def __init__(self, LayerParameter):
super(GNLayer, self).__init__(LayerParameter)
gn_param = LayerParameter.group_norm_param
scale_param = LayerParameter.scale_param
self._param = {
'group': int(gn_param.group),
'eps': gn_param.eps,
'axis': 1
}
scope = LayerParameter.name
scale = Tensor(scope + '/param:0')
scale_diff = Tensor(scope + '/param:0_grad')
bias = Tensor(scope + '/param:1')
bias_diff = Tensor(scope + '/param:1_grad')
if scale_param.HasField('filler'):
self.Fill(scale, scale_param, 'filler')
else:
scale.Constant(value=1.0)
self.Fill(bias, scale_param, 'bias_filler')
self.scale_blobs = [{
'data': scale,
'diff': scale_diff
}, {
'data': bias,
'diff': bias_diff
}]
self._blobs.extend(self.scale_blobs)
def __init__(self, LayerParameter):
super(BNLayer, self).__init__(LayerParameter)
bn_param = LayerParameter.batch_norm_param
scale_param = LayerParameter.scale_param
self._param = {'use_stats': int(bn_param.use_global_stats)
if bn_param.HasField('use_global_stats') else -1,
'momentum': bn_param.moving_average_fraction,
'eps': bn_param.eps}
mean = Tensor(LayerParameter.name + '@param0').Constant(value=0.0)
var = Tensor(LayerParameter.name + '@param1').Constant(value=0.0)
scale = Tensor(LayerParameter.name + '@param2')
scale_diff = Tensor(LayerParameter.name + '@param2_grad')
bias = Tensor(LayerParameter.name + '@param3')
bias_diff = Tensor(LayerParameter.name + '@param3_grad')
if scale_param.HasField('filler'):
self.Fill(scale, scale_param, 'filler')
else: scale.Constant(value=1.0)
self.Fill(bias, scale_param, 'bias_filler')
self.norm_blobs = [{'data': mean, 'diff': None},
{'data': var, 'diff': None}]
self.scale_blobs = [{'data': scale, 'diff': scale_diff},
{'data': bias, 'diff': bias_diff}]
self._blobs.extend(self.norm_blobs)
self._blobs.extend(self.scale_blobs)
def __init__(self, LayerParameter):
super(PReLULayer, self).__init__(LayerParameter)
param = LayerParameter.prelu_param
self._param = {
'channel_shared': param.channel_shared,
'data_format': 'NCHW'
}
slope = Tensor(LayerParameter.name + '@param0')
slope_diff = Tensor(LayerParameter.name + '@param0_grad')
if param.HasField('filler'):
self.Fill(slope, param, 'filler')
else:
slope.Constant(value=0.25)
self._blobs.append({'data': slope, 'diff': slope_diff})
def __init__(self, LayerParameter):
super(NormalizeLayer, self).__init__(LayerParameter)
param = LayerParameter.normalize_param
self._l2norm_param = {'axis': 1,
'num_axes': -1 if param.across_spatial else 1,
'eps': param.eps}
self._scale_param = {'axis': 1,
'num_axes': 0 if param.channel_shared else 1}
scale = Tensor(LayerParameter.name + '@param0')
if param.HasField('scale_filler'):
self.Fill(scale, param, 'scale_filler')
else: scale.Constant(value=1.0)
self.scale_blobs = [{'data': scale, 'diff': Tensor(scale.name + '_grad')}]
self._blobs.extend(self.scale_blobs)
def __init__(self, LayerParameter):
super(ScaleLayer, self).__init__(LayerParameter)
param = LayerParameter.scale_param
self._param = {'axis': param.axis, 'num_axes': param.num_axes}
scale = Tensor(LayerParameter.name + '@param0')
scale_diff = Tensor(LayerParameter.name + '@param0_grad')
if param.HasField('filler'):
self.Fill(scale, param, 'filler')
else:
scale.Constant(value=1.0)
self._blobs.append({'data': scale, 'diff': scale_diff})
if param.bias_term:
bias = Tensor(LayerParameter.name + '@param1')
bias_diff = Tensor(LayerParameter.name + '@param1_grad')
# auto fill 0 if not specficed bias_filler
self.Fill(bias, param, 'bias_filler')
self._blobs.append({'data': bias, 'diff': bias_diff})
def __init__(self, LayerParameter):
super(GNLayer, self).__init__(LayerParameter)
gn_param = LayerParameter.group_norm_param
scale_param = LayerParameter.scale_param
self._param = {
'group':
int(gn_param.group),
'use_stats':
int(gn_param.use_global_stats)
if gn_param.HasField('use_global_stats') else -1,
'momentum':
gn_param.moving_average_fraction,
'eps':
gn_param.eps,
'axis':
1
}
scope = LayerParameter.name
mean = Tensor(scope + '/param:0').Constant(value=0.0)
var = Tensor(scope + '/param:1').Constant(value=0.0)
scale = Tensor(scope + '/param:2')
scale_diff = Tensor(scope + '/param:2_grad')
bias = Tensor(scope + '/param:3')
bias_diff = Tensor(scope + '/param:3_grad')
if scale_param.HasField('filler'):
self.Fill(scale, scale_param, 'filler')
else:
scale.Constant(value=1.0)
self.Fill(bias, scale_param, 'bias_filler')
self.norm_blobs = [{
'data': mean,
'diff': None
}, {
'data': var,
'diff': None
}]
self.scale_blobs = [{
'data': scale,
'diff': scale_diff
}, {
'data': bias,
'diff': bias_diff
}]
self._blobs.extend(self.norm_blobs)
self._blobs.extend(self.scale_blobs)
