def __call__(self, feats, rois, spatial_scale):
roi, rois_num = rois
cur_l = 0
if self.start_level == self.end_level:
rois_feat = ops.roi_align(feats[self.start_level],
roi,
self.resolution,
spatial_scale,
rois_num=rois_num)
return rois_feat
offset = 2
k_min = self.start_level + offset
k_max = self.end_level + offset
rois_dist, restore_index, rois_num_dist = ops.distribute_fpn_proposals(
roi,
k_min,
k_max,
self.canconical_level,
self.canonical_size,
rois_num=rois_num)
rois_feat_list = []
for lvl in range(self.start_level, self.end_level + 1):
roi_feat = ops.roi_align(feats[lvl],
rois_dist[lvl],
self.resolution,
spatial_scale[lvl],
sampling_ratio=self.sampling_ratio,
rois_num=rois_num_dist[lvl])
rois_feat_list.append(roi_feat)
rois_feat_shuffle = paddle.concat(rois_feat_list)
rois_feat = paddle.gather(rois_feat_shuffle, restore_index)
return rois_feat
def __call__(self, feats, roi, rois_num):
roi = paddle.concat(roi) if len(roi) > 1 else roi[0]
if len(feats) == 1:
rois_feat = ops.roi_align(feats[self.start_level],
roi,
self.resolution,
self.spatial_scale[0],
rois_num=rois_num,
aligned=self.aligned)
else:
offset = 2
k_min = self.start_level + offset
k_max = self.end_level + offset
rois_dist, restore_index, rois_num_dist = ops.distribute_fpn_proposals(
roi,
k_min,
k_max,
self.canconical_level,
self.canonical_size,
rois_num=rois_num)
rois_feat_list = []
for lvl in range(self.start_level, self.end_level + 1):
roi_feat = ops.roi_align(feats[lvl],
rois_dist[lvl],
self.resolution,
self.spatial_scale[lvl],
sampling_ratio=self.sampling_ratio,
rois_num=rois_num_dist[lvl],
aligned=self.aligned)
if roi_feat.shape[0] > 0:
rois_feat_list.append(roi_feat)
rois_feat_shuffle = paddle.concat(rois_feat_list)
rois_feat = paddle.gather(rois_feat_shuffle, restore_index)
return rois_feat
def test_roi_align(self):
b, c, h, w = 2, 12, 20, 20
inputs_np = np.random.rand(b, c, h, w).astype('float32')
rois_num = [4, 6]
output_size = (7, 7)
rois_np = make_rois(h, w, rois_num, output_size)
rois_num_np = np.array(rois_num).astype('int32')
with self.static_graph():
inputs = paddle.static.data(name='inputs',
shape=[b, c, h, w],
dtype='float32')
rois = paddle.static.data(name='rois',
shape=[10, 4],
dtype='float32')
rois_num = paddle.static.data(name='rois_num',
shape=[None],
dtype='int32')
output = ops.roi_align(input=inputs,
rois=rois,
output_size=output_size,
rois_num=rois_num)
output_np, = self.get_static_graph_result(feed={
'inputs': inputs_np,
'rois': rois_np,
'rois_num': rois_num_np
},
fetch_list=output,
with_lod=False)
with self.dynamic_graph():
inputs_dy = base.to_variable(inputs_np)
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
output_dy = ops.roi_align(input=inputs_dy,
rois=rois_dy,
output_size=output_size,
rois_num=rois_num_dy)
output_dy_np = output_dy.numpy()
self.assertTrue(np.array_equal(output_np, output_dy_np))