def test_distribute_fpn_proposals(self):
rois_np = np.random.rand(10, 4).astype('float32')
rois_num_np = np.array([4, 6]).astype('int32')
with self.static_graph():
rois = fluid.data(name='rois', shape=[10, 4], dtype='float32')
rois_num = fluid.data(name='rois_num', shape=[None], dtype='int32')
multi_rois, restore_ind, rois_num_per_level = layers.distribute_fpn_proposals(
fpn_rois=rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224,
rois_num=rois_num)
fetch_list = multi_rois + [restore_ind] + rois_num_per_level
output_stat = self.get_static_graph_result(feed={
'rois': rois_np,
'rois_num': rois_num_np
},
fetch_list=fetch_list,
with_lod=True)
output_stat_np = []
for output in output_stat:
output_np = np.array(output)
if len(output_np) > 0:
output_stat_np.append(output_np)
with self.dynamic_graph():
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
multi_rois_dy, restore_ind_dy, rois_num_per_level_dy = layers.distribute_fpn_proposals(
fpn_rois=rois_dy,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224,
rois_num=rois_num_dy)
print(type(multi_rois_dy))
output_dy = multi_rois_dy + [restore_ind_dy
] + rois_num_per_level_dy
output_dy_np = []
for output in output_dy:
output_np = output.numpy()
if len(output_np) > 0:
output_dy_np.append(output_np)
for res_stat, res_dy in zip(output_stat_np, output_dy_np):
self.assertTrue(np.array_equal(res_stat, res_dy))
def test_distribute_fpn_proposals(self):
program = Program()
with program_guard(program):
fpn_rois = fluid.layers.data(
name='data', shape=[4], dtype='float32', lod_level=1)
multi_rois, restore_ind = layers.distribute_fpn_proposals(
fpn_rois=fpn_rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224)
self.assertIsNotNone(multi_rois)
self.assertIsNotNone(restore_ind)