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roi_average_pooling_2d.py
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roi_average_pooling_2d.py
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# Modified work:
# -----------------------------------------------------------------------------
# Copyright (c) 2015 Preferred Infrastructure, Inc.
# Copyright (c) 2015 Preferred Networks, Inc.
# -----------------------------------------------------------------------------
# Original work of _roi_pooling_slice, forward_cpu and backward_cpu:
# -----------------------------------------------------------------------------
# Copyright 2014 Nervana Systems Inc.
# 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
#
# https://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.
# -----------------------------------------------------------------------------
# Original work of forward_gpu and backward_gpu:
# -----------------------------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see fast-rcnn/LICENSE for details]
# Written by Ross Girshick
# -----------------------------------------------------------------------------
import numbers
import numpy
import six
from chainer.backends import cuda
from chainer import function
from chainer.functions.pooling.roi_pooling_2d import _roi_pooling_slice
from chainer import utils
from chainer.utils import collections_abc
from chainer.utils import type_check
def _pair(x):
if isinstance(x, collections_abc.Iterable):
return x
return x, x
class ROIAveragePooling2D(function.Function):
"""RoI average pooling over a set of 2d planes."""
def __init__(self, outsize, spatial_scale):
outh, outw = _pair(outsize)
if not (isinstance(outh, numbers.Integral) and outh > 0):
raise TypeError(
'outsize[0] must be positive integer: {}, {}'
.format(type(outh), outh))
if not (isinstance(outw, numbers.Integral) and outw > 0):
raise TypeError(
'outsize[1] must be positive integer: {}, {}'
.format(type(outw), outw))
if isinstance(spatial_scale, numbers.Integral):
spatial_scale = float(spatial_scale)
if not (isinstance(spatial_scale, numbers.Real) and
spatial_scale > 0):
raise TypeError(
'spatial_scale must be a positive float number: {}, {}'
.format(type(spatial_scale), spatial_scale))
self.outh, self.outw = outh, outw
self.spatial_scale = spatial_scale
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 3)
x_type, roi_type, roi_index_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim == 4,
x_type.dtype == roi_type.dtype,
roi_type.ndim == 2,
roi_type.shape[1] == 4,
roi_index_type.dtype == numpy.int32,
roi_index_type.ndim == 1,
roi_type.shape[0] == roi_index_type.shape[0],
)
def forward_cpu(self, inputs):
self.retain_inputs((1, 2))
self._bottom_data_shape = inputs[0].shape
bottom_data, bottom_rois, bottom_roi_indices = inputs
channels, height, width = bottom_data.shape[1:]
n_rois = bottom_rois.shape[0]
top_data = numpy.zeros((n_rois, channels, self.outh, self.outw),
dtype=bottom_data.dtype)
for i_roi in six.moves.range(n_rois):
idx = bottom_roi_indices[i_roi]
ymin, xmin, ymax, xmax = bottom_rois[i_roi]
ymin = int(round(ymin * self.spatial_scale))
xmin = int(round(xmin * self.spatial_scale))
ymax = int(round(ymax * self.spatial_scale))
xmax = int(round(xmax * self.spatial_scale))
roi_height = max(ymax - ymin, 1)
roi_width = max(xmax - xmin, 1)
strideh = 1. * roi_height / self.outh
stridew = 1. * roi_width / self.outw
for outh in six.moves.range(self.outh):
sliceh, lenh = _roi_pooling_slice(
outh, strideh, height, ymin)
if sliceh.stop <= sliceh.start:
continue
for outw in six.moves.range(self.outw):
slicew, lenw = _roi_pooling_slice(
outw, stridew, width, xmin)
if slicew.stop <= slicew.start:
continue
roi_data = bottom_data[int(idx), :, sliceh, slicew]\
.reshape(channels, -1)
top_data[i_roi, :, outh, outw] =\
numpy.average(roi_data, axis=1)
return top_data,
def forward_gpu(self, inputs):
self.retain_inputs((1, 2))
self._bottom_data_shape = inputs[0].shape
bottom_data, bottom_rois, bottom_roi_indices = inputs
channels, height, width = bottom_data.shape[1:]
n_rois = bottom_rois.shape[0]
top_data = cuda.cupy.empty((n_rois, channels, self.outh,
self.outw), dtype=bottom_data.dtype)
cuda.elementwise(
'''
raw T bottom_data, raw T bottom_rois, raw int32 bottom_roi_indices,
T spatial_scale, int32 channels, int32 height, int32 width,
int32 pooled_height, int32 pooled_width
''',
'T top_data',
'''
// pos in output filter
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
int c = (i / pooled_width / pooled_height) % channels;
int n = i / pooled_width / pooled_height / channels;
int roi_batch_ind = bottom_roi_indices[n];
int roi_start_h = round(bottom_rois[n * 4 + 0] * spatial_scale);
int roi_start_w = round(bottom_rois[n * 4 + 1] * spatial_scale);
int roi_end_h = round(bottom_rois[n * 4 + 2] * spatial_scale);
int roi_end_w = round(bottom_rois[n * 4 + 3] * spatial_scale);
// Force malformed ROIs to be 1x1
int roi_height = max(roi_end_h - roi_start_h, 1);
int roi_width = max(roi_end_w - roi_start_w, 1);
T bin_size_h = static_cast<T>(roi_height)
/ static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width)
/ static_cast<T>(pooled_width);
int hstart = static_cast<int>(floor(static_cast<T>(ph)
* bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw)
* bin_size_w));
int hend = static_cast<int>(ceil(static_cast<T>(ph + 1)
* bin_size_h));
int wend = static_cast<int>(ceil(static_cast<T>(pw + 1)
* bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
wend = min(max(wend + roi_start_w, 0), width);
bool is_empty = (hend <= hstart) || (wend <= wstart);
// Define an empty pooling region to be zero
T sumval = 0.;
T count = (hend - hstart) * (wend - wstart);
int data_offset = (roi_batch_ind * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int bottom_index = h * width + w;
sumval += bottom_data[data_offset + bottom_index];
}
}
top_data = is_empty ? 0. : sumval / count;
''', 'roi_average_pooling_2d_fwd'
)(bottom_data, bottom_rois, bottom_roi_indices, self.spatial_scale,
channels, height, width, self.outh, self.outw, top_data)
return top_data,
def backward_cpu(self, inputs, gy):
bottom_rois, bottom_roi_indices = inputs[1:]
channels, height, width = self._bottom_data_shape[1:]
n_rois = bottom_rois.shape[0]
bottom_diff = numpy.zeros(self._bottom_data_shape, gy[0].dtype)
for i_roi in six.moves.range(n_rois):
idx = bottom_roi_indices[i_roi]
ymin, xmin, ymax, xmax = bottom_rois[i_roi]
ymin = int(round(ymin * self.spatial_scale))
xmin = int(round(xmin * self.spatial_scale))
ymax = int(round(ymax * self.spatial_scale))
xmax = int(round(xmax * self.spatial_scale))
roi_height = max(ymax - ymin, 1)
roi_width = max(xmax - xmin, 1)
strideh = 1. * roi_height / self.outh
stridew = 1. * roi_width / self.outw
for outh in six.moves.range(self.outh):
sliceh, lenh = _roi_pooling_slice(
outh, strideh, height, ymin)
if sliceh.stop <= sliceh.start:
continue
for outw in six.moves.range(self.outw):
slicew, lenw = _roi_pooling_slice(
outw, stridew, width, xmin)
if slicew.stop <= slicew.start:
continue
diff_val = gy[0][i_roi, :, outh, outw]\
.reshape(channels, 1, 1)
diff_val = diff_val / lenh / lenw
bottom_diff[int(idx), :, sliceh, slicew] \
+= diff_val
return bottom_diff, None, None
def backward_gpu(self, inputs, gy):
utils.nondeterministic('atomicAdd')
bottom_rois, bottom_roi_indices = inputs[1:]
channels, height, width = self._bottom_data_shape[1:]
bottom_diff = cuda.cupy.zeros(
self._bottom_data_shape, gy[0].dtype)
cuda.elementwise(
'''
raw T top_diff, raw T bottom_rois, raw int32 bottom_roi_indices,
T spatial_scale, int32 channels, int32 height, int32 width,
int32 pooled_height, int32 pooled_width
''',
'raw T bottom_diff',
'''
// pos in output filter
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
int c = (i / pooled_width / pooled_height) % channels;
int n = i / pooled_width / pooled_height / channels;
int roi_batch_ind = bottom_roi_indices[n];
int roi_start_h = round(bottom_rois[n * 4 + 0] * spatial_scale);
int roi_start_w = round(bottom_rois[n * 4 + 1] * spatial_scale);
int roi_end_h = round(bottom_rois[n * 4 + 2] * spatial_scale);
int roi_end_w = round(bottom_rois[n * 4 + 3] * spatial_scale);
// Force malformed ROIs to be 1x1
int roi_height = max(roi_end_h - roi_start_h, 1);
int roi_width = max(roi_end_w - roi_start_w, 1);
T bin_size_h = static_cast<T>(roi_height)
/ static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width)
/ static_cast<T>(pooled_width);
int hstart = static_cast<int>(floor(static_cast<T>(ph)
* bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw)
* bin_size_w));
int hend = static_cast<int>(ceil(static_cast<T>(ph + 1)
* bin_size_h));
int wend = static_cast<int>(ceil(static_cast<T>(pw + 1)
* bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
wend = min(max(wend + roi_start_w, 0), width);
bool is_empty = (hend <= hstart) || (wend <= wstart);
int bottom_diff_offset =
(roi_batch_ind * channels + c) * height * width;
int top_offset =
(n * channels + c) * pooled_height * pooled_width;
T count = (hend - hstart) * (wend - wstart);
T diff_val = is_empty ? 0. :
top_diff[top_offset + ph * pooled_width + pw] / count;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int bottom_index = h * width + w;
atomicAdd(
&bottom_diff[bottom_diff_offset + bottom_index],
diff_val);
}
}
''', 'roi_average_pooling_2d_bwd'
)(gy[0], bottom_rois, bottom_roi_indices, self.spatial_scale,
channels, height, width, self.outh, self.outw,
bottom_diff, size=gy[0].size)
return bottom_diff, None, None
def roi_average_pooling_2d(x, rois, roi_indices, outsize, spatial_scale):
"""Spatial Region of Interest (ROI) average pooling function.
This function acts similarly to
:func:`~chainer.functions.average_pooling_2d`, but it computes the average
of input spatial patch for each channel with the region of interest.
Args:
x (~chainer.Variable): Input variable. The shape is expected to be
4 dimensional: (n: batch, c: channel, h, height, w: width).
rois (~chainer.Variable): Input roi variable. The shape is expected to
be (n: data size, 4), and each datum is set as below:
(y_min, x_min, y_max, x_max).
roi_indices (~chainer.Variable): Input roi variable. The shape is
expected to be (n: data size, ).
outsize ((int, int) or int): Expected output size after pooled
(height, width). ``outsize=o`` and ``outsize=(o, o)``
are equivalent.
spatial_scale (float): Scale of the roi is resized.
Returns:
~chainer.Variable: Output variable.
See the original paper proposing ROIPooling:
`Fast R-CNN <https://arxiv.org/abs/1504.08083>`_.
"""
return ROIAveragePooling2D(outsize, spatial_scale)(x, rois, roi_indices)