Spaces:
Runtime error
Runtime error
File size: 4,084 Bytes
ef31f77 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
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
|