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_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 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]
# `numpy.zeros` needs to be used because the arrays can be
# returned without having some of its values updated.
top_data = numpy.zeros((n_rois, channels, self.outh, self.outw),
dtype=bottom_data.dtype)
self.argmax_data = numpy.zeros(top_data.shape, numpy.int32)
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.max(roi_data, axis=1)
# get the max idx respect to feature_maps coordinates
max_idx_slice = numpy.unravel_index(
numpy.argmax(roi_data, axis=1), (lenh, lenw))
max_idx_slice_h = max_idx_slice[0] + sliceh.start
max_idx_slice_w = max_idx_slice[1] + slicew.start
max_idx_slice = max_idx_slice_h * width + max_idx_slice_w
self.argmax_data[i_roi, :, outh, outw] = max_idx_slice
return top_data,
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]
# `numpy.zeros` needs to be used because the arrays can be
# returned without having some of its values updated.
top_data = numpy.zeros((n_rois, channels, self.outh, self.outw),
dtype=bottom_data.dtype)
self.argmax_data = numpy.zeros(top_data.shape, numpy.int32)
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.max(roi_data, axis=1)
# get the max idx respect to feature_maps coordinates
max_idx_slice = numpy.unravel_index(
numpy.argmax(roi_data, axis=1), (lenh, lenw))
max_idx_slice_h = max_idx_slice[0] + sliceh.start
max_idx_slice_w = max_idx_slice[1] + slicew.start
max_idx_slice = max_idx_slice_h * width + max_idx_slice_w
self.argmax_data[i_roi, :, outh, outw] = max_idx_slice
return top_data,
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_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,
