# "self.voxel_dim", self.voxel_dim.shape, "len", len(self.voxel_dim.shape)) # self.gt_file_names = np.array(gt_slice_id) Self.img_file_names = np.array(img_slice_id) Self.voxel_dim = om_numpy(np.vstack(self.voxel_dim)) Self.label = om_numpy(np.expand_dims(np.vstack(labels), axis=1)) Self.data = om_numpy(np.expand_dims(np.vstack(images), axis=1)) Images, labels = self.unify_sizes(images, labels) Img_slice = cc359.img_transform(self, img_slice) Img_slice = np.expand_dims(img_slice, axis=0) # add channel dimension # loop over slices and save each slice with its corresponding file name and slice IDįor slice_id, img_slice in enumerate(nib_file.get_fdata('unchanged', dtype=np.float32)): Nib_file = nib.load(os.path.join(images_path, f)) Images_path = os.path.join(data_path, 'Original', self.folder)įiles = np.array(sorted(os.listdir(images_path))) Images_path = os.path.join(data_path, 'Original', self.folder, "val") Images_path = os.path.join(data_path, 'Original', self.folder, "train") Img_slice = np.moveaxis(img_slice, -1, 0) Img_slice = np.reshape(transformed, img_slice.shape) Transformed = scaler.fit_transform(np.reshape(img_slice, (-1, 1))) Input_labels = self.pad_image_w_size(input_labels, max_size) Input_images = self.pad_image_w_size(input_images, max_size) Sizes = np.zeros(len(input_images), np.int) Return np.pad(data_array, ((0, 0), (b, a), (b, a)), mode='edge')ĭef unify_sizes(self, input_images, input_labels):
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Below is its screenshot:īelow is the code of my data_set class: class cc359(Dataset):ĭef _init_(self, config, train = True, rotate=True, scale=True ): I checked the memory usage with watch nvidia-smi, but there is no other active process. It loads 3 images only and it crashes then. How can I resolve this, I figure out that the process in actually getting killed in the get_item(). Segmented_volume = segmented_volume.squeeze(1) # Remove the batch dimension from the segmented volume # Add the segmented volume for the current image to the list of segmented volumes Segmented_images_i = segmented_img_slices.permute(1, 0, 2, 3).unsqueeze(0) # Combine the segmented slices for the current image into a 3D volume # Add the segmented slice to the list of segmented slices for the current image Segmented_slice = segmented_slice.squeeze(0) # Remove the batch dimension from the segmented slice # Pass the current slice through the model to get the segmentation mask # Add a batch dimension to the current slice # Iterate over each slice in the current imageįor slice_id in range(img_slices.shape): Segmented_img_slices = torch.zeros((img_slices.shape)) # Initialize an empty tensor to store the segmented slices for the current image Segmented_volume = torch.zeros((input_samples.shape))įor img_id in range(input_samples.shape): # Initialize an empty tensor to store the segmented volume Var_gt = gt_samples.cuda(device = "cuda") Input_samples = input_samples.cuda(device= "cuda") Input_samples, gt_samples, voxel_dim = batch See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF for epoch in range(1, num_epochs + 1):įor i, batch in enumerate(train_dice_loader):
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Tried to allocate 20.00 MiB (GPU 0 23.70 GiB total capacity 5.36 GiB already allocated 13.00 MiB free 5.37 GiB reserved in total by PyTorch) If reserved memory is > allocated memory try setting max_split_size_mb to avoid fragmentation. However, given the slice dimension, I am still getting the cuda out of memory error. My batch size is 4, the slice dimension that is being passed to 2D model is (one image at a time). frame() modifier to specify the width and height of the image.I am using the following code to reconstruct 3D volume from the 2D segmented slices- which I am getting from my 2D model. resizable() modifier to allow the image to scale and the. This modifier allows you to specify the frame size of the image and have it automatically scale to fit the size you specify. To resize an image in SwiftUI, you can use the. In this tutorial, we'll walk through the steps for resizing an image in SwiftUI with code and output. Fortunately, SwiftUI provides an easy way to resize images directly within your code.
When working with images in SwiftUI, you may need to resize them to fit specific layout requirements.
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How to Resize Images in SwiftUI in 2023: A Step-by-Step Guide with Code Examples
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