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.empty((n_rois, channels, self.outh, self.outw),
dtype=bottom_data.dtype)
self.argmax_data = numpy.empty(top_data.shape, numpy.int32)
pooled_width, pooled_height = self.outw, self.outh
spatial_scale = self.spatial_scale
for i in six.moves.range(top_data.size):
pw = i % pooled_width
ph = int(i / pooled_width) % pooled_height
c = int(i / pooled_width / pooled_height) % channels
n = int(i / pooled_width / pooled_height / channels)
roi_batch_ind = bottom_roi_indices[n]
roi_start_h = bottom_rois[n, 0] * spatial_scale
roi_start_w = bottom_rois[n, 1] * spatial_scale
roi_end_h = bottom_rois[n, 2] * spatial_scale
roi_end_w = bottom_rois[n, 3] * spatial_scale
roi_width = max(roi_end_w - roi_start_w, 1.)
roi_height = max(roi_end_h - roi_start_h, 1.)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
if self.sampling_ratio[0] is None:
roi_bin_grid_h = int(numpy.ceil(roi_height / pooled_height))
else:
roi_bin_grid_h = self.sampling_ratio[0]
if self.sampling_ratio[1] is None:
roi_bin_grid_w = int(numpy.ceil(roi_width / pooled_width))
else:
roi_bin_grid_w = self.sampling_ratio[1]
max_val = -numpy.inf
max_index = -1
for iy in six.moves.range(roi_bin_grid_h):
y = roi_start_h + ph * bin_size_h + \
(iy + .5) * bin_size_h / roi_bin_grid_h
y, y_low, y_high = _get_bounds(y, height)
if y is None or y_low is None or y_high is None:
continue
for ix in six.moves.range(roi_bin_grid_w):
x = roi_start_w + pw * bin_size_w + \
(ix + .5) * bin_size_w / roi_bin_grid_w
x, x_low, x_high = _get_bounds(x, width)
if x is None or x_low is None or x_high is None:
continue
# bilinear interpolation {{
w1, w2, w3, w4 = _get_bilinear_interp_params(
y, x, y_low, x_low, y_high, x_high)
tmp_val = 0.
if w1 > 0 and y_low >= 0 and x_low >= 0:
v1 = bottom_data[roi_batch_ind, c, y_low, x_low]
tmp_val += w1 * v1
if w2 > 0 and y_low >= 0 and x_high <= width - 1:
v2 = bottom_data[roi_batch_ind, c, y_low, x_high]
tmp_val += w2 * v2
if w3 > 0 and y_high <= height - 1 and x_low >= 0:
v3 = bottom_data[roi_batch_ind, c, y_high, x_low]
tmp_val += w3 * v3
if w4 > 0 and y_high <= height - 1 and x_high <= width - 1:
v4 = bottom_data[roi_batch_ind, c, y_high, x_high]
tmp_val += w4 * v4
tmp_index = iy * roi_bin_grid_w + ix
if tmp_val > max_val:
max_val = tmp_val
max_index = tmp_index
# }}
top_data[n, c, ph, pw] = max_val
self.argmax_data[n, c, ph, pw] = max_index
return top_data,
def backward_cpu(self, inputs, gy):
bottom_rois, bottom_roi_indices = inputs[1:]
channels, height, width = self._bottom_data_shape[1:]
bottom_diff = numpy.zeros(self._bottom_data_shape, gy[0].dtype)
spatial_scale = self.spatial_scale
pooled_height = self.outh
pooled_width = self.outw
top_diff = gy[0]
for i in six.moves.range(top_diff.size):
pw = i % pooled_width
ph = int(i / pooled_width) % pooled_height
c = int(i / pooled_width / pooled_height) % channels
n = int(i / pooled_width / pooled_height / channels)
roi_batch_ind = bottom_roi_indices[n]
roi_start_h = bottom_rois[n, 0] * spatial_scale
roi_start_w = bottom_rois[n, 1] * spatial_scale
roi_end_h = bottom_rois[n, 2] * spatial_scale
roi_end_w = bottom_rois[n, 3] * spatial_scale
roi_width = max(roi_end_w - roi_start_w, 1.)
roi_height = max(roi_end_h - roi_start_h, 1.)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
top_diff_this_bin = top_diff[n, c, ph, pw]
max_index = self.argmax_data[n, c, ph, pw]
if max_index != -1:
if self.sampling_ratio[0] is None:
roi_bin_grid_h = numpy.ceil(roi_height / pooled_height)
else:
roi_bin_grid_h = self.sampling_ratio[0]
if self.sampling_ratio[1] is None:
roi_bin_grid_w = numpy.ceil(roi_width / pooled_width)
else:
roi_bin_grid_w = self.sampling_ratio[1]
iy = int(max_index / roi_bin_grid_w)
ix = max_index % roi_bin_grid_w
y = roi_start_h + ph * bin_size_h + \
(iy + .5) * bin_size_h / roi_bin_grid_h
x = roi_start_w + pw * bin_size_w + \
(ix + .5) * bin_size_w / roi_bin_grid_w
# bilinear_interpolation_gradient {{
y, y_low, y_high = _get_bounds(y, height)
if y is None or y_low is None or y_high is None:
continue
x, x_low, x_high = _get_bounds(x, width)
if x is None or x_low is None or x_high is None:
continue
w1, w2, w3, w4 = _get_bilinear_interp_params(
y, x, y_low, x_low, y_high, x_high)
if w1 > 0 and y_low >= 0 and x_low >= 0:
g1 = top_diff_this_bin * w1
bottom_diff[roi_batch_ind, c, y_low, x_low] += g1
if w2 > 0 and y_low >= 0 and x_high <= width - 1:
g2 = top_diff_this_bin * w2
bottom_diff[roi_batch_ind, c, y_low, x_high] += g2
if w3 > 0 and y_high <= height - 1 and x_low >= 0:
g3 = top_diff_this_bin * w3
bottom_diff[roi_batch_ind, c, y_high, x_low] += g3
if w4 > 0 and y_high <= height - 1 and x_high <= width - 1:
g4 = top_diff_this_bin * w4
bottom_diff[roi_batch_ind, c, y_high, x_high] += g4
# }}
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:]
bottom_diff = numpy.zeros(self._bottom_data_shape, gy[0].dtype)
spatial_scale = self.spatial_scale
pooled_height = self.outh
pooled_width = self.outw
top_diff = gy[0]
for i in six.moves.range(top_diff.size):
pw = i % pooled_width
ph = int(i / pooled_width) % pooled_height
c = int(i / pooled_width / pooled_height) % channels
n = int(i / pooled_width / pooled_height / channels)
roi_batch_ind = bottom_roi_indices[n]
roi_start_h = bottom_rois[n, 0] * spatial_scale
roi_start_w = bottom_rois[n, 1] * spatial_scale
roi_end_h = bottom_rois[n, 2] * spatial_scale
roi_end_w = bottom_rois[n, 3] * spatial_scale
roi_width = max(roi_end_w - roi_start_w, 1.)
roi_height = max(roi_end_h - roi_start_h, 1.)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
top_diff_this_bin = top_diff[n, c, ph, pw]
if self.sampling_ratio[0] is None:
roi_bin_grid_h = numpy.ceil(roi_height / pooled_height)
else:
roi_bin_grid_h = self.sampling_ratio[0]
if self.sampling_ratio[1] is None:
roi_bin_grid_w = numpy.ceil(roi_width / pooled_width)
else:
roi_bin_grid_w = self.sampling_ratio[1]
max_index = self.argmax_data[n, c, ph, pw]
iy = int(max_index / roi_bin_grid_w)
ix = max_index % roi_bin_grid_w
y = roi_start_h + ph * bin_size_h + \
(iy + .5) * bin_size_h / roi_bin_grid_h
x = roi_start_w + pw * bin_size_w + \
(ix + .5) * bin_size_w / roi_bin_grid_w
# bilinear_interpolation_gradient {{
y_low, x_low, y_high, x_high, w1, w2, w3, w4 = \
_get_bilinear_interp_params(y, x, height, width)
if y_low is None:
continue
g1 = top_diff_this_bin * w1
g2 = top_diff_this_bin * w2
g3 = top_diff_this_bin * w3
g4 = top_diff_this_bin * w4
if (x_low >= 0 and x_high >= 0 and
y_low >= 0 and y_high >= 0):
bottom_diff[roi_batch_ind, c, y_low, x_low] += g1
bottom_diff[roi_batch_ind, c, y_low, x_high] += g2
bottom_diff[roi_batch_ind, c, y_high, x_low] += g3
bottom_diff[roi_batch_ind, c, y_high, x_high] += g4
# }}
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]
top_data = numpy.empty((n_rois, channels, self.outh,
self.outw), dtype=bottom_data.dtype)
self.argmax_data = numpy.zeros(top_data.shape, numpy.int32)
pooled_width, pooled_height = self.outw, self.outh
spatial_scale = self.spatial_scale
for i in six.moves.range(top_data.size):
pw = i % pooled_width
ph = int(i / pooled_width) % pooled_height
c = int(i / pooled_width / pooled_height) % channels
n = int(i / pooled_width / pooled_height / channels)
roi_batch_ind = bottom_roi_indices[n]
roi_start_h = bottom_rois[n, 0] * spatial_scale
roi_start_w = bottom_rois[n, 1] * spatial_scale
roi_end_h = bottom_rois[n, 2] * spatial_scale
roi_end_w = bottom_rois[n, 3] * spatial_scale
roi_width = max(roi_end_w - roi_start_w, 1.)
roi_height = max(roi_end_h - roi_start_h, 1.)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
if self.sampling_ratio[0] is None:
roi_bin_grid_h = numpy.ceil(roi_height / pooled_height)
else:
roi_bin_grid_h = self.sampling_ratio[0]
if self.sampling_ratio[1] is None:
roi_bin_grid_w = numpy.ceil(roi_width / pooled_width)
else:
roi_bin_grid_w = self.sampling_ratio[1]
max_val = None
max_index = None
iy = 0
while iy < roi_bin_grid_h:
y = roi_start_h + ph * bin_size_h + \
(iy + .5) * bin_size_h / roi_bin_grid_h
ix = 0
while ix < roi_bin_grid_w:
x = roi_start_w + pw * bin_size_w + \
(ix + .5) * bin_size_w / roi_bin_grid_w
# bilinear interpolation {{
y_low, x_low, y_high, x_high, w1, w2, w3, w4 = \
_get_bilinear_interp_params(y, x, height, width)
if y_low is None:
continue
v1 = bottom_data[roi_batch_ind, c, y_low, x_low]
v2 = bottom_data[roi_batch_ind, c, y_low, x_high]
v3 = bottom_data[roi_batch_ind, c, y_high, x_low]
v4 = bottom_data[roi_batch_ind, c, y_high, x_high]
tmp_val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
tmp_index = iy * roi_bin_grid_w + ix
if max_val is None or max_index is None \
or (tmp_val > max_val):
max_val = tmp_val
max_index = tmp_index
# }}
ix += 1
iy += 1
top_data[n, c, ph, pw] = max_val
self.argmax_data[n, c, ph, pw] = max_index
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.empty((n_rois, channels, self.outh, self.outw),
dtype=bottom_data.dtype)
self.argmax_data = numpy.zeros(top_data.shape, numpy.int32)
pooled_width, pooled_height = self.outw, self.outh
spatial_scale = self.spatial_scale
for i in six.moves.range(top_data.size):
pw = i % pooled_width
ph = int(i / pooled_width) % pooled_height
c = int(i / pooled_width / pooled_height) % channels
n = int(i / pooled_width / pooled_height / channels)
roi_batch_ind = bottom_roi_indices[n]
roi_start_h = bottom_rois[n, 0] * spatial_scale
roi_start_w = bottom_rois[n, 1] * spatial_scale
roi_end_h = bottom_rois[n, 2] * spatial_scale
roi_end_w = bottom_rois[n, 3] * spatial_scale
roi_width = max(roi_end_w - roi_start_w, 1.)
roi_height = max(roi_end_h - roi_start_h, 1.)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
if self.sampling_ratio[0] is None:
roi_bin_grid_h = numpy.ceil(roi_height / pooled_height)
else:
roi_bin_grid_h = self.sampling_ratio[0]
if self.sampling_ratio[1] is None:
roi_bin_grid_w = numpy.ceil(roi_width / pooled_width)
else:
roi_bin_grid_w = self.sampling_ratio[1]
max_val = None
max_index = None
iy = 0
while iy < roi_bin_grid_h:
y = roi_start_h + ph * bin_size_h + \
(iy + .5) * bin_size_h / roi_bin_grid_h
ix = 0
y, y_low, y_high = _get_bounds(y, height)
if y is None or y_low is None or y_high is None:
continue
while ix < roi_bin_grid_w:
x = roi_start_w + pw * bin_size_w + \
(ix + .5) * bin_size_w / roi_bin_grid_w
x, x_low, x_high = _get_bounds(x, width)
if x is None or x_low is None or x_high is None:
continue
# bilinear interpolation {{
w1, w2, w3, w4 = _get_bilinear_interp_params(
y, x, y_low, x_low, y_high, x_high)
v1 = bottom_data[roi_batch_ind, c, y_low, x_low]
v2 = bottom_data[roi_batch_ind, c, y_low, x_high]
v3 = bottom_data[roi_batch_ind, c, y_high, x_low]
v4 = bottom_data[roi_batch_ind, c, y_high, x_high]
tmp_val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
tmp_index = iy * roi_bin_grid_w + ix
if max_val is None or max_index is None \
or (tmp_val > max_val):
max_val = tmp_val
max_index = tmp_index
# }}
ix += 1
iy += 1
top_data[n, c, ph, pw] = max_val
self.argmax_data[n, c, ph, pw] = max_index
return top_data,
