Inference ========= Here is an example that uses a grid sampler and aggregator to perform dense inference across a 3D image using patches:: >>> import torch >>> import torch.nn as nn >>> import torchio as tio >>> patch_overlap = 4, 4, 4 # or just 4 >>> patch_size = 88, 88, 60 >>> subject = tio.datasets.Colin27() >>> subject Colin27(Keys: ('t1', 'head', 'brain'); images: 3) >>> grid_sampler = tio.inference.GridSampler( ... subject, ... patch_size, ... patch_overlap, ... ) >>> patch_loader = tio.SubjectsLoader(grid_sampler, batch_size=4) >>> aggregator = tio.inference.GridAggregator(grid_sampler) >>> model = nn.Identity().eval() >>> with torch.no_grad(): ... for patches_batch in patch_loader: ... input_tensor = patches_batch['t1'][tio.DATA] ... locations = patches_batch[tio.LOCATION] ... logits = model(input_tensor) ... labels = logits.argmax(dim=tio.CHANNELS_DIMENSION, keepdim=True) ... outputs = labels ... aggregator.add_batch(outputs, locations) >>> output_tensor = aggregator.get_output_tensor() Grid sampler ------------ .. currentmodule:: torchio.data :class:`GridSampler` ~~~~~~~~~~~~~~~~~~~~ .. autoclass:: GridSampler :members: Grid aggregator --------------- :class:`GridAggregator` ~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: GridAggregator :members: