def __init__(self,
config,
batch_size,
num_classes,
use_sigmoid_cls,
target_means=(.0, .0, .0, .0),
target_stds=(1.0, 1.0, 1.0, 1.0)
):
super(Proposal, self).__init__()
cfg = config
self.batch_size = batch_size
self.num_classes = num_classes
self.target_means = target_means
self.target_stds = target_stds
self.use_sigmoid_cls = config.use_sigmoid_cls
if self.use_sigmoid_cls:
self.cls_out_channels = 1
self.activation = P.Sigmoid()
self.reshape_shape = (-1, 1)
else:
self.cls_out_channels = num_classes
self.activation = P.Softmax(axis=1)
self.reshape_shape = (-1, 2)
if self.cls_out_channels <= 0:
raise ValueError('num_classes={} is too small'.format(num_classes))
self.num_pre = cfg.rpn_proposal_nms_pre
self.min_box_size = cfg.rpn_proposal_min_bbox_size
self.nms_thr = cfg.rpn_proposal_nms_thr
self.nms_post = cfg.rpn_proposal_nms_post
self.nms_across_levels = cfg.rpn_proposal_nms_across_levels
self.max_num = cfg.rpn_proposal_max_num
# Op Define
self.squeeze = P.Squeeze()
self.reshape = P.Reshape()
self.cast = P.Cast()
self.feature_shapes = cfg.feature_shapes
self.transpose_shape = (1, 2, 0)
self.decode = BoundingBoxDecode()
self.nms = P.NMSWithMask(self.nms_thr)
self.concat_axis0 = P.Concat(axis=0)
self.concat_axis1 = P.Concat(axis=1)
self.split = P.Split(axis=1, output_num=5)
self.min = P.Minimum()
self.gatherND = P.GatherNd()
self.slice = P.Slice()
self.select = P.Select()
self.greater = P.Greater()
self.transpose = P.Transpose()
self.tile = P.Tile()
self.set_train_local(config, training=True)
self.multi_10 = Tensor(10.0, mstype.float16)
def test_nms_with_mask_edge_case_3():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
# CASE 3 - x2/x1 and y2/y1 sequence out of place
nms_op3 = P.NMSWithMask(0.7)
bbox3 = [[70, 70, 45, 75, 0.6], [30, 33, 43, 29, 0.1]]
expected_bbox = np.array([[70., 70., 45., 75.], [30., 33., 43., 29.]])
expected_score = np.array([0.6, 0.1])
sel_rows, sel_score = runMSRun(nms_op3, bbox3)
np.testing.assert_almost_equal(sel_rows, expected_bbox)
np.testing.assert_almost_equal(sel_score, expected_score)
def test_nms_with_mask_edge_case_2():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
# CASE 2 - 0 value boxes - with valid scores
nms_op2 = P.NMSWithMask(0.5)
bbox2 = [[0, 0, 0, 0, 0.6], [0, 0, 0, 0, 0.1]]
expected_bbox = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.]])
expected_score = np.array([0.6, 0.1])
sel_rows, sel_score = runMSRun(nms_op2, bbox2)
np.testing.assert_almost_equal(sel_rows, expected_bbox)
np.testing.assert_almost_equal(sel_score, expected_score)
def test_nms_with_mask_edge_case_1():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
# CASE 1 - FULL OVERLAP BOXES - Every box is duplicated and has a different score
nms_op1 = P.NMSWithMask(0.3)
bbox1 = [[12, 4, 33, 17, 0.6], [20, 11, 38, 23, 0.1], [20, 10, 45, 26, 0.9], [15, 17, 35, 38, 0.5],
[10, 20, 30, 40, 0.4], [35, 35, 89, 90, 0.8], [12, 4, 33, 17, 0.3], [20, 11, 38, 23, 0.2],
[20, 10, 45, 26, 0.1], [15, 17, 35, 38, 0.8], [10, 20, 30, 40, 0.41], [35, 35, 89, 90, 0.82]]
expected_bbox = np.array([[20., 10., 45., 26.],
[35., 35., 89., 90.],
[15., 17., 35., 38.],
[12., 4., 33., 17.]])
expected_score = np.array([0.9, 0.82, 0.8, 0.6])
sel_rows, sel_score = runMSRun(nms_op1, bbox1)
np.testing.assert_almost_equal(sel_rows, expected_bbox)
np.testing.assert_almost_equal(sel_score, expected_score)
def test_nms_with_mask_check_order():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
nms_op = P.NMSWithMask(0.5)
for _ in range(10):
count = 4000
box = np.random.randint(1, 100, size=(count, 4))
box[:, 2] = box[:, 0] + box[:, 2]
box[:, 3] = box[:, 1] + box[:, 3]
unsorted_scores = np.random.rand(count, 1)
bbox = np.hstack((box, unsorted_scores))
bbox = Tensor(bbox, dtype=mindspore.float32)
prop, _, _ = nms_op(bbox)
ms_sorted_scores = (prop.asnumpy()[:, -1]) # select just scores
np_sorted_scores = (np.sort(unsorted_scores, axis=0)[::-1][:, 0]) # sort manually
np.testing.assert_array_almost_equal(
ms_sorted_scores, np_sorted_scores)
def test_nms_with_masl_check_result():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_count = 500
for x in range(1, test_count + 1):
count = 20 # size of bbox lists
nms_op = P.NMSWithMask(x * 0.002) # will test full range b/w 0 and 1
box = np.random.randint(1, 100, size=(count, 4))
box[:, 2] = box[:, 0] + box[:, 2]
box[:, 3] = box[:, 1] + box[:, 3]
unsorted_scores = np.random.rand(count, 1)
sorted_scores = np.sort(unsorted_scores, axis=0)[::-1]
bbox = np.hstack((box, sorted_scores))
bbox = Tensor(bbox, dtype=mindspore.float32)
_, _, mask = nms_op(bbox)
mask = mask.asnumpy()
manual_mask = manualNMS(box, x * 0.002)
np.testing.assert_array_equal(mask, np.array(manual_mask))
def test_mode_init(self, config):
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(self.dtype))
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(self.dtype) *
config.test_score_thr)
self.test_score_zeros = Tensor(
np.ones((self.rpn_max_num, 1)).astype(self.dtype) * 0)
self.test_box_zeros = Tensor(
np.ones((self.rpn_max_num, 4)).astype(self.dtype) * -1)
self.test_iou_thr = Tensor(
np.ones((self.rpn_max_num, 1)).astype(self.dtype) *
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
'block': P.NPUGetFloatStatus(),
'desc_inputs': [Tensor(np.zeros([8]).astype(np.float32))],
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']}),
('NpuClearFloatStatus', {
'block': P.NPUClearFloatStatus(),
'desc_inputs': [Tensor(np.zeros([8]).astype(np.float32))],
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']}),
('CheckValid', {
'block': P.CheckValid(),
'desc_inputs': [[20000, 4], [3]],
'desc_bprop': [[20000]],
'skip': ['backward']}),
('NMSWithMask', {
'block': P.NMSWithMask(0.5),
'desc_inputs': [[128, 5]],
'desc_bprop': [[128, 5], [128], [128]],
'skip': ['backward']}),
('Abs', {
'block': P.Abs(),
'desc_inputs': [[4]],
'desc_bprop': [[4]]}),
('CumSum', {
'block': P.CumSum(),
'desc_const': [0],
'desc_inputs': [Tensor(np.array([[3, 4],[1, 6]]).astype(np.float16))],
'desc_bprop': [Tensor(np.array([[3, 4],[4, 10]]).astype(np.float16))]}),
('ReduceSum_3', {
'block': P.ReduceSum(),
'desc_const': [0],
'skip': ['backward']}),
# input is not tensor
('Sin0', {
'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(bool)
('Sin1', {
'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# input is not tensor
('NMSWithMask0', {
'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is not Tensor(float16) or Tensor(float32)
('NMSWithMask1', {
'block': (P.NMSWithMask(), {'exception': TypeError, 'error_keywords': ['NMSWithMask']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
'skip': ['backward']}),
# dims is not 2
('NMSWithMask2', {
'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
'desc_inputs': [Tensor(np.ones([3, 4, 2]).astype(np.float32))],
'skip': ['backward']}),
# shape[1] is not 5
('NMSWithMask3', {
'block': (P.NMSWithMask(), {'exception': ValueError, 'error_keywords': ['NMSWithMask']}),
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('Sin1', {
'block': (P.Sin(), {
'exception': TypeError,
'error_keywords': ['Sin']
}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']
}),
# input is not tensor
('NMSWithMask0', {
'block': (P.NMSWithMask(), {
'exception': TypeError,
'error_keywords': ['NMSWithMask']
}),
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is not Tensor(float16) or Tensor(float32)
('NMSWithMask1', {
'block': (P.NMSWithMask(), {
'exception': TypeError,
'error_keywords': ['NMSWithMask']
}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32))],
'skip': ['backward']
}),
def __init__(self, config):
super(Mask_Rcnn_Resnet50, 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)
# Backbone resnet50
self.backbone = ResNetFea(ResidualBlockUsing,
config.resnet_block,
config.resnet_in_channels,
config.resnet_out_channels,
False)
# Fpn
self.fpn_ncek = FeatPyramidNeck(config.fpn_in_channels,
config.fpn_out_channels,
config.fpn_num_outs)
# 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=(768, 1280), means=self.target_means, \
stds=self.target_stds)
# Roi
self.roi_align = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
self.train_batch_size,
config.roi_align_finest_scale,
mask=False)
self.roi_align.set_train_local(config, True)
self.roi_align_mask = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
self.train_batch_size,
config.roi_align_finest_scale,
mask=True)
self.roi_align_mask.set_train_local(config, True)
self.roi_align_test = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
1,
config.roi_align_finest_scale,
mask=False)
self.roi_align_test.set_train_local(config, False)
self.roi_align_mask_test = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
1,
config.roi_align_finest_scale,
mask=True)
self.roi_align_mask_test.set_train_local(config, False)
# Rcnn
self.rcnn_cls = RcnnCls(config, self.train_batch_size, self.num_classes)
self.rcnn_mask = RcnnMask(config, 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_fb_mask = 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.float16))
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.float16) * config.test_score_thr)
self.test_score_zeros = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * 0)
self.test_box_zeros = Tensor(np.ones((self.rpn_max_num, 4)).astype(np.float16) * -1)
self.test_iou_thr = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * 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 = min(self.num_classes - 2, 55)
self.concat_end = (self.num_classes - 1)
# Init tensor
roi_align_index = [np.array(np.ones((config.num_expected_pos_stage2 + config.num_expected_neg_stage2, 1)) * i,
dtype=np.float16) for i in range(self.train_batch_size)]
roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i, dtype=np.float16) \
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))
roi_align_index_pos = [np.array(np.ones((config.num_expected_pos_stage2, 1)) * i,
dtype=np.float16) for i in range(self.train_batch_size)]
self.roi_align_index_tensor_pos = Tensor(np.concatenate(roi_align_index_pos))
self.rcnn_loss_cls_weight = Tensor(np.array(config.rcnn_loss_cls_weight).astype(np.float16))
self.rcnn_loss_reg_weight = Tensor(np.array(config.rcnn_loss_reg_weight).astype(np.float16))
self.rcnn_loss_mask_fb_weight = Tensor(np.array(config.rcnn_loss_mask_fb_weight).astype(np.float16))
self.argmax_with_value = P.ArgMaxWithValue(axis=1)
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.onehot = P.OneHot()
self.reducesum = P.ReduceSum()
self.sigmoid = P.Sigmoid()
self.expand_dims = P.ExpandDims()
self.test_mask_fb_zeros = Tensor(np.zeros((self.rpn_max_num, 28, 28)).astype(np.float16))
self.value = Tensor(1.0, mstype.float16)
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()
