get forward pass working

src/backend/mxnet/model.jl

@@ -14,8 +14,36 @@ function mxargs(args)
   end
 end
 
-function mxnet(model::Model, input)
+function load!(model::MXModel)
-  vars = Dict{Symbol,Any}(:input => mx.zeros(mxdims(input)))
+  for (name, arr) in model.exec.arg_dict
-  node = graph(vars, model, mx.Variable(:input))
+    # TODO: don't allocate here
-  MXModel(model, vars, mx.bind(node, args = mxargs(vars), grad_req = mx.GRAD_NOP))
+    haskey(model.params, name) && mx.copy_ignore_shape!(arr, model.params[name]')
+  end
+  return model
 end
+
+function mxnet(model::Model, input)
+  vars = Dict{Symbol,Any}()
+  node = graph(vars, model, mx.Variable(:input))
+  args = merge(mxargs(vars), Dict(:input => mx.zeros(mxdims(input))))
+  model = MXModel(model, vars, mx.bind(node, args = args, grad_req = mx.GRAD_NOP))
+  load!(model)
+  return model
+end
+
+function (model::MXModel)(input)
+  inputnd = model.exec.arg_dict[:input]
+  mx.copy_ignore_shape!(inputnd, input')
+  mx.forward(model.exec)
+  copy(model.exec.outputs[1])'
+end
+
+# d = Dense(20, 10)
+
+# x = randn(20)
+
+# model = mxnet(d, (20,))
+
+# d(x)
+
+# model(x)

src/backend/mxnet/mxnet.jl

@@ -5,8 +5,4 @@ using MXNet, Flow, ..Flux
 include("graph.jl")
 include("model.jl")
 
-# d = Dense(20, 10)
-
-# model = mxnet(d, (1,20))
-
 end