def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
groups=1):
super(ConvBNReLU, self).__init__()
padding = (kernel_size - 1) // 2
if groups == 1:
conv = layers.Conv2d(in_channels,
out_channels,
kernel_size,
stride,
pad_mode='pad',
padding=padding)
else:
conv = layers.Conv2d(in_channels,
in_channels,
kernel_size,
stride,
pad_mode='pad',
padding=padding,
group=in_channels)
self.features = layers.SequentialLayer(
[conv, layers.BatchNorm2d(out_channels),
layers.ReLU6()])
def __init__(self,
in_planes,
out_planes,
kernel_size=4,
stride=2,
alpha=0.2,
norm_mode='batch',
pad_mode='CONSTANT',
use_relu=True,
padding=None):
super(ConvTransposeNormReLU, self).__init__()
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride=stride,
pad_mode='same')
norm = layers.BatchNorm2d(out_planes)
if norm_mode == 'instance':
# Use BatchNorm2d with batchsize=1, affine=False, training=True instead of InstanceNorm2d
norm = layers.BatchNorm2d(out_planes, affine=False)
has_bias = (norm_mode == 'instance')
if padding is None:
padding = (kernel_size - 1) // 2
if pad_mode == 'CONSTANT':
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride,
pad_mode='same',
has_bias=has_bias)
layer_list = [conv, norm]
else:
paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding))
pad = layers.Pad(paddings=paddings, mode=pad_mode)
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride,
pad_mode='pad',
has_bias=has_bias)
layer_list = [pad, conv, norm]
if use_relu:
relu = layers.ReLU()
if alpha > 0:
relu = layers.LeakyReLU(alpha)
layer_list.append(relu)
self.features = layers.SequentialLayer(layer_list)
def _bn_last(channel):
return layers.BatchNorm2d(channel,
eps=1e-4,
momentum=0.9,
gamma_init=0,
beta_init=0,
moving_mean_init=0,
moving_var_init=1)
def __init__(self, outer_nc, inner_nc, in_planes=None, dropout=False,
submodule=None, outermost=False, innermost=False, alpha=0.2, norm_mode='batch'):
super(UnetSkipConnectionBlock, self).__init__()
downnorm = layers.BatchNorm2d(inner_nc)
upnorm = layers.BatchNorm2d(outer_nc)
use_bias = False
if norm_mode == 'instance':
downnorm = layers.BatchNorm2d(inner_nc, affine=False)
upnorm = layers.BatchNorm2d(outer_nc, affine=False)
use_bias = True
if in_planes is None:
in_planes = outer_nc
downconv = layers.Conv2d(in_planes, inner_nc, kernel_size=4,
stride=2, padding=1, has_bias=use_bias, pad_mode='pad')
downrelu = layers.LeakyReLU(alpha)
uprelu = layers.ReLU()
if outermost:
upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, pad_mode='pad')
down = [downconv]
up = [uprelu, upconv, layers.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = layers.Conv2dTranspose(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, has_bias=use_bias, pad_mode='pad')
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, has_bias=use_bias, pad_mode='pad')
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
model = down + [submodule] + up
if dropout:
model.append(layers.Dropout(0.5))
self.model = layers.SequentialLayer(model)
self.skip_connections = not outermost
self.concat = Concat(axis=1)
def make_layers(cfg, batch_norm=False):
Layers = []
in_channels = 3
for v in cfg:
if v == 'M':
Layers += [layers.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = _conv3x3(in_channels, v)
if batch_norm:
Layers += [conv2d, layers.BatchNorm2d(v), layers.ReLU()]
else:
Layers += [conv2d, layers.ReLU()]
in_channels = v
return layers.SequentialLayer(Layers)
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = stride == 1 and inp == oup
residual_layers = []
if expand_ratio != 1:
residual_layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
residual_layers.extend([
ConvBNReLU(hidden_dim,
hidden_dim,
stride=stride,
groups=hidden_dim),
layers.Conv2d(hidden_dim,
oup,
kernel_size=1,
stride=1,
has_bias=False),
layers.BatchNorm2d(oup),
])
self.conv = layers.SequentialLayer(residual_layers)
