Coder Social home page Coder Social logo

knetml / neural-style-transfer Goto Github PK

View Code? Open in Web Editor NEW
5.0 2.0 0.0 13.88 MB

Knet Implementation of Neural Style Transfer

Jupyter Notebook 100.00%
neural-style-transfer cnn julia-language knet jupyter-notebook machine-learning deep-learning neural-networks

neural-style-transfer's Issues

DimensionMismatch("new array has 225 color channels, must have 3")

hello,

I tried to run the notebook in Julia 0.6.3 in OSX and I get the following error at the last cell (#Demo: Style transfer. [...]):

INFO: Recompiling stale cache file /Users/alage/.julia/lib/v0.6/QuartzImageIO.ji for module QuartzImageIO.
DimensionMismatch("new array has 225 color channels, must have 3")

Stacktrace:
 [1] check_ncolorchan(::Array{ColorTypes.RGB4{FixedPointNumbers.Normed{UInt8,8}},2}, ::Tuple{Int64,Int64,Int64}) at /Users/alage/.julia/v0.6/ImageCore/src/colorchannels.jl:360
 [2] similar(::ImageCore.ChannelView{FixedPointNumbers.Normed{UInt8,8},3,Array{ColorTypes.RGB4{FixedPointNumbers.Normed{UInt8,8}},2}}, ::Type{FixedPointNumbers.Normed{UInt8,8}}, ::Tuple{Int64,Int64,Int64}) at /Users/alage/.julia/v0.6/ImageCore/src/colorchannels.jl:117
 [3] permutedims(::ImageCore.ChannelView{FixedPointNumbers.Normed{UInt8,8},3,Array{ColorTypes.RGB4{FixedPointNumbers.Normed{UInt8,8}},2}}, ::Array{Int64,1}) at ./permuteddimsarray.jl:116
 [4] #preprocess#10(::Int64, ::Function, ::Array{ColorTypes.RGB4{FixedPointNumbers.Normed{UInt8,8}},2}) at ./In[3]:22
 [5] (::#kw##preprocess)(::Array{Any,1}, ::#preprocess, ::Array{ColorTypes.RGB4{FixedPointNumbers.Normed{UInt8,8}},2}) at ./<missing>:0
 [6] style_transfer(::String, ::String, ::Int64, ::Int64, ::Int64, ::Float64, ::NTuple{5,Int64}, ::Array{Float64,1}, ::Float64, ::Bool) at ./In[18]:25 (repeats 2 times)

example does not work under Julia >= 1.0

I made some obvious syntax changes to get the example to run under julia 1.3/Knet 132,
but it fails with this error

ERROR: LoadError: MethodError: Cannot `convert` an object of type UnitRange{Int64} to an object of type Colon
Closest candidates are:
  convert(::Type{T}, ::T) where T at essentials.jl:167
Stacktrace:
 [1] convert(::Type{Tuple{Colon,UnitRange{Int64},Colon,Colon}}, ::Tuple{UnitRange{Int64},Colon,Colon,Colon}) at ./essentials.jl:304
 [2] setindex!(::Array{Tuple{Colon,UnitRange{Int64},Colon,Colon},1}, ::Tuple{UnitRange{Int64},Colon,Colon,Colon}, ::Int64) at ./array.jl:766
 [3] copyto!(::Array{Tuple{Colon,UnitRange{Int64},Colon,Colon},1}, ::Int64, ::Array{Tuple{UnitRange{Int64},Colon,Colon,Colon},1}, ::Int64, ::Int64) at ./abstractarray.jl:842
 [4] append!(::Array{Tuple{Colon,UnitRange{Int64},Colon,Colon},1}, ::Array{Tuple{UnitRange{Int64},Colon,Colon,Colon},1}) at ./array.jl:895
 [5] addto!(::AutoGrad.Sparse{Float64,4}, ::AutoGrad.Sparse{Float64,4}) at /root/.julia/packages/AutoGrad/pTNVv/src/addto.jl:44
 [6] #differentiate#3(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(AutoGrad.differentiate), ::Function, ::Param{KnetArray{Float64,4}}, ::Vararg{Any,N} where N) at /root/.julia/packages/AutoGrad/pTNVv/src/core.jl:166
 [7] differentiate(::Function, ::Param{KnetArray{Float64,4}}, ::Vararg{Any,N} where N) at /root/.julia/packages/AutoGrad/pTNVv/src/core.jl:135
 [8] (::getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool})(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}}, ::KnetArray{Float64,4}, ::Vararg{Any,N} where N) at /root/.julia/packages/AutoGrad/pTNVv/src/core.jl:225
 [9] (::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}})(::KnetArray{Float64,4}, ::Vararg{Any,N} where N) at /root/.julia/packages/AutoGrad/pTNVv/src/core.jl:221
 [10] style_transfer(::String, ::String, ::Int64, ::Int64, ::Int64, ::Float64, ::NTuple{5,Int64}, ::Array{Float64,1}, ::Float64, ::Bool) at /work/neural_style_transfer.jl:365
 [11] style_transfer(::String, ::String, ::Int64, ::Int64, ::Int64, ::Float64, ::NTuple{5,Int64}, ::Array{Float64,1}, ::Float64) at /work/neural_style_transfer.jl:329
 [12] top-level scope at util.jl:156
 [13] include at ./boot.jl:328 [inlined]
 [14] include_relative(::Module, ::String) at ./loading.jl:1094
 [15] include(::Module, ::String) at ./Base.jl:31
 [16] include(::String) at ./client.jl:431
 [17] top-level scope at REPL[1]:1
in expression starting at /work/neural_style_transfer.jl:397

Error in last cell

While running the last cell I'm getting this error:

MethodError: Cannot convert an object of type AutoGrad.Rec{Array{Float64,4}} to an object of type Array
This may have arisen from a call to the constructor Array(...),
since type constructors fall back to convert methods.

Stacktrace:
[1] tv_loss(::AutoGrad.Rec{Array{Float64,4}}, ::Float64) at ./In[10]:12
[2] loss(::AutoGrad.Rec{Array{Float64,4}}, ::Float64, ::Int64, ::Array{Float64,4}, ::NTuple{5,Int64}, ::Array{Any,1}, ::Array{Float64,1}, ::Float64) at ./In[11]:6
[3] forward_pass(::Function, ::Tuple{Array{Float64,4},Float64,Int64,Array{Float64,4},NTuple{5,Int64},Array{Any,1},Array{Float64,1},Float64}, ::Array{Any,1}, ::Int64) at /home/subhankar/.julia/v0.6/AutoGrad/src/core.jl:88
[4] (::AutoGrad.##gradfun#4#6{#loss,Int64})(::Array{Any,1}, ::Function, ::Array{Float64,4}, ::Vararg{Any,N} where N) at /home/subhankar/.julia/v0.6/AutoGrad/src/core.jl:57
[5] (::AutoGrad.#gradfun#5)(::Array{Float64,4}, ::Vararg{Any,N} where N) at /home/subhankar/.julia/v0.6/AutoGrad/src/core.jl:57
[6] style_transfer(::String, ::String, ::Int64, ::Int64, ::Int64, ::Float64, ::NTuple{5,Int64}, ::Array{Float64,1}, ::Float64, ::Bool) at ./In[12]:60
[7] style_transfer(::String, ::String, ::Int64, ::Int64, ::Int64, ::Float64, ::NTuple{5,Int64}, ::Array{Float64,1}, ::Float64) at ./In[12]:25
[8] include_string(::String, ::String) at ./loading.jl:522
[9] execute_request(::ZMQ.Socket, ::IJulia.Msg) at /home/subhankar/.julia/v0.6/IJulia/src/execute_request.jl:193
[10] (::Compat.#inner#6{Array{Any,1},IJulia.#execute_request,Tuple{ZMQ.Socket,IJulia.Msg}})() at /home/subhankar/.julia/v0.6/Compat/src/Compat.jl:189
[11] eventloop(::ZMQ.Socket) at /home/subhankar/.julia/v0.6/IJulia/src/eventloop.jl:8
[12] (::IJulia.##13#16)() at ./task.jl:335

I'm on Julia 0.6.4 running on Elementary OS 5

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.