286 lines
10 KiB
Julia
286 lines
10 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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#=
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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))[1:50]))
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ene_data["mni"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(mni_100_fre_ene[s]["Ene"]), 100))[1:50]))
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ene_data["cif"]["bay"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_bay_ene[s]["Ene"]), 100))[1:50]))
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ene_data["cif"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_fre_ene[s]["Ene"]), 100))[1:50]))
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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 = Dict("LeNet" => Dict("MNIST" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
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"CIFAR" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])),
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"BCNN" => Dict("MNIST" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
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"CIFAR" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])))
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for i in 1:5
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original_vectors["LeNet"]["MNIST"][i] = (mni_100_fre_exp[i]["acc"][1:50] ./ ene_data["mni"]["fre"][i]) * 100
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original_vectors["BCNN"]["MNIST"][i] = (mni_100_bay_exp[i]["acc"][1:50] ./ ene_data["mni"]["bay"][i]) * 100
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original_vectors["LeNet"]["CIFAR"][i] = (cif_100_fre_exp[i]["acc"][1:50] ./ ene_data["cif"]["fre"][i]) * 100
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original_vectors["BCNN"]["CIFAR"][i] = (cif_100_bay_exp[i]["acc"][1:50] ./ ene_data["cif"]["bay"][i]) * 100
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end
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en_plot = plot(
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[
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["MNIST"][1],
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name = "LeNet 1",
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marker = attr(symbol = 4, color = "rgb(211,120,000)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["MNIST"][2],
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name = "LeNet 2",
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marker = attr(symbol = 17, color = "rgb(255,170,017)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["MNIST"][3],
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name = "LeNet 3",
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marker = attr(symbol = 2, color = "rgb(255,187,034)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["MNIST"][4],
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name = "LeNet 4",
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marker = attr(symbol = 0, color = "rgb(255,204,051)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["MNIST"][5],
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name = "LeNet 5",
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marker = attr(symbol = 4, color = "rgb(255,195,001)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["MNIST"][1],
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name = "BCNN 1",
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marker = attr(symbol = 4, color = "rgb(055,033,240)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["MNIST"][2],
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name = "BCNN 2",
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marker = attr(symbol = 4, color = "rgb(018,094,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["MNIST"][3],
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name = "BCNN 3",
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marker = attr(symbol = 4, color = "rgb(018,126,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["MNIST"][4],
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name = "BCNN 4",
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marker = attr(symbol = 4, color = "rgb(018,150,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["MNIST"][5],
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name = "BCNN 5",
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marker = attr(symbol = 4, color = "rgb(018,167,219)", line_width = 1.0),
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),
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],
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Layout(
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mode = "overlay",
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xaxis_tickangle = -45,
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yaxis_title_text = "Efficency",
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xaxis_title_text = "Epoch";
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opacity = 0.9,
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#xaxis_range = [-1, 5],
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#xaxis_type = "category",
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),
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)
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savefig(en_plot, "mnist_efficiency_epoch.png")
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en_plot = plot(
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[
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["CIFAR"][1],
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name = "LeNet 1",
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marker = attr(symbol = 4, color = "rgb(211,120,000)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["CIFAR"][2],
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name = "LeNet 2",
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marker = attr(symbol = 17, color = "rgb(255,170,017)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["CIFAR"][3],
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name = "LeNet 3",
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marker = attr(symbol = 2, color = "rgb(255,187,034)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["CIFAR"][4],
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name = "LeNet 4",
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marker = attr(symbol = 0, color = "rgb(255,204,051)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["LeNet"]["CIFAR"][5],
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name = "LeNet 5",
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marker = attr(symbol = 4, color = "rgb(255,195,001)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["CIFAR"][1],
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name = "BCNN 1",
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marker = attr(symbol = 4, color = "rgb(055,033,240)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["CIFAR"][2],
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name = "BCNN 2",
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marker = attr(symbol = 4, color = "rgb(018,094,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["CIFAR"][3],
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name = "BCNN 3",
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marker = attr(symbol = 4, color = "rgb(018,126,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["CIFAR"][4],
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name = "BCNN 4",
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marker = attr(symbol = 4, color = "rgb(018,150,219)", line_width = 1.0),
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),
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scatter(
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#x = ["1", "2", "3", "4", "5"],
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y = original_vectors["BCNN"]["CIFAR"][5],
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name = "BCNN 5",
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marker = attr(symbol = 4, color = "rgb(018,167,219)", line_width = 1.0),
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),
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],
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Layout(
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mode = "overlay",
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xaxis_tickangle = -45,
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yaxis_title_text = "Efficency",
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xaxis_title_text = "Epoch";
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opacity = 0.9,
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#xaxis_range = [-1, 5],
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#xaxis_type = "category",
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),
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)
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savefig(en_plot, "cifar_efficiency_epoch.png")
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