# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # 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 os import unittest import numpy as np import paddle from test_nms_op import nms import tempfile def _find(condition): """ Find the indices of elements saticfied the condition. Args: condition(Tensor[N] or np.ndarray([N,])): Element should be bool type. Returns: Tensor: Indices of True element. """ res = [] for i in range(condition.shape[0]): if condition[i]: res.append(i) return np.array(res) def multiclass_nms(boxes, scores, category_idxs, iou_threshold, top_k): mask = np.zeros_like(scores) for category_id in np.unique(category_idxs): cur_category_boxes_idxs = _find(category_idxs == category_id) cur_category_boxes = boxes[cur_category_boxes_idxs] cur_category_scores = scores[cur_category_boxes_idxs] cur_category_sorted_indices = np.argsort(-cur_category_scores) cur_category_sorted_boxes = cur_category_boxes[ cur_category_sorted_indices] cur_category_keep_boxes_sub_idxs = cur_category_sorted_indices[nms( cur_category_sorted_boxes, iou_threshold)] mask[cur_category_boxes_idxs[cur_category_keep_boxes_sub_idxs]] = True keep_boxes_idxs = _find(mask == True) topK_sub_indices = np.argsort(-scores[keep_boxes_idxs])[:top_k] return keep_boxes_idxs[topK_sub_indices] def gen_args(num_boxes, dtype): boxes = np.random.rand(num_boxes, 4).astype(dtype) boxes[:, 2] = boxes[:, 0] + boxes[:, 2] boxes[:, 3] = boxes[:, 1] + boxes[:, 3] scores = np.random.rand(num_boxes).astype(dtype) categories = [0, 1, 2, 3] category_idxs = np.random.choice(categories, num_boxes) return boxes, scores, category_idxs, categories class TestOpsNMS(unittest.TestCase): def setUp(self): self.num_boxes = 64 self.threshold = 0.5 self.topk = 20 self.dtypes = ['float32'] self.devices = ['cpu'] if paddle.is_compiled_with_cuda(): self.devices.append('gpu') self.temp_dir = tempfile.TemporaryDirectory() self.path = os.path.join(self.temp_dir.name, './net') def tearDown(self): self.temp_dir.cleanup() def test_nms(self): for device in self.devices: for dtype in self.dtypes: boxes, scores, category_idxs, categories = gen_args( self.num_boxes, dtype) paddle.set_device(device) out = paddle.vision.ops.nms(paddle.to_tensor(boxes), self.threshold, paddle.to_tensor(scores)) out = paddle.vision.ops.nms(paddle.to_tensor(boxes), self.threshold) out_py = nms(boxes, self.threshold) self.assertTrue( np.array_equal(out.numpy(), out_py), "paddle out: {}\n py out: {}\n".format(out, out_py)) def test_multiclass_nms_dynamic(self): for device in self.devices: for dtype in self.dtypes: boxes, scores, category_idxs, categories = gen_args( self.num_boxes, dtype) paddle.set_device(device) out = paddle.vision.ops.nms(paddle.to_tensor(boxes), self.threshold, paddle.to_tensor(scores), paddle.to_tensor(category_idxs), categories, self.topk) out_py = multiclass_nms(boxes, scores, category_idxs, self.threshold, self.topk) self.assertTrue( np.array_equal(out.numpy(), out_py), "paddle out: {}\n py out: {}\n".format(out, out_py)) def test_multiclass_nms_static(self): for device in self.devices: for dtype in self.dtypes: paddle.enable_static() boxes, scores, category_idxs, categories = gen_args( self.num_boxes, dtype) boxes_static = paddle.static.data(shape=boxes.shape, dtype=boxes.dtype, name="boxes") scores_static = paddle.static.data(shape=scores.shape, dtype=scores.dtype, name="scores") category_idxs_static = paddle.static.data( shape=category_idxs.shape, dtype=category_idxs.dtype, name="category_idxs") out = paddle.vision.ops.nms(boxes_static, self.threshold, scores_static, category_idxs_static, categories, self.topk) place = paddle.CPUPlace() if device == 'gpu': place = paddle.CUDAPlace(0) exe = paddle.static.Executor(place) out = exe.run(paddle.static.default_main_program(), feed={ 'boxes': boxes, 'scores': scores, 'category_idxs': category_idxs }, fetch_list=[out]) paddle.disable_static() out_py = multiclass_nms(boxes, scores, category_idxs, self.threshold, self.topk) out = np.array(out) out = np.squeeze(out) self.assertTrue( np.array_equal(out, out_py), "paddle out: {}\n py out: {}\n".format(out, out_py)) def test_multiclass_nms_dynamic_to_static(self): for device in self.devices: for dtype in self.dtypes: paddle.set_device(device) def fun(x): scores = np.arange(0, 64).astype('float32') categories = np.array([0, 1, 2, 3]) category_idxs = categories.repeat(16) out = paddle.vision.ops.nms(x, 0.1, paddle.to_tensor(scores), paddle.to_tensor(category_idxs), categories, 10) return out boxes = np.random.rand(64, 4).astype('float32') boxes[:, 2] = boxes[:, 0] + boxes[:, 2] boxes[:, 3] = boxes[:, 1] + boxes[:, 3] origin = fun(paddle.to_tensor(boxes)) paddle.jit.save( fun, self.path, input_spec=[ paddle.static.InputSpec(shape=[None, 4], dtype='float32', name='x') ], ) load_func = paddle.jit.load(self.path) res = load_func(paddle.to_tensor(boxes)) self.assertTrue( np.array_equal(origin, res), "origin out: {}\n inference model out: {}\n".format( origin, res)) def test_matrix_nms_dynamic(self): for device in self.devices: for dtype in self.dtypes: boxes, scores, category_idxs, categories = gen_args( self.num_boxes, dtype) scores = np.random.rand(1, 4, self.num_boxes).astype(dtype) paddle.set_device(device) out = paddle.vision.ops.matrix_nms( paddle.to_tensor(boxes).unsqueeze(0), paddle.to_tensor(scores), self.threshold, post_threshold=0., nms_top_k=400, keep_top_k=100, ) if __name__ == '__main__': unittest.main()