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196 lines
6.6 KiB
196 lines
6.6 KiB
# Copyright (c) 2019 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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from __future__ import division
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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from paddle.fluid import core
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-1.0 * x))
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def YoloBox(x, img_size, attrs):
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n, c, h, w = x.shape
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anchors = attrs['anchors']
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an_num = int(len(anchors) // 2)
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class_num = attrs['class_num']
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conf_thresh = attrs['conf_thresh']
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downsample = attrs['downsample']
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clip_bbox = attrs['clip_bbox']
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scale_x_y = attrs['scale_x_y']
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bias_x_y = -0.5 * (scale_x_y - 1.)
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input_size = downsample * h
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x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
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pred_box = x[:, :, :, :, :4].copy()
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grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
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grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
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pred_box[:, :, :, :, 0] = (
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grid_x + sigmoid(pred_box[:, :, :, :, 0]) * scale_x_y + bias_x_y) / w
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pred_box[:, :, :, :, 1] = (
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grid_y + sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
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anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
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anchors_s = np.array(
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[(an_w / input_size, an_h / input_size) for an_w, an_h in anchors])
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anchor_w = anchors_s[:, 0:1].reshape((1, an_num, 1, 1))
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anchor_h = anchors_s[:, 1:2].reshape((1, an_num, 1, 1))
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pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
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pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) * anchor_h
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pred_conf = sigmoid(x[:, :, :, :, 4:5])
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pred_conf[pred_conf < conf_thresh] = 0.
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pred_score = sigmoid(x[:, :, :, :, 5:]) * pred_conf
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pred_box = pred_box * (pred_conf > 0.).astype('float32')
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pred_box = pred_box.reshape((n, -1, 4))
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pred_box[:, :, :2], pred_box[:, :, 2:4] = \
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pred_box[:, :, :2] - pred_box[:, :, 2:4] / 2., \
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pred_box[:, :, :2] + pred_box[:, :, 2:4] / 2.0
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pred_box[:, :, 0] = pred_box[:, :, 0] * img_size[:, 1][:, np.newaxis]
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pred_box[:, :, 1] = pred_box[:, :, 1] * img_size[:, 0][:, np.newaxis]
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pred_box[:, :, 2] = pred_box[:, :, 2] * img_size[:, 1][:, np.newaxis]
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pred_box[:, :, 3] = pred_box[:, :, 3] * img_size[:, 0][:, np.newaxis]
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if clip_bbox:
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for i in range(len(pred_box)):
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pred_box[i, :, 0] = np.clip(pred_box[i, :, 0], 0, np.inf)
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pred_box[i, :, 1] = np.clip(pred_box[i, :, 1], 0, np.inf)
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pred_box[i, :, 2] = np.clip(pred_box[i, :, 2], -np.inf,
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img_size[i, 1] - 1)
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pred_box[i, :, 3] = np.clip(pred_box[i, :, 3], -np.inf,
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img_size[i, 0] - 1)
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return pred_box, pred_score.reshape((n, -1, class_num))
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class TestYoloBoxOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = 'yolo_box'
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x = np.random.random(self.x_shape).astype('float32')
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img_size = np.random.randint(10, 20, self.imgsize_shape).astype('int32')
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self.attrs = {
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"anchors": self.anchors,
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"class_num": self.class_num,
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"conf_thresh": self.conf_thresh,
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"downsample": self.downsample,
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"clip_bbox": self.clip_bbox,
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"scale_x_y": self.scale_x_y,
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}
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self.inputs = {
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'X': x,
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'ImgSize': img_size,
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}
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boxes, scores = YoloBox(x, img_size, self.attrs)
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self.outputs = {
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"Boxes": boxes,
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"Scores": scores,
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}
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def test_check_output(self):
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self.check_output()
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def initTestCase(self):
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self.anchors = [10, 13, 16, 30, 33, 23]
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an_num = int(len(self.anchors) // 2)
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self.batch_size = 32
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self.class_num = 2
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self.conf_thresh = 0.5
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self.downsample = 32
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self.clip_bbox = True
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self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
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self.imgsize_shape = (self.batch_size, 2)
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self.scale_x_y = 1.
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class TestYoloBoxOpNoClipBbox(TestYoloBoxOp):
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def initTestCase(self):
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self.anchors = [10, 13, 16, 30, 33, 23]
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an_num = int(len(self.anchors) // 2)
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self.batch_size = 32
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self.class_num = 2
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self.conf_thresh = 0.5
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self.downsample = 32
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self.clip_bbox = False
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self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
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self.imgsize_shape = (self.batch_size, 2)
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self.scale_x_y = 1.
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class TestYoloBoxOpScaleXY(TestYoloBoxOp):
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def initTestCase(self):
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self.anchors = [10, 13, 16, 30, 33, 23]
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an_num = int(len(self.anchors) // 2)
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self.batch_size = 32
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self.class_num = 2
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self.conf_thresh = 0.5
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self.downsample = 32
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self.clip_bbox = True
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self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
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self.imgsize_shape = (self.batch_size, 2)
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self.scale_x_y = 1.2
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class TestYoloBoxDygraph(unittest.TestCase):
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def test_dygraph(self):
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paddle.disable_static()
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x = np.random.random([2, 14, 8, 8]).astype('float32')
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img_size = np.ones((2, 2)).astype('int32')
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x = paddle.to_tensor(x)
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img_size = paddle.to_tensor(img_size)
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boxes, scores = paddle.vision.ops.yolo_box(
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x,
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img_size=img_size,
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anchors=[10, 13, 16, 30],
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class_num=2,
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conf_thresh=0.01,
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downsample_ratio=8,
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clip_bbox=True,
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scale_x_y=1.)
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assert boxes is not None and scores is not None
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paddle.enable_static()
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class TestYoloBoxStatic(unittest.TestCase):
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def test_static(self):
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x = paddle.static.data('x', [2, 14, 8, 8], 'float32')
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img_size = paddle.static.data('img_size', [2, 2], 'int32')
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boxes, scores = paddle.vision.ops.yolo_box(
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x,
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img_size=img_size,
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anchors=[10, 13, 16, 30],
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class_num=2,
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conf_thresh=0.01,
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downsample_ratio=8,
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clip_bbox=True,
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scale_x_y=1.)
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assert boxes is not None and scores is not None
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if __name__ == "__main__":
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unittest.main()
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