upload application files

main.py

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+import gradio as gr
+
+
+from src.inference import load_classifier, load_model, generate_images, convert_into_image, classify_image
+from src.models import ResUNetGenerator
+from src.explainer import GradCAM, preprocess_image
+
+# Loading Models
+classifier_path = 'models\\efficientnet_b1-epoch16-val_loss0.46_ft.ckpt'
+g_NP_checkpoint = 'models\\g_NP_best.ckpt'
+g_PN_checkpoint = 'models\\g_PN_best.ckpt'
+g_NP = load_model(g_NP_checkpoint, ResUNetGenerator(gf=32, channels=1))
+g_PN = load_model(g_PN_checkpoint, ResUNetGenerator(gf=32, channels=1))
+classifier = load_classifier(classifier_path)
+target_layer = classifier.model.features[-1]
+grad_cam = GradCAM(classifier, target_layer)
+
+
+def counterfactual_generation(input_image):
+
+    translated_images, recon_images = generate_images(input_image, classifier, g_PN, g_NP)
+    translated_images = convert_into_image(translated_images)
+    recon_images = convert_into_image(recon_images)
+    return translated_images, recon_images
+
+def image_classification(input_image):
+
+    result, target_class = classify_image(input_image, classifier=classifier)
+    input_tensor = preprocess_image(input_image)
+    cam = grad_cam.generate_cam(input_tensor, target_class)
+    cam_image = grad_cam.visualize_cam(cam, input_tensor)
+
+    return result, cam_image
+
+# Defining the components
+inputs1 = gr.Image(type="pil", format="png")
+inputs2 = gr.Image(type="pil", format="png")
+outputs1 = [gr.Image(type="pil", label="Translated Images", format="png"),
+           gr.Image(type="pil", label="Reconstructed Images", format="png")]
+
+outputs2 = [gr.Label(label="Classification Result"), gr.Image(label="Grad-CAM", format="png")]
+
+with gr.Blocks() as demo:
+    with gr.Tab("Counterfactual Generation"):
+        app1 = gr.Interface(fn=counterfactual_generation, inputs=inputs1, outputs=outputs1,
+                            title="Counterfactual Image Generation", allow_flagging="never",
+                            description="Generate counterfactual images to explain the classifier's decisions.")
+
+    with gr.Tab("Classification"):
+        app2 = gr.Interface(fn=image_classification, inputs=inputs2, outputs=outputs2,
+                            title="Image Classification", allow_flagging="never",
+                            description="Classify the input medical image and visualize Grad-CAM.")
+
+# Launch the app
+demo.launch(share=True)

models/cyclegan-epoch_epoch=38-vloss_val_generator_loss=1.64.ckpt

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+oid sha256:b2ed3c9f3a16222af626977aff6fc9763bf2862a56ea1475c41e783d56942f92
+size 110015900

models/efficientnet_b1-epoch16-val_loss0.46_ft.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3bba6f1e551baa0a91c6fa4d824d87ba12bcbcce27def144d05318f4cbbbb67c
+size 78886204

models/g_NP_best.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e4d7a90fbdd5adda11834b6496cad111cfb3a81d6cf347dfd2fb48373e75e1b
+size 6537396

models/g_PN_best.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1374c46226207f31ccf078e8bfaafdc299ed10a1b9de154a865f74b57d22387
+size 6537396

requirements.txt

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+pydicom
+wandb
+torchsummary
+torchvision
+torchmetrics 
+lightning==2.2.3
+pillow
+matplotlib
+pandas
+numpy
+gradio

src/classifier.py

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+import torch
+import torch.nn as nn
+import lightning as pl
+import torchvision.models as models
+from torchmetrics import Accuracy
+
+
+class Classifier(pl.LightningModule):
+    def __init__(self, transfer=True):
+        super(Classifier, self).__init__()
+        self.conv = nn.Conv2d(1, 3, kernel_size=3, stride=1, padding=1)  # Adjust input channels to 3
+        self.model = models.efficientnet_b1(weights='IMAGENET1K_V1')
+        if transfer:
+            # layers are frozen by using eval()
+            self.model.eval()
+            # freeze params
+            for p in self.model.parameters() : 
+                p.requires_grad = False
+        num_ftrs = 1280
+        self.model.classifier = nn.Sequential(
+            nn.LeakyReLU(),
+            nn.Dropout(p=0.3), 
+            nn.Linear(in_features=num_ftrs , out_features=2),
+            nn.Softmax(dim=1)  
+        )
+
+        self.criterion = nn.CrossEntropyLoss()
+        self.train_accuracy = Accuracy(task='binary')
+        self.val_accuracy = Accuracy(task='binary')
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.model(x)
+
+    def training_step(self, batch, batch_idx):
+        images, labels = batch
+        outputs = self(images)
+        loss = self.criterion(outputs, labels)
+        self.log('train_loss', loss, prog_bar=True, on_epoch=True, on_step=True)
+        # Calculate and log accuracy
+        _, preds = torch.max(outputs, 1)
+        acc = self.train_accuracy(preds, labels)
+        self.log('train_acc', acc, prog_bar=True, on_step=True, on_epoch=True)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        images, labels = batch
+        outputs = self(images)
+        loss = self.criterion(outputs, labels)
+        self.log('val_loss', loss, prog_bar=True, sync_dist=True)
+        # Calculate and log accuracy
+        _, preds = torch.max(outputs, 1)
+        acc = self.val_accuracy(preds, labels)
+        self.log('val_acc', acc, prog_bar=True, sync_dist=True)
+        return loss
+    
+    def on_train_epoch_end(self):
+        self.train_accuracy.reset()
+
+    def on_validation_epoch_end(self):
+        self.val_accuracy.reset()
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=0.0001)
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=5, verbose=True)
+        return {
+            'optimizer': optimizer,
+            'lr_scheduler': {
+                'scheduler': scheduler,
+                'monitor': 'val_loss',
+            },
+            'monitor': 'val_loss'
+        }

src/config.py

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+class CFG:
+    GAN_FILTERS = 32
+    DIS_FILTERS = 64

src/dataset.py

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+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+import os 
+
+class ClassifierDataset(Dataset):
+    def __init__(self, root_dir, transform=None):
+        self.root_dir = root_dir
+        self.transform = transform
+
+        self.classes = ['0', '1']
+        self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
+
+        self.samples = self._make_dataset()
+
+    def _make_dataset(self):
+        samples = []
+        for class_name in self.classes:
+            class_dir = os.path.join(self.root_dir, class_name)
+            for img_name in os.listdir(class_dir):
+                img_path = os.path.join(class_dir, img_name)
+                samples.append((img_path, self.class_to_idx[class_name]))
+        return samples
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        img_path, label = self.samples[idx]
+        img = Image.open(img_path).convert('L')  # Convert to grayscale
+        if self.transform:
+            img = self.transform(img)
+        return img, label
+    
+
+
+class CustomDataset(Dataset):
+    def __init__(self, root_dir, train_N, train_P, img_res):
+        self.root_dir = root_dir
+        self.train_N = train_N
+        self.train_P = train_P
+        self.img_res = img_res
+        self.transforms = transforms.Compose([
+            transforms.Resize(img_res),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5], std=[0.5])  # Assuming grayscale images
+        ])
+
+    def __len__(self):
+        return min(len(os.listdir(os.path.join(self.root_dir, self.train_N))),
+                   len(os.listdir(os.path.join(self.root_dir, self.train_P))))
+
+    def __getitem__(self, idx):
+        normal_path = os.path.join(self.root_dir, self.train_N, os.listdir(os.path.join(self.root_dir, self.train_N))[idx])
+        pneumo_path = os.path.join(self.root_dir, self.train_P, os.listdir(os.path.join(self.root_dir, self.train_P))[idx])
+        
+        normal_img = Image.open(normal_path).convert("L")  # Load as grayscale
+        pneumo_img = Image.open(pneumo_path).convert("L")  # Load as grayscale
+        
+        normal_img = self.transforms(normal_img)
+        pneumo_img = self.transforms(pneumo_img)
+        
+        return normal_img, pneumo_img
+
+

src/explainer.py

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+import numpy as np
+import cv2
+import torchvision.transforms as transforms
+
+
+class GradCAM:
+    def __init__(self, model, target_layer):
+        self.model = model
+        self.target_layer = target_layer
+        self.gradients = None
+        self.activations = None
+        self._register_hooks()
+
+    def _register_hooks(self):
+        def forward_hook(module, input, output):
+            self.activations = output
+
+        def backward_hook(module, grad_in, grad_out):
+            self.gradients = grad_out[0]
+
+        self.target_layer.register_forward_hook(forward_hook)
+        self.target_layer.register_backward_hook(backward_hook)
+
+    def generate_cam(self, input_image, target_class):
+        self.model.zero_grad()
+        output = self.model(input_image)
+        loss = output[:, target_class].sum()
+        loss.backward()
+
+        weights = self.gradients.mean(dim=(2, 3), keepdim=True)
+        cam = (weights * self.activations).sum(dim=1, keepdim=True)
+
+        cam = cam.detach().cpu().numpy()
+        cam = np.maximum(cam, 0)
+        cam = cam / cam.max()
+        cam = cam.squeeze()
+
+        return cam
+
+    def visualize_cam(self, cam, input_image):
+        cam = cv2.resize(cam, (input_image.shape[2], input_image.shape[3]))
+        heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)
+        heatmap = np.float32(heatmap) / 255
+        input_image = np.moveaxis(input_image.cpu().numpy()[0], 0, -1)
+        input_image = np.float32(input_image)
+        cam_image = heatmap + input_image
+        cam_image = cam_image / cam_image.max()
+        return cam_image
+
+def preprocess_image(image):
+    preprocess = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),
+        transforms.Resize((512, 512)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5], std=[0.5])
+    ])
+    image = preprocess(image)
+    image = image.unsqueeze(0)
+    return image

src/generate_images.py

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+import os
+import torch
+from PIL import Image
+import numpy as np
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from tqdm import tqdm
+
+from src.models import ResUNetGenerator
+
+# Custom Dataset
+class ImageDataset(Dataset):
+    def __init__(self, image_paths, transform=None):
+        self.image_paths = image_paths
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        img_path = self.image_paths[idx]
+        image = Image.open(img_path).convert('L')
+        if self.transform:
+            image = self.transform(image)
+        return image, img_path
+
+# Function to save image
+def save_image(tensor, path):
+    if tensor.is_cuda:
+        tensor = tensor.cpu()
+
+    array = tensor.permute(1, 2, 0).detach().numpy()
+    array = (array * 0.5 + 0.5) * 255
+    array = array.astype(np.uint8)
+    if array.shape[2] == 1:
+        array = array.squeeze(2)
+        image = Image.fromarray(array, mode='L')
+    else:
+        image = Image.fromarray(array)
+    image.save(path)
+
+# Function to load model
+def load_model(checkpoint_path, model_class, device):
+    model = model_class().to(device)
+    model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu')))
+    model.eval()
+    return model
+
+def generate_images(image_folder, g_NP_checkpoint, g_PN_checkpoint, output_dir='data/translated_images', batch_size=16):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    
+    # Load models
+    g_NP = load_model(g_NP_checkpoint, lambda: ResUNetGenerator(gf=32, channels=1), device)
+    g_PN = load_model(g_PN_checkpoint, lambda: ResUNetGenerator(gf=32, channels=1), device)
+    
+    # Create output directories
+    os.makedirs(os.path.join(output_dir, '0'), exist_ok=True)
+    os.makedirs(os.path.join(output_dir, '1'), exist_ok=True)
+
+    # Collect image paths
+    image_paths_0 = [os.path.join(image_folder, '0', fname) for fname in os.listdir(os.path.join(image_folder, '0')) if fname.endswith(('.png', '.jpg', '.jpeg'))]
+    image_paths_1 = [os.path.join(image_folder, '1', fname) for fname in os.listdir(os.path.join(image_folder, '1')) if fname.endswith(('.png', '.jpg', '.jpeg'))]
+
+    # Prepare dataset and dataloader
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485], std=[0.229])])
+    dataset_0 = ImageDataset(image_paths_0, transform)
+    dataset_1 = ImageDataset(image_paths_1, transform)
+    dataloader_0 = DataLoader(dataset_0, batch_size=batch_size, shuffle=False)
+    dataloader_1 = DataLoader(dataset_1, batch_size=batch_size, shuffle=False)
+    
+    # Process images from negative (0) to positive (1)
+    with torch.no_grad():
+        for batch, paths in tqdm(dataloader_0, desc="Converting N to P: "):
+            batch = batch.to(device)
+            translated_images = g_NP(batch)
+            translated_images = g_PN(translated_images)
+            for img, path in zip(translated_images, paths):
+                save_path = os.path.join(output_dir, '1', os.path.basename(path))
+                save_image(img, save_path)
+        
+        # Process images from positive (1) to negative (0)
+        for batch, paths in tqdm(dataloader_1, desc="Converting P to N: "):
+            batch = batch.to(device)
+            translated_images = g_PN(batch)
+            translated_images = g_NP(translated_images)
+            for img, path in zip(translated_images, paths):
+                save_path = os.path.join(output_dir, '0', os.path.basename(path))
+                save_image(img, save_path)
+
+if __name__ == '__main__':
+    image_folder = r'data\rsna-pneumonia-dataset\train'
+    g_NP_checkpoint = 'models\g_NP_best.ckpt'
+    g_PN_checkpoint = 'models\g_PN_best.ckpt'
+    
+    
+    generate_images(image_folder, g_NP_checkpoint, g_PN_checkpoint)

src/inference.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+
+from src.classifier import Classifier
+
+# Ensure the necessary directories exist
+# os.makedirs('results/translated_N', exist_ok=True)
+# os.makedirs('results/translated_P', exist_ok=True)
+
+# Load the classifier model
+def load_classifier(classifier_path):
+    classifier = Classifier()
+    classifier_checkpoint = torch.load(classifier_path, map_location=torch.device('cpu'))
+    classifier.load_state_dict(classifier_checkpoint['state_dict'])
+    classifier.eval()
+    return classifier
+
+# Load the generator models
+def load_model(checkpoint_path, model):
+    checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
+    model.load_state_dict(checkpoint)
+    model.eval()
+    return model
+
+def load_image(input_image, image_size):
+    transform = transforms.Compose([
+        transforms.Resize((image_size, image_size)),  # Resize image to 512x512
+        transforms.ToTensor(),          
+        transforms.Normalize(mean=[0.485], std=[0.229])  # Normalize image
+    ])
+    input_image = input_image.convert('L')
+    return transform(input_image).unsqueeze(0)
+
+def convert_into_image(tensor):
+    if tensor.is_cuda:
+        tensor = tensor.cpu()
+    array = tensor.squeeze(0).permute(1, 2, 0).detach().numpy()
+    array = (array * 0.5 + 0.5) * 255
+    array = array.astype(np.uint8)
+    
+    if array.shape[2] == 1:
+        array = array.squeeze(2)
+        image = Image.fromarray(array, mode='L')
+    else:
+        image = Image.fromarray(array)
+    
+    return image
+
+def generate_images(input_image, classifier, g_PN, g_NP, image_size=512):
+
+    image = load_image(input_image, image_size)
+
+    # Classify the image
+    classifier_output = classifier(image).cpu().detach().numpy()
+    pred = np.argmax(classifier_output, axis=1)[0]
+  
+    if pred > 0.5:
+        print("Classified as Domain P")
+        translate_to_domain = g_PN
+        folder_to_save = 'results/translated_N'
+        reverse_translate = g_NP
+    else:
+        print("Classified as Domain N")
+        translate_to_domain = g_NP
+        folder_to_save = 'results/translated_P'
+        reverse_translate = g_PN
+
+    # Perform translation and save images
+    with torch.no_grad():
+        for i in range(1):  # Generate and save 10 images
+            translated_image = translate_to_domain(image)
+            # save_image(translated_image, os.path.join(folder_to_save, f'translated_{i}.png'))
+
+            # Translate back to the original domain and save
+            recon_image = reverse_translate(translated_image)
+            # save_image(recon_image, os.path.join(folder_to_save, f'recon_{i}.png'))
+
+    return translated_image, recon_image
+
+def classify_image(input_image, classifier, image_size=512):
+    
+    image = load_image(input_image, image_size)
+    classifier_output = classifier(image).cpu().detach().numpy()
+    pred = np.argmax(classifier_output, axis=1)[0]
+    if pred > 0.5:
+        return {"Pneumonia": classifier_output[0][1]}, 1
+    
+    else:
+        return {"Normal": classifier_output[0][0]}, 0

src/models.py

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+import torch
+import torch.nn as nn
+import lightning as pl
+import wandb
+import itertools
+from torch.optim.lr_scheduler import LambdaLR
+from torch.utils.data import DataLoader
+import numpy as np
+import matplotlib.pyplot as plt
+
+from src.classifier import Classifier
+from src.dataset import CustomDataset
+
+
+class AttentionGate(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(AttentionGate, self).__init__()
+        self.conv_gate = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.conv_x = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x, g):
+        gate = self.conv_gate(g)
+        x = self.conv_x(x)
+        attention = self.softmax(gate)
+        x_att = x * attention
+        return x_att
+    
+class ResUNetGenerator(nn.Module):
+    def __init__(self, gf, channels):
+        super(ResUNetGenerator, self).__init__()
+        # self.img_shape = img_shape
+        self.channels = channels
+        
+        # Downsampling layers
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(channels, gf, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(gf, gf * 2, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf * 2)
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(gf * 2, gf * 4, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf * 4)
+        )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(gf * 4, gf * 8, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf * 8)
+        )
+
+        self.attn_layer = nn.ModuleList([
+            AttentionGate(gf * 2**(i), gf * 2**(i+1))
+            for i in range(3)
+        ])
+
+        # Upsampling layers
+        self.deconv1 = nn.Sequential(
+            nn.ConvTranspose2d(gf * 8, gf * 4, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf * 4)
+        )
+        self.deconv2 = nn.Sequential(
+            nn.ConvTranspose2d(gf * 8, gf * 2, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf * 2)
+        )
+        self.deconv3 = nn.Sequential(
+            nn.ConvTranspose2d(gf * 4, gf, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(inplace=True),
+            nn.GroupNorm(num_groups=1, num_channels=gf)
+        )
+        self.deconv4 = nn.Sequential(
+            nn.ConvTranspose2d(gf * 2, channels, kernel_size=4, stride=2, padding=1),
+            nn.Tanh()
+        )
+        
+    def forward(self, x):
+        # Downsampling
+        d1 = self.conv1(x)
+        d2 = self.conv2(d1)
+        d3 = self.conv3(d2)
+        d4 = self.conv4(d3)
+        
+        # Upsampling
+        u1 = self.deconv1(d4)
+        u1 = self.attn_layer[2](d3, u1)
+        
+        u2 = self.deconv2(u1)
+        u2 = self.attn_layer[1](d2, u2)
+        
+        u3 = self.deconv3(u2)
+        u3 = self.attn_layer[0](d1, u3)
+        
+        output = self.deconv4(u3)
+        
+        return output
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=0.0002, betas=(0.5, 0.999))
+        return optimizer
+
+
+class Discriminator(pl.LightningModule):
+    def __init__(self, df):
+        super(Discriminator, self).__init__()
+        self.df = df
+        # Define the layers for the discriminator
+        self.conv_layers = nn.ModuleList([nn.Sequential(
+            nn.Conv2d(1 if i == 0 else df * 2**(i-1), df * 2**i, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2),
+            nn.GroupNorm(8, df * 2**i)) for i in range(4)])
+        
+        self.final_conv = nn.Conv2d(df * 8, 1, kernel_size=4, stride=1, padding=1)
+
+    def forward(self, x):
+        out = x
+        for conv_layer in self.conv_layers:
+            out = conv_layer(out)
+        validity = self.final_conv(out)
+        return validity
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=0.0002, betas=(0.5, 0.999))
+        return optimizer
+
+
+
+class CycleGAN(pl.LightningModule):
+    def __init__(self, train_dir, val_dir, test_dataloader, classifier_path, checkpoint_dir, image_size=512, batch_size=4, channels=1, gf=32, df=64, lambda_cycle=10.0, lambda_id=0.1, classifier_weight=1):
+        super(CycleGAN, self).__init__()
+        self.image_size = image_size
+        self.batch_size = batch_size
+        self.channels = channels
+        self.gf = gf
+        self.df = df
+        self.lambda_cycle = lambda_cycle
+        self.lambda_id = lambda_id * lambda_cycle
+        self.classifier_path = classifier_path
+        self.classifier_weight = classifier_weight
+        self.lowest_val_loss = float('inf')
+        self.validation_step_outputs = []
+        self.train_dir = train_dir
+        self.val_dir = val_dir
+        self.test_dataloader = test_dataloader
+        self.checkpoint_dir = checkpoint_dir
+
+        # Initialize the generator, discriminator, and classifier models
+        self.g_NP = ResUNetGenerator(gf, channels=self.channels)
+        self.g_PN = ResUNetGenerator(gf, channels=self.channels)
+        self.d_N = Discriminator(df)
+        self.d_P = Discriminator(df)
+        self.automatic_optimization = False
+        
+        self.classifier = Classifier()
+        checkpoint = torch.load(classifier_path)
+        self.classifier.load_state_dict(checkpoint['state_dict'])
+        self.classifier.eval()
+        self.freeze_classifier()
+    
+    def freeze_classifier(self):
+        print("freezing Classifier...")
+        for p in self.classifier.parameters() : 
+                p.requires_grad = False
+
+
+    def generator_training_step(self, img_N, img_P, opt):
+        self.toggle_optimizer(opt)
+        # Translate images to the other domain
+        fake_P = self.g_NP(img_N)
+        fake_N = self.g_PN(img_P)
+
+        # Translate images back to original domain
+        reconstr_N = self.g_PN(fake_P)
+        reconstr_P = self.g_NP(fake_N)
+
+        # Identity mapping of images
+        img_N_id = self.g_PN(img_N)
+        img_P_id = self.g_NP(img_P)
+        # Discriminators determine validity of translated images
+        valid_N = self.d_N(fake_N)
+        valid_P = self.d_P(fake_P)
+
+        class_N_loss = self.classifier(fake_N)
+        class_P_loss = self.classifier(fake_P)
+        # Adversarial loss
+        valid_target = torch.ones_like(valid_N)
+        adversarial_loss = nn.MSELoss()(valid_N, valid_target) + nn.MSELoss()(valid_P, valid_target)
+
+        # Cycle consistency loss
+        cycle_loss = nn.L1Loss()(reconstr_N, img_N) + nn.L1Loss()(reconstr_P, img_P)
+
+        # Identity loss
+        identity_loss = nn.L1Loss()(img_N_id, img_N) + nn.L1Loss()(img_P_id, img_P)
+
+        # Classifier loss
+        class_loss = nn.MSELoss()(class_N_loss, torch.ones_like(class_N_loss)) + nn.MSELoss()(class_P_loss, torch.zeros_like(class_P_loss))
+        
+        # Total generator loss
+        total_loss = adversarial_loss + self.lambda_cycle * cycle_loss + self.lambda_id * identity_loss + self.classifier_weight * class_loss
+              
+        self.log('adversarial_loss', adversarial_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('reconstruction_loss', cycle_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('identity_loss', identity_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('class_loss', class_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('generator_loss', total_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        
+        opt.zero_grad()
+        self.manual_backward(total_loss)
+        opt.step()
+        self.untoggle_optimizer(opt)
+        
+        return total_loss, adversarial_loss, cycle_loss
+
+    def discriminator_training_step(self, img_N, img_P, opt):
+        # Pass real images through discriminator D_N
+        self.toggle_optimizer(opt)
+        pred_real_N = self.d_N(img_N)
+        mse_real_N = nn.MSELoss()(pred_real_N, torch.ones_like(pred_real_N))
+        fake_P = self.g_PN(img_P)
+        pred_fake_N = self.d_N(fake_P)
+        mse_fake_N = nn.MSELoss()(pred_fake_N, torch.zeros_like(pred_fake_N))
+
+        pred_real_P = self.d_P(img_P)
+        mse_real_P = nn.MSELoss()(pred_real_P, torch.ones_like(pred_real_P))
+        fake_N = self.g_NP(img_N)
+        pred_fake_P = self.d_P(fake_N)
+        mse_fake_P = nn.MSELoss()(pred_fake_P, torch.zeros_like(pred_fake_P))
+        
+        # Compute total discriminator loss
+        dis_loss = 0.5 * (mse_real_N + mse_fake_N + mse_real_P + mse_fake_P)
+        opt.zero_grad()
+        self.manual_backward(mse_fake_P)
+        opt.step()
+        self.untoggle_optimizer(opt)
+        
+        self.log('mse_fake_N', mse_fake_N, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('mse_fake_P', mse_fake_P, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('discriminator_loss', dis_loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+
+        return dis_loss, mse_fake_N, mse_fake_P
+    
+    def training_step(self, batch, batch_idx):
+        img_N, img_P = batch
+        optD, optG = self.optimizers()
+        
+        total_loss, adversarial_loss, cycle_loss = self.generator_training_step(img_N, img_P, optG)
+        dis_loss, mse_fake_N, mse_fake_P = self.discriminator_training_step(img_N, img_P, optD) 
+        
+        return {"generator_loss": total_loss, "adversarial_loss": adversarial_loss, "reconstruction_loss": cycle_loss, "discriminator_loss": dis_loss, "mse_fake_N": mse_fake_N, "mse_fake_P": mse_fake_P}
+    
+    def validation_step(self, batch, batch_idx):
+        img_N, img_P = batch
+
+        # Translate images to the other domain
+        fake_P = self.g_NP(img_N)
+        fake_N = self.g_PN(img_P)
+
+        # Translate images back to original domain
+        reconstr_N = self.g_PN(fake_P)
+        reconstr_P = self.g_NP(fake_N)
+
+        # Identity mapping of images
+        img_N_id = self.g_PN(img_N)
+        img_P_id = self.g_NP(img_P)
+
+        # Discriminators determine validity of translated images
+        valid_N = self.d_N(fake_N)
+        valid_P = self.d_P(fake_P)
+
+        class_N_loss = self.classifier(fake_N)
+        class_P_loss = self.classifier(fake_P)
+
+        # Adversarial loss
+        valid_target = torch.ones_like(valid_N)
+        adversarial_loss = nn.MSELoss()(valid_N, valid_target) + nn.MSELoss()(valid_P, valid_target)
+
+        # Cycle consistency loss
+        cycle_loss = nn.L1Loss()(reconstr_N, img_N) + nn.L1Loss()(reconstr_P, img_P)
+
+        # Identity loss
+        identity_loss = nn.L1Loss()(img_N_id, img_N) + nn.L1Loss()(img_P_id, img_P)
+
+        # Classifier loss
+        class_loss = nn.MSELoss()(class_N_loss, torch.ones_like(class_N_loss)) + nn.MSELoss()(class_P_loss, torch.zeros_like(class_P_loss))
+
+
+        # Total generator loss
+        total_loss = adversarial_loss + self.lambda_cycle * cycle_loss + self.lambda_id * identity_loss + self.classifier_weight * class_loss
+        self.validation_step_outputs.append(total_loss)
+
+        self.log('val_adversarial_loss', adversarial_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('val_cycle_loss', cycle_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('val_identity_loss', identity_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('val_class_loss', class_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('val_generator_loss', total_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+
+        return total_loss
+
+    def on_validation_end(self):
+        # Calculate average validation loss
+        avg_val_loss = torch.stack(self.validation_step_outputs).mean()
+
+        # Check if current validation loss is lower than the lowest recorded validation loss
+        if avg_val_loss < self.lowest_val_loss:
+            # Update lowest validation loss and corresponding epoch
+            self.lowest_val_loss = avg_val_loss
+
+            # Save the generators' state dictionaries
+            torch.save(self.g_NP.state_dict(), f"{self.checkpoint_dir}/g_NP_best.ckpt")
+            torch.save(self.g_PN.state_dict(), f"{self.checkpoint_dir}/g_PN_best.ckpt")
+            print(f"Model saved! loss reduced to {self.lowest_val_loss}")
+
+    def configure_optimizers(self):
+        optG = torch.optim.Adam(itertools.chain(self.g_NP.parameters(), self.g_PN.parameters()),lr=2e-4, betas=(0.5, 0.999))
+        optD = torch.optim.Adam(itertools.chain(self.d_N.parameters(), self.d_P.parameters()),lr=2e-4, betas=(0.5, 0.999))
+        
+        gamma = lambda epoch: 1 - max(0, epoch + 1 - 100) / 101
+        schD = LambdaLR(optD, lr_lambda=gamma)
+#         Optimizer= [optD, optG]
+        return optD, optG
+
+    def train_dataloader(self):
+        root_dir = self.train_dir
+        train_N = "0"
+        train_P = "1"
+        img_res = (self.image_size, self.image_size)
+
+        dataset = CustomDataset(root_dir=root_dir, train_N=train_N, train_P=train_P, img_res=img_res)
+
+        # Set up DataLoader for parallel processing and GPU acceleration
+        dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=4, pin_memory=True)
+
+        return dataloader
+    
+    def val_dataloader(self):
+        root_dir = self.val_dir
+        train_N = "0"
+        train_P = "1"
+        img_res = (self.image_size, self.image_size)
+
+        dataset = CustomDataset(root_dir=root_dir, train_N=train_N, train_P=train_P, img_res=img_res)
+
+        # Set up DataLoader for parallel processing and GPU acceleration
+        dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False, num_workers=4, pin_memory=True)
+
+        return dataloader
+     
+
+    def on_train_batch_end(self, outputs, batch, batch_idx):
+        if batch_idx % 100 == 0:
+            # Get a random batch from the test dataloader
+            batch = next(iter(self.test_dataloader))
+            img_N, img_P = batch
+
+            # Pick a random image from the batch
+            idx = np.random.randint(img_N.size(0))
+            img_N = img_N[idx].unsqueeze(0).to('cuda')
+            img_P = img_P[idx].unsqueeze(0).to('cuda')
+            # Translate images to the other domain
+            fake_P = self.g_NP(img_N)
+            fake_N = self.g_PN(img_P)
+
+            # Translate images back to original domain
+            reconstr_N = self.g_PN(fake_P)
+            reconstr_P = self.g_NP(fake_N)
+
+            # Plot the images
+            fig, axes = plt.subplots(2, 3, figsize=(15, 10))
+
+            # Plot real N, translated P, and reconstructed N
+            axes[0, 0].imshow(img_N.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[0, 0].set_title("Real N")
+            axes[0, 0].axis('off')
+
+            axes[0, 1].imshow(fake_P.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[0, 1].set_title("Translated P")
+            axes[0, 1].axis('off')
+
+            axes[0, 2].imshow(reconstr_N.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[0, 2].set_title("Reconstructed N")
+            axes[0, 2].axis('off')
+
+            # Plot real P, translated N, and reconstructed P
+            axes[1, 0].imshow(img_P.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[1, 0].set_title("Real P")
+            axes[1, 0].axis('off')
+
+            axes[1, 1].imshow(fake_N.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[1, 1].set_title("Translated N")
+            axes[1, 1].axis('off')
+
+            axes[1, 2].imshow(reconstr_P.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), cmap='gray')
+            axes[1, 2].set_title("Reconstructed P")
+            axes[1, 2].axis('off')
+
+            # Log the figure in WandB
+            wandb.log({"test_images": wandb.Image(fig)})
+
+            plt.close(fig)

src/train.py

@@ -0,0 +1,124 @@
+from torchvision import transforms
+from torch.utils.data import DataLoader
+from lightning.pytorch.loggers.wandb import WandbLogger
+from lightning.pytorch.callbacks import ModelCheckpoint
+import lightning as pl
+import wandb
+
+from src.dataset import ClassifierDataset, CustomDataset
+from src.classifier import Classifier
+from src.models import CycleGAN
+from src.config import CFG
+
+def train_classifier(image_size,
+                     batch_size,
+                     epochs,
+                     resume_ckpt_path,
+                     train_dir,
+                     val_dir,
+                     checkpoint_dir,
+                     project,
+                     job_name):
+    
+    clf_wandb_logger = WandbLogger(project=project, name=job_name, log_model="all")
+
+    transform = transforms.Compose([
+        transforms.Resize((image_size, image_size)),  # Resize image to 512x512
+        transforms.ToTensor(),          
+        transforms.Normalize(mean=[0.485], std=[0.229])  # Normalize image
+    ])
+
+    # Define dataset paths
+    # train_dir = "/kaggle/working/CycleGan-CFE/train-data/train"
+    # val_dir = "/kaggle/working/CycleGan-CFE/train-data/val"
+
+    # Create datasets
+    train_dataset = ClassifierDataset(root_dir=train_dir, transform=transform)
+    val_dataset = ClassifierDataset(root_dir=val_dir, transform=transform)
+    print("Total Training Images: ",len(train_dataset))
+    print("Total Validation Images: ",len(val_dataset))
+
+    # Create data loaders
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=4)
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)
+    # Instantiate the classifier model
+    clf = Classifier(transfer=True)
+
+    checkpoint_callback = ModelCheckpoint(
+        monitor='val_loss',
+        dirpath=checkpoint_dir,
+        filename='efficientnet_b2-epoch{epoch:02d}-val_loss{val_loss:.2f}',
+        auto_insert_metric_name=False,
+        save_weights_only=False,
+        save_top_k=3,
+        mode='min'
+    )
+    # Set up PyTorch Lightning Trainer with multiple GPUs and tqdm progress bar
+    trainer = pl.Trainer(
+        devices="auto",
+        precision="16-mixed",
+        accelerator="auto",
+        max_epochs=epochs,
+        accumulate_grad_batches=10,
+        log_every_n_steps=1,
+        check_val_every_n_epoch=1,
+        benchmark=True,
+        logger=clf_wandb_logger,
+        callbacks=[checkpoint_callback],
+    )
+
+    # Train the classifier
+    trainer.fit(clf, train_loader, val_loader, ckpt_path=resume_ckpt_path)
+    wandb.finish()
+
+
+def train_cyclegan(image_size,
+                   batch_size,
+                   epochs,
+                   classifier_path,
+                   resume_ckpt_path,
+                   train_dir,
+                   val_dir,
+                   test_dir,
+                   checkpoint_dir,
+                   project,
+                   job_name,
+):
+
+
+    testdata_dir = test_dir
+    train_N = "0"
+    train_P = "1"
+    img_res = (image_size, image_size)
+
+    test_dataset = CustomDataset(root_dir=testdata_dir, train_N=train_N, train_P=train_P, img_res=img_res)
+    test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)
+
+    wandb_logger = WandbLogger(project=project, name=job_name, log_model="all")
+    print(classifier_path)
+    cyclegan = CycleGAN(train_dir=train_dir, val_dir=val_dir, test_dataloader=test_dataloader, classifier_path=classifier_path, checkpoint_dir=checkpoint_dir, gf=CFG.GAN_FILTERS, df=CFG.DIS_FILTERS)
+
+    gan_checkpoint_callback = ModelCheckpoint(dirpath=checkpoint_dir,
+                                        filename='cyclegan-epoch_{epoch}-vloss_{val_generator_loss:.2f}',
+                                        monitor='val_generator_loss',
+                                        save_top_k=3,
+                                        save_last=True,
+                                        save_weights_only=False,
+                                        verbose=True,
+                                        mode='min')
+
+
+    # Create the trainer
+    trainer = pl.Trainer(
+        accelerator="auto",
+        precision="16-mixed",
+        max_epochs=epochs,
+        log_every_n_steps=1,
+        benchmark=True,
+        devices="auto",
+        logger=wandb_logger,
+        callbacks= [gan_checkpoint_callback]
+    )
+
+    # Train the CycleGAN model
+    trainer.fit(cyclegan, ckpt_path=resume_ckpt_path)

src/wandb/run-20240511_204545-zlcauue2/files/code/src/pipeline.py

@@ -0,0 +1,32 @@
+import argparse
+
+from train import train_classifier, train_cyclegan
+
+def main():
+    parser = argparse.ArgumentParser(description="Pipeline for training classifier or CycleGAN")
+    parser.add_argument("--model_type", type=str, choices=["classifier", "cycle-gan"], help="Type of model to train")
+    parser.add_argument("--image_size", type=int, default=512, help="Size of input images")
+    parser.add_argument("--batch_size", type=int, default=4, help="Batch size for training")
+    parser.add_argument("--epochs", type=int, default=10, help="Number of epochs for training")
+    parser.add_argument("--resume_ckpt_path", type=str, default=None, help="Checkpoint path for resuming the training")
+    parser.add_argument("--train_dir", type=str, required=True, help="Path to training data directory")
+    parser.add_argument("--val_dir", type=str, required=True, help="Path to validation data directory")
+    parser.add_argument("--test_dir", type=str, required=True, help="Path to test data directory (for CycleGAN)")
+    parser.add_argument("--project", type=str, default="CycleGAN-CounterFactual Explanation", help="Name of the project (for logging)")
+    parser.add_argument("--job_name", type=str, default="training", help="Name of the training job (for logging)")
+    parser.add_argument("--checkpoint_dir", type=str, default="./models", help="Directory to save model checkpoints")
+    parser.add_argument("--classifier_path", type=str, default=None, help="Path to pre-trained classifier model (for CycleGAN)")
+
+    args = parser.parse_args()
+
+    if args.model_type == "classifier":
+        train_classifier(args.image_size, args.batch_size, args.epochs, args.resume_ckpt_path, args.train_dir, args.val_dir, args.checkpoint_dir, args.project, args.job_name)
+    elif args.model_type == "cycle-gan":
+        if args.classifier_path == None:
+            raise ValueError("Please provide the 'classifier checkpoint path' to train the cyle GAN model")
+        train_cyclegan(args.image_size, args.batch_size, args.epochs, args.classifier_path, args.resume_ckpt_path, args.train_dir, args.val_dir, args.test_dir, args.checkpoint_dir, args.project, args.job_name)
+    else:
+        print("Invalid model type. Choose either 'classifier' or 'cycle-gan'.")
+
+if __name__ == "__main__":
+    main()

src/wandb/run-20240511_204545-zlcauue2/files/config.yaml

@@ -0,0 +1,37 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    code_path: code/src/pipeline.py
+    python_version: 3.11.7
+    cli_version: 0.17.0
+    framework: torch
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1715440545
+    t:
+      1:
+      - 1
+      - 41
+      - 55
+      2:
+      - 1
+      - 41
+      - 55
+      3:
+      - 2
+      - 7
+      - 13
+      - 23
+      - 66
+      4: 3.11.7
+      5: 0.17.0
+      8:
+      - 3
+      - 5
+      13: windows-amd64
+    m:
+    - 1: trainer/global_step
+      6:
+      - 3

src/wandb/run-20240511_204545-zlcauue2/files/diff.patch

@@ -0,0 +1,27 @@
+diff --git a/.gitignore b/.gitignore
+index 68bc17f..cdb9062 100644
+--- a/.gitignore
++++ b/.gitignore
+@@ -2,6 +2,7 @@
+ __pycache__/
+ *.py[cod]
+ *$py.class
++cfe/
+ 
+ # C extensions
+ *.so
+diff --git a/README.md b/README.md
+index 0c0d28c..82d90e4 100644
+--- a/README.md
++++ b/README.md
+@@ -5,3 +5,10 @@ This project addresses the critical need for transparent and interpretable decis
+ The primary objective is to generate realistic counterfactual images for image classifiers operating in medical contexts. By harnessing advanced techniques from adversarial learning, the project seeks to overcome the challenges associated with existing methods, such as saliency maps, which may not effectively capture the intricate nuances of decision-making processes rooted in texture and structure.
+ 
+ The proposed approach holds promise for enhancing the interpretability of deep learning models, especially in high-stakes domains like healthcare, where transparent decision-making is paramount. By providing realistic counterfactual explanations, the project aims to empower clinicians and stakeholders with insights into classifier decisions, thereby fostering trust and facilitating more informed medical interventions.
++
++
++### Python 3.11.0 recommended
++```
++python -m venv myenv
++.\myenv\Scripts\activate
++```

src/wandb/run-20240511_204545-zlcauue2/files/output.log

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+  warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
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+2 | criterion      | CrossEntropyLoss | 0
+3 | train_accuracy | BinaryAccuracy   | 0
+4 | val_accuracy   | BinaryAccuracy   | 0
+----------------------------------------------------
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+D:\Official\Reseach Projects\Personal Projects\Machine Learning\Deep Learning\Counterfactual-Image-Generation-using-CycleGAN\cfe\Lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:436: Consider setting `persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.
+
+
+
+
+D:\Official\Reseach Projects\Personal Projects\Machine Learning\Deep Learning\Counterfactual-Image-Generation-using-CycleGAN\cfe\Lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:436: Consider setting `persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.
+
+
+
+
+
+
+
+
+
+Epoch 0:   0%|▏                                                               | 9/4185 [02:58<23:03:15,  0.05it/s, v_num=uue2, train_acc=0.250]
+D:\Official\Reseach Projects\Personal Projects\Machine Learning\Deep Learning\Counterfactual-Image-Generation-using-CycleGAN\cfe\Lib\site-packages\lightning\pytorch\trainer\call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...

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src/wandb/run-20240511_204545-zlcauue2/logs/debug.log

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