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import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras import layers
from ..layers import BlockImages, SwapAxes, UnblockImages
def BlockGatingUnit(use_bias: bool = True, name: str = "block_gating_unit"):
"""A SpatialGatingUnit as defined in the gMLP paper.
The 'spatial' dim is defined as the **second last**.
If applied on other dims, you should swapaxes first.
"""
def apply(x):
u, v = tf.split(x, 2, axis=-1)
v = layers.LayerNormalization(
epsilon=1e-06, name=f"{name}_intermediate_layernorm"
)(v)
n = K.int_shape(x)[-2] # get spatial dim
v = SwapAxes()(v, -1, -2)
v = layers.Dense(n, use_bias=use_bias, name=f"{name}_Dense_0")(v)
v = SwapAxes()(v, -1, -2)
return u * (v + 1.0)
return apply
def BlockGmlpLayer(
block_size,
use_bias: bool = True,
factor: int = 2,
dropout_rate: float = 0.0,
name: str = "block_gmlp",
):
"""Block gMLP layer that performs local mixing of tokens."""
def apply(x):
n, h, w, num_channels = (
K.int_shape(x)[0],
K.int_shape(x)[1],
K.int_shape(x)[2],
K.int_shape(x)[3],
)
fh, fw = block_size
gh, gw = h // fh, w // fw
x = BlockImages()(x, patch_size=(fh, fw))
# MLP2: Local (block) mixing part, provides within-block communication.
y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
y = layers.Dense(
num_channels * factor,
use_bias=use_bias,
name=f"{name}_in_project",
)(y)
y = tf.nn.gelu(y, approximate=True)
y = BlockGatingUnit(use_bias=use_bias, name=f"{name}_BlockGatingUnit")(y)
y = layers.Dense(
num_channels,
use_bias=use_bias,
name=f"{name}_out_project",
)(y)
y = layers.Dropout(dropout_rate)(y)
x = x + y
x = UnblockImages()(x, grid_size=(gh, gw), patch_size=(fh, fw))
return x
return apply
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