From 1dcf0f0c237cb14a5683ab6b0cbd12c84fdc107f Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:15:06 +0800 Subject: [PATCH 01/12] Create ReadME.md --- Notes/GarvSachdev_Notes_and_Files/ReadME.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/ReadME.md diff --git a/Notes/GarvSachdev_Notes_and_Files/ReadME.md b/Notes/GarvSachdev_Notes_and_Files/ReadME.md new file mode 100644 index 0000000..41dbc4a --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ReadME.md @@ -0,0 +1 @@ +# folder creation From 452bd3a2e3d7f804942767d4fa0e5166f846c251 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:17:57 +0800 Subject: [PATCH 02/12] Create readme.md --- Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md new file mode 100644 index 0000000..f233a10 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md @@ -0,0 +1 @@ +creation From befa0583ae4bde318b380e164d166238a53dfabc Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:20:48 +0800 Subject: [PATCH 03/12] add files --- .../CAMUS_Dataset_test/dataset_camus.py | 87 +++++++++++++++ .../preprocessing script.py | 80 +++++++++++++ .../CAMUS_Dataset_test/requirements.txt | 7 ++ .../CAMUS_Dataset_test/test.py | 86 ++++++++++++++ .../CAMUS_Dataset_test/train.py | 76 +++++++++++++ .../CAMUS_Dataset_test/trainer.py | 105 ++++++++++++++++++ 6 files changed, 441 insertions(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/dataset_camus.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/preprocessing script.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/requirements.txt create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/test.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/train.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/trainer.py diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/dataset_camus.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/dataset_camus.py new file mode 100644 index 0000000..48567c2 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/dataset_camus.py @@ -0,0 +1,87 @@ +import os +import numpy as np +import torch +from torch.utils.data import Dataset +from scipy.ndimage import zoom, rotate +import random + +class RandomGenerator(object): + def __init__(self, output_size): + self.output_size = output_size + + def __call__(self, sample): + image, label = sample['image'], sample['label'] + + # Random rotation and flip + if random.random() > 0.5: + k = random.randint(0, 3) + image = np.rot90(image, k) + label = np.rot90(label, k) + if random.random() > 0.5: + axis = random.randint(0, 1) + image = np.flip(image, axis=axis).copy() + label = np.flip(label, axis=axis).copy() + + # Random rotate + if random.random() > 0.5: + angle = random.uniform(-20, 20) + image = rotate(image, angle, reshape=False, order=1, mode='nearest') + label = rotate(label, angle, reshape=False, order=0, mode='nearest') + + # Resize to output size + x, y = image.shape + if x != self.output_size[0] or y != self.output_size[1]: + image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) + label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0) + + # Convert to PyTorch tensors + # image = torch.from_numpy(image).float().unsqueeze(0) # Add channel dimension + image = torch.from_numpy(image.copy()).float().unsqueeze(0) # Add channel dimension + # label = torch.from_numpy(label).long() + label = torch.from_numpy(label.copy()).long() # Ensure no negative strides + return {'image': image, 'label': label} + +class CAMUSDataset(Dataset): + def __init__(self, base_dir, split, transform=None): + """ + Args: + base_dir (str): Path to preprocessed data directory. + split (str): One of 'train', 'val', 'test'. + transform (callable, optional): Transformations to apply to the data. + """ + self.base_dir = os.path.join(base_dir, split) + self.transform = transform + self.images = sorted([f for f in os.listdir(self.base_dir) if not f.endswith('_gt.npy')]) + self.labels = [f.replace('.npy', '_gt.npy') for f in self.images] + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + image_path = os.path.join(self.base_dir, self.images[idx]) + label_path = os.path.join(self.base_dir, self.labels[idx]) + case_name = os.path.basename(image_path).split('.')[0] + + # Load images and labels + image = np.load(image_path).astype(np.float32) + label = np.load(label_path).astype(np.uint8) + + sample = {'image': image, 'label': label} + if self.transform: + sample = self.transform(sample) + sample["case_name"] = case_name + + return sample + +if __name__ == "__main__": + base_dir = "preprocessed_data" + output_size = (224, 224) + + train_dataset = CAMUSDataset(base_dir, split='train', transform=RandomGenerator(output_size)) + train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4) + + for batch in train_loader: + images = batch['image'] + labels = batch['label'] + print("Batch size:", images.size(), labels.size()) + break diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/preprocessing script.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/preprocessing script.py new file mode 100644 index 0000000..47e3fde --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/preprocessing script.py @@ -0,0 +1,80 @@ +import os +import nibabel as nib +import numpy as np +from scipy.ndimage import zoom +from pathlib import Path + +# Parameters +INPUT_DIR = "database_nifti" +SPLIT_DIR = "database_split" +OUTPUT_DIR = "preprocessed_data" +TARGET_SIZE = (224, 224) + +def load_nii(file_path): + """Load NIfTI file and return the image data.""" + return nib.load(file_path).get_fdata() + +def resize_image(image, target_size): + """Resize a 3D image to the target size.""" + factors = [t / s for t, s in zip(target_size, image.shape[:2])] + resized_image = zoom(image, factors, order=1) + return resized_image + +def normalize_image(image): + """Normalize image to [0, 1] range.""" + return (image - np.min(image)) / (np.max(image) - np.min(image)) + +def process_and_save(patient_id, split, output_dir): + """Process images and save them in the specified output directory.""" + patient_dir = Path(INPUT_DIR) / patient_id + output_split_dir = Path(output_dir) / split + output_split_dir.mkdir(parents=True, exist_ok=True) + + for view in ["2CH", "4CH"]: + for phase in ["ED", "ES"]: + try: + # Paths for images and ground truths + image_path = patient_dir / f"{patient_id}_{view}_{phase}.nii.gz" + gt_path = patient_dir / f"{patient_id}_{view}_{phase}_gt.nii.gz" + + if image_path.exists() and gt_path.exists(): + image = load_nii(image_path) + gt = load_nii(gt_path) + + image_resized = resize_image(image, TARGET_SIZE) + gt_resized = resize_image(gt, TARGET_SIZE) + + image_normalized = normalize_image(image_resized) + + # Save preprocessed files + np.save(output_split_dir / f"{patient_id}_{view}_{phase}.npy", image_normalized) + np.save(output_split_dir / f"{patient_id}_{view}_{phase}_gt.npy", gt_resized) + except Exception as e: + print(f"Error processing {patient_id} {view} {phase}: {e}") + +def get_split_patients(split_file): + """Read patient IDs from split file.""" + with open(split_file, "r") as file: + return [line.strip() for line in file.readlines()] + +def main(): + # Create output directory + # Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True) + + # Load splits + splits = { + "train": get_split_patients(Path(SPLIT_DIR) / "subgroup_training.txt"), + "val": get_split_patients(Path(SPLIT_DIR) / "subgroup_validation.txt"), + "test": get_split_patients(Path(SPLIT_DIR) / "subgroup_testing.txt"), + } + + # Process each split + for split, patients in splits.items(): + print(f"Processing {split} split with {len(patients)} patients...") + for patient_id in patients: + process_and_save(patient_id, split, OUTPUT_DIR) + + print("Preprocessing complete!") + +if __name__ == "__main__": + main() diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/requirements.txt b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/requirements.txt new file mode 100644 index 0000000..6735f48 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/requirements.txt @@ -0,0 +1,7 @@ +torch==2.0.1 +numpy==1.23.5 +scipy==1.10.1 +h5py==3.8.0 +opencv-python==4.8.0.76 +matplotlib==3.7.1 +tqdm==4.65.0 diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/test.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/test.py new file mode 100644 index 0000000..2359fb5 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/test.py @@ -0,0 +1,86 @@ +import os +import torch +import numpy as np +from dataset_camus import CAMUSDataset, RandomGenerator +from networks.vit_seg_modeling import VisionTransformer as ViT_seg +from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg +from trainer import evaluate +import matplotlib.pyplot as plt + +def save_prediction(image, label, prediction, output_dir, case_name): + """Save the input image, ground truth, and predicted segmentation.""" + os.makedirs(output_dir, exist_ok=True) + + # Save as .npy + np.save(os.path.join(output_dir, f"{case_name}_image.npy"), image) + np.save(os.path.join(output_dir, f"{case_name}_label.npy"), label) + np.save(os.path.join(output_dir, f"{case_name}_prediction.npy"), prediction) + + # Save visualizations + plt.figure(figsize=(12, 4)) + plt.subplot(1, 3, 1) + plt.title("Image") + plt.imshow(image[0], cmap="gray") + plt.subplot(1, 3, 2) + plt.title("Ground Truth") + plt.imshow(label, cmap="jet") + plt.subplot(1, 3, 3) + plt.title("Prediction") + plt.imshow(prediction, cmap="jet") + plt.savefig(os.path.join(output_dir, f"{case_name}_visualization.png")) + plt.close() + +def main(): + # Configurations + model_path = "./output/best_model.pth" + vit_model_name = "R50-ViT-B_16" + output_size = (224, 224) + batch_size = 16 + data_dir = "preprocessed_data" + num_epochs = 100 + num_classes = 4 + img_size = 224 + vit_patches_size = 16 + prediction_output_dir = "./predictions" + + test_dataset = CAMUSDataset(data_dir, split="test", transform=RandomGenerator(output_size)) + test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4) + + config_vit = CONFIGS_ViT_seg[vit_model_name] + config_vit.n_classes = num_classes + if vit_model_name.find('R50') != -1: + config_vit.patches.grid = (int(img_size / vit_patches_size), int(img_size / vit_patches_size)) + #net.load_from(weights=np.load(config_vit.pretrained_path)) + #model = ViT_seg(img_size=224, num_classes=4, vit_name=vit_model_name) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = ViT_seg(config_vit, img_size=224, num_classes=num_classes) + model.load_state_dict(torch.load(model_path, map_location=device)) + model.to(device) + + # Evaluation + criterion = torch.nn.CrossEntropyLoss() + test_loss, test_dice, test_iou, test_hausdorff_95 = evaluate(model, test_loader, criterion, device) + print(f"Test Loss: {test_loss:.4f}, DICE: {test_dice:.4f}, IoU: {test_iou:.4f}, Hausdorff95: {test_hausdorff_95:.4f}") + + with torch.no_grad(): + for batch in test_loader: + images, labels = batch['image'].to(device), batch['label'].to(device) + outputs = model(images) + predictions = torch.argmax(outputs, dim=1).cpu().numpy() + + case_names = batch.get('case_name', ["unknown_case"] * len(predictions)) # Fallback for missing case_name + + for i in range(len(predictions)): + case_name = batch['case_name'][i] + save_prediction( + image=images[i].cpu().numpy(), + label=labels[i].cpu().numpy(), + prediction=predictions[i], + output_dir=prediction_output_dir, + case_name=case_name, + ) + print(f"Predictions saved in {prediction_output_dir}") + +if __name__ == "__main__": + main() diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/train.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/train.py new file mode 100644 index 0000000..0e50c33 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/train.py @@ -0,0 +1,76 @@ +import os +import torch +import torch.nn as nn +import numpy as np +import torch.optim as optim +from torch.utils.data import DataLoader +from dataset_camus import CAMUSDataset, RandomGenerator +from networks.vit_seg_modeling import VisionTransformer as ViT_seg +from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg +from trainer import train_one_epoch, evaluate + +def main(): + # Configurations + data_dir = "preprocessed_data" + output_dir = "./output" + vit_model_name = "R50-ViT-B_16" + output_size = (224, 224) + num_epochs = 100 + num_classes = 4 + batch_size = 16 + learning_rate = 1e-4 + img_size = 224 + vit_patches_size = 16 + + os.makedirs(output_dir, exist_ok=True) + + train_dataset = CAMUSDataset(data_dir, split="train", transform=RandomGenerator(output_size)) + val_dataset = CAMUSDataset(data_dir, split="val", transform=RandomGenerator(output_size)) + + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4) + + # Load Model + config_vit = CONFIGS_ViT_seg[vit_model_name] + config_vit.n_classes = num_classes + if vit_model_name.find('R50') != -1: + config_vit.patches.grid = (int(img_size / vit_patches_size), int(img_size / vit_patches_size)) + model = ViT_seg(config_vit, img_size=224, num_classes=num_classes) + + #net.load_from(weights=np.load(config_vit.pretrained_path)) + #model = ViT_seg(img_size=224, num_classes=4, vit_name=vit_model_name) + + vit_path = f"../model/vit_checkpoint/imagenet21k/{vit_model_name}.npz" + #model.load_from(weights=vit_path) + model.load_from(weights=np.load(config_vit.pretrained_path)) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device) + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + + # Training + best_val_dice = 0.0 + for epoch in range(num_epochs): + print(f"\nEpoch {epoch + 1}/{num_epochs}") + + train_loss = train_one_epoch(model, train_loader, criterion, optimizer, device) + print(f"Train Loss: {train_loss:.4f}") + + val_loss, val_dice, val_iou, val_hausdorff_95 = evaluate(model, val_loader, criterion, device) + print(f"Validation Loss: {val_loss:.4f}, DICE: {val_dice:.4f}, IoU: {val_iou:.4f}, Hausdorff95: {val_hausdorff_95:.4f}") + + if val_dice > best_val_dice: + best_val_dice = val_dice + torch.save(model.state_dict(), os.path.join(output_dir, "best_model.pth")) + print("Saved best model!") + + if (epoch + 1) % 10 == 0: + model_path = os.path.join(output_dir, f"model_epoch_{epoch + 1}.pth") + torch.save(model.state_dict(), model_path) + print(f"Saved model at epoch {epoch + 1}.") + + print("Training Complete. Best Validation DICE:", best_val_dice) + +if __name__ == "__main__": + main() diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/trainer.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/trainer.py new file mode 100644 index 0000000..7ff22cb --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/trainer.py @@ -0,0 +1,105 @@ +import torch +import numpy as np +from scipy.ndimage import distance_transform_edt +from scipy.spatial.distance import directed_hausdorff + +def train_one_epoch(model, dataloader, criterion, optimizer, device): + """ + Train the model for one epoch. + """ + model.train() + total_loss = 0.0 + + for batch in dataloader: + images = batch["image"].to(device) + labels = batch["label"].to(device) + + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + total_loss += loss.item() * images.size(0) + + return total_loss / len(dataloader.dataset) + + +def evaluate(model, dataloader, criterion, device): + """ + Evaluate the model on the validation or test dataset. + """ + model.eval() + total_loss, total_dice, total_iou, total_hausdorff_95 = 0.0, 0.0, 0.0, 0.0 + num_samples = 0 + + with torch.no_grad(): + for batch in dataloader: + images = batch["image"].to(device) + labels = batch["label"].to(device) + + outputs = model(images) + loss = criterion(outputs, labels) + total_loss += loss.item() * images.size(0) + + preds = torch.argmax(torch.softmax(outputs, dim=1), dim=1).cpu().numpy() + labels = labels.cpu().numpy() + + for pred, label in zip(preds, labels): + total_dice += dice_coeff(pred, label) + total_iou += iou_score(pred, label) + total_hausdorff_95 += hausdorff_distance(label, pred) + num_samples += 1 + + return (total_loss / num_samples, + total_dice / num_samples, + total_iou / num_samples, + total_hausdorff_95 / num_samples) + +def dice_coeff(pred, target, epsilon=1e-6): + """ + Compute the DICE coefficient for multi-class segmentation. + """ + num_classes = max(pred.max(), target.max()) + 1 + dice_scores = [] + for c in range(num_classes): + pred_c = (pred == c).astype(np.uint8) + target_c = (target == c).astype(np.uint8) + + intersection = (pred_c * target_c).sum() + union = pred_c.sum() + target_c.sum() + dice_scores.append((2.0 * intersection + epsilon) / (union + epsilon)) + return np.mean(dice_scores) + +def iou_score(pred, target, epsilon=1e-6): + """ + Compute the Intersection over Union (IoU) score for multi-class segmentation. + """ + num_classes = max(pred.max(), target.max()) + 1 + iou_scores = [] + for c in range(num_classes): + pred_c = (pred == c).astype(np.uint8) + target_c = (target == c).astype(np.uint8) + + intersection = (pred_c * target_c).sum() + union = pred_c.sum() + target_c.sum() - intersection + iou_scores.append((intersection + epsilon) / (union + epsilon)) + return np.mean(iou_scores) + +def hausdorff_distance(label, pred): + """ + Compute the 95th percentile of the Hausdorff Distance between the label and prediction. + """ + label_points = np.argwhere(label > 0) + pred_points = np.argwhere(pred > 0) + + if label_points.size == 0 or pred_points.size == 0: + return np.inf + + # Compute distance transform for both label and prediction + label_distances = distance_transform_edt(1 - label)[pred_points[:, 0], pred_points[:, 1]] + pred_distances = distance_transform_edt(1 - pred)[label_points[:, 0], label_points[:, 1]] + + # Combine distances and take the 95th percentile + all_distances = np.concatenate([label_distances, pred_distances]) + return np.percentile(all_distances, 95) From 8df14720f15a8f40373b9c28e0e07be989209bb3 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:22:20 +0800 Subject: [PATCH 04/12] Create readme.md --- Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md new file mode 100644 index 0000000..623df3e --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md @@ -0,0 +1 @@ +creation From efb7848ab855fd2701afec9a7456e694990a5963 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:23:21 +0800 Subject: [PATCH 05/12] add files --- .../ACDC_Dataset_test/ACDC visualiser.ipynb | 841354 +++++++++++++++ .../ACDC_Dataset_test/dataset_acdc.py | 142 + .../ACDC_Dataset_test/requirements.txt | 11 + .../ACDC_Dataset_test/test.py | 164 + .../ACDC_Dataset_test/train.py | 99 + .../ACDC_Dataset_test/trainer.py | 203 + .../ACDC_Dataset_test/utils.py | 114 + 7 files changed, 842087 insertions(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/ACDC 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index is provided, extract that slice\n", + " if slice_idx is not None:\n", + " return image_data[:, :, slice_idx] # Return 2D slice along z-axis (depth)\n", + " else:\n", + " # If no slice index is provided, return the middle slice\n", + " middle_slice = image_data.shape[2] // 2\n", + " return image_data[:, :, middle_slice]\n", + "\n", + "# Paths to your NIfTI images\n", + "img_path_1 = 'patient001_frame01_img.nii.gz'\n", + "gt_path_1 = 'patient001_frame01_gt.nii.gz'\n", + "pred_path_1 = 'patient001_frame01_pred.nii.gz'\n", + "\n", + "img_path_2 = 'patient001_frame12_img.nii.gz'\n", + "gt_path_2 = 'patient001_frame12_gt.nii.gz'\n", + "pred_path_2 = 'patient001_frame12_pred.nii.gz'\n", + "\n", + "# Extract slices from the 3D volumes\n", + "slice_img_1 = load_nii_and_extract_slice(img_path_1)\n", + "slice_gt_1 = load_nii_and_extract_slice(gt_path_1)\n", + "slice_pred_1 = load_nii_and_extract_slice(pred_path_1)\n", + "\n", + "slice_img_2 = load_nii_and_extract_slice(img_path_2)\n", + "slice_gt_2 = load_nii_and_extract_slice(gt_path_2)\n", + "slice_pred_2 = load_nii_and_extract_slice(pred_path_2)\n", + "\n", + "# Function to create a Plotly figure for side-by-side comparison\n", + "def create_side_by_side_plot(img, gt, pred, title=\"Frame Comparison\"):\n", + " # Create subplots: 1 row, 3 columns\n", + " fig = go.Figure()\n", + "\n", + " # Original Image\n", + " fig.add_trace(go.Image(z=img, name=\"Original Image\"))\n", + " \n", + " # Ground Truth Image\n", + " fig.add_trace(go.Image(z=gt, name=\"Ground Truth\", opacity=0.6))\n", + " \n", + " # Predicted Image\n", + " fig.add_trace(go.Image(z=pred, name=\"Predicted Mask\", opacity=0.6))\n", + "\n", + " fig.update_layout(\n", + " title=title,\n", + " xaxis=dict(showticklabels=False),\n", + " yaxis=dict(showticklabels=False),\n", + " updatemenus=[\n", + " dict(\n", + " type=\"buttons\", \n", + " x=0.5, y=-0.1,\n", + " showactive=False,\n", + " buttons=[dict(label=\"Reset\", method=\"relayout\", args=[{\"xaxis.range\": [0, img.shape[1]], \"yaxis.range\": [0, img.shape[0]]}])]\n", + " )\n", + " ],\n", + " height=600\n", + " )\n", + " \n", + " return fig\n", + "\n", + "# Create a comparison plot for Frame 1\n", + "fig_1 = create_side_by_side_plot(slice_img_1, slice_gt_1, slice_pred_1, title=\"Frame 1 Comparison\")\n", + "\n", + "# Create a comparison plot for Frame 2\n", + "fig_2 = create_side_by_side_plot(slice_img_2, slice_gt_2, slice_pred_2, title=\"Frame 12 Comparison\")\n", + "\n", + "# Show both plots\n", + "fig_1.show()\n", + "fig_2.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "index 50 is out of bounds for axis 2 with size 10", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/kp/45v3c3g91ds_t7twxv3r3xgr0000gp/T/ipykernel_62899/3565788321.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# Normalize the images to be in the range [0, 1] for better visualization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mgt_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgt_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mpred_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpred_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: index 50 is out of bounds for axis 2 with size 10" + ] + } + ], + "source": [ + "import nibabel as nib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objects as go\n", + "\n", + "# Load the .nii.gz images using nibabel\n", + "gt_image_path = 'patient001_frame01_gt.nii.gz'\n", + "pred_image_path = 'patient001_frame01_pred.nii.gz'\n", + "\n", + "gt_img = nib.load(gt_image_path)\n", + "pred_img = nib.load(pred_image_path)\n", + "\n", + "gt_data = gt_img.get_fdata()\n", + "pred_data = pred_img.get_fdata()\n", + "\n", + "# Choose a slice to visualize (you can change the slice index as needed)\n", + "slice_idx = 50 # You can change this depending on which slice you're interested in\n", + "\n", + "# Normalize the images to be in the range [0, 1] for better visualization\n", + "gt_slice = gt_data[:, :, slice_idx]\n", + "pred_slice = pred_data[:, :, slice_idx]\n", + "\n", + "gt_slice = (gt_slice - np.min(gt_slice)) / (np.max(gt_slice) - np.min(gt_slice))\n", + "pred_slice = (pred_slice - np.min(pred_slice)) / (np.max(pred_slice) - np.min(pred_slice))\n", + "\n", + "# Plot using Matplotlib (for side-by-side comparison)\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Ground truth slice\n", + "ax[0].imshow(gt_slice, cmap='gray')\n", + "ax[0].set_title(\"Ground Truth\")\n", + "ax[0].axis('off')\n", + "\n", + "# Predicted slice\n", + "ax[1].imshow(pred_slice, cmap='gray')\n", + "ax[1].set_title(\"Prediction\")\n", + "ax[1].axis('off')\n", + "\n", + "plt.show()\n", + "\n", + "# Optional: Plot using Plotly for better interactivity\n", + "fig_plotly = go.Figure()\n", + "\n", + "# Add ground truth image to Plotly\n", + "fig_plotly.add_trace(go.Image(z=gt_slice, name=\"Ground Truth\", colorscale='gray'))\n", + "\n", + "# Add predicted image to Plotly\n", + "fig_plotly.add_trace(go.Image(z=pred_slice, name=\"Prediction\", colorscale='gray'))\n", + "\n", + "# Update the layout\n", + "fig_plotly.update_layout(\n", + " title=\"Ground Truth vs. Prediction\",\n", + " xaxis=dict(showgrid=False),\n", + " yaxis=dict(showgrid=False),\n", + ")\n", + "\n", + "fig_plotly.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth shape: (216, 256, 10)\n", + "Prediction shape: (216, 256, 10)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garv/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning:\n", + "\n", + "invalid value encountered in true_divide\n", + "\n" + ] + }, + { + "data": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Invalid property specified for object of type plotly.graph_objs.Image: 'colorscale'\n\nDid you mean \"colormodel\"?\n\n Valid properties:\n colormodel\n Color model used to map the numerical color components\n described in `z` into colors. If `source` is specified,\n this attribute will be set to `rgba256` otherwise it\n defaults to `rgb`.\n customdata\n Assigns extra data each datum. This may be useful when\n listening to hover, click and selection events. Note\n that, \"scatter\" traces also appends customdata items in\n the markers DOM elements\n customdatasrc\n Sets the source reference on Chart Studio Cloud for\n `customdata`.\n dx\n Set the pixel's horizontal size.\n dy\n Set the pixel's vertical size\n hoverinfo\n Determines which trace information appear on hover. If\n `none` or `skip` are set, no information is displayed\n upon hovering. But, if `none` is set, click and hover\n events are still fired.\n hoverinfosrc\n Sets the source reference on Chart Studio Cloud for\n `hoverinfo`.\n hoverlabel\n :class:`plotly.graph_objects.image.Hoverlabel` instance\n or dict with compatible properties\n hovertemplate\n Template string used for rendering the information that\n appear on hover box. Note that this will override\n `hoverinfo`. Variables are inserted using %{variable},\n for example \"y: %{y}\" as well as %{xother}, {%_xother},\n {%_xother_}, {%xother_}. When showing info for several\n points, \"xother\" will be added to those with different\n x positions from the first point. An underscore before\n or after \"(x|y)other\" will add a space on that side,\n only when this field is shown. Numbers are formatted\n using d3-format's syntax %{variable:d3-format}, for\n example \"Price: %{y:$.2f}\".\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n for details on the formatting syntax. Dates are\n formatted using d3-time-format's syntax\n %{variable|d3-time-format}, for example \"Day:\n %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n format/tree/v2.2.3#locale_format for details on the\n date formatting syntax. The variables available in\n `hovertemplate` are the ones emitted as event data\n described at this link\n https://plotly.com/javascript/plotlyjs-events/#event-\n data. Additionally, every attributes that can be\n specified per-point (the ones that are `arrayOk: true`)\n are available. Finally, the template string has access\n to variables `z`, `color` and `colormodel`. Anything\n contained in tag `` is displayed in the\n secondary box, for example\n \"{fullData.name}\". To hide the secondary\n box completely, use an empty tag ``.\n hovertemplatesrc\n Sets the source reference on Chart Studio Cloud for\n `hovertemplate`.\n hovertext\n Same as `text`.\n hovertextsrc\n Sets the source reference on Chart Studio Cloud for\n `hovertext`.\n ids\n Assigns id labels to each datum. These ids for object\n constancy of data points during animation. Should be an\n array of strings, not numbers or any other type.\n idssrc\n Sets the source reference on Chart Studio Cloud for\n `ids`.\n legend\n Sets the reference to a legend to show this trace in.\n References to these legends are \"legend\", \"legend2\",\n \"legend3\", etc. Settings for these legends are set in\n the layout, under `layout.legend`, `layout.legend2`,\n etc.\n legendgrouptitle\n :class:`plotly.graph_objects.image.Legendgrouptitle`\n instance or dict with compatible properties\n legendrank\n Sets the legend rank for this trace. Items and groups\n with smaller ranks are presented on top/left side while\n with \"reversed\" `legend.traceorder` they are on\n bottom/right side. The default legendrank is 1000, so\n that you can use ranks less than 1000 to place certain\n items before all unranked items, and ranks greater than\n 1000 to go after all unranked items. When having\n unranked or equal rank items shapes would be displayed\n after traces i.e. according to their order in data and\n layout.\n legendwidth\n Sets the width (in px or fraction) of the legend for\n this trace.\n meta\n Assigns extra meta information associated with this\n trace that can be used in various text attributes.\n Attributes such as trace `name`, graph, axis and\n colorbar `title.text`, annotation `text`\n `rangeselector`, `updatemenues` and `sliders` `label`\n text all support `meta`. To access the trace `meta`\n values in an attribute in the same trace, simply use\n `%{meta[i]}` where `i` is the index or key of the\n `meta` item in question. To access trace `meta` in\n layout attributes, use `%{data[n[.meta[i]}` where `i`\n is the index or key of the `meta` and `n` is the trace\n index.\n metasrc\n Sets the source reference on Chart Studio Cloud for\n `meta`.\n name\n Sets the trace name. The trace name appears as the\n legend item and on hover.\n opacity\n Sets the opacity of the trace.\n source\n Specifies the data URI of the image to be visualized.\n The URI consists of \"data:image/[][;base64],\"\n stream\n :class:`plotly.graph_objects.image.Stream` instance or\n dict with compatible properties\n text\n Sets the text elements associated with each z value.\n textsrc\n Sets the source reference on Chart Studio Cloud for\n `text`.\n uid\n Assign an id to this trace, Use this to provide object\n constancy between traces during animations and\n transitions.\n uirevision\n Controls persistence of some user-driven changes to the\n trace: `constraintrange` in `parcoords` traces, as well\n as some `editable: true` modifications such as `name`\n and `colorbar.title`. Defaults to `layout.uirevision`.\n Note that other user-driven trace attribute changes are\n controlled by `layout` attributes: `trace.visible` is\n controlled by `layout.legend.uirevision`,\n `selectedpoints` is controlled by\n `layout.selectionrevision`, and `colorbar.(x|y)`\n (accessible with `config: {editable: true}`) is\n controlled by `layout.editrevision`. Trace changes are\n tracked by `uid`, which only falls back on trace index\n if no `uid` is provided. So if your app can add/remove\n traces before the end of the `data` array, such that\n the same trace has a different index, you can still\n preserve user-driven changes if you give each trace a\n `uid` that stays with it as it moves.\n visible\n Determines whether or not this trace is visible. If\n \"legendonly\", the trace is not drawn, but can appear as\n a legend item (provided that the legend itself is\n visible).\n x0\n Set the image's x position. The left edge of the image\n (or the right edge if the x axis is reversed or dx is\n negative) will be found at xmin=x0-dx/2\n xaxis\n Sets a reference between this trace's x coordinates and\n a 2D cartesian x axis. If \"x\" (the default value), the\n x coordinates refer to `layout.xaxis`. If \"x2\", the x\n coordinates refer to `layout.xaxis2`, and so on.\n y0\n Set the image's y position. The top edge of the image\n (or the bottom edge if the y axis is NOT reversed or if\n dy is negative) will be found at ymin=y0-dy/2. By\n default when an image trace is included, the y axis\n will be reversed so that the image is right-side-up,\n but you can disable this by setting\n yaxis.autorange=true or by providing an explicit y axis\n range.\n yaxis\n Sets a reference between this trace's y coordinates and\n a 2D cartesian y axis. If \"y\" (the default value), the\n y coordinates refer to `layout.yaxis`. If \"y2\", the y\n coordinates refer to `layout.yaxis2`, and so on.\n z\n A 2-dimensional array in which each element is an array\n of 3 or 4 numbers representing a color.\n zmax\n Array defining the higher bound for each color\n component. Note that the default value will depend on\n the colormodel. For the `rgb` colormodel, it is [255,\n 255, 255]. For the `rgba` colormodel, it is [255, 255,\n 255, 1]. For the `rgba256` colormodel, it is [255, 255,\n 255, 255]. For the `hsl` colormodel, it is [360, 100,\n 100]. For the `hsla` colormodel, it is [360, 100, 100,\n 1].\n zmin\n Array defining the lower bound for each color\n component. Note that the default value will depend on\n the colormodel. For the `rgb` colormodel, it is [0, 0,\n 0]. For the `rgba` colormodel, it is [0, 0, 0, 0]. For\n the `rgba256` colormodel, it is [0, 0, 0, 0]. For the\n `hsl` colormodel, it is [0, 0, 0]. For the `hsla`\n colormodel, it is [0, 0, 0, 0].\n zsmooth\n Picks a smoothing algorithm used to smooth `z` data.\n This only applies for image traces that use the\n `source` attribute.\n zsrc\n Sets the source reference on Chart Studio Cloud for\n `z`.\n \nDid you mean \"colormodel\"?\n\nBad property path:\ncolorscale\n^^^^^^^^^^", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/kp/45v3c3g91ds_t7twxv3r3xgr0000gp/T/ipykernel_62899/3334525214.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;31m# Add ground truth image to Plotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mfig_plotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgt_slice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Ground Truth\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolorscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;31m# Add predicted image to Plotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/plotly/graph_objs/_image.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg, colormodel, customdata, customdatasrc, dx, dy, hoverinfo, hoverinfosrc, hoverlabel, hovertemplate, hovertemplatesrc, hovertext, hovertextsrc, ids, idssrc, legend, legendgrouptitle, legendrank, legendwidth, meta, metasrc, name, opacity, source, stream, text, textsrc, uid, uirevision, visible, x0, xaxis, y0, yaxis, z, zmax, zmin, zsmooth, zsrc, **kwargs)\u001b[0m\n\u001b[1;32m 1684\u001b[0m \u001b[0;31m# Process unknown kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1685\u001b[0m \u001b[0;31m# ----------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1686\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1688\u001b[0m \u001b[0;31m# Reset skip_invalid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_process_kwargs\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 4390\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4391\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_skip_invalid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4392\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4393\u001b[0m \u001b[0;31m# No need to call _raise_on_invalid_property_error here,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4394\u001b[0m \u001b[0;31m# because we have it set up so that the singular case of calling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Invalid property specified for object of type plotly.graph_objs.Image: 'colorscale'\n\nDid you mean \"colormodel\"?\n\n Valid properties:\n colormodel\n Color model used to map the numerical color components\n described in `z` into colors. If `source` is specified,\n this attribute will be set to `rgba256` otherwise it\n defaults to `rgb`.\n customdata\n Assigns extra data each datum. This may be useful when\n listening to hover, click and selection events. Note\n that, \"scatter\" traces also appends customdata items in\n the markers DOM elements\n customdatasrc\n Sets the source reference on Chart Studio Cloud for\n `customdata`.\n dx\n Set the pixel's horizontal size.\n dy\n Set the pixel's vertical size\n hoverinfo\n Determines which trace information appear on hover. If\n `none` or `skip` are set, no information is displayed\n upon hovering. But, if `none` is set, click and hover\n events are still fired.\n hoverinfosrc\n Sets the source reference on Chart Studio Cloud for\n `hoverinfo`.\n hoverlabel\n :class:`plotly.graph_objects.image.Hoverlabel` instance\n or dict with compatible properties\n hovertemplate\n Template string used for rendering the information that\n appear on hover box. Note that this will override\n `hoverinfo`. Variables are inserted using %{variable},\n for example \"y: %{y}\" as well as %{xother}, {%_xother},\n {%_xother_}, {%xother_}. When showing info for several\n points, \"xother\" will be added to those with different\n x positions from the first point. An underscore before\n or after \"(x|y)other\" will add a space on that side,\n only when this field is shown. Numbers are formatted\n using d3-format's syntax %{variable:d3-format}, for\n example \"Price: %{y:$.2f}\".\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n for details on the formatting syntax. Dates are\n formatted using d3-time-format's syntax\n %{variable|d3-time-format}, for example \"Day:\n %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n format/tree/v2.2.3#locale_format for details on the\n date formatting syntax. The variables available in\n `hovertemplate` are the ones emitted as event data\n described at this link\n https://plotly.com/javascript/plotlyjs-events/#event-\n data. Additionally, every attributes that can be\n specified per-point (the ones that are `arrayOk: true`)\n are available. Finally, the template string has access\n to variables `z`, `color` and `colormodel`. Anything\n contained in tag `` is displayed in the\n secondary box, for example\n \"{fullData.name}\". To hide the secondary\n box completely, use an empty tag ``.\n hovertemplatesrc\n Sets the source reference on Chart Studio Cloud for\n `hovertemplate`.\n hovertext\n Same as `text`.\n hovertextsrc\n Sets the source reference on Chart Studio Cloud for\n `hovertext`.\n ids\n Assigns id labels to each datum. These ids for object\n constancy of data points during animation. Should be an\n array of strings, not numbers or any other type.\n idssrc\n Sets the source reference on Chart Studio Cloud for\n `ids`.\n legend\n Sets the reference to a legend to show this trace in.\n References to these legends are \"legend\", \"legend2\",\n \"legend3\", etc. Settings for these legends are set in\n the layout, under `layout.legend`, `layout.legend2`,\n etc.\n legendgrouptitle\n :class:`plotly.graph_objects.image.Legendgrouptitle`\n instance or dict with compatible properties\n legendrank\n Sets the legend rank for this trace. Items and groups\n with smaller ranks are presented on top/left side while\n with \"reversed\" `legend.traceorder` they are on\n bottom/right side. The default legendrank is 1000, so\n that you can use ranks less than 1000 to place certain\n items before all unranked items, and ranks greater than\n 1000 to go after all unranked items. When having\n unranked or equal rank items shapes would be displayed\n after traces i.e. according to their order in data and\n layout.\n legendwidth\n Sets the width (in px or fraction) of the legend for\n this trace.\n meta\n Assigns extra meta information associated with this\n trace that can be used in various text attributes.\n Attributes such as trace `name`, graph, axis and\n colorbar `title.text`, annotation `text`\n `rangeselector`, `updatemenues` and `sliders` `label`\n text all support `meta`. To access the trace `meta`\n values in an attribute in the same trace, simply use\n `%{meta[i]}` where `i` is the index or key of the\n `meta` item in question. To access trace `meta` in\n layout attributes, use `%{data[n[.meta[i]}` where `i`\n is the index or key of the `meta` and `n` is the trace\n index.\n metasrc\n Sets the source reference on Chart Studio Cloud for\n `meta`.\n name\n Sets the trace name. The trace name appears as the\n legend item and on hover.\n opacity\n Sets the opacity of the trace.\n source\n Specifies the data URI of the image to be visualized.\n The URI consists of \"data:image/[][;base64],\"\n stream\n :class:`plotly.graph_objects.image.Stream` instance or\n dict with compatible properties\n text\n Sets the text elements associated with each z value.\n textsrc\n Sets the source reference on Chart Studio Cloud for\n `text`.\n uid\n Assign an id to this trace, Use this to provide object\n constancy between traces during animations and\n transitions.\n uirevision\n Controls persistence of some user-driven changes to the\n trace: `constraintrange` in `parcoords` traces, as well\n as some `editable: true` modifications such as `name`\n and `colorbar.title`. Defaults to `layout.uirevision`.\n Note that other user-driven trace attribute changes are\n controlled by `layout` attributes: `trace.visible` is\n controlled by `layout.legend.uirevision`,\n `selectedpoints` is controlled by\n `layout.selectionrevision`, and `colorbar.(x|y)`\n (accessible with `config: {editable: true}`) is\n controlled by `layout.editrevision`. Trace changes are\n tracked by `uid`, which only falls back on trace index\n if no `uid` is provided. So if your app can add/remove\n traces before the end of the `data` array, such that\n the same trace has a different index, you can still\n preserve user-driven changes if you give each trace a\n `uid` that stays with it as it moves.\n visible\n Determines whether or not this trace is visible. If\n \"legendonly\", the trace is not drawn, but can appear as\n a legend item (provided that the legend itself is\n visible).\n x0\n Set the image's x position. The left edge of the image\n (or the right edge if the x axis is reversed or dx is\n negative) will be found at xmin=x0-dx/2\n xaxis\n Sets a reference between this trace's x coordinates and\n a 2D cartesian x axis. If \"x\" (the default value), the\n x coordinates refer to `layout.xaxis`. If \"x2\", the x\n coordinates refer to `layout.xaxis2`, and so on.\n y0\n Set the image's y position. The top edge of the image\n (or the bottom edge if the y axis is NOT reversed or if\n dy is negative) will be found at ymin=y0-dy/2. By\n default when an image trace is included, the y axis\n will be reversed so that the image is right-side-up,\n but you can disable this by setting\n yaxis.autorange=true or by providing an explicit y axis\n range.\n yaxis\n Sets a reference between this trace's y coordinates and\n a 2D cartesian y axis. If \"y\" (the default value), the\n y coordinates refer to `layout.yaxis`. If \"y2\", the y\n coordinates refer to `layout.yaxis2`, and so on.\n z\n A 2-dimensional array in which each element is an array\n of 3 or 4 numbers representing a color.\n zmax\n Array defining the higher bound for each color\n component. Note that the default value will depend on\n the colormodel. For the `rgb` colormodel, it is [255,\n 255, 255]. For the `rgba` colormodel, it is [255, 255,\n 255, 1]. For the `rgba256` colormodel, it is [255, 255,\n 255, 255]. For the `hsl` colormodel, it is [360, 100,\n 100]. For the `hsla` colormodel, it is [360, 100, 100,\n 1].\n zmin\n Array defining the lower bound for each color\n component. Note that the default value will depend on\n the colormodel. For the `rgb` colormodel, it is [0, 0,\n 0]. For the `rgba` colormodel, it is [0, 0, 0, 0]. For\n the `rgba256` colormodel, it is [0, 0, 0, 0]. For the\n `hsl` colormodel, it is [0, 0, 0]. For the `hsla`\n colormodel, it is [0, 0, 0, 0].\n zsmooth\n Picks a smoothing algorithm used to smooth `z` data.\n This only applies for image traces that use the\n `source` attribute.\n zsrc\n Sets the source reference on Chart Studio Cloud for\n `z`.\n \nDid you mean \"colormodel\"?\n\nBad property path:\ncolorscale\n^^^^^^^^^^" + ] + } + ], + "source": [ + "import nibabel as nib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objects as go\n", + "\n", + "# Load the .nii.gz images using nibabel\n", + "gt_image_path = 'patient001_frame01_gt.nii.gz'\n", + "pred_image_path = 'patient001_frame01_pred.nii.gz'\n", + "\n", + "gt_img = nib.load(gt_image_path)\n", + "pred_img = nib.load(pred_image_path)\n", + "\n", + "gt_data = gt_img.get_fdata()\n", + "pred_data = pred_img.get_fdata()\n", + "\n", + "# Print the shape of the image to understand the dimensions\n", + "print(f\"Ground truth shape: {gt_data.shape}\")\n", + "print(f\"Prediction shape: {pred_data.shape}\")\n", + "\n", + "# Choose a valid slice index\n", + "# The third axis is usually the slice axis, and we can get the size of it\n", + "slice_idx = 0 # You can change this to any index between 0 and (number of slices - 1)\n", + "\n", + "# Normalize the images to be in the range [0, 1] for better visualization\n", + "gt_slice = gt_data[:, :, slice_idx]\n", + "pred_slice = pred_data[:, :, slice_idx]\n", + "\n", + "gt_slice = (gt_slice - np.min(gt_slice)) / (np.max(gt_slice) - np.min(gt_slice))\n", + "pred_slice = (pred_slice - np.min(pred_slice)) / (np.max(pred_slice) - np.min(pred_slice))\n", + "\n", + "# Plot using Matplotlib (for side-by-side comparison)\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Ground truth slice\n", + "ax[0].imshow(gt_slice, cmap='gray')\n", + "ax[0].set_title(\"Ground Truth\")\n", + "ax[0].axis('off')\n", + "\n", + "# Predicted slice\n", + "ax[1].imshow(pred_slice, cmap='gray')\n", + "ax[1].set_title(\"Prediction\")\n", + "ax[1].axis('off')\n", + "\n", + "plt.show()\n", + "\n", + "# Optional: Plot using Plotly for better interactivity\n", + "fig_plotly = go.Figure()\n", + "\n", + "# Add ground truth image to Plotly\n", + "fig_plotly.add_trace(go.Image(z=gt_slice, name=\"Ground Truth\", colorscale='gray'))\n", + "\n", + "# Add predicted image to Plotly\n", + "fig_plotly.add_trace(go.Image(z=pred_slice, name=\"Prediction\", colorscale='gray'))\n", + "\n", + "# Update the layout\n", + "fig_plotly.update_layout(\n", + " title=\"Ground Truth vs. Prediction\",\n", + " xaxis=dict(showgrid=False),\n", + " yaxis=dict(showgrid=False),\n", + ")\n", + "\n", + "fig_plotly.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "colorbar": { + "title": { + "text": "Intensity" + } + }, + "colorscale": [ + [ + 0, + "rgb(0, 0, 0)" + ], + [ + 0.09090909090909091, + "rgb(16, 16, 16)" + ], + [ + 0.18181818181818182, + "rgb(38, 38, 38)" + ], + [ + 0.2727272727272727, + "rgb(59, 59, 59)" + ], + [ + 0.36363636363636365, + "rgb(81, 80, 80)" + ], + [ + 0.45454545454545453, + "rgb(102, 101, 101)" + ], + [ + 0.5454545454545454, + "rgb(124, 123, 122)" + ], + [ + 0.6363636363636364, + "rgb(146, 146, 145)" + ], + [ + 0.7272727272727273, + "rgb(171, 171, 170)" + ], + [ + 0.8181818181818182, + "rgb(197, 197, 195)" + ], + [ + 0.9090909090909091, + "rgb(224, 224, 223)" + ], + [ + 1, + "rgb(254, 254, 253)" + ] + ], + "name": "Ground Truth", + "type": "heatmap", + "z": [ + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 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+ "import plotly.graph_objects as go\n", + "import nibabel as nib\n", + "import numpy as np\n", + "\n", + "# Load ground truth, predicted, and original images (replace with correct paths)\n", + "gt_path = 'patient001_frame01_gt.nii.gz'\n", + "pred_path = 'patient001_frame01_pred.nii.gz'\n", + "img_path = 'patient001_frame01_img.nii.gz'\n", + "\n", + "# Load the NIfTI files\n", + "gt_img = nib.load(gt_path)\n", + "pred_img = nib.load(pred_path)\n", + "img_img = nib.load(img_path)\n", + "\n", + "# Convert images to numpy arrays\n", + "gt_data = gt_img.get_fdata()\n", + "pred_data = pred_img.get_fdata()\n", + "img_data = img_img.get_fdata()\n", + "\n", + "# Select a slice to display\n", + "slice_idx = 0 # You can adjust this to select different slices\n", + "gt_slice = gt_data[:, :, slice_idx]\n", + "pred_slice = pred_data[:, :, slice_idx]\n", + "img_slice = img_data[:, :, slice_idx]\n", + "\n", + "# Create the Plotly figure\n", + "fig_plotly = go.Figure()\n", + "\n", + "# Add ground truth image to Plotly (using Heatmap for colormap control)\n", + "fig_plotly.add_trace(go.Heatmap(z=gt_slice, name=\"Ground Truth\", colorscale='Gray', colorbar=dict(title=\"Intensity\")))\n", + "\n", + "# Add predicted image to Plotly\n", + "fig_plotly.add_trace(go.Heatmap(z=pred_slice, name=\"Prediction\", colorscale='Viridis', colorbar=dict(title=\"Intensity\")))\n", + "\n", + "# Add original image to Plotly\n", + "fig_plotly.add_trace(go.Heatmap(z=img_slice, name=\"Image\", colorscale='Jet', colorbar=dict(title=\"Intensity\")))\n", + "\n", + "# Update layout for better visualization\n", + "fig_plotly.update_layout(\n", + " title=\"Segmentation Results\",\n", + " xaxis_title=\"X Axis\",\n", + " yaxis_title=\"Y Axis\",\n", + " showlegend=True\n", + ")\n", + "\n", + "# Show the figure\n", + "fig_plotly.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ground truth shape: (216, 256, 10)\n", + "Prediction shape: (216, 256, 10)\n" + ] + }, + { + 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go\n", + "import plotly.subplots as sp\n", + "import nibabel as nib\n", + "import numpy as np\n", + "\n", + "# Load ground truth, predicted, and original images (replace with correct paths)\n", + "gt_path = 'patient001_frame01_gt.nii.gz'\n", + "pred_path = 'patient001_frame01_pred.nii.gz'\n", + "img_path = 'patient001_frame01_img.nii.gz'\n", + "\n", + "# Load the NIfTI files\n", + "gt_img = nib.load(gt_path)\n", + "pred_img = nib.load(pred_path)\n", + "img_img = nib.load(img_path)\n", + "\n", + "# Convert images to numpy arrays\n", + "gt_data = gt_img.get_fdata()\n", + "pred_data = pred_img.get_fdata()\n", + "img_data = img_img.get_fdata()\n", + "\n", + "# Select a slice to display\n", + "slice_idx = 1 # You can adjust this to select different slices\n", + "gt_slice = gt_data[:, :, slice_idx]\n", + "pred_slice = pred_data[:, :, slice_idx]\n", + "img_slice = img_data[:, :, slice_idx]\n", + "\n", + "# Create subplots\n", + "fig = sp.make_subplots(\n", + " rows=1, cols=3,\n", + " subplot_titles=(\"Ground Truth\", \"Prediction\", \"Original Image\"),\n", + " shared_yaxes=True\n", + ")\n", + "\n", + "# Add ground truth image to subplot\n", + "fig.add_trace(go.Heatmap(z=gt_slice, colorscale='Gray', colorbar=dict(title=\"Intensity\")), row=1, col=1)\n", + "\n", + "# Add predicted image to subplot\n", + "fig.add_trace(go.Heatmap(z=pred_slice, colorscale='Viridis', colorbar=dict(title=\"Intensity\")), row=1, col=2)\n", + "\n", + "# Add original image to subplot\n", + "fig.add_trace(go.Heatmap(z=img_slice, colorscale='Jet', colorbar=dict(title=\"Intensity\")), row=1, col=3)\n", + "\n", + "# Update layout for better visualization\n", + "fig.update_layout(\n", + " title=\"Segmentation Results\",\n", + " xaxis_title=\"X Axis\",\n", + " yaxis_title=\"Y Axis\",\n", + " showlegend=False,\n", + " height=500,\n", + " width=1000\n", + ")\n", + "\n", + "# Show the figure\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + 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+ "# Load ground truth, predicted, and original images (replace with correct paths)\n", + "gt_path = 'patient001_frame12_gt.nii.gz'\n", + "pred_path = 'patient001_frame12_pred.nii.gz'\n", + "img_path = 'patient001_frame12_img.nii.gz'\n", + "\n", + "# Load the NIfTI files\n", + "gt_img = nib.load(gt_path)\n", + "pred_img = nib.load(pred_path)\n", + "img_img = nib.load(img_path)\n", + "\n", + "# Convert images to numpy arrays\n", + "gt_data = gt_img.get_fdata()\n", + "pred_data = pred_img.get_fdata()\n", + "img_data = img_img.get_fdata()\n", + "\n", + "# Select a slice to display\n", + "slice_idx = 5 # You can adjust this to select different slices\n", + "gt_slice = gt_data[:, :, slice_idx]\n", + "pred_slice = pred_data[:, :, slice_idx]\n", + "img_slice = img_data[:, :, slice_idx]\n", + "\n", + "# Create subplots\n", + "fig = sp.make_subplots(\n", + " rows=1, cols=3,\n", + " subplot_titles=(\"Ground Truth\", \"Prediction\", \"Original Image\"),\n", + " shared_yaxes=True\n", + ")\n", + "\n", + "# Add ground truth image to subplot\n", + "fig.add_trace(go.Heatmap(z=gt_slice, colorscale='Gray', colorbar=dict(title=\"Intensity\")), row=1, col=1)\n", + "\n", + "# Add predicted image to subplot\n", + "fig.add_trace(go.Heatmap(z=pred_slice, colorscale='Viridis', colorbar=dict(title=\"Intensity\")), row=1, col=2)\n", + "\n", + "# Add original image to subplot\n", + "fig.add_trace(go.Heatmap(z=img_slice, colorscale='Jet', colorbar=dict(title=\"Intensity\")), row=1, col=3)\n", + "\n", + "# Update layout for better visualization\n", + "fig.update_layout(\n", + " title=\"Segmentation Results\",\n", + " xaxis_title=\"X Axis\",\n", + " yaxis_title=\"Y Axis\",\n", + " showlegend=False,\n", + " height=500,\n", + " width=1000\n", + ")\n", + "\n", + "# Show the figure\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/dataset_acdc.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/dataset_acdc.py new file mode 100644 index 0000000..3ab1a96 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/dataset_acdc.py @@ -0,0 +1,142 @@ +import itertools +import os +import random +import re +from glob import glob + +import cv2 +import h5py +import numpy as np +import torch +from scipy import ndimage +from scipy.ndimage.interpolation import zoom +from torch.utils.data import Dataset +from skimage import io +import cv2 +class BaseDataSets(Dataset): + def __init__(self, base_dir=None, split='train', list_dir=None, transform=None): + self._base_dir = base_dir + self.sample_list = [] + self.split = split + self.transform = transform + train_ids, val_ids, test_ids = self._get_ids() + if self.split.find('train') != -1: + print(f"Looking for slices in: {os.path.abspath('./data/ACDC_training_slices')}") + self.all_slices = os.listdir( + self._base_dir + "/ACDC_training_slices") + self.sample_list = [] + for ids in train_ids: + new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_slices)) + self.sample_list.extend(new_data_list) + + elif self.split.find('val') != -1: + self.all_volumes = os.listdir( + self._base_dir + "/ACDC_training_volumes") + self.sample_list = [] + for ids in val_ids: + new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_volumes)) + self.sample_list.extend(new_data_list) + + elif self.split.find('test') != -1: + self.all_volumes = os.listdir( + self._base_dir + "/ACDC_training_volumes") + self.sample_list = [] + for ids in test_ids: + new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_volumes)) + self.sample_list.extend(new_data_list) + + # if num is not None and self.split == "train": + # self.sample_list = self.sample_list[:num] + print("total {} samples".format(len(self.sample_list))) + + def _get_ids(self): + all_cases_set = ["patient{:0>3}".format(i) for i in range(1, 101)] + testing_set = ["patient{:0>3}".format(i) for i in range(1, 21)] + validation_set = ["patient{:0>3}".format(i) for i in range(21, 31)] + training_set = [i for i in all_cases_set if i not in testing_set+validation_set] + + return [training_set, validation_set, testing_set] + + def __len__(self): + return len(self.sample_list) + + def __getitem__(self, idx): + case = self.sample_list[idx] + + # image = h5f['image'][:] + # label = h5f['label'][:] + # sample = {'image': image, 'label': label} + if self.split == "train": + h5f = h5py.File(self._base_dir + "/ACDC_training_slices/{}".format(case), 'r') + image = h5f['image'][:] + label = h5f['label'][:] # fix sup_type to label + sample = {'image': image, 'label': label} + sample = self.transform(sample) + else: + h5f = h5py.File(self._base_dir + "/ACDC_training_volumes/{}".format(case), 'r') + image = h5f['image'][:] + label = h5f['label'][:] + sample = {'image': image, 'label': label} + sample["idx"] = idx + sample['case_name'] = case.replace('.h5', '') + return sample + + +def random_rot_flip(image, label): + k = np.random.randint(0, 4) + image = np.rot90(image, k) + label = np.rot90(label, k) + axis = np.random.randint(0, 2) + image = np.flip(image, axis=axis).copy() + label = np.flip(label, axis=axis).copy() + return image, label + + +def random_rotate(image, label): + angle = np.random.randint(-20, 20) + image = ndimage.rotate(image, angle, order=0, reshape=False) + label = ndimage.rotate(label, angle, order=0, reshape=False) + return image, label + + +class RandomGenerator(object): + def __init__(self, output_size): + self.output_size = output_size + + def __call__(self, sample): + image, label = sample['image'], sample['label'] + # ind = random.randrange(0, img.shape[0]) + # image = img[ind, ...] + # label = lab[ind, ...] + if random.random() > 0.5: + image, label = random_rot_flip(image, label) + elif random.random() > 0.5: + image, label = random_rotate(image, label) + x, y = image.shape + if x != self.output_size[0] or y != self.output_size[1]: + image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=0) # the default is 0 + label = zoom( label, (self.output_size[0] / x, self.output_size[1] / y), order=0) + + assert (image.shape[0] == self.output_size[0]) and (image.shape[1] == self.output_size[1]) + image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0) + label = torch.from_numpy(label.astype(np.uint8)) + sample = {'image': image, 'label': label} + return sample + + +def iterate_once(iterable): + return np.random.permutation(iterable) + + +def iterate_eternally(indices): + def infinite_shuffles(): + while True: + yield np.random.permutation(indices) + return itertools.chain.from_iterable(infinite_shuffles()) + + +def grouper(iterable, n): + "Collect data into fixed-length chunks or blocks" + # grouper('ABCDEFG', 3) --> ABC DEF" + args = [iter(iterable)] * n + return zip(*args) diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/requirements.txt b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/requirements.txt new file mode 100644 index 0000000..69f732d --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/requirements.txt @@ -0,0 +1,11 @@ +torch +torchvision +numpy +tqdm +tensorboard +tensorboardX +ml-collections +medpy +SimpleITK +scipy +h5py diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/test.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/test.py new file mode 100644 index 0000000..6fd8156 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/test.py @@ -0,0 +1,164 @@ +import argparse +import logging +import os +import random +import sys +import numpy as np +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +from torch.utils.data import DataLoader +from tqdm import tqdm +#from datasets.dataset_synapse import Synapse_dataset +#try: +from dataset_acdc import BaseDataSets as ACDC_dataset +#except: + #pass +from utils import test_single_volume +from networks.vit_seg_modeling import VisionTransformer as ViT_seg +from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg +#from networks import VisionTransformer as ViT_seg +#from networks import CONFIGS as CONFIGS_ViT_seg + +parser = argparse.ArgumentParser() +parser.add_argument('--volume_path', type=str, + default='./data/ACDC', help='root dir for validation volume data') # for acdc volume_path=root_dir +parser.add_argument('--dataset', type=str, + default='Synapse', help='experiment_name') +parser.add_argument('--num_classes', type=int, + default=4, help='output channel of network') +parser.add_argument('--list_dir', type=str, + default='./lists/lists_Synapse', help='list dir') + +parser.add_argument('--max_iterations', type=int,default=20000, help='maximum epoch number to train') +parser.add_argument('--max_epochs', type=int, default=30, help='maximum epoch number to train') +parser.add_argument('--batch_size', type=int, default=24, + help='batch_size per gpu') +parser.add_argument('--img_size', type=int, default=224, help='input patch size of network input') +parser.add_argument('--is_savenii', action="store_true", help='whether to save results during inference') + +parser.add_argument('--n_skip', type=int, default=3, help='using number of skip-connect, default is num') +parser.add_argument('--vit_name', type=str, default='ViT-B_16', help='select one vit model') + +parser.add_argument('--test_save_dir', type=str, default='../predictions', help='saving prediction as nii!') +parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training') +parser.add_argument('--base_lr', type=float, default=0.01, help='segmentation network learning rate') +parser.add_argument('--seed', type=int, default=1234, help='random seed') +parser.add_argument('--vit_patches_size', type=int, default=16, help='vit_patches_size, default is 16') +args = parser.parse_args() + +def inference(args, model, test_save_path=None): + db_test = args.Dataset(base_dir=args.volume_path, split="test", list_dir=args.list_dir) + testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1) + logging.info("{} test iterations per epoch".format(len(testloader))) + model.eval() + metric_list = 0.0 + for i_batch, sampled_batch in tqdm(enumerate(testloader)): + h, w = sampled_batch["image"].size()[2:] + image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0] + dice_scores, hd95_scores, iou_scores = test_single_volume( + image, label, model, classes=args.num_classes, + patch_size=[args.img_size, args.img_size], + test_save_path=test_save_path, case=case_name, z_spacing=args.z_spacing + ) + metric_list += np.array([dice_scores, hd95_scores, iou_scores]) + logging.info('idx %d case %s mean_dice %f mean_hd95 %f mean_iou %f' % ( + i_batch, case_name, np.mean(dice_scores), np.mean(hd95_scores), np.mean(iou_scores) + )) + metric_list = metric_list / len(db_test) + for i in range(1, args.num_classes): + logging.info('Mean class %d mean_dice %f mean_hd95 %f mean_iou %f' % ( + i, metric_list[i-1][0], metric_list[i-1][1], metric_list[i-1][2] + )) + performance = np.mean(metric_list, axis=0)[0] + mean_hd95 = np.mean(metric_list, axis=0)[1] + mean_iou = np.mean(metric_list, axis=0)[2] + logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f mean_iou : %f' % ( + performance, mean_hd95, mean_iou + )) + return "Testing Finished!" + + + +if __name__ == "__main__": + + if not args.deterministic: + cudnn.benchmark = True + cudnn.deterministic = False + else: + cudnn.benchmark = False + cudnn.deterministic = True + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed(args.seed) + + dataset_config = { + 'ACDC': { + 'Dataset': ACDC_dataset, # datasets.dataset_acdc.BaseDataSets, + 'volume_path': './data/ACDC', + 'list_dir': None, + 'num_classes': 4, + 'z_spacing': 5, + 'info': '3D' + }, + 'Synapse': { + 'Dataset': 'Synapse_dataset', + 'volume_path': '../data/Synapse/test_vol_h5', + 'list_dir': './lists/lists_Synapse', + 'num_classes': 9, + 'z_spacing': 1, + }, + } + dataset_name = args.dataset + args.num_classes = dataset_config[dataset_name]['num_classes'] + args.volume_path = dataset_config[dataset_name]['volume_path'] + args.Dataset = dataset_config[dataset_name]['Dataset'] + args.list_dir = dataset_config[dataset_name]['list_dir'] + args.z_spacing = dataset_config[dataset_name]['z_spacing'] + args.is_pretrain = True + + # name the same snapshot defined in train script! + args.exp = 'TU_' + dataset_name + str(args.img_size) + snapshot_path = "../model/{}/{}".format(args.exp, 'TU') + snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path + snapshot_path += '_' + args.vit_name + snapshot_path = snapshot_path + '_skip' + str(args.n_skip) + snapshot_path = snapshot_path + '_vitpatch' + str(args.vit_patches_size) if args.vit_patches_size!=16 else snapshot_path + snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 30 else snapshot_path + #if dataset_name == 'ACDC': # using max_epoch instead of iteration to control training duration + #snapshot_path = snapshot_path + '_' + str(args.max_iterations)[0:2] + 'k' if args.max_iterations != 30000 else snapshot_path + snapshot_path = snapshot_path+'_bs'+str(args.batch_size) + snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.01 else snapshot_path + snapshot_path = snapshot_path + '_'+str(args.img_size) + snapshot_path = snapshot_path + '_s'+str(args.seed) if args.seed!=1234 else snapshot_path + + config_vit = CONFIGS_ViT_seg[args.vit_name] + config_vit.n_classes = args.num_classes + config_vit.n_skip = args.n_skip + config_vit.patches.size = (args.vit_patches_size, args.vit_patches_size) + if args.vit_name.find('R50') !=-1: + config_vit.patches.grid = (int(args.img_size/args.vit_patches_size), int(args.img_size/args.vit_patches_size)) + net = ViT_seg(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda() + + snapshot = os.path.join(snapshot_path, 'best_model.pth') + if not os.path.exists(snapshot): snapshot = snapshot.replace('best_model', 'epoch_'+str(args.max_epochs-1)) + net.load_state_dict(torch.load(snapshot)) + snapshot_name = snapshot_path.split('/')[-1] + + log_folder = './test_log/test_log_' + args.exp + os.makedirs(log_folder, exist_ok=True) + logging.basicConfig(filename=log_folder + '/'+snapshot_name+".txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') + logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) + logging.info(str(args)) + logging.info(snapshot_name) + + if args.is_savenii: + args.test_save_dir = '../predictions' + test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name) + os.makedirs(test_save_path, exist_ok=True) + else: + test_save_path = None + inference(args, net, test_save_path) + + diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/train.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/train.py new file mode 100644 index 0000000..2b1626a --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/train.py @@ -0,0 +1,99 @@ +import argparse +import logging +import os +import random +import numpy as np +import torch +import torch.backends.cudnn as cudnn +from networks.vit_seg_modeling import VisionTransformer as ViT_seg +from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg +from trainer import trainer_synapse, trainer_acdc + +parser = argparse.ArgumentParser() +parser.add_argument('--root_path', type=str, + default='../data/Synapse/train_npz', help='root dir for data') +parser.add_argument('--dataset', type=str, + default='Synapse', help='experiment_name') +parser.add_argument('--list_dir', type=str, + default='./lists/lists_Synapse', help='list dir') +parser.add_argument('--num_classes', type=int, + default=9, help='output channel of network') +parser.add_argument('--max_iterations', type=int, + default=30000, help='maximum epoch number to train') +parser.add_argument('--max_epochs', type=int, + default=150, help='maximum epoch number to train') +parser.add_argument('--batch_size', type=int, + default=24, help='batch_size per gpu') +parser.add_argument('--n_gpu', type=int, default=1, help='total gpu') +parser.add_argument('--deterministic', type=int, default=1, + help='whether use deterministic training') +parser.add_argument('--base_lr', type=float, default=0.01, + help='segmentation network learning rate') +parser.add_argument('--img_size', type=int, + default=224, help='input patch size of network input') +parser.add_argument('--seed', type=int, + default=1234, help='random seed') +parser.add_argument('--n_skip', type=int, + default=3, help='using number of skip-connect, default is num') +parser.add_argument('--vit_name', type=str, + default='R50-ViT-B_16', help='select one vit model') +parser.add_argument('--vit_patches_size', type=int, + default=16, help='vit_patches_size, default is 16') +args = parser.parse_args() + + +if __name__ == "__main__": + if not args.deterministic: + cudnn.benchmark = True + cudnn.deterministic = False + else: + cudnn.benchmark = False + cudnn.deterministic = True + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed(args.seed) + dataset_name = args.dataset + dataset_config = { + 'ACDC': { + #'Dataset': ACDC_dataset, # datasets.dataset_acdc.BaseDataSets, + 'root_path': './data/ACDC', + 'list_dir': None, + 'num_classes': 4, + }, + 'Synapse': { + 'root_path': '../data/Synapse/train_npz', + 'list_dir': './lists/lists_Synapse', + 'num_classes': 9, + }, + } + args.num_classes = dataset_config[dataset_name]['num_classes'] + args.root_path = dataset_config[dataset_name]['root_path'] + args.list_dir = dataset_config[dataset_name]['list_dir'] + args.is_pretrain = True + args.exp = 'TU_' + dataset_name + str(args.img_size) + snapshot_path = "../model/{}/{}".format(args.exp, 'TU') + snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path + snapshot_path += '_' + args.vit_name + snapshot_path = snapshot_path + '_skip' + str(args.n_skip) + snapshot_path = snapshot_path + '_vitpatch' + str(args.vit_patches_size) if args.vit_patches_size!=16 else snapshot_path + snapshot_path = snapshot_path+'_'+str(args.max_iterations)[0:2]+'k' if args.max_iterations != 30000 else snapshot_path + snapshot_path = snapshot_path + '_epo' +str(args.max_epochs) if args.max_epochs != 30 else snapshot_path + snapshot_path = snapshot_path+'_bs'+str(args.batch_size) + snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.01 else snapshot_path + snapshot_path = snapshot_path + '_'+str(args.img_size) + snapshot_path = snapshot_path + '_s'+str(args.seed) if args.seed!=1234 else snapshot_path + + if not os.path.exists(snapshot_path): + os.makedirs(snapshot_path) + config_vit = CONFIGS_ViT_seg[args.vit_name] + config_vit.n_classes = args.num_classes + config_vit.n_skip = args.n_skip + if args.vit_name.find('R50') != -1: + config_vit.patches.grid = (int(args.img_size / args.vit_patches_size), int(args.img_size / args.vit_patches_size)) + net = ViT_seg(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda() + net.load_from(weights=np.load(config_vit.pretrained_path)) + + trainer = {'Synapse': trainer_synapse,'ACDC': trainer_acdc,} + trainer[dataset_name](args, net, snapshot_path) diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/trainer.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/trainer.py new file mode 100644 index 0000000..fd5c18e --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/trainer.py @@ -0,0 +1,203 @@ +import argparse +import logging +import os +import random +import sys +import time +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from tensorboardX import SummaryWriter +from torch.nn.modules.loss import CrossEntropyLoss +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils import DiceLoss, test_single_volume +from torchvision import transforms +import multiprocessing +num_workers = min(4, multiprocessing.cpu_count() - 1) + +def trainer_acdc(args, model, snapshot_path): + from dataset_acdc import BaseDataSets, RandomGenerator + base_lr = args.base_lr + num_classes = args.num_classes + batch_size = args.batch_size + max_iterations = args.max_iterations + + db_train = BaseDataSets(base_dir=args.root_path, split="train", transform=transforms.Compose([ + RandomGenerator([args.img_size, args.img_size])])) + db_val = BaseDataSets(base_dir=args.root_path, split="val") + + #addition for testloader + #db_test = BaseDataSets(base_dir=args.root_path, split="test") + #testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1) + #end + + def worker_init_fn(worker_id): + random.seed(args.seed + worker_id) + trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, + num_workers=num_workers, pin_memory=True, worker_init_fn=worker_init_fn) + valloader = DataLoader(db_val, batch_size=1, shuffle=False, + num_workers=max(1, num_workers // 2)) + model.train() + optimizer = optim.SGD(model.parameters(), lr=base_lr, + momentum=0.9, weight_decay=0.0001) + ce_loss = CrossEntropyLoss(ignore_index=4) + dice_loss = DiceLoss(num_classes) + + writer = SummaryWriter(snapshot_path + '/log') + logging.info("{} iterations per epoch".format(len(trainloader))) + logging.info("{} val iterations per epoch".format(len(valloader))) + #logging.info("{} test iterations per epoch".format(len(testloader))) + + iter_num = 0 + max_epoch = max_iterations // len(trainloader) + 1 + best_performance = 0.0 + iterator = tqdm(range(max_epoch), ncols=70) + for epoch_num in iterator: + for i_batch, sampled_batch in enumerate(trainloader): + volume_batch, label_batch = sampled_batch['image'], sampled_batch['label'] + volume_batch, label_batch = volume_batch.cuda(), label_batch.cuda() + outputs = model(volume_batch) + loss_ce = ce_loss(outputs, label_batch[:].long()) + loss_dice = dice_loss(outputs, label_batch, softmax=True) + loss = 0.5 * loss_ce + 0.5 * loss_dice + optimizer.zero_grad() + loss.backward() + optimizer.step() + lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9 + for param_group in optimizer.param_groups: + param_group['lr'] = lr_ + + iter_num = iter_num + 1 + writer.add_scalar('info/lr', lr_, iter_num) + writer.add_scalar('info/total_loss', loss, iter_num) + writer.add_scalar('info/loss_ce', loss_ce, iter_num) + + logging.info('iteration %d : loss : %f, loss_ce: %f' % (iter_num, loss.item(), loss_ce.item())) + + if iter_num % 20 == 0: + image = volume_batch[1, 0:1, :, :] + image = (image - image.min()) / (image.max() - image.min()) + writer.add_image('train/Image', image, iter_num) + outputs = torch.argmax(torch.softmax( + outputs, dim=1), dim=1, keepdim=True) + writer.add_image('train/Prediction', + outputs[1, ...] * 50, iter_num) + labs = label_batch[1, ...].unsqueeze(0) * 50 + writer.add_image('train/GroundTruth', labs, iter_num) + + if iter_num > 0 and iter_num % 500 == 0: # 500 + model.eval() + metric_list = 0.0 + for i_batch, sampled_batch in enumerate(valloader): + image, label = sampled_batch["image"], sampled_batch["label"] + metric_i = test_single_volume(image, label, model, classes=num_classes, + patch_size=[args.img_size, args.img_size]) + metric_list += np.array(metric_i) + metric_list = metric_list / len(db_val) + for class_i in range(num_classes - 1): + writer.add_scalar('info/val_{}_dice'.format(class_i + 1), + metric_list[class_i, 0], iter_num) + writer.add_scalar('info/val_{}_hd95'.format(class_i + 1), + metric_list[class_i, 1], iter_num) + + performance = np.mean(metric_list, axis=0)[0] + + mean_hd95 = np.mean(metric_list, axis=0)[1] + writer.add_scalar('info/val_mean_dice', performance, iter_num) + writer.add_scalar('info/val_mean_hd95', mean_hd95, iter_num) + + if performance > best_performance: + best_iteration, best_performance, best_hd95 = iter_num, performance, mean_hd95 + save_best = os.path.join(snapshot_path, 'best_model.pth') + torch.save(model.state_dict(), save_best) + logging.info('Best model | iteration %d : mean_dice : %f mean_hd95 : %f' % ( + iter_num, performance, mean_hd95)) + + logging.info('iteration %d : mean_dice : %f mean_hd95 : %f' % (iter_num, performance, mean_hd95)) + model.train() + + if iter_num >= max_iterations: + break + +def trainer_synapse(args, model, snapshot_path): + from datasets.dataset_synapse import Synapse_dataset, RandomGenerator + logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO, + format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') + logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) + logging.info(str(args)) + base_lr = args.base_lr + num_classes = args.num_classes + batch_size = args.batch_size * args.n_gpu + # max_iterations = args.max_iterations + db_train = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train", + transform=transforms.Compose( + [RandomGenerator(output_size=[args.img_size, args.img_size])])) + print("The length of train set is: {}".format(len(db_train))) + + def worker_init_fn(worker_id): + random.seed(args.seed + worker_id) + + trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True, + worker_init_fn=worker_init_fn) + if args.n_gpu > 1: + model = nn.DataParallel(model) + model.train() + ce_loss = CrossEntropyLoss() + dice_loss = DiceLoss(num_classes) + optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001) + writer = SummaryWriter(snapshot_path + '/log') + iter_num = 0 + max_epoch = args.max_epochs + max_iterations = args.max_epochs * len(trainloader) # max_epoch = max_iterations // len(trainloader) + 1 + logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations)) + best_performance = 0.0 + iterator = tqdm(range(max_epoch), ncols=70) + for epoch_num in iterator: + for i_batch, sampled_batch in enumerate(trainloader): + image_batch, label_batch = sampled_batch['image'], sampled_batch['label'] + image_batch, label_batch = image_batch.cuda(), label_batch.cuda() + outputs = model(image_batch) + loss_ce = ce_loss(outputs, label_batch[:].long()) + loss_dice = dice_loss(outputs, label_batch, softmax=True) + loss = 0.5 * loss_ce + 0.5 * loss_dice + optimizer.zero_grad() + loss.backward() + optimizer.step() + lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9 + for param_group in optimizer.param_groups: + param_group['lr'] = lr_ + + iter_num = iter_num + 1 + writer.add_scalar('info/lr', lr_, iter_num) + writer.add_scalar('info/total_loss', loss, iter_num) + writer.add_scalar('info/loss_ce', loss_ce, iter_num) + + logging.info('iteration %d : loss : %f, loss_ce: %f' % (iter_num, loss.item(), loss_ce.item())) + + if iter_num % 20 == 0: + image = image_batch[1, 0:1, :, :] + image = (image - image.min()) / (image.max() - image.min()) + writer.add_image('train/Image', image, iter_num) + outputs = torch.argmax(torch.softmax(outputs, dim=1), dim=1, keepdim=True) + writer.add_image('train/Prediction', outputs[1, ...] * 50, iter_num) + labs = label_batch[1, ...].unsqueeze(0) * 50 + writer.add_image('train/GroundTruth', labs, iter_num) + + save_interval = 50 # int(max_epoch/6) + if epoch_num > int(max_epoch / 2) and (epoch_num + 1) % save_interval == 0: + save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') + torch.save(model.state_dict(), save_mode_path) + logging.info("save model to {}".format(save_mode_path)) + + if epoch_num >= max_epoch - 1: + save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') + torch.save(model.state_dict(), save_mode_path) + logging.info("save model to {}".format(save_mode_path)) + iterator.close() + break + + writer.close() + return "Training Finished!" + diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/utils.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/utils.py new file mode 100644 index 0000000..9a749f2 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/utils.py @@ -0,0 +1,114 @@ +import numpy as np +import torch +from medpy import metric +from scipy.ndimage import zoom +import torch.nn as nn +import SimpleITK as sitk + + +class DiceLoss(nn.Module): + def __init__(self, n_classes): + super(DiceLoss, self).__init__() + self.n_classes = n_classes + + def _one_hot_encoder(self, input_tensor): + tensor_list = [] + for i in range(self.n_classes): + temp_prob = input_tensor == i # * torch.ones_like(input_tensor) + tensor_list.append(temp_prob.unsqueeze(1)) + output_tensor = torch.cat(tensor_list, dim=1) + return output_tensor.float() + + def _dice_loss(self, score, target): + target = target.float() + smooth = 1e-5 + intersect = torch.sum(score * target) + y_sum = torch.sum(target * target) + z_sum = torch.sum(score * score) + loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth) + loss = 1 - loss + return loss + + def forward(self, inputs, target, weight=None, softmax=False): + if softmax: + inputs = torch.softmax(inputs, dim=1) + target = self._one_hot_encoder(target) + if weight is None: + weight = [1] * self.n_classes + assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size()) + class_wise_dice = [] + loss = 0.0 + for i in range(0, self.n_classes): + dice = self._dice_loss(inputs[:, i], target[:, i]) + class_wise_dice.append(1.0 - dice.item()) + loss += dice * weight[i] + return loss / self.n_classes + + +def calculate_metric_percase(pred, gt): + """ + Calculates Dice, HD95, and IoU for a single case. + """ + pred[pred > 0] = 1 + gt[gt > 0] = 1 + if pred.sum() > 0 and gt.sum() > 0: + dice = metric.binary.dc(pred, gt) # Dice coefficient + hd95 = metric.binary.hd95(pred, gt) # Hausdorff distance 95% + intersection = np.sum(pred * gt) + union = np.sum(pred) + np.sum(gt) - intersection + iou = intersection / union # IoU calculation + return dice, hd95, iou + elif pred.sum() > 0 and gt.sum() == 0: + return 1, 0, 0 # Perfect prediction when ground truth is empty + else: + return 0, 0, 0 # No overlap when both are empty + + +def test_single_volume(image, label, net, classes, patch_size=[256, 256], test_save_path=None, case=None, z_spacing=1): + """ + Performs inference on a single volume and computes metrics (Dice, HD95, IoU). + """ + image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy() + if len(image.shape) == 3: # 3D case + prediction = np.zeros_like(label) + for ind in range(image.shape[0]): + slice = image[ind, :, :] + x, y = slice.shape[0], slice.shape[1] + if x != patch_size[0] or y != patch_size[1]: + slice = zoom(slice, (patch_size[0] / x, patch_size[1] / y), order=3) # Resize to patch size + input = torch.from_numpy(slice).unsqueeze(0).unsqueeze(0).float().cuda() + net.eval() + with torch.no_grad(): + outputs = net(input) + out = torch.argmax(torch.softmax(outputs, dim=1), dim=1).squeeze(0) + out = out.cpu().detach().numpy() + if x != patch_size[0] or y != patch_size[1]: + pred = zoom(out, (x / patch_size[0], y / patch_size[1]), order=0) + else: + pred = out + prediction[ind] = pred + else: # 2D case + input = torch.from_numpy(image).unsqueeze(0).unsqueeze(0).float().cuda() + net.eval() + with torch.no_grad(): + out = torch.argmax(torch.softmax(net(input), dim=1), dim=1).squeeze(0) + prediction = out.cpu().detach().numpy() + + # Compute metrics for each class + metric_list = [] + for i in range(1, classes): + metric_list.append(calculate_metric_percase(prediction == i, label == i)) + + # Save predictions if a path is specified + if test_save_path is not None: + img_itk = sitk.GetImageFromArray(image.astype(np.float32)) + prd_itk = sitk.GetImageFromArray(prediction.astype(np.float32)) + lab_itk = sitk.GetImageFromArray(label.astype(np.float32)) + img_itk.SetSpacing((1, 1, z_spacing)) + prd_itk.SetSpacing((1, 1, z_spacing)) + lab_itk.SetSpacing((1, 1, z_spacing)) + sitk.WriteImage(prd_itk, test_save_path + '/' + case + "_pred.nii.gz") + sitk.WriteImage(img_itk, test_save_path + '/' + case + "_img.nii.gz") + sitk.WriteImage(lab_itk, test_save_path + '/' + case + "_gt.nii.gz") + + return metric_list From 9dc84fd7e01b1728a13d227144a5c705669567eb Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:28:39 +0800 Subject: [PATCH 06/12] Update README.md --- .../ACDC_Dataset_test/readme.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md index 623df3e..577ec60 100644 --- a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md @@ -1 +1,15 @@ -creation +# Implementation of TransUNet Segmentation model on ACDC Dataset + +- to run directly on your system, please change the data path from "./data/ACDC" in test.py or train.py to the path to your Dataset. + +- this folder does NOT include the dataset due to size reasons. + +- Download Google pre-trained ViT models +* [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16... +```bash +wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz && +mkdir ../model/vit_checkpoint/imagenet21k && +mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz +``` +I used the R50_ViT-B_16 model + From 37faa1ac5ffd8e5f4e53dcdeb1d6108bc8e6841e Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:33:15 +0800 Subject: [PATCH 07/12] Update and rename readme.md to README.md --- .../CAMUS_Dataset_test/README.md | 23 +++++++++++++++++++ .../CAMUS_Dataset_test/readme.md | 1 - 2 files changed, 23 insertions(+), 1 deletion(-) create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/README.md delete mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/README.md b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/README.md new file mode 100644 index 0000000..01fa710 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/README.md @@ -0,0 +1,23 @@ +# Implementation of TransUNet Segmentation model on CAMUS Dataset + +- to run directly on your system, please change the data path from "preprocessed_data" in test.py or train.py to the path to your Dataset. + +- the data (after running preprocessing script.py) is stored in the following manner: + ```markdown + preprocessed_data + |-> test + |-> train + |-> val + ``` + change the directories in test.py, train.py accordingly to suit your own data path. + +- this folder does NOT include the dataset due to size concerns. + +- Download Google pre-trained ViT models + * [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16... + ```bash + wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz && + mkdir ../model/vit_checkpoint/imagenet21k && + mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz + ``` + I used the R50-ViT-B_16 model. diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md deleted file mode 100644 index f233a10..0000000 --- a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/readme.md +++ /dev/null @@ -1 +0,0 @@ -creation From 36de34813e933d01fd3ee815a1f327ff19195ae5 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:35:24 +0800 Subject: [PATCH 08/12] Update and rename readme.md to README.md --- .../ACDC_Dataset_test/README.md | 17 +++++++++++++++++ .../ACDC_Dataset_test/readme.md | 15 --------------- 2 files changed, 17 insertions(+), 15 deletions(-) create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/README.md delete mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/README.md b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/README.md new file mode 100644 index 0000000..3f8fe5b --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/README.md @@ -0,0 +1,17 @@ +# Implementation of TransUNet Segmentation model on ACDC Dataset + +- to run directly on your system, please change the data path from "./data/ACDC" in test.py or train.py to the path to your Dataset. + +- this folder does NOT include the dataset due to size reasons. + +- test.py in this folder does NOT save the visualised images for every predicted file. Please modify and run the ACDCvisualiser.ipynb notebook to visualise your data slice by slice. + +- Download Google pre-trained ViT models + * [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16... + ```bash + wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz && + mkdir ../model/vit_checkpoint/imagenet21k && + mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz + ``` + I used the R50_ViT-B_16 model + diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md deleted file mode 100644 index 577ec60..0000000 --- a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/readme.md +++ /dev/null @@ -1,15 +0,0 @@ -# Implementation of TransUNet Segmentation model on ACDC Dataset - -- to run directly on your system, please change the data path from "./data/ACDC" in test.py or train.py to the path to your Dataset. - -- this folder does NOT include the dataset due to size reasons. - -- Download Google pre-trained ViT models -* [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16... -```bash -wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz && -mkdir ../model/vit_checkpoint/imagenet21k && -mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz -``` -I used the R50_ViT-B_16 model - From c10b5f7e26b426427399426d732573ebbcb4b683 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:36:16 +0800 Subject: [PATCH 09/12] Add files via upload --- .../networks/vit_seg_configs.py | 130 +++++ .../networks/vit_seg_modeling.py | 453 ++++++++++++++++++ .../networks/vit_seg_modeling_resnet_skip.py | 160 +++++++ 3 files changed, 743 insertions(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_configs.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling_resnet_skip.py diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_configs.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_configs.py new file mode 100644 index 0000000..1bc4c78 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_configs.py @@ -0,0 +1,130 @@ +import ml_collections + +def get_b16_config(): + """Returns the ViT-B/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 768 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 3072 + config.transformer.num_heads = 12 + config.transformer.num_layers = 12 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + + config.classifier = 'seg' + config.representation_size = None + config.resnet_pretrained_path = None + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_16.npz' + config.patch_size = 16 + + config.decoder_channels = (256, 128, 64, 16) + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_testing(): + """Returns a minimal configuration for testing.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 1 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 1 + config.transformer.num_heads = 1 + config.transformer.num_layers = 1 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.classifier = 'token' + config.representation_size = None + return config + +def get_r50_b16_config(): + """Returns the Resnet50 + ViT-B/16 configuration.""" + config = get_b16_config() + config.patches.grid = (16, 16) + config.resnet = ml_collections.ConfigDict() + config.resnet.num_layers = (3, 4, 9) + config.resnet.width_factor = 1 + + config.classifier = 'seg' + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.skip_channels = [512, 256, 64, 16] + config.n_classes = 2 + config.n_skip = 3 + config.activation = 'softmax' + + return config + + +def get_b32_config(): + """Returns the ViT-B/32 configuration.""" + config = get_b16_config() + config.patches.size = (32, 32) + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_32.npz' + return config + + +def get_l16_config(): + """Returns the ViT-L/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 1024 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 4096 + config.transformer.num_heads = 16 + config.transformer.num_layers = 24 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.representation_size = None + + # custom + config.classifier = 'seg' + config.resnet_pretrained_path = None + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-L_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_r50_l16_config(): + """Returns the Resnet50 + ViT-L/16 configuration. customized """ + config = get_l16_config() + config.patches.grid = (16, 16) + config.resnet = ml_collections.ConfigDict() + config.resnet.num_layers = (3, 4, 9) + config.resnet.width_factor = 1 + + config.classifier = 'seg' + config.resnet_pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.skip_channels = [512, 256, 64, 16] + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_l32_config(): + """Returns the ViT-L/32 configuration.""" + config = get_l16_config() + config.patches.size = (32, 32) + return config + + +def get_h14_config(): + """Returns the ViT-L/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (14, 14)}) + config.hidden_size = 1280 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 5120 + config.transformer.num_heads = 16 + config.transformer.num_layers = 32 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.classifier = 'token' + config.representation_size = None + + return config diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling.py new file mode 100644 index 0000000..8346d9f --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling.py @@ -0,0 +1,453 @@ +# coding=utf-8 +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import logging +import math + +from os.path import join as pjoin + +import torch +import torch.nn as nn +import numpy as np + +from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm +from torch.nn.modules.utils import _pair +from scipy import ndimage +from . import vit_seg_configs as configs +from .vit_seg_modeling_resnet_skip import ResNetV2 + + +logger = logging.getLogger(__name__) + + +ATTENTION_Q = "MultiHeadDotProductAttention_1/query" +ATTENTION_K = "MultiHeadDotProductAttention_1/key" +ATTENTION_V = "MultiHeadDotProductAttention_1/value" +ATTENTION_OUT = "MultiHeadDotProductAttention_1/out" +FC_0 = "MlpBlock_3/Dense_0" +FC_1 = "MlpBlock_3/Dense_1" +ATTENTION_NORM = "LayerNorm_0" +MLP_NORM = "LayerNorm_2" + + +def np2th(weights, conv=False): + """Possibly convert HWIO to OIHW.""" + if conv: + weights = weights.transpose([3, 2, 0, 1]) + return torch.from_numpy(weights) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish} + + +class Attention(nn.Module): + def __init__(self, config, vis): + super(Attention, self).__init__() + self.vis = vis + self.num_attention_heads = config.transformer["num_heads"] + self.attention_head_size = int(config.hidden_size / self.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = Linear(config.hidden_size, self.all_head_size) + self.key = Linear(config.hidden_size, self.all_head_size) + self.value = Linear(config.hidden_size, self.all_head_size) + + self.out = Linear(config.hidden_size, config.hidden_size) + self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"]) + self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"]) + + self.softmax = Softmax(dim=-1) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states): + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + attention_probs = self.softmax(attention_scores) + weights = attention_probs if self.vis else None + attention_probs = self.attn_dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + attention_output = self.out(context_layer) + attention_output = self.proj_dropout(attention_output) + return attention_output, weights + + +class Mlp(nn.Module): + def __init__(self, config): + super(Mlp, self).__init__() + self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"]) + self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size) + self.act_fn = ACT2FN["gelu"] + self.dropout = Dropout(config.transformer["dropout_rate"]) + + self._init_weights() + + def _init_weights(self): + nn.init.xavier_uniform_(self.fc1.weight) + nn.init.xavier_uniform_(self.fc2.weight) + nn.init.normal_(self.fc1.bias, std=1e-6) + nn.init.normal_(self.fc2.bias, std=1e-6) + + def forward(self, x): + x = self.fc1(x) + x = self.act_fn(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + + +class Embeddings(nn.Module): + """Construct the embeddings from patch, position embeddings. + """ + def __init__(self, config, img_size, in_channels=3): + super(Embeddings, self).__init__() + self.hybrid = None + self.config = config + img_size = _pair(img_size) + + if config.patches.get("grid") is not None: # ResNet + grid_size = config.patches["grid"] + patch_size = (img_size[0] // 16 // grid_size[0], img_size[1] // 16 // grid_size[1]) + patch_size_real = (patch_size[0] * 16, patch_size[1] * 16) + n_patches = (img_size[0] // patch_size_real[0]) * (img_size[1] // patch_size_real[1]) + self.hybrid = True + else: + patch_size = _pair(config.patches["size"]) + n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1]) + self.hybrid = False + + if self.hybrid: + self.hybrid_model = ResNetV2(block_units=config.resnet.num_layers, width_factor=config.resnet.width_factor) + in_channels = self.hybrid_model.width * 16 + self.patch_embeddings = Conv2d(in_channels=in_channels, + out_channels=config.hidden_size, + kernel_size=patch_size, + stride=patch_size) + self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, config.hidden_size)) + + self.dropout = Dropout(config.transformer["dropout_rate"]) + + + def forward(self, x): + if self.hybrid: + x, features = self.hybrid_model(x) + else: + features = None + x = self.patch_embeddings(x) # (B, hidden. n_patches^(1/2), n_patches^(1/2)) + x = x.flatten(2) + x = x.transpose(-1, -2) # (B, n_patches, hidden) + + embeddings = x + self.position_embeddings + embeddings = self.dropout(embeddings) + return embeddings, features + + +class Block(nn.Module): + def __init__(self, config, vis): + super(Block, self).__init__() + self.hidden_size = config.hidden_size + self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6) + self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6) + self.ffn = Mlp(config) + self.attn = Attention(config, vis) + + def forward(self, x): + h = x + x = self.attention_norm(x) + x, weights = self.attn(x) + x = x + h + + h = x + x = self.ffn_norm(x) + x = self.ffn(x) + x = x + h + return x, weights + + def load_from(self, weights, n_block): + ROOT = f"Transformer/encoderblock_{n_block}" + with torch.no_grad(): + query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t() + key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t() + value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t() + out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t() + + query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1) + key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1) + value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1) + out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1) + + self.attn.query.weight.copy_(query_weight) + self.attn.key.weight.copy_(key_weight) + self.attn.value.weight.copy_(value_weight) + self.attn.out.weight.copy_(out_weight) + self.attn.query.bias.copy_(query_bias) + self.attn.key.bias.copy_(key_bias) + self.attn.value.bias.copy_(value_bias) + self.attn.out.bias.copy_(out_bias) + + mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t() + mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t() + mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t() + mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t() + + self.ffn.fc1.weight.copy_(mlp_weight_0) + self.ffn.fc2.weight.copy_(mlp_weight_1) + self.ffn.fc1.bias.copy_(mlp_bias_0) + self.ffn.fc2.bias.copy_(mlp_bias_1) + + self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")])) + self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")])) + self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")])) + self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")])) + + +class Encoder(nn.Module): + def __init__(self, config, vis): + super(Encoder, self).__init__() + self.vis = vis + self.layer = nn.ModuleList() + self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6) + for _ in range(config.transformer["num_layers"]): + layer = Block(config, vis) + self.layer.append(copy.deepcopy(layer)) + + def forward(self, hidden_states): + attn_weights = [] + for layer_block in self.layer: + hidden_states, weights = layer_block(hidden_states) + if self.vis: + attn_weights.append(weights) + encoded = self.encoder_norm(hidden_states) + return encoded, attn_weights + + +class Transformer(nn.Module): + def __init__(self, config, img_size, vis): + super(Transformer, self).__init__() + self.embeddings = Embeddings(config, img_size=img_size) + self.encoder = Encoder(config, vis) + + def forward(self, input_ids): + embedding_output, features = self.embeddings(input_ids) + encoded, attn_weights = self.encoder(embedding_output) # (B, n_patch, hidden) + return encoded, attn_weights, features + + +class Conv2dReLU(nn.Sequential): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + padding=0, + stride=1, + use_batchnorm=True, + ): + conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + bias=not (use_batchnorm), + ) + relu = nn.ReLU(inplace=True) + + bn = nn.BatchNorm2d(out_channels) + + super(Conv2dReLU, self).__init__(conv, bn, relu) + + +class DecoderBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + skip_channels=0, + use_batchnorm=True, + ): + super().__init__() + self.conv1 = Conv2dReLU( + in_channels + skip_channels, + out_channels, + kernel_size=3, + padding=1, + use_batchnorm=use_batchnorm, + ) + self.conv2 = Conv2dReLU( + out_channels, + out_channels, + kernel_size=3, + padding=1, + use_batchnorm=use_batchnorm, + ) + self.up = nn.UpsamplingBilinear2d(scale_factor=2) + + def forward(self, x, skip=None): + x = self.up(x) + if skip is not None: + x = torch.cat([x, skip], dim=1) + x = self.conv1(x) + x = self.conv2(x) + return x + + +class SegmentationHead(nn.Sequential): + + def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1): + conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2) + upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity() + super().__init__(conv2d, upsampling) + + +class DecoderCup(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + head_channels = 512 + self.conv_more = Conv2dReLU( + config.hidden_size, + head_channels, + kernel_size=3, + padding=1, + use_batchnorm=True, + ) + decoder_channels = config.decoder_channels + in_channels = [head_channels] + list(decoder_channels[:-1]) + out_channels = decoder_channels + + if self.config.n_skip != 0: + skip_channels = self.config.skip_channels + for i in range(4-self.config.n_skip): # re-select the skip channels according to n_skip + skip_channels[3-i]=0 + + else: + skip_channels=[0,0,0,0] + + blocks = [ + DecoderBlock(in_ch, out_ch, sk_ch) for in_ch, out_ch, sk_ch in zip(in_channels, out_channels, skip_channels) + ] + self.blocks = nn.ModuleList(blocks) + + def forward(self, hidden_states, features=None): + B, n_patch, hidden = hidden_states.size() # reshape from (B, n_patch, hidden) to (B, h, w, hidden) + h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch)) + x = hidden_states.permute(0, 2, 1) + x = x.contiguous().view(B, hidden, h, w) + x = self.conv_more(x) + for i, decoder_block in enumerate(self.blocks): + if features is not None: + skip = features[i] if (i < self.config.n_skip) else None + else: + skip = None + x = decoder_block(x, skip=skip) + return x + + +class VisionTransformer(nn.Module): + def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False): + super(VisionTransformer, self).__init__() + self.num_classes = num_classes + self.zero_head = zero_head + self.classifier = config.classifier + self.transformer = Transformer(config, img_size, vis) + self.decoder = DecoderCup(config) + self.segmentation_head = SegmentationHead( + in_channels=config['decoder_channels'][-1], + out_channels=config['n_classes'], + kernel_size=3, + ) + self.config = config + + def forward(self, x): + if x.size()[1] == 1: + x = x.repeat(1,3,1,1) + x, attn_weights, features = self.transformer(x) # (B, n_patch, hidden) + x = self.decoder(x, features) + logits = self.segmentation_head(x) + return logits + + def load_from(self, weights): + with torch.no_grad(): + + res_weight = weights + self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True)) + self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"])) + + self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"])) + self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"])) + + posemb = np2th(weights["Transformer/posembed_input/pos_embedding"]) + + posemb_new = self.transformer.embeddings.position_embeddings + if posemb.size() == posemb_new.size(): + self.transformer.embeddings.position_embeddings.copy_(posemb) + elif posemb.size()[1]-1 == posemb_new.size()[1]: + posemb = posemb[:, 1:] + self.transformer.embeddings.position_embeddings.copy_(posemb) + else: + logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size())) + ntok_new = posemb_new.size(1) + if self.classifier == "seg": + _, posemb_grid = posemb[:, :1], posemb[0, 1:] + gs_old = int(np.sqrt(len(posemb_grid))) + gs_new = int(np.sqrt(ntok_new)) + print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new)) + posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1) + zoom = (gs_new / gs_old, gs_new / gs_old, 1) + posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1) # th2np + posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1) + posemb = posemb_grid + self.transformer.embeddings.position_embeddings.copy_(np2th(posemb)) + + # Encoder whole + for bname, block in self.transformer.encoder.named_children(): + for uname, unit in block.named_children(): + unit.load_from(weights, n_block=uname) + + if self.transformer.embeddings.hybrid: + self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(res_weight["conv_root/kernel"], conv=True)) + gn_weight = np2th(res_weight["gn_root/scale"]).view(-1) + gn_bias = np2th(res_weight["gn_root/bias"]).view(-1) + self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight) + self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias) + + for bname, block in self.transformer.embeddings.hybrid_model.body.named_children(): + for uname, unit in block.named_children(): + unit.load_from(res_weight, n_block=bname, n_unit=uname) + +CONFIGS = { + 'ViT-B_16': configs.get_b16_config(), + 'ViT-B_32': configs.get_b32_config(), + 'ViT-L_16': configs.get_l16_config(), + 'ViT-L_32': configs.get_l32_config(), + 'ViT-H_14': configs.get_h14_config(), + 'R50-ViT-B_16': configs.get_r50_b16_config(), + 'R50-ViT-L_16': configs.get_r50_l16_config(), + 'testing': configs.get_testing(), +} + + diff --git a/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling_resnet_skip.py b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling_resnet_skip.py new file mode 100644 index 0000000..9753d52 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_modeling_resnet_skip.py @@ -0,0 +1,160 @@ +import math + +from os.path import join as pjoin +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def np2th(weights, conv=False): + """Possibly convert HWIO to OIHW.""" + if conv: + weights = weights.transpose([3, 2, 0, 1]) + return torch.from_numpy(weights) + + +class StdConv2d(nn.Conv2d): + + def forward(self, x): + w = self.weight + v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False) + w = (w - m) / torch.sqrt(v + 1e-5) + return F.conv2d(x, w, self.bias, self.stride, self.padding, + self.dilation, self.groups) + + +def conv3x3(cin, cout, stride=1, groups=1, bias=False): + return StdConv2d(cin, cout, kernel_size=3, stride=stride, + padding=1, bias=bias, groups=groups) + + +def conv1x1(cin, cout, stride=1, bias=False): + return StdConv2d(cin, cout, kernel_size=1, stride=stride, + padding=0, bias=bias) + + +class PreActBottleneck(nn.Module): + """Pre-activation (v2) bottleneck block. + """ + + def __init__(self, cin, cout=None, cmid=None, stride=1): + super().__init__() + cout = cout or cin + cmid = cmid or cout//4 + + self.gn1 = nn.GroupNorm(32, cmid, eps=1e-6) + self.conv1 = conv1x1(cin, cmid, bias=False) + self.gn2 = nn.GroupNorm(32, cmid, eps=1e-6) + self.conv2 = conv3x3(cmid, cmid, stride, bias=False) # Original code has it on conv1!! + self.gn3 = nn.GroupNorm(32, cout, eps=1e-6) + self.conv3 = conv1x1(cmid, cout, bias=False) + self.relu = nn.ReLU(inplace=True) + + if (stride != 1 or cin != cout): + # Projection also with pre-activation according to paper. + self.downsample = conv1x1(cin, cout, stride, bias=False) + self.gn_proj = nn.GroupNorm(cout, cout) + + def forward(self, x): + + # Residual branch + residual = x + if hasattr(self, 'downsample'): + residual = self.downsample(x) + residual = self.gn_proj(residual) + + # Unit's branch + y = self.relu(self.gn1(self.conv1(x))) + y = self.relu(self.gn2(self.conv2(y))) + y = self.gn3(self.conv3(y)) + + y = self.relu(residual + y) + return y + + def load_from(self, weights, n_block, n_unit): + conv1_weight = np2th(weights[pjoin(n_block, n_unit, "conv1/kernel")], conv=True) + conv2_weight = np2th(weights[pjoin(n_block, n_unit, "conv2/kernel")], conv=True) + conv3_weight = np2th(weights[pjoin(n_block, n_unit, "conv3/kernel")], conv=True) + + gn1_weight = np2th(weights[pjoin(n_block, n_unit, "gn1/scale")]) + gn1_bias = np2th(weights[pjoin(n_block, n_unit, "gn1/bias")]) + + gn2_weight = np2th(weights[pjoin(n_block, n_unit, "gn2/scale")]) + gn2_bias = np2th(weights[pjoin(n_block, n_unit, "gn2/bias")]) + + gn3_weight = np2th(weights[pjoin(n_block, n_unit, "gn3/scale")]) + gn3_bias = np2th(weights[pjoin(n_block, n_unit, "gn3/bias")]) + + self.conv1.weight.copy_(conv1_weight) + self.conv2.weight.copy_(conv2_weight) + self.conv3.weight.copy_(conv3_weight) + + self.gn1.weight.copy_(gn1_weight.view(-1)) + self.gn1.bias.copy_(gn1_bias.view(-1)) + + self.gn2.weight.copy_(gn2_weight.view(-1)) + self.gn2.bias.copy_(gn2_bias.view(-1)) + + self.gn3.weight.copy_(gn3_weight.view(-1)) + self.gn3.bias.copy_(gn3_bias.view(-1)) + + if hasattr(self, 'downsample'): + proj_conv_weight = np2th(weights[pjoin(n_block, n_unit, "conv_proj/kernel")], conv=True) + proj_gn_weight = np2th(weights[pjoin(n_block, n_unit, "gn_proj/scale")]) + proj_gn_bias = np2th(weights[pjoin(n_block, n_unit, "gn_proj/bias")]) + + self.downsample.weight.copy_(proj_conv_weight) + self.gn_proj.weight.copy_(proj_gn_weight.view(-1)) + self.gn_proj.bias.copy_(proj_gn_bias.view(-1)) + +class ResNetV2(nn.Module): + """Implementation of Pre-activation (v2) ResNet mode.""" + + def __init__(self, block_units, width_factor): + super().__init__() + width = int(64 * width_factor) + self.width = width + + self.root = nn.Sequential(OrderedDict([ + ('conv', StdConv2d(3, width, kernel_size=7, stride=2, bias=False, padding=3)), + ('gn', nn.GroupNorm(32, width, eps=1e-6)), + ('relu', nn.ReLU(inplace=True)), + # ('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0)) + ])) + + self.body = nn.Sequential(OrderedDict([ + ('block1', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width, cout=width*4, cmid=width))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*4, cout=width*4, cmid=width)) for i in range(2, block_units[0] + 1)], + ))), + ('block2', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width*4, cout=width*8, cmid=width*2, stride=2))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*8, cout=width*8, cmid=width*2)) for i in range(2, block_units[1] + 1)], + ))), + ('block3', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width*8, cout=width*16, cmid=width*4, stride=2))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*16, cout=width*16, cmid=width*4)) for i in range(2, block_units[2] + 1)], + ))), + ])) + + def forward(self, x): + features = [] + b, c, in_size, _ = x.size() + x = self.root(x) + features.append(x) + x = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)(x) + for i in range(len(self.body)-1): + x = self.body[i](x) + right_size = int(in_size / 4 / (i+1)) + if x.size()[2] != right_size: + pad = right_size - x.size()[2] + assert pad < 3 and pad > 0, "x {} should {}".format(x.size(), right_size) + feat = torch.zeros((b, x.size()[1], right_size, right_size), device=x.device) + feat[:, :, 0:x.size()[2], 0:x.size()[3]] = x[:] + else: + feat = x + features.append(feat) + x = self.body[-1](x) + return x, features[::-1] From 42b7e2e0690a06ceb102387afd5e5aff0998b3c9 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:36:45 +0800 Subject: [PATCH 10/12] Add files via upload --- .../networks/vit_seg_configs.py | 130 +++++ .../networks/vit_seg_modeling.py | 453 ++++++++++++++++++ .../networks/vit_seg_modeling_resnet_skip.py | 160 +++++++ 3 files changed, 743 insertions(+) create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_configs.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling.py create mode 100644 Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling_resnet_skip.py diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_configs.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_configs.py new file mode 100644 index 0000000..1bc4c78 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_configs.py @@ -0,0 +1,130 @@ +import ml_collections + +def get_b16_config(): + """Returns the ViT-B/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 768 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 3072 + config.transformer.num_heads = 12 + config.transformer.num_layers = 12 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + + config.classifier = 'seg' + config.representation_size = None + config.resnet_pretrained_path = None + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_16.npz' + config.patch_size = 16 + + config.decoder_channels = (256, 128, 64, 16) + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_testing(): + """Returns a minimal configuration for testing.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 1 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 1 + config.transformer.num_heads = 1 + config.transformer.num_layers = 1 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.classifier = 'token' + config.representation_size = None + return config + +def get_r50_b16_config(): + """Returns the Resnet50 + ViT-B/16 configuration.""" + config = get_b16_config() + config.patches.grid = (16, 16) + config.resnet = ml_collections.ConfigDict() + config.resnet.num_layers = (3, 4, 9) + config.resnet.width_factor = 1 + + config.classifier = 'seg' + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.skip_channels = [512, 256, 64, 16] + config.n_classes = 2 + config.n_skip = 3 + config.activation = 'softmax' + + return config + + +def get_b32_config(): + """Returns the ViT-B/32 configuration.""" + config = get_b16_config() + config.patches.size = (32, 32) + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_32.npz' + return config + + +def get_l16_config(): + """Returns the ViT-L/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (16, 16)}) + config.hidden_size = 1024 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 4096 + config.transformer.num_heads = 16 + config.transformer.num_layers = 24 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.representation_size = None + + # custom + config.classifier = 'seg' + config.resnet_pretrained_path = None + config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-L_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_r50_l16_config(): + """Returns the Resnet50 + ViT-L/16 configuration. customized """ + config = get_l16_config() + config.patches.grid = (16, 16) + config.resnet = ml_collections.ConfigDict() + config.resnet.num_layers = (3, 4, 9) + config.resnet.width_factor = 1 + + config.classifier = 'seg' + config.resnet_pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz' + config.decoder_channels = (256, 128, 64, 16) + config.skip_channels = [512, 256, 64, 16] + config.n_classes = 2 + config.activation = 'softmax' + return config + + +def get_l32_config(): + """Returns the ViT-L/32 configuration.""" + config = get_l16_config() + config.patches.size = (32, 32) + return config + + +def get_h14_config(): + """Returns the ViT-L/16 configuration.""" + config = ml_collections.ConfigDict() + config.patches = ml_collections.ConfigDict({'size': (14, 14)}) + config.hidden_size = 1280 + config.transformer = ml_collections.ConfigDict() + config.transformer.mlp_dim = 5120 + config.transformer.num_heads = 16 + config.transformer.num_layers = 32 + config.transformer.attention_dropout_rate = 0.0 + config.transformer.dropout_rate = 0.1 + config.classifier = 'token' + config.representation_size = None + + return config diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling.py new file mode 100644 index 0000000..8346d9f --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling.py @@ -0,0 +1,453 @@ +# coding=utf-8 +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import logging +import math + +from os.path import join as pjoin + +import torch +import torch.nn as nn +import numpy as np + +from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm +from torch.nn.modules.utils import _pair +from scipy import ndimage +from . import vit_seg_configs as configs +from .vit_seg_modeling_resnet_skip import ResNetV2 + + +logger = logging.getLogger(__name__) + + +ATTENTION_Q = "MultiHeadDotProductAttention_1/query" +ATTENTION_K = "MultiHeadDotProductAttention_1/key" +ATTENTION_V = "MultiHeadDotProductAttention_1/value" +ATTENTION_OUT = "MultiHeadDotProductAttention_1/out" +FC_0 = "MlpBlock_3/Dense_0" +FC_1 = "MlpBlock_3/Dense_1" +ATTENTION_NORM = "LayerNorm_0" +MLP_NORM = "LayerNorm_2" + + +def np2th(weights, conv=False): + """Possibly convert HWIO to OIHW.""" + if conv: + weights = weights.transpose([3, 2, 0, 1]) + return torch.from_numpy(weights) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish} + + +class Attention(nn.Module): + def __init__(self, config, vis): + super(Attention, self).__init__() + self.vis = vis + self.num_attention_heads = config.transformer["num_heads"] + self.attention_head_size = int(config.hidden_size / self.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = Linear(config.hidden_size, self.all_head_size) + self.key = Linear(config.hidden_size, self.all_head_size) + self.value = Linear(config.hidden_size, self.all_head_size) + + self.out = Linear(config.hidden_size, config.hidden_size) + self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"]) + self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"]) + + self.softmax = Softmax(dim=-1) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states): + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + attention_probs = self.softmax(attention_scores) + weights = attention_probs if self.vis else None + attention_probs = self.attn_dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + attention_output = self.out(context_layer) + attention_output = self.proj_dropout(attention_output) + return attention_output, weights + + +class Mlp(nn.Module): + def __init__(self, config): + super(Mlp, self).__init__() + self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"]) + self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size) + self.act_fn = ACT2FN["gelu"] + self.dropout = Dropout(config.transformer["dropout_rate"]) + + self._init_weights() + + def _init_weights(self): + nn.init.xavier_uniform_(self.fc1.weight) + nn.init.xavier_uniform_(self.fc2.weight) + nn.init.normal_(self.fc1.bias, std=1e-6) + nn.init.normal_(self.fc2.bias, std=1e-6) + + def forward(self, x): + x = self.fc1(x) + x = self.act_fn(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + + +class Embeddings(nn.Module): + """Construct the embeddings from patch, position embeddings. + """ + def __init__(self, config, img_size, in_channels=3): + super(Embeddings, self).__init__() + self.hybrid = None + self.config = config + img_size = _pair(img_size) + + if config.patches.get("grid") is not None: # ResNet + grid_size = config.patches["grid"] + patch_size = (img_size[0] // 16 // grid_size[0], img_size[1] // 16 // grid_size[1]) + patch_size_real = (patch_size[0] * 16, patch_size[1] * 16) + n_patches = (img_size[0] // patch_size_real[0]) * (img_size[1] // patch_size_real[1]) + self.hybrid = True + else: + patch_size = _pair(config.patches["size"]) + n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1]) + self.hybrid = False + + if self.hybrid: + self.hybrid_model = ResNetV2(block_units=config.resnet.num_layers, width_factor=config.resnet.width_factor) + in_channels = self.hybrid_model.width * 16 + self.patch_embeddings = Conv2d(in_channels=in_channels, + out_channels=config.hidden_size, + kernel_size=patch_size, + stride=patch_size) + self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, config.hidden_size)) + + self.dropout = Dropout(config.transformer["dropout_rate"]) + + + def forward(self, x): + if self.hybrid: + x, features = self.hybrid_model(x) + else: + features = None + x = self.patch_embeddings(x) # (B, hidden. n_patches^(1/2), n_patches^(1/2)) + x = x.flatten(2) + x = x.transpose(-1, -2) # (B, n_patches, hidden) + + embeddings = x + self.position_embeddings + embeddings = self.dropout(embeddings) + return embeddings, features + + +class Block(nn.Module): + def __init__(self, config, vis): + super(Block, self).__init__() + self.hidden_size = config.hidden_size + self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6) + self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6) + self.ffn = Mlp(config) + self.attn = Attention(config, vis) + + def forward(self, x): + h = x + x = self.attention_norm(x) + x, weights = self.attn(x) + x = x + h + + h = x + x = self.ffn_norm(x) + x = self.ffn(x) + x = x + h + return x, weights + + def load_from(self, weights, n_block): + ROOT = f"Transformer/encoderblock_{n_block}" + with torch.no_grad(): + query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t() + key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t() + value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t() + out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t() + + query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1) + key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1) + value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1) + out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1) + + self.attn.query.weight.copy_(query_weight) + self.attn.key.weight.copy_(key_weight) + self.attn.value.weight.copy_(value_weight) + self.attn.out.weight.copy_(out_weight) + self.attn.query.bias.copy_(query_bias) + self.attn.key.bias.copy_(key_bias) + self.attn.value.bias.copy_(value_bias) + self.attn.out.bias.copy_(out_bias) + + mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t() + mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t() + mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t() + mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t() + + self.ffn.fc1.weight.copy_(mlp_weight_0) + self.ffn.fc2.weight.copy_(mlp_weight_1) + self.ffn.fc1.bias.copy_(mlp_bias_0) + self.ffn.fc2.bias.copy_(mlp_bias_1) + + self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")])) + self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")])) + self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")])) + self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")])) + + +class Encoder(nn.Module): + def __init__(self, config, vis): + super(Encoder, self).__init__() + self.vis = vis + self.layer = nn.ModuleList() + self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6) + for _ in range(config.transformer["num_layers"]): + layer = Block(config, vis) + self.layer.append(copy.deepcopy(layer)) + + def forward(self, hidden_states): + attn_weights = [] + for layer_block in self.layer: + hidden_states, weights = layer_block(hidden_states) + if self.vis: + attn_weights.append(weights) + encoded = self.encoder_norm(hidden_states) + return encoded, attn_weights + + +class Transformer(nn.Module): + def __init__(self, config, img_size, vis): + super(Transformer, self).__init__() + self.embeddings = Embeddings(config, img_size=img_size) + self.encoder = Encoder(config, vis) + + def forward(self, input_ids): + embedding_output, features = self.embeddings(input_ids) + encoded, attn_weights = self.encoder(embedding_output) # (B, n_patch, hidden) + return encoded, attn_weights, features + + +class Conv2dReLU(nn.Sequential): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + padding=0, + stride=1, + use_batchnorm=True, + ): + conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + bias=not (use_batchnorm), + ) + relu = nn.ReLU(inplace=True) + + bn = nn.BatchNorm2d(out_channels) + + super(Conv2dReLU, self).__init__(conv, bn, relu) + + +class DecoderBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + skip_channels=0, + use_batchnorm=True, + ): + super().__init__() + self.conv1 = Conv2dReLU( + in_channels + skip_channels, + out_channels, + kernel_size=3, + padding=1, + use_batchnorm=use_batchnorm, + ) + self.conv2 = Conv2dReLU( + out_channels, + out_channels, + kernel_size=3, + padding=1, + use_batchnorm=use_batchnorm, + ) + self.up = nn.UpsamplingBilinear2d(scale_factor=2) + + def forward(self, x, skip=None): + x = self.up(x) + if skip is not None: + x = torch.cat([x, skip], dim=1) + x = self.conv1(x) + x = self.conv2(x) + return x + + +class SegmentationHead(nn.Sequential): + + def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1): + conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2) + upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity() + super().__init__(conv2d, upsampling) + + +class DecoderCup(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + head_channels = 512 + self.conv_more = Conv2dReLU( + config.hidden_size, + head_channels, + kernel_size=3, + padding=1, + use_batchnorm=True, + ) + decoder_channels = config.decoder_channels + in_channels = [head_channels] + list(decoder_channels[:-1]) + out_channels = decoder_channels + + if self.config.n_skip != 0: + skip_channels = self.config.skip_channels + for i in range(4-self.config.n_skip): # re-select the skip channels according to n_skip + skip_channels[3-i]=0 + + else: + skip_channels=[0,0,0,0] + + blocks = [ + DecoderBlock(in_ch, out_ch, sk_ch) for in_ch, out_ch, sk_ch in zip(in_channels, out_channels, skip_channels) + ] + self.blocks = nn.ModuleList(blocks) + + def forward(self, hidden_states, features=None): + B, n_patch, hidden = hidden_states.size() # reshape from (B, n_patch, hidden) to (B, h, w, hidden) + h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch)) + x = hidden_states.permute(0, 2, 1) + x = x.contiguous().view(B, hidden, h, w) + x = self.conv_more(x) + for i, decoder_block in enumerate(self.blocks): + if features is not None: + skip = features[i] if (i < self.config.n_skip) else None + else: + skip = None + x = decoder_block(x, skip=skip) + return x + + +class VisionTransformer(nn.Module): + def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False): + super(VisionTransformer, self).__init__() + self.num_classes = num_classes + self.zero_head = zero_head + self.classifier = config.classifier + self.transformer = Transformer(config, img_size, vis) + self.decoder = DecoderCup(config) + self.segmentation_head = SegmentationHead( + in_channels=config['decoder_channels'][-1], + out_channels=config['n_classes'], + kernel_size=3, + ) + self.config = config + + def forward(self, x): + if x.size()[1] == 1: + x = x.repeat(1,3,1,1) + x, attn_weights, features = self.transformer(x) # (B, n_patch, hidden) + x = self.decoder(x, features) + logits = self.segmentation_head(x) + return logits + + def load_from(self, weights): + with torch.no_grad(): + + res_weight = weights + self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True)) + self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"])) + + self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"])) + self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"])) + + posemb = np2th(weights["Transformer/posembed_input/pos_embedding"]) + + posemb_new = self.transformer.embeddings.position_embeddings + if posemb.size() == posemb_new.size(): + self.transformer.embeddings.position_embeddings.copy_(posemb) + elif posemb.size()[1]-1 == posemb_new.size()[1]: + posemb = posemb[:, 1:] + self.transformer.embeddings.position_embeddings.copy_(posemb) + else: + logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size())) + ntok_new = posemb_new.size(1) + if self.classifier == "seg": + _, posemb_grid = posemb[:, :1], posemb[0, 1:] + gs_old = int(np.sqrt(len(posemb_grid))) + gs_new = int(np.sqrt(ntok_new)) + print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new)) + posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1) + zoom = (gs_new / gs_old, gs_new / gs_old, 1) + posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1) # th2np + posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1) + posemb = posemb_grid + self.transformer.embeddings.position_embeddings.copy_(np2th(posemb)) + + # Encoder whole + for bname, block in self.transformer.encoder.named_children(): + for uname, unit in block.named_children(): + unit.load_from(weights, n_block=uname) + + if self.transformer.embeddings.hybrid: + self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(res_weight["conv_root/kernel"], conv=True)) + gn_weight = np2th(res_weight["gn_root/scale"]).view(-1) + gn_bias = np2th(res_weight["gn_root/bias"]).view(-1) + self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight) + self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias) + + for bname, block in self.transformer.embeddings.hybrid_model.body.named_children(): + for uname, unit in block.named_children(): + unit.load_from(res_weight, n_block=bname, n_unit=uname) + +CONFIGS = { + 'ViT-B_16': configs.get_b16_config(), + 'ViT-B_32': configs.get_b32_config(), + 'ViT-L_16': configs.get_l16_config(), + 'ViT-L_32': configs.get_l32_config(), + 'ViT-H_14': configs.get_h14_config(), + 'R50-ViT-B_16': configs.get_r50_b16_config(), + 'R50-ViT-L_16': configs.get_r50_l16_config(), + 'testing': configs.get_testing(), +} + + diff --git a/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling_resnet_skip.py b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling_resnet_skip.py new file mode 100644 index 0000000..9753d52 --- /dev/null +++ b/Notes/GarvSachdev_Notes_and_Files/CAMUS_Dataset_test/networks/vit_seg_modeling_resnet_skip.py @@ -0,0 +1,160 @@ +import math + +from os.path import join as pjoin +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def np2th(weights, conv=False): + """Possibly convert HWIO to OIHW.""" + if conv: + weights = weights.transpose([3, 2, 0, 1]) + return torch.from_numpy(weights) + + +class StdConv2d(nn.Conv2d): + + def forward(self, x): + w = self.weight + v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False) + w = (w - m) / torch.sqrt(v + 1e-5) + return F.conv2d(x, w, self.bias, self.stride, self.padding, + self.dilation, self.groups) + + +def conv3x3(cin, cout, stride=1, groups=1, bias=False): + return StdConv2d(cin, cout, kernel_size=3, stride=stride, + padding=1, bias=bias, groups=groups) + + +def conv1x1(cin, cout, stride=1, bias=False): + return StdConv2d(cin, cout, kernel_size=1, stride=stride, + padding=0, bias=bias) + + +class PreActBottleneck(nn.Module): + """Pre-activation (v2) bottleneck block. + """ + + def __init__(self, cin, cout=None, cmid=None, stride=1): + super().__init__() + cout = cout or cin + cmid = cmid or cout//4 + + self.gn1 = nn.GroupNorm(32, cmid, eps=1e-6) + self.conv1 = conv1x1(cin, cmid, bias=False) + self.gn2 = nn.GroupNorm(32, cmid, eps=1e-6) + self.conv2 = conv3x3(cmid, cmid, stride, bias=False) # Original code has it on conv1!! + self.gn3 = nn.GroupNorm(32, cout, eps=1e-6) + self.conv3 = conv1x1(cmid, cout, bias=False) + self.relu = nn.ReLU(inplace=True) + + if (stride != 1 or cin != cout): + # Projection also with pre-activation according to paper. + self.downsample = conv1x1(cin, cout, stride, bias=False) + self.gn_proj = nn.GroupNorm(cout, cout) + + def forward(self, x): + + # Residual branch + residual = x + if hasattr(self, 'downsample'): + residual = self.downsample(x) + residual = self.gn_proj(residual) + + # Unit's branch + y = self.relu(self.gn1(self.conv1(x))) + y = self.relu(self.gn2(self.conv2(y))) + y = self.gn3(self.conv3(y)) + + y = self.relu(residual + y) + return y + + def load_from(self, weights, n_block, n_unit): + conv1_weight = np2th(weights[pjoin(n_block, n_unit, "conv1/kernel")], conv=True) + conv2_weight = np2th(weights[pjoin(n_block, n_unit, "conv2/kernel")], conv=True) + conv3_weight = np2th(weights[pjoin(n_block, n_unit, "conv3/kernel")], conv=True) + + gn1_weight = np2th(weights[pjoin(n_block, n_unit, "gn1/scale")]) + gn1_bias = np2th(weights[pjoin(n_block, n_unit, "gn1/bias")]) + + gn2_weight = np2th(weights[pjoin(n_block, n_unit, "gn2/scale")]) + gn2_bias = np2th(weights[pjoin(n_block, n_unit, "gn2/bias")]) + + gn3_weight = np2th(weights[pjoin(n_block, n_unit, "gn3/scale")]) + gn3_bias = np2th(weights[pjoin(n_block, n_unit, "gn3/bias")]) + + self.conv1.weight.copy_(conv1_weight) + self.conv2.weight.copy_(conv2_weight) + self.conv3.weight.copy_(conv3_weight) + + self.gn1.weight.copy_(gn1_weight.view(-1)) + self.gn1.bias.copy_(gn1_bias.view(-1)) + + self.gn2.weight.copy_(gn2_weight.view(-1)) + self.gn2.bias.copy_(gn2_bias.view(-1)) + + self.gn3.weight.copy_(gn3_weight.view(-1)) + self.gn3.bias.copy_(gn3_bias.view(-1)) + + if hasattr(self, 'downsample'): + proj_conv_weight = np2th(weights[pjoin(n_block, n_unit, "conv_proj/kernel")], conv=True) + proj_gn_weight = np2th(weights[pjoin(n_block, n_unit, "gn_proj/scale")]) + proj_gn_bias = np2th(weights[pjoin(n_block, n_unit, "gn_proj/bias")]) + + self.downsample.weight.copy_(proj_conv_weight) + self.gn_proj.weight.copy_(proj_gn_weight.view(-1)) + self.gn_proj.bias.copy_(proj_gn_bias.view(-1)) + +class ResNetV2(nn.Module): + """Implementation of Pre-activation (v2) ResNet mode.""" + + def __init__(self, block_units, width_factor): + super().__init__() + width = int(64 * width_factor) + self.width = width + + self.root = nn.Sequential(OrderedDict([ + ('conv', StdConv2d(3, width, kernel_size=7, stride=2, bias=False, padding=3)), + ('gn', nn.GroupNorm(32, width, eps=1e-6)), + ('relu', nn.ReLU(inplace=True)), + # ('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0)) + ])) + + self.body = nn.Sequential(OrderedDict([ + ('block1', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width, cout=width*4, cmid=width))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*4, cout=width*4, cmid=width)) for i in range(2, block_units[0] + 1)], + ))), + ('block2', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width*4, cout=width*8, cmid=width*2, stride=2))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*8, cout=width*8, cmid=width*2)) for i in range(2, block_units[1] + 1)], + ))), + ('block3', nn.Sequential(OrderedDict( + [('unit1', PreActBottleneck(cin=width*8, cout=width*16, cmid=width*4, stride=2))] + + [(f'unit{i:d}', PreActBottleneck(cin=width*16, cout=width*16, cmid=width*4)) for i in range(2, block_units[2] + 1)], + ))), + ])) + + def forward(self, x): + features = [] + b, c, in_size, _ = x.size() + x = self.root(x) + features.append(x) + x = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)(x) + for i in range(len(self.body)-1): + x = self.body[i](x) + right_size = int(in_size / 4 / (i+1)) + if x.size()[2] != right_size: + pad = right_size - x.size()[2] + assert pad < 3 and pad > 0, "x {} should {}".format(x.size(), right_size) + feat = torch.zeros((b, x.size()[1], right_size, right_size), device=x.device) + feat[:, :, 0:x.size()[2], 0:x.size()[3]] = x[:] + else: + feat = x + features.append(feat) + x = self.body[-1](x) + return x, features[::-1] From 4c25767cffdfa336c69d3c0653904903e1bb4895 Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:37:16 +0800 Subject: [PATCH 11/12] Delete Notes/GarvSachdev_Notes_and_Files/ReadME.md --- Notes/GarvSachdev_Notes_and_Files/ReadME.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Notes/GarvSachdev_Notes_and_Files/ReadME.md diff --git a/Notes/GarvSachdev_Notes_and_Files/ReadME.md b/Notes/GarvSachdev_Notes_and_Files/ReadME.md deleted file mode 100644 index 41dbc4a..0000000 --- a/Notes/GarvSachdev_Notes_and_Files/ReadME.md +++ /dev/null @@ -1 +0,0 @@ -# folder creation From 7f3fca2b2de73ed3d1203389c440efbbe83f318d Mon Sep 17 00:00:00 2001 From: garv sachdev <83745829+gavkujo@users.noreply.github.com> Date: Mon, 30 Dec 2024 19:43:44 +0800 Subject: [PATCH 12/12] Add files via upload --- .../Report_Task1.pdf | Bin 0 -> 801114 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Notes/GarvSachdev_Notes_and_Files/Report_Task1.pdf diff --git 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