Source code for torchvision.ops.poolers
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.fx
import torchvision
from torch import nn, Tensor
from torchvision.ops.boxes import box_area
from ..utils import _log_api_usage_once
from .roi_align import roi_align
# copying result_idx_in_level to a specific index in result[]
# is not supported by ONNX tracing yet.
# _onnx_merge_levels() is an implementation supported by ONNX
# that merges the levels to the right indices
@torch.jit.unused
def _onnx_merge_levels(levels: Tensor, unmerged_results: List[Tensor]) -> Tensor:
first_result = unmerged_results[0]
dtype, device = first_result.dtype, first_result.device
res = torch.zeros(
(levels.size(0), first_result.size(1), first_result.size(2), first_result.size(3)), dtype=dtype, device=device
)
for level in range(len(unmerged_results)):
index = torch.where(levels == level)[0].view(-1, 1, 1, 1)
index = index.expand(
index.size(0),
unmerged_results[level].size(1),
unmerged_results[level].size(2),
unmerged_results[level].size(3),
)
res = res.scatter(0, index, unmerged_results[level])
return res
# TODO: (eellison) T54974082 https://github.com/pytorch/pytorch/issues/26744/pytorch/issues/26744
def initLevelMapper(
k_min: int,
k_max: int,
canonical_scale: int = 224,
canonical_level: int = 4,
eps: float = 1e-6,
):
return LevelMapper(k_min, k_max, canonical_scale, canonical_level, eps)
class LevelMapper:
"""Determine which FPN level each RoI in a set of RoIs should map to based
on the heuristic in the FPN paper.
Args:
k_min (int)
k_max (int)
canonical_scale (int)
canonical_level (int)
eps (float)
"""
def __init__(
self,
k_min: int,
k_max: int,
canonical_scale: int = 224,
canonical_level: int = 4,
eps: float = 1e-6,
):
self.k_min = k_min
self.k_max = k_max
self.s0 = canonical_scale
self.lvl0 = canonical_level
self.eps = eps
def __call__(self, boxlists: List[Tensor]) -> Tensor:
"""
Args:
boxlists (list[BoxList])
"""
# Compute level ids
s = torch.sqrt(torch.cat([box_area(boxlist) for boxlist in boxlists]))
# Eqn.(1) in FPN paper
target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0) + torch.tensor(self.eps, dtype=s.dtype))
target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
return (target_lvls.to(torch.int64) - self.k_min).to(torch.int64)
def _convert_to_roi_format(boxes: List[Tensor]) -> Tensor:
concat_boxes = torch.cat(boxes, dim=0)
device, dtype = concat_boxes.device, concat_boxes.dtype
ids = torch.cat(
[torch.full_like(b[:, :1], i, dtype=dtype, layout=torch.strided, device=device) for i, b in enumerate(boxes)],
dim=0,
)
rois = torch.cat([ids, concat_boxes], dim=1)
return rois
def _infer_scale(feature: Tensor, original_size: List[int]) -> float:
# assumption: the scale is of the form 2 ** (-k), with k integer
size = feature.shape[-2:]
possible_scales: List[float] = []
for s1, s2 in zip(size, original_size):
approx_scale = float(s1) / float(s2)
scale = 2 ** float(torch.tensor(approx_scale).log2().round())
possible_scales.append(scale)
return possible_scales[0]
@torch.fx.wrap
def _setup_scales(
features: List[Tensor], image_shapes: List[Tuple[int, int]], canonical_scale: int, canonical_level: int
) -> Tuple[List[float], LevelMapper]:
if not image_shapes:
raise ValueError("images list should not be empty")
max_x = 0
max_y = 0
for shape in image_shapes:
max_x = max(shape[0], max_x)
max_y = max(shape[1], max_y)
original_input_shape = (max_x, max_y)
scales = [_infer_scale(feat, original_input_shape) for feat in features]
# get the levels in the feature map by leveraging the fact that the network always
# downsamples by a factor of 2 at each level.
lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
map_levels = initLevelMapper(
int(lvl_min),
int(lvl_max),
canonical_scale=canonical_scale,
canonical_level=canonical_level,
)
return scales, map_levels
@torch.fx.wrap
def _filter_input(x: Dict[str, Tensor], featmap_names: List[str]) -> List[Tensor]:
x_filtered = []
for k, v in x.items():
if k in featmap_names:
x_filtered.append(v)
return x_filtered
@torch.fx.wrap
def _multiscale_roi_align(
x_filtered: List[Tensor],
boxes: List[Tensor],
output_size: List[int],
sampling_ratio: int,
scales: Optional[List[float]],
mapper: Optional[LevelMapper],
) -> Tensor:
"""
Args:
x_filtered (List[Tensor]): List of input tensors.
boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in
(x1, y1, x2, y2) format and in the image reference size, not the feature map
reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
output_size (Union[List[Tuple[int, int]], List[int]]): size of the output
sampling_ratio (int): sampling ratio for ROIAlign
scales (Optional[List[float]]): If None, scales will be automatically inferred. Default value is None.
mapper (Optional[LevelMapper]): If none, mapper will be automatically inferred. Default value is None.
Returns:
result (Tensor)
"""
if scales is None or mapper is None:
raise ValueError("scales and mapper should not be None")
num_levels = len(x_filtered)
rois = _convert_to_roi_format(boxes)
if num_levels == 1:
return roi_align(
x_filtered[0],
rois,
output_size=output_size,
spatial_scale=scales[0],
sampling_ratio=sampling_ratio,
)
levels = mapper(boxes)
num_rois = len(rois)
num_channels = x_filtered[0].shape[1]
dtype, device = x_filtered[0].dtype, x_filtered[0].device
result = torch.zeros(
(
num_rois,
num_channels,
)
+ output_size,
dtype=dtype,
device=device,
)
tracing_results = []
for level, (per_level_feature, scale) in enumerate(zip(x_filtered, scales)):
idx_in_level = torch.where(levels == level)[0]
rois_per_level = rois[idx_in_level]
result_idx_in_level = roi_align(
per_level_feature,
rois_per_level,
output_size=output_size,
spatial_scale=scale,
sampling_ratio=sampling_ratio,
)
if torchvision._is_tracing():
tracing_results.append(result_idx_in_level.to(dtype))
else:
# result and result_idx_in_level's dtypes are based on dtypes of different
# elements in x_filtered. x_filtered contains tensors output by different
# layers. When autocast is active, it may choose different dtypes for
# different layers' outputs. Therefore, we defensively match result's dtype
# before copying elements from result_idx_in_level in the following op.
# We need to cast manually (can't rely on autocast to cast for us) because
# the op acts on result in-place, and autocast only affects out-of-place ops.
result[idx_in_level] = result_idx_in_level.to(result.dtype)
if torchvision._is_tracing():
result = _onnx_merge_levels(levels, tracing_results)
return result
[docs]class MultiScaleRoIAlign(nn.Module):
"""
Multi-scale RoIAlign pooling, which is useful for detection with or without FPN.
It infers the scale of the pooling via the heuristics specified in eq. 1
of the `Feature Pyramid Network paper <https://arxiv.org/abs/1612.03144>`_.
They keyword-only parameters ``canonical_scale`` and ``canonical_level``
correspond respectively to ``224`` and ``k0=4`` in eq. 1, and
have the following meaning: ``canonical_level`` is the target level of the pyramid from
which to pool a region of interest with ``w x h = canonical_scale x canonical_scale``.
Args:
featmap_names (List[str]): the names of the feature maps that will be used
for the pooling.
output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region
sampling_ratio (int): sampling ratio for ROIAlign
canonical_scale (int, optional): canonical_scale for LevelMapper
canonical_level (int, optional): canonical_level for LevelMapper
Examples::
>>> m = torchvision.ops.MultiScaleRoIAlign(['feat1', 'feat3'], 3, 2)
>>> i = OrderedDict()
>>> i['feat1'] = torch.rand(1, 5, 64, 64)
>>> i['feat2'] = torch.rand(1, 5, 32, 32) # this feature won't be used in the pooling
>>> i['feat3'] = torch.rand(1, 5, 16, 16)
>>> # create some random bounding boxes
>>> boxes = torch.rand(6, 4) * 256; boxes[:, 2:] += boxes[:, :2]
>>> # original image size, before computing the feature maps
>>> image_sizes = [(512, 512)]
>>> output = m(i, [boxes], image_sizes)
>>> print(output.shape)
>>> torch.Size([6, 5, 3, 3])
"""
__annotations__ = {"scales": Optional[List[float]], "map_levels": Optional[LevelMapper]}
def __init__(
self,
featmap_names: List[str],
output_size: Union[int, Tuple[int], List[int]],
sampling_ratio: int,
*,
canonical_scale: int = 224,
canonical_level: int = 4,
):
super().__init__()
_log_api_usage_once(self)
if isinstance(output_size, int):
output_size = (output_size, output_size)
self.featmap_names = featmap_names
self.sampling_ratio = sampling_ratio
self.output_size = tuple(output_size)
self.scales = None
self.map_levels = None
self.canonical_scale = canonical_scale
self.canonical_level = canonical_level
[docs] def forward(
self,
x: Dict[str, Tensor],
boxes: List[Tensor],
image_shapes: List[Tuple[int, int]],
) -> Tensor:
"""
Args:
x (OrderedDict[Tensor]): feature maps for each level. They are assumed to have
all the same number of channels, but they can have different sizes.
boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in
(x1, y1, x2, y2) format and in the image reference size, not the feature map
reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
image_shapes (List[Tuple[height, width]]): the sizes of each image before they
have been fed to a CNN to obtain feature maps. This allows us to infer the
scale factor for each one of the levels to be pooled.
Returns:
result (Tensor)
"""
x_filtered = _filter_input(x, self.featmap_names)
if self.scales is None or self.map_levels is None:
self.scales, self.map_levels = _setup_scales(
x_filtered, image_shapes, self.canonical_scale, self.canonical_level
)
return _multiscale_roi_align(
x_filtered,
boxes,
self.output_size,
self.sampling_ratio,
self.scales,
self.map_levels,
)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(featmap_names={self.featmap_names}, "
f"output_size={self.output_size}, sampling_ratio={self.sampling_ratio})"
)
