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add generate_proposals into paddle.vision
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@zoooo0820
zoooo0820 committed Jun 17, 2022
1 parent 8bc1c82 commit f694865
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180 changes: 180 additions & 0 deletions python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py
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Expand Up @@ -254,6 +254,186 @@ def init_test_params(self):
self.pixel_offset = False


class testGenerateProposalsFunctionAPI(unittest.TestCase):

def setUp(self):
np.random.seed(678)
self.scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
self.bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
self.im_shape_np = np.array([[8, 8], [6, 6]]).astype('float32')
self.anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4),
[4, 4, 3, 4]).astype('float32')
self.variances_np = np.ones((4, 4, 3, 4)).astype('float32')

self.roi_expected, self.roi_probs_expected, self.rois_num_expected = generate_proposals_v2_in_python(
self.scores_np,
self.bbox_deltas_np,
self.im_shape_np,
self.anchors_np,
self.variances_np,
pre_nms_topN=10,
post_nms_topN=5,
nms_thresh=0.5,
min_size=0.1,
eta=1.0,
pixel_offset=False)
self.roi_expected = np.array(self.roi_expected).squeeze(1)
self.roi_probs_expected = np.array(self.roi_probs_expected).squeeze(1)
self.rois_num_expected = np.array(self.rois_num_expected)

def test_dynamic(self):
paddle.disable_static()
scores = paddle.to_tensor(self.scores_np)
bbox_deltas = paddle.to_tensor(self.bbox_deltas_np)
im_shape = paddle.to_tensor(self.im_shape_np)
anchors = paddle.to_tensor(self.anchors_np)
variances = paddle.to_tensor(self.variances_np)

rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
im_shape,
anchors,
variances,
pre_nms_top_n=10,
post_nms_top_n=5,
return_rois_num=True)
self.assertTrue(np.allclose(self.roi_expected, rois.numpy()))
self.assertTrue(np.allclose(self.roi_probs_expected, roi_probs.numpy()))
self.assertTrue(np.allclose(self.rois_num_expected, rois_num.numpy()))

def test_static(self):
paddle.enable_static()
scores = paddle.static.data(name='scores',
shape=[2, 3, 4, 4],
dtype='float32')
bbox_deltas = paddle.static.data(name='bbox_deltas',
shape=[2, 12, 4, 4],
dtype='float32')
im_shape = paddle.static.data(name='im_shape',
shape=[2, 2],
dtype='float32')
anchors = paddle.static.data(name='anchors',
shape=[4, 4, 3, 4],
dtype='float32')
variances = paddle.static.data(name='variances',
shape=[4, 4, 3, 4],
dtype='float32')
rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
im_shape,
anchors,
variances,
pre_nms_top_n=10,
post_nms_top_n=5,
return_rois_num=True)
exe = paddle.static.Executor()
rois, roi_probs, rois_num = exe.run(
paddle.static.default_main_program(),
feed={
'scores': self.scores_np,
'bbox_deltas': self.bbox_deltas_np,
'im_shape': self.im_shape_np,
'anchors': self.anchors_np,
'variances': self.variances_np,
},
fetch_list=[rois.name, roi_probs.name, rois_num.name],
return_numpy=False)

self.assertTrue(np.allclose(self.roi_expected, np.array(rois)))
self.assertTrue(
np.allclose(self.roi_probs_expected, np.array(roi_probs)))
self.assertTrue(np.allclose(self.rois_num_expected, np.array(rois_num)))


class testGenerateProposalsClassAPI(unittest.TestCase):

def setUp(self):
np.random.seed(678)
self.scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
self.bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
self.im_shape_np = np.array([[8, 8], [6, 6]]).astype('float32')
self.anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4),
[4, 4, 3, 4]).astype('float32')
self.variances_np = np.ones((4, 4, 3, 4)).astype('float32')

self.roi_expected, self.roi_probs_expected, self.rois_num_expected = generate_proposals_v2_in_python(
self.scores_np,
self.bbox_deltas_np,
self.im_shape_np,
self.anchors_np,
self.variances_np,
pre_nms_topN=10,
post_nms_topN=5,
nms_thresh=0.5,
min_size=0.1,
eta=1.0,
pixel_offset=False)
self.roi_expected = np.array(self.roi_expected).squeeze(1)
self.roi_probs_expected = np.array(self.roi_probs_expected).squeeze(1)
self.rois_num_expected = np.array(self.rois_num_expected)

def test_dynamic(self):
paddle.disable_static()

generate_proposals_module = paddle.vision.ops.GenerateProposals(
pre_nms_top_n=10, post_nms_top_n=5, return_rois_num=True)
scores = paddle.to_tensor(self.scores_np)
bbox_deltas = paddle.to_tensor(self.bbox_deltas_np)
im_shape = paddle.to_tensor(self.im_shape_np)
anchors = paddle.to_tensor(self.anchors_np)
variances = paddle.to_tensor(self.variances_np)

rois, roi_probs, rois_num = generate_proposals_module(
scores, bbox_deltas, im_shape, anchors, variances)

self.assertTrue(np.allclose(self.roi_expected, rois.numpy()))
self.assertTrue(np.allclose(self.roi_probs_expected, roi_probs.numpy()))
self.assertTrue(np.allclose(self.rois_num_expected, rois_num.numpy()))

def test_static(self):
paddle.enable_static()

generate_proposals_module = paddle.vision.ops.GenerateProposals(
pre_nms_top_n=10, post_nms_top_n=5, return_rois_num=True)

scores = paddle.static.data(name='scores',
shape=[2, 3, 4, 4],
dtype='float32')
bbox_deltas = paddle.static.data(name='bbox_deltas',
shape=[2, 12, 4, 4],
dtype='float32')
im_shape = paddle.static.data(name='im_shape',
shape=[2, 2],
dtype='float32')
anchors = paddle.static.data(name='anchors',
shape=[4, 4, 3, 4],
dtype='float32')
variances = paddle.static.data(name='variances',
shape=[4, 4, 3, 4],
dtype='float32')
rois, roi_probs, rois_num = generate_proposals_module(
scores, bbox_deltas, im_shape, anchors, variances)
exe = paddle.static.Executor()
rois, roi_probs, rois_num = exe.run(
paddle.static.default_main_program(),
feed={
'scores': self.scores_np,
'bbox_deltas': self.bbox_deltas_np,
'im_shape': self.im_shape_np,
'anchors': self.anchors_np,
'variances': self.variances_np,
},
fetch_list=[rois.name, roi_probs.name, rois_num.name],
return_numpy=False)

self.assertTrue(np.allclose(self.roi_expected, np.array(rois)))
self.assertTrue(
np.allclose(self.roi_probs_expected, np.array(roi_probs)))
self.assertTrue(np.allclose(self.rois_num_expected, np.array(rois_num)))


if __name__ == '__main__':
paddle.enable_static()
unittest.main()

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