def __init__(self, config, batch_size, in_channels, feat_channels,
num_anchors, cls_out_channels):
super(RPN, self).__init__()
cfg_rpn = config
self.dtype = np.float32
self.ms_type = mstype.float32
self.num_bboxes = cfg_rpn.num_bboxes
self.slice_index = ()
self.feature_anchor_shape = ()
self.slice_index += (0, )
index = 0
for shape in cfg_rpn.feature_shapes:
self.slice_index += (self.slice_index[index] +
shape[0] * shape[1] * num_anchors, )
self.feature_anchor_shape += (shape[0] * shape[1] * num_anchors *
batch_size, )
index += 1
self.num_anchors = num_anchors
self.batch_size = batch_size
self.test_batch_size = cfg_rpn.test_batch_size
self.num_layers = 5
self.real_ratio = Tensor(np.ones((1, 1)).astype(self.dtype))
self.rpn_convs_list = nn.layer.CellList(
self._make_rpn_layer(self.num_layers, in_channels, feat_channels,
num_anchors, cls_out_channels))
self.transpose = P.Transpose()
self.reshape = P.Reshape()
self.concat = P.Concat(axis=0)
self.fill = P.Fill()
self.placeh1 = Tensor(np.ones((1, )).astype(self.dtype))
self.trans_shape = (0, 2, 3, 1)
self.reshape_shape_reg = (-1, 4)
self.reshape_shape_cls = (-1, )
self.rpn_loss_reg_weight = Tensor(
np.array(cfg_rpn.rpn_loss_reg_weight).astype(self.dtype))
self.rpn_loss_cls_weight = Tensor(
np.array(cfg_rpn.rpn_loss_cls_weight).astype(self.dtype))
self.num_expected_total = Tensor(
np.array(cfg_rpn.num_expected_neg * self.batch_size).astype(
self.dtype))
self.num_bboxes = cfg_rpn.num_bboxes
self.get_targets = BboxAssignSample(cfg_rpn, self.batch_size,
self.num_bboxes, False)
self.CheckValid = P.CheckValid()
self.sum_loss = P.ReduceSum()
self.loss_cls = P.SigmoidCrossEntropyWithLogits()
self.loss_bbox = P.SmoothL1Loss(beta=1.0 / 9.0)
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.tile = P.Tile()
self.zeros_like = P.ZerosLike()
self.loss = Tensor(np.zeros((1, )).astype(self.dtype))
self.clsloss = Tensor(np.zeros((1, )).astype(self.dtype))
self.regloss = Tensor(np.zeros((1, )).astype(self.dtype))
def __init__(self, config, batch_size, in_channels, feat_channels,
num_anchors, cls_out_channels):
super(RPN, self).__init__()
cfg_rpn = config
self.cfg = config
self.num_bboxes = cfg_rpn.num_bboxes
self.feature_anchor_shape = cfg_rpn.feature_shapes
self.feature_anchor_shape = self.feature_anchor_shape[0] * \
self.feature_anchor_shape[1] * num_anchors * batch_size
self.num_anchors = num_anchors
self.batch_size = batch_size
self.test_batch_size = cfg_rpn.test_batch_size
self.num_layers = 1
self.real_ratio = Tensor(np.ones((1, 1)).astype(np.float16))
self.use_sigmoid_cls = config.use_sigmoid_cls
if config.use_sigmoid_cls:
self.reshape_shape_cls = (-1, )
self.loss_cls = P.SigmoidCrossEntropyWithLogits()
cls_out_channels = 1
else:
self.reshape_shape_cls = (-1, cls_out_channels)
self.loss_cls = nn.SoftmaxCrossEntropyWithLogits(sparse=True,
reduction="none")
self.rpn_convs_list = self._make_rpn_layer(self.num_layers, in_channels, feat_channels,\
num_anchors, cls_out_channels)
self.transpose = P.Transpose()
self.reshape = P.Reshape()
self.concat = P.Concat(axis=0)
self.fill = P.Fill()
self.placeh1 = Tensor(np.ones((1, )).astype(np.float16))
self.trans_shape = (0, 2, 3, 1)
self.reshape_shape_reg = (-1, 4)
self.softmax = nn.Softmax()
self.rpn_loss_reg_weight = Tensor(
np.array(cfg_rpn.rpn_loss_reg_weight).astype(np.float16))
self.rpn_loss_cls_weight = Tensor(
np.array(cfg_rpn.rpn_loss_cls_weight).astype(np.float16))
self.num_expected_total = Tensor(
np.array(cfg_rpn.num_expected_neg * self.batch_size).astype(
np.float16))
self.num_bboxes = cfg_rpn.num_bboxes
self.get_targets = BboxAssignSample(cfg_rpn, self.batch_size,
self.num_bboxes, False)
self.CheckValid = P.CheckValid()
self.sum_loss = P.ReduceSum()
self.loss_bbox = P.SmoothL1Loss(beta=1.0 / 9.0)
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.tile = P.Tile()
self.zeros_like = P.ZerosLike()
self.loss = Tensor(np.zeros((1, )).astype(np.float16))
self.clsloss = Tensor(np.zeros((1, )).astype(np.float16))
self.regloss = Tensor(np.zeros((1, )).astype(np.float16))
self.print = P.Print()
def __init__(self):
super(NetCheckValid, self).__init__()
self.valid = P.CheckValid()
'add_fack_input': True,
'fack_input_type': np.float32,
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']}),
('NpuGetFloatStatus', {
'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],
