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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
# http://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.
import unittest
import numpy as np
import random
from op_test import OpTest
def gen_match_and_neg_indices(num_prior, gt_lod, neg_lod):
if len(gt_lod) != len(neg_lod):
raise AssertionError("The input arguments are illegal.")
batch_size = len(gt_lod) - 1
match_indices = -1 * np.ones((batch_size, num_prior)).astype('int32')
neg_indices = np.zeros((neg_lod[-1], 1)).astype('int32')
for n in range(batch_size):
gt_num = gt_lod[n + 1] - gt_lod[n]
ids = random.sample([i for i in range(num_prior)], gt_num)
match_indices[n, ids] = [i for i in range(gt_num)]
ret_ids = set([i for i in range(num_prior)]) - set(ids)
s = neg_lod[n]
e = neg_lod[n + 1]
l = e - s
neg_ids = random.sample(ret_ids, l)
neg_indices[s:e, :] = np.array(neg_ids).astype('int32').reshape(l, 1)
return match_indices, neg_indices
def target_assign(encoded_box, gt_label, match_indices, neg_indices, gt_lod,
neg_lod, background_label):
batch_size, num_prior = match_indices.shape
# init target bbox
trg_box = np.zeros((batch_size, num_prior, 4)).astype('float32')
# init weight for target bbox
trg_box_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
# init target label
trg_label = np.ones((batch_size, num_prior, 1)).astype('int32')
trg_label = trg_label * background_label
# init weight for target label
trg_label_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
for i in range(batch_size):
cur_indices = match_indices[i]
col_ids = np.where(cur_indices > -1)
col_val = cur_indices[col_ids]
gt_start = gt_lod[i]
# target bbox
for v, c in zip(col_val + gt_start, col_ids[0].tolist()):
trg_box[i][c][:] = encoded_box[v][c][:]
# weight for target bbox
trg_box_wt[i][col_ids] = 1.0
trg_label[i][col_ids] = gt_label[col_val + gt_start]
trg_label_wt[i][col_ids] = 1.0
# set target label weight to 1.0 for the negative samples
neg_ids = neg_indices[neg_lod[i]:neg_lod[i + 1]]
trg_label_wt[i][neg_ids] = 1.0
return trg_box, trg_box_wt, trg_label, trg_label_wt
class TestTargetAssginOp(OpTest):
def setUp(self):
self.op_type = "target_assign"
num_prior = 120
num_class = 21
gt_lod = [0, 5, 11, 23]
neg_lod = [0, 4, 7, 13]
batch_size = len(gt_lod) - 1
num_gt = gt_lod[-1]
background_label = 0
encoded_box = np.random.random((num_gt, num_prior, 4)).astype('float32')
gt_label = np.random.randint(
num_class, size=(num_gt, 1)).astype('int32')
match_indices, neg_indices = gen_match_and_neg_indices(num_prior,
gt_lod, neg_lod)
trg_box, trg_box_wt, trg_label, trg_label_wt = target_assign(
encoded_box, gt_label, match_indices, neg_indices, gt_lod, neg_lod,
background_label)
self.inputs = {
'EncodedGTBBox': (encoded_box, [gt_lod]),
'GTScoreLabel': (gt_label, [gt_lod]),
'MatchIndices': (match_indices),
'NegIndices': (neg_indices, [neg_lod]),
}
self.attrs = {'background_label': background_label}
self.outputs = {
'PredBBoxLabel': (trg_box),
'PredBBoxWeight': (trg_box_wt),
'PredScoreLabel': (trg_label),
'PredScoreWeight': (trg_label_wt),
}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()