Python mxnet.gluon.nn.InstanceNorm() Examples
The following are 13
code examples of mxnet.gluon.nn.InstanceNorm().
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mxnet.gluon.nn
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Example #1
| Source File: test_gluon.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
|
def test_instancenorm():
layer = nn.InstanceNorm(in_channels=10)
check_layer_forward(layer, (2, 10, 10, 10))
Example #2
| Source File: train_cgan.py From gluon-cv with Apache License 2.0 | 5 votes |
|
def __init__(self, output_nc, ngf=64, use_dropout=False, n_blocks=6, padding_type='reflect'):
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.output_nc = output_nc
self.ngf = ngf
self.model = nn.HybridSequential()
with self.name_scope():
self.model.add(
nn.ReflectionPad2D(3),
nn.Conv2D(ngf, kernel_size=7, padding=0),
nn.InstanceNorm(),
nn.Activation('relu')
)
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
self.model.add(
nn.Conv2D(ngf * mult * 2, kernel_size=3,strides=2, padding=1),
nn.InstanceNorm(),
nn.Activation('relu')
)
mult = 2**n_downsampling
for i in range(n_blocks):
self.model.add(
ResnetBlock(ngf * mult, padding_type=padding_type, use_dropout=use_dropout)
)
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
self.model.add(
nn.Conv2DTranspose(int(ngf * mult / 2),kernel_size=3,strides=2,padding=1,output_padding=1),
nn.InstanceNorm(),
nn.Activation('relu')
)
self.model.add(
nn.ReflectionPad2D(3),
nn.Conv2D(output_nc,kernel_size=7,padding=0),
nn.Activation('tanh')
)
Example #3
| Source File: train_cgan.py From gluon-cv with Apache License 2.0 | 5 votes |
|
def build_conv_block(self, dim, padding_type, use_dropout):
conv_block = nn.HybridSequential()
p = 0
with self.name_scope():
if padding_type == 'reflect':
conv_block.add(nn.ReflectionPad2D(1))
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block.add(
nn.Conv2D(dim, kernel_size=3, padding=p),
nn.InstanceNorm(),
nn.Activation('relu')
)
if use_dropout:
conv_block.add(nn.Dropout(0.5))
p = 0
if padding_type == 'reflect':
conv_block.add(nn.ReflectionPad2D(1))
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block.add(
nn.Conv2D(dim, kernel_size=3, padding=p),
nn.InstanceNorm()
)
return conv_block
Example #4
| Source File: train_cgan.py From gluon-cv with Apache License 2.0 | 5 votes |
|
def __init__(self, ndf=64, n_layers=3, use_sigmoid=False):
super(NLayerDiscriminator, self).__init__()
self.model = nn.HybridSequential()
kw = 4
padw = 1
with self.name_scope():
self.model.add(
nn.Conv2D(ndf, kernel_size=kw, strides=2, padding=padw),
nn.LeakyReLU(0.2),
)
nf_mult = 1
for n in range(1, n_layers):
nf_mult = min(2**n, 8)
self.model.add(
nn.Conv2D(ndf * nf_mult,kernel_size=kw, strides=2, padding=padw),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
)
nf_mult = min(2**n_layers, 8)
self.model.add(
nn.Conv2D(ndf * nf_mult,kernel_size=kw, strides=1, padding=padw),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
)
self.model.add(
nn.Conv2D(1, kernel_size=kw, strides=1, padding=padw)
)
if use_sigmoid:
self.model.add(nn.Activation('sigmoid'))
Example #5
| Source File: common.py From imgclsmob with MIT License | 5 votes |
|
def __init__(self,
channels,
bn_use_global_stats=False,
first_fraction=0.5,
inst_first=True,
**kwargs):
super(IBN, self).__init__(**kwargs)
self.inst_first = inst_first
h1_channels = int(math.floor(channels * first_fraction))
h2_channels = channels - h1_channels
self.split_sections = [h1_channels, h2_channels]
if self.inst_first:
self.inst_norm = nn.InstanceNorm(
in_channels=h1_channels,
scale=True)
self.batch_norm = nn.BatchNorm(
in_channels=h2_channels,
use_global_stats=bn_use_global_stats)
else:
self.batch_norm = nn.BatchNorm(
in_channels=h1_channels,
use_global_stats=bn_use_global_stats)
self.inst_norm = nn.InstanceNorm(
in_channels=h2_channels,
scale=True)
Example #6
| Source File: ibnbresnet.py From imgclsmob with MIT License | 5 votes |
|
def __init__(self,
in_channels,
out_channels,
kernel_size,
strides,
padding,
dilation=1,
groups=1,
use_bias=False,
activate=True,
**kwargs):
super(IBNbConvBlock, self).__init__(**kwargs)
self.activate = activate
with self.name_scope():
self.conv = nn.Conv2D(
channels=out_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
dilation=dilation,
groups=groups,
use_bias=use_bias,
in_channels=in_channels)
self.inst_norm = nn.InstanceNorm(
in_channels=out_channels,
scale=True)
if self.activate:
self.activ = nn.Activation("relu")
Example #7
| Source File: ibnbresnet.py From imgclsmob with MIT License | 5 votes |
|
def __init__(self,
in_channels,
out_channels,
strides,
use_inst_norm,
bn_use_global_stats,
**kwargs):
super(IBNbResUnit, self).__init__(**kwargs)
self.use_inst_norm = use_inst_norm
self.resize_identity = (in_channels != out_channels) or (strides != 1)
with self.name_scope():
self.body = ResBottleneck(
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
bn_use_global_stats=bn_use_global_stats,
conv1_stride=False)
if self.resize_identity:
self.identity_conv = conv1x1_block(
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
bn_use_global_stats=bn_use_global_stats,
activation=None)
if self.use_inst_norm:
self.inst_norm = nn.InstanceNorm(
in_channels=out_channels,
scale=True)
self.activ = nn.Activation("relu")
Example #8
| Source File: train_cgan.py From panoptic-fpn-gluon with Apache License 2.0 | 5 votes |
|
def __init__(self, output_nc, ngf=64, use_dropout=False, n_blocks=6, padding_type='reflect'):
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.output_nc = output_nc
self.ngf = ngf
self.model = nn.HybridSequential()
with self.name_scope():
self.model.add(
nn.ReflectionPad2D(3),
nn.Conv2D(ngf, kernel_size=7, padding=0),
nn.InstanceNorm(),
nn.Activation('relu')
)
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
self.model.add(
nn.Conv2D(ngf * mult * 2, kernel_size=3,strides=2, padding=1),
nn.InstanceNorm(),
nn.Activation('relu')
)
mult = 2**n_downsampling
for i in range(n_blocks):
self.model.add(
ResnetBlock(ngf * mult, padding_type=padding_type, use_dropout=use_dropout)
)
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
self.model.add(
nn.Conv2DTranspose(int(ngf * mult / 2),kernel_size=3,strides=2,padding=1,output_padding=1),
nn.InstanceNorm(),
nn.Activation('relu')
)
self.model.add(
nn.ReflectionPad2D(3),
nn.Conv2D(output_nc,kernel_size=7,padding=0),
nn.Activation('tanh')
)
Example #9
| Source File: train_cgan.py From panoptic-fpn-gluon with Apache License 2.0 | 5 votes |
|
def build_conv_block(self, dim, padding_type, use_dropout):
conv_block = nn.HybridSequential()
p = 0
with self.name_scope():
if padding_type == 'reflect':
conv_block.add(nn.ReflectionPad2D(1))
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block.add(
nn.Conv2D(dim, kernel_size=3, padding=p),
nn.InstanceNorm(),
nn.Activation('relu')
)
if use_dropout:
conv_block.add(nn.Dropout(0.5))
p = 0
if padding_type == 'reflect':
conv_block.add(nn.ReflectionPad2D(1))
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block.add(
nn.Conv2D(dim, kernel_size=3, padding=p),
nn.InstanceNorm()
)
return conv_block
Example #10
| Source File: train_cgan.py From panoptic-fpn-gluon with Apache License 2.0 | 5 votes |
|
def __init__(self, ndf=64, n_layers=3, use_sigmoid=False):
super(NLayerDiscriminator, self).__init__()
self.model = nn.HybridSequential()
kw = 4
padw = 1
with self.name_scope():
self.model.add(
nn.Conv2D(ndf, kernel_size=kw, strides=2, padding=padw),
nn.LeakyReLU(0.2),
)
nf_mult = 1
for n in range(1, n_layers):
nf_mult = min(2**n, 8)
self.model.add(
nn.Conv2D(ndf * nf_mult,kernel_size=kw, strides=2, padding=padw),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
)
nf_mult = min(2**n_layers, 8)
self.model.add(
nn.Conv2D(ndf * nf_mult,kernel_size=kw, strides=1, padding=padw),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
)
self.model.add(
nn.Conv2D(1, kernel_size=kw, strides=1, padding=padw)
)
if use_sigmoid:
self.model.add(nn.Activation('sigmoid'))
Example #11
| Source File: test_gluon.py From SNIPER-mxnet with Apache License 2.0 | 5 votes |
|
def test_instancenorm():
layer = nn.InstanceNorm(in_channels=10)
check_layer_forward(layer, (2, 10, 10, 10))
Example #12
| Source File: train_cgan.py From gluon-cv with Apache License 2.0 | 4 votes |
|
def __init__(self, outer_nc, inner_nc,submodule=None, outermost=False, innermost=False, use_dropout=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
downconv = nn.Conv2D(inner_nc, kernel_size=4,strides=2, padding=1)
downrelu = nn.LeakyReLU(0.2)
downnorm = nn.InstanceNorm()
uprelu = nn.Activation('relu')
upnorm = nn.InstanceNorm()
self.model = nn.HybridSequential()
with self.model.name_scope():
if outermost:
self.model.add(
downconv
)
if submodule is not None:
self.model.add(
submodule
)
self.model.add(
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
nn.Activation('tanh')
)
elif innermost:
self.model.add(
downrelu,
downconv,
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
upnorm
)
else:
self.model.add(
downrelu,
downconv,
downnorm,
)
if submodule is not None:
self.model.add(
submodule
)
self.model.add(
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
upnorm,
)
if use_dropout:
self.model.add(nn.Dropout(0.5))
Example #13
| Source File: train_cgan.py From panoptic-fpn-gluon with Apache License 2.0 | 4 votes |
|
def __init__(self, outer_nc, inner_nc,submodule=None, outermost=False, innermost=False, use_dropout=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
downconv = nn.Conv2D(inner_nc, kernel_size=4,strides=2, padding=1)
downrelu = nn.LeakyReLU(0.2)
downnorm = nn.InstanceNorm()
uprelu = nn.Activation('relu')
upnorm = nn.InstanceNorm()
self.model = nn.HybridSequential()
with self.model.name_scope():
if outermost:
self.model.add(
downconv
)
if submodule is not None:
self.model.add(
submodule
)
self.model.add(
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
nn.Activation('tanh')
)
elif innermost:
self.model.add(
downrelu,
downconv,
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
upnorm
)
else:
self.model.add(
downrelu,
downconv,
downnorm,
)
if submodule is not None:
self.model.add(
submodule
)
self.model.add(
uprelu,
nn.Conv2DTranspose(outer_nc, kernel_size=4, strides=2, padding=1),
upnorm,
)
if use_dropout:
self.model.add(nn.Dropout(0.5))
