def generator_three_layer_block(input_layer,
out_channels,
do_pixel_norm=False,
conditional_layer=None,
unet_end_points=None):
# Upsample
ret = pggan_utils.resize_twice_as_big(input_layer)
# Concat extra layers.
ret = pggan_utils.maybe_concat_conditional_layer(ret, conditional_layer)
ret = pggan_utils.maybe_concat_unet_layer(ret, unet_end_points)
# Conv
conv2d_out = ret
conv2d_out = pggan_utils.maybe_pixel_norm(
pggan_utils.maybe_equalized_conv2d(
conv2d_out,
out_channels,
),
do_pixel_norm=do_pixel_norm)
conv2d_out = pggan_utils.maybe_pixel_norm(
pggan_utils.maybe_equalized_conv2d(
conv2d_out,
out_channels,
),
do_pixel_norm=do_pixel_norm)
ret = pggan_utils.maybe_resblock(ret, out_channels, conv2d_out)
return ret
def encoder_from_rgb_block(input_layer, out_channels, do_pixel_norm=False):
conv2d_out = pggan_utils.maybe_pixel_norm(
pggan_utils.maybe_equalized_conv2d(input_layer,
out_channels,
kernel_size=1),
do_pixel_norm=do_pixel_norm)
ret = pggan_utils.maybe_resblock(input_layer, out_channels, conv2d_out)
return ret
def discriminator_from_rgb_block(input_layer, out_channels):
conv2d_out = input_layer
conv2d_out = pggan_utils.maybe_equalized_conv2d(conv2d_out,
out_channels,
kernel_size=1,
is_discriminator=True)
ret = pggan_utils.maybe_resblock(input_layer,
out_channels,
conv2d_out,
is_discriminator=True)
return ret
def discriminator_two_layer_block(input_layer, out_channels, maybe_gdrop_fn):
input_shape = input_layer.shape
input_channels = input_shape[3]
conv2d_out = input_layer
# The first layer's depth is the same as input.
conv2d_out = pggan_utils.maybe_equalized_conv2d(maybe_gdrop_fn(conv2d_out),
input_channels,
is_discriminator=True)
# The second layer's depth is the output channel.
conv2d_out = pggan_utils.maybe_equalized_conv2d(maybe_gdrop_fn(conv2d_out),
out_channels,
kernel_size=3,
is_discriminator=True)
ret = pggan_utils.maybe_resblock(input_layer,
out_channels,
conv2d_out,
is_discriminator=True)
return ret
def encoder_two_layer_block(input_layer, out_channels, do_pixel_norm=False):
input_shape = input_layer.shape
input_channels = input_shape[3]
conv2d_out = input_layer
# The first layer's depth is the same as input.
conv2d_out = pggan_utils.maybe_pixel_norm(
pggan_utils.maybe_equalized_conv2d(
conv2d_out,
input_channels,
),
do_pixel_norm=do_pixel_norm)
# The second layer's depth is the output channel.
conv2d_out = pggan_utils.maybe_pixel_norm(
pggan_utils.maybe_equalized_conv2d(conv2d_out,
out_channels,
kernel_size=3),
do_pixel_norm=do_pixel_norm)
ret = pggan_utils.maybe_resblock(input_layer, out_channels, conv2d_out)
return ret