def __init__(self, num_input_features, args):
super().__init__()
self.memory_efficient = args.memory_efficient
self.drop_rate = args.convolution_dropout
bn_size = args.bn_size
growth_rate = args.growth_rate
inter_features = bn_size * growth_rate
kernel_size = args.kernel_size
self.norm1 = PervasiveBatchNorm(num_input_features)
self.conv1 = nn.Conv2d(num_input_features,
inter_features,
kernel_size=1,
stride=1,
bias=False)
self.norm2 = PervasiveBatchNorm(inter_features)
dilsrc = args.source_dilation
diltrg = args.target_dilation
padding_trg = diltrg * (kernel_size - 1) // 2
padding_src = dilsrc * (kernel_size - 1) // 2
padding = (padding_trg, padding_src)
self.mconv2 = MaskedConvolution(
inter_features,
growth_rate,
kernel_size,
args,
padding=padding,
)
def __init__(self, num_input_features, args):
super().__init__()
self.memory_efficient = args.memory_efficient
self.drop_rate = args.convolution_dropout
bn_size = args.bn_size
growth_rate = args.growth_rate
inter_features = bn_size * growth_rate
kernel_size = args.kernel_size
ffn_dim = args.ffn_dim
self.conv1 = nn.Conv2d(num_input_features,
inter_features,
kernel_size=1,
stride=1,
bias=False
)
dilsrc = args.source_dilation
diltrg = args.target_dilation
padding_trg = diltrg * (kernel_size - 1) // 2
padding_src = dilsrc * (kernel_size - 1) // 2
padding = (padding_trg, padding_src)
self.mconv2 = MaskedConvolution(
inter_features, growth_rate,
kernel_size, args,
padding=padding,
)
self.fc1 = Linear(growth_rate, ffn_dim)
self.fc2 = Linear(ffn_dim, growth_rate)
def __init__(self, num_input_features, kernel_size,
growth_rate, bn_size, drop_rate, args, bias=False):
super().__init__()
self.drop_rate = drop_rate
inter_features = bn_size * growth_rate
self.layer = nn.Sequential()
self.bn1 = nn.BatchNorm2d(num_input_features)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(num_input_features, inter_features,
kernel_size=1, stride=1, bias=bias)
self.layer2 = nn.Sequential()
self.layer2.add_module('bn2', nn.BatchNorm2d(inter_features))
self.layer2.add_module('relu2', nn.ReLU(inplace=True))
mask = torch.ones(growth_rate, inter_features,
kernel_size, kernel_size)
if kernel_size > 1:
mask[:, :, kernel_size // 2 + 1:, :] = 0
padding = (kernel_size - 1) // 2
self.layer2.add_module(
'mconv2',
MaskedConvolution(
mask, inter_features, growth_rate,
kernel_size, padding, bias=bias
)
)
self.layer2.add_module('do1', nn.Dropout(p=self.drop_rate))
def __init__(self, num_features, kernel_size, args):
super().__init__()
self.drop_rate = args.convolution_dropout
mid_features = args.reduce_dim
stride = args.conv_stride # source dimension stride
dilsrc = args.source_dilation
diltrg = args.target_dilation
resolution = args.maintain_resolution
if resolution:
if not stride == 1:
raise ValueError('Could not maintain the resolution with stride=%d' % stride)
# choose the padding accordingly:
padding_trg = diltrg * (kernel_size - 1) // 2
padding_src = dilsrc * (kernel_size - 1) // 2
padding = (padding_trg, padding_src)
else:
# must maintain the target resolution:
padding = (diltrg * (kernel_size - 1) // 2, 0)
# Reduce dim should be dividible by groups
self.conv1 = nn.Conv2d(num_features,
mid_features,
kernel_size=1,
stride=1,
bias=args.conv_bias)
self.mconv2 = MaskedConvolution(
mid_features, num_features,
kernel_size, args,
padding=padding,
)
self.scale = 0.5 ** .5
self.nonzero_padding = args.nonzero_padding
def __init__(self, num_features, kernel_size, args, source_dilation,
target_dilation):
super().__init__()
self.drop_rate = args.convolution_dropout
ffn_dim = args.ffn_dim
mid_features = args.reduce_dim
stride = args.conv_stride # source dimension stride
resolution = args.maintain_resolution
if resolution:
if not stride == 1:
raise ValueError(
'Could not maintain the resolution with stride=%d' %
stride)
# choose the padding accordingly:
padding_trg = source_dilation * (kernel_size - 1) // 2
padding_src = target_dilation * (kernel_size - 1) // 2
padding = (padding_trg, padding_src)
else:
# must maintain the target resolution:
padding = (diltrg * (kernel_size - 1) // 2, 0)
# Reduce dim should be dividible by groups
self.conv1 = nn.Conv2d(num_features,
mid_features,
kernel_size=1,
stride=1,
bias=False)
self.mconv2 = MaskedConvolution(
mid_features,
num_features,
kernel_size,
args,
padding=padding,
source_dilation=source_dilation,
target_dilation=target_dilation,
)
self.fc1 = Linear(num_features, ffn_dim)
self.fc2 = Linear(ffn_dim, num_features)
self.scale = 0.5**.5
def __init__(self, num_features, kernel_size, args):
super().__init__()
self.drop_rate = args.convolution_dropout
ffn_dim = args.ffn_dim
mid_features = args.reduce_dim
self.conv1 = nn.Conv2d(num_features,
mid_features,
kernel_size=1,
stride=1,
bias=False)
self.mconv2 = MaskedConvolution(mid_features,
num_features,
kernel_size,
bias=False,
separable=True)
self.ln1 = nn.LayerNorm(num_features)
self.fc1 = Linear(num_features, ffn_dim)
self.fc2 = Linear(ffn_dim, num_features)
self.ln2 = nn.LayerNorm(num_features)
self.scale = 2**.5
