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ops.jl
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ops.jl
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import Base:*, broadcast, reshape, exp, log, tanh, sum,
adjoint, inv, argmax, argmin, ^, max, maximum, min, minimum,
vec, \, cos, sin, sign, map, prod, reverse
import LinearAlgebra: tr, diag, det, norm, diagm, dot, I, svd, tril, triu
import Statistics: mean, std
import FFTW: fft, ifft
export
*,
^,
einsum,
sigmoid,
tanh,
mean,
log,
exp,
softplus,
softmax,
softsign,
sum,
relu,
relu6,
squeeze,
adjoint,
diag,
diagm,
det,
inv,
triangular_solve,
argmin,
argmax,
max,
min,
group_assign,
assign,
maximum,
minimum,
cast,
group,
clip,
scatter_add,
scatter_sub,
scatter_update,
stack,
concat,
unstack,
norm,
cvec,
rvec,
vec,
sqrt,
mean,
pad,
leaky_relu,
fft,
ifft,
I,
svd,
vector,
pmap,
std,
lgamma,
topk,
argsort,
batch_matmul,
dot,
set_shape,
selu,
elu,
tr,
tril,
triu,
solve_batch,
swish, hard_sigmoid, hard_swish, concat_elu, concat_hard_swish, concat_relu, fourier,
rollmean, rollsum, rollvar, rollstd,
softmax_cross_entropy_with_logits
@doc raw"""
batch_matmul(o1::PyObject, o2::PyObject)
Computes `o1[i,:,:] * o2[i, :]` or `o1[i,:,:] * o2[i, :, :]` for each index `i`.
"""
function batch_matmul(o1::PyObject, o2::PyObject)
flag = false
if length(size(o2))==2
flag = true
o2 = tf.expand_dims(o2, 2)
end
if length(size(o1))!=3 || length(size(o2))!=3
error("The size of o1 or o2 is not valid.")
end
out = tf.matmul(o1, o2)
if flag
squeeze(out)
else
out
end
end
batch_matmul(o1::Array{<:Real}, o2::PyObject) = batch_matmul(constant(o1), o2)
batch_matmul(o1::PyObject, o2::Array{<:Real}) = batch_matmul(o1, constant(o2))
batch_matmul(o1::Array{<:Real}, o2::Array{<:Real}) = batch_matmul(constant(o1), constant(o2))
function PyCall.:*(o1::PyObject, o2::PyObject)
s1 = size(o1)
s2 = size(o2)
if s1==nothing || s2==nothing
error("o1 and o2 should be tensors of rank 0, 1, 2")
end
if length(s1) == 0 || length(s2)==0
return tf.multiply(o1, o2)
end
if length(s1)==2 && length(s2)==2
return tf.matmul(o1, o2)
elseif length(s1)==2 && length(s2)==1
return tf.einsum("nm,m->n", o1, o2)
elseif length(s1)==2 && length(s2)==0
return tf.multiply(o1, o2)
elseif length(s1)==1 && length(s2)==2
error("[rand 1] x [rank 2] not defined")
elseif length(s1)==1 && length(s2)==1
return tf.multiply(o1, o2)
else
@warn("Unusual usage of multiplication. Check carefully")
tf.matmul(o1,o2)
end
end
Base.:*(o1::PyObject, o2::AbstractArray{<:Real}) = *(o1, constant(Array(o2), dtype=get_dtype(o1)))
Base.:*(o1::AbstractArray{<:Real}, o2::PyObject) = *(constant(Array(o1), dtype=get_dtype(o2)), o2)
Base.:*(o1::Number, o2::PyObject) = *(constant(o1, dtype=get_dtype(o2)), o2)
Base.:*(o1::PyObject, o2::Number) = *(o1, constant(o2, dtype=get_dtype(o1)))
Base.Broadcast.broadcasted(::typeof(*), o1::PyObject, o2::AbstractArray{<:Real}) = tf.multiply(o1, Array(o2))
Base.Broadcast.broadcasted(::typeof(*), o1::AbstractArray{<:Real}, o2::PyObject) = tf.multiply(Array(o1), o2)
Base.Broadcast.broadcasted(::typeof(*), o1::PyObject, o2::PyObject) = tf.multiply(o1, o2)
Base.Broadcast.broadcasted(::typeof(*), o1::PyObject, o2::Number) = tf.multiply(o1, o2)
Base.Broadcast.broadcasted(::typeof(*), o1::Number, o2::PyObject) = tf.multiply(o1, o2)
Base.Broadcast.broadcasted(::typeof(/), o1::PyObject, o2::AbstractArray{<:Real}) = tf.divide(o1, Array(o2))
Base.Broadcast.broadcasted(::typeof(/), o1::AbstractArray{<:Real}, o2::PyObject) = tf.divide(Array(o1), o2)
Base.Broadcast.broadcasted(::typeof(/), o1::PyObject, o2::PyObject) = tf.divide(o1, o2)
Base.Broadcast.broadcasted(::typeof(/), o1::PyObject, o2::Number) = tf.divide(o1, o2)
Base.Broadcast.broadcasted(::typeof(/), o1::Number, o2::PyObject) = tf.divide(o1, o2)
Base.Broadcast.broadcasted(::typeof(+), o1::PyObject, o2::AbstractArray{<:Real}) = o1 + Array(o2)
Base.Broadcast.broadcasted(::typeof(+), o1::AbstractArray{<:Real}, o2::PyObject) = Array(o1) + o2
Base.Broadcast.broadcasted(::typeof(+), o1::PyObject, o2::PyObject) = o1 + o2
Base.Broadcast.broadcasted(::typeof(+), o1::PyObject, o2::Number) = o1 + o2
Base.Broadcast.broadcasted(::typeof(+), o1::Number, o2::PyObject) = o1 + o2
Base.Broadcast.broadcasted(::typeof(-), o1::PyObject, o2::AbstractArray{<:Real}) = o1 - Array(o2)
Base.Broadcast.broadcasted(::typeof(-), o1::AbstractArray{<:Real}, o2::PyObject) = Array(o1) - o2
Base.Broadcast.broadcasted(::typeof(-), o1::PyObject, o2::PyObject) = o1 - o2
Base.Broadcast.broadcasted(::typeof(-), o1::PyObject, o2::Number) = o1 - o2
Base.Broadcast.broadcasted(::typeof(-), o1::Number, o2::PyObject) = o1 - o2
warn_broadcast_pow() = error(".^ is disabled due to eager evaluation. Use ^ instead.")
Base.Broadcast.broadcasted(::typeof(^), o1::PyObject, o2::Union{AbstractArray{<:Real},Number}) = warn_broadcast_pow()
Base.Broadcast.broadcasted(::typeof(^), o1::PyObject, o2::PyObject) = warn_broadcast_pow()
Base.Broadcast.broadcasted(::typeof(^), o1::Union{AbstractArray{<:Real},Number}, o2::PyObject) = warn_broadcast_pow()
function einsum(equation, args...; kwargs...)
tf.einsum(equation, args...; kwargs...)
end
"""
reshape(o::PyObject, s::Union{Array{<:Integer}, Tuple{Vararg{<:Integer, N}}}) where N
reshape(o::PyObject, s::Integer; kwargs...)
reshape(o::PyObject, m::Integer, n::Integer; kwargs...)
reshape(o::PyObject, ::Colon, n::Integer)
reshape(o::PyObject, n::Integer, ::Colon)
Reshapes the tensor according to row major if the "TensorFlow style" syntax is used; otherwise
reshaping according to column major is assumed.
# Example
```julia
reshape(a, [10,5]) # row major
reshape(a, 10, 5) # column major
```
"""
function reshape(o::PyObject, s::Union{Array{<:Integer}, Tuple{Vararg{<:Integer, N}}}) where N
tf.reshape(o, s)
end
function reshape(o::PyObject, s::Integer; kwargs...)
if length(size(o))>=2
return tf.reshape(o', [s]; kwargs...)
end
tf.reshape(o, [s]; kwargs...)
end
function reshape(o::PyObject, m::Integer, n::Integer; kwargs...)
if length(size(o))==1
return tf.reshape(o, [n,m]; kwargs...)'
elseif length(size(o))==2
return tf.reshape(o', [n,m]; kwargs...)'
end
tf.reshape(o, [m, n]; kwargs...)
end
reshape(o::PyObject, ::Colon, n::Integer) = reshape(o, -1, n)
reshape(o::PyObject, n::Integer, ::Colon) = reshape(o, n, -1)
"""
rvec(o::PyObject; kwargs...)
Vectorizes the tensor `o` to a row vector, assuming column major.
"""
function rvec(o::PyObject; kwargs...)
s = size(o)
if length(s)==0
return reshape(o, 1, 1, kwargs...)
elseif length(s)==1
return reshape(o, 1, s[1], kwargs...)
elseif length(s)==2
return reshape(o, 1, s[1]*s[2], kwargs...)
else
error("Invalid argument")
end
end
"""
rvec(o::PyObject; kwargs...)
Vectorizes the tensor `o` to a column vector, assuming column major.
"""
function cvec(o::PyObject;kwargs...)
s = size(o)
if length(s)==0
return reshape(o, 1, 1,kwargs...)
elseif length(s)==1
return reshape(o, s[1], 1,kwargs...)
elseif length(s)==2
return reshape(o, s[1]*s[2], 1,kwargs...)
else
error("Invalid argument")
end
end
"""
vec(o::PyObject;kwargs...)
Vectorizes the tensor `o` assuming column major.
"""
function vec(o::PyObject;kwargs...)
s = size(o)
if length(s)==0
return reshape(o, 1, kwargs...)
elseif length(s)==1
return o
elseif length(s)>=2
return reshape(o, s[1]*s[2])
end
end
"""
set_shape(o::PyObject, s::Union{Array{<:Integer}, Tuple{Vararg{<:Integer, N}}}) where N
set_shape(o::PyObject, s::Integer...)
Sets the shape of `o` to `s`. `s` must be the actual shape of `o`. This function is used to convert a
tensor with unknown dimensions to a tensor with concrete dimensions.
# Example
```julia
a = placeholder(Float64, shape=[nothing, 10])
b = set_shape(a, 3, 10)
run(sess, b, a=>rand(3,10)) # OK
run(sess, b, a=>rand(5,10)) # Error
run(sess, b, a=>rand(10,3)) # Error
```
"""
function set_shape(o::PyObject, s::Union{Array{<:Integer}, Tuple{Vararg{<:Integer, N}}}) where N
o.set_shape(s)
return o
end
set_shape(o::PyObject, s::Integer...) = set_shape(o, s)
function sigmoid(x::Real)
return 1/(1+exp(-x))
end
function sigmoid(o::PyObject; kwargs...)
tf.math.sigmoid(o; kwargs...)
end
relu(x::Real) = max(zero(x), x)
function relu(o::PyObject; kwargs...)
tf.nn.relu(o; kwargs...)
end
relu6(x::Real) = min(relu(x), one(x)*oftype(x, 6))
relu6(o::PyObject; kwargs...) = tf.nn.relu6(o; kwargs...)
function tan(o::PyObject; kwargs...)
tf.math.tan(o; kwargs...)
end
function leaky_relu(x::Real, a = oftype(x / 1, 0.2))
max(a * x, x / one(x))
end
function leaky_relu(o::PyObject; kwargs...)
tf.nn.leaky_relu(o; kwargs...)
end
function tanh(o::PyObject; kwargs...)
tf.tanh(o; kwargs...)
end
function selu(x::Real)
λ = oftype(x / 1, 1.0507009873554804934193349852946)
α = oftype(x / 1, 1.6732632423543772848170429916717)
λ * ifelse(x > 0, x / one(x), α * (exp(x) - one(x)))
end
selu(o::PyObject; kwargs...) = tf.nn.selu(o; kwargs...)
elu(x, α = one(x)) = ifelse(x ≥ 0, x / one(x), α * (exp(x) - one(x)))
elu(o::PyObject; kwargs...) = tf.nn.elu(o; kwargs...)
softsign(x::Real) = x / (one(x) + abs(x))
softsign(o::PyObject; kwargs...) = tf.nn.softsign(o; kwargs...)
function argmax(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.argmax(o; kwargs...) + 1
end
function Base.:sqrt(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.sqrt(o)
end
function argmin(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.argmin(o; kwargs...) + 1
end
function max(o1::PyObject, o2::PyObject; kwargs...)
tf.maximum(o1, o2; kwargs...)
end
function min(o1::PyObject, o2::PyObject; kwargs...)
tf.minimum(o1, o2; kwargs...)
end
function maximum(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.reduce_max(o; kwargs...)
end
function minimum(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.reduce_min(o; kwargs...)
end
function std(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.math.reduce_std(o; kwargs...)
end
function cast(x::PyObject, dtype::Type;kwargs...)
dtype = DTYPE[dtype]
tf.cast(x, dtype; kwargs...)
end
function cast(dtype::Type, x::PyObject;kwargs...)
dtype = DTYPE[dtype]
tf.cast(x, dtype; kwargs...)
end
softplus(x::Real) = ifelse(x > 0, x + log1p(exp(-x)), log1p(exp(x)))
function softplus(x;kwargs...)
tf.math.softplus(x; kwargs...)
end
function log(o::PyObject; kwargs...)
tf.math.log(o; kwargs...)
end
function exp(o::PyObject; kwargs...)
tf.exp(o; kwargs...)
end
function cos(o::PyObject; kwargs...)
tf.cos(o; kwargs...)
end
function sin(o::PyObject; kwargs...)
tf.sin(o; kwargs...)
end
function sign(o::PyObject; kwargs...)
tf.sign(o; kwargs...)
end
function softmax(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.math.softmax(o; kwargs...)
end
function sum(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.reduce_sum(o; kwargs...)
end
function mean(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.reduce_mean(o; kwargs...)
end
function prod(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.reduce_prod(o; kwargs...)
end
function squeeze(o::PyObject; kwargs...)
kwargs = jlargs(kwargs)
tf.squeeze(o;kwargs...)
end
"""
pad(o::PyObject, paddings::Array{Int64, 2}, args...; kwargs...)
Pads `o` with values on the boundary.
# Example
```julia
o = rand(3,3)
o = pad(o, [1 4 # first dimension
2 3]) # second dimension
run(sess, o)
```
Expected:
```
8×8 Array{Float64,2}:
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.250457 0.666905 0.823611 0.0 0.0 0.0
0.0 0.0 0.23456 0.625145 0.646713 0.0 0.0 0.0
0.0 0.0 0.552415 0.226417 0.67802 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
```
"""
function pad(o::Union{Array{<:Real}, PyObject}, paddings::Array{Int64, 2}, args...; kwargs...)
o = constant(o)
tf.pad(o, paddings, args...; kwargs...)
end
function assign(o::PyObject,value, args...; kwargs...)
tf.compat.v1.assign(o, value, args...;kwargs...)
end
function group(args...; kwargs...)
tf.group(args...; kwargs...)
end
assign(o::Array{PyObject}, value::Array, args...;kwargs...) = group_assign(o, value, args...; kwargs...)
@deprecate group_assign assign
function group_assign(os::Array{PyObject}, values, args...; kwargs...)
ops = Array{PyObject}(undef, length(os))
for i = 1:length(os)
ops[i] = tf.compat.v1.assign(os[i], values[i], args...; kwargs...)
end
ops
end
"""
adjoint(o::PyObject; kwargs...)
Returns the conjugate adjoint of `o`.
When the dimension of `o` is greater than 2, only the last two dimensions are permuted, i.e., `permutedims(o, [1,2,...,n,n-1])`
"""
function adjoint(o::PyObject; kwargs...)
if length(size(o))==0
return o
elseif length(size(o))==1
return rvec(o)
else
return tf.linalg.adjoint(o; kwargs...)
end
end
diagm(o::PyObject; kwargs...) = tf.linalg.diag(o; kwargs...)
diag(o::PyObject; kwargs...) = tf.linalg.diag_part(o; kwargs...)
det(o::PyObject; kwargs...) = tf.linalg.det(o; kwargs...)
inv(o::PyObject; kwargs...) = tf.linalg.inv(o; kwargs...)
@doc raw"""
solve_batch(A::Union{PyObject, Array{<:Real, 2}}, rhs::Union{PyObject, Array{<:Real,2}})
Solves $$Ax = b$$ for a batch of right hand sides.
- `A`: a $m\times n$ matrix, where $m\geq n$
- `rhs`: a $n_b\times m$ matrix. Each row is a new right hand side to solve.
The returned value is a $n_b\times n$ matrix.
# Example
```julia
a = rand(10,5)
b = rand(100, 10)
sol = solve_batch(a, b)
@assert run(sess, sol) ≈ (a\b')'
```
!!! note
Internally, the matrix $A$ is factorized first and then the factorization is used to solve multiple right hand side.
"""
function solve_batch(A::Union{PyObject, Array{<:Real, 2}}, rhs::Union{PyObject, Array{<:Real,2}})
solve_batched_rhs_ = load_system_op("solve_batched_rhs"; multiple=false)
a,rhs = convert_to_tensor([A,rhs], [Float64,Float64])
sol = solve_batched_rhs_(a,rhs)
if size(a, 2)!=nothing && size(rhs,1) != nothing
sol = set_shape(sol, (size(rhs,1), size(a,2)))
end
return sol
end
function solve(matrix, rhs; kwargs...)
rhs = constant(rhs)
matrix = constant(matrix)
flag = false
if length(size(rhs))==1
flag = true
rhs = reshape(rhs, size(rhs, 1), 1)
end
if size(matrix,1)==size(matrix,2)
ret = tf.linalg.solve(matrix, rhs; kwargs...)
else
# @show matrix, rhs
ret = tf.linalg.lstsq(matrix, rhs;kwargs...)
end
if flag
ret = squeeze(ret, dims=2)
end
return ret
end
Base.:\(o1::PyObject, o2::PyObject) = solve(o1, o2)
Base.:\(o1::PyObject, o2::Array) = solve(o1, o2)
Base.:\(o1::Array, o2::PyObject) = solve(o1, o2)
function triangular_solve(matrix, rhs; kwargs...)
flag = false
if length(size(rhs))==1
flag = true
rhs = reshape(rhs, size(rhs, 1), 1)
end
ret = tf.linalg.triangular_solve(matrix, rhs; kwargs...)
if flag
ret = squeeze(ret, dims=2)
end
return ret
end
# reference: https://blog.csdn.net/LoseInVain/article/details/79638183
function concat(o::Union{PyObject,Array{PyObject}}, args...;kwargs...)
if isa(o, PyObject)
@warn "Only one input is consumed by concat" maxlog=1
return o
end
kwargs = jlargs(kwargs)
if length(size(o[1]))==0
return tf.stack(o)
end
tf.concat(o, args...; kwargs...)
end
function stack(o::Array{PyObject}, args...;kwargs...)
kwargs = jlargs(kwargs)
tf.stack(o, args...; kwargs...)
end
Base.:vcat(args::PyObject...) = concat([args...],0)
function Base.:hcat(args::PyObject...)
if length(size(args[1]))>=2
concat([args...],1)
elseif length(size(args[1]))==1
stack([args...],dims=2)
else
vcat(args...)'
end
end
"""
stack(o::PyObject)
Convert a `TensorArray` `o` to a normal tensor. The leading dimension is the size of the tensor array.
"""
function stack(o::PyObject)
o.stack()
end
function unstack(o::PyObject, args...;kwargs...)
kwargs = jlargs(kwargs)
tf.unstack(o, args...; kwargs...)
end
function _jlindex2indices(len::Int64,
indices::Union{PyObject, Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}})
if isa(indices, BitArray{1}) || isa(indices, Array{Bool,1})
indices = findall(indices)
elseif isa(indices, UnitRange{Int64}) || isa(indices, StepRange{Int64, Int64})
indices = collect(indices)
elseif isa(indices, Colon)
indices = Array(1:len)
elseif isa(indices, Int64)
indices = [indices]
end
if isa(indices, PyObject)
return indices - 1
else
return constant(indices .- 1)
end
end
for (op1, op2) = [(:_scatter_add, :tensor_scatter_nd_add), (:_scatter_sub, :tensor_scatter_nd_sub),
(:_scatter_update,:tensor_scatter_nd_update)]
@eval begin
function $op1(ref::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
updates = convert_to_tensor(updates, dtype=get_dtype(ref))
@assert length(size(updates)) <= 1
@assert length(size(ref))==1
if length(size(updates))==0
updates = reshape(updates, (-1,))
end
indices = _jlindex2indices(length(ref),indices)
indices = reshape(indices, (-1,1))
# @info ref, indices, updates
tf.$op2(ref, indices, updates)
end
end
end
"""
scatter_update(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
Updates array `a`
```
a[indices] = updates
```
# Example
Julia:
```julia
A[[1;2;3]] = rand(3)
A[2] = 1.0
```
ADCME:
```
A = scatter_update(A, [1;2;3], rand(3))
A = scatter_update(A, 2, 1.0)
```
"""
scatter_update(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = _scatter_update(a, indices, updates)
"""
scatter_sub(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
Updates array `a`
```
a[indices] -= updates
```
# Example
Julia:
```julia
A[[1;2;3]] -= rand(3)
A[2] -= 1.0
```
ADCME:
```
A = scatter_sub(A, [1;2;3], rand(3))
A = scatter_sub(A, 2, 1.0)
```
"""
scatter_sub(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = _scatter_sub(a, indices, updates)
"""
scatter_add(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
Updates array `add`
```
a[indices] += updates
```
# Example
Julia:
```julia
A[[1;2;3]] += rand(3)
A[2] += 1.0
```
ADCME:
```
A = scatter_add(A, [1;2;3], rand(3))
A = scatter_add(A, 2, 1.0)
```
"""
scatter_add(a::PyObject,
indices::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = _scatter_add(a, indices, updates)
for (op1, op2) = [(:scatter_update2, :scatter_update), (:scatter_add2, :scatter_add),
(:scatter_sub2,:scatter_sub)]
@eval begin
function $op1(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
m, n = size(A)
updates = convert_to_tensor(updates, dtype=get_dtype(A))
@assert length(size(A))==2
xind = _jlindex2indices(m,xind)
yind = _jlindex2indices(n,yind)
if length(size(updates))==0
updates = reshape(updates, (1,1))
end
indices = reshape(repeat(xind, 1, length(yind)), (-1,)) * n + repeat(yind, length(xind))
out = $op2(reshape(A, (-1,)), indices + 1, reshape(updates, (-1,)))
reshape(out, (m, n))
end
end
end
"""
scatter_update(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
```julia
A[xind, yind] = updates
```
"""
scatter_update(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = scatter_update2(A, xind, yind, updates)
"""
scatter_add(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
```julia
A[xind, yind] += updates
```
"""
scatter_add(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = scatter_add2(A, xind, yind, updates)
"""
scatter_add(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject})
```julia
A[xind, yind] -= updates
```
"""
scatter_sub(A::PyObject,
xind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
yind::Union{Colon, Int64, Array{Int64}, BitArray{1}, Array{Bool,1}, UnitRange{Int64}, StepRange{Int64, Int64}, PyObject},
updates::Union{Array{<:Real}, Real, PyObject}) = scatter_sub2(A, xind, yind, updates)
function norm(o::PyObject, args...;kwargs...)
kwargs = jlargs(kwargs)
tf.norm(o, args...;kwargs...)
end
function Base.:diff(o::PyObject; dims::Union{Int64,Nothing}=nothing)
if dims==nothing
if length(size(o))!=1
error("expect rank=1")
end
return o[2:end]-o[1:end-1]
elseif length(size(o))==2
if dims==1
return o[2:end,:]-o[1:end-1,:]
elseif dims==2
return o[:,2:end]-o[:,1:end-1]
end
else
error("Arguments not understood")
end
end
clip(o::PyObject, vmin, vmax, args...;kwargs...) = tf.clip_by_value(o, vmin, vmax, args...;kwargs...)
@doc raw"""
clip(o::Union{Array{Any}, Array{PyObject}}, vmin, vmax, args...;kwargs...)
Clips the values of `o` to the range [`vmin`, `vmax`]
# Example
```julia
a = constant(3.0)
a = clip(a, 1.0, 2.0)
b = constant(rand(3))
b = clip(b, 0.5, 1.0)
```
"""
function clip(o::Union{Array{Any}, Array{PyObject}}, vmin, vmax, args...;kwargs...)
out = Array{PyObject}(undef, length(o))
for i = 1:length(o)
out[i] = clip(o[i], vmin, vmax, args...;kwargs...)
end
out
end
function fft(o::PyObject, args...; kwargs...)
if length(size(o))==1
tf.fft(o, args...; kwargs...)
elseif length(size(o))==2
tf.fft2d(o, args...; kwargs...)
elseif length(size(o))==3
tf.fft3d(o, args...; kwargs...)
else
error("FFT for d>=4 not supported")
end
end
# mimic the Julia SVD
struct TFSVD
S::PyObject
U::PyObject
V::PyObject
Vt::PyObject
end
"""
svd(o::PyObject, args...; kwargs...)
Returns a `TFSVD` structure which holds the following data structures
```julia
S::PyObject
U::PyObject
V::PyObject
Vt::PyObject
```
We have the equality
``o = USV'``
# Example
```julia
A = rand(10,20)
r = svd(constant(A))
A2 = r.U*diagm(r.S)*r.Vt # The value of `A2` should be equal to `A`
```
"""
function svd(o::PyObject, args...; kwargs...)
s,u,v = tf.linalg.svd(o)
TFSVD(s, u, v, v')
end
function ifft(o::PyObject, args...; kwargs...)
if length(size(o))==1
tf.ifft(o, args...; kwargs...)
elseif length(size(o))==2
tf.ifft2d(o, args...; kwargs...)
elseif length(size(o))==3
tf.ifft3d(o, args...; kwargs...)
else
error("IFFT for d>=4 not supported")
end
end
function Base.:real(o::PyObject, args...; kwargs...)
tf.real(o, args...; kwargs...)
end
function Base.:imag(o::PyObject, args...; kwargs...)
tf.imag(o, args...; kwargs...)
end
@doc raw"""
map(fn::Function, o::Union{Array{PyObject},PyObject};
kwargs...)
Applies `fn` to each element of `o`.
- `o`∈`Array{PyObject}` : returns `[fn(x) for x in o]`
- `o`∈PyObject : splits `o` according to the first dimension and then applies `fn`.
# Example
```julia
a = constant(rand(10,5))
b = map(x->sum(x), a) # equivalent to `sum(a, dims=2)`
```
!!! note
If `fn` is a multivariate function, we need to specify the output type using `dtype` keyword. For example,
```julia
a = constant(ones(10))
b = constant(ones(10))
fn = x->x[1]+x[2]
c = map(fn, [a, b], dtype=Float64)
```
"""
function map(fn::Function, o::Union{Array{PyObject},PyObject};
kwargs...)
# if `o` is not a tensorflow tensor, roll back to normal `map`
if (isa(o, Array) && !hasproperty(o[1], :graph)) ||
(isa(o, PyObject) && !hasproperty(o, :graph))
return fn.(o)
end
kwargs = jlargs(kwargs)
tf.map_fn(fn, o;kwargs...)
end
"""
pmap(fn::Function, o::Union{Array{PyObject}, PyObject})
Parallel for loop. There should be no data dependency between different iterations.
# Example
```julia
x = constant(ones(10))
y1 = pmap(x->2.0*x, x)
y2 = pmap(x->x[1]+x[2], [x,x])
y3 = pmap(1:10, x) do z
i = z[1]
xi = z[2]
xi + cast(Float64, i)
end
run(sess, y1)
run(sess, y2)
run(sess, y3)
```
"""
function pmap(fn::Function, o::Union{Array{PyObject}, PyObject})
tf.compat.v1.vectorized_map(fn, o)
end
function pmap(fn::Function, range_::Union{Array{Int64},UnitRange{Int64},StepRange{Int64}, PyObject},
o::Union{Array{PyObject}, PyObject})
ipt = convert_to_tensor(collect(range_))
if isa(o, PyObject)
return tf.compat.v1.vectorized_map(fn, [ipt,o])
end
if length(o)==0
return tf.compat.v1.vectorized_map(fn, ipt)
end
if length(o)>0
tf.compat.v1.vectorized_map(fn, [ipt, o...])
end
end
dot(x::PyObject, y::PyObject) = sum(x.*y)
dot(x::PyObject, y::AbstractArray{<:Real}{<:Real}) = sum(x.*constant(y))
dot(x::AbstractArray{<:Real}{<:Real}, y::PyObject) = sum(constant(x).*y)
import PyCall: +, -
function +(o::PyObject, I::UniformScaling{Bool})
@assert size(o,1)==size(o,2)
o + diagm(0=>ones(size(o,1)))
end
function -(o::PyObject, I::UniformScaling{Bool})
@assert size(o,1)==size(o,2)
o - diagm(0=>ones(size(o,1)))
end