def setUp(self):
self.link = DummyFasterRCNN(n_anchor_base=1,
feat_stride=16,
n_fg_class=21,
n_roi=1,
min_size=self.min_size,
max_size=self.max_size)
class TestFasterRCNNPrepare(unittest.TestCase):
min_size = 200
max_size = 400
def setUp(self):
self.link = DummyFasterRCNN(n_anchor_base=1,
feat_stride=16,
n_fg_class=21,
n_roi=1,
min_size=self.min_size,
max_size=self.max_size)
def check_prepare(self):
x = _random_array(np, self.in_shape)
out = self.link.prepare(x)
self.assertIsInstance(out, np.ndarray)
self.assertEqual(out.shape, self.expected_shape)
def test_prepare_cpu(self):
self.check_prepare()
@attr.gpu
def test_prepare_gpu(self):
self.link.to_gpu()
self.check_prepare()
class TestFasterRCNN(unittest.TestCase):
def setUp(self):
self.n_anchor_base = 6
self.feat_stride = 4
n_fg_class = 4
self.n_class = n_fg_class + 1
self.n_roi = 24
self.link = DummyFasterRCNN(
n_anchor_base=self.n_anchor_base,
feat_stride=self.feat_stride,
n_fg_class=n_fg_class,
n_roi=self.n_roi,
min_size=600,
max_size=1000,
)
def check_call(self):
xp = self.link.xp
x1 = chainer.Variable(_random_array(xp, (1, 3, 600, 800)))
scales = chainer.Variable(xp.array([1.], dtype=np.float32))
roi_cls_locs, roi_scores, rois, roi_indices = self.link(x1, scales)
self.assertIsInstance(roi_cls_locs, chainer.Variable)
self.assertIsInstance(roi_cls_locs.array, xp.ndarray)
self.assertEqual(roi_cls_locs.shape, (self.n_roi, self.n_class * 4))
self.assertIsInstance(roi_scores, chainer.Variable)
self.assertIsInstance(roi_scores.array, xp.ndarray)
self.assertEqual(roi_scores.shape, (self.n_roi, self.n_class))
self.assertIsInstance(rois, xp.ndarray)
self.assertEqual(rois.shape, (self.n_roi, 4))
self.assertIsInstance(roi_indices, xp.ndarray)
self.assertEqual(roi_indices.shape, (self.n_roi, ))
def test_call_cpu(self):
self.check_call()
@attr.gpu
def test_call_gpu(self):
self.link.to_gpu()
self.check_call()
def test_predict_cpu(self):
assert_is_detection_link(self.link, self.n_class - 1)
@attr.gpu
def test_predict_gpu(self):
self.link.to_gpu()
assert_is_detection_link(self.link, self.n_class - 1)
def setUp(self):
self.n_anchor_base = 6
self.feat_stride = 4
n_fg_class = 4
self.n_class = n_fg_class + 1
self.n_roi = 24
self.link = DummyFasterRCNN(
n_anchor_base=self.n_anchor_base,
feat_stride=self.feat_stride,
n_fg_class=n_fg_class,
n_roi=self.n_roi,
min_size=600,
max_size=1000,
)
def setUp(self):
self.n_anchor_base = 6
self.feat_stride = 4
self.n_fg_class = 3
self.n_roi = 24
self.n_bbox = 3
self.link = FasterRCNNTrainChain(DummyFasterRCNN(
n_anchor_base=self.n_anchor_base,
feat_stride=self.feat_stride,
n_fg_class=self.n_fg_class,
n_roi=self.n_roi,
min_size=600,
max_size=800,
))
self.bboxes = chainer.Variable(
generate_random_bbox(self.n_bbox, (600, 800), 16, 350)[np.newaxis])
_labels = np.random.randint(
0, self.n_fg_class, size=(1, self.n_bbox)).astype(np.int32)
self.labels = chainer.Variable(_labels)
self.imgs = chainer.Variable(_random_array((1, 3, 600, 800)))
self.scale = chainer.Variable(np.array(1.))