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import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from torchio import LOCATION, DATA
from torchio.data.inference import GridSampler, GridAggregator
from ...utils import TorchioTestCase
class TestInference(TorchioTestCase):
"""Tests for `inference` module."""
def test_inference_no_padding(self):
self.try_inference(None)
def test_inference_padding(self):
self.try_inference(3)
def try_inference(self, padding_mode):
for n in 17, 27:
patch_size = 10, 15, n
patch_overlap = 4, 6, 8
batch_size = 6
grid_sampler = GridSampler(
self.sample,
patch_size,
patch_overlap,
padding_mode=padding_mode,
)
aggregator = GridAggregator(grid_sampler)
patch_loader = DataLoader(grid_sampler, batch_size=batch_size)
with torch.no_grad():
for patches_batch in tqdm(patch_loader):
input_tensor = patches_batch['t1'][DATA]
locations = patches_batch[LOCATION]
logits = model(input_tensor) # some model
outputs = logits
aggregator.add_batch(outputs, locations)
output = aggregator.get_output_tensor()
assert (output == -5).all()
assert output.shape == self.sample.t1.shape
def model(tensor):
tensor[:] = -5
return tensor
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