this
with
modules
frozenbatchnorm2d
parameters
output
layernorm2d
type
spaces
posenc
max
hardswish
creating
zero
total
or
dropout
convolutions
2d
network
5
by
last
after
gaussian
batchnorm
two
function
none
pool
separable
weighted
normalization
3
does
producing
analogue
map
correct
?
value
smoother
it
swish
average
not
7
features
lastlevelmaxpool
units
elements
architecture
maximum
what
include
input
of
neural
norm
silu
4d
use
feature
rectified
conv2d
across
used
sep
number
hardsigmoid
calculating
adaptive
have
level
dil
has
exist
inserting
frozen
reducing
1
hard
identity
linear
overall
model
more
sigmoid
6
relu6
instead
over
pooling
while
are
kernel
expensive
comes
general
unit
piecewise
module
same
into
there
for
between
the
avgpool2d
convoloution
version
maxpool2d
)
which
applying
calculates
single
wider
layer
along
convolution
been
size
gelu
followed
each
patch
layers
adaptiveavgpool2d
relu
replacing
dimension
(
to
activation
in
_
included
and
error
performs
is
convolve
*
dilated
layernorm
dividing
batchnorm2d
kind
computationally
a
batch
