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import functools
import tensorflow as tf
from tensorflow.keras import layers
from .attentions import RCAB
from .misc_gating import CrossGatingBlock, ResidualSplitHeadMultiAxisGmlpLayer
Conv1x1 = functools.partial(layers.Conv2D, kernel_size=(1, 1), padding="same")
Conv3x3 = functools.partial(layers.Conv2D, kernel_size=(3, 3), padding="same")
ConvT_up = functools.partial(
layers.Conv2DTranspose, kernel_size=(2, 2), strides=(2, 2), padding="same"
)
Conv_down = functools.partial(
layers.Conv2D, kernel_size=(4, 4), strides=(2, 2), padding="same"
)
def UNetEncoderBlock(
num_channels: int,
block_size,
grid_size,
num_groups: int = 1,
lrelu_slope: float = 0.2,
block_gmlp_factor: int = 2,
grid_gmlp_factor: int = 2,
input_proj_factor: int = 2,
channels_reduction: int = 4,
dropout_rate: float = 0.0,
downsample: bool = True,
use_global_mlp: bool = True,
use_bias: bool = True,
use_cross_gating: bool = False,
name: str = "unet_encoder",
):
"""Encoder block in MAXIM."""
def apply(x, skip=None, enc=None, dec=None):
if skip is not None:
x = tf.concat([x, skip], axis=-1)
# convolution-in
x = Conv1x1(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_0")(x)
shortcut_long = x
for i in range(num_groups):
if use_global_mlp:
x = ResidualSplitHeadMultiAxisGmlpLayer(
grid_size=grid_size,
block_size=block_size,
grid_gmlp_factor=grid_gmlp_factor,
block_gmlp_factor=block_gmlp_factor,
input_proj_factor=input_proj_factor,
use_bias=use_bias,
dropout_rate=dropout_rate,
name=f"{name}_SplitHeadMultiAxisGmlpLayer_{i}",
)(x)
x = RCAB(
num_channels=num_channels,
reduction=channels_reduction,
lrelu_slope=lrelu_slope,
use_bias=use_bias,
name=f"{name}_channel_attention_block_1{i}",
)(x)
x = x + shortcut_long
if enc is not None and dec is not None:
assert use_cross_gating
x, _ = CrossGatingBlock(
features=num_channels,
block_size=block_size,
grid_size=grid_size,
dropout_rate=dropout_rate,
input_proj_factor=input_proj_factor,
upsample_y=False,
use_bias=use_bias,
name=f"{name}_cross_gating_block",
)(x, enc + dec)
if downsample:
x_down = Conv_down(
filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_1"
)(x)
return x_down, x
else:
return x
return apply
def UNetDecoderBlock(
num_channels: int,
block_size,
grid_size,
num_groups: int = 1,
lrelu_slope: float = 0.2,
block_gmlp_factor: int = 2,
grid_gmlp_factor: int = 2,
input_proj_factor: int = 2,
channels_reduction: int = 4,
dropout_rate: float = 0.0,
downsample: bool = True,
use_global_mlp: bool = True,
use_bias: bool = True,
name: str = "unet_decoder",
):
"""Decoder block in MAXIM."""
def apply(x, bridge=None):
x = ConvT_up(
filters=num_channels, use_bias=use_bias, name=f"{name}_ConvTranspose_0"
)(x)
x = UNetEncoderBlock(
num_channels=num_channels,
num_groups=num_groups,
lrelu_slope=lrelu_slope,
block_size=block_size,
grid_size=grid_size,
block_gmlp_factor=block_gmlp_factor,
grid_gmlp_factor=grid_gmlp_factor,
channels_reduction=channels_reduction,
use_global_mlp=use_global_mlp,
dropout_rate=dropout_rate,
downsample=False,
use_bias=use_bias,
name=f"{name}_UNetEncoderBlock_0",
)(x, skip=bridge)
return x
return apply
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