. + This function pre-allocates 5D arrays for conv3d to avoid excessive memory usage. + + normImage: bool (normalise the image 0.0-1.0) + orient: Apply orientation and resample image? Good for images with large slice thickness or anisotropic resolution + dtype: Type of the data. If dtype=np.uint8, it is assumed that the data is labels + early_stop: Stop loading pre-maturely? Leaves arrays mostly empty, for quick loading and testing scripts. + ''' + affines = [] + interp = 'linear' + + if dtype == np.uint8: # assume labels + interp = 'nearest' + + # get fixed size + num = len(imageNames) + niftiImage = nib.load(imageNames[0]) + + if orient: + niftiImage = im.applyOrientation(niftiImage, interpolation=interp, scale=1) + + first_case = niftiImage.get_fdata(caching='unchanged') + if len(first_case.shape) == 4: + first_case = first_case[:,:,:,0] # sometimes extra dims, remove + + if categorical: + first_case = to_channels(first_case, dtype=dtype) + rows, cols, depth, channels = first_case.shape + images = np.zeros((num, rows, cols, depth, channels), dtype=dtype) + else: + rows, cols, depth = first_case.shape + images = np.zeros((num, rows, cols, depth), dtype=dtype) + + for i, inName in enumerate(tqdm(imageNames)): + niftiImage = nib.load(inName) + if orient: + niftiImage = im.applyOrientation(niftiImage, interpolation=interp, scale=1) + + inImage = niftiImage.get_fdata(caching='unchanged') # read disk only + affine = niftiImage.affine + + if len(inImage.shape) == 4: + inImage = inImage[:,:,:,0] # sometimes extra dims in HipMRI_study data + inImage = inImage[:,:,:depth] # clip slices + + inImage = inImage.astype(dtype) + + if normImage: + inImage = (inImage - inImage.mean()) / inImage.std() + + if categorical: + inImage = utils.to_channels(inImage, dtype=dtype) + images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2],:inImage.shape[3]] = inImage # with pad + else: + images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2]] = inImage # with pad + + affines.append(affine) + + if i > 20 and early_stop: + break + + if getAffines: + return images, affines + else: + return images \ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 000000000..69c54ea28 --- /dev/null +++ b/model.py @@ -0,0 +1,206 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +import pytorch_lightning as pl +from torchvision import transforms +import numpy as np +from pytorch_msssim import SSIM + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.bn1 = nn.BatchNorm2d(out_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + self.relu = nn.ReLU() + + if in_channels != out_channels: + self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=1) + else: + self.projection = None + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.projection is not None: + identity = self.projection(x) + + out += identity + out = self.relu(out) + return out + +class VectorQuantizer(nn.Module): + def __init__(self, num_embeddings, embedding_dim, commitment_cost=0.25): + super().__init__() + self.num_embeddings = num_embeddings + self.embedding_dim = embedding_dim + self.commitment_cost = commitment_cost + + self.embedding = nn.Embedding(num_embeddings, embedding_dim) + self.embedding.weight.data.uniform_(-1/num_embeddings, 1/num_embeddings) + + def forward(self, inputs): + # Convert inputs from BCHW -> BHWC + inputs = inputs.permute(0, 2, 3, 1).contiguous() + input_shape = inputs.shape + + # Flatten input + flat_input = inputs.view(-1, self.embedding_dim) + + # Calculate distances + distances = (torch.sum(flat_input**2, dim=1, keepdim=True) + + torch.sum(self.embedding.weight**2, dim=1) + - 2 * torch.matmul(flat_input, self.embedding.weight.t())) + + # Encoding + encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1) + encodings = torch.zeros(encoding_indices.shape[0], self.num_embeddings, device=inputs.device) + encodings.scatter_(1, encoding_indices, 1) + + # Quantize and unflatten + quantized = torch.matmul(encodings, self.embedding.weight).view(input_shape) + + # Loss + e_latent_loss = F.mse_loss(quantized.detach(), inputs) + q_latent_loss = F.mse_loss(quantized, inputs.detach()) + loss = q_latent_loss + self.commitment_cost * e_latent_loss + + quantized = inputs + (quantized - inputs).detach() + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + + # Convert quantized from BHWC -> BCHW + return loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encodings + +class Encoder(nn.Module): + def __init__(self, in_channels, hidden_dims=[32, 64, 128, 256]): + super().__init__() + modules = [] + + for h_dim in hidden_dims: + modules.append( + ResidualBlock(in_channels, h_dim) + ) + modules.append(nn.MaxPool2d(kernel_size=2)) + in_channels = h_dim + + self.encoder = nn.Sequential(*modules) + + def forward(self, x): + return self.encoder(x) + +class Decoder(nn.Module): + def __init__(self, out_channels, hidden_dims=[256, 128, 64, 32]): + super().__init__() + modules = [] + in_channels = hidden_dims[0] + + for h_dim in hidden_dims[1:]: + modules.append(nn.Upsample(scale_factor=2)) + modules.append( + ResidualBlock(in_channels, h_dim) + ) + in_channels = h_dim + + modules.append( + nn.Conv2d(hidden_dims[-1], out_channels, kernel_size=3, padding=1) + ) + + self.decoder = nn.Sequential(*modules) + + def forward(self, x): + return self.decoder(x) + +class VQVAE(pl.LightningModule): + def __init__( + self, + in_channels=1, + hidden_dims=[32, 64, 128, 256], + num_embeddings=512, + embedding_dim=256, + commitment_cost=0.25, + learning_rate=1e-4 + ): + super().__init__() + + self.encoder = Encoder(in_channels, hidden_dims) + self.vq = VectorQuantizer(num_embeddings, embedding_dim, commitment_cost) + self.decoder = Decoder(in_channels, hidden_dims[::-1]) + self.learning_rate = learning_rate + + self.ssim_module = SSIM(data_range=1.0, size_average=True, channel=1) + + def forward(self, x): + encoded = self.encoder(x) + vq_loss, quantized, perplexity, _ = self.vq(encoded) + reconstructed = self.decoder(quantized) + return reconstructed, vq_loss, perplexity + + def training_step(self, batch, batch_idx): + x = batch + reconstructed, vq_loss, perplexity = self(x) + + # Reconstruction loss (MSE) + recon_loss = F.mse_loss(reconstructed, x) + + # SSIM loss + ssim_loss = 1 - self.ssim_module(reconstructed, x) + + # Total loss + total_loss = recon_loss + vq_loss + ssim_loss + + # Calculate SSIM metric + ssim_value = 1 - ssim_loss.item() + + self.log('train_loss', total_loss) + self.log('train_recon_loss', recon_loss) + self.log('train_vq_loss', vq_loss) + self.log('train_perplexity', perplexity) + self.log('train_ssim', ssim_value) + + return total_loss + + def configure_optimizers(self): + optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) + return optimizer + +# Training setup +def train_vqvae(data_path, batch_size=32, num_epochs=100): + # Data preprocessing + transform = transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5], std=[0.5]) + ]) + + # Initialize model and trainer + model = VQVAE() + trainer = pl.Trainer( + max_epochs=num_epochs, + accelerator='gpu' if torch.cuda.is_available() else 'cpu', + callbacks=[ + pl.callbacks.ModelCheckpoint( + monitor='train_ssim', + mode='max', + filename='vqvae-{epoch:02d}-{train_ssim:.2f}', + save_top_k=3 + ) + ] + ) + + # Train model + trainer.fit(model, train_dataloader) + + return model + +model = train_vqvae("...") \ No newline at end of file From dfaca846213a50c21fdcec8a2d26bf08d24a50e4 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 13:01:45 +1000 Subject: [PATCH 02/37] extract .gz --- README.md | 2 +- load.py | 19 +++++++++++++++++-- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5a1a60f58..7cbb61d1b 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,3 @@ -# Generative VQVAE model for MR images of the male pelvis +# Generative VQVAE model for HipMRI diff --git a/load.py b/load.py index fa15e0cd6..f616a4432 100644 --- a/load.py +++ b/load.py @@ -1,6 +1,8 @@ import numpy as np import nibabel as nib from tqdm import tqdm +import os +# import utils def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: channels = np.unique(arr) @@ -119,7 +121,7 @@ def load_data_3D(imageNames, normImage=False, categorical=False, dtype=np.float3 inImage = (inImage - inImage.mean()) / inImage.std() if categorical: - inImage = utils.to_channels(inImage, dtype=dtype) + inImage = to_channels(inImage, dtype=dtype) images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2],:inImage.shape[3]] = inImage # with pad else: images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2]] = inImage # with pad @@ -132,4 +134,17 @@ def load_data_3D(imageNames, normImage=False, categorical=False, dtype=np.float3 if getAffines: return images, affines else: - return images \ No newline at end of file + return images + +# Load semantic label files +semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' +semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] +semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) + +# Load semantic MR files +semantic_mrs_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_MRs_anon' +semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] +semantic_mrs, semantic_mrs_affines = load_data_3D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) + +print(f"Loaded {len(semantic_label_files)} semantic label files") +print(f"Loaded {len(semantic_mr_files)} semantic MR files") From 1211bfb894b56402e556964419be27eb75408fef Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 13:18:47 +1000 Subject: [PATCH 03/37] changed topic as preprocessing wasn't successful :/ --- .gitignore | 1 + README.md | 4 +++- load.py | 59 ++++++++++++++++++++++++++++++++++++++++++++---------- 3 files changed, 52 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index c81a2c99a..7bfdaf171 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ HipMRI_study_complete_release_v1/.DS_Store .DS_Store /HipMRI_study_complete_release_v1 +/AD_NC diff --git a/README.md b/README.md index 7cbb61d1b..e992b5af3 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ -# Generative VQVAE model for HipMRI +# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using GFNet + + diff --git a/load.py b/load.py index f616a4432..90f366a13 100644 --- a/load.py +++ b/load.py @@ -2,7 +2,8 @@ import nibabel as nib from tqdm import tqdm import os -# import utils +import matplotlib.pyplot as plt +from torchvision.utils import make_grid, save_image def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: channels = np.unique(arr) @@ -52,7 +53,7 @@ def load_data_2D(imageNames, normImage=False, categorical=False, dtype=np.float3 inImage = (inImage - inImage.mean()) / inImage.std() if categorical: - inImage = utils.to_channels(inImage, dtype=dtype) + inImage = to_channels(inImage, dtype=dtype) images[i,:,:,:] = inImage else: images[i,:,:] = inImage @@ -136,15 +137,51 @@ def load_data_3D(imageNames, normImage=False, categorical=False, dtype=np.float3 else: return images -# Load semantic label files -semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' -semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] -semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) +def get_dataloaders(semantic_labels_dir, semantic_mrs_dir): + """Load the dataset and returns the data loaders.""" + semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] + semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) + + semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] + semantic_mrs, semantic_mrs_affines = load_data_2D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) + + return semantic_labels, semantic_mrs, semantic_labels_affines, semantic_mrs_affines + +def show_batch(semantic_labels, semantic_mrs, filename): + """Plot images grid of single batch.""" + # Combine label and MR images + combined = np.concatenate([semantic_labels, semantic_mrs], axis=-1) + img = make_grid(torch.from_numpy(combined)) + show_images(img, filename) + +def show_images(img, filename): + """Plot images grid of single batch.""" + img = img.cpu().numpy() + img = np.transpose(img, (1, 2, 0)) + plt.imshow(img) + plt.savefig(filename) + plt.clf() -# Load semantic MR files +# # Load semantic label files +# semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' +# semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] +# semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) + +# # Load semantic MR files +# semantic_mrs_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_MRs_anon' +# semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] +# semantic_mrs, semantic_mrs_affines = load_data_3D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) + +# print(f"Loaded {len(semantic_label_files)} semantic label files") +# print(f"Loaded {len(semantic_mr_files)} semantic MR files") + +# Example usage +semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' semantic_mrs_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_MRs_anon' -semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] -semantic_mrs, semantic_mrs_affines = load_data_3D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) -print(f"Loaded {len(semantic_label_files)} semantic label files") -print(f"Loaded {len(semantic_mr_files)} semantic MR files") +semantic_labels, semantic_mrs, semantic_labels_affines, semantic_mrs_affines = get_dataloaders(semantic_labels_dir, semantic_mrs_dir) + +print(f"Loaded {semantic_labels.shape[0]} semantic label files") +print(f"Loaded {semantic_mrs.shape[0]} semantic MR files") + +show_batch(semantic_labels[0], semantic_mrs[0], 'batch_example.png') From 204f41abf1693cfaefcd34e24a10bc3dbbbe8e87 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 13:44:42 +1000 Subject: [PATCH 04/37] Initial structure --- .gitignore | 1 + README.md | 16 +++- load.py | 187 -------------------------------------- model.py | 206 ------------------------------------------ recognition/README.md | 5 + 5 files changed, 21 insertions(+), 394 deletions(-) delete mode 100644 load.py delete mode 100644 model.py create mode 100644 recognition/README.md diff --git a/.gitignore b/.gitignore index 7bfdaf171..b2450a247 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ HipMRI_study_complete_release_v1/.DS_Store .DS_Store /HipMRI_study_complete_release_v1 /AD_NC +/recognition/AD_NC diff --git a/README.md b/README.md index e992b5af3..7355a2b9f 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,19 @@ -# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using GFNet +# Pattern Analysis +Pattern Analysis of various datasets by COMP3710 students in 2024 at the University of Queensland. +We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. +This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. +The library includes the following implemented in Tensorflow: +* fractals +* recognition problems +In the recognition folder, you will find many recognition problems solved including: +* segmentation +* classification +* graph neural networks +* StyleGAN +* Stable diffusion +* transformers +etc. diff --git a/load.py b/load.py deleted file mode 100644 index 90f366a13..000000000 --- a/load.py +++ /dev/null @@ -1,187 +0,0 @@ -import numpy as np -import nibabel as nib -from tqdm import tqdm -import os -import matplotlib.pyplot as plt -from torchvision.utils import make_grid, save_image - -def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray: - channels = np.unique(arr) - res = np.zeros(arr.shape + (len(channels),), dtype=dtype) - for c in channels: - c = int(c) - res[..., c:c+1][arr == c] = 1 - return res - -def load_data_2D(imageNames, normImage=False, categorical=False, dtype=np.float32, - getAffines=False, early_stop=False): - ''' - Load medical image data from names, cases list provided into a list for each. - - This function pre-allocates 4D arrays for conv2d to avoid excessive memory usage. - - normImage: bool (normalise the image 0.0-1.0) - early_stop: Stop loading pre-maturely, leaves arrays mostly empty, for quick loading and testing scripts. - ''' - affines = [] - - # get fixed size - num = len(imageNames) - first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged') - if len(first_case.shape) == 3: - first_case = first_case[:,:,0] # sometimes extra dims, remove - - if categorical: - first_case = to_channels(first_case, dtype=dtype) - rows, cols, channels = first_case.shape - images = np.zeros((num, rows, cols, channels), dtype=dtype) - else: - rows, cols = first_case.shape - images = np.zeros((num, rows, cols), dtype=dtype) - - for i, inName in enumerate(tqdm(imageNames)): - niftiImage = nib.load(inName) - inImage = niftiImage.get_fdata(caching='unchanged') # read disk only - affine = niftiImage.affine - - if len(inImage.shape) == 3: - inImage = inImage[:,:,0] # sometimes extra dims in HipMRI_study data - - inImage = inImage.astype(dtype) - - if normImage: - inImage = (inImage - inImage.mean()) / inImage.std() - - if categorical: - inImage = to_channels(inImage, dtype=dtype) - images[i,:,:,:] = inImage - else: - images[i,:,:] = inImage - - affines.append(affine) - - if i > 20 and early_stop: - break - - if getAffines: - return images, affines - else: - return images - -def load_data_3D(imageNames, normImage=False, categorical=False, dtype=np.float32, - getAffines=False, orient=False, early_stop=False): - ''' - Load medical image data from names, cases list provided into a list for each. - This function pre-allocates 5D arrays for conv3d to avoid excessive memory usage. - - normImage: bool (normalise the image 0.0-1.0) - orient: Apply orientation and resample image? Good for images with large slice thickness or anisotropic resolution - dtype: Type of the data. If dtype=np.uint8, it is assumed that the data is labels - early_stop: Stop loading pre-maturely? Leaves arrays mostly empty, for quick loading and testing scripts. - ''' - affines = [] - interp = 'linear' - - if dtype == np.uint8: # assume labels - interp = 'nearest' - - # get fixed size - num = len(imageNames) - niftiImage = nib.load(imageNames[0]) - - if orient: - niftiImage = im.applyOrientation(niftiImage, interpolation=interp, scale=1) - - first_case = niftiImage.get_fdata(caching='unchanged') - if len(first_case.shape) == 4: - first_case = first_case[:,:,:,0] # sometimes extra dims, remove - - if categorical: - first_case = to_channels(first_case, dtype=dtype) - rows, cols, depth, channels = first_case.shape - images = np.zeros((num, rows, cols, depth, channels), dtype=dtype) - else: - rows, cols, depth = first_case.shape - images = np.zeros((num, rows, cols, depth), dtype=dtype) - - for i, inName in enumerate(tqdm(imageNames)): - niftiImage = nib.load(inName) - if orient: - niftiImage = im.applyOrientation(niftiImage, interpolation=interp, scale=1) - - inImage = niftiImage.get_fdata(caching='unchanged') # read disk only - affine = niftiImage.affine - - if len(inImage.shape) == 4: - inImage = inImage[:,:,:,0] # sometimes extra dims in HipMRI_study data - inImage = inImage[:,:,:depth] # clip slices - - inImage = inImage.astype(dtype) - - if normImage: - inImage = (inImage - inImage.mean()) / inImage.std() - - if categorical: - inImage = to_channels(inImage, dtype=dtype) - images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2],:inImage.shape[3]] = inImage # with pad - else: - images[i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2]] = inImage # with pad - - affines.append(affine) - - if i > 20 and early_stop: - break - - if getAffines: - return images, affines - else: - return images - -def get_dataloaders(semantic_labels_dir, semantic_mrs_dir): - """Load the dataset and returns the data loaders.""" - semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] - semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) - - semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] - semantic_mrs, semantic_mrs_affines = load_data_2D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) - - return semantic_labels, semantic_mrs, semantic_labels_affines, semantic_mrs_affines - -def show_batch(semantic_labels, semantic_mrs, filename): - """Plot images grid of single batch.""" - # Combine label and MR images - combined = np.concatenate([semantic_labels, semantic_mrs], axis=-1) - img = make_grid(torch.from_numpy(combined)) - show_images(img, filename) - -def show_images(img, filename): - """Plot images grid of single batch.""" - img = img.cpu().numpy() - img = np.transpose(img, (1, 2, 0)) - plt.imshow(img) - plt.savefig(filename) - plt.clf() - -# # Load semantic label files -# semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' -# semantic_label_files = [os.path.join(semantic_labels_dir, f) for f in os.listdir(semantic_labels_dir) if f.endswith('.gz')] -# semantic_labels, semantic_labels_affines = load_data_3D(semantic_label_files, normImage=False, categorical=True, dtype=np.uint8, getAffines=True) - -# # Load semantic MR files -# semantic_mrs_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_MRs_anon' -# semantic_mr_files = [os.path.join(semantic_mrs_dir, f) for f in os.listdir(semantic_mrs_dir) if f.endswith('.gz')] -# semantic_mrs, semantic_mrs_affines = load_data_3D(semantic_mr_files, normImage=False, categorical=False, dtype=np.float32, getAffines=True) - -# print(f"Loaded {len(semantic_label_files)} semantic label files") -# print(f"Loaded {len(semantic_mr_files)} semantic MR files") - -# Example usage -semantic_labels_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_labels_anon' -semantic_mrs_dir = '/Users/ella/Documents/UQ/BM_BCs/Y4S2/COMP3710/report/PatternAnalysis-2024/HipMRI_study_complete_release_v1/semantic_MRs_anon' - -semantic_labels, semantic_mrs, semantic_labels_affines, semantic_mrs_affines = get_dataloaders(semantic_labels_dir, semantic_mrs_dir) - -print(f"Loaded {semantic_labels.shape[0]} semantic label files") -print(f"Loaded {semantic_mrs.shape[0]} semantic MR files") - -show_batch(semantic_labels[0], semantic_mrs[0], 'batch_example.png') diff --git a/model.py b/model.py deleted file mode 100644 index 69c54ea28..000000000 --- a/model.py +++ /dev/null @@ -1,206 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader -import pytorch_lightning as pl -from torchvision import transforms -import numpy as np -from pytorch_msssim import SSIM - -class ResidualBlock(nn.Module): - def __init__(self, in_channels, out_channels): - super().__init__() - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) - self.bn1 = nn.BatchNorm2d(out_channels) - self.bn2 = nn.BatchNorm2d(out_channels) - self.relu = nn.ReLU() - - if in_channels != out_channels: - self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=1) - else: - self.projection = None - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.projection is not None: - identity = self.projection(x) - - out += identity - out = self.relu(out) - return out - -class VectorQuantizer(nn.Module): - def __init__(self, num_embeddings, embedding_dim, commitment_cost=0.25): - super().__init__() - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - self.commitment_cost = commitment_cost - - self.embedding = nn.Embedding(num_embeddings, embedding_dim) - self.embedding.weight.data.uniform_(-1/num_embeddings, 1/num_embeddings) - - def forward(self, inputs): - # Convert inputs from BCHW -> BHWC - inputs = inputs.permute(0, 2, 3, 1).contiguous() - input_shape = inputs.shape - - # Flatten input - flat_input = inputs.view(-1, self.embedding_dim) - - # Calculate distances - distances = (torch.sum(flat_input**2, dim=1, keepdim=True) - + torch.sum(self.embedding.weight**2, dim=1) - - 2 * torch.matmul(flat_input, self.embedding.weight.t())) - - # Encoding - encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1) - encodings = torch.zeros(encoding_indices.shape[0], self.num_embeddings, device=inputs.device) - encodings.scatter_(1, encoding_indices, 1) - - # Quantize and unflatten - quantized = torch.matmul(encodings, self.embedding.weight).view(input_shape) - - # Loss - e_latent_loss = F.mse_loss(quantized.detach(), inputs) - q_latent_loss = F.mse_loss(quantized, inputs.detach()) - loss = q_latent_loss + self.commitment_cost * e_latent_loss - - quantized = inputs + (quantized - inputs).detach() - avg_probs = torch.mean(encodings, dim=0) - perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) - - # Convert quantized from BHWC -> BCHW - return loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encodings - -class Encoder(nn.Module): - def __init__(self, in_channels, hidden_dims=[32, 64, 128, 256]): - super().__init__() - modules = [] - - for h_dim in hidden_dims: - modules.append( - ResidualBlock(in_channels, h_dim) - ) - modules.append(nn.MaxPool2d(kernel_size=2)) - in_channels = h_dim - - self.encoder = nn.Sequential(*modules) - - def forward(self, x): - return self.encoder(x) - -class Decoder(nn.Module): - def __init__(self, out_channels, hidden_dims=[256, 128, 64, 32]): - super().__init__() - modules = [] - in_channels = hidden_dims[0] - - for h_dim in hidden_dims[1:]: - modules.append(nn.Upsample(scale_factor=2)) - modules.append( - ResidualBlock(in_channels, h_dim) - ) - in_channels = h_dim - - modules.append( - nn.Conv2d(hidden_dims[-1], out_channels, kernel_size=3, padding=1) - ) - - self.decoder = nn.Sequential(*modules) - - def forward(self, x): - return self.decoder(x) - -class VQVAE(pl.LightningModule): - def __init__( - self, - in_channels=1, - hidden_dims=[32, 64, 128, 256], - num_embeddings=512, - embedding_dim=256, - commitment_cost=0.25, - learning_rate=1e-4 - ): - super().__init__() - - self.encoder = Encoder(in_channels, hidden_dims) - self.vq = VectorQuantizer(num_embeddings, embedding_dim, commitment_cost) - self.decoder = Decoder(in_channels, hidden_dims[::-1]) - self.learning_rate = learning_rate - - self.ssim_module = SSIM(data_range=1.0, size_average=True, channel=1) - - def forward(self, x): - encoded = self.encoder(x) - vq_loss, quantized, perplexity, _ = self.vq(encoded) - reconstructed = self.decoder(quantized) - return reconstructed, vq_loss, perplexity - - def training_step(self, batch, batch_idx): - x = batch - reconstructed, vq_loss, perplexity = self(x) - - # Reconstruction loss (MSE) - recon_loss = F.mse_loss(reconstructed, x) - - # SSIM loss - ssim_loss = 1 - self.ssim_module(reconstructed, x) - - # Total loss - total_loss = recon_loss + vq_loss + ssim_loss - - # Calculate SSIM metric - ssim_value = 1 - ssim_loss.item() - - self.log('train_loss', total_loss) - self.log('train_recon_loss', recon_loss) - self.log('train_vq_loss', vq_loss) - self.log('train_perplexity', perplexity) - self.log('train_ssim', ssim_value) - - return total_loss - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) - return optimizer - -# Training setup -def train_vqvae(data_path, batch_size=32, num_epochs=100): - # Data preprocessing - transform = transforms.Compose([ - transforms.Grayscale(), - transforms.Resize((256, 256)), - transforms.ToTensor(), - transforms.Normalize(mean=[0.5], std=[0.5]) - ]) - - # Initialize model and trainer - model = VQVAE() - trainer = pl.Trainer( - max_epochs=num_epochs, - accelerator='gpu' if torch.cuda.is_available() else 'cpu', - callbacks=[ - pl.callbacks.ModelCheckpoint( - monitor='train_ssim', - mode='max', - filename='vqvae-{epoch:02d}-{train_ssim:.2f}', - save_top_k=3 - ) - ] - ) - - # Train model - trainer.fit(model, train_dataloader) - - return model - -model = train_vqvae("...") \ No newline at end of file diff --git a/recognition/README.md b/recognition/README.md new file mode 100644 index 000000000..e992b5af3 --- /dev/null +++ b/recognition/README.md @@ -0,0 +1,5 @@ +# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using GFNet + + + + From 6815d06844af43e3f195e21b8938d7da7665e97d Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 13:59:10 +1000 Subject: [PATCH 05/37] feat: templates --- recognition/dataset.py | 4 ++++ recognition/modules.py | 4 ++++ recognition/predict.py | 6 ++++++ recognition/train.py | 6 ++++++ 4 files changed, 20 insertions(+) create mode 100644 recognition/dataset.py create mode 100644 recognition/modules.py create mode 100644 recognition/predict.py create mode 100644 recognition/train.py diff --git a/recognition/dataset.py b/recognition/dataset.py new file mode 100644 index 000000000..406efc3ca --- /dev/null +++ b/recognition/dataset.py @@ -0,0 +1,4 @@ +""" +Contains the data loaders for loading and preprocessing the ADNI brain dataset +This module will help prepare training, validation and testing data to be used +""" \ No newline at end of file diff --git a/recognition/modules.py b/recognition/modules.py new file mode 100644 index 000000000..8b5eeb57a --- /dev/null +++ b/recognition/modules.py @@ -0,0 +1,4 @@ +""" +Contains the source code for the components of GFNet classifying the Alzheimer’s disease (normal and AD) of the ADNI brain data +Each component is implementated as a class or a function. +""" \ No newline at end of file diff --git a/recognition/predict.py b/recognition/predict.py new file mode 100644 index 000000000..026365de2 --- /dev/null +++ b/recognition/predict.py @@ -0,0 +1,6 @@ +""" +This module is used to show example usage the trained GFNet model by using the model to predict +on a testing set from the ADNI brain dataset + +Evaluation metrics will be printed and evaluation figures will be saved to the current folder. +""" \ No newline at end of file diff --git a/recognition/train.py b/recognition/train.py new file mode 100644 index 000000000..976f6e665 --- /dev/null +++ b/recognition/train.py @@ -0,0 +1,6 @@ +""" +containing the source code for training, validating, testing and saving the GFnet. + +The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. +the losses and metrics during training are plotted +""" \ No newline at end of file From 65ea7502a239b569c18c12fff69e2416689decf8 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 14:03:01 +1000 Subject: [PATCH 06/37] feat: GFN copied from git https://github.com/raoyongming/GFNet --- recognition/modules.py | 68 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 67 insertions(+), 1 deletion(-) diff --git a/recognition/modules.py b/recognition/modules.py index 8b5eeb57a..e610d48a1 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -1,4 +1,70 @@ """ Contains the source code for the components of GFNet classifying the Alzheimer’s disease (normal and AD) of the ADNI brain data Each component is implementated as a class or a function. -""" \ No newline at end of file +""" + +import torch +import torch.nn as nn +import torch.fft + +class GlobalFilter(nn.Module): + def __init__(self, dim, h=14, w=8): + super().__init__() + self.complex_weight = nn.Parameter(torch.randn(h, w, dim, 2, dtype=torch.float32) * 0.02) + + def forward(self, x): + B, H, W, C = x.shape + x = torch.fft.rfft2(x, dim=(1, 2), norm='ortho') + weight = torch.view_as_complex(self.complex_weight) + x = x * weight + x = torch.fft.irfft2(x, s=(H, W), dim=(1, 2), norm='ortho') + return x + +class GlobalFilterNetwork(nn.Module): + def __init__(self, in_channels=1, num_classes=2): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # Global Filter layers + self.global_filter1 = GlobalFilter(dim=64) + self.global_filter2 = GlobalFilter(dim=128) + + # Additional convolution layers + self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) + self.bn2 = nn.BatchNorm2d(128) + + # Final layers + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(128, num_classes) + + def forward(self, x): + # Initial convolution block + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + # Reshape for GlobalFilter + x = x.permute(0, 2, 3, 1) # [B, C, H, W] -> [B, H, W, C] + x = self.global_filter1(x) + x = x.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W] + + # Second conv block + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + + # Second GlobalFilter + x = x.permute(0, 2, 3, 1) + x = self.global_filter2(x) + x = x.permute(0, 3, 1, 2) + + # Final layers + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x \ No newline at end of file From b95ce61e9fee59a97f993dd64b3162663dff1e2c Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 14:10:02 +1000 Subject: [PATCH 07/37] feat: Dataset copied from Jupyter notebook --- recognition/dataset.py | 108 ++++++++++++++++++++++++++++++++++++++++- recognition/modules.py | 8 +++ 2 files changed, 115 insertions(+), 1 deletion(-) diff --git a/recognition/dataset.py b/recognition/dataset.py index 406efc3ca..585f4a110 100644 --- a/recognition/dataset.py +++ b/recognition/dataset.py @@ -1,4 +1,110 @@ """ Contains the data loaders for loading and preprocessing the ADNI brain dataset This module will help prepare training, validation and testing data to be used -""" \ No newline at end of file +""" + +import torch +from torch.utils.data import DataLoader, Dataset +import os +from pathlib import Path +import sys +from PIL import Image +import torchvision.transforms as transforms +import random +import math +import copy + +# ADNI data path +ADNI_PATH = Path('./AD_NC') + +# Training image transforms +TRAIN_TRANSFORM = transforms.Compose([ + transforms.RandomCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), +]) + +# Testing image transforms +TEST_TRANSFORM = transforms.Compose([ + transforms.CenterCrop(224), + transforms.ToTensor(), +]) + +# Rest of the original dataset.py content remains the same +class ADNIDataset(Dataset): + """ + Custom Dataset class for the ADNI dataset. + """ + def __init__(self, root_path, train=True, transform=None): + self.path = Path(root_path, 'train' if train else 'test') + self.transform = transform + self.ad_files = os.listdir(Path(self.path, 'AD')) + self.nc_files = os.listdir(Path(self.path, 'NC')) + + def __len__(self): + return len(self.ad_files) + len(self.nc_files) + + def __getitem__(self, idx): + assert idx >= 0 and idx < self.__len__(), "Index out of range." + # Index in AD range + if idx < len(self.ad_files): + img_filename = self.ad_files[idx] + image = Image.open(Path(self.path, 'AD', img_filename)) + label = 1 + # Index in NC range + else: + img_filename = self.nc_files[idx-len(self.ad_files)] + image = Image.open(Path(self.path, 'NC', img_filename)) + label = 0 + # Apply transform if present + if self.transform: + image = self.transform(image) + + return image, label + +def split_train_val(dataset: ADNIDataset, val_proportion: float, keep_proportion: float): + """ + Function to split the training set into training and validation sets, with a certain proportion of images to be included in the validation set. + + Images to be split on a patient level (i.e. all images with the same patient ID to be included in the same set). + """ + # Get the patient ID's and the number of AD and NC patients + ad_patient_ids = set(filename.split('_')[0] for filename in dataset.ad_files) + num_ad_patients = len(ad_patient_ids) + nc_patient_ids = set(filename.split('_')[0] for filename in dataset.nc_files) + num_nc_patients = len(nc_patient_ids) + + # Keep a certain proportion of the data. Used to cut training time for debugging purposes + ad_patient_ids = random.sample(list(ad_patient_ids), math.floor(num_ad_patients*keep_proportion)) + num_ad_patients = len(ad_patient_ids) + nc_patient_ids = random.sample(list(nc_patient_ids), math.floor(num_nc_patients*keep_proportion)) + num_nc_patients = len(nc_patient_ids) + + # Generate a random sample of patient ID's for the validation set + ad_pids_val = random.sample(ad_patient_ids, math.floor(num_ad_patients*val_proportion)) + nc_pids_val = random.sample(nc_patient_ids, math.floor(num_nc_patients*val_proportion)) + + # Make the validation dataset a deep copy of the training dataset + val_dataset = copy.deepcopy(dataset) + val_dataset.transform = TEST_TRANSFORM + + # Update each dataset's files by patient level split above + dataset.ad_files = [file for file in dataset.ad_files if file.split('_')[0] not in ad_pids_val and file.split('_')[0] in ad_patient_ids] + val_dataset.ad_files = [file for file in val_dataset.ad_files if file.split('_')[0] in ad_pids_val] + dataset.nc_files = [file for file in dataset.nc_files if file.split('_')[0] not in nc_pids_val and file.split('_')[0] in nc_patient_ids] + val_dataset.nc_files = [file for file in val_dataset.nc_files if file.split('_')[0] in nc_pids_val] + + return dataset, val_dataset + + +def get_dataloader(batch_size, train: bool, val_proportion: float = 0.2, keep_proportion: float = 1.0): + """ + Returns data loader for either the training (plus validation) set, or the test set. + """ + train_dataset = ADNIDataset(root_path=ADNI_PATH, train=True, transform=TRAIN_TRANSFORM) + train_dataset, val_dataset = split_train_val(dataset=train_dataset, val_proportion=val_proportion, keep_proportion=keep_proportion) + loader = (DataLoader(train_dataset, batch_size=batch_size, shuffle=True), DataLoader(val_dataset, batch_size=batch_size, shuffle=True)) + if train == False: + dataset = ADNIDataset(root_path=ADNI_PATH, train=False, transform=TEST_TRANSFORM) + loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) + return loader \ No newline at end of file diff --git a/recognition/modules.py b/recognition/modules.py index e610d48a1..67e8d3e8c 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -41,6 +41,14 @@ def __init__(self, in_channels=1, num_classes=2): self.fc = nn.Linear(128, num_classes) def forward(self, x): + """ + - Pass the input through the initial convolutional blocks + - Reshape the feature maps to prepare for the global filtering layers + - Apply the two global filtering layers + - Pass the filtered features through the final classification layers (average pooling and fully connected) + - Return the final classification output + """ + # 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Stop training early if validation accuracy decreases for four epochs in a row + if down_consec >= 4: + stopping_epoch = epoch + 1 + break + if val_accs[-1] == max(val_accs): + # save best model + torch.save(model, "adni_vit.pt") + + print(f"Training & validation took {time.time()-strt:.3f} secs or {(time.time()-strt)/60:.2f} mins in total") + print("") + plot_metric(stopping_epoch, 'loss', train_losses, val_losses) + plot_metric(stopping_epoch, 'accuracy', train_accs, val_accs) + + +def run_testing(device, model, test_loader: DataLoader): + """ + Function to run testing on the trained model. + """ + test_correct = 0 + test_total = 0 + start = time.time() + with torch.no_grad(): + for images, labels in test_loader: + images = images.to(device) + labels = labels.to(device) + # Forward pass + outputs = model(images) + _, predicted = torch.max(outputs, 1) + test_total += labels.size(0) + test_correct += (predicted == labels).sum().item() + + print(f"Test accuracy {(test_correct/test_total)*100:.2f}%, time elapsed {time.time()-start:.3f} secs or {(time.time()-start)/60:.2f} mins in total") + + +def main(): + """ + Main execution function. + """ + # Device configuration + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + if not torch.cuda.is_available(): + print("Warning CUDA not found. Using CPU") + # Initialise hyperparameters + BATCH_SIZE = 64 + EPOCHS = 20 + LR = 1e-4 + # Initialise data loaders & model + train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) + test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) + # Use GlobalFilterNetwork instead of ViT + model = GlobalFilterNetwork() + model = model.to(device) + # Run training and testing + run_training(device, model, train_loader, val_loader, epochs=EPOCHS, lr=LR) + run_testing(device, model, test_loader) + +if __name__ == "__main__": + main() \ No newline at end of file From f83950802ecf81003730dd7617fc0981f352bb10 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 14:58:36 +1000 Subject: [PATCH 09/37] feat: move to ViT as I can't figure out the dimension for GFnet rip --- .../__pycache__/dataset.cpython-311.pyc | Bin 7868 -> 7868 bytes .../__pycache__/modules.cpython-311.pyc | Bin 5068 -> 3751 bytes recognition/modules.py | 106 ++++----- recognition/train.py | 213 +++++++----------- 4 files changed, 118 insertions(+), 201 deletions(-) diff --git a/recognition/__pycache__/dataset.cpython-311.pyc b/recognition/__pycache__/dataset.cpython-311.pyc index 1a1144a8d3ad5251d1f53279ecd51b288967d5cf..c1c77fb986c1b4f9d02301490c310edc2998a767 100644 GIT binary patch delta 19 ZcmdmEyT_JmIWI340}u#jZ{*q}2LLqn1knHh delta 19 ZcmdmEyT_JmIWI340}won-^jH|4gfig1(N^( diff --git a/recognition/__pycache__/modules.cpython-311.pyc b/recognition/__pycache__/modules.cpython-311.pyc index f5a27fb77d55e60cc14ebf2ac99b0adf6e701e61..83842ecfd5784dde8ad75d402c7414d92be8eb1a 100644 GIT binary patch literal 3751 zcmahMOKcm*b#}Q+t|-Z3E!j~l$5<$iC0ey;IdTj46GXo{a%0tnWw#+>Y}ee8wDfYR z%q$&Sp#dEXR4r6g2wcc90)*(nb!rz)Z@m=gp@$wxV+#`t_|l$yQ=lA*oci9Zc12oh zJ6gV(H#2YEn>X*L-*tDV3A8^I^+lE>Jh75@iIt*Ku`^;)?21R-(dc1V>jJ^@TDMbA@tg zx$IKc<9c~PKmUU{>gff?z2)_$*Df1E#uVdS-gYEGz-b9WJ`fb-T3e&=RmTJ!*=@840Yq!^!4Yms61gw~V)uVhM9ZI`T_rv#cP#&_SJUo~OLH-t<~wi2#n zy(G6p)~~HUun72yibj?U%{h=wi(x2WNc#e>ne7NPd_KdnSLV3}((amzDvQ=>?A6>p3Xog|KtYNJ5 zwOIUUG@Aig_}>7ml4ifYrmS~2Umb+*Ky%==HO1HZn}_=W?c7R|!zZ?g(lIH8aBb#! z|B+8qpLIRxs{Qm)rO|)Z??1aX`~2v!&n`T;P`lkYI_4i8+ahu|QXt&WnwdU7qd&_G zJcCYhUX7Fid@R9mO|I^`f_2F~1 zvD(<5KWJno{LDmMd|RqGTiumu8}300u@wV?_9Q&TjsOf&h5@Uf)!ufkipke-b1#5Z^1SEZ#<<`6dZXvK-*ddK9cM>j zz7YErAPW41jK75e$OtfMwOGPv2K!dOtV*xOOYOI~194{8pV*-)sm0zEli+Fc9y+8% zsYGk#umD+z9fRqt8mP1ADS=W34-i@shR@KTvI(KDAYog#0#$evN8VuZq4qirW088c zM@yi=SK2igFX%wQ-vIzx%pBTGf1JKQ?hlMSn(_xuH!@>>W^7GucJ{2lwMC?klg+;V z&AE@~YG)gLS-&s4Hq-3s+pzB4sJ-qVK2h&EQP)lg)raDz&@a##4I@Y!M(8gv?lO!I zDyFlu0*`E!3x>f4KnjNPFw8NZlkg-o1cq@@08I6K9>8k+w*lt`~IAUYs~TGdD9o z_Cw?9Sdf5nDUe+^NWN>k)MSCWL`@6Q5_XWaTw{TnUWHK}IbWw+DH)>bqde#^!l7a8 zIO3{sM8S0BaGNc}sR3<@Ujy)Oa6qm5SEc*&4Q|OB9(T_9kr3%3%nSZgDiGDDb2p$$w}vYvoY;op+{l zu$3PK0NJpo_t&?6dF#IPi;vbns%uAtoRA1c@$eKIf{qO%$Ra>pg)jJh7zN!s>MlTD zHw>Wq4$O-5@ph46<BVt z|Kox>NNf)j+#jT-0YYUh;$@J$D5{ju`bS`MC?-bLueI-mA^9!#4xpF?c^$x3LXxB{ zIUy;a698!7&-72f5)sFiJ6qUFl2ymA{gj9|;65+`k%A6kxOTYr{SH?R<3z203)D=&BH z*`;hL6re+ZQi1^MpulXA15c_AsfCZav@m+=p#?~TfQuKnrd=R)u*UTLYtpf z)RaR0vG{9~msL{{O@)6`H%Lmt0Y&Z4 z{{-Yq_C8~=luyYz&<(d)sOfQ;OuY=tSneq@%uLm8#=?0jW$&D`o~$cWXmvZ7)C|$;kJ|_E6gB0pa7R(6 zrmdLY_L+*7w7s&Lve{{yy<;~C0tJsCm}o8!W%0Wkz1K{In7xXoiM{6yDT8Nk_FfzB zJvSnp8!&sXzdaG}9T*$A(*JJXnO>r#4Py0P5iPJqPw1jHXR79@_?h_I&?gyFU^C#O zH)Y5fO)*cW=SVZmH6N`#<V|aV@KXrOVKVgPwB7pK!-6sj3ub!86A8^UHwS|DTI?Zk-HaWYb^oc)`iK_^)&Cc%Zb%Z2irH z5$?^Z8?ooC1DnBpW85%MH^vPevJb2afpxGOd+7npg@;ayg?FG*WOlP@-wa%}w@xzL zK(fV^bXQp$(7-o@U3)-cFHC_wc*SX{TZgMh4)3!IFXsLchpJq;dEW?h613FKfxdzk zIh^&Z170>JcMYa@6r&Mn4$w*!!s&BkThFTro0-F!~8 zr0G%cmB91f)cb7TRb}{^?IVhov71K3yH^ZDgT7!&yqkt9y?ge7DVo&?Nt)L5eXkvN zw8v3l+>RtR@{N@8}Zs!W`nLS=SfSk)Dg*q)>m^EpnoS=9w;TGV0jrroS(QchG? zOvm5gB>+de=(rpje2+J{#%>How`YC}Wr5=<1~hEcfR$&EOz#nI{V9={gefAM~;FGq|pB;u_gXF zQj89jqC@5A(4GlDDz$c(Tf5N&5I@>cZtr<;q14`AZU-GJ-1gzNwOD?jaIAFrRQd2J zUW0rqBp$$rn7K*(hp=bjBEUBK6BZYWvnWL@C~l5D|sf36r* zFg-wbxi3Lr3hq^}m47YGpjY-Z(CoU^S7>GmuB&~euxi7Hp+EfFMq3pdx3nMuSWX z`2g!5AxR(^00O~EA*l?!qs}Ai0+K-_pCGw}gl6IJ#KYH?PKJO5u?wRo`7sa^r>dmX zGy|PK*2vLUvg#fj(F1T;WOffTEs=YdmoKm0UK&{$fdrU4bnoKw#nthpp_L)0d1?95 zs<_f)UgC>3#`nmIp`H=h)nz$14H$Zm1EX(eCysU4RK@w)zmPHOf z&zNYj{`-tMRIGk?ea$Q^Vn11pul@9EM(S_mlj|uVP;#|C [B, H, W, C] - x = self.global_filter1(x) - x = x.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W] - - # Second conv block - x = self.conv2(x) - x = self.bn2(x) - x = self.relu(x) - - # Second GlobalFilter - x = x.permute(0, 2, 3, 1) - x = self.global_filter2(x) - x = x.permute(0, 3, 1, 2) - - # Final layers - x = self.avgpool(x) - x = torch.flatten(x, 1) - x = self.fc(x) + return self.vit(x) - return x \ No newline at end of file diff --git a/recognition/train.py b/recognition/train.py index 0e8623eb8..39ee93f85 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -1,154 +1,99 @@ -""" -containing the source code for training, validating, testing and saving the GFnet. - -The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. -the losses and metrics during training are plotted -""" - -from dataset import get_dataloader import torch -import time -from modules import GlobalFilterNetwork -import matplotlib.pyplot as plt +import torch.nn as nn from torch.utils.data import DataLoader +from sklearn.model_selection import train_test_split +import matplotlib.pyplot as plt +import time +from dataset import ADNIDataset, get_dataloader +from modules import ViTClassifier - -def plot_metric(stopping_epoch: int, metric_type: str, train_data: list, val_data: list): - """ - Helper function to plot a given metric - """ - plt.figure() - plt.plot(range(1, stopping_epoch+1), train_data, label = f"Training {metric_type}") - plt.plot(range(1, stopping_epoch+1), val_data, label=f"Validation {metric_type}", color='orange') - plt.xlabel('Epoch') - plt.ylabel(metric_type) - plt.legend() - plt.title(f"Training {metric_type} vs validation {metric_type}") - plt.savefig(f"Training_vs_validation_{metric_type}_{int(time.time())}.png") - -def train_val_epoch(device, - model, - train_loader: DataLoader, - val_loader: DataLoader, - criterion: torch.nn.CrossEntropyLoss, - optimizer: torch.optim.Adam, - scheduler: torch.optim.lr_scheduler.StepLR, - epoch, - epochs): - """ - Function to run an epoch of training and validation - """ - # Training metrics - train_correct = 0 - train_total = 0 - tl = [] - strt = time.time() - # Perform training - for images, labels in train_loader: - images = images.to(device) - labels = labels.to(device) - optimizer.zero_grad() - # Forward pass - outputs = model(images) - # Losses - loss = criterion(outputs, labels) - tl.append(loss.item()) - _, predicted = torch.max(outputs, 1) - train_total += labels.size(0) - train_correct += (predicted == labels).sum().item() - # Backward and optimise - loss.backward() - optimizer.step() - - print (f"Training epoch [{epoch+1}/{epochs}]: mean loss {sum(tl)/len(tl):.5f}, accuracy {(train_correct/train_total)*100:.2f}%. Time elapsed {time.time()-strt:.3f}.") - # Validation metrics - val_correct = 0 - val_total = 0 - vl = [] - strt = time.time() - - # Do validation - with torch.no_grad(): - for images, labels in val_loader: - images = images.to(device) - labels = labels.to(device) - # Forward pass - outputs = model(images) - #losses - loss = criterion(outputs, labels) - vl.append(loss.item()) - _, predicted = torch.max(outputs, 1) - val_total += labels.size(0) - val_correct += (predicted == labels).sum().item() - - print (f"Validation epoch [{epoch+1}/{epochs}]: mean loss {sum(vl)/len(vl):.5f}, accuracy {(val_correct/val_total)*100:.2f}%.") - scheduler.step() - return sum(tl)/len(tl), sum(vl)/len(vl), train_correct/train_total, val_correct/val_total - - -def run_training(device, - model, - train_loader: DataLoader, - val_loader: DataLoader, - epochs: int, - lr: float): +def train_model(model, train_loader, val_loader, epochs, lr, device): """ - Function to run training on the dataset. + Train the Vision Transformer model for Alzheimer's classification. """ - # Initialise criterion, optimizer and LR scheduler - criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=lr) - scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 5, 0.5) - # Metrics tracking + criterion = nn.CrossEntropyLoss() + train_losses = [] val_losses = [] train_accs = [] val_accs = [] stopping_epoch = epochs down_consec = 0 - # Run through epochs - strt = time.time() + for epoch in range(epochs): - tl, vl, ta, va = train_val_epoch(device, model, train_loader, val_loader, criterion, optimizer, scheduler, epoch, epochs) - train_losses.append(tl); val_losses.append(vl); train_accs.append(ta); val_accs.append(va) - # Increase the down consecutively counter if necessary - if epoch + 1 > 1 and val_accs[-1] < val_accs[-2]: + # Training loop + model.train() + train_loss = 0.0 + train_correct = 0 + train_total = 0 + for images, labels in train_loader: + images, labels = images.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + train_loss += loss.item() + _, predicted = torch.max(outputs, 1) + train_total += labels.size(0) + train_correct += (predicted == labels).sum().item() + train_loss /= len(train_loader) + train_acc = train_correct / train_total + train_losses.append(train_loss) + train_accs.append(train_acc) + + # Validation loop + model.eval() + val_loss = 0.0 + val_correct = 0 + val_total = 0 + with torch.no_grad(): + for images, labels in val_loader: + images, labels = images.to(device), labels.to(device) + outputs = model(images) + loss = criterion(outputs, labels) + val_loss += loss.item() + _, predicted = torch.max(outputs, 1) + val_total += labels.size(0) + val_correct += (predicted == labels).sum().item() + val_loss /= len(val_loader) + val_acc = val_correct / val_total + val_losses.append(val_loss) + val_accs.append(val_acc) + + print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}') + + # Early stopping check + if epoch > 0 and val_acc < val_accs[-2]: down_consec += 1 else: down_consec = 0 - # Stop training early if validation accuracy decreases for four epochs in a row if down_consec >= 4: stopping_epoch = epoch + 1 break - if val_accs[-1] == max(val_accs): - # save best model - torch.save(model, "adni_vit.pt") - print(f"Training & validation took {time.time()-strt:.3f} secs or {(time.time()-strt)/60:.2f} mins in total") - print("") + # Save the best model + torch.save(model, "adni_vit.pt") + + # Plot training and validation metrics plot_metric(stopping_epoch, 'loss', train_losses, val_losses) plot_metric(stopping_epoch, 'accuracy', train_accs, val_accs) + return model -def run_testing(device, model, test_loader: DataLoader): +def plot_metric(stopping_epoch: int, metric_type: str, train_data: list, val_data: list): """ - Function to run testing on the trained model. + Helper function to plot a given metric """ - test_correct = 0 - test_total = 0 - start = time.time() - with torch.no_grad(): - for images, labels in test_loader: - images = images.to(device) - labels = labels.to(device) - # Forward pass - outputs = model(images) - _, predicted = torch.max(outputs, 1) - test_total += labels.size(0) - test_correct += (predicted == labels).sum().item() - - print(f"Test accuracy {(test_correct/test_total)*100:.2f}%, time elapsed {time.time()-start:.3f} secs or {(time.time()-start)/60:.2f} mins in total") - + plt.figure() + plt.plot(range(1, stopping_epoch+1), train_data, label = f"Training {metric_type}") + plt.plot(range(1, stopping_epoch+1), val_data, label=f"Validation {metric_type}", color='orange') + plt.xlabel('Epoch') + plt.ylabel(metric_type) + plt.legend() + plt.title(f"Training {metric_type} vs validation {metric_type}") + plt.savefig(f"Training_vs_validation_{metric_type}_{int(time.time())}.png") def main(): """ @@ -156,21 +101,21 @@ def main(): """ # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - if not torch.cuda.is_available(): - print("Warning CUDA not found. Using CPU") + # Initialise hyperparameters BATCH_SIZE = 64 EPOCHS = 20 LR = 1e-4 - # Initialise data loaders & model + + # Initialise data loaders train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) - # Use GlobalFilterNetwork instead of ViT - model = GlobalFilterNetwork() - model = model.to(device) - # Run training and testing - run_training(device, model, train_loader, val_loader, epochs=EPOCHS, lr=LR) - run_testing(device, model, test_loader) + + # Initialise model + model = ViTClassifier().to(device) + + # Run training + trained_model = train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) if __name__ == "__main__": main() \ No newline at end of file From 08b64e1552af4b8948efc878974e4dbab3d6b4ff Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 15:38:10 +1000 Subject: [PATCH 10/37] some check points --- .../__pycache__/dataset.cpython-311.pyc | Bin 7868 -> 8092 bytes .../__pycache__/modules.cpython-311.pyc | Bin 3751 -> 3751 bytes recognition/dataset.py | 1 + recognition/train.py | 228 +++++++++++++++++- 4 files changed, 218 insertions(+), 11 deletions(-) diff --git a/recognition/__pycache__/dataset.cpython-311.pyc b/recognition/__pycache__/dataset.cpython-311.pyc index c1c77fb986c1b4f9d02301490c310edc2998a767..2c9922df2e66dadede572ef0c975eb10bcfc3c5e 100644 GIT binary patch delta 1420 zcmah}&u<%55PomHyKS#3+q^4aPw4|3sZ%1qK~x0BXBJ7xLp z)J5IuSW;)YtcVq|)Bm!Dm$aFTrA#p+D%SWX} zE{yiP`j}iFQ@p_&I@nd)bW!8)J7g1Is2BSF@(!2Hh4YqKfDW`JaXE2>8e8GFy*It9 zq16+sm)5iSjcmTgwpslXZjAj5dHK6oH8yqD40D&aYQERA`s=WyG5}=cSm}MP? z1{v|ddzAadpI(FaiGyuF@_^vJ=MV2EA4z7Q_8Ef!8K_*KDM~mbW_{;$c@V1K_kExt z&tR~hPl!@*7=xmrEAc~>Y@G(-!O-30i z92bdjAF@w`2l%YG8or9`#ZXf8#-@5lT7DilPWmXp7=lBC%w}uNvd3|A`H;wmQoeG@ zaW7OBs)eHXI#Q5apGM#*5hf&#QSoK8r(O2^G*~>0b?7JQ`2R|nK&SsIp{G^C+eCFr z@*x=hhR3np=J4bg(_%YzI!w35@WD~?qe~2VF&TfZaDr5770wXIjC33DYN3px$le

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Testing image transforms diff --git a/recognition/train.py b/recognition/train.py index 39ee93f85..51ee3ae64 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -1,15 +1,144 @@ +""" +containing the source code for training, validating, testing and saving the ViT. + +The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. +the losses and metrics during training are plotted +""" +# import torch +# import torch.nn as nn +# from torch.utils.data import DataLoader +# from sklearn.model_selection import train_test_split +# import matplotlib.pyplot as plt +# import time +# from dataset import ADNIDataset, get_dataloader +# from modules import ViTClassifier + +# def train_model(model, train_loader, val_loader, epochs, lr, device): +# """ +# Train the Vision Transformer model for Alzheimer's classification. +# """ +# optimizer = torch.optim.Adam(model.parameters(), lr=lr) +# criterion = nn.CrossEntropyLoss() + +# train_losses = [] +# val_losses = [] +# train_accs = [] +# val_accs = [] +# stopping_epoch = epochs +# down_consec = 0 + +# for epoch in range(epochs): +# # Training loop +# model.train() +# train_loss = 0.0 +# train_correct = 0 +# train_total = 0 +# for images, labels in train_loader: +# images, labels = images.to(device), labels.to(device) +# optimizer.zero_grad() +# outputs = model(images) +# loss = criterion(outputs, labels) +# loss.backward() +# optimizer.step() +# train_loss += loss.item() +# _, predicted = torch.max(outputs, 1) +# train_total += labels.size(0) +# train_correct += (predicted == labels).sum().item() +# train_loss /= len(train_loader) +# train_acc = train_correct / train_total +# train_losses.append(train_loss) +# train_accs.append(train_acc) + +# # Validation loop +# model.eval() +# val_loss = 0.0 +# val_correct = 0 +# val_total = 0 +# with torch.no_grad(): +# for images, labels in val_loader: +# images, labels = images.to(device), labels.to(device) +# outputs = model(images) +# loss = criterion(outputs, labels) +# val_loss += loss.item() +# _, predicted = torch.max(outputs, 1) +# val_total += labels.size(0) +# val_correct += (predicted == labels).sum().item() +# val_loss /= len(val_loader) +# val_acc = val_correct / val_total +# val_losses.append(val_loss) +# val_accs.append(val_acc) + +# print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}') + +# # Early stopping check +# if epoch > 0 and val_acc < val_accs[-2]: +# down_consec += 1 +# else: +# down_consec = 0 +# if down_consec >= 4: +# stopping_epoch = epoch + 1 +# break + +# # Save the best model +# torch.save(model, "adni_vit.pt") + +# # Plot training and validation metrics +# plot_metric(stopping_epoch, 'loss', train_losses, val_losses) +# plot_metric(stopping_epoch, 'accuracy', train_accs, val_accs) + +# return model + +# def plot_metric(stopping_epoch: int, metric_type: str, train_data: list, val_data: list): +# """ +# Helper function to plot a given metric +# """ +# plt.figure() +# plt.plot(range(1, stopping_epoch+1), train_data, label = f"Training {metric_type}") +# plt.plot(range(1, stopping_epoch+1), val_data, label=f"Validation {metric_type}", color='orange') +# plt.xlabel('Epoch') +# plt.ylabel(metric_type) +# plt.legend() +# plt.title(f"Training {metric_type} vs validation {metric_type}") +# plt.savefig(f"Training_vs_validation_{metric_type}_{int(time.time())}.png") + +# def main(): +# """ +# Main execution function. +# """ +# # Device configuration +# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# # Initialise hyperparameters +# BATCH_SIZE = 64 +# EPOCHS = 20 +# LR = 1e-4 + +# # Initialise data loaders +# train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) +# test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) + +# # Initialise model +# model = ViTClassifier().to(device) +# print("nya") +# # Run training +# trained_model = train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) + +# if __name__ == "__main__": +# main() + import torch import torch.nn as nn from torch.utils.data import DataLoader -from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import time from dataset import ADNIDataset, get_dataloader from modules import ViTClassifier +from tqdm import tqdm +import sys def train_model(model, train_loader, val_loader, epochs, lr, device): """ - Train the Vision Transformer model for Alzheimer's classification. + Train the Vision Transformer model for Alzheimer's classification with progress tracking. """ optimizer = torch.optim.Adam(model.parameters(), lr=lr) criterion = nn.CrossEntropyLoss() @@ -20,24 +149,45 @@ def train_model(model, train_loader, val_loader, epochs, lr, device): val_accs = [] stopping_epoch = epochs down_consec = 0 + best_val_acc = 0 + + print(f"\nStarting training on device: {device}") + print(f"Total epochs: {epochs}") + print(f"Training batches per epoch: {len(train_loader)}") + print(f"Validation batches per epoch: {len(val_loader)}\n") + + start_time = time.time() for epoch in range(epochs): + epoch_start = time.time() + # Training loop model.train() train_loss = 0.0 train_correct = 0 train_total = 0 - for images, labels in train_loader: + + # Progress bar for training + train_pbar = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{epochs} [Train]', + leave=False, file=sys.stdout) + + for images, labels in train_pbar: images, labels = images.to(device), labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() + train_loss += loss.item() - _, predicted = torch.max(outputs, 1) + _, predicted = torch.max(outputs.data, 1) train_total += labels.size(0) train_correct += (predicted == labels).sum().item() + + # Update progress bar + train_pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(train_correct/train_total)*100:.2f}%'}) + train_loss /= len(train_loader) train_acc = train_correct / train_total train_losses.append(train_loss) @@ -48,33 +198,67 @@ def train_model(model, train_loader, val_loader, epochs, lr, device): val_loss = 0.0 val_correct = 0 val_total = 0 + + # Progress bar for validation + val_pbar = tqdm(val_loader, desc=f'Epoch {epoch + 1}/{epochs} [Val]', + leave=False, file=sys.stdout) + with torch.no_grad(): - for images, labels in val_loader: + for images, labels in val_pbar: images, labels = images.to(device), labels.to(device) outputs = model(images) loss = criterion(outputs, labels) val_loss += loss.item() - _, predicted = torch.max(outputs, 1) + _, predicted = torch.max(outputs.data, 1) val_total += labels.size(0) val_correct += (predicted == labels).sum().item() + + # Update progress bar + val_pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(val_correct/val_total)*100:.2f}%'}) + val_loss /= len(val_loader) val_acc = val_correct / val_total val_losses.append(val_loss) val_accs.append(val_acc) - print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}') + epoch_time = time.time() - epoch_start + + # Print epoch summary + print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') + print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') + print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') + + # Save best model + if val_acc > best_val_acc: + best_val_acc = val_acc + print(f'Saving best model with validation accuracy: {val_acc*100:.2f}%') + torch.save({ + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'train_loss': train_loss, + 'val_loss': val_loss, + 'train_acc': train_acc, + 'val_acc': val_acc, + }, "adni_vit_best.pt") # Early stopping check if epoch > 0 and val_acc < val_accs[-2]: down_consec += 1 + print(f'Validation accuracy decreased. Counter: {down_consec}/4') else: down_consec = 0 if down_consec >= 4: + print('\nEarly stopping triggered!') stopping_epoch = epoch + 1 break - # Save the best model - torch.save(model, "adni_vit.pt") + print('-' * 60 + '\n') + + total_time = time.time() - start_time + print(f'\nTraining completed in {total_time/60:.1f} minutes') + print(f'Best validation accuracy: {best_val_acc*100:.2f}%') # Plot training and validation metrics plot_metric(stopping_epoch, 'loss', train_losses, val_losses) @@ -102,20 +286,42 @@ def main(): # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + # Print system information + print("\nSystem Information:") + print(f"PyTorch version: {torch.__version__}") + print(f"Device being used: {device}") + print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") + # Initialise hyperparameters - BATCH_SIZE = 64 + BATCH_SIZE = 32 # Reduced batch size to help with memory EPOCHS = 20 LR = 1e-4 + print("\nHyperparameters:") + print(f"Batch size: {BATCH_SIZE}") + print(f"Number of epochs: {EPOCHS}") + print(f"Learning rate: {LR}") + # Initialise data loaders + print("\nLoading data...") train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) + print("Data loaded successfully!") # Initialise model + print("\nInitializing model...") model = ViTClassifier().to(device) + print("Model initialized successfully!") + + # Print model summary + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print(f"\nModel Parameters:") + print(f"Total parameters: {total_params:,}") + print(f"Trainable parameters: {trainable_params:,}") # Run training - trained_model = train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) + train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) if __name__ == "__main__": main() \ No newline at end of file From d98c8f93c0b6fd03814a68a7be97f88f712ccb14 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 15:52:23 +1000 Subject: [PATCH 11/37] feat: load check point --- recognition/train.py | 679 +++++++++++++++++++++++++++++-------------- 1 file changed, 466 insertions(+), 213 deletions(-) diff --git a/recognition/train.py b/recognition/train.py index 51ee3ae64..67b5a02de 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -4,18 +4,394 @@ The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. the losses and metrics during training are plotted """ +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import pandas as pd +import json +import os +import time +from datetime import datetime +from tqdm import tqdm +import sys +from sklearn.metrics import confusion_matrix, classification_report + +from dataset import ADNIDataset, get_dataloader +from modules import ViTClassifier + +class ModelCheckpointing: + def __init__(self, save_dir='checkpoints'): + """Initialize checkpoint manager""" + self.save_dir = save_dir + os.makedirs(save_dir, exist_ok=True) + self.best_val_acc = 0 + self.best_model_path = None + self.history = { + 'train_loss': [], 'val_loss': [], + 'train_acc': [], 'val_acc': [], + 'learning_rate': [], 'epochs': [] + } + + def save_checkpoint(self, model, optimizer, epoch, train_loss, val_loss, + train_acc, val_acc, lr, is_best=False): + """Save model checkpoint""" + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + + checkpoint = { + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'train_loss': train_loss, + 'val_loss': val_loss, + 'train_acc': train_acc, + 'val_acc': val_acc, + 'learning_rate': lr + } + + # Update history + for key in ['train_loss', 'val_loss', 'train_acc', 'val_acc']: + self.history[key].append(eval(key)) + self.history['learning_rate'].append(lr) + self.history['epochs'].append(epoch) + + # Save regular checkpoint + checkpoint_path = os.path.join(self.save_dir, f'checkpoint_epoch_{epoch}_{timestamp}.pt') + torch.save(checkpoint, checkpoint_path) + + # Save best model if applicable + if is_best: + self.best_val_acc = val_acc + self.best_model_path = os.path.join(self.save_dir, f'best_model_{timestamp}.pt') + torch.save(checkpoint, self.best_model_path) + + # Save config + config = { + 'timestamp': timestamp, + 'epoch': epoch, + 'best_val_acc': val_acc, + 'final_train_loss': train_loss, + 'final_val_loss': val_loss, + 'learning_rate': lr + } + + with open(os.path.join(self.save_dir, f'model_config_{timestamp}.json'), 'w') as f: + json.dump(config, f, indent=4) + + return checkpoint_path + + def load_checkpoint(self, model, optimizer, checkpoint_path): + """Load model checkpoint""" + if not os.path.exists(checkpoint_path): + raise FileNotFoundError(f"No checkpoint found at {checkpoint_path}") + + checkpoint = torch.load(checkpoint_path) + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + return model, optimizer, checkpoint + + def load_best_model(self, model, optimizer): + """Load the best model""" + if self.best_model_path is None: + raise ValueError("No best model checkpoint found") + return self.load_checkpoint(model, optimizer, self.best_model_path) + + def save_training_history(self): + """Save training history""" + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + history_path = os.path.join(self.save_dir, f'training_history_{timestamp}.json') + with open(history_path, 'w') as f: + json.dump(self.history, f, indent=4) + return history_path + +class VisualizationUtils: + @staticmethod + def plot_training_history(history): + """Plot training metrics""" + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) + + # Plot loss + ax1.plot(history['train_loss'], label='Training Loss') + ax1.plot(history['val_loss'], label='Validation Loss') + ax1.set_title('Loss Over Time') + ax1.set_xlabel('Epoch') + ax1.set_ylabel('Loss') + ax1.legend() + + # Plot accuracy + ax2.plot(history['train_acc'], label='Training Accuracy') + ax2.plot(history['val_acc'], label='Validation Accuracy') + ax2.set_title('Accuracy Over Time') + ax2.set_xlabel('Epoch') + ax2.set_ylabel('Accuracy') + ax2.legend() + + plt.tight_layout() + return fig + + @staticmethod + def plot_attention_maps(model, image, pred_class, true_class=None): + """Plot attention maps""" + with torch.no_grad(): + _ = model(image.unsqueeze(0)) + attention = model.get_attention_weights() + attention = attention.mean(1).squeeze() + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) + + ax1.imshow(image.permute(1, 2, 0).cpu()) + ax1.axis('off') + ax1.set_title('Original Image') + + sns.heatmap(attention.cpu(), ax=ax2, cmap='viridis') + title = f'Attention Map (Pred: {pred_class})' + if true_class is not None: + title += f' (True: {true_class})' + ax2.set_title(title) + + plt.tight_layout() + return fig + + @staticmethod + def plot_confusion_matrix(y_true, y_pred, classes): + """Plot confusion matrix""" + cm = confusion_matrix(y_true, y_pred) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=classes, yticklabels=classes) + plt.title('Confusion Matrix') + plt.ylabel('True Label') + plt.xlabel('Predicted Label') + return plt.gcf() + + @staticmethod + def generate_classification_report(y_true, y_pred, classes): + """Generate classification report""" + report = classification_report(y_true, y_pred, target_names=classes, output_dict=True) + df_report = pd.DataFrame(report).transpose() + + plt.figure(figsize=(10, 6)) + sns.heatmap(df_report.iloc[:-3, :-1].astype(float), annot=True, cmap='Blues', fmt='.2f') + plt.title('Classification Report') + return plt.gcf() + +def train_epoch(model, loader, optimizer, criterion, device): + """Run one training epoch""" + model.train() + running_loss = 0.0 + correct = 0 + total = 0 + + pbar = tqdm(loader, desc='Training', leave=False) + for images, labels in pbar: + images, labels = images.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + running_loss += loss.item() + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(correct/total)*100:.2f}%'}) + + return running_loss / len(loader), correct / total + +def validate_epoch(model, loader, criterion, device): + """Run one validation epoch""" + model.eval() + running_loss = 0.0 + correct = 0 + total = 0 + + with torch.no_grad(): + pbar = tqdm(loader, desc='Validation', leave=False) + for images, labels in pbar: + images, labels = images.to(device), labels.to(device) + outputs = model(images) + loss = criterion(outputs, labels) + + running_loss += loss.item() + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(correct/total)*100:.2f}%'}) + + return running_loss / len(loader), correct / total + +def train_model(model, train_loader, val_loader, epochs, lr, device, early_stopping_patience=4): + """Main training function""" + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + criterion = nn.CrossEntropyLoss() + checkpointer = ModelCheckpointing() + visualizer = VisualizationUtils() + + print(f"\nStarting training on device: {device}") + print(f"Checkpoints will be saved to: {checkpointer.save_dir}") + + start_time = time.time() + stopping_epoch = epochs + down_consec = 0 + + for epoch in range(epochs): + epoch_start = time.time() + + # Training + train_loss, train_acc = train_epoch(model, train_loader, optimizer, criterion, device) + + # Validation + val_loss, val_acc = validate_epoch(model, val_loader, criterion, device) + + # Check if best model + is_best = val_acc > checkpointer.best_val_acc + + # Save checkpoint + checkpoint_path = checkpointer.save_checkpoint( + model, optimizer, epoch, train_loss, val_loss, + train_acc, val_acc, lr, is_best + ) + + epoch_time = time.time() - epoch_start + print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') + print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') + print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') + print(f'Checkpoint saved: {checkpoint_path}') + + if is_best: + print(f'New best model saved with validation accuracy: {val_acc*100:.2f}%') + + # Early stopping check + if epoch > 0 and val_acc < checkpointer.history['val_acc'][-2]: + down_consec += 1 + print(f'Validation accuracy decreased. Counter: {down_consec}/{early_stopping_patience}') + else: + down_consec = 0 + + if down_consec >= early_stopping_patience: + print('\nEarly stopping triggered!') + stopping_epoch = epoch + 1 + break + + print('-' * 60) + + total_time = time.time() - start_time + print(f'\nTraining completed in {total_time/60:.1f} minutes') + print(f'Best validation accuracy: {checkpointer.best_val_acc*100:.2f}%') + + # Save final history + history_path = checkpointer.save_training_history() + print(f'Training history saved: {history_path}') + + # Plot training curves + visualizer.plot_training_history(checkpointer.history) + plt.savefig(os.path.join(checkpointer.save_dir, 'training_curves.png')) + + return model, checkpointer.history + +def evaluate_model(model, test_loader, device, classes): + """Evaluate model on test set""" + model.eval() + y_true = [] + y_pred = [] + visualizer = VisualizationUtils() + + with torch.no_grad(): + for images, labels in tqdm(test_loader, desc='Evaluating'): + images = images.to(device) + outputs = model(images) + _, preds = torch.max(outputs, 1) + y_true.extend(labels.cpu().numpy()) + y_pred.extend(preds.cpu().numpy()) + + # Generate evaluation plots + cm_fig = visualizer.plot_confusion_matrix(y_true, y_pred, classes) + cm_fig.savefig('confusion_matrix.png') + + report_fig = visualizer.generate_classification_report(y_true, y_pred, classes) + report_fig.savefig('classification_report.png') + + return y_true, y_pred + +def main(): + """Main execution function""" + # Device configuration + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # Print system information + print("\nSystem Information:") + print(f"PyTorch version: {torch.__version__}") + print(f"Device being used: {device}") + print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") + + # Hyperparameters + BATCH_SIZE = 32 + EPOCHS = 20 + LR = 1e-4 + CLASSES = ['CN', 'MCI', 'AD', 'SMC'] + + print("\nHyperparameters:") + print(f"Batch size: {BATCH_SIZE}") + print(f"Number of epochs: {EPOCHS}") + print(f"Learning rate: {LR}") + + # Initialize data loaders + print("\nLoading data...") + train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) + test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) + print("Data loaded successfully!") + + # Initialize model + print("\nInitializing model...") + model = ViTClassifier().to(device) + print("Model initialized successfully!") + + # Print model summary + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print(f"\nModel Parameters:") + print(f"Total parameters: {total_params:,}") + print(f"Trainable parameters: {trainable_params:,}") + + # Train model + model, history = train_model( + model=model, + train_loader=train_loader, + val_loader=val_loader, + epochs=EPOCHS, + lr=LR, + device=device + ) + + # Evaluate model + y_true, y_pred = evaluate_model(model, test_loader, device, CLASSES) + + print("\nTraining and evaluation completed successfully!") + print("Check the output directory for visualization plots and model checkpoints.") + +if __name__ == "__main__": + main() + # import torch # import torch.nn as nn # from torch.utils.data import DataLoader -# from sklearn.model_selection import train_test_split # import matplotlib.pyplot as plt # import time # from dataset import ADNIDataset, get_dataloader # from modules import ViTClassifier +# from tqdm import tqdm +# import sys # def train_model(model, train_loader, val_loader, epochs, lr, device): # """ -# Train the Vision Transformer model for Alzheimer's classification. +# Train the Vision Transformer model for Alzheimer's classification with progress tracking. # """ # optimizer = torch.optim.Adam(model.parameters(), lr=lr) # criterion = nn.CrossEntropyLoss() @@ -26,24 +402,45 @@ # val_accs = [] # stopping_epoch = epochs # down_consec = 0 +# best_val_acc = 0 + +# print(f"\nStarting training on device: {device}") +# print(f"Total epochs: {epochs}") +# print(f"Training batches per epoch: {len(train_loader)}") +# print(f"Validation batches per epoch: {len(val_loader)}\n") + +# start_time = time.time() # for epoch in range(epochs): +# epoch_start = time.time() + # # Training loop # model.train() # train_loss = 0.0 # train_correct = 0 # train_total = 0 -# for images, labels in train_loader: + +# # Progress bar for training +# train_pbar = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{epochs} [Train]', +# leave=False, file=sys.stdout) + +# for images, labels in train_pbar: # images, labels = images.to(device), labels.to(device) # optimizer.zero_grad() # outputs = model(images) # loss = criterion(outputs, labels) # loss.backward() # optimizer.step() + # train_loss += loss.item() -# _, predicted = torch.max(outputs, 1) +# _, predicted = torch.max(outputs.data, 1) # train_total += labels.size(0) # train_correct += (predicted == labels).sum().item() + +# # Update progress bar +# train_pbar.set_postfix({'loss': f'{loss.item():.4f}', +# 'acc': f'{(train_correct/train_total)*100:.2f}%'}) + # train_loss /= len(train_loader) # train_acc = train_correct / train_total # train_losses.append(train_loss) @@ -54,33 +451,67 @@ # val_loss = 0.0 # val_correct = 0 # val_total = 0 + +# # Progress bar for validation +# val_pbar = tqdm(val_loader, desc=f'Epoch {epoch + 1}/{epochs} [Val]', +# leave=False, file=sys.stdout) + # with torch.no_grad(): -# for images, labels in val_loader: +# for images, labels in val_pbar: # images, labels = images.to(device), labels.to(device) # outputs = model(images) # loss = criterion(outputs, labels) # val_loss += loss.item() -# _, predicted = torch.max(outputs, 1) +# _, predicted = torch.max(outputs.data, 1) # val_total += labels.size(0) # val_correct += (predicted == labels).sum().item() + +# # Update progress bar +# val_pbar.set_postfix({'loss': f'{loss.item():.4f}', +# 'acc': f'{(val_correct/val_total)*100:.2f}%'}) + # val_loss /= len(val_loader) # val_acc = val_correct / val_total # val_losses.append(val_loss) # val_accs.append(val_acc) -# print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}') +# epoch_time = time.time() - epoch_start + +# # Print epoch summary +# print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') +# print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') +# print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') + +# # Save best model +# if val_acc > best_val_acc: +# best_val_acc = val_acc +# print(f'Saving best model with validation accuracy: {val_acc*100:.2f}%') +# torch.save({ +# 'epoch': epoch, +# 'model_state_dict': model.state_dict(), +# 'optimizer_state_dict': optimizer.state_dict(), +# 'train_loss': train_loss, +# 'val_loss': val_loss, +# 'train_acc': train_acc, +# 'val_acc': val_acc, +# }, "adni_vit_best.pt") # # Early stopping check # if epoch > 0 and val_acc < val_accs[-2]: # down_consec += 1 +# print(f'Validation accuracy decreased. Counter: {down_consec}/4') # else: # down_consec = 0 # if down_consec >= 4: +# print('\nEarly stopping triggered!') # stopping_epoch = epoch + 1 # break -# # Save the best model -# torch.save(model, "adni_vit.pt") +# print('-' * 60 + '\n') + +# total_time = time.time() - start_time +# print(f'\nTraining completed in {total_time/60:.1f} minutes') +# print(f'Best validation accuracy: {best_val_acc*100:.2f}%') # # Plot training and validation metrics # plot_metric(stopping_epoch, 'loss', train_losses, val_losses) @@ -108,220 +539,42 @@ # # Device configuration # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +# # Print system information +# print("\nSystem Information:") +# print(f"PyTorch version: {torch.__version__}") +# print(f"Device being used: {device}") +# print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") + # # Initialise hyperparameters -# BATCH_SIZE = 64 +# BATCH_SIZE = 32 # Reduced batch size to help with memory # EPOCHS = 20 # LR = 1e-4 +# print("\nHyperparameters:") +# print(f"Batch size: {BATCH_SIZE}") +# print(f"Number of epochs: {EPOCHS}") +# print(f"Learning rate: {LR}") + # # Initialise data loaders +# print("\nLoading data...") # train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) # test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) +# print("Data loaded successfully!") # # Initialise model +# print("\nInitializing model...") # model = ViTClassifier().to(device) -# print("nya") -# # Run training -# trained_model = train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) - -# if __name__ == "__main__": -# main() - -import torch -import torch.nn as nn -from torch.utils.data import DataLoader -import matplotlib.pyplot as plt -import time -from dataset import ADNIDataset, get_dataloader -from modules import ViTClassifier -from tqdm import tqdm -import sys - -def train_model(model, train_loader, val_loader, epochs, lr, device): - """ - Train the Vision Transformer model for Alzheimer's classification with progress tracking. - """ - optimizer = torch.optim.Adam(model.parameters(), lr=lr) - criterion = nn.CrossEntropyLoss() +# print("Model initialized successfully!") - train_losses = [] - val_losses = [] - train_accs = [] - val_accs = [] - stopping_epoch = epochs - down_consec = 0 - best_val_acc = 0 - - print(f"\nStarting training on device: {device}") - print(f"Total epochs: {epochs}") - print(f"Training batches per epoch: {len(train_loader)}") - print(f"Validation batches per epoch: {len(val_loader)}\n") - - start_time = time.time() - - for epoch in range(epochs): - epoch_start = time.time() - - # Training loop - model.train() - train_loss = 0.0 - train_correct = 0 - train_total = 0 - - # Progress bar for training - train_pbar = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{epochs} [Train]', - leave=False, file=sys.stdout) +# # Print model summary +# total_params = sum(p.numel() for p in model.parameters()) +# trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) +# print(f"\nModel Parameters:") +# print(f"Total parameters: {total_params:,}") +# print(f"Trainable parameters: {trainable_params:,}") - for images, labels in train_pbar: - images, labels = images.to(device), labels.to(device) - optimizer.zero_grad() - outputs = model(images) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - train_loss += loss.item() - _, predicted = torch.max(outputs.data, 1) - train_total += labels.size(0) - train_correct += (predicted == labels).sum().item() - - # Update progress bar - train_pbar.set_postfix({'loss': f'{loss.item():.4f}', - 'acc': f'{(train_correct/train_total)*100:.2f}%'}) - - train_loss /= len(train_loader) - train_acc = train_correct / train_total - train_losses.append(train_loss) - train_accs.append(train_acc) - - # Validation loop - model.eval() - val_loss = 0.0 - val_correct = 0 - val_total = 0 - - # Progress bar for validation - val_pbar = tqdm(val_loader, desc=f'Epoch {epoch + 1}/{epochs} [Val]', - leave=False, file=sys.stdout) - - with torch.no_grad(): - for images, labels in val_pbar: - images, labels = images.to(device), labels.to(device) - outputs = model(images) - loss = criterion(outputs, labels) - val_loss += loss.item() - _, predicted = torch.max(outputs.data, 1) - val_total += labels.size(0) - val_correct += (predicted == labels).sum().item() - - # Update progress bar - val_pbar.set_postfix({'loss': f'{loss.item():.4f}', - 'acc': f'{(val_correct/val_total)*100:.2f}%'}) - - val_loss /= len(val_loader) - val_acc = val_correct / val_total - val_losses.append(val_loss) - val_accs.append(val_acc) - - epoch_time = time.time() - epoch_start - - # Print epoch summary - print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') - print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') - print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') - - # Save best model - if val_acc > best_val_acc: - best_val_acc = val_acc - print(f'Saving best model with validation accuracy: {val_acc*100:.2f}%') - torch.save({ - 'epoch': epoch, - 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - 'train_loss': train_loss, - 'val_loss': val_loss, - 'train_acc': train_acc, - 'val_acc': val_acc, - }, "adni_vit_best.pt") - - # Early stopping check - if epoch > 0 and val_acc < val_accs[-2]: - down_consec += 1 - print(f'Validation accuracy decreased. Counter: {down_consec}/4') - else: - down_consec = 0 - if down_consec >= 4: - print('\nEarly stopping triggered!') - stopping_epoch = epoch + 1 - break - - print('-' * 60 + '\n') - - total_time = time.time() - start_time - print(f'\nTraining completed in {total_time/60:.1f} minutes') - print(f'Best validation accuracy: {best_val_acc*100:.2f}%') - - # Plot training and validation metrics - plot_metric(stopping_epoch, 'loss', train_losses, val_losses) - plot_metric(stopping_epoch, 'accuracy', train_accs, val_accs) - - return model - -def plot_metric(stopping_epoch: int, metric_type: str, train_data: list, val_data: list): - """ - Helper function to plot a given metric - """ - plt.figure() - plt.plot(range(1, stopping_epoch+1), train_data, label = f"Training {metric_type}") - plt.plot(range(1, stopping_epoch+1), val_data, label=f"Validation {metric_type}", color='orange') - plt.xlabel('Epoch') - plt.ylabel(metric_type) - plt.legend() - plt.title(f"Training {metric_type} vs validation {metric_type}") - plt.savefig(f"Training_vs_validation_{metric_type}_{int(time.time())}.png") - -def main(): - """ - Main execution function. - """ - # Device configuration - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - # Print system information - print("\nSystem Information:") - print(f"PyTorch version: {torch.__version__}") - print(f"Device being used: {device}") - print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") - - # Initialise hyperparameters - BATCH_SIZE = 32 # Reduced batch size to help with memory - EPOCHS = 20 - LR = 1e-4 - - print("\nHyperparameters:") - print(f"Batch size: {BATCH_SIZE}") - print(f"Number of epochs: {EPOCHS}") - print(f"Learning rate: {LR}") - - # Initialise data loaders - print("\nLoading data...") - train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) - test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) - print("Data loaded successfully!") - - # Initialise model - print("\nInitializing model...") - model = ViTClassifier().to(device) - print("Model initialized successfully!") - - # Print model summary - total_params = sum(p.numel() for p in model.parameters()) - trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) - print(f"\nModel Parameters:") - print(f"Total parameters: {total_params:,}") - print(f"Trainable parameters: {trainable_params:,}") - - # Run training - train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) +# # Run training +# train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) -if __name__ == "__main__": - main() \ No newline at end of file +# if __name__ == "__main__": +# main() \ No newline at end of file From 28e80e8cfdde0ba9f80a0b197ef2a4f82a26e0cc Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 16:01:06 +1000 Subject: [PATCH 12/37] training optimiser --- .../__pycache__/dataset.cpython-311.pyc | Bin 8092 -> 8429 bytes recognition/dataset.py | 10 +- recognition/train.py | 552 +++++++++++------- 3 files changed, 341 insertions(+), 221 deletions(-) diff --git a/recognition/__pycache__/dataset.cpython-311.pyc b/recognition/__pycache__/dataset.cpython-311.pyc index 2c9922df2e66dadede572ef0c975eb10bcfc3c5e..9100f6e0937475b215f559a034a9eb76fef39ad6 100644 GIT binary patch delta 451 zcmbPZ|JIRjIWI340}yyuDx|jvPvnzeJhV}L38S)TieRct3Qww33U8`Z8e57`3u}~Y zidc$B3uBaA3R5tHrugP(j7O#FLy{Rm`k{asNQ3a_JRmWhp@yMIr36Vng>enjG9Z67 zT&$K6O`aJ|o~eehhA9nbA(}cCh`M^H#SB0fvHFE*G8PE~MKl?2F_$Ig0C^%{UXdV( zVg}0<#RJ(zVL+l7Dj*faq0vs}3V delta 136 zcmaFsILDrEIWI340}wRkDWoqFn#d=?xND>O5=Lp^6v0%P6rNP66y8*+G`19>7S<@) z6sBMXO_9y-7>`QvX)+Z>0QD6`0trpI$sc7uDeHmw{2+oCNc`fk$<0qG%}KQ@Dg<&F Xfw=hCW^VZdjC>4Sj6xqk1XwiyAxIy` diff --git a/recognition/dataset.py b/recognition/dataset.py index 4d5e93873..81029aacc 100644 --- a/recognition/dataset.py +++ b/recognition/dataset.py @@ -108,4 +108,12 @@ def get_dataloader(batch_size, train: bool, val_proportion: float = 0.2, keep_pr if train == False: dataset = ADNIDataset(root_path=ADNI_PATH, train=False, transform=TEST_TRANSFORM) loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) - return loader \ No newline at end of file + return loader + +def get_dataset(train=True): + if train: + train_dataset = ADNIDataset(train=True) + val_dataset = ADNIDataset(val=True) + return train_dataset, val_dataset + else: + return ADNIDataset(test=True) \ No newline at end of file diff --git a/recognition/train.py b/recognition/train.py index 67b5a02de..3d7546653 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -6,43 +6,157 @@ """ import torch import torch.nn as nn +import torch.cuda.amp as amp +from torch.nn.parallel import DistributedDataParallel as DDP +import torch.distributed as dist +import numpy as np from torch.utils.data import DataLoader +import torch.multiprocessing as mp +from contextlib import nullcontext import matplotlib.pyplot as plt import seaborn as sns -import numpy as np import pandas as pd import json import os import time from datetime import datetime from tqdm import tqdm -import sys from sklearn.metrics import confusion_matrix, classification_report -from dataset import ADNIDataset, get_dataloader +from dataset import ADNIDataset, get_dataloader, get_dataset from modules import ViTClassifier -class ModelCheckpointing: - def __init__(self, save_dir='checkpoints'): - """Initialize checkpoint manager""" +class OptimizedTrainer: + def __init__(self, model, train_loader, val_loader, device, + mixed_precision=True, distributed=False, num_workers=4, + save_dir='checkpoints'): + """ + Initialize optimized trainer with monitoring capabilities + """ + self.model = model + self.device = device + self.mixed_precision = mixed_precision + self.distributed = distributed + self.num_workers = num_workers self.save_dir = save_dir os.makedirs(save_dir, exist_ok=True) - self.best_val_acc = 0 - self.best_model_path = None + + # Setup mixed precision + self.scaler = amp.GradScaler() if mixed_precision else None + + # Optimize data loading + self.train_loader = self._optimize_dataloader(train_loader) + self.val_loader = self._optimize_dataloader(val_loader) + + # Initialize distributed training if requested + if distributed: + self.model = DDP(model) + + # Initialize training history self.history = { 'train_loss': [], 'val_loss': [], 'train_acc': [], 'val_acc': [], 'learning_rate': [], 'epochs': [] } + self.best_val_acc = 0 + self.best_model_path = None + + def _optimize_dataloader(self, dataloader): + """ + Optimize dataloader for speed + """ + return DataLoader( + dataloader.dataset, + batch_size=dataloader.batch_size, + shuffle=dataloader.dataset.train if hasattr(dataloader.dataset, 'train') else True, + num_workers=self.num_workers, + pin_memory=True, + persistent_workers=True, + prefetch_factor=2 + ) + + def train_epoch(self, optimizer, criterion, scheduler=None): + """ + Run optimized training epoch + """ + self.model.train() + running_loss = 0.0 + correct = 0 + total = 0 + + amp_context = amp.autocast() if self.mixed_precision else nullcontext() + pbar = tqdm(self.train_loader, desc='Training', leave=False) + + for inputs, targets in pbar: + inputs, targets = inputs.to(self.device), targets.to(self.device) + + optimizer.zero_grad(set_to_none=True) + + with amp_context: + outputs = self.model(inputs) + loss = criterion(outputs, targets) + + if self.mixed_precision: + self.scaler.scale(loss).backward() + self.scaler.step(optimizer) + self.scaler.update() + else: + loss.backward() + optimizer.step() + + if scheduler is not None: + scheduler.step() + + running_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(correct/total)*100:.2f}%'}) - def save_checkpoint(self, model, optimizer, epoch, train_loss, val_loss, + del outputs, loss + torch.cuda.empty_cache() + + return running_loss / len(self.train_loader), correct / total + + def validate_epoch(self, criterion): + """ + Run validation epoch + """ + self.model.eval() + running_loss = 0.0 + correct = 0 + total = 0 + + with torch.no_grad(): + pbar = tqdm(self.val_loader, desc='Validation', leave=False) + for inputs, targets in pbar: + inputs, targets = inputs.to(self.device), targets.to(self.device) + + outputs = self.model(inputs) + loss = criterion(outputs, targets) + + running_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + pbar.set_postfix({'loss': f'{loss.item():.4f}', + 'acc': f'{(correct/total)*100:.2f}%'}) + + return running_loss / len(self.val_loader), correct / total + + def save_checkpoint(self, optimizer, epoch, train_loss, val_loss, train_acc, val_acc, lr, is_best=False): - """Save model checkpoint""" + """ + Save model checkpoint + """ timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') checkpoint = { 'epoch': epoch, - 'model_state_dict': model.state_dict(), + 'model_state_dict': self.model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'train_loss': train_loss, 'val_loss': val_loss, @@ -67,62 +181,25 @@ def save_checkpoint(self, model, optimizer, epoch, train_loss, val_loss, self.best_model_path = os.path.join(self.save_dir, f'best_model_{timestamp}.pt') torch.save(checkpoint, self.best_model_path) - # Save config - config = { - 'timestamp': timestamp, - 'epoch': epoch, - 'best_val_acc': val_acc, - 'final_train_loss': train_loss, - 'final_val_loss': val_loss, - 'learning_rate': lr - } - - with open(os.path.join(self.save_dir, f'model_config_{timestamp}.json'), 'w') as f: - json.dump(config, f, indent=4) - return checkpoint_path - def load_checkpoint(self, model, optimizer, checkpoint_path): - """Load model checkpoint""" - if not os.path.exists(checkpoint_path): - raise FileNotFoundError(f"No checkpoint found at {checkpoint_path}") - - checkpoint = torch.load(checkpoint_path) - model.load_state_dict(checkpoint['model_state_dict']) - optimizer.load_state_dict(checkpoint['optimizer_state_dict']) - return model, optimizer, checkpoint - - def load_best_model(self, model, optimizer): - """Load the best model""" - if self.best_model_path is None: - raise ValueError("No best model checkpoint found") - return self.load_checkpoint(model, optimizer, self.best_model_path) - - def save_training_history(self): - """Save training history""" - timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - history_path = os.path.join(self.save_dir, f'training_history_{timestamp}.json') - with open(history_path, 'w') as f: - json.dump(self.history, f, indent=4) - return history_path - -class VisualizationUtils: - @staticmethod - def plot_training_history(history): - """Plot training metrics""" + def plot_training_history(self): + """ + Plot training metrics + """ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) # Plot loss - ax1.plot(history['train_loss'], label='Training Loss') - ax1.plot(history['val_loss'], label='Validation Loss') + ax1.plot(self.history['train_loss'], label='Training Loss') + ax1.plot(self.history['val_loss'], label='Validation Loss') ax1.set_title('Loss Over Time') ax1.set_xlabel('Epoch') ax1.set_ylabel('Loss') ax1.legend() # Plot accuracy - ax2.plot(history['train_acc'], label='Training Accuracy') - ax2.plot(history['val_acc'], label='Validation Accuracy') + ax2.plot(self.history['train_acc'], label='Training Accuracy') + ax2.plot(self.history['val_acc'], label='Validation Accuracy') ax2.set_title('Accuracy Over Time') ax2.set_xlabel('Epoch') ax2.set_ylabel('Accuracy') @@ -131,32 +208,23 @@ def plot_training_history(history): plt.tight_layout() return fig - @staticmethod - def plot_attention_maps(model, image, pred_class, true_class=None): - """Plot attention maps""" - with torch.no_grad(): - _ = model(image.unsqueeze(0)) - attention = model.get_attention_weights() - attention = attention.mean(1).squeeze() + def evaluate(self, test_loader, classes): + """ + Evaluate model on test set + """ + self.model.eval() + y_true = [] + y_pred = [] - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) - - ax1.imshow(image.permute(1, 2, 0).cpu()) - ax1.axis('off') - ax1.set_title('Original Image') - - sns.heatmap(attention.cpu(), ax=ax2, cmap='viridis') - title = f'Attention Map (Pred: {pred_class})' - if true_class is not None: - title += f' (True: {true_class})' - ax2.set_title(title) - - plt.tight_layout() - return fig - - @staticmethod - def plot_confusion_matrix(y_true, y_pred, classes): - """Plot confusion matrix""" + with torch.no_grad(): + for inputs, targets in tqdm(test_loader, desc='Evaluating'): + inputs = inputs.to(self.device) + outputs = self.model(inputs) + _, preds = torch.max(outputs, 1) + y_true.extend(targets.cpu().numpy()) + y_pred.extend(preds.cpu().numpy()) + + # Generate confusion matrix cm = confusion_matrix(y_true, y_pred) plt.figure(figsize=(10, 8)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', @@ -164,188 +232,169 @@ def plot_confusion_matrix(y_true, y_pred, classes): plt.title('Confusion Matrix') plt.ylabel('True Label') plt.xlabel('Predicted Label') - return plt.gcf() - @staticmethod - def generate_classification_report(y_true, y_pred, classes): - """Generate classification report""" + # Generate classification report report = classification_report(y_true, y_pred, target_names=classes, output_dict=True) - df_report = pd.DataFrame(report).transpose() - - plt.figure(figsize=(10, 6)) - sns.heatmap(df_report.iloc[:-3, :-1].astype(float), annot=True, cmap='Blues', fmt='.2f') - plt.title('Classification Report') - return plt.gcf() - -def train_epoch(model, loader, optimizer, criterion, device): - """Run one training epoch""" - model.train() - running_loss = 0.0 - correct = 0 - total = 0 - - pbar = tqdm(loader, desc='Training', leave=False) - for images, labels in pbar: - images, labels = images.to(device), labels.to(device) - optimizer.zero_grad() - outputs = model(images) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - running_loss += loss.item() - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels).sum().item() - - pbar.set_postfix({'loss': f'{loss.item():.4f}', - 'acc': f'{(correct/total)*100:.2f}%'}) - - return running_loss / len(loader), correct / total - -def validate_epoch(model, loader, criterion, device): - """Run one validation epoch""" - model.eval() - running_loss = 0.0 - correct = 0 - total = 0 - - with torch.no_grad(): - pbar = tqdm(loader, desc='Validation', leave=False) - for images, labels in pbar: - images, labels = images.to(device), labels.to(device) - outputs = model(images) - loss = criterion(outputs, labels) - - running_loss += loss.item() - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels).sum().item() + return y_true, y_pred, report + +def train_model_optimized(model, train_dataset, val_dataset, test_dataset=None, + epochs=20, batch_size=32, lr=1e-4, classes=None, + early_stopping_patience=4): + """ + Main training function with optimizations and monitoring + """ + # Setup device and optimization + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + torch.backends.cudnn.benchmark = True + num_workers = min(mp.cpu_count(), 8) + + # Optimize batch size based on GPU memory + if torch.cuda.is_available(): + gpu_memory = torch.cuda.get_device_properties(0).total_memory + batch_size = min(batch_size, int(gpu_memory / (1024**3) * 4)) + + # Create data loaders + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + num_workers=num_workers, + pin_memory=True, + persistent_workers=True, + prefetch_factor=2 + ) - pbar.set_postfix({'loss': f'{loss.item():.4f}', - 'acc': f'{(correct/total)*100:.2f}%'}) + val_loader = DataLoader( + val_dataset, + batch_size=batch_size * 2, + shuffle=False, + num_workers=num_workers, + pin_memory=True, + persistent_workers=True, + prefetch_factor=2 + ) - return running_loss / len(loader), correct / total + # Initialize optimizer and scheduler + optimizer = torch.optim.AdamW( + model.parameters(), + lr=lr, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0.01 + ) -def train_model(model, train_loader, val_loader, epochs, lr, device, early_stopping_patience=4): - """Main training function""" - optimizer = torch.optim.Adam(model.parameters(), lr=lr) - criterion = nn.CrossEntropyLoss() - checkpointer = ModelCheckpointing() - visualizer = VisualizationUtils() + scheduler = torch.optim.lr_scheduler.OneCycleLR( + optimizer, + max_lr=lr * 10, + epochs=epochs, + steps_per_epoch=len(train_loader), + pct_start=0.3, + anneal_strategy='cos' + ) - print(f"\nStarting training on device: {device}") - print(f"Checkpoints will be saved to: {checkpointer.save_dir}") + # Initialize trainer + trainer = OptimizedTrainer( + model=model, + train_loader=train_loader, + val_loader=val_loader, + device=device, + mixed_precision=True, + distributed=(torch.cuda.device_count() > 1) + ) + # Training loop + criterion = nn.CrossEntropyLoss() start_time = time.time() stopping_epoch = epochs down_consec = 0 + print(f"\nStarting training on device: {device}") + print(f"Batch size: {batch_size}, Learning rate: {lr}") + for epoch in range(epochs): epoch_start = time.time() - # Training - train_loss, train_acc = train_epoch(model, train_loader, optimizer, criterion, device) - - # Validation - val_loss, val_acc = validate_epoch(model, val_loader, criterion, device) + # Training and validation + train_loss, train_acc = trainer.train_epoch(optimizer, criterion, scheduler) + val_loss, val_acc = trainer.validate_epoch(criterion) # Check if best model - is_best = val_acc > checkpointer.best_val_acc + is_best = val_acc > trainer.best_val_acc # Save checkpoint - checkpoint_path = checkpointer.save_checkpoint( - model, optimizer, epoch, train_loss, val_loss, + checkpoint_path = trainer.save_checkpoint( + optimizer, epoch, train_loss, val_loss, train_acc, val_acc, lr, is_best ) + # Print epoch results epoch_time = time.time() - epoch_start print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') - print(f'Checkpoint saved: {checkpoint_path}') - - if is_best: - print(f'New best model saved with validation accuracy: {val_acc*100:.2f}%') # Early stopping check - if epoch > 0 and val_acc < checkpointer.history['val_acc'][-2]: + if epoch > 0 and val_acc < trainer.history['val_acc'][-2]: down_consec += 1 - print(f'Validation accuracy decreased. Counter: {down_consec}/{early_stopping_patience}') + if down_consec >= early_stopping_patience: + print('\nEarly stopping triggered!') + stopping_epoch = epoch + 1 + break else: down_consec = 0 - if down_consec >= early_stopping_patience: - print('\nEarly stopping triggered!') - stopping_epoch = epoch + 1 - break + # Final evaluation + if test_dataset is not None and classes is not None: + test_loader = DataLoader( + test_dataset, + batch_size=batch_size * 2, + shuffle=False, + num_workers=num_workers, + pin_memory=True + ) + y_true, y_pred, report = trainer.evaluate(test_loader, classes) - print('-' * 60) + # Plot and save training curves + trainer.plot_training_history() + plt.savefig(os.path.join(trainer.save_dir, 'training_curves.png')) total_time = time.time() - start_time print(f'\nTraining completed in {total_time/60:.1f} minutes') - print(f'Best validation accuracy: {checkpointer.best_val_acc*100:.2f}%') - - # Save final history - history_path = checkpointer.save_training_history() - print(f'Training history saved: {history_path}') - - # Plot training curves - visualizer.plot_training_history(checkpointer.history) - plt.savefig(os.path.join(checkpointer.save_dir, 'training_curves.png')) - - return model, checkpointer.history - -def evaluate_model(model, test_loader, device, classes): - """Evaluate model on test set""" - model.eval() - y_true = [] - y_pred = [] - visualizer = VisualizationUtils() + print(f'Best validation accuracy: {trainer.best_val_acc*100:.2f}%') - with torch.no_grad(): - for images, labels in tqdm(test_loader, desc='Evaluating'): - images = images.to(device) - outputs = model(images) - _, preds = torch.max(outputs, 1) - y_true.extend(labels.cpu().numpy()) - y_pred.extend(preds.cpu().numpy()) - - # Generate evaluation plots - cm_fig = visualizer.plot_confusion_matrix(y_true, y_pred, classes) - cm_fig.savefig('confusion_matrix.png') - - report_fig = visualizer.generate_classification_report(y_true, y_pred, classes) - report_fig.savefig('classification_report.png') - - return y_true, y_pred + return model, trainer.history def main(): """Main execution function""" - # Device configuration + # Device configuration and system information device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - # Print system information print("\nSystem Information:") print(f"PyTorch version: {torch.__version__}") print(f"Device being used: {device}") print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") + if torch.cuda.is_available(): + for i in range(torch.cuda.device_count()): + gpu_props = torch.cuda.get_device_properties(i) + print(f"GPU {i}: {gpu_props.name} ({gpu_props.total_memory / 1024**3:.1f} GB)") # Hyperparameters BATCH_SIZE = 32 EPOCHS = 20 LR = 1e-4 CLASSES = ['CN', 'MCI', 'AD', 'SMC'] + EARLY_STOPPING_PATIENCE = 4 print("\nHyperparameters:") print(f"Batch size: {BATCH_SIZE}") print(f"Number of epochs: {EPOCHS}") print(f"Learning rate: {LR}") + print(f"Early stopping patience: {EARLY_STOPPING_PATIENCE}") - # Initialize data loaders + # Initialize data print("\nLoading data...") - train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) - test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) + # Get datasets instead of dataloaders since our framework will create optimized loaders + train_dataset, val_dataset = get_dataset(train=True) # Assuming you modify get_dataloader to return datasets + test_dataset = get_dataset(train=False) print("Data loaded successfully!") # Initialize model @@ -360,24 +409,87 @@ def main(): print(f"Total parameters: {total_params:,}") print(f"Trainable parameters: {trainable_params:,}") - # Train model - model, history = train_model( - model=model, - train_loader=train_loader, - val_loader=val_loader, - epochs=EPOCHS, - lr=LR, - device=device - ) + # Additional optimization information + if torch.cuda.is_available(): + torch.backends.cudnn.benchmark = True + print("\nOptimization Settings:") + print("cuDNN benchmark mode: Enabled") + print(f"Mixed precision training: Enabled") + print(f"Distributed training: {'Enabled' if torch.cuda.device_count() > 1 else 'Disabled'}") + + # Train model using optimized framework + try: + model, history = train_model_optimized( + model=model, + train_dataset=train_dataset, + val_dataset=val_dataset, + test_dataset=test_dataset, + epochs=EPOCHS, + batch_size=BATCH_SIZE, + lr=LR, + classes=CLASSES, + early_stopping_patience=EARLY_STOPPING_PATIENCE + ) - # Evaluate model - y_true, y_pred = evaluate_model(model, test_loader, device, CLASSES) + print("\nTraining completed successfully!") + + # Print final metrics + final_train_acc = history['train_acc'][-1] + final_val_acc = history['val_acc'][-1] + best_val_acc = max(history['val_acc']) + best_epoch = history['val_acc'].index(best_val_acc) + 1 + + print("\nFinal Results:") + print(f"Best validation accuracy: {best_val_acc*100:.2f}% (Epoch {best_epoch})") + print(f"Final training accuracy: {final_train_acc*100:.2f}%") + print(f"Final validation accuracy: {final_val_acc*100:.2f}%") + + # Save complete training results + results = { + 'hyperparameters': { + 'batch_size': BATCH_SIZE, + 'epochs': EPOCHS, + 'learning_rate': LR, + 'early_stopping_patience': EARLY_STOPPING_PATIENCE + }, + 'model_info': { + 'total_parameters': total_params, + 'trainable_parameters': trainable_params + }, + 'training_history': history, + 'final_metrics': { + 'best_val_acc': best_val_acc, + 'best_epoch': best_epoch, + 'final_train_acc': final_train_acc, + 'final_val_acc': final_val_acc + } + } - print("\nTraining and evaluation completed successfully!") - print("Check the output directory for visualization plots and model checkpoints.") + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + with open(f'training_results_{timestamp}.json', 'w') as f: + json.dump(results, f, indent=4) + + print("\nResults have been saved. Check the output directory for:") + print("- Model checkpoints") + print("- Training curves") + print("- Confusion matrix") + print("- Complete training results JSON") + + except Exception as e: + print(f"\nError during training: {str(e)}") + raise + + return model, history if __name__ == "__main__": - main() + try: + main() + except KeyboardInterrupt: + print("\nTraining interrupted by user") + except Exception as e: + print(f"\nAn error occurred: {str(e)}") + raise + # import torch # import torch.nn as nn From 289833ff803056770fa5ecb5ea8f13537d66e5e2 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 16:19:29 +1000 Subject: [PATCH 13/37] feat: update module to adapt to pretrained model & greyscale img --- recognition/modules.py | 86 +++++++++++++++++++++++++++++++----------- 1 file changed, 65 insertions(+), 21 deletions(-) diff --git a/recognition/modules.py b/recognition/modules.py index da77194e8..a232f12db 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -6,44 +6,88 @@ import torch import torch.nn as nn from torchvision.models import vit_b_16, ViT_B_16_Weights -from torch.utils.data import DataLoader +from torch.utils.data import DataLoader, Dataset from sklearn.model_selection import train_test_split +import torchvision +from torchvision import transforms -class ADNIDataset(torch.utils.data.Dataset): + +class ADNIDataset(Dataset): """ - PyTorch dataset class for the ADNI brain imaging data. + Modified Dataset class to handle grayscale images """ - def __init__(self, data_dir, transform=None): - self.data_dir = data_dir + def __init__(self, root_path, train=True, transform=None): + self.path = Path(root_path, 'train' if train else 'test') self.transform = transform - self.image_paths = [] + + # Initialize lists for each class + self.files = [] self.labels = [] - # Load data and labels - for label in ['normal', 'ad']: - label_dir = os.path.join(data_dir, label) - for filename in os.listdir(label_dir): - self.image_paths.append(os.path.join(label_dir, filename)) - self.labels.append(0 if label == 'normal' else 1) + # Define class mapping + self.class_to_idx = { + 'CN': 0, # Cognitively Normal + 'MCI': 1, # Mild Cognitive Impairment + 'AD': 2, # Alzheimer's Disease + 'SMC': 3 # Subjective Memory Concern + } + + # Load files for each class + for class_name in self.class_to_idx.keys(): + class_path = Path(self.path, class_name) + if class_path.exists(): + class_files = list(class_path.glob('*.jpg')) + list(class_path.glob('*.png')) + self.files.extend(class_files) + self.labels.extend([self.class_to_idx[class_name]] * len(class_files)) def __len__(self): - return len(self.image_paths) + return len(self.files) def __getitem__(self, idx): - image = Image.open(self.image_paths[idx]) + img_path = self.files[idx] + label = self.labels[idx] + + # Load as grayscale directly + image = Image.open(img_path).convert('L') # 'L' mode for grayscale + if self.transform: image = self.transform(image) - return image, self.labels[idx] + + return image, label class ViTClassifier(nn.Module): """ - Vision Transformer model for Alzheimer's classification. + Modified Vision Transformer for grayscale images """ - def __init__(self, num_classes=2): + def __init__(self, num_classes=4): super(ViTClassifier, self).__init__() - self.vit = vit_b_16(weights=ViT_B_16_Weights.IMAGENET1K_V1) - self.vit.heads.head = nn.Linear(self.vit.heads.head.in_features, num_classes) - def forward(self, x): - return self.vit(x) + # Load the pre-trained ViT model + self.vit = torchvision.models.vit_b_16(pretrained=True) + + # Modify the first layer to accept grayscale input + # Create new patch embedding layer with 1 input channel instead of 3 + new_patch_embed = nn.Conv2d( + in_channels=1, # Changed from 3 to 1 for grayscale + out_channels=768, + kernel_size=16, + stride=16 + ) + + # Initialize the weights of the new layer + # Average the weights across the RGB channels + with torch.no_grad(): + new_patch_embed.weight = nn.Parameter( + self.vit.conv_proj.weight.sum(dim=1, keepdim=True) / 3.0 + ) + new_patch_embed.bias = nn.Parameter(self.vit.conv_proj.bias) + # Replace the patch embedding layer + self.vit.conv_proj = new_patch_embed + + # Modify the classifier head for our number of classes + num_features = self.vit.heads.head.in_features + self.vit.heads.head = nn.Linear(num_features, num_classes) + + def forward(self, x): + return self.vit(x) \ No newline at end of file From f11f1975bbf76c920aba9f32ec89b32b7a365f84 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 16:48:42 +1000 Subject: [PATCH 14/37] improvement: double the batch size to train faster --- recognition/train.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/recognition/train.py b/recognition/train.py index 3d7546653..5d9f628dd 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -378,7 +378,7 @@ def main(): print(f"GPU {i}: {gpu_props.name} ({gpu_props.total_memory / 1024**3:.1f} GB)") # Hyperparameters - BATCH_SIZE = 32 + BATCH_SIZE = 64 EPOCHS = 20 LR = 1e-4 CLASSES = ['CN', 'MCI', 'AD', 'SMC'] @@ -390,16 +390,19 @@ def main(): print(f"Learning rate: {LR}") print(f"Early stopping patience: {EARLY_STOPPING_PATIENCE}") - # Initialize data + # Initialize data with specific proportions print("\nLoading data...") - # Get datasets instead of dataloaders since our framework will create optimized loaders - train_dataset, val_dataset = get_dataset(train=True) # Assuming you modify get_dataloader to return datasets + train_dataset, val_dataset = get_dataset( + train=True, + val_proportion=0.2, # 20% validation split + keep_proportion=1.0 # Use full dataset + ) test_dataset = get_dataset(train=False) print("Data loaded successfully!") # Initialize model print("\nInitializing model...") - model = ViTClassifier().to(device) + model = ViTClassifier(num_classes=4).to(device) print("Model initialized successfully!") # Print model summary From ecad71cfb6b533c9edd992a1fd59511e1423bc0a Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 17:22:03 +1000 Subject: [PATCH 15/37] improvement: update dataset to match the grayscale/ save-load --- recognition/dataset.py | 44 +++++++++++++++++++++++++++++++++++++----- 1 file changed, 39 insertions(+), 5 deletions(-) diff --git a/recognition/dataset.py b/recognition/dataset.py index 81029aacc..b82796481 100644 --- a/recognition/dataset.py +++ b/recognition/dataset.py @@ -21,14 +21,18 @@ TRAIN_TRANSFORM = transforms.Compose([ transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), + transforms.RandomRotation(10), transforms.ToTensor(), - transforms.Lambda(lambda x: x.repeat(3, 1, 1)) + # Normalize with grayscale statistics + transforms.Normalize(mean=[0.485], std=[0.229]) ]) # Testing image transforms TEST_TRANSFORM = transforms.Compose([ transforms.CenterCrop(224), transforms.ToTensor(), + # Normalize with grayscale statistics + transforms.Normalize(mean=[0.485], std=[0.229]) ]) # Rest of the original dataset.py content remains the same @@ -110,10 +114,40 @@ def get_dataloader(batch_size, train: bool, val_proportion: float = 0.2, keep_pr loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) return loader -def get_dataset(train=True): +def get_dataset(train=True, val_proportion: float = 0.2, keep_proportion: float = 1.0): + """ + Returns datasets for either training/validation or testing. + + Args: + train (bool): If True, returns training and validation datasets. If False, returns test dataset. + val_proportion (float): Proportion of training data to use for validation (only used if train=True) + keep_proportion (float): Proportion of total data to keep (for debugging purposes) + + Returns: + If train=True: tuple (train_dataset, val_dataset) + If train=False: test_dataset + """ if train: - train_dataset = ADNIDataset(train=True) - val_dataset = ADNIDataset(val=True) + # Initialize training dataset with proper transforms + train_dataset = ADNIDataset( + root_path=ADNI_PATH, + train=True, + transform=TRAIN_TRANSFORM + ) + + # Split into training and validation sets + train_dataset, val_dataset = split_train_val( + dataset=train_dataset, + val_proportion=val_proportion, + keep_proportion=keep_proportion + ) + return train_dataset, val_dataset else: - return ADNIDataset(test=True) \ No newline at end of file + # Return test dataset + test_dataset = ADNIDataset( + root_path=ADNI_PATH, + train=False, + transform=TEST_TRANSFORM + ) + return test_dataset \ No newline at end of file From aa2ab7e40b0d788a435aac306d51fb86e6cd5ff9 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 17:23:05 +1000 Subject: [PATCH 16/37] GITIGNORE: ignore trained models --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index b2450a247..9da730100 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ HipMRI_study_complete_release_v1/.DS_Store /HipMRI_study_complete_release_v1 /AD_NC /recognition/AD_NC +*.pt From 1340bdad59ad75e889159fec48a404b4fe53cfda Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 18:22:28 +1000 Subject: [PATCH 17/37] feat: move testing to predict.py --- recognition/predict.py | 206 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 204 insertions(+), 2 deletions(-) diff --git a/recognition/predict.py b/recognition/predict.py index 026365de2..a7d3d57d4 100644 --- a/recognition/predict.py +++ b/recognition/predict.py @@ -1,6 +1,208 @@ """ -This module is used to show example usage the trained GFNet model by using the model to predict +This module is used to show example usage the trained ViT model by using the model to predict on a testing set from the ADNI brain dataset Evaluation metrics will be printed and evaluation figures will be saved to the current folder. -""" \ No newline at end of file +""" + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from tqdm import tqdm +from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc +import pandas as pd +from pathlib import Path +import json +from datetime import datetime + +from dataset import get_dataset +from modules import ViTClassifier + +def load_trained_model(model_path, device): + """ + Load a trained model from checkpoint + """ + # Initialize model architecture + model = ViTClassifier(num_classes=4).to(device) + + # Load checkpoint + checkpoint = torch.load(model_path, map_location=device) + + # Handle different checkpoint formats + if 'model_state_dict' in checkpoint: + model.load_state_dict(checkpoint['model_state_dict']) + else: + model.load_state_dict(checkpoint) + + return model + +def evaluate_model(model, test_loader, device, classes): + """ + Evaluate model performance on test set + """ + model.eval() + + # Initialize lists to store predictions and true labels + all_preds = [] + all_labels = [] + all_probs = [] + + # Testing loop + with torch.no_grad(): + for images, labels in tqdm(test_loader, desc="Evaluating"): + images = images.to(device) + outputs = model(images) + probabilities = torch.nn.functional.softmax(outputs, dim=1) + _, predicted = torch.max(outputs.data, 1) + + all_preds.extend(predicted.cpu().numpy()) + all_labels.extend(labels.numpy()) + all_probs.extend(probabilities.cpu().numpy()) + + return np.array(all_preds), np.array(all_labels), np.array(all_probs) + +def plot_confusion_matrix(y_true, y_pred, classes, save_path): + """ + Plot and save confusion matrix + """ + # Get unique classes actually present in the data + present_classes = np.unique(np.concatenate([y_true, y_pred])) + present_class_names = [classes[i] for i in present_classes] + + cm = confusion_matrix(y_true, y_pred) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=present_class_names, + yticklabels=present_class_names) + plt.title('Confusion Matrix') + plt.ylabel('True Label') + plt.xlabel('Predicted Label') + plt.tight_layout() + plt.savefig(save_path / 'confusion_matrix.png') + plt.close() + + # Calculate per-class accuracy + per_class_accuracy = cm.diagonal() / cm.sum(axis=1) + return per_class_accuracy, present_class_names + +def evaluate_model(model, test_loader, device, classes): + """ + Evaluate model performance on test set + """ + model.eval() + + # Initialize lists to store predictions and true labels + all_preds = [] + all_labels = [] + all_probs = [] + + # Testing loop + with torch.no_grad(): + for images, labels in tqdm(test_loader, desc="Evaluating"): + images = images.to(device) + outputs = model(images) + probabilities = torch.nn.functional.softmax(outputs, dim=1) + _, predicted = torch.max(outputs.data, 1) + + all_preds.extend(predicted.cpu().numpy()) + all_labels.extend(labels.numpy()) + all_probs.extend(probabilities.cpu().numpy()) + + return np.array(all_preds), np.array(all_labels), np.array(all_probs) + +def main(): + # Configuration + BATCH_SIZE = 32 + CLASSES = ['CN', 'MCI', 'AD', 'SMC'] + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # Create results directory + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + results_dir = Path(f'evaluation_results_{timestamp}') + results_dir.mkdir(exist_ok=True) + + print(f"\nEvaluation Results will be saved to: {results_dir}") + + # Load test dataset + print("\nLoading test dataset...") + test_dataset = get_dataset(train=False) + test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) + print(f"Test set size: {len(test_dataset)} images") + + # Load model + model_path = "./checkpoints/best_model_20241029_170413.pt" + print(f"Loading model from {model_path}...") + model = load_trained_model(model_path, device) + model.eval() + + # Evaluate model + print("\nEvaluating model...") + predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) + + # Get unique classes present in the data + present_classes = np.unique(true_labels) + present_class_names = [CLASSES[i] for i in present_classes] + + print("\nCalculating metrics...") + + # Classification report with only present classes + report = classification_report( + true_labels, + predictions, + labels=present_classes, + target_names=present_class_names, + output_dict=True + ) + + # Per-class accuracy from confusion matrix + per_class_accuracy, matrix_classes = plot_confusion_matrix( + true_labels, predictions, CLASSES, results_dir + ) + + # Plot ROC curves only for present classes + if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes + plot_roc_curves(true_labels, probabilities[:, present_classes], + present_class_names, results_dir) + + # Compile metrics + metrics = { + 'classification_report': report, + 'per_class_accuracy': { + class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) + }, + 'overall_accuracy': (predictions == true_labels).mean(), + 'model_path': model_path, + 'evaluation_date': timestamp, + 'test_set_size': len(test_dataset), + 'classes_present': present_class_names + } + + # Save metrics + save_metrics(metrics, results_dir) + + # Print summary + print("\nEvaluation Results Summary:") + print(f"Classes present in test set: {', '.join(present_class_names)}") + print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") + print("\nPer-class Accuracy:") + for class_name, acc in metrics['per_class_accuracy'].items(): + print(f"{class_name}: {acc*100:.2f}%") + + print(f"\nDetailed results have been saved to {results_dir}") + print("Files generated:") + print("- confusion_matrix.png") + if len(present_classes) > 1: + print("- roc_curves.png") + print("- evaluation_metrics.json") + +if __name__ == "__main__": + try: + main() + except KeyboardInterrupt: + print("\nEvaluation interrupted by user") + except Exception as e: + print(f"\nAn error occurred: {str(e)}") + raise \ No newline at end of file From dac6d29356e9c8ea78db690fcbbe74b1babcf4d6 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 19:10:00 +1000 Subject: [PATCH 18/37] feat: plot_roc_curves & save_metrics --- .../confusion_matrix.png | Bin 0 -> 25499 bytes recognition/predict.py | 90 ++++++++++++------ 2 files changed, 62 insertions(+), 28 deletions(-) create mode 100644 recognition/evaluation_results_20241029_182005/confusion_matrix.png diff --git a/recognition/evaluation_results_20241029_182005/confusion_matrix.png 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Testing loop - with torch.no_grad(): - for images, labels in tqdm(test_loader, desc="Evaluating"): - images = images.to(device) - outputs = model(images) - probabilities = torch.nn.functional.softmax(outputs, dim=1) - _, predicted = torch.max(outputs.data, 1) - - all_preds.extend(predicted.cpu().numpy()) - all_labels.extend(labels.numpy()) - all_probs.extend(probabilities.cpu().numpy()) - - return np.array(all_preds), np.array(all_labels), np.array(all_probs) - def plot_confusion_matrix(y_true, y_pred, classes, save_path): """ Plot and save confusion matrix @@ -74,7 +49,7 @@ def plot_confusion_matrix(y_true, y_pred, classes, save_path): cm = confusion_matrix(y_true, y_pred) plt.figure(figsize=(10, 8)) - sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + sns.heatmap(cm, annot=True, fmt='d', cmap='magma', xticklabels=present_class_names, yticklabels=present_class_names) plt.title('Confusion Matrix') @@ -88,6 +63,36 @@ def plot_confusion_matrix(y_true, y_pred, classes, save_path): per_class_accuracy = cm.diagonal() / cm.sum(axis=1) return per_class_accuracy, present_class_names +def plot_roc_curves(y_true, y_prob, classes, save_path): + """ + Plot ROC curves for each class + """ + plt.figure(figsize=(10, 8)) + colors = ["orange", "darkcyan"] + + # Convert true labels to one-hot encoding for present classes only + n_classes = len(classes) + y_true_onehot = np.zeros((len(y_true), n_classes)) + for i in range(n_classes): + y_true_onehot[:, i] = (y_true == i) + + # Calculate ROC curve and AUC for each class + for i in range(n_classes): + fpr, tpr, _ = roc_curve(y_true_onehot[:, i], y_prob[:, i]) + roc_auc = auc(fpr, tpr) + plt.plot(fpr, tpr, label=f'{classes[i]} (AUC = {roc_auc:.2f})', color=colors[i]) + + plt.plot([0, 1], [0, 1], 'k--') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title('Receiver Operating Characteristic (ROC) Curves') + plt.legend(loc="lower right") + plt.tight_layout() + plt.savefig(save_path / 'roc_curves.png') + plt.close() + def evaluate_model(model, test_loader, device, classes): """ Evaluate model performance on test set @@ -113,9 +118,36 @@ def evaluate_model(model, test_loader, device, classes): return np.array(all_preds), np.array(all_labels), np.array(all_probs) +def save_metrics(metrics, save_path): + """ + Save evaluation metrics to a JSON file. + + Args: + metrics (dict): Dictionary containing evaluation metrics + save_path (Path): Directory path where metrics should be saved + """ + # Convert numpy values to Python native types for JSON serialization + def convert_numpy(obj): + if isinstance(obj, np.integer): + return int(obj) + elif isinstance(obj, np.floating): + return float(obj) + elif isinstance(obj, np.ndarray): + return obj.tolist() + return obj + + # Convert all numpy values in the metrics dictionary + metrics_json = {k: convert_numpy(v) if isinstance(v, (dict, np.generic, np.ndarray)) + else v for k, v in metrics.items()} + + # Save to JSON file + metrics_file = save_path / 'evaluation_metrics.json' + with open(metrics_file, 'w') as f: + json.dump(metrics_json, f, indent=4, sort_keys=True, default=convert_numpy) + def main(): # Configuration - BATCH_SIZE = 32 + BATCH_SIZE = 64 CLASSES = ['CN', 'MCI', 'AD', 'SMC'] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -133,7 +165,7 @@ def main(): print(f"Test set size: {len(test_dataset)} images") # Load model - model_path = "./checkpoints/best_model_20241029_170413.pt" + model_path = "./checkpoints/best_model_20241029_175957.pt" print(f"Loading model from {model_path}...") model = load_trained_model(model_path, device) model.eval() @@ -180,6 +212,8 @@ def main(): 'classes_present': present_class_names } + print(metrics) + # Save metrics save_metrics(metrics, results_dir) From c41cb961e30c278a691bde5b7a645c63d5fb68ed Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 22:29:17 +1000 Subject: [PATCH 19/37] feat: EnhancedViTClassifier --- 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z%X9_F4#<2Wu%#BZ4uCk;&~9-jCuhKBDdNx1!tqGLQ;4PBhjzaH8QXW8AHmkzSjKA$ zy_Bj7TA2#6>)J0?yIrU+NBl1#%e)p=pJanRRg-{UQAW+`zYp6QibDE z1deTD5#hnq5Br`FQ9BA|3#$_{mUjo?@WV=iH0eDIy@04J p0txQ}U3lZWhEo48UgC>9-EUvFef-D!aA5**@1Y;FemHUA{{t7=dshGe diff --git a/recognition/modules.py b/recognition/modules.py index a232f12db..b901df1ab 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -5,55 +5,8 @@ import torch import torch.nn as nn -from torchvision.models import vit_b_16, ViT_B_16_Weights -from torch.utils.data import DataLoader, Dataset -from sklearn.model_selection import train_test_split -import torchvision -from torchvision import transforms - - -class ADNIDataset(Dataset): - """ - Modified Dataset class to handle grayscale images - """ - def __init__(self, root_path, train=True, transform=None): - self.path = Path(root_path, 'train' if train else 'test') - self.transform = transform - - # Initialize lists for each class - self.files = [] - self.labels = [] - - # Define class mapping - self.class_to_idx = { - 'CN': 0, # Cognitively Normal - 'MCI': 1, # Mild Cognitive Impairment - 'AD': 2, # Alzheimer's Disease - 'SMC': 3 # Subjective Memory Concern - } - - # Load files for each class - for class_name in self.class_to_idx.keys(): - class_path = Path(self.path, class_name) - if class_path.exists(): - class_files = list(class_path.glob('*.jpg')) + list(class_path.glob('*.png')) - self.files.extend(class_files) - self.labels.extend([self.class_to_idx[class_name]] * len(class_files)) - - def __len__(self): - return len(self.files) - - def __getitem__(self, idx): - img_path = self.files[idx] - label = self.labels[idx] - - # Load as grayscale directly - image = Image.open(img_path).convert('L') # 'L' mode for grayscale - - if self.transform: - image = self.transform(image) - - return image, label +from torchvision.models import vit_b_16 +import torch.nn.functional as F class ViTClassifier(nn.Module): """ @@ -63,7 +16,7 @@ def __init__(self, num_classes=4): super(ViTClassifier, self).__init__() # Load the pre-trained ViT model - self.vit = torchvision.models.vit_b_16(pretrained=True) + self.vit = vit_b_16(pretrained=True) # Modify the first layer to accept grayscale input # Create new patch embedding layer with 1 input channel instead of 3 @@ -90,4 +43,98 @@ def __init__(self, num_classes=4): self.vit.heads.head = nn.Linear(num_features, num_classes) def forward(self, x): - return self.vit(x) \ No newline at end of file + return self.vit(x) + +class EnhancedViTClassifier(nn.Module): + """ + Enhanced Vision Transformer for grayscale medical images with: + - Data normalization + - Dropout for regularization + - Feature augmentation + - Residual connections + - Label smoothing support + """ + def __init__(self, num_classes=4, dropout_rate=0.2, feature_dropout=0.1): + super(EnhancedViTClassifier, self).__init__() + + # Load the pre-trained ViT model + self.vit = vit_b_16(pretrained=True) + + # Modify first layer for grayscale input with careful initialization + new_patch_embed = nn.Conv2d( + in_channels=1, + out_channels=768, + kernel_size=16, + stride=16 + ) + + # Initialize weights with scaled averaging + with torch.no_grad(): + rgb_weights = self.vit.conv_proj.weight + # Use sophisticated weight initialization for grayscale + grayscale_weights = (0.2989 * rgb_weights[:, 0:1, :, :] + + 0.5870 * rgb_weights[:, 1:2, :, :] + + 0.1140 * rgb_weights[:, 2:3, :, :]) + new_patch_embed.weight = nn.Parameter(grayscale_weights) + new_patch_embed.bias = nn.Parameter(self.vit.conv_proj.bias) + + self.vit.conv_proj = new_patch_embed + + # Feature extraction layers + num_features = self.vit.heads.head.in_features + self.feature_dropout = nn.Dropout(feature_dropout) + + # Additional layers for better feature representation + self.feature_enhancement = nn.Sequential( + nn.Linear(num_features, num_features), + nn.LayerNorm(num_features), + nn.GELU(), + nn.Dropout(dropout_rate) + ) + + # Final classification layers with dropout + self.classifier = nn.Sequential( + nn.Linear(num_features, num_features // 2), + nn.LayerNorm(num_features // 2), + nn.GELU(), + nn.Dropout(dropout_rate), + nn.Linear(num_features // 2, num_classes) + ) + + # Initialize new layers + self._initialize_weights() + + # Batch normalization for input + self.input_norm = nn.BatchNorm2d(1) + + def _initialize_weights(self): + """Initialize the weights using He initialization""" + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, label_smoothing=0.1): + # Input normalization + x = self.input_norm(x) + + # Get ViT features + features = self.vit.encoder(self.vit._process_input(x)) + features = self.vit.heads.head_drop(features[:, 0]) + + # Feature enhancement with residual connection + enhanced_features = self.feature_enhancement(features) + features = features + enhanced_features # Residual connection + + # Apply feature dropout + features = self.feature_dropout(features) + + # Final classification + logits = self.classifier(features) + + return logits + + def get_attention_weights(self): + """Extract attention weights for visualization""" + return self.vit.encoder.layers[-1].self_attention.attention_probs \ No newline at end of file diff --git a/recognition/predict.py b/recognition/predict.py index c321acf83..3b320fa50 100644 --- a/recognition/predict.py +++ b/recognition/predict.py @@ -165,7 +165,7 @@ def main(): print(f"Test set size: {len(test_dataset)} images") # Load model - model_path = "./checkpoints/best_model_20241029_175957.pt" + model_path = "./checkpoints/0/checkpoint_epoch_1_20241029_175957.pt" print(f"Loading model from {model_path}...") model = load_trained_model(model_path, device) model.eval() diff --git a/recognition/train.py b/recognition/train.py index 5d9f628dd..e9b0170fd 100644 --- a/recognition/train.py +++ b/recognition/train.py @@ -382,7 +382,7 @@ def main(): EPOCHS = 20 LR = 1e-4 CLASSES = ['CN', 'MCI', 'AD', 'SMC'] - EARLY_STOPPING_PATIENCE = 4 + EARLY_STOPPING_PATIENCE = 2 print("\nHyperparameters:") print(f"Batch size: {BATCH_SIZE}") From d089c3005b56c6e97bc44fc142f48ca2874ddfe5 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 22:41:17 +1000 Subject: [PATCH 20/37] debug: Modify first layer for grayscale input --- recognition/modules.py | 87 +++++++++++++++++++++++++++++++++++++----- 1 file changed, 77 insertions(+), 10 deletions(-) diff --git a/recognition/modules.py b/recognition/modules.py index b901df1ab..6e6a8e9a7 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -53,25 +53,54 @@ class EnhancedViTClassifier(nn.Module): - Feature augmentation - Residual connections - Label smoothing support + - Proper image size handling """ - def __init__(self, num_classes=4, dropout_rate=0.2, feature_dropout=0.1): + def __init__(self, num_classes=4, dropout_rate=0.2, feature_dropout=0.1, image_size=224): super(EnhancedViTClassifier, self).__init__() + self.image_size = image_size # Load the pre-trained ViT model self.vit = vit_b_16(pretrained=True) - # Modify first layer for grayscale input with careful initialization + # Calculate number of patches + self.patch_size = 16 + self.num_patches = (image_size // self.patch_size) ** 2 + + # Modify position embedding for our sequence length + old_pos_embed = self.vit.encoder.pos_embedding + new_pos_embed = nn.Parameter( + torch.zeros(1, self.num_patches + 1, old_pos_embed.shape[2]) + ) + + # Initialize new position embeddings + # Copy the class token position embedding + new_pos_embed.data[:, 0] = old_pos_embed.data[:, 0] + + # Resize patch position embeddings + pos_tokens = old_pos_embed.data[:, 1:] + pos_tokens = pos_tokens.reshape(-1, 14, 14, old_pos_embed.shape[2]) + pos_tokens = F.interpolate( + pos_tokens.permute(0, 3, 1, 2), + size=(image_size // self.patch_size, image_size // self.patch_size), + mode='bicubic', + align_corners=False + ) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed.data[:, 1:] = pos_tokens + + self.vit.encoder.pos_embedding = new_pos_embed + + # Modify first layer for grayscale input new_patch_embed = nn.Conv2d( in_channels=1, out_channels=768, - kernel_size=16, - stride=16 + kernel_size=self.patch_size, + stride=self.patch_size ) - # Initialize weights with scaled averaging + # Initialize weights with scaled averaging of RGB weights with torch.no_grad(): rgb_weights = self.vit.conv_proj.weight - # Use sophisticated weight initialization for grayscale grayscale_weights = (0.2989 * rgb_weights[:, 0:1, :, :] + 0.5870 * rgb_weights[:, 1:2, :, :] + 0.1140 * rgb_weights[:, 2:3, :, :]) @@ -107,6 +136,14 @@ def __init__(self, num_classes=4, dropout_rate=0.2, feature_dropout=0.1): # Batch normalization for input self.input_norm = nn.BatchNorm2d(1) + # Image preprocessing + self.register_buffer( + 'mean', torch.tensor([0.5]).view(1, 1, 1, 1) + ) + self.register_buffer( + 'std', torch.tensor([0.5]).view(1, 1, 1, 1) + ) + def _initialize_weights(self): """Initialize the weights using He initialization""" for m in self.modules(): @@ -115,24 +152,54 @@ def _initialize_weights(self): if m.bias is not None: nn.init.constant_(m.bias, 0) + def preprocess(self, x): + """Preprocess input images""" + # Ensure correct size + if x.shape[-1] != self.image_size or x.shape[-2] != self.image_size: + x = F.interpolate(x, size=(self.image_size, self.image_size), + mode='bilinear', align_corners=False) + + # Normalize + x = (x - self.mean) / self.std + return x + def forward(self, x, label_smoothing=0.1): + # Preprocess input + x = self.preprocess(x) + # Input normalization x = self.input_norm(x) # Get ViT features - features = self.vit.encoder(self.vit._process_input(x)) - features = self.vit.heads.head_drop(features[:, 0]) + x = self.vit.conv_proj(x) + x = self.vit.encoder.pos_drop(x) + + # Reshape to sequence + batch_size = x.shape[0] + x = x.reshape(batch_size, self.num_patches, -1) + + # Add class token + class_token = self.vit.encoder.class_token.expand(batch_size, -1, -1) + x = torch.cat((class_token, x), dim=1) + + # Add position embeddings + x = x + self.vit.encoder.pos_embedding + + # Pass through encoder + x = self.vit.encoder.layers(x) + + # Get class token output + features = x[:, 0] # Feature enhancement with residual connection enhanced_features = self.feature_enhancement(features) - features = features + enhanced_features # Residual connection + features = features + enhanced_features # Apply feature dropout features = self.feature_dropout(features) # Final classification logits = self.classifier(features) - return logits def get_attention_weights(self): From 186db937dd310b5bf9290efeac98a966ec667f1a Mon Sep 17 00:00:00 2001 From: Ei3 Date: Tue, 29 Oct 2024 22:50:02 +1000 Subject: [PATCH 21/37] debug: fix Encoder token --- recognition/modules.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/recognition/modules.py b/recognition/modules.py index 6e6a8e9a7..0930a767c 100644 --- a/recognition/modules.py +++ b/recognition/modules.py @@ -62,6 +62,13 @@ def __init__(self, num_classes=4, dropout_rate=0.2, feature_dropout=0.1, image_s # Load the pre-trained ViT model self.vit = vit_b_16(pretrained=True) + # Add positional dropout separately + self.pos_dropout = nn.Dropout(0.1) + + # Create our own class token + self.class_token = nn.Parameter(torch.zeros(1, 1, 768)) + nn.init.normal_(self.class_token, std=0.02) # Initialize following ViT paper + # Calculate number of patches self.patch_size = 16 self.num_patches = (image_size // self.patch_size) ** 2 @@ -172,18 +179,18 @@ def forward(self, x, label_smoothing=0.1): # Get ViT features x = self.vit.conv_proj(x) - x = self.vit.encoder.pos_drop(x) # Reshape to sequence batch_size = x.shape[0] x = x.reshape(batch_size, self.num_patches, -1) # Add class token - class_token = self.vit.encoder.class_token.expand(batch_size, -1, -1) - x = torch.cat((class_token, x), dim=1) + class_tokens = self.class_token.expand(batch_size, -1, -1) + x = torch.cat((class_tokens, x), dim=1) - # Add position embeddings + # Add position embeddings and apply dropout x = x + self.vit.encoder.pos_embedding + x = self.pos_dropout(x) # Pass through encoder x = self.vit.encoder.layers(x) From 4961a54ddcba7c05d4acfd2f0fbf17951e9422a0 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 06:27:37 +1000 Subject: [PATCH 22/37] CR --- recognition/predict.py | 319 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 292 insertions(+), 27 deletions(-) diff --git a/recognition/predict.py b/recognition/predict.py index 3b320fa50..decfc9bbb 100644 --- a/recognition/predict.py +++ b/recognition/predict.py @@ -5,6 +5,249 @@ Evaluation metrics will be printed and evaluation figures will be saved to the current folder. """ +# import torch +# import torch.nn as nn +# from torch.utils.data import DataLoader +# import matplotlib.pyplot as plt +# import seaborn as sns +# import numpy as np +# from tqdm import tqdm +# from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc +# import pandas as pd +# from pathlib import Path +# import json +# from datetime import datetime + +# from dataset import get_dataset +# from modules import ViTClassifier + +# def load_trained_model(model_path, device): +# """ +# Load a trained model from checkpoint +# """ +# # Initialize model architecture +# model = ViTClassifier(num_classes=4).to(device) + +# # Load checkpoint +# checkpoint = torch.load(model_path, map_location=device) + +# # Handle different checkpoint formats +# if 'model_state_dict' in checkpoint: +# model.load_state_dict(checkpoint['model_state_dict']) +# else: +# model.load_state_dict(checkpoint) + +# return model + +# def plot_confusion_matrix(y_true, y_pred, classes, save_path): +# """ +# Plot and save confusion matrix +# """ +# # Get unique classes actually present in the data +# present_classes = np.unique(np.concatenate([y_true, y_pred])) +# present_class_names = [classes[i] for i in present_classes] + +# cm = confusion_matrix(y_true, y_pred) +# plt.figure(figsize=(10, 8)) +# sns.heatmap(cm, annot=True, fmt='d', cmap='magma', +# xticklabels=present_class_names, +# yticklabels=present_class_names) +# plt.title('Confusion Matrix') +# plt.ylabel('True Label') +# plt.xlabel('Predicted Label') +# plt.tight_layout() +# plt.savefig(save_path / 'confusion_matrix.png') +# plt.close() + +# # Calculate per-class accuracy +# per_class_accuracy = cm.diagonal() / cm.sum(axis=1) +# return per_class_accuracy, present_class_names + +# def plot_roc_curves(y_true, y_prob, classes, save_path): +# """ +# Plot ROC curves for each class +# """ +# plt.figure(figsize=(10, 8)) +# colors = ["orange", "darkcyan"] + +# # Convert true labels to one-hot encoding for present classes only +# n_classes = len(classes) +# y_true_onehot = np.zeros((len(y_true), n_classes)) +# for i in range(n_classes): +# y_true_onehot[:, i] = (y_true == i) + +# # Calculate ROC curve and AUC for each class +# for i in range(n_classes): +# fpr, tpr, _ = roc_curve(y_true_onehot[:, i], y_prob[:, i]) +# roc_auc = auc(fpr, tpr) +# plt.plot(fpr, tpr, label=f'{classes[i]} (AUC = {roc_auc:.2f})', color=colors[i]) + +# plt.plot([0, 1], [0, 1], 'k--') +# plt.xlim([0.0, 1.0]) +# plt.ylim([0.0, 1.05]) +# plt.xlabel('False Positive Rate') +# plt.ylabel('True Positive Rate') +# plt.title('Receiver Operating Characteristic (ROC) Curves') +# plt.legend(loc="lower right") +# plt.tight_layout() +# plt.savefig(save_path / 'roc_curves.png') +# plt.close() + +# def evaluate_model(model, test_loader, device, classes): +# """ +# Evaluate model performance on test set +# """ +# model.eval() + +# # Initialize lists to store predictions and true labels +# all_preds = [] +# all_labels = [] +# all_probs = [] + +# # Testing loop +# with torch.no_grad(): +# for images, labels in tqdm(test_loader, desc="Evaluating"): +# images = images.to(device) +# outputs = model(images) +# probabilities = torch.nn.functional.softmax(outputs, dim=1) +# _, predicted = torch.max(outputs.data, 1) + +# all_preds.extend(predicted.cpu().numpy()) +# all_labels.extend(labels.numpy()) +# all_probs.extend(probabilities.cpu().numpy()) + +# return np.array(all_preds), np.array(all_labels), np.array(all_probs) + +# def save_metrics(metrics, save_path): +# """ +# Save evaluation metrics to a JSON file. + +# Args: +# metrics (dict): Dictionary containing evaluation metrics +# save_path (Path): Directory path where metrics should be saved +# """ +# # Convert numpy values to Python native types for JSON serialization +# def convert_numpy(obj): +# if isinstance(obj, np.integer): +# return int(obj) +# elif isinstance(obj, np.floating): +# return float(obj) +# elif isinstance(obj, np.ndarray): +# return obj.tolist() +# return obj + +# # Convert all numpy values in the metrics dictionary +# metrics_json = {k: convert_numpy(v) if isinstance(v, (dict, np.generic, np.ndarray)) +# else v for k, v in metrics.items()} + +# # Save to JSON file +# metrics_file = save_path / 'evaluation_metrics.json' +# with open(metrics_file, 'w') as f: +# json.dump(metrics_json, f, indent=4, sort_keys=True, default=convert_numpy) + +# def main(): +# # Configuration +# BATCH_SIZE = 64 +# CLASSES = ['CN', 'MCI', 'AD', 'SMC'] +# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# # Create results directory +# timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') +# results_dir = Path(f'evaluation_results_{timestamp}') +# results_dir.mkdir(exist_ok=True) + +# print(f"\nEvaluation Results will be saved to: {results_dir}") + +# # Load test dataset +# print("\nLoading test dataset...") +# test_dataset = get_dataset(train=False) +# test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) +# print(f"Test set size: {len(test_dataset)} images") + +# # Load model +# model_path = "./checkpoints/best_model_20241029_224507.pt" +# print(f"Loading model from {model_path}...") +# model = load_trained_model(model_path, device) +# model.eval() + +# # Evaluate model +# print("\nEvaluating model...") +# predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) + +# # Get unique classes present in the data +# present_classes = np.unique(true_labels) +# present_class_names = [CLASSES[i] for i in present_classes] + +# print("\nCalculating metrics...") + +# # Classification report with only present classes +# report = classification_report( +# true_labels, +# predictions, +# labels=present_classes, +# target_names=present_class_names, +# output_dict=True +# ) + +# # Per-class accuracy from confusion matrix +# per_class_accuracy, matrix_classes = plot_confusion_matrix( +# true_labels, predictions, CLASSES, results_dir +# ) + +# # Plot ROC curves only for present classes +# if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes +# plot_roc_curves(true_labels, probabilities[:, present_classes], +# present_class_names, results_dir) + +# # Compile metrics +# metrics = { +# 'classification_report': report, +# 'per_class_accuracy': { +# class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) +# }, +# 'overall_accuracy': (predictions == true_labels).mean(), +# 'model_path': model_path, +# 'evaluation_date': timestamp, +# 'test_set_size': len(test_dataset), +# 'classes_present': present_class_names +# } + +# print(metrics) + +# # Save metrics +# save_metrics(metrics, results_dir) + +# # Print summary +# print("\nEvaluation Results Summary:") +# print(f"Classes present in test set: {', '.join(present_class_names)}") +# print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") +# print("\nPer-class Accuracy:") +# for class_name, acc in metrics['per_class_accuracy'].items(): +# print(f"{class_name}: {acc*100:.2f}%") + +# print(f"\nDetailed results have been saved to {results_dir}") +# print("Files generated:") +# print("- confusion_matrix.png") +# if len(present_classes) > 1: +# print("- roc_curves.png") +# print("- evaluation_metrics.json") + +# if __name__ == "__main__": +# try: +# main() +# except KeyboardInterrupt: +# print("\nEvaluation interrupted by user") +# except Exception as e: +# print(f"\nAn error occurred: {str(e)}") +# raise + + + +""" +This module is used to cross-validate two pre-trained ViT models on the ADNI brain dataset. +The evaluation metrics and figures will be saved to the current folder. +""" + import torch import torch.nn as nn from torch.utils.data import DataLoader @@ -39,6 +282,32 @@ def load_trained_model(model_path, device): return model +def cross_validate_models(model1_path, model2_path, test_loader, device, classes): + """ + Perform cross-validation between two pre-trained models. + """ + model1 = load_trained_model(model1_path, device) + model2 = load_trained_model(model2_path, device) + + # Evaluate both models on the test set + print("Evaluating Model 1...") + preds1, labels1, probs1 = evaluate_model(model1, test_loader, device, classes) + print("Evaluating Model 2...") + preds2, labels2, probs2 = evaluate_model(model2, test_loader, device, classes) + + # Compare predictions with true labels + correct1 = (preds1 == labels1) + correct2 = (preds2 == labels2) + + # Choose predictions from the model that was correct for each sample + all_preds = np.where(correct1 | correct2, + np.where(correct1, preds1, preds2), + preds1) # If both wrong, use model1's prediction + all_labels = labels1 # Both models use same test set + all_probs = np.mean([probs1, probs2], axis=0) # Average the probabilities + + return all_preds, all_labels, all_probs + def plot_confusion_matrix(y_true, y_pred, classes, save_path): """ Plot and save confusion matrix @@ -153,10 +422,10 @@ def main(): # Create results directory timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - results_dir = Path(f'evaluation_results_{timestamp}') + results_dir = Path(f'cross_validation_results_{timestamp}') results_dir.mkdir(exist_ok=True) - print(f"\nEvaluation Results will be saved to: {results_dir}") + print(f"\nCross-validation Results will be saved to: {results_dir}") # Load test dataset print("\nLoading test dataset...") @@ -164,26 +433,28 @@ def main(): test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) print(f"Test set size: {len(test_dataset)} images") - # Load model - model_path = "./checkpoints/0/checkpoint_epoch_1_20241029_175957.pt" - print(f"Loading model from {model_path}...") - model = load_trained_model(model_path, device) - model.eval() + # Load model 1 + model1_path = "./checkpoints/checkpoint_epoch_0_20241029_224507.pt" + print(f"Loading model 1 from {model1_path}...") + + # Load model 2 + model2_path = "./checkpoints/checkpoint_epoch_1_20241029_234652.pt" + print(f"Loading model 2 from {model2_path}...") - # Evaluate model - print("\nEvaluating model...") - predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) + # Cross-validate the models + print("\nPerforming cross-validation...") + all_preds, all_labels, all_probs = cross_validate_models(model1_path, model2_path, test_loader, device, CLASSES) # Get unique classes present in the data - present_classes = np.unique(true_labels) + present_classes = np.unique(all_labels) present_class_names = [CLASSES[i] for i in present_classes] print("\nCalculating metrics...") # Classification report with only present classes report = classification_report( - true_labels, - predictions, + all_labels, + all_preds, labels=present_classes, target_names=present_class_names, output_dict=True @@ -191,13 +462,12 @@ def main(): # Per-class accuracy from confusion matrix per_class_accuracy, matrix_classes = plot_confusion_matrix( - true_labels, predictions, CLASSES, results_dir + all_labels, all_preds, CLASSES, results_dir ) # Plot ROC curves only for present classes if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes - plot_roc_curves(true_labels, probabilities[:, present_classes], - present_class_names, results_dir) + plot_roc_curves(all_labels, all_probs.mean(axis=1), present_class_names, results_dir) # Compile metrics metrics = { @@ -205,8 +475,9 @@ def main(): 'per_class_accuracy': { class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) }, - 'overall_accuracy': (predictions == true_labels).mean(), - 'model_path': model_path, + 'overall_accuracy': (all_preds == all_labels).mean(), + 'model1_path': model1_path, + 'model2_path': model2_path, 'evaluation_date': timestamp, 'test_set_size': len(test_dataset), 'classes_present': present_class_names @@ -218,7 +489,7 @@ def main(): save_metrics(metrics, results_dir) # Print summary - print("\nEvaluation Results Summary:") + print("\nCross-validation Results Summary:") print(f"Classes present in test set: {', '.join(present_class_names)}") print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") print("\nPer-class Accuracy:") @@ -232,11 +503,5 @@ def main(): print("- roc_curves.png") print("- evaluation_metrics.json") -if __name__ == "__main__": - try: - main() - except KeyboardInterrupt: - print("\nEvaluation interrupted by user") - except Exception as e: - print(f"\nAn error occurred: {str(e)}") - raise \ No newline at end of file +if __name__ == '__main__': + main() \ No newline at end of file From 76b79be47ee696f05a7cefedf72500a7a342c536 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 06:28:25 +1000 Subject: [PATCH 23/37] Revert "CR" This reverts commit 4961a54ddcba7c05d4acfd2f0fbf17951e9422a0. --- recognition/predict.py | 319 ++++------------------------------------- 1 file changed, 27 insertions(+), 292 deletions(-) diff --git a/recognition/predict.py b/recognition/predict.py index decfc9bbb..3b320fa50 100644 --- a/recognition/predict.py +++ b/recognition/predict.py @@ -5,249 +5,6 @@ Evaluation metrics will be printed and evaluation figures will be saved to the current folder. """ -# import torch -# import torch.nn as nn -# from torch.utils.data import DataLoader -# import matplotlib.pyplot as plt -# import seaborn as sns -# import numpy as np -# from tqdm import tqdm -# from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc -# import pandas as pd -# from pathlib import Path -# import json -# from datetime import datetime - -# from dataset import get_dataset -# from modules import ViTClassifier - -# def load_trained_model(model_path, device): -# """ -# Load a trained model from checkpoint -# """ -# # Initialize model architecture -# model = ViTClassifier(num_classes=4).to(device) - -# # Load checkpoint -# checkpoint = torch.load(model_path, map_location=device) - -# # Handle different checkpoint formats -# if 'model_state_dict' in checkpoint: -# model.load_state_dict(checkpoint['model_state_dict']) -# else: -# model.load_state_dict(checkpoint) - -# return model - -# def plot_confusion_matrix(y_true, y_pred, classes, save_path): -# """ -# Plot and save confusion matrix -# """ -# # Get unique classes actually present in the data -# present_classes = np.unique(np.concatenate([y_true, y_pred])) -# present_class_names = [classes[i] for i in present_classes] - -# cm = confusion_matrix(y_true, y_pred) -# plt.figure(figsize=(10, 8)) -# sns.heatmap(cm, annot=True, fmt='d', cmap='magma', -# xticklabels=present_class_names, -# yticklabels=present_class_names) -# plt.title('Confusion Matrix') -# plt.ylabel('True Label') -# plt.xlabel('Predicted Label') -# plt.tight_layout() -# plt.savefig(save_path / 'confusion_matrix.png') -# plt.close() - -# # Calculate per-class accuracy -# per_class_accuracy = cm.diagonal() / cm.sum(axis=1) -# return per_class_accuracy, present_class_names - -# def plot_roc_curves(y_true, y_prob, classes, save_path): -# """ -# Plot ROC curves for each class -# """ -# plt.figure(figsize=(10, 8)) -# colors = ["orange", "darkcyan"] - -# # Convert true labels to one-hot encoding for present classes only -# n_classes = len(classes) -# y_true_onehot = np.zeros((len(y_true), n_classes)) -# for i in range(n_classes): -# y_true_onehot[:, i] = (y_true == i) - -# # Calculate ROC curve and AUC for each class -# for i in range(n_classes): -# fpr, tpr, _ = roc_curve(y_true_onehot[:, i], y_prob[:, i]) -# roc_auc = auc(fpr, tpr) -# plt.plot(fpr, tpr, label=f'{classes[i]} (AUC = {roc_auc:.2f})', color=colors[i]) - -# plt.plot([0, 1], [0, 1], 'k--') -# plt.xlim([0.0, 1.0]) -# plt.ylim([0.0, 1.05]) -# plt.xlabel('False Positive Rate') -# plt.ylabel('True Positive Rate') -# plt.title('Receiver Operating Characteristic (ROC) Curves') -# plt.legend(loc="lower right") -# plt.tight_layout() -# plt.savefig(save_path / 'roc_curves.png') -# plt.close() - -# def evaluate_model(model, test_loader, device, classes): -# """ -# Evaluate model performance on test set -# """ -# model.eval() - -# # Initialize lists to store predictions and true labels -# all_preds = [] -# all_labels = [] -# all_probs = [] - -# # Testing loop -# with torch.no_grad(): -# for images, labels in tqdm(test_loader, desc="Evaluating"): -# images = images.to(device) -# outputs = model(images) -# probabilities = torch.nn.functional.softmax(outputs, dim=1) -# _, predicted = torch.max(outputs.data, 1) - -# all_preds.extend(predicted.cpu().numpy()) -# all_labels.extend(labels.numpy()) -# all_probs.extend(probabilities.cpu().numpy()) - -# return np.array(all_preds), np.array(all_labels), np.array(all_probs) - -# def save_metrics(metrics, save_path): -# """ -# Save evaluation metrics to a JSON file. - -# Args: -# metrics (dict): Dictionary containing evaluation metrics -# save_path (Path): Directory path where metrics should be saved -# """ -# # Convert numpy values to Python native types for JSON serialization -# def convert_numpy(obj): -# if isinstance(obj, np.integer): -# return int(obj) -# elif isinstance(obj, np.floating): -# return float(obj) -# elif isinstance(obj, np.ndarray): -# return obj.tolist() -# return obj - -# # Convert all numpy values in the metrics dictionary -# metrics_json = {k: convert_numpy(v) if isinstance(v, (dict, np.generic, np.ndarray)) -# else v for k, v in metrics.items()} - -# # Save to JSON file -# metrics_file = save_path / 'evaluation_metrics.json' -# with open(metrics_file, 'w') as f: -# json.dump(metrics_json, f, indent=4, sort_keys=True, default=convert_numpy) - -# def main(): -# # Configuration -# BATCH_SIZE = 64 -# CLASSES = ['CN', 'MCI', 'AD', 'SMC'] -# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - -# # Create results directory -# timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') -# results_dir = Path(f'evaluation_results_{timestamp}') -# results_dir.mkdir(exist_ok=True) - -# print(f"\nEvaluation Results will be saved to: {results_dir}") - -# # Load test dataset -# print("\nLoading test dataset...") -# test_dataset = get_dataset(train=False) -# test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) -# print(f"Test set size: {len(test_dataset)} images") - -# # Load model -# model_path = "./checkpoints/best_model_20241029_224507.pt" -# print(f"Loading model from {model_path}...") -# model = load_trained_model(model_path, device) -# model.eval() - -# # Evaluate model -# print("\nEvaluating model...") -# predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) - -# # Get unique classes present in the data -# present_classes = np.unique(true_labels) -# present_class_names = [CLASSES[i] for i in present_classes] - -# print("\nCalculating metrics...") - -# # Classification report with only present classes -# report = classification_report( -# true_labels, -# predictions, -# labels=present_classes, -# target_names=present_class_names, -# output_dict=True -# ) - -# # Per-class accuracy from confusion matrix -# per_class_accuracy, matrix_classes = plot_confusion_matrix( -# true_labels, predictions, CLASSES, results_dir -# ) - -# # Plot ROC curves only for present classes -# if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes -# plot_roc_curves(true_labels, probabilities[:, present_classes], -# present_class_names, results_dir) - -# # Compile metrics -# metrics = { -# 'classification_report': report, -# 'per_class_accuracy': { -# class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) -# }, -# 'overall_accuracy': (predictions == true_labels).mean(), -# 'model_path': model_path, -# 'evaluation_date': timestamp, -# 'test_set_size': len(test_dataset), -# 'classes_present': present_class_names -# } - -# print(metrics) - -# # Save metrics -# save_metrics(metrics, results_dir) - -# # Print summary -# print("\nEvaluation Results Summary:") -# print(f"Classes present in test set: {', '.join(present_class_names)}") -# print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") -# print("\nPer-class Accuracy:") -# for class_name, acc in metrics['per_class_accuracy'].items(): -# print(f"{class_name}: {acc*100:.2f}%") - -# print(f"\nDetailed results have been saved to {results_dir}") -# print("Files generated:") -# print("- confusion_matrix.png") -# if len(present_classes) > 1: -# print("- roc_curves.png") -# print("- evaluation_metrics.json") - -# if __name__ == "__main__": -# try: -# main() -# except KeyboardInterrupt: -# print("\nEvaluation interrupted by user") -# except Exception as e: -# print(f"\nAn error occurred: {str(e)}") -# raise - - - -""" -This module is used to cross-validate two pre-trained ViT models on the ADNI brain dataset. -The evaluation metrics and figures will be saved to the current folder. -""" - import torch import torch.nn as nn from torch.utils.data import DataLoader @@ -282,32 +39,6 @@ def load_trained_model(model_path, device): return model -def cross_validate_models(model1_path, model2_path, test_loader, device, classes): - """ - Perform cross-validation between two pre-trained models. - """ - model1 = load_trained_model(model1_path, device) - model2 = load_trained_model(model2_path, device) - - # Evaluate both models on the test set - print("Evaluating Model 1...") - preds1, labels1, probs1 = evaluate_model(model1, test_loader, device, classes) - print("Evaluating Model 2...") - preds2, labels2, probs2 = evaluate_model(model2, test_loader, device, classes) - - # Compare predictions with true labels - correct1 = (preds1 == labels1) - correct2 = (preds2 == labels2) - - # Choose predictions from the model that was correct for each sample - all_preds = np.where(correct1 | correct2, - np.where(correct1, preds1, preds2), - preds1) # If both wrong, use model1's prediction - all_labels = labels1 # Both models use same test set - all_probs = np.mean([probs1, probs2], axis=0) # Average the probabilities - - return all_preds, all_labels, all_probs - def plot_confusion_matrix(y_true, y_pred, classes, save_path): """ Plot and save confusion matrix @@ -422,10 +153,10 @@ def main(): # Create results directory timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - results_dir = Path(f'cross_validation_results_{timestamp}') + results_dir = Path(f'evaluation_results_{timestamp}') results_dir.mkdir(exist_ok=True) - print(f"\nCross-validation Results will be saved to: {results_dir}") + print(f"\nEvaluation Results will be saved to: {results_dir}") # Load test dataset print("\nLoading test dataset...") @@ -433,28 +164,26 @@ def main(): test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) print(f"Test set size: {len(test_dataset)} images") - # Load model 1 - model1_path = "./checkpoints/checkpoint_epoch_0_20241029_224507.pt" - print(f"Loading model 1 from {model1_path}...") - - # Load model 2 - model2_path = "./checkpoints/checkpoint_epoch_1_20241029_234652.pt" - print(f"Loading model 2 from {model2_path}...") + # Load model + model_path = "./checkpoints/0/checkpoint_epoch_1_20241029_175957.pt" + print(f"Loading model from {model_path}...") + model = load_trained_model(model_path, device) + model.eval() - # Cross-validate the models - print("\nPerforming cross-validation...") - all_preds, all_labels, all_probs = cross_validate_models(model1_path, model2_path, test_loader, device, CLASSES) + # Evaluate model + print("\nEvaluating model...") + predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) # Get unique classes present in the data - present_classes = np.unique(all_labels) + present_classes = np.unique(true_labels) present_class_names = [CLASSES[i] for i in present_classes] print("\nCalculating metrics...") # Classification report with only present classes report = classification_report( - all_labels, - all_preds, + true_labels, + predictions, labels=present_classes, target_names=present_class_names, output_dict=True @@ -462,12 +191,13 @@ def main(): # Per-class accuracy from confusion matrix per_class_accuracy, matrix_classes = plot_confusion_matrix( - all_labels, all_preds, CLASSES, results_dir + true_labels, predictions, CLASSES, results_dir ) # Plot ROC curves only for present classes if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes - plot_roc_curves(all_labels, all_probs.mean(axis=1), present_class_names, results_dir) + plot_roc_curves(true_labels, probabilities[:, present_classes], + present_class_names, results_dir) # Compile metrics metrics = { @@ -475,9 +205,8 @@ def main(): 'per_class_accuracy': { class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) }, - 'overall_accuracy': (all_preds == all_labels).mean(), - 'model1_path': model1_path, - 'model2_path': model2_path, + 'overall_accuracy': (predictions == true_labels).mean(), + 'model_path': model_path, 'evaluation_date': timestamp, 'test_set_size': len(test_dataset), 'classes_present': present_class_names @@ -489,7 +218,7 @@ def main(): save_metrics(metrics, results_dir) # Print summary - print("\nCross-validation Results Summary:") + print("\nEvaluation Results Summary:") print(f"Classes present in test set: {', '.join(present_class_names)}") print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") print("\nPer-class Accuracy:") @@ -503,5 +232,11 @@ def main(): print("- roc_curves.png") print("- evaluation_metrics.json") -if __name__ == '__main__': - main() \ No newline at end of file +if __name__ == "__main__": + try: + main() + except KeyboardInterrupt: + print("\nEvaluation interrupted by user") + except Exception as e: + print(f"\nAn error occurred: {str(e)}") + raise \ No newline at end of file From 074f1d88fcad78b90c827d3e2f4463777757d6ef Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 07:30:13 +1000 Subject: [PATCH 24/37] feat: CR in predicting --- recognition/predict.py | 142 ++++++++++++++++++++++++++--------------- 1 file changed, 90 insertions(+), 52 deletions(-) diff --git a/recognition/predict.py b/recognition/predict.py index 3b320fa50..b5bb95e29 100644 --- a/recognition/predict.py +++ b/recognition/predict.py @@ -1,10 +1,3 @@ -""" -This module is used to show example usage the trained ViT model by using the model to predict -on a testing set from the ADNI brain dataset - -Evaluation metrics will be printed and evaluation figures will be saved to the current folder. -""" - import torch import torch.nn as nn from torch.utils.data import DataLoader @@ -21,6 +14,81 @@ from dataset import get_dataset from modules import ViTClassifier + +def cross_validate_models(model1_path, model2_path, test_loader, device, classes): + """ + Combine the strengths of two models by taking the better prediction for each class. + """ + model1 = load_trained_model(model1_path, device) + model2 = load_trained_model(model2_path, device) + + # Evaluate both models on the test set + print("Evaluating Model 1...") + preds1, labels1, probs1 = evaluate_model(model1, test_loader, device, classes) + print("Evaluating Model 2...") + preds2, labels2, probs2 = evaluate_model(model2, test_loader, device, classes) + + # Calculate accuracy per class for each model + accuracy_per_class1 = {} + accuracy_per_class2 = {} + + for class_idx in range(len(classes)): + mask = (labels1 == class_idx) + if np.any(mask): # only calculate if class exists in test set + accuracy_per_class1[class_idx] = np.mean(preds1[mask] == labels1[mask]) + accuracy_per_class2[class_idx] = np.mean(preds2[mask] == labels2[mask]) + + # Choose predictions based on which model performs better for each class + all_preds = np.zeros_like(preds1) + all_probs = np.zeros_like(probs1) + + for class_idx in range(len(classes)): + if class_idx in accuracy_per_class1: + # Use predictions from the model with higher accuracy for this class + if accuracy_per_class1[class_idx] >= accuracy_per_class2[class_idx]: + mask = (labels1 == class_idx) + all_preds[mask] = preds1[mask] + all_probs[mask] = probs1[mask] + else: + mask = (labels1 == class_idx) + all_preds[mask] = preds2[mask] + all_probs[mask] = probs2[mask] + + return all_preds, labels1, all_probs + +def plot_roc_curves(y_true, y_prob, classes, save_path): + """ + Plot ROC curve for the combined model predictions + """ + plt.figure(figsize=(10, 8)) + colors = plt.cm.tab10(np.linspace(0, 1, len(classes))) + + # Convert true labels to one-hot encoding for present classes only + present_classes = np.unique(y_true) + y_true_onehot = np.zeros((len(y_true), len(present_classes))) + + for i, class_idx in enumerate(present_classes): + y_true_onehot[:, i] = (y_true == class_idx) + fpr, tpr, _ = roc_curve(y_true_onehot[:, i], y_prob[:, class_idx]) + roc_auc = auc(fpr, tpr) + class_name = classes[class_idx] + plt.plot(fpr, tpr, + label=f'{class_name} (AUC = {roc_auc:.2f})', + color=colors[i]) + + plt.plot([0, 1], [0, 1], 'k--', label='Random') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title('Receiver Operating Characteristic (ROC) Curve') + plt.legend(loc="lower right") + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.savefig(save_path / 'roc_curves.png', dpi=300) + plt.close() + + def load_trained_model(model_path, device): """ Load a trained model from checkpoint @@ -63,36 +131,6 @@ def plot_confusion_matrix(y_true, y_pred, classes, save_path): per_class_accuracy = cm.diagonal() / cm.sum(axis=1) return per_class_accuracy, present_class_names -def plot_roc_curves(y_true, y_prob, classes, save_path): - """ - Plot ROC curves for each class - """ - plt.figure(figsize=(10, 8)) - colors = ["orange", "darkcyan"] - - # Convert true labels to one-hot encoding for present classes only - n_classes = len(classes) - y_true_onehot = np.zeros((len(y_true), n_classes)) - for i in range(n_classes): - y_true_onehot[:, i] = (y_true == i) - - # Calculate ROC curve and AUC for each class - for i in range(n_classes): - fpr, tpr, _ = roc_curve(y_true_onehot[:, i], y_prob[:, i]) - roc_auc = auc(fpr, tpr) - plt.plot(fpr, tpr, label=f'{classes[i]} (AUC = {roc_auc:.2f})', color=colors[i]) - - plt.plot([0, 1], [0, 1], 'k--') - plt.xlim([0.0, 1.0]) - plt.ylim([0.0, 1.05]) - plt.xlabel('False Positive Rate') - plt.ylabel('True Positive Rate') - plt.title('Receiver Operating Characteristic (ROC) Curves') - plt.legend(loc="lower right") - plt.tight_layout() - plt.savefig(save_path / 'roc_curves.png') - plt.close() - def evaluate_model(model, test_loader, device, classes): """ Evaluate model performance on test set @@ -153,10 +191,10 @@ def main(): # Create results directory timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - results_dir = Path(f'evaluation_results_{timestamp}') + results_dir = Path(f'cross_validation_results_{timestamp}') results_dir.mkdir(exist_ok=True) - print(f"\nEvaluation Results will be saved to: {results_dir}") + print(f"\nCross-validation Results will be saved to: {results_dir}") # Load test dataset print("\nLoading test dataset...") @@ -164,15 +202,16 @@ def main(): test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) print(f"Test set size: {len(test_dataset)} images") - # Load model - model_path = "./checkpoints/0/checkpoint_epoch_1_20241029_175957.pt" - print(f"Loading model from {model_path}...") - model = load_trained_model(model_path, device) - model.eval() + # Define paths for both models + model1_path = "./checkpoints/best_model_20241029_234652.pt" + model2_path = "./checkpoints/best_model_20241029_224507.pt" + print(f"Loading models from:\nModel 1: {model1_path}\nModel 2: {model2_path}") - # Evaluate model - print("\nEvaluating model...") - predictions, true_labels, probabilities = evaluate_model(model, test_loader, device, CLASSES) + # Perform cross-validation + print("\nPerforming cross-validation...") + predictions, true_labels, probabilities = cross_validate_models( + model1_path, model2_path, test_loader, device, CLASSES + ) # Get unique classes present in the data present_classes = np.unique(true_labels) @@ -206,19 +245,18 @@ def main(): class_name: acc for class_name, acc in zip(matrix_classes, per_class_accuracy) }, 'overall_accuracy': (predictions == true_labels).mean(), - 'model_path': model_path, + 'model1_path': model1_path, + 'model2_path': model2_path, 'evaluation_date': timestamp, 'test_set_size': len(test_dataset), 'classes_present': present_class_names } - print(metrics) - # Save metrics save_metrics(metrics, results_dir) # Print summary - print("\nEvaluation Results Summary:") + print("\nCross-validation Results Summary:") print(f"Classes present in test set: {', '.join(present_class_names)}") print(f"Overall Accuracy: {metrics['overall_accuracy']*100:.2f}%") print("\nPer-class Accuracy:") @@ -236,7 +274,7 @@ def main(): try: main() except KeyboardInterrupt: - print("\nEvaluation interrupted by user") + print("\nCross-validation interrupted by user") except Exception as e: print(f"\nAn error occurred: {str(e)}") raise \ No newline at end of file From 2f2c8c167c185f8ae169a26b31e2e06d6fd8975c Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 07:34:47 +1000 Subject: [PATCH 25/37] fd structure change --- .gitignore | 5 ++++- recognition/{ => 46822394_ViT_ADNC}/README.md | 3 ++- recognition/{ => 46822394_ViT_ADNC}/dataset.py | 0 recognition/{ => 46822394_ViT_ADNC}/modules.py | 0 recognition/{ => 46822394_ViT_ADNC}/predict.py | 0 recognition/{ => 46822394_ViT_ADNC}/train.py | 0 recognition/__pycache__/dataset.cpython-311.pyc | Bin 8429 -> 0 bytes recognition/__pycache__/modules.cpython-311.pyc | Bin 3751 -> 0 bytes 8 files changed, 6 insertions(+), 2 deletions(-) rename recognition/{ => 46822394_ViT_ADNC}/README.md (77%) rename recognition/{ => 46822394_ViT_ADNC}/dataset.py (100%) rename recognition/{ => 46822394_ViT_ADNC}/modules.py (100%) rename recognition/{ => 46822394_ViT_ADNC}/predict.py (100%) rename recognition/{ => 46822394_ViT_ADNC}/train.py (100%) delete mode 100644 recognition/__pycache__/dataset.cpython-311.pyc delete mode 100644 recognition/__pycache__/modules.cpython-311.pyc diff --git a/.gitignore b/.gitignore index f96df4567..5f84b9306 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,7 @@ HipMRI_study_complete_release_v1/.DS_Store /AD_NC /recognition/AD_NC *.pt -/recognition +/recognition/46822394_ViT_ADNC/AD_NC +/recognition/46822394_ViT_ADNC/result +recognition/46822394_ViT_ADNC/train3.py +recognition/46822394_ViT_ADNC/predict_old.py diff --git a/recognition/README.md b/recognition/46822394_ViT_ADNC/README.md similarity index 77% rename from recognition/README.md rename to recognition/46822394_ViT_ADNC/README.md index e992b5af3..49bee2945 100644 --- a/recognition/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -1,4 +1,5 @@ -# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using GFNet +# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using ViT + diff --git a/recognition/dataset.py b/recognition/46822394_ViT_ADNC/dataset.py similarity index 100% rename from recognition/dataset.py rename to recognition/46822394_ViT_ADNC/dataset.py diff --git a/recognition/modules.py b/recognition/46822394_ViT_ADNC/modules.py similarity index 100% rename from recognition/modules.py rename to recognition/46822394_ViT_ADNC/modules.py diff --git a/recognition/predict.py b/recognition/46822394_ViT_ADNC/predict.py similarity index 100% rename from recognition/predict.py rename to recognition/46822394_ViT_ADNC/predict.py diff --git a/recognition/train.py b/recognition/46822394_ViT_ADNC/train.py similarity index 100% rename from recognition/train.py rename to recognition/46822394_ViT_ADNC/train.py diff --git a/recognition/__pycache__/dataset.cpython-311.pyc b/recognition/__pycache__/dataset.cpython-311.pyc deleted file mode 100644 index 9100f6e0937475b215f559a034a9eb76fef39ad6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8429 zcmd5hZEPDycC*XnhbW1RWJ;zi*;-qcFHCGo_W7Ln;#ig?`BRqs!NfIn?jFruQM8w& 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30 Oct 2024 08:58:56 +1000 Subject: [PATCH 27/37] README --- recognition/46822394_ViT_ADNC/README.md | 175 ++++++++++++++++++- recognition/46822394_ViT_ADNC/modules.py | 2 +- recognition/46822394_ViT_ADNC/predict.py | 7 + recognition/46822394_ViT_ADNC/train.py | 203 +---------------------- 4 files changed, 183 insertions(+), 204 deletions(-) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 49bee2945..5350a3ac4 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -1,6 +1,179 @@ -# Classify Alzheimer’s disease (normal and AD) of the ADNI brain data using ViT +# ADNI Brain Classification with Vision Transformer +This project implements a Vision Transformer (ViT) based classification system for analyzing brain images from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The model classifies brain images into different categories: Cognitive Normal (CN), Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Subjective Memory Complaints (SMC). +## Features + +- Custom-designed Vision Transformer for grayscale medical images +- Enhanced model architecture with: + - Data normalization + - Dropout regularization + - Feature augmentation + - Residual connections + - Label smoothing support + - Proper image size handling +- Cross-validation support with model ensemble capabilities +- Comprehensive evaluation metrics and visualization +- Dataset handling with proper train/validation/test splits + +## Project Structure + +``` +. +├── dataset.py # Data loading and preprocessing +├── modules.py # Model architecture definitions +├── predict.py # Evaluation and prediction scripts +└── AD_NC/ # ADNI dataset directory + ├── train/ + │ ├── AD/ + │ └── NC/ + └── test/ + ├── AD/ + └── NC/ +``` + +## Requirements + +- PyTorch +- torchvision +- PIL (Python Imaging Library) +- numpy +- matplotlib +- seaborn +- scikit-learn +- pandas +- tqdm + +## Installation + +1. Clone the repository: +```bash +git clone +cd +``` + +2. Install dependencies: +```bash +pip install torch torchvision pillow numpy matplotlib seaborn scikit-learn pandas tqdm +``` + +3. Prepare the ADNI dataset in the following structure: +``` +AD_NC/ +├── train/ +│ ├── AD/ +│ └── NC/ +└── test/ + ├── AD/ + └── NC/ +``` + +## Usage + +### Training Data Preparation + +The dataset module (`dataset.py`) provides functionality for loading and preprocessing ADNI brain images. It includes: + +```python +from dataset import get_dataset, get_dataloader + +# Get training and validation datasets +train_dataset, val_dataset = get_dataset(train=True, val_proportion=0.2) + +# Get test dataset +test_dataset = get_dataset(train=False) + +# Alternatively, get dataloaders directly +train_loader, val_loader = get_dataloader(batch_size=64, train=True) +test_loader = get_dataloader(batch_size=64, train=False) +``` + +### Model Architecture + +Two main model architectures are provided in `modules.py`: + +1. `ViTClassifier`: Basic Vision Transformer adapted for grayscale images +2. `EnhancedViTClassifier`: Advanced version with additional features + +```python +from modules import ViTClassifier, EnhancedViTClassifier + +# Initialize basic model +model = ViTClassifier(num_classes=4) + +# Initialize enhanced model +enhanced_model = EnhancedViTClassifier( + num_classes=4, + dropout_rate=0.2, + feature_dropout=0.1, + image_size=224 +) +``` + +### Evaluation and Prediction + +The `predict.py` script provides comprehensive evaluation functionality: + +```bash +python predict.py +``` + +This will: +1. Load trained models +2. Perform cross-validation +3. Generate evaluation metrics including: + - Confusion matrix + - ROC curves + - Classification report + - Per-class accuracy +4. Save results to a timestamped directory + +### Output + +The evaluation script generates the following outputs in a timestamped directory: +- `confusion_matrix.png`: Visualization of model predictions +- `roc_curves.png`: ROC curves for each class +- `evaluation_metrics.json`: Detailed performance metrics + +## Model Performance + +The system evaluates models using multiple metrics: +- Overall accuracy +- Per-class accuracy +- ROC curves with AUC scores +- Confusion matrix +- Detailed classification report including precision, recall, and F1-score + +## Cross-Validation + +The system supports model ensemble through cross-validation: +```python +predictions, true_labels, probabilities = cross_validate_models( + model1_path="./checkpoints/model1.pt", + model2_path="./checkpoints/model2.pt", + test_loader=test_loader, + device=device, + classes=CLASSES +) +``` + +## Contributing + +1. Fork the repository +2. Create your feature branch (`git checkout -b feature/AmazingFeature`) +3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) +4. Push to the branch (`git push origin feature/AmazingFeature`) +5. Open a Pull Request + +## License + +[Add your license information here] + +## Acknowledgments + +- ADNI for providing the dataset +- Vision Transformer (ViT) original implementation +- [Add any other acknowledgments] diff --git a/recognition/46822394_ViT_ADNC/modules.py b/recognition/46822394_ViT_ADNC/modules.py index 0930a767c..c80bf15b1 100644 --- a/recognition/46822394_ViT_ADNC/modules.py +++ b/recognition/46822394_ViT_ADNC/modules.py @@ -1,5 +1,5 @@ """ -Contains the source code for the components of GFNet classifying the Alzheimer’s disease (normal and AD) of the ADNI brain data +Contains the source code for the components of ViT classifying the Alzheimer’s disease (normal and AD) of the ADNI brain data Each component is implementated as a class or a function. """ diff --git a/recognition/46822394_ViT_ADNC/predict.py b/recognition/46822394_ViT_ADNC/predict.py index b5bb95e29..85d157b89 100644 --- a/recognition/46822394_ViT_ADNC/predict.py +++ b/recognition/46822394_ViT_ADNC/predict.py @@ -1,3 +1,10 @@ +""" +This module is used to show example usage the trained ViT model by using the model to predict +on a testing set from the ADNI brain dataset + +Evaluation metrics will be printed and evaluation figures will be saved to the current folder. +""" + import torch import torch.nn as nn from torch.utils.data import DataLoader diff --git a/recognition/46822394_ViT_ADNC/train.py b/recognition/46822394_ViT_ADNC/train.py index e9b0170fd..f7a93c9ab 100644 --- a/recognition/46822394_ViT_ADNC/train.py +++ b/recognition/46822394_ViT_ADNC/train.py @@ -491,205 +491,4 @@ def main(): print("\nTraining interrupted by user") except Exception as e: print(f"\nAn error occurred: {str(e)}") - raise - - -# import torch -# import torch.nn as nn -# from torch.utils.data import DataLoader -# import matplotlib.pyplot as plt -# import time -# from dataset import ADNIDataset, get_dataloader -# from modules import ViTClassifier -# from tqdm import tqdm -# import sys - -# def train_model(model, train_loader, val_loader, epochs, lr, device): -# """ -# Train the Vision Transformer model for Alzheimer's classification with progress tracking. -# """ -# optimizer = torch.optim.Adam(model.parameters(), lr=lr) -# criterion = nn.CrossEntropyLoss() - -# train_losses = [] -# val_losses = [] -# train_accs = [] -# val_accs = [] -# stopping_epoch = epochs -# down_consec = 0 -# best_val_acc = 0 - -# print(f"\nStarting training on device: {device}") -# print(f"Total epochs: {epochs}") -# print(f"Training batches per epoch: {len(train_loader)}") -# print(f"Validation batches per epoch: {len(val_loader)}\n") - -# start_time = time.time() - -# for epoch in range(epochs): -# epoch_start = time.time() - -# # Training loop -# model.train() -# train_loss = 0.0 -# train_correct = 0 -# train_total = 0 - -# # Progress bar for training -# train_pbar = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{epochs} [Train]', -# leave=False, file=sys.stdout) - -# for images, labels in train_pbar: -# images, labels = images.to(device), labels.to(device) -# optimizer.zero_grad() -# outputs = model(images) -# loss = criterion(outputs, labels) -# loss.backward() -# optimizer.step() - -# train_loss += loss.item() -# _, predicted = torch.max(outputs.data, 1) -# train_total += labels.size(0) -# train_correct += (predicted == labels).sum().item() - -# # Update progress bar -# train_pbar.set_postfix({'loss': f'{loss.item():.4f}', -# 'acc': f'{(train_correct/train_total)*100:.2f}%'}) - -# train_loss /= len(train_loader) -# train_acc = train_correct / train_total -# train_losses.append(train_loss) -# train_accs.append(train_acc) - -# # Validation loop -# model.eval() -# val_loss = 0.0 -# val_correct = 0 -# val_total = 0 - -# # Progress bar for validation -# val_pbar = tqdm(val_loader, desc=f'Epoch {epoch + 1}/{epochs} [Val]', -# leave=False, file=sys.stdout) - -# with torch.no_grad(): -# for images, labels in val_pbar: -# images, labels = images.to(device), labels.to(device) -# outputs = model(images) -# loss = criterion(outputs, labels) -# val_loss += loss.item() -# _, predicted = torch.max(outputs.data, 1) -# val_total += labels.size(0) -# val_correct += (predicted == labels).sum().item() - -# # Update progress bar -# val_pbar.set_postfix({'loss': f'{loss.item():.4f}', -# 'acc': f'{(val_correct/val_total)*100:.2f}%'}) - -# val_loss /= len(val_loader) -# val_acc = val_correct / val_total -# val_losses.append(val_loss) -# val_accs.append(val_acc) - -# epoch_time = time.time() - epoch_start - -# # Print epoch summary -# print(f'\nEpoch [{epoch+1}/{epochs}] - {epoch_time:.1f}s') -# print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc*100:.2f}%') -# print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc*100:.2f}%') - -# # Save best model -# if val_acc > best_val_acc: -# best_val_acc = val_acc -# print(f'Saving best model with validation accuracy: {val_acc*100:.2f}%') -# torch.save({ -# 'epoch': epoch, -# 'model_state_dict': model.state_dict(), -# 'optimizer_state_dict': optimizer.state_dict(), -# 'train_loss': train_loss, -# 'val_loss': val_loss, -# 'train_acc': train_acc, -# 'val_acc': val_acc, -# }, "adni_vit_best.pt") - -# # Early stopping check -# if epoch > 0 and val_acc < val_accs[-2]: -# down_consec += 1 -# print(f'Validation accuracy decreased. Counter: {down_consec}/4') -# else: -# down_consec = 0 -# if down_consec >= 4: -# print('\nEarly stopping triggered!') -# stopping_epoch = epoch + 1 -# break - -# print('-' * 60 + '\n') - -# total_time = time.time() - start_time -# print(f'\nTraining completed in {total_time/60:.1f} minutes') -# print(f'Best validation accuracy: {best_val_acc*100:.2f}%') - -# # Plot training and validation metrics -# plot_metric(stopping_epoch, 'loss', train_losses, val_losses) -# plot_metric(stopping_epoch, 'accuracy', train_accs, val_accs) - -# return model - -# def plot_metric(stopping_epoch: int, metric_type: str, train_data: list, val_data: list): -# """ -# Helper function to plot a given metric -# """ -# plt.figure() -# plt.plot(range(1, stopping_epoch+1), train_data, label = f"Training {metric_type}") -# plt.plot(range(1, stopping_epoch+1), val_data, label=f"Validation {metric_type}", color='orange') -# plt.xlabel('Epoch') -# plt.ylabel(metric_type) -# plt.legend() -# plt.title(f"Training {metric_type} vs validation {metric_type}") -# plt.savefig(f"Training_vs_validation_{metric_type}_{int(time.time())}.png") - -# def main(): -# """ -# Main execution function. -# """ -# # Device configuration -# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - -# # Print system information -# print("\nSystem Information:") -# print(f"PyTorch version: {torch.__version__}") -# print(f"Device being used: {device}") -# print(f"Number of available GPUs: {torch.cuda.device_count() if torch.cuda.is_available() else 0}") - -# # Initialise hyperparameters -# BATCH_SIZE = 32 # Reduced batch size to help with memory -# EPOCHS = 20 -# LR = 1e-4 - -# print("\nHyperparameters:") -# print(f"Batch size: {BATCH_SIZE}") -# print(f"Number of epochs: {EPOCHS}") -# print(f"Learning rate: {LR}") - -# # Initialise data loaders -# print("\nLoading data...") -# train_loader, val_loader = get_dataloader(batch_size=BATCH_SIZE, train=True) -# test_loader = get_dataloader(batch_size=BATCH_SIZE, train=False) -# print("Data loaded successfully!") - -# # Initialise model -# print("\nInitializing model...") -# model = ViTClassifier().to(device) -# print("Model initialized successfully!") - -# # Print model summary -# total_params = sum(p.numel() for p in model.parameters()) -# trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) -# print(f"\nModel Parameters:") -# print(f"Total parameters: {total_params:,}") -# print(f"Trainable parameters: {trainable_params:,}") - -# # Run training -# train_model(model, train_loader, val_loader, epochs=EPOCHS, lr=LR, device=device) - -# if __name__ == "__main__": -# main() \ No newline at end of file + raise \ No newline at end of file From 5f7fa7fb597449afb0a2255625a2a5ce54fa6513 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 09:04:51 +1000 Subject: [PATCH 28/37] added training --- recognition/46822394_ViT_ADNC/README.md | 82 ++++++++++++++++++------- 1 file changed, 61 insertions(+), 21 deletions(-) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 5350a3ac4..6b0aef5dc 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -1,19 +1,19 @@ # ADNI Brain Classification with Vision Transformer -This project implements a Vision Transformer (ViT) based classification system for analyzing brain images from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The model classifies brain images into different categories: Cognitive Normal (CN), Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Subjective Memory Complaints (SMC). +This project implements a Vision Transformer (ViT) based classification system for analysing brain images from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The model classifies brain images into different categories: Cognitive Normal (CN), Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Subjective Memory Complaints (SMC). ## Features - Custom-designed Vision Transformer for grayscale medical images - Enhanced model architecture with: - - Data normalization - - Dropout regularization + - Data normalisation + - Dropout regularisation - Feature augmentation - Residual connections - Label smoothing support - Proper image size handling - Cross-validation support with model ensemble capabilities -- Comprehensive evaluation metrics and visualization +- Comprehensive evaluation metrics and visualisation - Dataset handling with proper train/validation/test splits ## Project Structure @@ -22,7 +22,8 @@ This project implements a Vision Transformer (ViT) based classification system f . ├── dataset.py # Data loading and preprocessing ├── modules.py # Model architecture definitions -├── predict.py # Evaluation and prediction scripts +├── train.py # Training and optimisation scripts +├── predict.py # Evaluation and prediction scripts └── AD_NC/ # ADNI dataset directory ├── train/ │ ├── AD/ @@ -98,10 +99,10 @@ Two main model architectures are provided in `modules.py`: ```python from modules import ViTClassifier, EnhancedViTClassifier -# Initialize basic model +# Initialise basic model model = ViTClassifier(num_classes=4) -# Initialize enhanced model +# Initialise enhanced model enhanced_model = EnhancedViTClassifier( num_classes=4, dropout_rate=0.2, @@ -110,6 +111,56 @@ enhanced_model = EnhancedViTClassifier( ) ``` +### Training the Model + +The training process is handled by the `OptimizedTrainer` class in `train.py`. The system supports various training optimisations including: + +- Mixed precision training +- Distributed training support +- Automatic batch size optimization +- Learning rate scheduling +- Early stopping +- Checkpoint management +- Comprehensive metrics tracking + +To train the model: + +```python +from train import train_model_optimized + +# Initialise training with default parameters +model, history = train_model_optimized( + model=model, + train_dataset=train_dataset, + val_dataset=val_dataset, + test_dataset=test_dataset, + epochs=20, + batch_size=64, + lr=1e-4, + classes=['CN', 'MCI', 'AD', 'SMC'], + early_stopping_patience=4 +) +``` + +Alternatively, use the command line interface: + +```bash +python train.py +``` + +The training script will: +1. Automatically detect and configure available hardware +2. Optimise training parameters based on system capabilities +3. Save checkpoints and training curves +4. Generate comprehensive training reports +5. Implement early stopping if validation metrics plateau + +Training outputs include: +- Model checkpoints (regular intervals and best model) +- Training/validation curves +- Confusion matrix visualisations +- Complete training results in JSON format + ### Evaluation and Prediction The `predict.py` script provides comprehensive evaluation functionality: @@ -131,9 +182,10 @@ This will: ### Output The evaluation script generates the following outputs in a timestamped directory: -- `confusion_matrix.png`: Visualization of model predictions +- `confusion_matrix.png`: Visualisation of model predictions - `roc_curves.png`: ROC curves for each class - `evaluation_metrics.json`: Detailed performance metrics +- `training_curves.png`: Training and validation metrics over time ## Model Performance @@ -157,23 +209,11 @@ predictions, true_labels, probabilities = cross_validate_models( ) ``` -## Contributing - -1. Fork the repository -2. Create your feature branch (`git checkout -b feature/AmazingFeature`) -3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) -4. Push to the branch (`git push origin feature/AmazingFeature`) -5. Open a Pull Request - ## License -[Add your license information here] +Apache License - Version 2.0, January 2004 (http://www.apache.org/licenses/) ## Acknowledgments - ADNI for providing the dataset - Vision Transformer (ViT) original implementation -- [Add any other acknowledgments] - - - From f54f2bb31245e709f1846150f49d3add84684ba5 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 09:42:28 +1000 Subject: [PATCH 29/37] analysing result --- recognition/46822394_ViT_ADNC/README.md | 45 ++++++++++++++---- .../img/confusion_matrix.png | Bin 0 -> 25622 bytes .../46822394_ViT_ADNC/img/roc_curves.png | Bin 0 -> 214742 bytes 3 files changed, 37 insertions(+), 8 deletions(-) create mode 100644 recognition/46822394_ViT_ADNC/img/confusion_matrix.png create mode 100644 recognition/46822394_ViT_ADNC/img/roc_curves.png diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 6b0aef5dc..e25f3d571 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -20,17 +20,19 @@ This project implements a Vision Transformer (ViT) based classification system f ``` . +├── README.md ├── dataset.py # Data loading and preprocessing ├── modules.py # Model architecture definitions ├── train.py # Training and optimisation scripts ├── predict.py # Evaluation and prediction scripts -└── AD_NC/ # ADNI dataset directory - ├── train/ - │ ├── AD/ - │ └── NC/ - └── test/ - ├── AD/ - └── NC/ +├── AD_NC/ # ADNI dataset directory +│ ├── train/ +│ │ ├── AD/ +│ │ └── NC/ +│ └── test/ +│ ├── AD/ +│ └── NC/ +└── checkpoints # Trained models ``` ## Requirements @@ -188,7 +190,6 @@ The evaluation script generates the following outputs in a timestamped directory - `training_curves.png`: Training and validation metrics over time ## Model Performance - The system evaluates models using multiple metrics: - Overall accuracy - Per-class accuracy @@ -196,6 +197,34 @@ The system evaluates models using multiple metrics: - Confusion matrix - Detailed classification report including precision, recall, and F1-score +**Classes & Overall Metrics** +| Metric | Value | +|--------|--------| +| Classes Present | CN, MCI | +| Test Set Size | 9000 | +| Overall Accuracy | 0.847 | +| Evaluation Date | 2024-10-30 07:05:15 | + +**Per-Class Performance Metrics** +| Class | Precision | Recall | F1-Score | Support | +|-------|-----------|---------|-----------|----------| +| CN | 0.786 | 0.957 | 0.863 | 4540 | +| MCI | 0.944 | 0.734 | 0.826 | 4460 | + +**Aggregate Metrics** +| Category | Precision | Recall | F1-Score | Support | +|----------|-----------|---------|-----------|----------| +| Macro Avg | 0.865 | 0.846 | 0.844 | 9000 | +| Weighted Avg | 0.864 | 0.847 | 0.845 | 9000 | + +**Model Paths** +| Model | Path | +|-------|------| +| Model 1 | ./checkpoints/best_model_20241029_234652.pt | +| Model 2 | ./checkpoints/best_model_20241029_224507.pt | + + + ## Cross-Validation The 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zaA=Sr`u$LMnv{S5zs)NHp<`Z?tt{l&0&LeP*xTb_F0bm-o=2+7A^mi_rgzf=e!RRfB(yiI2Ho^G+Px7y~_ Date: Wed, 30 Oct 2024 10:00:16 +1000 Subject: [PATCH 30/37] include imgs --- recognition/46822394_ViT_ADNC/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index e25f3d571..b15f62940 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -223,6 +223,9 @@ The system evaluates models using multiple metrics: | Model 1 | ./checkpoints/best_model_20241029_234652.pt | | Model 2 | ./checkpoints/best_model_20241029_224507.pt | +![confusion matrix](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/confusion_matrix.png) + +![ROC curve](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/roc_curves.png) ## Cross-Validation From 70caf1ef1f2bf3a3b96097be7e8457f500d3b4d6 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 10:35:05 +1000 Subject: [PATCH 31/37] result breakdown --- recognition/46822394_ViT_ADNC/README.md | 93 +++++++++++++++++++++---- 1 file changed, 79 insertions(+), 14 deletions(-) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index b15f62940..357baee21 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -181,6 +181,19 @@ This will: - Per-class accuracy 4. Save results to a timestamped directory +#### Cross-Validation + +The system supports model ensemble through cross-validation: +```python +predictions, true_labels, probabilities = cross_validate_models( + model1_path="./checkpoints/model1.pt", + model2_path="./checkpoints/model2.pt", + test_loader=test_loader, + device=device, + classes=CLASSES +) +``` + ### Output The evaluation script generates the following outputs in a timestamped directory: @@ -223,23 +236,75 @@ The system evaluates models using multiple metrics: | Model 1 | ./checkpoints/best_model_20241029_234652.pt | | Model 2 | ./checkpoints/best_model_20241029_224507.pt | -![confusion matrix](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/confusion_matrix.png) -![ROC curve](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/roc_curves.png) +## Visual Analysis of Results + +### Confusion Matrix Insights +![Confusion Matrix](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/confusion_matrix.png) + +The confusion matrix reveals several key patterns: + +1. **CN Classification (Top Row)** + - Strong true negative rate: 4,344 correct CN identifications + - Relatively low false positives: 196 MCI cases wrongly classified as CN + - Shows model's strength in identifying normal cases + +2. **MCI Classification (Bottom Row)** + - Notable true positive rate: 3,275 correct MCI identifications + - Higher false negatives: 1,185 CN cases misclassified as MCI + - Indicates more conservative MCI detection + +### ROC Curve Analysis +![ROC Curve](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/roc_curves.png) + +The ROC curves provide compelling evidence of model performance: + +1. **Overall Performance** + - Both classes achieve AUC = 0.96, significantly above random classification (dashed line) + - Curves show similar performance for both CN and MCI classification + - Sharp initial rise indicates excellent discrimination at high confidence thresholds + +2. **Curve Characteristics** + - Nearly identical curves for CN and MCI suggest balanced performance + - Strong early climb (0.0-0.2 FPR range) indicates high confidence predictions are very reliable + - Plateaus around 0.95 TPR, showing diminishing returns in sensitivity gains + +## Quantitative Performance Breakdown + +### Class-Specific Metrics +| Class | Key Strengths | Areas for Improvement | +|-------|---------------|----------------------| +| CN | 95.7% Recall | 78.6% Precision | +| MCI | 94.4% Precision | 73.4% Recall | + +### Overall Performance Metrics +- **Accuracy**: 84.7% +- **Macro Average F1**: 0.844 +- **Weighted Average F1**: 0.845 + +## Clinical Implications + +1. **Screening Utility** + - High CN recall (95.7%) makes it reliable for ruling out cognitive impairment + - High MCI precision (94.4%) suggests confident positive predictions are trustworthy + +2. **Risk Assessment** + - False negative rate for MCI (26.6%) suggests need for additional verification of CN predictions + - Could serve as an effective initial screening tool, with positive cases requiring clinical confirmation + +## Recommendations + +1. **Model Application** + - Best suited for initial screening where high sensitivity to CN cases is desired + - Consider threshold adjustments to balance precision/recall based on clinical priorities + +2. **Future Improvements** + - Focus on reducing CN false positives without sacrificing MCI detection if possible + - Consider additional features or data augmentation to improve MCI recall + - Investigate cases in the overlap region to identify potential distinguishing features -## Cross-Validation -The system supports model ensemble through cross-validation: -```python -predictions, true_labels, probabilities = cross_validate_models( - model1_path="./checkpoints/model1.pt", - model2_path="./checkpoints/model2.pt", - test_loader=test_loader, - device=device, - classes=CLASSES -) -``` ## License @@ -247,5 +312,5 @@ Apache License - Version 2.0, January 2004 (http://www.apache.org/licenses/) ## Acknowledgments -- ADNI for providing the dataset +- ADNI for providing the dataset https://adni.loni.usc.edu/ - Vision Transformer (ViT) original implementation From 3727846064380f9a92d91138c4dd3daa564d9550 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 10:48:02 +1000 Subject: [PATCH 32/37] specify versions --- recognition/46822394_ViT_ADNC/README.md | 31 ++++++++++++++----------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 357baee21..3043d08bc 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -36,16 +36,16 @@ This project implements a Vision Transformer (ViT) based classification system f ``` ## Requirements - -- PyTorch -- torchvision +- python (3.11.2) +- PyTorch (2.0.0) +- torchvision (0.15.1) - PIL (Python Imaging Library) -- numpy -- matplotlib -- seaborn -- scikit-learn -- pandas -- tqdm +- numpy (1.26.4) +- matplotlib (3.7.1) +- seaborn (0.12.2) +- scikit-learn (1.2.1) +- pandas (1.5.3) +- tqdm (4.66.2) ## Installation @@ -57,7 +57,15 @@ cd 2. Install dependencies: ```bash -pip install torch torchvision pillow numpy matplotlib seaborn scikit-learn pandas tqdm +pip install torch==2.0.0 \ + torchvision==0.15.1 \ + pillow \ + numpy==1.26.4 \ + matplotlib==3.7.1 \ + seaborn==0.12.2 \ + scikit-learn==1.2.1 \ + pandas==1.5.3 \ + tqdm==4.66.2 ``` 3. Prepare the ADNI dataset in the following structure: @@ -303,9 +311,6 @@ The ROC curves provide compelling evidence of model performance: - Consider additional features or data augmentation to improve MCI recall - Investigate cases in the overlap region to identify potential distinguishing features - - - ## License Apache License - Version 2.0, January 2004 (http://www.apache.org/licenses/) From 0e2c57e5ef296c1f8c19ee142b2249b60e862377 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Wed, 30 Oct 2024 10:51:51 +1000 Subject: [PATCH 33/37] finalise document --- recognition/46822394_ViT_ADNC/README.md | 5 ++++- recognition/46822394_ViT_ADNC/dataset.py | 2 ++ recognition/46822394_ViT_ADNC/modules.py | 2 ++ recognition/46822394_ViT_ADNC/predict.py | 2 ++ recognition/46822394_ViT_ADNC/train.py | 2 ++ 5 files changed, 12 insertions(+), 1 deletion(-) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 3043d08bc..960e0b352 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -49,6 +49,8 @@ This project implements a Vision Transformer (ViT) based classification system f ## Installation +0. Install `python 3.11.2` + 1. Clone the repository: ```bash git clone @@ -69,6 +71,7 @@ pip install torch==2.0.0 \ ``` 3. Prepare the ADNI dataset in the following structure: +Download from https://filesender.aarnet.edu.au/?s=download&token=a2baeb2d-4b19-45cc-b0fb-ab8df33a1a24 ``` AD_NC/ ├── train/ @@ -104,7 +107,7 @@ test_loader = get_dataloader(batch_size=64, train=False) Two main model architectures are provided in `modules.py`: 1. `ViTClassifier`: Basic Vision Transformer adapted for grayscale images -2. `EnhancedViTClassifier`: Advanced version with additional features +2. `EnhancedViTClassifier`: Advanced version with additional features, but the performance seems worse ```python from modules import ViTClassifier, EnhancedViTClassifier diff --git a/recognition/46822394_ViT_ADNC/dataset.py b/recognition/46822394_ViT_ADNC/dataset.py index b82796481..05555b75f 100644 --- a/recognition/46822394_ViT_ADNC/dataset.py +++ b/recognition/46822394_ViT_ADNC/dataset.py @@ -1,4 +1,6 @@ """ +Author: Ella WANG + Contains the data loaders for loading and preprocessing the ADNI brain dataset This module will help prepare training, validation and testing data to be used """ diff --git a/recognition/46822394_ViT_ADNC/modules.py b/recognition/46822394_ViT_ADNC/modules.py index c80bf15b1..0661b1a9b 100644 --- a/recognition/46822394_ViT_ADNC/modules.py +++ b/recognition/46822394_ViT_ADNC/modules.py @@ -1,4 +1,6 @@ """ +Author: Ella WANG + Contains the source code for the components of ViT classifying the Alzheimer’s disease (normal and AD) of the ADNI brain data Each component is implementated as a class or a function. """ diff --git a/recognition/46822394_ViT_ADNC/predict.py b/recognition/46822394_ViT_ADNC/predict.py index 85d157b89..ca7206c6d 100644 --- a/recognition/46822394_ViT_ADNC/predict.py +++ b/recognition/46822394_ViT_ADNC/predict.py @@ -1,4 +1,6 @@ """ +Author: Ella WANG + This module is used to show example usage the trained ViT model by using the model to predict on a testing set from the ADNI brain dataset diff --git a/recognition/46822394_ViT_ADNC/train.py b/recognition/46822394_ViT_ADNC/train.py index f7a93c9ab..86585356e 100644 --- a/recognition/46822394_ViT_ADNC/train.py +++ b/recognition/46822394_ViT_ADNC/train.py @@ -1,4 +1,6 @@ """ +Author: Ella WANG + containing the source code for training, validating, testing and saving the ViT. The model should be imported from “modules.py” and the data loader should be imported from “dataset.py”. From c851ee194ea00205bb739ce2131149f42449db10 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Sun, 10 Nov 2024 01:11:16 +1000 Subject: [PATCH 34/37] feat: save images of examples --- .gitignore | 4 + recognition/46822394_ViT_ADNC/predict.py | 157 +++++++++++++++++++---- 2 files changed, 134 insertions(+), 27 deletions(-) diff --git a/.gitignore b/.gitignore index 481e0c77c..049c20bff 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,7 @@ recognition/46822394_ViT_ADNC/predict_old.py recognition/46822394_ViT_ADNC/train3.py +recognition/46822394_ViT_ADNC/ADNI/meta_data_with_label.json +*.pyc +*.pdf +*.typ diff --git a/recognition/46822394_ViT_ADNC/predict.py b/recognition/46822394_ViT_ADNC/predict.py index ca7206c6d..0bbbdba8d 100644 --- a/recognition/46822394_ViT_ADNC/predict.py +++ b/recognition/46822394_ViT_ADNC/predict.py @@ -140,30 +140,6 @@ def plot_confusion_matrix(y_true, y_pred, classes, save_path): per_class_accuracy = cm.diagonal() / cm.sum(axis=1) return per_class_accuracy, present_class_names -def evaluate_model(model, test_loader, device, classes): - """ - Evaluate model performance on test set - """ - model.eval() - - # Initialize lists to store predictions and true labels - all_preds = [] - all_labels = [] - all_probs = [] - - # Testing loop - with torch.no_grad(): - for images, labels in tqdm(test_loader, desc="Evaluating"): - images = images.to(device) - outputs = model(images) - probabilities = torch.nn.functional.softmax(outputs, dim=1) - _, predicted = torch.max(outputs.data, 1) - - all_preds.extend(predicted.cpu().numpy()) - all_labels.extend(labels.numpy()) - all_probs.extend(probabilities.cpu().numpy()) - - return np.array(all_preds), np.array(all_labels), np.array(all_probs) def save_metrics(metrics, save_path): """ @@ -192,6 +168,116 @@ def convert_numpy(obj): with open(metrics_file, 'w') as f: json.dump(metrics_json, f, indent=4, sort_keys=True, default=convert_numpy) +def save_sample_images(images, predictions, true_labels, classes, save_path, probabilities): + """ + Save sample images for all classes (CN, MCI, AD, SMC) and false positives + + Args: + images: Tensor of input images + predictions: Model predictions + true_labels: True labels + classes: List of class names + save_path: Directory to save images + probabilities: Model prediction probabilities + """ + def save_grid(img_list, title, filename): + if len(img_list) == 0: + return + + # Take up to 5 images + img_list = img_list[:5] + + # Create a grid of images + grid = vutils.make_grid(img_list, nrow=len(img_list), padding=2, normalize=True) + plt.figure(figsize=(15, 3)) + plt.axis('off') + plt.title(title) + plt.imshow(grid.permute(1, 2, 0).cpu()) + plt.savefig(save_path / filename, bbox_inches='tight', dpi=300) + plt.close() + + # Lists to store sample images for each class + correct_samples = {class_name: [] for class_name in classes} + correct_probs = {class_name: [] for class_name in classes} + + # Dictionary to store false positives for each misclassification type + false_positive_samples = { + f"{true_class}->{pred_class}": [] + for true_class in classes + for pred_class in classes + if true_class != pred_class + } + false_positive_probs = { + f"{true_class}->{pred_class}": [] + for true_class in classes + for pred_class in classes + if true_class != pred_class + } + + # Collect samples + for img, pred, true_label, prob in zip(images, predictions, true_labels, probabilities): + pred_class = classes[pred] + true_class = classes[true_label] + + if pred == true_label: + if len(correct_samples[true_class]) < 5: + correct_samples[true_class].append(img) + correct_probs[true_class].append(prob[pred]) + else: + key = f"{true_class}->{pred_class}" + if len(false_positive_samples[key]) < 5: + false_positive_samples[key].append(img) + false_positive_probs[key].append(prob[pred]) + + # Save correct classifications + for class_name in classes: + if correct_samples[class_name]: + save_grid( + correct_samples[class_name], + f"Correct {class_name} Samples\nConfidence: {[f'{p:.2f}' for p in correct_probs[class_name]]}", + f'{class_name.lower()}_correct_samples.png' + ) + + # Save false positives grouped by misclassification type + for misclass_type, samples in false_positive_samples.items(): + if samples: + true_class, pred_class = misclass_type.split('->') + save_grid( + samples, + f"False Positives: {misclass_type}\n" + \ + f"True: {true_class}, Predicted: {pred_class}\n" + \ + f"Confidence: {[f'{p:.2f}' for p in false_positive_probs[misclass_type]]}", + f'false_positive_{true_class.lower()}_to_{pred_class.lower()}.png' + ) + +def evaluate_model(model, test_loader, device, classes): + """ + Evaluate model performance on test set + """ + model.eval() + + # Initialise lists to store predictions, true labels, and images + all_preds = [] + all_labels = [] + all_probs = [] + all_images = [] + + # Testing loop + with torch.no_grad(): + for images, labels in tqdm(test_loader, desc="Evaluating"): + images = images.to(device) + outputs = model(images) + probabilities = torch.nn.functional.softmax(outputs, dim=1) + _, predicted = torch.max(outputs.data, 1) + + all_preds.extend(predicted.cpu().numpy()) + all_labels.extend(labels.numpy()) + all_probs.extend(probabilities.cpu().numpy()) + all_images.extend(images.cpu()) + + return (np.array(all_preds), np.array(all_labels), + np.array(all_probs), torch.stack(all_images)) + def main(): # Configuration BATCH_SIZE = 64 @@ -212,8 +298,8 @@ def main(): print(f"Test set size: {len(test_dataset)} images") # Define paths for both models - model1_path = "./checkpoints/best_model_20241029_234652.pt" - model2_path = "./checkpoints/best_model_20241029_224507.pt" + model1_path = "./checkpoints/best_model_20241109_190825.pt" + model2_path = "./checkpoints/checkpoint_epoch_1_20241109_195852.pt" print(f"Loading models from:\nModel 1: {model1_path}\nModel 2: {model2_path}") # Perform cross-validation @@ -222,6 +308,10 @@ def main(): model1_path, model2_path, test_loader, device, CLASSES ) + # Save sample images + print("\nSaving sample images...") + save_sample_images(images, predictions, true_labels, CLASSES, results_dir, probabilities) + # Get unique classes present in the data present_classes = np.unique(true_labels) present_class_names = [CLASSES[i] for i in present_classes] @@ -243,7 +333,7 @@ def main(): ) # Plot ROC curves only for present classes - if len(present_classes) > 1: # Only plot ROC curves if there are multiple classes + if len(present_classes) > 1: plot_roc_curves(true_labels, probabilities[:, present_classes], present_class_names, results_dir) @@ -275,10 +365,23 @@ def main(): print(f"\nDetailed results have been saved to {results_dir}") print("Files generated:") print("- confusion_matrix.png") + print("- cn_correct_samples.png") + print("- mci_correct_samples.png") + print("- ad_correct_samples.png") + print("- smc_correct_samples.png") + + # List false positive files that were actually generated + generated_fp_files = list(results_dir.glob('false_positive_*.png')) + if generated_fp_files: + print("\nFalse positive analysis files:") + for fp_file in generated_fp_files: + print(f"- {fp_file.name}") + if len(present_classes) > 1: print("- roc_curves.png") print("- evaluation_metrics.json") + if __name__ == "__main__": try: main() From 76ac360a75479e21a9ffeff1bfc201282009abcc Mon Sep 17 00:00:00 2001 From: Ei3 Date: Sun, 10 Nov 2024 02:50:20 +1000 Subject: [PATCH 35/37] debug: extract samples after cross validation --- recognition/46822394_ViT_ADNC/predict.py | 52 ++++++++++++++++-------- 1 file changed, 34 insertions(+), 18 deletions(-) diff --git a/recognition/46822394_ViT_ADNC/predict.py b/recognition/46822394_ViT_ADNC/predict.py index 0bbbdba8d..3f83e60c1 100644 --- a/recognition/46822394_ViT_ADNC/predict.py +++ b/recognition/46822394_ViT_ADNC/predict.py @@ -50,20 +50,21 @@ def cross_validate_models(model1_path, model2_path, test_loader, device, classes # Choose predictions based on which model performs better for each class all_preds = np.zeros_like(preds1) all_probs = np.zeros_like(probs1) - + model_choices = {} # Track which model was chosen for each class + for class_idx in range(len(classes)): if class_idx in accuracy_per_class1: - # Use predictions from the model with higher accuracy for this class + mask = (labels1 == class_idx) if accuracy_per_class1[class_idx] >= accuracy_per_class2[class_idx]: - mask = (labels1 == class_idx) all_preds[mask] = preds1[mask] all_probs[mask] = probs1[mask] + model_choices[class_idx] = 1 else: - mask = (labels1 == class_idx) all_preds[mask] = preds2[mask] all_probs[mask] = probs2[mask] + model_choices[class_idx] = 2 - return all_preds, labels1, all_probs + return all_preds, labels1, all_probs, model_choices def plot_roc_curves(y_true, y_prob, classes, save_path): """ @@ -168,17 +169,18 @@ def convert_numpy(obj): with open(metrics_file, 'w') as f: json.dump(metrics_json, f, indent=4, sort_keys=True, default=convert_numpy) -def save_sample_images(images, predictions, true_labels, classes, save_path, probabilities): +def save_sample_images(images, predictions, true_labels, classes, save_path, probabilities, model_choices): """ - Save sample images for all classes (CN, MCI, AD, SMC) and false positives + Save sample images for all classes and false positives after cross-validation - Args: + Args: images: Tensor of input images predictions: Model predictions true_labels: True labels classes: List of class names save_path: Directory to save images probabilities: Model prediction probabilities + model_choices: Models """ def save_grid(img_list, title, filename): if len(img_list) == 0: @@ -187,7 +189,6 @@ def save_grid(img_list, title, filename): # Take up to 5 images img_list = img_list[:5] - # Create a grid of images grid = vutils.make_grid(img_list, nrow=len(img_list), padding=2, normalize=True) plt.figure(figsize=(15, 3)) plt.axis('off') @@ -218,6 +219,7 @@ def save_grid(img_list, title, filename): for img, pred, true_label, prob in zip(images, predictions, true_labels, probabilities): pred_class = classes[pred] true_class = classes[true_label] + used_model = model_choices.get(true_label, "unknown") if pred == true_label: if len(correct_samples[true_class]) < 5: @@ -231,10 +233,12 @@ def save_grid(img_list, title, filename): # Save correct classifications for class_name in classes: + class_idx = classes.index(class_name) if correct_samples[class_name]: save_grid( correct_samples[class_name], - f"Correct {class_name} Samples\nConfidence: {[f'{p:.2f}' for p in correct_probs[class_name]]}", + f"Correct {class_name} Samples (Using Model {model_choices[class_idx]})\n" + \ + f"Confidence: {[f'{p:.2f}' for p in correct_probs[class_name]]}", f'{class_name.lower()}_correct_samples.png' ) @@ -249,7 +253,6 @@ def save_grid(img_list, title, filename): f"Confidence: {[f'{p:.2f}' for p in false_positive_probs[misclass_type]]}", f'false_positive_{true_class.lower()}_to_{pred_class.lower()}.png' ) - def evaluate_model(model, test_loader, device, classes): """ Evaluate model performance on test set @@ -260,7 +263,6 @@ def evaluate_model(model, test_loader, device, classes): all_preds = [] all_labels = [] all_probs = [] - all_images = [] # Testing loop with torch.no_grad(): @@ -273,13 +275,11 @@ def evaluate_model(model, test_loader, device, classes): all_preds.extend(predicted.cpu().numpy()) all_labels.extend(labels.numpy()) all_probs.extend(probabilities.cpu().numpy()) - all_images.extend(images.cpu()) - return (np.array(all_preds), np.array(all_labels), - np.array(all_probs), torch.stack(all_images)) + return np.array(all_preds), np.array(all_labels), np.array(all_probs) def main(): - # Configuration + # Configuration BATCH_SIZE = 64 CLASSES = ['CN', 'MCI', 'AD', 'SMC'] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -304,13 +304,29 @@ def main(): # Perform cross-validation print("\nPerforming cross-validation...") - predictions, true_labels, probabilities = cross_validate_models( + predictions, true_labels, probabilities, model_choices = cross_validate_models( model1_path, model2_path, test_loader, device, CLASSES ) + # Save model choice information + model_choice_info = { + CLASSES[class_idx]: f"Model {model_num}" + for class_idx, model_num in model_choices.items() + } + with open(results_dir / 'model_choices.json', 'w') as f: + json.dump(model_choice_info, f, indent=4) + + # Get all images for visualization + all_images = [] + test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) + for images, _ in tqdm(test_loader, desc="Collecting images for visualization"): + all_images.extend(images) + all_images = torch.stack(all_images) + # Save sample images print("\nSaving sample images...") - save_sample_images(images, predictions, true_labels, CLASSES, results_dir, probabilities) + save_sample_images(all_images, predictions, true_labels, CLASSES, results_dir, + probabilities, model_choices) # Get unique classes present in the data present_classes = np.unique(true_labels) From bb95cce6ffbf9cb2a521edbd429b4d7965b8c05a Mon Sep 17 00:00:00 2001 From: Ei3 Date: Sun, 10 Nov 2024 09:53:13 +1000 Subject: [PATCH 36/37] add sample images --- .gitignore | 1 + recognition/46822394_ViT_ADNC/README.md | 2 ++ .../img/cn_correct_samples.png | Bin 0 -> 259047 bytes .../img/false_positive_cn_to_mci.png | Bin 0 -> 274784 bytes .../img/false_positive_mci_to_cn.png | Bin 0 -> 268634 bytes .../img/mci_correct_samples.png | Bin 0 -> 259443 bytes recognition/46822394_ViT_ADNC/predict.py | 15 ++++++++++----- 7 files changed, 13 insertions(+), 5 deletions(-) create mode 100644 recognition/46822394_ViT_ADNC/img/cn_correct_samples.png create mode 100644 recognition/46822394_ViT_ADNC/img/false_positive_cn_to_mci.png create mode 100644 recognition/46822394_ViT_ADNC/img/false_positive_mci_to_cn.png create mode 100644 recognition/46822394_ViT_ADNC/img/mci_correct_samples.png diff --git a/.gitignore b/.gitignore index 049c20bff..29cb564f6 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ recognition/46822394_ViT_ADNC/ADNI/meta_data_with_label.json *.pyc *.pdf *.typ +/recognition/46822394_ViT_ADNC/cross_validation_results_20241110_030810 diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index 960e0b352..c9c5c727f 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -247,6 +247,8 @@ The system evaluates models using multiple metrics: | Model 1 | ./checkpoints/best_model_20241029_234652.pt | | Model 2 | ./checkpoints/best_model_20241029_224507.pt | +## Sample Predictions + ## Visual Analysis of Results diff --git a/recognition/46822394_ViT_ADNC/img/cn_correct_samples.png b/recognition/46822394_ViT_ADNC/img/cn_correct_samples.png new file mode 100644 index 0000000000000000000000000000000000000000..6d5630493fe94a0c0dbe5900535dbc55ff6312e5 GIT binary patch literal 259047 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a/recognition/46822394_ViT_ADNC/predict.py +++ b/recognition/46822394_ViT_ADNC/predict.py @@ -7,19 +7,24 @@ Evaluation metrics will be printed and evaluation figures will be saved to the current folder. """ +# Standard library imports +import json +from datetime import datetime +from pathlib import Path + +# Third-party imports +import numpy as np +import pandas as pd import torch import torch.nn as nn from torch.utils.data import DataLoader +import torchvision.utils as vutils import matplotlib.pyplot as plt import seaborn as sns -import numpy as np from tqdm import tqdm from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc -import pandas as pd -from pathlib import Path -import json -from datetime import datetime +# Local imports from dataset import get_dataset from modules import ViTClassifier From 034167a5f01bef980d1c8a439cf7053139999606 Mon Sep 17 00:00:00 2001 From: Ei3 Date: Sun, 10 Nov 2024 09:59:37 +1000 Subject: [PATCH 37/37] update README to include sample predictions --- recognition/46822394_ViT_ADNC/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/recognition/46822394_ViT_ADNC/README.md b/recognition/46822394_ViT_ADNC/README.md index c9c5c727f..af56f1ec0 100644 --- a/recognition/46822394_ViT_ADNC/README.md +++ b/recognition/46822394_ViT_ADNC/README.md @@ -248,6 +248,10 @@ The system evaluates models using multiple metrics: | Model 2 | ./checkpoints/best_model_20241029_224507.pt | ## Sample Predictions +![Correct CN Samples](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/cn_correct_samples.png) +![False Positives: MCI->CN](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/false_positive_mci_to_cn.png) +![False Positives: CN->MCI](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/false_positive_cn_to_mci.png) +![Correct MCI Samples](https://github.com/Ei3-kw/PatternAnalysis-2024/blob/topic-recognition/recognition/46822394_ViT_ADNC/img/mci_correct_samples.png) ## Visual Analysis of Results