def __init__(self, config_file, **kwargs):
super(DeepLTE, self).__init__(config_file, **kwargs)
self.num_frames = self.config['model']['num_frames']
self.order = self.config['model']['order']
self.nodes = self.config['model']['nodes']
self.targets = self.config['model']['targets']
self.interps = self.config['model']['interps']
self.alpha = self.config['model']['alpha']
self.dropout = self.config['model']['dropout']
self.perceptual_cost = self.config['model']['perceptual_cost']
self.vgg_weight_file = self.config['model']['vgg_weight_file']
self.input_tensor_shape = (None,) + self.input_shape[1:]
enc = nn.model_zoo.resnet34(self.input_tensor_shape, 64, 'lrelu', False, False, name='encoder', alpha=self.alpha)
self.model.append(enc)
subnet = 'decoder'
dec = nn.Sequential(input_shape=enc.output_shape, layer_name='decoder')
dec.append(nn.ResizingLayer(dec.input_shape, 2, layer_name=subnet + '_up1'))
dec.append(nn.StackingConv(dec.output_shape, 3, 256, 5, batch_norm=False, layer_name=subnet + '_block5',
He_init='normal', stride=(1, 1), activation='lrelu', alpha=self.alpha))
dec.append(nn.ResizingLayer(dec.output_shape, 2, layer_name=subnet + '_up2'))
dec.append(nn.StackingConv(dec.output_shape, 5, 128, 5, batch_norm=False, layer_name=subnet + '_block6',
He_init='normal', stride=(1, 1), activation='lrelu', alpha=self.alpha))
dec.append(nn.ResizingLayer(dec.output_shape, 2, layer_name=subnet + '_up3'))
dec.append(nn.StackingConv(dec.output_shape, 6, 128, 5, batch_norm=False, layer_name=subnet + '_block7',
He_init='normal', stride=(1, 1), activation='lrelu', alpha=self.alpha))
dec.append(nn.ConvolutionalLayer(dec.output_shape, 128, 5, activation='linear', layer_name=subnet+'_conv7'))
if self.dropout:
dec.append(nn.DropoutLayer(dec.output_shape, drop_prob=.5, layer_name=subnet + '_dropout7'))
dec.append(nn.ActivationLayer(dec.output_shape, 'lrelu', subnet+'_act7', alpha=self.alpha))
dec.append(nn.ResizingLayer(dec.output_shape, 2, layer_name=subnet + '_up4'))
dec.append(nn.StackingConv(dec.output_shape, 8, 64, 5, batch_norm=False, layer_name=subnet + '_block8',
He_init='normal', stride=(1, 1), activation='lrelu', alpha=self.alpha))
dec.append(nn.ConvolutionalLayer(dec.output_shape, 64, 5, activation='linear', layer_name=subnet + '_conv8'))
if self.dropout:
dec.append(nn.DropoutLayer(dec.output_shape, drop_prob=.5, layer_name=subnet + '_dropout8'))
dec.append(nn.ActivationLayer(dec.output_shape, 'lrelu', subnet + '_act8', alpha=self.alpha))
dec.append(nn.ConvolutionalLayer(dec.output_shape, 3, 5, activation='tanh', no_bias=False,
layer_name=subnet + '_output'))
self.model.append(dec)
def __init__(self,
input_shape,
fc=True,
num_classes=1000,
first_output=16,
growth_rate=12,
num_blocks=3,
depth=40,
dropout=False,
name='DenseNet'):
super(DenseNet, self).__init__(input_shape=input_shape,
layer_name=name)
self.append(
nn.ConvolutionalLayer(self.input_shape,
first_output,
3,
activation='linear',
layer_name=name + 'pre_conv'))
n = (depth - 1) // num_blocks
for b in range(num_blocks):
self.append(
nn.DenseBlock(self.output_shape,
num_conv_layer=n - 1,
growth_rate=growth_rate,
dropout=dropout,
layer_name=name + 'dense_block_%d' % b))
if b < num_blocks - 1:
self.append(
nn.DenseBlock(self.output_shape,
True,
None,
None,
dropout,
layer_name=name +
'dense_block_transit_%d' % b))
self.append(
nn.BatchNormLayer(self.output_shape, layer_name=name + 'post_bn'))
if fc:
self.append(
nn.GlobalAveragePoolingLayer(input_shape,
name + '_glbavgpooling'))
self.append(
nn.SoftmaxLayer(self.output_shape, num_classes,
name + '_softmax'))
def __init__(self,
input_shape,
ch=512,
wscale=0.02,
output_dim=1,
layer_name='SNDCGANDis'):
super(SNDCGANDiscriminator, self).__init__(input_shape=input_shape,
layer_name=layer_name)
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch // 8,
3,
nn.Normal(wscale),
False,
stride=1,
activation='lrelu',
layer_name=layer_name + '/conv1',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch // 4,
4,
nn.Normal(wscale),
False,
stride=2,
border_mode=1,
activation='lrelu',
layer_name=layer_name + '/conv2',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch // 4,
3,
nn.Normal(wscale),
False,
stride=1,
activation='lrelu',
layer_name=layer_name + '/conv3',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch // 2,
4,
nn.Normal(wscale),
False,
stride=2,
border_mode=1,
activation='lrelu',
layer_name=layer_name + '/conv4',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch // 2,
3,
nn.Normal(wscale),
False,
stride=1,
activation='lrelu',
layer_name=layer_name + '/conv5',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch,
4,
nn.Normal(wscale),
False,
stride=2,
border_mode=1,
activation='lrelu',
layer_name=layer_name + '/conv6',
alpha=.2))
self.append(
nn.ConvolutionalLayer(self.output_shape,
ch,
3,
nn.Normal(wscale),
False,
stride=1,
activation='lrelu',
layer_name=layer_name + '/conv7',
alpha=.2))
self.append(
nn.FullyConnectedLayer(self.output_shape,
output_dim,
nn.Normal(wscale),
activation='linear',
layer_name=layer_name + '/output'))
def __init__(self, input_shape, output_size):
super(LSGAN, self).__init__(input_shape=input_shape,
layer_name='LSGAN')
s2, s4, s8, s16 = output_size // 2, output_size // 4, output_size // 8, output_size // 16
subnet = 'generator'
self.gen = nn.Sequential(input_shape=input_shape, layer_name=subnet)
self.gen.append(
nn.FullyConnectedLayer(self.gen.output_shape,
256 * s16 * s16,
activation='linear',
layer_name=subnet + '/fc1',
init=nn.Normal(.02)))
self.gen.append(
nn.ReshapingLayer(self.gen.output_shape, (-1, 256, s16, s16),
subnet + '/reshape'))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn1',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
256,
3, (s8, s8),
activation='linear',
layer_name=subnet + '/deconv1',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn2',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
256,
3, (s8, s8),
stride=(1, 1),
activation='linear',
layer_name=subnet + '/deconv2',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn3',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
256,
3, (s4, s4),
activation='linear',
layer_name=subnet + '/deconv3',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn4',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
256,
3, (s4, s4),
stride=(1, 1),
activation='linear',
layer_name=subnet + '/deconv4',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn5',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
128,
3, (s2, s2),
activation='linear',
layer_name=subnet + '/deconv5',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn6',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
64,
3, (output_size, output_size),
activation='linear',
layer_name=subnet + '/deconv6',
init=nn.Normal(.02)))
self.gen.append(
nn.BatchNormLayer(self.gen.output_shape,
subnet + '/bn7',
activation='relu',
epsilon=1.1e-5))
self.gen.append(
nn.TransposedConvolutionalLayer(self.gen.output_shape,
3,
3, (output_size, output_size),
stride=(1, 1),
activation='tanh',
layer_name=subnet + '/output',
init=nn.Normal(.02)))
self.append(self.gen)
subnet = 'discriminator'
self.dis = nn.Sequential(input_shape=self.gen.output_shape,
layer_name=subnet)
self.dis.append(
nn.ConvolutionalLayer(self.dis.output_shape,
64,
5,
stride=2,
activation='lrelu',
no_bias=False,
layer_name=subnet + '/first_conv',
init=nn.TruncatedNormal(.02),
alpha=.2))
self.dis.append(
nn.ConvNormAct(self.dis.output_shape,
64 * 2,
5,
stride=2,
activation='lrelu',
no_bias=False,
layer_name=subnet + '/conv1',
init=nn.TruncatedNormal(.02),
epsilon=1.1e-5,
alpha=.2))
self.dis.append(
nn.ConvNormAct(self.dis.output_shape,
64 * 4,
5,
stride=2,
activation='lrelu',
no_bias=False,
layer_name=subnet + '/conv2',
init=nn.TruncatedNormal(.02),
epsilon=1.1e-5,
alpha=.2))
self.dis.append(
nn.ConvNormAct(self.dis.output_shape,
64 * 8,
5,
stride=2,
activation='lrelu',
no_bias=False,
layer_name=subnet + '/conv3',
init=nn.TruncatedNormal(.02),
epsilon=1.1e-5,
alpha=.2))
self.dis.append(
nn.FullyConnectedLayer(self.dis.output_shape,
1,
layer_name=subnet + '/output',
activation='linear'))
self.append(self.dis)