wehub-resource-sync
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle
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from paddle.base.layer_helper import LayerHelper
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paddle.enable_static()
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def yolo_box_post(
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box0,
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box1,
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box2,
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im_shape,
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im_scale,
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anchors0=[116, 90, 156, 198, 373, 326],
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anchors1=[30, 61, 62, 45, 59, 119],
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anchors2=[10, 13, 16, 30, 33, 23],
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class_num=80,
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conf_thresh=0.005,
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downsample_ratio0=32,
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downsample_ratio1=16,
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downsample_ratio2=8,
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clip_bbox=True,
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scale_x_y=1.0,
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nms_threshold=0.45,
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):
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helper = LayerHelper('yolo_box_post', **locals())
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output = helper.create_variable_for_type_inference(dtype=box0.dtype)
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nms_rois_num = helper.create_variable_for_type_inference(dtype='int32')
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inputs = {
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'Boxes0': box0,
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'Boxes1': box1,
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'Boxes2': box2,
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"ImageShape": im_shape,
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"ImageScale": im_scale,
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}
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outputs = {'Out': output, 'NmsRoisNum': nms_rois_num}
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helper.append_op(
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type="yolo_box_post",
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inputs=inputs,
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attrs={
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'anchors0': anchors0,
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'anchors1': anchors1,
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'anchors2': anchors2,
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'class_num': class_num,
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'conf_thresh': conf_thresh,
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'downsample_ratio0': downsample_ratio0,
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'downsample_ratio1': downsample_ratio1,
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'downsample_ratio2': downsample_ratio2,
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'clip_bbox': clip_bbox,
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'scale_x_y': scale_x_y,
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'nms_threshold': nms_threshold,
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},
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outputs=outputs,
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)
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output.stop_gradient = True
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nms_rois_num.stop_gradient = True
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return output, nms_rois_num
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@unittest.skipIf(
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not paddle.is_compiled_with_cuda(), "only support cuda kernel."
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)
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class TestYoloBoxPost(unittest.TestCase):
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def test_yolo_box_post(self):
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place = paddle.CUDAPlace(0)
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with paddle.pir_utils.OldIrGuard():
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program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(program, startup_program):
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box0 = paddle.static.data("box0", [1, 255, 19, 19])
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box1 = paddle.static.data("box1", [1, 255, 38, 38])
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box2 = paddle.static.data("box2", [1, 255, 76, 76])
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im_shape = paddle.static.data("im_shape", [1, 2])
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im_scale = paddle.static.data("im_scale", [1, 2])
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out, rois_num = yolo_box_post(
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box0, box1, box2, im_shape, im_scale
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)
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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feed = {
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"box0": np.random.uniform(size=[1, 255, 19, 19]).astype(
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"float32"
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),
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"box1": np.random.uniform(size=[1, 255, 38, 38]).astype(
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"float32"
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),
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"box2": np.random.uniform(size=[1, 255, 76, 76]).astype(
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"float32"
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),
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"im_shape": np.array([[608.0, 608.0]], "float32"),
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"im_scale": np.array([[1.0, 1.0]], "float32"),
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}
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outs = exe.run(program, feed=feed, fetch_list=[out, rois_num])
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if __name__ == '__main__':
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unittest.main()
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