158 lines
6.2 KiB
Julia
158 lines
6.2 KiB
Julia
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include("aux_func.jl")
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using PlotlyJS: savefig
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using Statistics
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using PlotlyJS
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using Random
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using Printf
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#=
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Obtain energy data
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=#
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#mni_folder_1 = "ini_exp_data/"
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mni_folder_100 = "exp_100_epochs/";
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cif_folder_100 = "CIFAR_100_epoch/";
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mni_folder_acc = "data_bounded/";
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cif_folder_acc = "CIFAR_acc_bound/";
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mni_folder_wat = "data_budget/";
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cif_folder_wat = "CIFAR_energy_bound/";
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mni_folder_est = "early_stop_res/";
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cif_folder_est = "CIFAR_early_stop/";
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bayes_model = "bayes";
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freq_model = "freq";
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w_type = "watt";
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e_type = "exp";
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#=
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Load GPU data
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=#
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mni_100_bay_ene = getgpudata(mni_folder_100, bayes_model, w_type, "eo");
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mni_100_fre_ene = getgpudata(mni_folder_100, freq_model, w_type, "eo");
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cif_100_bay_ene = getgpudata(cif_folder_100, bayes_model, w_type, "eo");
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cif_100_fre_ene = getgpudata(cif_folder_100, freq_model, w_type, "eo");
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#=
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Define CPU paths
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=#
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cif = "cifar";
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mni = "mnist";
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bay = "bayes";
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wat = "cpu_watts";
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frq = "freq";
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ram = "ram_use";
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_100 = "100";
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acc = "acc";
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es = "es";
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wbud = "wbud";
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#=
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Load CPU data
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=#
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bay_cif_100_ene = readcpudata(cif, bay, wat, _100);
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bay_mni_100_ene = readcpudata(mni, bay, wat, _100);
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frq_cif_100_ene = readcpudata(cif, frq, wat, _100);
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frq_mni_100_ene = readcpudata(mni, frq, wat, _100);
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for s = 1:5
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bay_cif_100_ene[s] = round.(getcpuwatt(bay_cif_100_ene[s]))
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bay_mni_100_ene[s] = round.(getcpuwatt(bay_mni_100_ene[s]))
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frq_cif_100_ene[s] = round.(getcpuwatt(frq_cif_100_ene[s]))
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frq_mni_100_ene[s] = round.(getcpuwatt(frq_mni_100_ene[s]))
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end
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for s = 1:5
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mni_100_bay_ene[s]["Ene"] = vcat(mni_100_bay_ene[s]["Ene"], bay_mni_100_ene[s])
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mni_100_fre_ene[s]["Ene"] = vcat(mni_100_fre_ene[s]["Ene"], frq_mni_100_ene[s])
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cif_100_bay_ene[s]["Ene"] = vcat(cif_100_bay_ene[s]["Ene"], bay_cif_100_ene[s])
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cif_100_fre_ene[s]["Ene"] = vcat(cif_100_fre_ene[s]["Ene"], frq_cif_100_ene[s])
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end
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ene_data = Dict("mni" => Dict("bay" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
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"fre" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])),
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"cif" => Dict("bay" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
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"fre" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])))
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#ene_data = Dict("mni" => Dict("bay" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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# "fre" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
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# "cif" => Dict("bay" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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# "fre" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))
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for s = 1:5
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ene_data["mni"]["bay"][s] = sum.(join_vectors(resize1(makechunks(shuffle(mni_100_bay_ene[s]["Ene"]), 100))[1:100]))
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ene_data["mni"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(mni_100_fre_ene[s]["Ene"]), 100))[1:100]))
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ene_data["cif"]["bay"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_bay_ene[s]["Ene"]), 100))[1:100]))
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ene_data["cif"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_fre_ene[s]["Ene"]), 100))[1:100]))
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end
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#for s = 1:5
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# ene_data["mni"]["bay"][s] = sum(mni_100_bay_ene[s]["Ene"]) / 100
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# ene_data["mni"]["fre"][s] = sum(mni_100_fre_ene[s]["Ene"]) / 100
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# ene_data["cif"]["bay"][s] = sum(cif_100_bay_ene[s]["Ene"]) / 100
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# ene_data["cif"]["fre"][s] = sum(cif_100_fre_ene[s]["Ene"]) / 100
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#end
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#=
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Load Accuracy data
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=#
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mni_100_bay_exp = getgpudata(mni_folder_100, bayes_model, e_type, "eo");
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mni_100_fre_exp = getgpudata(mni_folder_100, freq_model, e_type, "eo");
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cif_100_bay_exp = getgpudata(cif_folder_100, bayes_model, e_type, "eo");
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cif_100_fre_exp = getgpudata(cif_folder_100, freq_model, e_type, "eo");
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for i = 1:5
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mni_100_bay_exp[i] = expdatatodict(mni_100_bay_exp[i])
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mni_100_fre_exp[i] = expdatatodict(mni_100_fre_exp[i])
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cif_100_bay_exp[i] = expdatatodict(cif_100_bay_exp[i])
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cif_100_fre_exp[i] = expdatatodict(cif_100_fre_exp[i])
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end
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original_vectors_acc = Dict("LeNet" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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"CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
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"BCNN" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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"CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))
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for i in 1:5
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original_vectors_acc["LeNet"]["MNIST"][i] = mean(mni_100_fre_exp[i]["acc"][1:100])
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@printf "LeNet-5 on MNIST acc size %i Metric: %-.8f\n" i original_vectors_acc["LeNet"]["MNIST"][i]
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original_vectors_acc["BCNN"]["MNIST"][i] = mean(mni_100_bay_exp[i]["acc"][1:100])
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@printf "BCNN-5 on MNIST acc size %i Metric: %-.8f\n" i original_vectors_acc["BCNN"]["MNIST"][i]
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original_vectors_acc["LeNet"]["CIFAR"][i] = mean(cif_100_fre_exp[i]["acc"][1:100])
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@printf "LeNet-5 on CIFAR acc size %i Metric: %-.8f\n" i original_vectors_acc["LeNet"]["CIFAR"][i]
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original_vectors_acc["BCNN"]["CIFAR"][i] = mean(cif_100_bay_exp[i]["acc"][1:100])
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@printf "BCNN-5 on CIFAR acc size %i Metric: %-.8f\n" i original_vectors_acc["BCNN"]["CIFAR"][i]
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end
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println("\n\n\n")
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original_vectors_pre = Dict("LeNet" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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"CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
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"BCNN" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
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"CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))
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for i in 1:5
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original_vectors_pre["LeNet"]["MNIST"][i] = mean(mni_100_fre_exp[i]["pre"][1:100])
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@printf "LeNet-5 on MNIST pre size %i Metric: %-.8f\n" i original_vectors_pre["LeNet"]["MNIST"][i]
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original_vectors_pre["BCNN"]["MNIST"][i] = mean(mni_100_bay_exp[i]["pre"][1:100])
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@printf "BCNN-5 on MNIST pre size %i Metric: %-.8f\n" i original_vectors_pre["BCNN"]["MNIST"][i]
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original_vectors_pre["LeNet"]["CIFAR"][i] = mean(cif_100_fre_exp[i]["pre"][1:100])
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@printf "LeNet-5 on CIFAR pre size %i Metric: %-.8f\n" i original_vectors_pre["LeNet"]["CIFAR"][i]
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original_vectors_pre["BCNN"]["CIFAR"][i] = mean(cif_100_bay_exp[i]["pre"][1:100])
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@printf "BCNN-5 on CIFAR pre size %i Metric: %-.8f\n" i original_vectors_pre["BCNN"]["CIFAR"][i]
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end
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