def test_roi_align():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
x = Tensor(
np.array([[[[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
[13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24],
[25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36]]]],
np.float32))
rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], np.float32))
# test case 1
pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num)
output = roi_align(x, rois)
print(output)
expect = [[[[2.75, 4.5, 6.5], [13.25, 15., 17.], [25.25, 27., 29.]]]]
assert (output.asnumpy() == expect).all()
# test case 2
pooled_height, pooled_width, spatial_scale, sample_num = 4, 4, 0.2, 3
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num)
output = roi_align(x, rois)
print(output)
expect = [[[[1.2333, 2.1000, 3.3000, 4.5000],
[6.4333, 7.3000, 8.5000, 9.7000],
[13.6333, 14.5000, 15.7000, 16.9000],
[20.8333, 21.7000, 22.9000, 24.1000]]]]
np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)
# test case 3
pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.3, 3
rois = Tensor(
np.array([[0, -2.0, -2.0, 22.0, 22.0], [0, 1.0, 0.0, 19.0, 18.0]],
np.float32))
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num)
output = roi_align(x, rois)
print(output)
expect = [[[[3.3333, 5.5000, 7.6667], [16.3333, 18.5000, 20.6667],
[29.3333, 31.5000, 33.6667]]],
[[[4.5000, 6.3000, 8.1000], [14.9000, 16.7000, 18.5000],
[25.7000, 27.5000, 29.3000]]]]
np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)
# test case 4
pooled_height, pooled_width, spatial_scale, sample_num = 2, 2, 1.0, -1
rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], np.float32))
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num)
output = roi_align(x, rois)
print(output)
expect = [[[[8.2222, 0.], [0., 0.]]]]
np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)
def __init__(self, out_size_h, out_size_w, spatial_scale, sample_num=0):
super(ROIAlign, self).__init__()
self.out_size = (out_size_h, out_size_w)
self.spatial_scale = float(spatial_scale)
self.sample_num = int(sample_num)
self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1],
self.spatial_scale, self.sample_num)
def roi_align_case(data_type):
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
x = Tensor(
np.array([[[[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
[13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24],
[25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36]]]],
data_type))
# test case 1
rois = Tensor(np.array([[0, -2.0, -2.0, 21.0, 21.0]], data_type))
pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num, 1)
output = roi_align(x, rois)
print(output)
expect = [[[[4.5, 6.5, 8.5], [16.5, 18.5, 20.5], [28.5, 30.5, 32.5]]]]
assert (output.asnumpy() == expect).all()
# test case 2
rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], data_type))
pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num, 0)
output = roi_align(x, rois)
print(output)
expect = [[[[4.5, 6.5, 8.5], [16.5, 18.5, 20.5], [28.5, 30.5, 32.5]]]]
assert (output.asnumpy() == expect).all()
# test case 3
pooled_height, pooled_width, spatial_scale, sample_num = 2, 2, 1.0, -1
rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], data_type))
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num, 0)
output = roi_align(x, rois)
print(output)
expect = [[[[6.295, 0.], [0., 0.]]]]
np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=2)
def test_roi_align_half():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
x = Tensor(
np.array([[[[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
[13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24],
[25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36]]]],
np.float16))
rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], np.float16))
# test case 1
pooled_height, pooled_width, spatial_scale, sample_num = 4, 4, 0.2, 3
roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale,
sample_num, 0)
output = roi_align(x, rois)
print(output)
expect = [[[[1.2333, 2.1000, 3.3000, 4.5000],
[6.4333, 7.3000, 8.5000, 9.7000],
[13.6333, 14.5000, 15.7000, 16.9000],
[20.8333, 21.7000, 22.9000, 24.1000]]]]
np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=1)
def __init__(self, config):
super(Deeptext_VGG16, self).__init__()
self.train_batch_size = config.batch_size
self.num_classes = config.num_classes
self.anchor_scales = config.anchor_scales
self.anchor_ratios = config.anchor_ratios
self.anchor_strides = config.anchor_strides
self.target_means = tuple(config.rcnn_target_means)
self.target_stds = tuple(config.rcnn_target_stds)
# Anchor generator
anchor_base_sizes = None
self.anchor_base_sizes = list(
self.anchor_strides
) if anchor_base_sizes is None else anchor_base_sizes
self.anchor_generators = []
for anchor_base in self.anchor_base_sizes:
self.anchor_generators.append(
AnchorGenerator(anchor_base, self.anchor_scales,
self.anchor_ratios))
self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
featmap_sizes = config.feature_shapes
assert len(featmap_sizes) == len(self.anchor_generators)
self.anchor_list = self.get_anchors(featmap_sizes)
# Rpn and rpn loss
self.gt_labels_stage1 = Tensor(
np.ones((self.train_batch_size, config.num_gts)).astype(np.uint8))
self.rpn_with_loss = RPN(config, self.train_batch_size,
config.rpn_in_channels,
config.rpn_feat_channels, config.num_anchors,
config.rpn_cls_out_channels)
# Proposal
self.proposal_generator = Proposal(config, self.train_batch_size,
config.activate_num_classes,
config.use_sigmoid_cls)
self.proposal_generator.set_train_local(config, True)
self.proposal_generator_test = Proposal(config, config.test_batch_size,
config.activate_num_classes,
config.use_sigmoid_cls)
self.proposal_generator_test.set_train_local(config, False)
# Assign and sampler stage two
self.bbox_assigner_sampler_for_rcnn = BboxAssignSampleForRcnn(
config, self.train_batch_size, config.num_bboxes_stage2, True)
self.decode = P.BoundingBoxDecode(max_shape=(576, 960), means=self.target_means, \
stds=self.target_stds)
# Rcnn
self.rcnn = Rcnn(
config, config.rcnn_in_channels * config.roi_layer['out_size'] *
config.roi_layer['out_size'], self.train_batch_size,
self.num_classes)
# Op declare
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.concat = P.Concat(axis=0)
self.concat_1 = P.Concat(axis=1)
self.concat_2 = P.Concat(axis=2)
self.reshape = P.Reshape()
self.select = P.Select()
self.greater = P.Greater()
self.transpose = P.Transpose()
# Test mode
self.test_batch_size = config.test_batch_size
self.split = P.Split(axis=0, output_num=self.test_batch_size)
self.split_shape = P.Split(axis=0, output_num=4)
self.split_scores = P.Split(axis=1, output_num=self.num_classes)
self.split_cls = P.Split(axis=0, output_num=self.num_classes - 1)
self.tile = P.Tile()
self.gather = P.GatherNd()
self.rpn_max_num = config.rpn_max_num
self.zeros_for_nms = Tensor(
np.zeros((self.rpn_max_num, 3)).astype(np.float32))
self.ones_mask = np.ones((self.rpn_max_num, 1)).astype(np.bool)
self.zeros_mask = np.zeros((self.rpn_max_num, 1)).astype(np.bool)
self.bbox_mask = Tensor(
np.concatenate((self.ones_mask, self.zeros_mask, self.ones_mask,
self.zeros_mask),
axis=1))
self.nms_pad_mask = Tensor(
np.concatenate((self.ones_mask, self.ones_mask, self.ones_mask,
self.ones_mask, self.zeros_mask),
axis=1))
self.test_score_thresh = Tensor(
np.ones((self.rpn_max_num, 1)).astype(np.float32) *
config.test_score_thr)
self.test_score_zeros = Tensor(
np.ones((self.rpn_max_num, 1)).astype(np.float32) * 0)
self.test_box_zeros = Tensor(
np.ones((self.rpn_max_num, 4)).astype(np.float32) * -1)
self.test_iou_thr = Tensor(
np.ones((self.rpn_max_num, 1)).astype(np.float32) *
config.test_iou_thr)
self.test_max_per_img = config.test_max_per_img
self.nms_test = P.NMSWithMask(config.test_iou_thr)
self.softmax = P.Softmax(axis=1)
self.logicand = P.LogicalAnd()
self.oneslike = P.OnesLike()
self.test_topk = P.TopK(sorted=True)
self.test_num_proposal = self.test_batch_size * self.rpn_max_num
# Improve speed
self.concat_start = (self.num_classes - 2)
self.concat_end = (self.num_classes - 1)
# Init tensor
self.use_ambigous_sample = config.use_ambigous_sample
roi_align_index = [
np.array(np.ones((config.num_expected_pos_stage2 +
config.num_expected_neg_stage2, 1)) * i,
dtype=np.float32) for i in range(self.train_batch_size)
]
if self.use_ambigous_sample:
roi_align_index = [
np.array(np.ones((config.num_expected_pos_stage2 +
config.num_expected_amb_stage2 +
config.num_expected_neg_stage2, 1)) * i,
dtype=np.float32)
for i in range(self.train_batch_size)
]
roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i, dtype=np.float32) \
for i in range(self.test_batch_size)]
self.roi_align_index_tensor = Tensor(np.concatenate(roi_align_index))
self.roi_align_index_test_tensor = Tensor(
np.concatenate(roi_align_index_test))
self.roi_align4 = P.ROIAlign(pooled_width=7,
pooled_height=7,
spatial_scale=0.125)
self.roi_align5 = P.ROIAlign(pooled_width=7,
pooled_height=7,
spatial_scale=0.0625)
self.concat1 = P.Concat(axis=1)
self.roi_align_fuse = _conv(in_channels=1024,
out_channels=512,
kernel_size=1,
padding=0,
stride=1)
self.vgg16_feature_extractor = VGG16FeatureExtraction()
