Mix.install([
{:kino, git: "https://github.com/livebook-dev/kino", override: true},
{:kino_db, "~> 0.1.2"},
{:postgrex, "~> 0.16.3"},
{:kino_vega_lite, "~> 0.1.1"},
{:kino_maplibre, "~> 0.1.3"},
{:exla, "~> 0.2"},
{:axon_onnx, "~> 0.1"},
{:stb_image, "~> 0.5"},
{:download, "~> 0.0"},
{:jason, "~> 1.3"},
{:scidata, "~> 0.1.4"}
]):ok
memory_plot =
VegaLite.new(width: 600, height: 400, padding: 20)
|> VegaLite.repeat(
[layer: ["total", "processes", "atom", "binary", "code", "ets"]],
VegaLite.new()
|> VegaLite.mark(:line)
|> VegaLite.encode_field(:x, "iter", type: :quantitative, title: "Measurement")
|> VegaLite.encode_repeat(:y, :layer, type: :quantitative, title: "Memory usage (MB)")
|> VegaLite.encode(:color, datum: [repeat: :layer], type: :nominal)
)
|> Kino.VegaLite.new()Kino.VegaLite.periodically(memory_plot, 200, 1, fn i ->
point =
:erlang.memory()
|> Enum.map(fn {type, bytes} -> {type, bytes / 1_000_000} end)
|> Map.new()
|> Map.put(:iter, i)
Kino.VegaLite.push(memory_plot, point, window: 1000)
{:cont, i + 1}
end):ok
for i <- 1..8000, do: String.duplicate("piacere", i)my_data = %{a: [89, 124, 09, 67, 45], b: [12, 45, 67, 83, 32]}%{a: 'Y|\tC-', b: '\f-CS '}
Kino.Process.render_seq_trace(fn -> IO.puts("hello") end)hello
:ok
parent = self()
Kino.Process.render_seq_trace(fn ->
ponger_by_ping =
spawn(fn ->
receive do
:ping -> send(parent, :pong) |> dbg
end
end)
send(ponger_by_ping, :ping) |> IO.inspect()
receive do
:pong -> :ponged!
end
end):ping
:ponged!
earthquakes = "https://maplibre.org/maplibre-gl-js-docs/assets/earthquakes.geojson"
conference = %Geo.Point{coordinates: {100.4933, 13.7551}, properties: %{year: 2004}}%Geo.Point{coordinates: {100.4933, 13.7551}, srid: nil, properties: %{year: 2004}}
:pong
MapLibre.new(style: :terrain)
|> MapLibre.add_source("earthquakes", type: :geojson, data: earthquakes)
|> MapLibre.add_layer(
id: "test",
source: "earthquakes",
type: :circle,
paint: [circle_color: "#f50000", circle_radius: 7, circle_opacity: 0.4]
)opts = [
hostname: "localhost",
port: 5432,
username: "postgres",
password: "postgres",
database: "basic_dev",
socket_options: [:inet6]
]
{:ok, conn2} = Kino.start_child({Postgrex, opts}){:ok, #PID<0.430.0>}
result2 = Postgrex.query!(conn2, "select schemaname, tablename, tableowner from pg_tables", [])result = Postgrex.query!(conn2, "select * from blogs", [])[1, 2, 3]
|> Enum.map(&(&1 * 3))
|> Enum.map(&(&1 - 2))
|> Enum.map(&(&1 / 2))
|> dbg[0.5, 2.0, 3.5]
[1, 2, 3]
|> Enum.map(&(&1 * 3))
|> Enum.map(&(&1 - 2))
|> Enum.map(&(&1 / 2))
|> dbg[0.5, 2.0, 3.5]
今回、動かしているLivebookは、Githubから構築した最新版のため、mixプロジェクトをロードするメニューが出ませんでした…
livebook serverで起動できる下記インストールでお試し出来ます
mix escript.install hex livebook
livebook server
data =
Download.from("https://pbs.twimg.com/media/FcyOH_WaAAQf6GV?format=jpg&name=small")
|> elem(1)
|> File.read!()
Kino.Image.new(data, :jpeg)EXLA.set_as_nx_default([:tpu, :cuda, :rocm, :host])
File.rm("imagenet_class_index.json")
classes =
Download.from(
"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
)
|> elem(1)
|> File.read!()
|> Jason.decode!()
File.rm("resnet18-v1-7.onnx")
{model, params} =
Download.from(
"https://media.githubusercontent.com/media/onnx/models/main/vision/classification/resnet/model/resnet18-v1-7.onnx"
)
|> elem(1)
|> AxonOnnx.import()warning: EXLA.set_as_nx_default/1 is deprecated. Configure the Nx backend directly
/home/piacere/new_feature.livemd#cell:gaj7gbiact2aimwub33cmiq5xlb6xfb5:1
13:27:50.652 [info] XLA service 0x7fd7805fe730 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
13:27:50.657 [info] StreamExecutor device (0): Host, Default Version
13:27:52.744 [warn] N has no specified dimension, assuming nil
{#Axon<
inputs: %{"data" => {nil, 3, 224, 224}}
outputs: "resnetv15_dense0_fwd"
nodes: 78
>,
%{
"resnetv15_stage2_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[128][128][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155553>
[
[
[
[-0.004372711759060621, -0.05419772490859032, -3.91293375287205e-4],
[-0.04232256859540939, -0.06853722780942917, 0.04243552312254906],
[-0.00898411963135004, -0.07863212376832962, -0.016110705211758614]
],
[
[-0.010281874798238277, 0.03421446308493614, -0.014962800778448582],
[-0.016651591286063194, 0.06242304667830467, -0.021968599408864975],
[0.008408828638494015, -0.01319062802940607, -0.032806456089019775]
],
[
[0.006438169162720442, -0.022291747853159904, 0.008700958453118801],
[-0.006003286223858595, 0.012804224155843258, -0.03372650220990181],
[0.006644513923674822, 0.03296489641070366, -0.008054926060140133]
],
[
[0.03198675438761711, -0.008067324757575989, 0.02648865059018135],
[0.02297864854335785, 0.0386367104947567, -0.07900068163871765],
[0.033621758222579956, 0.02159077301621437, 0.02256469801068306]
],
[
[-0.04729733243584633, 0.024080155417323112, -0.005679002031683922],
[-0.008684554137289524, -0.030438365414738655, 0.002640919527038932],
[-2.89778381556971e-5, -0.010822935961186886, -0.014380409382283688]
],
[
[-0.002085881307721138, ...],
...
],
...
],
...
]
>
},
"resnetv15_stage3_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[256][128][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155557>
[
[
[
[-0.0132809579372406, -0.021667124703526497, -0.007288098335266113],
[-0.007261752150952816, -0.011978251859545708, -0.00867552775889635],
[0.024430986493825912, 0.0010231132619082928, 0.00932250078767538]
],
[
[-0.004223249386996031, -0.0020390385761857033, 0.0123683400452137],
[0.018303273245692253, 0.026677964255213737, 0.01098636444658041],
[0.04307591915130615, 0.06255745142698288, 0.05567341297864914]
],
[
[0.012428343296051025, 0.0259371530264616, 0.02102091908454895],
[0.002833062317222357, -0.012572483159601688, 0.053992487490177155],
[-0.002169080777093768, 0.029913710430264473, 0.05847463384270668]
],
[
[-0.006782576907426119, -0.015180363319814205, -0.0024544952902942896],
[0.014065624214708805, -0.014143011532723904, 0.004663258325308561],
[0.017230762168765068, -0.005719166714698076, -0.014543636702001095]
],
[
[-0.0087962681427598, -0.003275044495239854, 0.014437062665820122],
[0.0033619666937738657, -0.005276537965983152, 0.00811026245355606],
[-0.00519958883523941, 0.008937902748584747, 0.01126845832914114]
],
...
],
...
]
>
},
"resnetv15_stage1_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][64][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155549>
[
[
[
[-0.04273908957839012, -0.0016209344612434506, -0.024602342396974564],
[-0.0014729746617376804, 0.0407138429582119, 0.003976970445364714],
[-0.024285735562443733, 0.008288075216114521, 0.026334460824728012]
],
[
[0.022718101739883423, 0.027617767453193665, -0.0015894880052655935],
[0.029189737513661385, -0.0296456478536129, 0.005020724609494209],
[-0.016052044928073883, 0.003710768651217222, 0.003594387089833617]
],
[
[0.0022521952632814646, 0.03973611816763878, -0.008867218159139156],
[0.03960597887635231, 0.09111523628234863, 0.04423365741968155],
[-0.006665181368589401, 0.05003541335463524, 0.021319540217518806]
],
[
[-0.02817159704864025, -0.020342908799648285, -0.035205960273742676],
[-0.021228399127721786, -0.015519125387072563, -0.019682085141539574],
[-0.029222961515188217, -0.019630378112196922, -0.02652803249657154]
],
[
[-0.028428852558135986, -0.06003900617361069, -0.011422639712691307],
[0.0047211111523211, 0.017041660845279694, 0.018938405439257622],
[0.01961064338684082, 0.05447525531053543, ...]
],
...
],
...
]
>
},
"resnetv15_stage3_conv3_fwd" => %{
"kernel" => #Nx.Tensor<
f32[256][256][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155559>
[
[
[
[0.007360897492617369, 0.011684455908834934, 0.014390671625733376],
[0.01795717515051365, 0.018082698807120323, 0.007120896130800247],
[-0.01249797735363245, -0.021108508110046387, -0.018952926620841026]
],
[
[0.005976986140012741, 0.003644037526100874, -0.009489438496530056],
[0.003749158466234803, -0.006711691617965698, -0.006486326921731234],
[-0.020584726706147194, -0.02576063759624958, -0.032088130712509155]
],
[
[-0.04379397630691528, 0.06337196379899979, 0.04274430871009827],
[-0.04211288318037987, 0.10768246650695801, 0.03375661000609398],
[-0.04575773701071739, -0.0370059609413147, -0.02428184635937214]
],
[
[-0.011994347907602787, -0.008994431234896183, -0.004394493997097015],
[0.006494964938610792, 0.008632502518594265, -0.006265564821660519],
[-0.0022780282888561487, 0.010660781525075436, -0.001458185026422143]
],
[
[0.0013403133489191532, -0.004409696441143751, -0.009679187089204788],
[0.008197332732379436, -0.004781861323863268, -0.012495175004005432],
[0.005880873650312424, ...]
],
...
],
...
]
>
},
"resnetv15_stage1_batchnorm3_fwd" => %{
"beta" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156574>
[0.17209984362125397, 0.006118499208241701, 0.046158578246831894, 0.2616331875324249, 0.047538693994283676, -0.1332138031721115, -0.14159579575061798, -0.22409465909004211, -0.1705334335565567, 0.17851437628269196, -0.14194239675998688, 0.14119787514209747, 0.24726499617099762, -0.24126304686069489, 0.0788675844669342, 0.02458156831562519, -0.16053283214569092, -0.03937358036637306, 0.0768241211771965, 0.009212430566549301, 0.11453794687986374, -0.058193571865558624, 0.10432858020067215, 0.1427590698003769, -0.006379001308232546, 0.10411231219768524, -0.11333339661359787, -0.01300036907196045, -0.22290268540382385, 0.15458111464977264, 0.047290921211242676, 9.417664841748774e-4, 0.18037591874599457, -0.2871352434158325, 0.03729450702667236, 0.158797025680542, -0.0427778996527195, -0.06669793277978897, -0.12869243323802948, 0.046405766159296036, -0.10199212282896042, -0.06456106156110764, ...]
>,
"gamma" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156573>
[0.21748007833957672, 0.3514665961265564, 0.2732861340045929, 0.27742791175842285, 0.24738048017024994, 0.3718911409378052, 0.34789225459098816, 0.42112666368484497, 0.2833738625049591, 0.3125789165496826, 0.4478318989276886, 0.2905173599720001, 0.5661211013793945, 0.5346167683601379, 0.39130064845085144, 0.6248013377189636, 0.6175164580345154, 0.5105664730072021, 0.31301483511924744, 0.49441030621528625, 0.29812195897102356, 0.48537227511405945, 0.2841026484966278, 0.27279117703437805, 0.4470939338207245, 0.43783998489379883, 0.34781044721603394, 0.2684362828731537, 0.32683250308036804, 0.696101725101471, 0.3728120028972626, 0.1681371033191681, 0.2642030417919159, 0.28523433208465576, 0.27973616123199463, 0.5557174682617188, 0.666621744632721, 0.38617250323295593, 0.4075431227684021, 0.6257655620574951, 0.37075087428092957, ...]
>,
"mean" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156575>
[0.05512537062168121, 0.2595690190792084, -0.049237582832574844, 0.04469703510403633, -0.07464569061994553, -0.09598635137081146, 0.03816966712474823, -0.004327676724642515, 0.11293864250183105, 0.0017437192145735025, -0.04388900846242905, 0.010042845271527767, 5.892590852454305e-4, -0.1037544459104538, 0.022028744220733643, 0.06744462251663208, -0.12486346065998077, -0.13421498239040375, -0.19209356606006622, 0.10132186859846115, 0.16112294793128967, 0.047579772770404816, -0.0735909640789032, 0.16654402017593384, -0.13080161809921265, -0.2900204658508301, -0.05816976726055145, -0.08223054558038712, 0.11685419082641602, -0.015925955027341843, -0.29348576068878174, -0.13051675260066986, -0.2572914958000183, -0.1295301765203476, 0.09597738832235336, -0.1798400580883026, 0.030478280037641525, -0.09064535796642303, -0.24201823770999908, -0.029760124161839485, ...]
>,
"var" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156576>
[0.036362603306770325, 0.02163962833583355, 0.028978446498513222, 0.013921423815190792, 0.017633484676480293, 0.06508386880159378, 0.03457463160157204, 0.01873803324997425, 0.0314139761030674, 0.03456990793347359, 0.021263349801301956, 0.01834406703710556, 0.029071129858493805, 0.03549467772245407, 0.023502057418227196, 0.02790575474500656, 0.03459765017032623, 0.021767428144812584, 0.038501542061567307, 0.04073057323694229, 0.020157456398010254, 0.019277233630418777, 0.027829118072986603, 0.022200150415301323, 0.030972301959991455, 0.022777006030082703, 0.03643421828746796, 0.02056802809238434, 0.043726444244384766, 0.0440472811460495, 0.027097592130303383, 0.02111688256263733, 0.017365621402859688, 0.02980935387313366, 0.02249041385948658, 0.03220800310373306, 0.03904491662979126, 0.039861176162958145, 0.041399721056222916, ...]
>
},
"resnetv15_conv0_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][3][7][7]
EXLA.Backend<host:0, 0.1909074504.761397274.155546>
[
[
[
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...
],
...
],
...
]
>
},
"resnetv15_stage2_conv2_fwd" => %{
"kernel" => #Nx.Tensor<
f32[128][64][1][1]
EXLA.Backend<host:0, 0.1909074504.761397274.155551>
[
[
[
[-0.07025585323572159]
],
[
[-0.026780370622873306]
],
[
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],
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],
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],
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[
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[-0.07711481302976608]
],
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],
[
[0.03447660431265831]
],
[
[-0.019013287499547005]
],
[
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],
[
[-0.03312944248318672]
],
[
[-0.05929741635918617]
],
[
[0.0471075177192688]
],
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],
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[0.012288697995245457]
],
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[-0.04691522940993309]
],
[
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],
[
[-0.003479759907349944]
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],
[
[0.03304138407111168]
],
[
[-0.01724536530673504]
],
[
[0.00598535779863596]
],
[
[-0.01786188594996929]
],
[
[-0.07275085896253586]
],
[
[0.007575713563710451]
],
[
[0.028518645092844963]
],
[
[0.02167900651693344]
],
[
[0.0045365202240645885]
],
[
[0.06451937556266785]
],
...
],
...
]
>
},
"resnetv15_stage4_batchnorm1_fwd" => %{
"beta" => #Nx.Tensor<
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>,
"gamma" => #Nx.Tensor<
f32[512]
EXLA.Backend<host:0, 0.1909074504.761397290.156649>
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>,
"mean" => #Nx.Tensor<
f32[512]
EXLA.Backend<host:0, 0.1909074504.761397290.156651>
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>,
"var" => #Nx.Tensor<
f32[512]
EXLA.Backend<host:0, 0.1909074504.761397290.156652>
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>
},
"resnetv15_stage1_batchnorm2_fwd" => %{
"beta" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156569>
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>,
"gamma" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156568>
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>,
"mean" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156570>
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f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156571>
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>
},
"resnetv15_stage1_conv1_fwd" => %{
"kernel" => #Nx.Tensor<
f32[64][64][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155548>
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],
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[0.009184181690216064, 0.023880526423454285, -0.01875920780003071],
[0.06694190949201584, ...],
...
],
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],
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>
},
"resnetv15_stage1_batchnorm1_fwd" => %{
"beta" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156564>
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>,
"gamma" => #Nx.Tensor<
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EXLA.Backend<host:0, 0.1909074504.761397290.156563>
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>,
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f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156565>
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f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156566>
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>
},
"resnetv15_stage4_conv4_fwd" => %{
"kernel" => #Nx.Tensor<
f32[512][512][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155565>
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>
},
"resnetv15_stage1_conv0_fwd" => %{
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f32[64][64][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155547>
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>
},
"resnetv15_stage1_batchnorm0_fwd" => %{
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f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156559>
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EXLA.Backend<host:0, 0.1909074504.761397290.156558>
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>,
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f32[64]
EXLA.Backend<host:0, 0.1909074504.761397290.156560>
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EXLA.Backend<host:0, 0.1909074504.761397290.156561>
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},
"resnetv15_stage3_batchnorm0_fwd" => %{
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f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156609>
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>,
"gamma" => #Nx.Tensor<
f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156608>
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>,
"mean" => #Nx.Tensor<
f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156610>
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>,
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f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156611>
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>
},
"resnetv15_stage3_batchnorm3_fwd" => %{
"beta" => #Nx.Tensor<
f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156620>
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>,
"gamma" => #Nx.Tensor<
f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156619>
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>,
"mean" => #Nx.Tensor<
f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156621>
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>,
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f32[256]
EXLA.Backend<host:0, 0.1909074504.761397290.156622>
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>
},
"resnetv15_stage2_conv3_fwd" => %{
"kernel" => #Nx.Tensor<
f32[128][128][3][3]
EXLA.Backend<host:0, 0.1909074504.761397274.155554>
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>
}
}}
File.rm("greatwhiteshark_157273892.jpg")
data =
Download.from("https://www.collinsdictionary.com/images/full/greatwhiteshark_157273892.jpg")
|> elem(1)
|> File.read!()
Kino.Image.new(data, :jpeg)nx_image =
data
|> StbImage.read_binary!()
|> StbImage.resize(224, 224)
|> StbImage.to_nx()
nx_channels =
nx_image
|> Nx.axis_size(2)
tensor =
case nx_channels do
3 -> nx_image
4 -> Nx.slice(nx_image, [0, 0, 0], [224, 224, 3])
end
|> Nx.divide(255)
|> Nx.subtract(Nx.tensor([0.485, 0.456, 0.406]))
|> Nx.divide(Nx.tensor([0.229, 0.224, 0.225]))
|> Nx.transpose()
|> Nx.new_axis(0)
Axon.predict(model, params, tensor)
|> Nx.flatten()
|> Nx.argsort()
|> Nx.reverse()
|> Nx.slice([0], [5])
|> Nx.to_flat_list()
|> Enum.with_index()
|> Enum.map(fn {no, index} -> {index, Map.get(classes, to_string(no))} end)[
{0, ["n01491361", "tiger_shark"]},
{1, ["n01484850", "great_white_shark"]},
{2, ["n01494475", "hammerhead"]},
{3, ["n01496331", "electric_ray"]},
{4, ["n02074367", "dugong"]}
]