152 lines
4.2 KiB
Julia
152 lines
4.2 KiB
Julia
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#using HDF5
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include("aux_func.jl")
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#using Plots
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#using StatsPlots
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using Statistics
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using LinearAlgebra
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using PlotlyJS, Random, StatsBase
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using PlotlyJS: savefig
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folder = "ini_exp_data/"
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#fcnn = h5open("fcnn_watt_data.h5", "r") do file
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# read(file,"fcnn")
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#end
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#bcnn = h5open("bcnn_watt_data.h5", "r") do file
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# read(file,"bcnn")
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#end
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fcnn = load_pickle("$(folder)fcnn_wattdata.pkl")
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bcnn = load_pickle("$(folder)bcnn_wattdata.pkl")
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bcnn_w = removewatt(bcnn[:, 1])
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mem_bcnn = removewatt(bcnn[:, 3])
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#bcnn_ch = bcnn_w .> 28
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#bcnn_start = findall(x -> x==1, bcnn_ch)[1]
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#bcnn_end = findall(x -> x==1, bcnn_ch)[end]
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#bcnn_w_tot = bcnn_w[bcnn_start:bcnn_end]
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#bcnn_tot = sum(bcnn_w_tot)
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#mean_bcnn_w = mean(bcnn_w_tot)
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#bcnn_s = size(bcnn_w_tot)[1]
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#mean_bcnn_w_v = mean_bcnn_w .* ones(bcnn_s)
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#x_bcnn = collect(1:bcnn_s)
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fcnn_w = removewatt(fcnn[:, 1])
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mem_fcnn = removewatt(fcnn[:, 3])
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#val_ch = fcnn_w .> 28
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#fcnn_start = findall(x -> x==1, val_ch)[1]
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#fcnn_end = findall(x -> x==1, val_ch)[end]
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#fcnn_w_tot = fcnn_w[fcnn_start:fcnn_end]
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#fcnn_tot = sum(fcnn_w_tot)
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#mean_fcnn_w = mean(fcnn_w_tot)
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#fcnn_s = size(fcnn_w_tot)[1]
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#mean_fcnn_w_v = mean_fcnn_w .* ones(fcnn_s)
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#x_fcnn = collect(1:fcnn_s)
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mem_bcnn = parse.(Int64, replace.(bcnn[:, 3], r"[^0-9]+" => ""))
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mem_fcnn = parse.(Int64, replace.(fcnn[:, 3], r"[^0-9]+" => ""))
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#en_plot = boxplot(bcnn_w_tot,x_bcnn,title="Energy per sample",xlabel="Watts",ylabel="No of samples",label="BCNN",seriesalpha=0.5)
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#boxplot!(fcnn_w_tot,x_fcnn,label="CNN",seriesalpha=0.5,color="green")
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#savefig(en_plot,"energy.png")
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#en_plot = histogram(bcnn_w,xlabel="Watts",ylabel="No of samples",label="BCNN",color="blue",normalize=:pdf,bar_position = :overlay)
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#histogram!(fcnn_w,label="LeNet",seriesalpha=0.9,color="#ff9900",normalize=:pdf,bar_position=:overlay)
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#savefig(en_plot,"energy.png")
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#bcnn_w = incwzeros(bcnn_w,length(fcnn_w)-length(bcnn_w))
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#bcnn_w = reshape(bcnn_w,:,1)
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#fcnn_w = reshape(fcnn_w,:,1)
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#w_mat = hcat(bcnn_w,fcnn_w)
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#groupedbar(rand(length(bcnn_w),2),bar_width=0.5,bar_position = :dodge)
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#w_mat = reshape(w_mat,2,:)
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#mem_plot = plot(mem_fcnn,title="Memory usage, one epoch in MiB's",label="CNN Memory",lw=2,seriesalpha=0.5)
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#plot!(mem_bcnn,label="BCNN Memory",lw=2,seriesalpha=0.5,fmt=:png)
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#savefig(mem_plot,"memory.png")
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#mem_plot = histogram(mem_bcnn,xlabel="MiB",ylabel="No of samples",label="BCNN",color="blue")
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#histogram!(mem_fcnn,label="LeNet",seriesalpha=0.9,color="#ff9900")
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#savefig(mem_plot,"memory.png")
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#plot(bcnn_w_tot,title="BCNN energy in one epoch",label="Watt",lw=2);plot!(mean_bcnn_w_v,label="Mean")
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#plot(fcnn_w_tot,title="CNN energy in one epoch",label="Watt",lw=2);plot!(mean_fcnn_w_v,label="Mean")
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#scatter(bcnn[1,:],title="Energy usage in W",label="Bayesian CNN",lw=2);scatter!(fcnn[1,1:463],label="CNN",lw=2)
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bcnn_d = countmap(bcnn_w)
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bcnn_u = unique(bcnn_w)
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bcnn_v = Vector()
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for u in bcnn_u
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push!(bcnn_v, bcnn_d[u])
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end
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m_bcnn_d = countmap(mem_bcnn)
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m_bcnn_u = unique(mem_bcnn)
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m_bcnn_v = Vector()
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for u in m_bcnn_u
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push!(m_bcnn_v, m_bcnn_d[u])
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end
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fcnn_d = countmap(fcnn_w)
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fcnn_u = unique(fcnn_w)
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fcnn_v = Vector()
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for u in fcnn_u
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push!(fcnn_v, fcnn_d[u])
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end
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m_fcnn_d = countmap(mem_fcnn)
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m_fcnn_u = unique(mem_fcnn)
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m_fcnn_v = Vector()
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for u in fcnn_u
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push!(m_fcnn_v, fcnn_d[u])
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end
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bcnn_v = bcnn_v / norm(bcnn_v)
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fcnn_v = fcnn_v / norm(fcnn_v)
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m_bcnn_v = m_bcnn_v / norm(m_bcnn_v)
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m_fcnn_v = m_fcnn_v / norm(m_fcnn_v)
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en_plot = plot(
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[
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bar(x = bcnn_u, y = bcnn_v, name = "BCNN", marker_color = "blue"),
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bar(x = fcnn_u, y = fcnn_v, name = "LeNet", marker_color = "#ff9900"),
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],
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Layout(
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barmode = "group",
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xaxis_tickangle = -45,
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yaxis_title_text = "Samples",
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xaxis_title_text = "Watts",
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title = "Energy distribution experiment 1",
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),
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)
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savefig(en_plot, "energy.png")
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mem_plot = plot(
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[
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bar(x = m_bcnn_u, y = m_bcnn_v, name = "BCNN", marker_color = "blue"),
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bar(x = m_fcnn_u, y = m_fcnn_v, name = "LeNet", marker_color = "#ff9900"),
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],
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Layout(
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barmode = "group",
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xaxis_tickangle = -45,
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yaxis_title_text = "Samples",
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xaxis_title_text = "MiB",
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title = "MRW distribution experiment 1",
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),
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)
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savefig(mem_plot, "memory.png")
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