def __init__(self, num_layers=36, feature_dim=128, shape=(96, 64)):
super(SphereNet_float32, self).__init__()
assert num_layers in [12, 20, 36, 64], 'SphereNet num_layers should be 12, 20 or 64'
if num_layers == 12:
layers = [1, 1, 1, 1]
filter_list = [3, 16, 32, 64, 128]
fc_size = 128 * 6 * 4
elif num_layers == 20:
layers = [1, 2, 4, 1]
filter_list = [3, 64, 128, 256, 512]
fc_size = 512 * 6 * 4
elif num_layers == 36:
layers = [2, 4, 4, 2]
filter_list = [3, 32, 64, 128, 256]
fc_size = 256 * 6 * 4
elif num_layers == 64:
layers = [3, 7, 16, 3]
filter_list = [3, 64, 128, 256, 512]
fc_size = 512 * 6 * 4
else:
raise ValueError('sphere' + str(num_layers) + " IS NOT SUPPORTED! (sphere20 or sphere64)")
self.shape = P.Shape()
self.reshape = P.Reshape()
block = BaseBlock
self.layer1 = MakeLayer(block, filter_list[0], filter_list[1], layers[0], stride=2)
self.layer2 = MakeLayer(block, filter_list[1], filter_list[2], layers[1], stride=2)
self.layer3 = MakeLayer(block, filter_list[2], filter_list[3], layers[2], stride=2)
self.layer4 = MakeLayer(block, filter_list[3], filter_list[4], layers[3], stride=2)
self.fc = fc_with_initialize(fc_size, feature_dim)
self.last_bn = nn.BatchNorm1d(feature_dim, momentum=0.9).add_flags_recursive(fp32=True)
self.last_bn_sub = nn.BatchNorm2d(feature_dim, momentum=0.9).add_flags_recursive(fp32=True)
self.cast = P.Cast()
self.l2norm = P.L2Normalize(axis=1)
for _, cell in self.cells_and_names():
if isinstance(cell, (nn.Conv2d, nn.Dense)):
if cell.bias is not None:
cell.weight.set_data(initializer(me_init.ReidKaimingUniform(a=math.sqrt(5), mode='fan_out'),
cell.weight.shape))
cell.bias.set_data(initializer('zeros', cell.bias.shape))
else:
cell.weight.set_data(initializer(me_init.ReidXavierUniform(), cell.weight.shape))
self.device_target = context.get_context('device_target')
def __init__(self, block, layers, args):
super(FaceResNet, self).__init__()
self.act_type = args.act_type
self.inplanes = 64
self.use_se = args.use_se
self.conv1 = conv3x3(3, 64, stride=1)
self.bn1 = bn_with_initialize(64, use_inference=args.inference)
self.prelu = nn.PReLU(64) if self.act_type == 'prelu' else P.ReLU()
self.layer1 = MakeLayer(block,
planes=64,
inplanes=self.inplanes,
blocks=layers[0],
stride=2,
args=args)
self.inplanes = 64
self.layer2 = MakeLayer(block,
planes=128,
inplanes=self.inplanes,
blocks=layers[1],
stride=2,
args=args)
self.inplanes = 128
self.layer3 = MakeLayer(block,
planes=256,
inplanes=self.inplanes,
blocks=layers[2],
stride=2,
args=args)
self.inplanes = 256
self.layer4 = MakeLayer(block,
planes=512,
inplanes=self.inplanes,
blocks=layers[3],
stride=2,
args=args)
self.head = get_head(args)
np.random.seed(1)
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(
initializer(
me_init.ReidKaimingUniform(a=math.sqrt(5),
mode='fan_out'),
cell.weight.shape))
if cell.bias is not None:
cell.bias.set_data(initializer('zeros', cell.bias.shape))
elif isinstance(cell, nn.Dense):
cell.weight.set_data(
initializer(
me_init.ReidKaimingNormal(a=math.sqrt(5),
mode='fan_out'),
cell.weight.shape))
if cell.bias is not None:
cell.bias.set_data(initializer('zeros', cell.bias.shape))
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
# defulat gamma 1 and beta 0, and if you set should be careful for the IRBlock gamma value
pass
for _, cell in self.cells_and_names():
if isinstance(cell, IRBlock):
# be careful for bn3 Do not change the name unless IRBlock last bn change name
cell.bn3.gamma.set_data(
initializer('zeros', cell.bn3.gamma.shape))