ONNXNaiveNASflux

ONNX import and export for Flux.

Models are imported as NaiveNASflux graphs, meaning that things like removing/inserting layers and pruning pre-trained models is a breeze.

Model export does not require the model to have any particular format. Almost any julia function can be exported as long as the primitives are recognized by ONNXNaiveNASflux.

Basic usage

] add ONNXNaiveNASflux

Exporting is done using the save function which accepts a filename String or an IO as first argument:

# Save model as model.onnx where inputshapes are tuples with sizes of input.
save("model.onnx", model, inputshapes...)

# Load model as a CompGraph
graph = load("model.onnx", inputshapes...)

Input shapes can be omitted in which case an attempt to infer the shapes will be made. If supplied, one tuple with size as the dimensions of the corresponding input the Julia model (including batch dimension) is expected. For example, if model expects an array of size (s1, s2, s3) then (s1, s2, s3) shall be given as inputshapes to both save and load.

Elements of input shape tuples can have one of the following types:

• Integer: The size of the corresponding dimension
• Missing: No shape info will be recorded for this dimension
• Symbol : Use the provided symbol as a variable name in the exported ONNX model

Names can be attached to inputs by providing a Pair where the first element is the name as a string, for example "imageinput" => (:W, :H, 3, missing). Note that non-integer input sizes will be ignored when loading a model.

More elaborate example with a model defined as a plain Julia function:

using ONNXNaiveNASflux, Test, Statistics

l1 = Conv((3,3), 2=>3, relu)
l2 = Dense(3, 4, elu)

f = function(x,y)
    x = l1(x)
    # Home-brewed global average pool
    x = dropdims(mean(x, dims=(1,2)), dims=(1,2))
    x = l2(x)
    return x + y
end

# Serialize f. Use a string to save to file instead
io = PipeBuffer()
x_shape = (:W, :H, 2, :Batch)
y_shape = (4, :Batch)
save(io, f, x_shape, y_shape)

# Deserialize as a NaiveNASflux CompGraph
g = load(io)

x = ones(Float32, 5,4,2,3)
y = ones(Float32, 4, 3)
@test g(x,y) ≈ f(x,y)

# Serialization of CompGraphs does not require input shapes to be provided as they can be inferred.
io = PipeBuffer()
save(io, g)

g = load(io)
@test g(x,y) ≈ f(x,y)

Supported Operations

Add
AveragePool
BatchNormalization
Concat
Constant
Conv
ConvTranspose
Div
Dropout
Elu
Flatten
Gemm
GlobalAveragePool
GlobalMaxPool
InstanceNormalization
LSTM
LeakyRelu
MatMul
MaxPool
Mul
RNN
ReduceMean
Relu
Reshape
Selu
Sigmoid
Softmax
Squeeze
Tanh
Unsqueeze

Adding Operations

While the list of supported operations is currently quite meager, it is relatively straightforward to add support for more.

Serialization uses a lightweight graph tracing mechanism where AbstractProbes are sent through the function to collect all ONNX operations they encounter.

To map the function myfun(args::SomeType....) to an ONNX operation one just defines a method myfun(args::AbstractProbe...) which

1. Adds ONNX information to one of the inputs (does not matter which one)
2. Returns at least one AbstractProbe with information for the next function

This function typically looks something like this:

import ONNXNaiveNASflux: AbstractProbe, recursename, nextname, newfrom, add!, name
function myfun(probes::AbstractProbe...)
    p = probes[1] # select any probe
    optype = "MyOpType"
    # Naming strategy (e.g. how to avoid duplicate names) is provided by the probe
    # Not strictly needed, but the onnx model is basically corrupt if duplicates exist
    nodename = recursename(optype, nextname(p))

    # Add ONNX node info
    add!(p, ONNX.NodeProto(
    # Names of input is provided by probes. This is why new probes need to be provided as output
    input = collect(name.(probes)),
    # Name of output from this node
    output = [nodename],
    op_type = optype))

    # Probes can procreate like this
    return newfrom(p, nodename, s -> s)
end

See serialize.jl for existing operations.

Deserialization is done by simply mapping operation types to functions in a dictionary. This allows for both easy extension as well as overwriting of existing mappings with own implementations:

import ONNXNaiveNASflux: actfuns

# All inputs which are not output from another node in the graph are provided in the method call
actfuns[:SomeOp] = (params, α, β) -> x -> x^α + β
# Params contains a Dict with attributes.
actfuns[:AnotherOp] = function(params)
    α = get(params, :alpha, 1)
    return x -> α / x
end
ONNXNaiveNASflux.refresh()

Note: After adding/changing an operation mapping one needs to call ONNXNaiveNASflux.refresh() for it to take effect. See ops.jl for existing operations.

Contributing

All contributions are welcome. Please file an issue before creating a PR.