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jcreinhold/test_flux_gpu.ipynb

Created December 23, 2018 20:25
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Test Flux with GPU
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test_flux_gpu.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Flux with GPU\n",
"\n",
"This is with `julia v1.0.3`\n",
"\n",
"FWIW, if I type the following in python, I get the following cudnn version:\n",
"\n",
"```python\n",
" torch.backends.cudnn.version()\n",
" 7401\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sun Dec 23 15:18:25 2018 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 396.54 Driver Version: 396.54 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla M40 24GB Off | 00000000:02:00.0 Off | 0 |\n",
"| N/A 39C P0 58W / 250W | 314MiB / 22945MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
"| 1 Tesla M40 24GB Off | 00000000:03:00.0 Off | 0 |\n",
"| N/A 31C P8 17W / 250W | 11MiB / 22945MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: GPU Memory |\n",
"| GPU PID Type Process name Usage |\n",
"|=============================================================================|\n",
"| 0 2727 C /home/jacobr/code/julia-1.0.3/bin/julia 303MiB |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
";nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32m\u001b[1m Status\u001b[22m\u001b[39m `~/.julia/environments/v1.0/Project.toml`\n",
" \u001b[90m [be33ccc6]\u001b[39m\u001b[37m CUDAnative v0.9.1\u001b[39m\n",
" \u001b[90m [3a865a2d]\u001b[39m\u001b[37m CuArrays v0.8.1\u001b[39m\n",
" \u001b[90m [31c24e10]\u001b[39m\u001b[37m Distributions v0.16.4\u001b[39m\n",
" \u001b[90m [587475ba]\u001b[39m\u001b[37m Flux v0.6.10\u001b[39m\n",
" \u001b[90m [7073ff75]\u001b[39m\u001b[37m IJulia v1.14.1\u001b[39m\n",
" \u001b[90m [6218d12a]\u001b[39m\u001b[37m ImageMagick v0.7.1\u001b[39m\n",
" \u001b[90m [916415d5]\u001b[39m\u001b[37m Images v0.17.0\u001b[39m\n",
" \u001b[90m [e5e0dc1b]\u001b[39m\u001b[37m Juno v0.5.3\u001b[39m\n",
" \u001b[90m [dbeba491]\u001b[39m\u001b[37m Metalhead v0.3.0\u001b[39m\n",
" \u001b[90m [a3a9e032]\u001b[39m\u001b[37m NIfTI v0.4.0\u001b[39m\n",
" \u001b[90m [91a5bcdd]\u001b[39m\u001b[37m Plots v0.22.0\u001b[39m\n",
" \u001b[90m [5e47fb64]\u001b[39m\u001b[37m TestImages v0.4.1\u001b[39m\n"
]
}
],
"source": [
"] st"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variational Autoencoder\n",
"\n",
"This fails with:\n",
"\n",
"```sh\n",
"KernelError: recursion is currently not supported\n",
"```\n",
"\n",
"Full error and stacktrace below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"using Flux, Flux.Data.MNIST, Statistics\n",
"using Flux: throttle, params\n",
"using Juno: @progress\n",
"using CuArrays"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: using Distributions.params in module Main conflicts with an existing identifier.\n"
]
}
],
"source": [
"using Distributions\n",
"import Distributions: logpdf\n",
"# Extend distributions slightly to have a numerically stable logpdf for `p` close to 1 or 0.\n",
"logpdf(b::Bernoulli, y::Bool) = y * log(b.p + eps()) + (1 - y) * log(1 - b.p + eps());"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Load data, binarise it, and partition into mini-batches of M.\n",
"X = float.(hcat(vec.(MNIST.images())...)) .> 0.5\n",
"N, M = size(X, 2), 100\n",
"data = [X[:,i] for i in Iterators.partition(1:N,M)]\n",
"data = gpu.(data)\n",
"#X = gpu.(X);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"################################# Define Model #################################\n",
"\n",
"# Latent dimensionality, # hidden units.\n",
"Dz, Dh = 5, 500\n",
"\n",
"# Components of recognition model / \"encoder\" MLP.\n",
"A, μ, logσ = gpu(Dense(28^2, Dh, tanh)), gpu(Dense(Dh, Dz)), gpu(Dense(Dh, Dz))\n",
"g(X) = (h = A(X); (μ(h), logσ(h)))\n",
"z(μ, logσ) = μ + exp(logσ) * randn()\n",
"\n",
"# Generative model / \"decoder\" MLP.\n",
"f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ)) |> gpu;"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"####################### Define ways of doing things with the model. #######################\n",
"\n",
"# KL-divergence between approximation posterior and N(0, 1) prior.\n",
"kl_q_p(μ, logσ) = 0.5 * sum(exp.(2 .* logσ) + μ.^2 .- 1 .+ 2*logσ)\n",
"\n",
"# logp(x|z) - conditional probability of data given latents.\n",
"logp_x_z(x, z) = sum(logpdf.(Bernoulli.(f(z)), x))\n",
"\n",
"# Monte Carlo estimator of mean ELBO using M samples.\n",
"L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, z.(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) / M)\n",
"\n",
"loss(X) = -L̄(X) + 0.01 * sum(x->sum(x.^2), params(f))\n",
"\n",
"# Sample from the learned model.\n",
"modelsample() = rand.(Bernoulli.(f(z.(zeros(Dz), zeros(Dz)))));"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Epoch 1\n",
"└ @ Main In[8]:6\n",
"┌ Warning: calls to Base intrinsics might be GPU incompatible\n",
"│ exception = (CUDAnative.MethodSubstitutionWarning(exp(x::T) where T<:Union{Float32, Float64} in Base.Math at special/exp.jl:75, exp(x::Float32) in CUDAnative at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/device/libdevice.jl:90), Base.StackTraces.StackFrame[exp at exp.jl:75, z at In[6]:9, #25 at broadcast.jl:58])\n",
"└ @ CUDAnative /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:111\n",
"┌ Warning: calls to Base intrinsics might be GPU incompatible\n",
"│ exception = (CUDAnative.MethodSubstitutionWarning(log(x::Float64) in Base.Math at special/log.jl:254, log(x::Float64) in CUDAnative at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/device/libdevice.jl:65), Base.StackTraces.StackFrame[log at log.jl:254, randn_unlikely at normal.jl:49, z at In[6]:9, #25 at broadcast.jl:58])\n",
"└ @ CUDAnative /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:111\n",
"┌ Warning: calls to Base intrinsics might be GPU incompatible\n",
"│ exception = (CUDAnative.MethodSubstitutionWarning(log(x::Float64) in Base.Math at special/log.jl:254, log(x::Float64) in CUDAnative at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/device/libdevice.jl:65), Base.StackTraces.StackFrame[log at log.jl:254, randn_unlikely at normal.jl:49, z at In[6]:9, #25 at broadcast.jl:58])\n",
"└ @ CUDAnative /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:111\n",
"┌ Warning: calls to Base intrinsics might be GPU incompatible\n",
"│ exception = (CUDAnative.MethodSubstitutionWarning(exp(x::T) where T<:Union{Float32, Float64} in Base.Math at special/exp.jl:75, exp(x::Float64) in CUDAnative at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/device/libdevice.jl:89), Base.StackTraces.StackFrame[exp at exp.jl:75, randn_unlikely at normal.jl:49, z at In[6]:9, #25 at broadcast.jl:58])\n",
"└ @ CUDAnative /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:111\n"
]
},
{
"ename": "CUDAnative.KernelError",
"evalue": "GPU compilation of #25(CuArrays.CuKernelState, CUDAnative.CuDeviceArray{Float64,2,CUDAnative.AS.Global}, Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},typeof(z),Tuple{Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}) failed\nKernelError: recursion is currently not supported\n\nTry inspecting the generated code with any of the @device_code_... macros.\n\nStacktrace:\n [1] randn_unlikely at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:49\n [2] randn at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:37\n [3] randn_unlikely at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:49\n [4] z at In[6]:9\n [5] #25 at /home/jacobr/.julia/packages/GPUArrays/hzyWn/src/broadcast.jl:58",
"output_type": "error",
"traceback": [
"GPU compilation of #25(CuArrays.CuKernelState, CUDAnative.CuDeviceArray{Float64,2,CUDAnative.AS.Global}, Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},typeof(z),Tuple{Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}) failed\nKernelError: recursion is currently not supported\n\nTry inspecting the generated code with any of the @device_code_... macros.\n\nStacktrace:\n [1] randn_unlikely at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:49\n [2] randn at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:37\n [3] randn_unlikely at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.0/Random/src/normal.jl:49\n [4] z at In[6]:9\n [5] #25 at /home/jacobr/.julia/packages/GPUArrays/hzyWn/src/broadcast.jl:58",
"",
"Stacktrace:",
" [1] (::getfield(CUDAnative, Symbol(\"#hook_emit_function#58\")){CUDAnative.CompilerContext,Array{Core.MethodInstance,1}})(::Core.MethodInstance, ::Core.CodeInfo, ::UInt64) at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:97",
" [2] irgen(::CUDAnative.CompilerContext) at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/irgen.jl:133",
" [3] #compile_function#78(::Bool, ::Function, ::CUDAnative.CompilerContext) at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/driver.jl:63",
" [4] compile_function at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/driver.jl:56 [inlined]",
" [5] #cufunction#77(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::CUDAdrv.CuDevice, ::Any, ::Any) at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/driver.jl:22",
" [6] cufunction at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/compiler/driver.jl:10 [inlined]",
" [7] macro expansion at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/execution.jl:180 [inlined]",
" [8] _cuda(::getfield(GPUArrays, Symbol(\"##25#26\")), ::Tuple{}, ::NamedTuple{(:blocks, :threads),Tuple{Tuple{Int64},Tuple{Int64}}}, ::CuArrays.CuKernelState, ::CUDAnative.CuDeviceArray{Float64,2,CUDAnative.AS.Global}, ::Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},typeof(z),Tuple{Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{CUDAnative.CuDeviceArray{Float32,2,CUDAnative.AS.Global},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}) at /home/jacobr/.julia/packages/CUDAnative/AGfq2/src/execution.jl:139",
" [9] _gpu_call(::CuArrays.CuArrayBackend, ::Function, ::CuArray{Float64,2}, ::Tuple{CuArray{Float64,2},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},typeof(z),Tuple{Base.Broadcast.Extruded{CuArray{Float32,2},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{CuArray{Float32,2},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}}, ::Tuple{Tuple{Int64},Tuple{Int64}}) at ./gcutils.jl:87",
" [10] gpu_call(::Function, ::CuArray{Float64,2}, ::Tuple{CuArray{Float64,2},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},typeof(z),Tuple{Base.Broadcast.Extruded{CuArray{Float32,2},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{CuArray{Float32,2},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}}, ::Int64) at /home/jacobr/.julia/packages/GPUArrays/hzyWn/src/abstract_gpu_interface.jl:151",
" [11] gpu_call at /home/jacobr/.julia/packages/GPUArrays/hzyWn/src/abstract_gpu_interface.jl:128 [inlined]",
" [12] copyto! at /home/jacobr/.julia/packages/GPUArrays/hzyWn/src/broadcast.jl:57 [inlined]",
" [13] copyto! at ./broadcast.jl:800 [inlined]",
" [14] copy at ./broadcast.jl:776 [inlined]",
" [15] materialize at ./broadcast.jl:756 [inlined]",
" [16] broadcast(::typeof(z), ::CuArray{Float32,2}, ::CuArray{Float32,2}) at ./broadcast.jl:710",
" [17] ∇broadcast at /home/jacobr/.julia/packages/Flux/jsf3Y/src/tracker/array.jl:393 [inlined]",
" [18] materialize(::Base.Broadcast.Broadcasted{Flux.Tracker.TrackedStyle,Nothing,typeof(z),Tuple{TrackedArray{…,CuArray{Float32,2}},TrackedArray{…,CuArray{Float32,2}}}}) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/tracker/array.jl:424",
" [19] L̄(::CuArray{Float32,2}) at ./In[7]:10",
" [20] loss(::CuArray{Float32,2}) at ./In[7]:12",
" [21] macro expansion at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:60 [inlined]",
" [22] macro expansion at /home/jacobr/.julia/packages/Juno/46C8i/src/progress.jl:133 [inlined]",
" [23] #train!#121(::getfield(Flux, Symbol(\"#throttled#18\")){getfield(Flux, Symbol(\"##throttled#10#14\")){Bool,Bool,getfield(Main, Symbol(\"##7#8\")),Int64}}, ::Function, ::Function, ::Base.Iterators.Zip1{Array{CuArray{Float32,2},1}}, ::getfield(Flux.Optimise, Symbol(\"##43#47\"))) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:58",
" [24] (::getfield(Flux.Optimise, Symbol(\"#kw##train!\")))(::NamedTuple{(:cb,),Tuple{getfield(Flux, Symbol(\"#throttled#18\")){getfield(Flux, Symbol(\"##throttled#10#14\")){Bool,Bool,getfield(Main, Symbol(\"##7#8\")),Int64}}}}, ::typeof(Flux.Optimise.train!), ::Function, ::Base.Iterators.Zip1{Array{CuArray{Float32,2},1}}, ::Function) at ./none:0",
" [25] macro expansion at ./In[8]:7 [inlined]",
" [26] macro expansion at /home/jacobr/.julia/packages/Juno/46C8i/src/progress.jl:133 [inlined]",
" [27] top-level scope at ./In[8]:5 [inlined]",
" [28] top-level scope at ./none:0"
]
}
],
"source": [
"################################# Learn Parameters ##############################\n",
"\n",
"evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 30)\n",
"opt = ADAM(params(A, μ, logσ, f))\n",
"@progress for i = 1:20\n",
" @info \"Epoch $i\"\n",
" Flux.train!(loss, zip(data), opt, cb=evalcb)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"################################# Sample Output ##############################\n",
"\n",
"#using Images\n",
"\n",
"#img(x) = Gray.(reshape(x, 28, 28))\n",
"\n",
"#cd(@__DIR__)\n",
"#sample = hcat(img.([modelsample() for i = 1:10])...)\n",
"#save(\"sample.png\", sample);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vanilla Autoencoder\n",
"\n",
"This works."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Epoch 1\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss(data[1]) = 0.10304373f0 (tracked)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Epoch 2\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 3\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 4\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 5\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 6\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 7\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 8\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 9\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 10\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n"
]
},
{
"data": {
"text/plain": [
"sample (generic function with 1 method)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Flux, Flux.Data.MNIST\n",
"using Flux: @epochs, onehotbatch, mse, throttle\n",
"using Base.Iterators: partition\n",
"using Juno: @progress\n",
"using CuArrays\n",
"\n",
"# Encode MNIST images as compressed vectors that can later be decoded back into\n",
"# images.\n",
"\n",
"imgs = MNIST.images()\n",
"\n",
"# Partition into batches of size 1000\n",
"data = [float(hcat(vec.(imgs)...)) for imgs in partition(imgs, 1000)]\n",
"data = gpu.(data)\n",
"\n",
"N = 32 # Size of the encoding\n",
"\n",
"# You can try to make the encoder/decoder network larger\n",
"# Also, the output of encoder is a coding of the given input.\n",
"# In this case, the input dimension is 28^2 and the output dimension of\n",
"# encoder is 32. This implies that the coding is a compressed representation.\n",
"# We can make lossy compression via this `encoder`.\n",
"encoder = Dense(28^2, N, leakyrelu) |> gpu\n",
"decoder = Dense(N, 28^2, leakyrelu) |> gpu\n",
"\n",
"m = Chain(encoder, decoder)\n",
"\n",
"loss(x) = mse(m(x), x)\n",
"\n",
"evalcb = throttle(() -> @show(loss(data[1])), 5)\n",
"opt = ADAM(params(m))\n",
"\n",
"@epochs 10 Flux.train!(loss, zip(data), opt, cb = evalcb)\n",
"\n",
"# Sample output\n",
"\n",
"using Images\n",
"\n",
"img(x::Vector) = Gray.(reshape(clamp.(x, 0, 1), 28, 28))\n",
"\n",
"function sample()\n",
" # 20 random digits\n",
" before = [imgs[i] for i in rand(1:length(imgs), 20)]\n",
" # Before and after images\n",
" after = img.(map(x -> cpu(m)(float(vec(x))).data, before))\n",
" # Stack them all together\n",
" hcat(vcat.(before, after)...)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"56×560 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) … Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) … Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) … Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" ⋮ ⋱ \n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) … Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
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" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
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" Gray{Float64}(0.0) Gray{Float64}(0.0) Gray{Float64}(0.0)\n",
" Gray{Float64}(0.0) Gray{Float64}(0.0) … Gray{Float64}(0.0)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Custom Test\n",
"\n",
"This fails with:\n",
"\n",
"```sh\n",
"conversion to pointer not defined for CuArray{Float32,2}\n",
"```\n",
"\n",
"The full stacktrace is below."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"using NIfTI, Flux, Plots, CuArrays, Random"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data_dir = \"/iacl/pg19/jacobr/isbi15/norm_ss_1\"\n",
"t1_dir = string(data_dir, \"/t1\")\n",
"t2_dir = string(data_dir, \"/t2\")\n",
"pd_dir = string(data_dir, \"/pd\")\n",
"fl_dir = string(data_dir, \"/flair\");"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"t1_fn = string(t1_dir, \"/training01_04_mprage_fcm.nii.gz\")\n",
"t2_fn = string(t2_dir, \"/training01_04_t2_reg_fcm.nii.gz\")\n",
"pd_fn = string(pd_dir, \"/training01_04_pd_reg_fcm.nii.gz\")\n",
"fl_fn = string(fl_dir, \"/training01_04_flair_reg_fcm.nii.gz\");"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"fns = [t1_fn, t2_fn, pd_fn, fl_fn]\n",
"imgs = map(niread, fns);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
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},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heatmap(imgs[1][:,:,80],aspect_ratio=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Chain(Conv((3, 3), 1=>16, NNlib.relu), Conv((3, 3), 16=>1))"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = Chain(\n",
" Conv((3,3), 1=>16, relu, pad=(1,1)),\n",
" Conv((3,3), 16=>1, pad=(1,1))) |> gpu"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"xᵣ = [imgs[i].raw[:,:,80:83] for i in 1:4];\n",
"szₓ = size(xᵣ[1])\n",
"x = [reshape(xᵣ[i], (szₓ[1], szₓ[2], 1, szₓ[3])) for i in 1:4];\n",
"data = gpu.(x);"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Epoch 1\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n"
]
},
{
"ename": "ErrorException",
"evalue": "conversion to pointer not defined for CuArray{Float32,2}",
"output_type": "error",
"traceback": [
"conversion to pointer not defined for CuArray{Float32,2}",
"",
"Stacktrace:",
" [1] error(::String) at ./error.jl:33",
" [2] unsafe_convert(::Type{Ptr{Float32}}, ::CuArray{Float32,2}) at ./pointer.jl:67",
" [3] pointer(::CuArray{Float32,2}) at ./abstractarray.jl:858",
" [4] #conv2d!#40(::Tuple{Int64,Int64}, ::Tuple{Int64,Int64}, ::Tuple{Int64,Int64}, ::Int64, ::Float32, ::Function, ::CuArray{Float32,4}, ::CuArray{Float32,4}, ::CuArray{Float32,4}) at /home/jacobr/.julia/packages/NNlib/0EAe7/src/impl/conv.jl:275",
" [5] #conv2d! at ./none:0 [inlined]",
" [6] #conv!#62 at /home/jacobr/.julia/packages/NNlib/0EAe7/src/conv.jl:65 [inlined]",
" [7] #conv! at ./none:0 [inlined]",
" [8] #conv#50(::Tuple{Int64,Int64}, ::Tuple{Int64,Int64}, ::Tuple{Int64,Int64}, ::Function, ::CuArray{Float32,4}, ::CuArray{Float32,4}) at /home/jacobr/.julia/packages/NNlib/0EAe7/src/conv.jl:29",
" [9] #_forward#454 at ./none:0 [inlined]",
" [10] #_forward at ./none:0 [inlined]",
" [11] #track#1 at /home/jacobr/.julia/packages/Flux/jsf3Y/src/tracker/Tracker.jl:50 [inlined]",
" [12] #track at ./none:0 [inlined]",
" [13] #conv#452 at /home/jacobr/.julia/packages/Flux/jsf3Y/src/tracker/array.jl:347 [inlined]",
" [14] #conv at ./none:0 [inlined]",
" [15] Conv at /home/jacobr/.julia/packages/Flux/jsf3Y/src/layers/conv.jl:44 [inlined]",
" [16] (::getfield(Flux, Symbol(\"##60#61\")))(::CuArray{Float32,4}, ::Conv{2,typeof(relu),TrackedArray{…,CuArray{Float32,4}},TrackedArray{…,CuArray{Float32,1}}}) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/layers/basic.jl:31",
" [17] mapfoldl_impl(::typeof(identity), ::getfield(Flux, Symbol(\"##60#61\")), ::NamedTuple{(:init,),Tuple{CuArray{Float32,4}}}, ::Array{Any,1}) at ./reduce.jl:43",
" [18] #mapfoldl#170 at ./reduce.jl:70 [inlined]",
" [19] #mapfoldl at ./none:0 [inlined]",
" [20] #foldl#171 at ./reduce.jl:88 [inlined]",
" [21] #foldl at ./none:0 [inlined]",
" [22] (::Chain)(::CuArray{Float32,4}) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/layers/basic.jl:31",
" [23] loss(::CuArray{Float32,4}) at ./In[18]:3",
" [24] macro expansion at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:60 [inlined]",
" [25] macro expansion at /home/jacobr/.julia/packages/Juno/46C8i/src/progress.jl:133 [inlined]",
" [26] #train!#121(::getfield(Flux.Optimise, Symbol(\"##123#127\")), ::Function, ::Function, ::Base.Iterators.Zip1{Array{CuArray{Float32,4},1}}, ::getfield(Flux.Optimise, Symbol(\"##43#47\"))) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:58",
" [27] train!(::Function, ::Base.Iterators.Zip1{Array{CuArray{Float32,4},1}}, ::Function) at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:56",
" [28] macro expansion at /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:94 [inlined]",
" [29] macro expansion at /home/jacobr/.julia/packages/Juno/46C8i/src/progress.jl:133 [inlined]",
" [30] top-level scope at ./In[18]:5 [inlined]",
" [31] top-level scope at ./none:0"
]
}
],
"source": [
"using Flux: @epochs, mse\n",
"\n",
"loss(x) = mse(m(x), x)\n",
"opt = ADAM(params(m))\n",
"@epochs 10 Flux.train!(loss, zip(data), opt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test without GPU\n",
"\n",
"This works."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Chain(Conv((3, 3), 1=>16, NNlib.relu), Conv((3, 3), 16=>1))"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = Chain(\n",
" Conv((3,3), 1=>16, relu, pad=(1,1)),\n",
" Conv((3,3), 16=>1, pad=(1,1)))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"xᵣ = [imgs[i].raw[:,:,80:83] for i in 1:4];\n",
"szₓ = size(xᵣ[1])\n",
"x = [Float64.(reshape(xᵣ[i], (szₓ[1], szₓ[2], 1, szₓ[3]))) for i in 1:4];\n",
"data = x;"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Epoch 1\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 2\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 3\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 4\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 5\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 6\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 7\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 8\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 9\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n",
"┌ Info: Epoch 10\n",
"└ @ Main /home/jacobr/.julia/packages/Flux/jsf3Y/src/optimise/train.jl:93\n"
]
}
],
"source": [
"using Flux: @epochs, mse\n",
"\n",
"loss(x) = mse(m(x), x)\n",
"opt = ADAM(params(m))\n",
"@epochs 10 Flux.train!(loss, zip(data), opt)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.0.3",
"language": "julia",
"name": "julia-1.0"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.0.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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