def __init__(self, record=None):
super(BCELoss, self).__init__(record)
self.sm_scalar = P.ScalarSummary()
self.cast = P.Cast()
self.record = record
self.weight = None
self.bce = P.BinaryCrossEntropy()
def __init__(self, weight=None, reduction='none'):
super(BCELoss, self).__init__()
self.binary_cross_entropy = P.BinaryCrossEntropy(reduction=reduction)
self.weight_one = weight is None
if not self.weight_one:
self.weight = weight
else:
self.ones = P.OnesLike()
'desc_inputs': [[10, 32, 32, 32], [1, 32, 3, 3]],
'desc_bprop': [[10, 32, 16, 16]]}),
('DepthwiseConv2dNative_2', {
'block': P.DepthwiseConv2dNative(1, (3, 3), pad_mode="same", pad=0, stride=1),
'desc_inputs': [[2592, 2048, 4, 4], [1, 2048, 3, 3]],
'desc_bprop': [[2592, 2048, 4, 4]]}),
('SigmoidCrossEntropyWithLogits', {
'block': P.SigmoidCrossEntropyWithLogits(),
'desc_inputs': [[128, 10], [128, 10]],
'desc_bprop': [[128, 10]]}),
('Pad', {
'block': P.Pad(((1, 2), (2, 3))),
'desc_inputs': [[7, 7]],
'desc_bprop': [[10, 12]]}),
('BinaryCrossEntropy', {
'block': P.BinaryCrossEntropy(),
'desc_inputs': [[1, 2, 3], [1, 2, 3], [1, 2, 3]],
'desc_bprop': []}),
('SparseApplyAdagrad', {
'block': P.SparseApplyAdagrad(0.5),
'desc_inputs': [[3, 3], [3, 3], [3, 3], Tensor(np.ones((3,), np.int32))],
'desc_bprop': [3, 3],
'skip': ['backward']}),
('Flatten_1', {
'block': NetForFlatten(),
'desc_inputs': [Tensor(np.ones([2, 3, 4]).astype(np.int32)), Tensor(np.ones([2, 12]).astype(np.int32))],
'desc_bprop': [Tensor(np.ones([2, 12]).astype(np.int32))],
'skip': ['backward']}),
('Flatten_2', {
'block': NetForFlatten(),
'desc_inputs': [Tensor(np.ones([8]).astype(np.int32)), Tensor(np.ones([8, 3]).astype(np.int32))],
def __init__(self, reduction="none"):
super(Net, self).__init__()
self.BinaryCrossEntropy = P.BinaryCrossEntropy("none")
