File size: 8,585 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | # Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from monai.data.utils import compute_importance_map, dense_patch_slices, get_valid_patch_size
from monai.utils import BlendMode, PytorchPadMode, fall_back_tuple
def sliding_window_inference(
inputs: torch.Tensor,
roi_size: Union[Sequence[int], int],
sw_batch_size: int,
predictor: Callable[[torch.Tensor], torch.Tensor],
overlap: float = 0.25,
mode: Union[BlendMode, str] = BlendMode.CONSTANT,
padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,
cval: float = 0.0,
device: Optional[torch.device] = None,
) -> torch.Tensor:
"""
Sliding window inference on `inputs` with `predictor`.
When roi_size is larger than the inputs' spatial size, the input image are padded during inference.
To maintain the same spatial sizes, the output image will be cropped to the original input size.
Args:
inputs: input image to be processed (assuming NCHW[D])
roi_size: the spatial window size for inferences.
When its components have None or non-positives, the corresponding inputs dimension will be used.
if the components of the `roi_size` are non-positive values, the transform will use the
corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
to `(32, 64)` if the second spatial dimension size of img is `64`.
sw_batch_size: the batch size to run window slices.
predictor: given input tensor `patch_data` in shape NCHW[D], `predictor(patch_data)`
should return a prediction with the same spatial shape and batch_size, i.e. NMHW[D];
where HW[D] represents the patch spatial size, M is the number of output channels, N is `sw_batch_size`.
overlap: Amount of overlap between scans.
mode: {``"constant"``, ``"gaussian"``}
How to blend output of overlapping windows. Defaults to ``"constant"``.
- ``"constant``": gives equal weight to all predictions.
- ``"gaussian``": gives less weight to predictions on edges of windows.
padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
Padding mode when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
See also: https://pytorch.org/docs/stable/nn.functional.html#pad
cval: fill value for 'constant' padding mode. Default: 0
device: device running the concatenation of the windows.
By default the device and accordingly the memory of the input device is used. If for example
set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
input and roi_size parameter. Output is on the device set or if not set the inputs device.
Raises:
NotImplementedError: When ``inputs`` does not have batch size = 1.
Note:
- input must be channel-first and have a batch dim, support both spatial 2D and 3D.
- currently only supports `inputs` with batch_size=1.
"""
num_spatial_dims = len(inputs.shape) - 2
assert 0 <= overlap < 1, "overlap must be >= 0 and < 1."
# determine image spatial size and batch size
# Note: all input images must have the same image size and batch size
image_size_ = list(inputs.shape[2:])
batch_size = inputs.shape[0]
# TODO: Enable batch sizes > 1 in future
if batch_size > 1:
raise NotImplementedError("Currently only inputs with batch size = 1 are supported.")
if device is None:
device = inputs.device
roi_size = fall_back_tuple(roi_size, image_size_)
# in case that image size is smaller than roi size
image_size = tuple(max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims))
pad_size = []
for k in range(len(inputs.shape) - 1, 1, -1):
diff = max(roi_size[k - 2] - inputs.shape[k], 0)
half = diff // 2
pad_size.extend([half, diff - half])
inputs = F.pad(inputs, pad=pad_size, mode=PytorchPadMode(padding_mode).value, value=cval)
scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
# Store all slices in list
slices = dense_patch_slices(image_size, roi_size, scan_interval)
slice_batches = []
for slice_index in range(0, len(slices), sw_batch_size):
slice_index_range = range(slice_index, min(slice_index + sw_batch_size, len(slices)))
input_slices = []
for curr_index in slice_index_range:
curr_slice = slices[curr_index]
if len(curr_slice) == 3:
input_slices.append(inputs[0, :, curr_slice[0], curr_slice[1], curr_slice[2]])
else:
input_slices.append(inputs[0, :, curr_slice[0], curr_slice[1]])
slice_batches.append(torch.stack(input_slices))
# Perform predictions
output_rois = list()
for data in slice_batches:
seg_prob = predictor(data) # batched patch segmentation
output_rois.append(seg_prob.to(device))
# stitching output image
output_classes = output_rois[0].shape[1]
output_shape = [batch_size, output_classes] + list(image_size)
# Create importance map
importance_map = compute_importance_map(get_valid_patch_size(image_size, roi_size), mode=mode, device=device)
# allocate memory to store the full output and the count for overlapping parts
output_image = torch.zeros(output_shape, dtype=torch.float32, device=device)
count_map = torch.zeros(output_shape, dtype=torch.float32, device=device)
for window_id, slice_index in enumerate(range(0, len(slices), sw_batch_size)):
slice_index_range = range(slice_index, min(slice_index + sw_batch_size, len(slices)))
# store the result in the proper location of the full output. Apply weights from importance map.
for curr_index in slice_index_range:
curr_slice = slices[curr_index]
if len(curr_slice) == 3:
output_image[0, :, curr_slice[0], curr_slice[1], curr_slice[2]] += (
importance_map * output_rois[window_id][curr_index - slice_index, :]
)
count_map[0, :, curr_slice[0], curr_slice[1], curr_slice[2]] += importance_map
else:
output_image[0, :, curr_slice[0], curr_slice[1]] += (
importance_map * output_rois[window_id][curr_index - slice_index, :]
)
count_map[0, :, curr_slice[0], curr_slice[1]] += importance_map
# account for any overlapping sections
output_image = output_image / count_map
if num_spatial_dims == 3:
return output_image[
...,
pad_size[4] : image_size_[0] + pad_size[4],
pad_size[2] : image_size_[1] + pad_size[2],
pad_size[0] : image_size_[2] + pad_size[0],
]
return output_image[
..., pad_size[2] : image_size_[0] + pad_size[2], pad_size[0] : image_size_[1] + pad_size[0]
] # 2D
def _get_scan_interval(
image_size: Sequence[int], roi_size: Sequence[int], num_spatial_dims: int, overlap: float
) -> Tuple[int, ...]:
"""
Compute scan interval according to the image size, roi size and overlap.
Scan interval will be `int((1 - overlap) * roi_size)`, if interval is 0,
use 1 instead to make sure sliding window works.
"""
if len(image_size) != num_spatial_dims:
raise ValueError("image coord different from spatial dims.")
if len(roi_size) != num_spatial_dims:
raise ValueError("roi coord different from spatial dims.")
scan_interval = []
for i in range(num_spatial_dims):
if roi_size[i] == image_size[i]:
scan_interval.append(int(roi_size[i]))
else:
interval = int(roi_size[i] * (1 - overlap))
scan_interval.append(interval if interval > 0 else 1)
return tuple(scan_interval)
|