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from typing import Optional, Union, Callable
from torch.nn import Conv2d, Module, Sequential, ConvTranspose2d
from tha3.module.module_factory import ModuleFactory
from tha3.nn.nonlinearity_factory import resolve_nonlinearity_factory
from tha3.nn.normalization import NormalizationLayerFactory
from tha3.nn.util import wrap_conv_or_linear_module, BlockArgs
def create_conv7(in_channels: int, out_channels: int,
bias: bool = False,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
use_spectral_norm: bool = False) -> Module:
return wrap_conv_or_linear_module(
Conv2d(in_channels, out_channels, kernel_size=7, stride=1, padding=3, bias=bias),
initialization_method,
use_spectral_norm)
def create_conv7_from_block_args(in_channels: int,
out_channels: int,
bias: bool = False,
block_args: Optional[BlockArgs] = None) -> Module:
if block_args is None:
block_args = BlockArgs()
return create_conv7(
in_channels, out_channels, bias,
block_args.initialization_method,
block_args.use_spectral_norm)
def create_conv3(in_channels: int,
out_channels: int,
bias: bool = False,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
use_spectral_norm: bool = False) -> Module:
return wrap_conv_or_linear_module(
Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias),
initialization_method,
use_spectral_norm)
def create_conv3_from_block_args(in_channels: int, out_channels: int,
bias: bool = False,
block_args: Optional[BlockArgs] = None):
if block_args is None:
block_args = BlockArgs()
return create_conv3(in_channels, out_channels, bias,
block_args.initialization_method,
block_args.use_spectral_norm)
def create_conv1(in_channels: int, out_channels: int,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
bias: bool = False,
use_spectral_norm: bool = False) -> Module:
return wrap_conv_or_linear_module(
Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias),
initialization_method,
use_spectral_norm)
def create_conv1_from_block_args(in_channels: int,
out_channels: int,
bias: bool = False,
block_args: Optional[BlockArgs] = None) -> Module:
if block_args is None:
block_args = BlockArgs()
return create_conv1(
in_channels=in_channels,
out_channels=out_channels,
initialization_method=block_args.initialization_method,
bias=bias,
use_spectral_norm=block_args.use_spectral_norm)
def create_conv7_block(in_channels: int, out_channels: int,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
nonlinearity_factory: Optional[ModuleFactory] = None,
normalization_layer_factory: Optional[NormalizationLayerFactory] = None,
use_spectral_norm: bool = False) -> Module:
nonlinearity_factory = resolve_nonlinearity_factory(nonlinearity_factory)
return Sequential(
create_conv7(in_channels, out_channels,
bias=False, initialization_method=initialization_method, use_spectral_norm=use_spectral_norm),
NormalizationLayerFactory.resolve_2d(normalization_layer_factory).create(out_channels, affine=True),
resolve_nonlinearity_factory(nonlinearity_factory).create())
def create_conv7_block_from_block_args(
in_channels: int, out_channels: int,
block_args: Optional[BlockArgs] = None) -> Module:
if block_args is None:
block_args = BlockArgs()
return create_conv7_block(in_channels, out_channels,
block_args.initialization_method,
block_args.nonlinearity_factory,
block_args.normalization_layer_factory,
block_args.use_spectral_norm)
def create_conv3_block(in_channels: int, out_channels: int,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
nonlinearity_factory: Optional[ModuleFactory] = None,
normalization_layer_factory: Optional[NormalizationLayerFactory] = None,
use_spectral_norm: bool = False) -> Module:
nonlinearity_factory = resolve_nonlinearity_factory(nonlinearity_factory)
return Sequential(
create_conv3(in_channels, out_channels,
bias=False, initialization_method=initialization_method, use_spectral_norm=use_spectral_norm),
NormalizationLayerFactory.resolve_2d(normalization_layer_factory).create(out_channels, affine=True),
resolve_nonlinearity_factory(nonlinearity_factory).create())
def create_conv3_block_from_block_args(
in_channels: int, out_channels: int, block_args: Optional[BlockArgs] = None):
if block_args is None:
block_args = BlockArgs()
return create_conv3_block(in_channels, out_channels,
block_args.initialization_method,
block_args.nonlinearity_factory,
block_args.normalization_layer_factory,
block_args.use_spectral_norm)
def create_downsample_block(in_channels: int, out_channels: int,
is_output_1x1: bool = False,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
nonlinearity_factory: Optional[ModuleFactory] = None,
normalization_layer_factory: Optional[NormalizationLayerFactory] = None,
use_spectral_norm: bool = False) -> Module:
if is_output_1x1:
return Sequential(
wrap_conv_or_linear_module(
Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False),
initialization_method,
use_spectral_norm),
resolve_nonlinearity_factory(nonlinearity_factory).create())
else:
return Sequential(
wrap_conv_or_linear_module(
Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False),
initialization_method,
use_spectral_norm),
NormalizationLayerFactory.resolve_2d(normalization_layer_factory).create(out_channels, affine=True),
resolve_nonlinearity_factory(nonlinearity_factory).create())
def create_downsample_block_from_block_args(in_channels: int, out_channels: int,
is_output_1x1: bool = False,
block_args: Optional[BlockArgs] = None):
if block_args is None:
block_args = BlockArgs()
return create_downsample_block(
in_channels, out_channels,
is_output_1x1,
block_args.initialization_method,
block_args.nonlinearity_factory,
block_args.normalization_layer_factory,
block_args.use_spectral_norm)
def create_upsample_block(in_channels: int,
out_channels: int,
initialization_method: Union[str, Callable[[Module], Module]] = 'he',
nonlinearity_factory: Optional[ModuleFactory] = None,
normalization_layer_factory: Optional[NormalizationLayerFactory] = None,
use_spectral_norm: bool = False) -> Module:
nonlinearity_factory = resolve_nonlinearity_factory(nonlinearity_factory)
return Sequential(
wrap_conv_or_linear_module(
ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False),
initialization_method,
use_spectral_norm),
NormalizationLayerFactory.resolve_2d(normalization_layer_factory).create(out_channels, affine=True),
resolve_nonlinearity_factory(nonlinearity_factory).create())
def create_upsample_block_from_block_args(in_channels: int,
out_channels: int,
block_args: Optional[BlockArgs] = None) -> Module:
if block_args is None:
block_args = BlockArgs()
return create_upsample_block(in_channels, out_channels,
block_args.initialization_method,
block_args.nonlinearity_factory,
block_args.normalization_layer_factory,
block_args.use_spectral_norm)
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