From ca1b2b7630039ef3f44ddc66c7b4ac2a4122fdac Mon Sep 17 00:00:00 2001 From: jakmro Date: Wed, 19 Jun 2024 13:05:21 +0200 Subject: [PATCH 1/4] Add EfficientNetV2 and SqueezeNet --- .gitattributes | 4 ++ README.md | 6 +-- .../classification/efficientnet_v2_l.ex | 40 ++++++++++++++++ .../classification/efficientnet_v2_m.ex | 40 ++++++++++++++++ .../classification/efficientnet_v2_s.ex | 40 ++++++++++++++++ lib/ex_vision/classification/squeezenet1_1.ex | 40 ++++++++++++++++ mix.exs | 6 ++- mix.lock | 8 ++-- .../classification/efficientnet_v2_l.py | 48 +++++++++++++++++++ .../classification/efficientnet_v2_m.py | 48 +++++++++++++++++++ .../classification/efficientnet_v2_s.py | 48 +++++++++++++++++++ .../exports/classification/squeezenet1_1.py | 48 +++++++++++++++++++ .../classification/efficientnet_v2_l.json | 1 + .../classification/efficientnet_v2_m.json | 1 + .../classification/efficientnet_v2_s.json | 1 + .../results/classification/squeezenet1_1.json | 1 + .../classification/efficientnet_v2_l_test.exs | 18 +++++++ .../classification/efficientnet_v2_m_test.exs | 18 +++++++ .../classification/efficientnet_v2_s_test.exs | 18 +++++++ .../classification/squeezenet1_1_test.exs | 18 +++++++ 20 files changed, 444 insertions(+), 8 deletions(-) create mode 100644 lib/ex_vision/classification/efficientnet_v2_l.ex create mode 100644 lib/ex_vision/classification/efficientnet_v2_m.ex create mode 100644 lib/ex_vision/classification/efficientnet_v2_s.ex create mode 100644 lib/ex_vision/classification/squeezenet1_1.ex create mode 100644 python/exports/classification/efficientnet_v2_l.py create mode 100644 python/exports/classification/efficientnet_v2_m.py create mode 100644 python/exports/classification/efficientnet_v2_s.py create mode 100644 python/exports/classification/squeezenet1_1.py create mode 100644 test/assets/results/classification/efficientnet_v2_l.json create mode 100644 test/assets/results/classification/efficientnet_v2_m.json create mode 100644 test/assets/results/classification/efficientnet_v2_s.json create mode 100644 test/assets/results/classification/squeezenet1_1.json create mode 100644 test/ex_vision/classification/efficientnet_v2_l_test.exs create mode 100644 test/ex_vision/classification/efficientnet_v2_m_test.exs create mode 100644 test/ex_vision/classification/efficientnet_v2_s_test.exs create mode 100644 test/ex_vision/classification/squeezenet1_1_test.exs diff --git a/.gitattributes b/.gitattributes index 2b6a975..0f7a8f0 100644 --- a/.gitattributes +++ b/.gitattributes @@ -2,4 +2,8 @@ models/**/*.onnx filter=lfs diff=lfs merge=lfs -text models/deeplab_v3_mobilenetv3_segmentation.onnx filter=lfs diff=lfs merge=lfs -text models/fasterrcnn_resnet50_fpn_detector.onnx filter=lfs diff=lfs merge=lfs -text models/mobilenetv3small-classifier.onnx filter=lfs diff=lfs merge=lfs -text +models/efficientnet_v2_s_classifier.onnx filter=lfs diff=lfs merge=lfs -text +models/efficientnet_v2_m_classifier.onnx filter=lfs diff=lfs merge=lfs -text +models/efficientnet_v2_l_classifier.onnx filter=lfs diff=lfs merge=lfs -text +models/squeezenet1_1_classifier.onnx filter=lfs diff=lfs merge=lfs -text models/ssdlite320_mobilenetv3_detector.onnx filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 5e5679d..9d0008f 100644 --- a/README.md +++ b/README.md @@ -78,10 +78,10 @@ In order to compile, ExVision **requires Rust and Cargo** to be installed on you We have identified a set of models that we would like to support. If the model that you would like to use is missing, feel free to open the issue, express interest in an existing one or contribute the model directly. -- [ ] Classification +- [x] Classification - [x] MobileNetV3 Small - - [ ] EfficientNetV2 - - [ ] SqueezeNet + - [x] EfficientNetV2 + - [x] SqueezeNet - [x] Object detection - [x] SSDLite320 - MobileNetV3 Large backbone - [x] FasterRCNN ResNet50 FPN diff --git a/lib/ex_vision/classification/efficientnet_v2_l.ex b/lib/ex_vision/classification/efficientnet_v2_l.ex new file mode 100644 index 0000000..75eda80 --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_l.ex @@ -0,0 +1,40 @@ +defmodule ExVision.Classification.EfficientNet_V2_L do + @moduledoc """ + An object classifier based on EfficientNet_V2_L. + Exported from `torchvision`. + Weights from Imagenet 1k. + """ + use ExVision.Model.Definition.Ortex, + model: "efficientnet_v2_l_classifier.onnx", + categories: "priv/categories/imagenet_v2_categories.json" + + require Bunch.Typespec + alias ExVision.Utils + + @typedoc """ + A type describing the output of EfficientNet_V2_L classifier as a mapping of category to probability. + """ + @type output_t() :: %{category_t() => number()} + + @impl true + def preprocessing(image, _metadata) do + image + |> ExVision.Utils.resize({480, 480}) + |> NxImage.normalize( + Nx.tensor([0.5, 0.5, 0.5]), + Nx.tensor([0.5, 0.5, 0.5]), + channels: :first + ) + end + + @impl true + def postprocessing(%{"output" => scores}, _metadata) do + scores + |> Nx.backend_transfer() + |> Nx.flatten() + |> Utils.softmax() + |> Nx.to_flat_list() + |> then(&Enum.zip(categories(), &1)) + |> Map.new() + end +end diff --git a/lib/ex_vision/classification/efficientnet_v2_m.ex b/lib/ex_vision/classification/efficientnet_v2_m.ex new file mode 100644 index 0000000..c7f09b6 --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_m.ex @@ -0,0 +1,40 @@ +defmodule ExVision.Classification.EfficientNet_V2_M do + @moduledoc """ + An object classifier based on EfficientNet_V2_M. + Exported from `torchvision`. + Weights from Imagenet 1k. + """ + use ExVision.Model.Definition.Ortex, + model: "efficientnet_v2_m_classifier.onnx", + categories: "priv/categories/imagenet_v2_categories.json" + + require Bunch.Typespec + alias ExVision.Utils + + @typedoc """ + A type describing the output of EfficientNet_V2_M classifier as a mapping of category to probability. + """ + @type output_t() :: %{category_t() => number()} + + @impl true + def preprocessing(image, _metadata) do + image + |> ExVision.Utils.resize({480, 480}) + |> NxImage.normalize( + Nx.tensor([0.485, 0.456, 0.406]), + Nx.tensor([0.229, 0.224, 0.225]), + channels: :first + ) + end + + @impl true + def postprocessing(%{"output" => scores}, _metadata) do + scores + |> Nx.backend_transfer() + |> Nx.flatten() + |> Utils.softmax() + |> Nx.to_flat_list() + |> then(&Enum.zip(categories(), &1)) + |> Map.new() + end +end diff --git a/lib/ex_vision/classification/efficientnet_v2_s.ex b/lib/ex_vision/classification/efficientnet_v2_s.ex new file mode 100644 index 0000000..4e1fe7f --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_s.ex @@ -0,0 +1,40 @@ +defmodule ExVision.Classification.EfficientNet_V2_S do + @moduledoc """ + An object classifier based on EfficientNet_V2_S. + Exported from `torchvision`. + Weights from Imagenet 1k. + """ + use ExVision.Model.Definition.Ortex, + model: "efficientnet_v2_s_classifier.onnx", + categories: "priv/categories/imagenet_v2_categories.json" + + require Bunch.Typespec + alias ExVision.Utils + + @typedoc """ + A type describing the output of EfficientNet_V2_S classifier as a mapping of category to probability. + """ + @type output_t() :: %{category_t() => number()} + + @impl true + def preprocessing(image, _metadata) do + image + |> ExVision.Utils.resize({384, 384}) + |> NxImage.normalize( + Nx.tensor([0.485, 0.456, 0.406]), + Nx.tensor([0.229, 0.224, 0.225]), + channels: :first + ) + end + + @impl true + def postprocessing(%{"output" => scores}, _metadata) do + scores + |> Nx.backend_transfer() + |> Nx.flatten() + |> Utils.softmax() + |> Nx.to_flat_list() + |> then(&Enum.zip(categories(), &1)) + |> Map.new() + end +end diff --git a/lib/ex_vision/classification/squeezenet1_1.ex b/lib/ex_vision/classification/squeezenet1_1.ex new file mode 100644 index 0000000..af43a61 --- /dev/null +++ b/lib/ex_vision/classification/squeezenet1_1.ex @@ -0,0 +1,40 @@ +defmodule ExVision.Classification.SqueezeNet1_1 do + @moduledoc """ + An object classifier based on SqueezeNet1_1. + Exported from `torchvision`. + Weights from Imagenet 1k. + """ + use ExVision.Model.Definition.Ortex, + model: "squeezenet1_1_classifier.onnx", + categories: "priv/categories/imagenet_v2_categories.json" + + require Bunch.Typespec + alias ExVision.Utils + + @typedoc """ + A type describing the output of SqueezeNet1_1 classifier as a mapping of category to probability. + """ + @type output_t() :: %{category_t() => number()} + + @impl true + def preprocessing(image, _metadata) do + image + |> ExVision.Utils.resize({224, 224}) + |> NxImage.normalize( + Nx.tensor([0.485, 0.456, 0.406]), + Nx.tensor([0.229, 0.224, 0.225]), + channels: :first + ) + end + + @impl true + def postprocessing(%{"output" => scores}, _metadata) do + scores + |> Nx.backend_transfer() + |> Nx.flatten() + |> Utils.softmax() + |> Nx.to_flat_list() + |> then(&Enum.zip(categories(), &1)) + |> Map.new() + end +end diff --git a/mix.exs b/mix.exs index 7912654..70d4155 100644 --- a/mix.exs +++ b/mix.exs @@ -1,7 +1,7 @@ defmodule ExVision.Mixfile do use Mix.Project - @version "0.1.0" + @version "0.2.0" @github_url "https://github.com/membraneframework-labs/ex_vision/" def project do @@ -94,6 +94,10 @@ defmodule ExVision.Mixfile do groups_for_modules: [ Models: [ ExVision.Classification.MobileNetV3Small, + ExVision.Classification.EfficientNet_V2_S, + ExVision.Classification.EfficientNet_V2_M, + ExVision.Classification.EfficientNet_V2_L, + ExVision.Classification.SqueezeNet1_1, ExVision.Segmentation.DeepLabV3_MobileNetV3, ExVision.Detection.Ssdlite320_MobileNetv3, ExVision.Detection.FasterRCNN_ResNet50_FPN diff --git a/mix.lock b/mix.lock index e5a9e6a..9931294 100644 --- a/mix.lock +++ b/mix.lock @@ -2,18 +2,18 @@ "axon": {:hex, :axon, "0.6.1", "1d042fdba1c1b4413a3d65800524feebd1bc8ed218f8cdefe7a97510c3f427f3", [:mix], [{:kino, "~> 0.7", [hex: :kino, repo: "hexpm", optional: true]}, {:kino_vega_lite, "~> 0.1.7", [hex: :kino_vega_lite, repo: "hexpm", optional: true]}, {:nx, "~> 0.6.0 or ~> 0.7.0", [hex: :nx, repo: "hexpm", optional: false]}, {:polaris, "~> 0.1", [hex: :polaris, repo: "hexpm", optional: false]}, {:table_rex, "~> 3.1.1", [hex: :table_rex, repo: "hexpm", optional: true]}], "hexpm", "d6b0ae2f0dd284f6bf702edcab71e790d6c01ca502dd06c4070836554f5a48e1"}, "bunch": {:hex, :bunch, "1.6.1", "5393d827a64d5f846092703441ea50e65bc09f37fd8e320878f13e63d410aec7", [:mix], [], "hexpm", "286cc3add551628b30605efbe2fca4e38cc1bea89bcd0a1a7226920b3364fe4a"}, "bunt": {:hex, :bunt, "1.0.0", "081c2c665f086849e6d57900292b3a161727ab40431219529f13c4ddcf3e7a44", [:mix], [], "hexpm", "dc5f86aa08a5f6fa6b8096f0735c4e76d54ae5c9fa2c143e5a1fc7c1cd9bb6b5"}, - "castore": {:hex, :castore, "1.0.6", "ffc42f110ebfdafab0ea159cd43d31365fa0af0ce4a02ecebf1707ae619ee727", [:mix], [], "hexpm", "374c6e7ca752296be3d6780a6d5b922854ffcc74123da90f2f328996b962d33a"}, + "castore": {:hex, :castore, "1.0.7", "b651241514e5f6956028147fe6637f7ac13802537e895a724f90bf3e36ddd1dd", [:mix], [], "hexpm", "da7785a4b0d2a021cd1292a60875a784b6caef71e76bf4917bdee1f390455cf5"}, "cc_precompiler": {:hex, :cc_precompiler, "0.1.10", "47c9c08d8869cf09b41da36538f62bc1abd3e19e41701c2cea2675b53c704258", [:mix], [{:elixir_make, "~> 0.7", [hex: :elixir_make, repo: "hexpm", optional: false]}], "hexpm", "f6e046254e53cd6b41c6bacd70ae728011aa82b2742a80d6e2214855c6e06b22"}, "coerce": {:hex, :coerce, "1.0.1", "211c27386315dc2894ac11bc1f413a0e38505d808153367bd5c6e75a4003d096", [:mix], [], "hexpm", "b44a691700f7a1a15b4b7e2ff1fa30bebd669929ac8aa43cffe9e2f8bf051cf1"}, "complex": {:hex, :complex, "0.5.0", "af2d2331ff6170b61bb738695e481b27a66780e18763e066ee2cd863d0b1dd92", [:mix], [], "hexpm", "2683bd3c184466cfb94fad74cbfddfaa94b860e27ad4ca1bffe3bff169d91ef1"}, "credo": {:hex, :credo, "1.7.5", "643213503b1c766ec0496d828c90c424471ea54da77c8a168c725686377b9545", [:mix], [{:bunt, "~> 0.2.1 or ~> 1.0", [hex: :bunt, repo: "hexpm", optional: false]}, {:file_system, "~> 0.2 or ~> 1.0", [hex: :file_system, repo: "hexpm", optional: false]}, {:jason, "~> 1.0", [hex: :jason, repo: "hexpm", optional: false]}], "hexpm", "f799e9b5cd1891577d8c773d245668aa74a2fcd15eb277f51a0131690ebfb3fd"}, "dialyxir": {:hex, :dialyxir, "1.4.3", "edd0124f358f0b9e95bfe53a9fcf806d615d8f838e2202a9f430d59566b6b53b", [:mix], [{:erlex, ">= 0.2.6", [hex: :erlex, repo: "hexpm", optional: false]}], "hexpm", "bf2cfb75cd5c5006bec30141b131663299c661a864ec7fbbc72dfa557487a986"}, "earmark_parser": {:hex, :earmark_parser, "1.4.39", "424642f8335b05bb9eb611aa1564c148a8ee35c9c8a8bba6e129d51a3e3c6769", [:mix], [], "hexpm", "06553a88d1f1846da9ef066b87b57c6f605552cfbe40d20bd8d59cc6bde41944"}, - "elixir_make": {:hex, :elixir_make, "0.8.3", "d38d7ee1578d722d89b4d452a3e36bcfdc644c618f0d063b874661876e708683", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: true]}, {:certifi, "~> 2.0", [hex: :certifi, repo: "hexpm", optional: true]}], "hexpm", "5c99a18571a756d4af7a4d89ca75c28ac899e6103af6f223982f09ce44942cc9"}, + "elixir_make": {:hex, :elixir_make, "0.8.4", "4960a03ce79081dee8fe119d80ad372c4e7badb84c493cc75983f9d3bc8bde0f", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: true]}, {:certifi, "~> 2.0", [hex: :certifi, repo: "hexpm", optional: true]}], "hexpm", "6e7f1d619b5f61dfabd0a20aa268e575572b542ac31723293a4c1a567d5ef040"}, "erlex": {:hex, :erlex, "0.2.6", "c7987d15e899c7a2f34f5420d2a2ea0d659682c06ac607572df55a43753aa12e", [:mix], [], "hexpm", "2ed2e25711feb44d52b17d2780eabf998452f6efda104877a3881c2f8c0c0c75"}, "evision": {:hex, :evision, "0.1.38", "f8b23ad685c3ebd70969a3457027b5c74b5bc8dc51588661c516098c3240b92d", [:make, :mix, :rebar3], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: false]}, {:elixir_make, "~> 0.7", [hex: :elixir_make, repo: "hexpm", optional: false]}, {:kino, "~> 0.11", [hex: :kino, repo: "hexpm", optional: true]}, {:nx, "~> 0.4", [hex: :nx, repo: "hexpm", optional: false]}, {:progress_bar, "~> 2.0 or ~> 3.0", [hex: :progress_bar, repo: "hexpm", optional: true]}], "hexpm", "f9302547d76c5e4ad7022ffdc76be13e33c990fdd67ad2af203f24ab5d3aee20"}, "ex_doc": {:hex, :ex_doc, "0.32.1", "21e40f939515373bcdc9cffe65f3b3543f05015ac6c3d01d991874129d173420", [:mix], [{:earmark_parser, "~> 1.4.39", [hex: :earmark_parser, repo: "hexpm", optional: false]}, {:makeup_c, ">= 0.1.1", [hex: :makeup_c, repo: "hexpm", optional: true]}, {:makeup_elixir, "~> 0.14", [hex: :makeup_elixir, repo: "hexpm", optional: false]}, {:makeup_erlang, "~> 0.1", [hex: :makeup_erlang, repo: "hexpm", optional: false]}], "hexpm", "5142c9db521f106d61ff33250f779807ed2a88620e472ac95dc7d59c380113da"}, - "exla": {:hex, :exla, "0.7.1", "790493288cf4441abed98df0c4e98da15a2e3a7fa27cd2a1f74ec0693952c579", [:make, :mix], [{:elixir_make, "~> 0.6", [hex: :elixir_make, repo: "hexpm", optional: false]}, {:nimble_pool, "~> 1.0", [hex: :nimble_pool, repo: "hexpm", optional: false]}, {:nx, "~> 0.7.1", [hex: :nx, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}, {:xla, "~> 0.6.0", [hex: :xla, repo: "hexpm", optional: false]}], "hexpm", "ec9c1698a9a17b859d79f9b3c1d75c370335580cdd0353db9c2017f86155e2ec"}, + "exla": {:hex, :exla, "0.7.2", "8ac573093df8e5e6b36845beeb3f5a0ea92b05082bf2fa4678f80170cfc887f6", [:make, :mix], [{:elixir_make, "~> 0.6", [hex: :elixir_make, repo: "hexpm", optional: false]}, {:nimble_pool, "~> 1.0", [hex: :nimble_pool, repo: "hexpm", optional: false]}, {:nx, "~> 0.7.1", [hex: :nx, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}, {:xla, "~> 0.6.0", [hex: :xla, repo: "hexpm", optional: false]}], "hexpm", "d061ea87858415e5585cbd4b7bdae5489000339519a2c6a7f51eb0defd73b588"}, "file_system": {:hex, :file_system, "1.0.0", "b689cc7dcee665f774de94b5a832e578bd7963c8e637ef940cd44327db7de2cd", [:mix], [], "hexpm", "6752092d66aec5a10e662aefeed8ddb9531d79db0bc145bb8c40325ca1d8536d"}, "finch": {:hex, :finch, "0.18.0", "944ac7d34d0bd2ac8998f79f7a811b21d87d911e77a786bc5810adb75632ada4", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: false]}, {:mime, "~> 1.0 or ~> 2.0", [hex: :mime, repo: "hexpm", optional: false]}, {:mint, "~> 1.3", [hex: :mint, repo: "hexpm", optional: false]}, {:nimble_options, "~> 0.4 or ~> 1.0", [hex: :nimble_options, repo: "hexpm", optional: false]}, {:nimble_pool, "~> 0.2.6 or ~> 1.0", [hex: :nimble_pool, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "69f5045b042e531e53edc2574f15e25e735b522c37e2ddb766e15b979e03aa65"}, "hpax": {:hex, :hpax, "0.2.0", "5a58219adcb75977b2edce5eb22051de9362f08236220c9e859a47111c194ff5", [:mix], [], "hexpm", "bea06558cdae85bed075e6c036993d43cd54d447f76d8190a8db0dc5893fa2f1"}, @@ -31,7 +31,7 @@ "nimble_parsec": {:hex, :nimble_parsec, "1.4.0", "51f9b613ea62cfa97b25ccc2c1b4216e81df970acd8e16e8d1bdc58fef21370d", [:mix], [], "hexpm", "9c565862810fb383e9838c1dd2d7d2c437b3d13b267414ba6af33e50d2d1cf28"}, "nimble_pool": {:hex, :nimble_pool, "1.1.0", "bf9c29fbdcba3564a8b800d1eeb5a3c58f36e1e11d7b7fb2e084a643f645f06b", [:mix], [], "hexpm", "af2e4e6b34197db81f7aad230c1118eac993acc0dae6bc83bac0126d4ae0813a"}, "numbers": {:hex, :numbers, "5.2.4", "f123d5bb7f6acc366f8f445e10a32bd403c8469bdbce8ce049e1f0972b607080", [:mix], [{:coerce, "~> 1.0", [hex: :coerce, repo: "hexpm", optional: false]}, {:decimal, "~> 1.9 or ~> 2.0", [hex: :decimal, repo: "hexpm", optional: true]}], "hexpm", "eeccf5c61d5f4922198395bf87a465b6f980b8b862dd22d28198c5e6fab38582"}, - "nx": {:hex, :nx, "0.7.1", "5f6376e3d18408116e8a84b8f4ac851fb07dfe61764a5410ebf0b5dcb69c1b7e", [:mix], [{:complex, "~> 0.5", [hex: :complex, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "e3ddd6a3f2a9bac79c67b3933368c25bb5ec814a883fc68aba8fd8a236751777"}, + "nx": {:hex, :nx, "0.7.2", "7f6f6584585e49ffbf81769e7ccc2d01c5639074e399c1f94adc2b509869673e", [:mix], [{:complex, "~> 0.5", [hex: :complex, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "e2c0680066eec5af8b8ef00c99e9bf40a0d08d8b2bbba77f59f801ec54a3f90e"}, "nx_image": {:hex, :nx_image, "0.1.2", "0c6e3453c1dc30fc80c723a54861204304cebc8a89ed3b806b972c73ee5d119d", [:mix], [{:nx, "~> 0.4", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "9161863c42405ddccb6dbbbeae078ad23e30201509cc804b3b3a7c9e98764b81"}, "ortex": {:hex, :ortex, "0.1.9", "a9b14552ef6058961a3e300f973a51887328a13c2ffa6f2cad1b0785f9c7e73c", [:mix], [{:nx, "~> 0.6", [hex: :nx, repo: "hexpm", optional: false]}, {:rustler, "~> 0.29.0", [hex: :rustler, repo: "hexpm", optional: false]}], "hexpm", "5201b9aa8e22a86f3a04e819266bfd1c5a8194f0c51f917c1d0cffe8bdbb76d8"}, "phoenix_html": {:hex, :phoenix_html, "4.1.1", "4c064fd3873d12ebb1388425a8f2a19348cef56e7289e1998e2d2fa758aa982e", [:mix], [], "hexpm", "f2f2df5a72bc9a2f510b21497fd7d2b86d932ec0598f0210fed4114adc546c6f"}, diff --git a/python/exports/classification/efficientnet_v2_l.py b/python/exports/classification/efficientnet_v2_l.py new file mode 100644 index 0000000..80ab3cb --- /dev/null +++ b/python/exports/classification/efficientnet_v2_l.py @@ -0,0 +1,48 @@ +from torchvision.models import efficientnet_v2_l, EfficientNet_V2_L_Weights +from torchvision.transforms.functional import to_tensor, resize +import torch +import json +from pathlib import Path +from PIL import Image + +base_dir = Path("models/classification/efficientnet_v2_l") +base_dir.mkdir(parents=True, exist_ok=True) + +model_file = base_dir / "model.onnx" +categories_file = base_dir / "categories.json" + +weights = EfficientNet_V2_L_Weights.DEFAULT +model = efficientnet_v2_l(weights=weights) +model.eval() + +categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] +transforms = weights.transforms() + +with open(categories_file, "w") as f: + json.dump(categories, f) + +onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) +onnx_input = resize(onnx_input, [480, 480]) +onnx_input = transforms(onnx_input) + +torch.onnx.export( + model, + onnx_input, + str(model_file), + verbose=False, + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, + export_params=True, +) + +expected_output: torch.Tensor = model(onnx_input) +expected_output = expected_output.softmax(dim=1) + +result = dict(zip(categories, expected_output[0].tolist())) + +file = Path("test/assets/results/classification/efficientnet_v2_l.json") +file.parent.mkdir(exist_ok=True, parents=True) + +with file.open("w") as f: + json.dump(result, f) diff --git a/python/exports/classification/efficientnet_v2_m.py b/python/exports/classification/efficientnet_v2_m.py new file mode 100644 index 0000000..0912fe5 --- /dev/null +++ b/python/exports/classification/efficientnet_v2_m.py @@ -0,0 +1,48 @@ +from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights +from torchvision.transforms.functional import to_tensor, resize +import torch +import json +from pathlib import Path +from PIL import Image + +base_dir = Path("models/classification/efficientnet_v2_m") +base_dir.mkdir(parents=True, exist_ok=True) + +model_file = base_dir / "model.onnx" +categories_file = base_dir / "categories.json" + +weights = EfficientNet_V2_M_Weights.DEFAULT +model = efficientnet_v2_m(weights=weights) +model.eval() + +categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] +transforms = weights.transforms() + +with open(categories_file, "w") as f: + json.dump(categories, f) + +onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) +onnx_input = resize(onnx_input, [480, 480]) +onnx_input = transforms(onnx_input) + +torch.onnx.export( + model, + onnx_input, + str(model_file), + verbose=False, + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, + export_params=True, +) + +expected_output: torch.Tensor = model(onnx_input) +expected_output = expected_output.softmax(dim=1) + +result = dict(zip(categories, expected_output[0].tolist())) + +file = Path("test/assets/results/classification/efficientnet_v2_m.json") +file.parent.mkdir(exist_ok=True, parents=True) + +with file.open("w") as f: + json.dump(result, f) diff --git a/python/exports/classification/efficientnet_v2_s.py b/python/exports/classification/efficientnet_v2_s.py new file mode 100644 index 0000000..6d9fbd2 --- /dev/null +++ b/python/exports/classification/efficientnet_v2_s.py @@ -0,0 +1,48 @@ +from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights +from torchvision.transforms.functional import to_tensor, resize +import torch +import json +from pathlib import Path +from PIL import Image + +base_dir = Path("models/classification/efficientnet_v2_s") +base_dir.mkdir(parents=True, exist_ok=True) + +model_file = base_dir / "model.onnx" +categories_file = base_dir / "categories.json" + +weights = EfficientNet_V2_S_Weights.DEFAULT +model = efficientnet_v2_s(weights=weights) +model.eval() + +categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] +transforms = weights.transforms() + +with open(categories_file, "w") as f: + json.dump(categories, f) + +onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) +onnx_input = resize(onnx_input, [384, 384]) +onnx_input = transforms(onnx_input) + +torch.onnx.export( + model, + onnx_input, + str(model_file), + verbose=False, + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, + export_params=True, +) + +expected_output: torch.Tensor = model(onnx_input) +expected_output = expected_output.softmax(dim=1) + +result = dict(zip(categories, expected_output[0].tolist())) + +file = Path("test/assets/results/classification/efficientnet_v2_s.json") +file.parent.mkdir(exist_ok=True, parents=True) + +with file.open("w") as f: + json.dump(result, f) diff --git a/python/exports/classification/squeezenet1_1.py b/python/exports/classification/squeezenet1_1.py new file mode 100644 index 0000000..54b507f --- /dev/null +++ b/python/exports/classification/squeezenet1_1.py @@ -0,0 +1,48 @@ +from torchvision.models import squeezenet1_1, SqueezeNet1_1_Weights +from torchvision.transforms.functional import to_tensor, resize +import torch +import json +from pathlib import Path +from PIL import Image + +base_dir = Path("models/classification/squeezenet1_1") +base_dir.mkdir(parents=True, exist_ok=True) + +model_file = base_dir / "model.onnx" +categories_file = base_dir / "categories.json" + +weights = SqueezeNet1_1_Weights.DEFAULT +model = squeezenet1_1(weights=weights) +model.eval() + +categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] +transforms = weights.transforms() + +with open(categories_file, "w") as f: + json.dump(categories, f) + +onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) +onnx_input = resize(onnx_input, [224, 224]) +onnx_input = transforms(onnx_input) + +torch.onnx.export( + model, + onnx_input, + str(model_file), + verbose=False, + input_names=["input"], + output_names=["output"], + dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, + export_params=True, +) + +expected_output: torch.Tensor = model(onnx_input) +expected_output = expected_output.softmax(dim=1) + +result = dict(zip(categories, expected_output[0].tolist())) + +file = Path("test/assets/results/classification/squeezenet1_1.json") +file.parent.mkdir(exist_ok=True, parents=True) + +with file.open("w") as f: + json.dump(result, f) diff --git a/test/assets/results/classification/efficientnet_v2_l.json b/test/assets/results/classification/efficientnet_v2_l.json new file mode 100644 index 0000000..7b17c1e --- /dev/null +++ b/test/assets/results/classification/efficientnet_v2_l.json @@ -0,0 +1 @@ +{"tench": 4.2528696212684736e-05, "goldfish": 5.480377876665443e-05, "great_white_shark": 6.552657578140497e-05, "tiger_shark": 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1.4392227687398673e-10, "carbonara": 8.055891023706074e-12, "chocolate_sauce": 7.959813363989099e-10, "dough": 4.321668023443692e-11, "meat_loaf": 1.7600349996871323e-11, "pizza": 4.9489419506887344e-11, "potpie": 3.0324489447286496e-11, "burrito": 1.0218711432585792e-09, "red_wine": 1.3523793640501935e-08, "espresso": 3.293073799759405e-11, "cup": 8.630903494122322e-07, "eggnog": 2.8421717956916837e-07, "alp": 5.68638940188354e-13, "bubble": 1.6790810519751176e-08, "cliff": 2.4840138279635937e-10, "coral_reef": 2.487697825515056e-10, "geyser": 1.382813568512331e-12, "lakeside": 5.989111290238824e-11, "promontory": 1.4093638365728078e-12, "sandbar": 1.5645663931085352e-10, "seashore": 7.591077766377907e-10, "valley": 1.3175736543469263e-12, "volcano": 3.7982979391934513e-13, "ballplayer": 2.1473649247649718e-11, "groom": 6.2485749907637e-10, "scuba_diver": 2.5898350304587314e-12, "rapeseed": 1.6124557244978632e-10, "daisy": 1.2044890258799956e-11, "yellow_lady's_slipper": 3.4347601152173723e-11, "corn": 5.481978178067948e-07, "acorn": 3.193484587882267e-08, "hip": 3.6400008007753115e-11, "buckeye": 5.2242392634127555e-09, "coral_fungus": 2.239148970406557e-12, "agaric": 2.6264225169714273e-12, "gyromitra": 8.30220094061307e-12, "stinkhorn": 3.1925861954107404e-10, "earthstar": 2.3212485752988066e-11, "hen-of-the-woods": 6.670229906607927e-12, "bolete": 1.7389095371966867e-11, "ear": 2.987068725701647e-08, "toilet_tissue": 1.5202968484118173e-07} \ No newline at end of file diff --git a/test/ex_vision/classification/efficientnet_v2_l_test.exs b/test/ex_vision/classification/efficientnet_v2_l_test.exs new file mode 100644 index 0000000..8468c75 --- /dev/null +++ b/test/ex_vision/classification/efficientnet_v2_l_test.exs @@ -0,0 +1,18 @@ +defmodule ExVision.Classification.EfficientNet_V2_L_Test do + @moduledoc false + use ExVision.Model.Case, module: ExVision.Classification.EfficientNet_V2_L + use ExVision.TestUtils + + @expected_result "test/assets/results/classification/efficientnet_v2_l.json" + |> File.read!() + |> Jason.decode!() + |> Map.new(fn {k, v} -> {ExVision.Utils.normalize_category_name(k), v} end) + + @impl true + def test_inference_result(result) do + assert_float_dicts_equal(@expected_result, result) + + top_result = Enum.max_by(result, &elem(&1, 1)) + assert {:egyptian_cat, _pred} = top_result + end +end diff --git a/test/ex_vision/classification/efficientnet_v2_m_test.exs b/test/ex_vision/classification/efficientnet_v2_m_test.exs new file mode 100644 index 0000000..4db3f1b --- /dev/null +++ b/test/ex_vision/classification/efficientnet_v2_m_test.exs @@ -0,0 +1,18 @@ +defmodule ExVision.Classification.EfficientNet_V2_M_Test do + @moduledoc false + use ExVision.Model.Case, module: ExVision.Classification.EfficientNet_V2_M + use ExVision.TestUtils + + @expected_result "test/assets/results/classification/efficientnet_v2_m.json" + |> File.read!() + |> Jason.decode!() + |> Map.new(fn {k, v} -> {ExVision.Utils.normalize_category_name(k), v} end) + + @impl true + def test_inference_result(result) do + assert_float_dicts_equal(@expected_result, result) + + top_result = Enum.max_by(result, &elem(&1, 1)) + assert {:egyptian_cat, _pred} = top_result + end +end diff --git a/test/ex_vision/classification/efficientnet_v2_s_test.exs b/test/ex_vision/classification/efficientnet_v2_s_test.exs new file mode 100644 index 0000000..0a51cd5 --- /dev/null +++ b/test/ex_vision/classification/efficientnet_v2_s_test.exs @@ -0,0 +1,18 @@ +defmodule ExVision.Classification.EfficientNet_V2_S_Test do + @moduledoc false + use ExVision.Model.Case, module: ExVision.Classification.EfficientNet_V2_S + use ExVision.TestUtils + + @expected_result "test/assets/results/classification/efficientnet_v2_s.json" + |> File.read!() + |> Jason.decode!() + |> Map.new(fn {k, v} -> {ExVision.Utils.normalize_category_name(k), v} end) + + @impl true + def test_inference_result(result) do + assert_float_dicts_equal(@expected_result, result) + + top_result = Enum.max_by(result, &elem(&1, 1)) + assert {:egyptian_cat, _pred} = top_result + end +end diff --git a/test/ex_vision/classification/squeezenet1_1_test.exs b/test/ex_vision/classification/squeezenet1_1_test.exs new file mode 100644 index 0000000..c92fb53 --- /dev/null +++ b/test/ex_vision/classification/squeezenet1_1_test.exs @@ -0,0 +1,18 @@ +defmodule ExVision.Classification.SqueezeNet1_1_Test do + @moduledoc false + use ExVision.Model.Case, module: ExVision.Classification.SqueezeNet1_1 + use ExVision.TestUtils + + @expected_result "test/assets/results/classification/squeezenet1_1.json" + |> File.read!() + |> Jason.decode!() + |> Map.new(fn {k, v} -> {ExVision.Utils.normalize_category_name(k), v} end) + + @impl true + def test_inference_result(result) do + assert_float_dicts_equal(@expected_result, result, 0.21) + + top_result = Enum.max_by(result, &elem(&1, 1)) + assert {:egyptian_cat, _pred} = top_result + end +end From 8c52dc732f8a633c7aea75a52210d71ed055b6f2 Mon Sep 17 00:00:00 2001 From: Jakub Mroz <115979017+jakmro@users.noreply.github.com> Date: Wed, 19 Jun 2024 13:26:52 +0200 Subject: [PATCH 2/4] Change version to 0.2.0 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9d0008f..5f238e0 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ The package can be installed by adding `ex_vision` to your list of dependencies ```elixir def deps do [ - {:ex_vision, "~> 0.1.0"} + {:ex_vision, "~> 0.2.0"} ] end ``` From df4ff4b8255838c82e9c7ce6c9dbe42ef0248bdb Mon Sep 17 00:00:00 2001 From: jakmro Date: Thu, 20 Jun 2024 17:59:01 +0200 Subject: [PATCH 3/4] Reduce code duplication --- .../classification/efficientnet_v2_l.ex | 19 +---- .../classification/efficientnet_v2_m.ex | 19 +---- .../classification/efficientnet_v2_s.ex | 19 +---- .../classification/generic_classifier.ex | 30 +++++++ .../classification/mobilenet_v3_small.ex | 19 +---- lib/ex_vision/classification/squeezenet1_1.ex | 19 +---- python/exports/classification.py | 80 +++++++++++++++++++ .../classification/efficientnet_v2_l.py | 48 ----------- .../classification/efficientnet_v2_m.py | 48 ----------- .../classification/efficientnet_v2_s.py | 48 ----------- .../exports/classification/squeezenet1_1.py | 48 ----------- python/exports/mobilenetv3.py | 53 ------------ .../classification/mobilenet_v3_small.json | 1 + .../results/classification/mobilenetv3.json | 1 - .../mobilenet_v3_small_test.exs | 2 +- 15 files changed, 117 insertions(+), 337 deletions(-) create mode 100644 lib/ex_vision/classification/generic_classifier.ex create mode 100644 python/exports/classification.py delete mode 100644 python/exports/classification/efficientnet_v2_l.py delete mode 100644 python/exports/classification/efficientnet_v2_m.py delete mode 100644 python/exports/classification/efficientnet_v2_s.py delete mode 100644 python/exports/classification/squeezenet1_1.py delete mode 100644 python/exports/mobilenetv3.py create mode 100644 test/assets/results/classification/mobilenet_v3_small.json delete mode 100644 test/assets/results/classification/mobilenetv3.json diff --git a/lib/ex_vision/classification/efficientnet_v2_l.ex b/lib/ex_vision/classification/efficientnet_v2_l.ex index 75eda80..a4e86db 100644 --- a/lib/ex_vision/classification/efficientnet_v2_l.ex +++ b/lib/ex_vision/classification/efficientnet_v2_l.ex @@ -8,13 +8,7 @@ defmodule ExVision.Classification.EfficientNet_V2_L do model: "efficientnet_v2_l_classifier.onnx", categories: "priv/categories/imagenet_v2_categories.json" - require Bunch.Typespec - alias ExVision.Utils - - @typedoc """ - A type describing the output of EfficientNet_V2_L classifier as a mapping of category to probability. - """ - @type output_t() :: %{category_t() => number()} + use ExVision.Classification.GenericClassifier @impl true def preprocessing(image, _metadata) do @@ -26,15 +20,4 @@ defmodule ExVision.Classification.EfficientNet_V2_L do channels: :first ) end - - @impl true - def postprocessing(%{"output" => scores}, _metadata) do - scores - |> Nx.backend_transfer() - |> Nx.flatten() - |> Utils.softmax() - |> Nx.to_flat_list() - |> then(&Enum.zip(categories(), &1)) - |> Map.new() - end end diff --git a/lib/ex_vision/classification/efficientnet_v2_m.ex b/lib/ex_vision/classification/efficientnet_v2_m.ex index c7f09b6..83ccafd 100644 --- a/lib/ex_vision/classification/efficientnet_v2_m.ex +++ b/lib/ex_vision/classification/efficientnet_v2_m.ex @@ -8,13 +8,7 @@ defmodule ExVision.Classification.EfficientNet_V2_M do model: "efficientnet_v2_m_classifier.onnx", categories: "priv/categories/imagenet_v2_categories.json" - require Bunch.Typespec - alias ExVision.Utils - - @typedoc """ - A type describing the output of EfficientNet_V2_M classifier as a mapping of category to probability. - """ - @type output_t() :: %{category_t() => number()} + use ExVision.Classification.GenericClassifier @impl true def preprocessing(image, _metadata) do @@ -26,15 +20,4 @@ defmodule ExVision.Classification.EfficientNet_V2_M do channels: :first ) end - - @impl true - def postprocessing(%{"output" => scores}, _metadata) do - scores - |> Nx.backend_transfer() - |> Nx.flatten() - |> Utils.softmax() - |> Nx.to_flat_list() - |> then(&Enum.zip(categories(), &1)) - |> Map.new() - end end diff --git a/lib/ex_vision/classification/efficientnet_v2_s.ex b/lib/ex_vision/classification/efficientnet_v2_s.ex index 4e1fe7f..277f9ce 100644 --- a/lib/ex_vision/classification/efficientnet_v2_s.ex +++ b/lib/ex_vision/classification/efficientnet_v2_s.ex @@ -8,13 +8,7 @@ defmodule ExVision.Classification.EfficientNet_V2_S do model: "efficientnet_v2_s_classifier.onnx", categories: "priv/categories/imagenet_v2_categories.json" - require Bunch.Typespec - alias ExVision.Utils - - @typedoc """ - A type describing the output of EfficientNet_V2_S classifier as a mapping of category to probability. - """ - @type output_t() :: %{category_t() => number()} + use ExVision.Classification.GenericClassifier @impl true def preprocessing(image, _metadata) do @@ -26,15 +20,4 @@ defmodule ExVision.Classification.EfficientNet_V2_S do channels: :first ) end - - @impl true - def postprocessing(%{"output" => scores}, _metadata) do - scores - |> Nx.backend_transfer() - |> Nx.flatten() - |> Utils.softmax() - |> Nx.to_flat_list() - |> then(&Enum.zip(categories(), &1)) - |> Map.new() - end end diff --git a/lib/ex_vision/classification/generic_classifier.ex b/lib/ex_vision/classification/generic_classifier.ex new file mode 100644 index 0000000..64b8b0a --- /dev/null +++ b/lib/ex_vision/classification/generic_classifier.ex @@ -0,0 +1,30 @@ +defmodule ExVision.Classification.GenericClassifier do + alias ExVision.Utils + + def postprocessing(%{"output" => scores}, _metadata, categories) do + scores + |> Nx.backend_transfer() + |> Nx.flatten() + |> Utils.softmax() + |> Nx.to_flat_list() + |> then(&Enum.zip(categories, &1)) + |> Map.new() + end + + defmacro __using__(_opts) do + quote do + @typedoc """ + A type describing the output of a classification model as a mapping of category to probability. + """ + @type output_t() :: %{category_t() => number()} + + @impl true + @spec postprocessing(map(), ExVision.Types.ImageMetadata.t()) :: output_t() + def postprocessing(output, metadata) do + ExVision.Classification.GenericClassifier.postprocessing(output, metadata, categories()) + end + + defoverridable postprocessing: 2 + end + end +end diff --git a/lib/ex_vision/classification/mobilenet_v3_small.ex b/lib/ex_vision/classification/mobilenet_v3_small.ex index ac30093..2c4d207 100644 --- a/lib/ex_vision/classification/mobilenet_v3_small.ex +++ b/lib/ex_vision/classification/mobilenet_v3_small.ex @@ -8,13 +8,7 @@ defmodule ExVision.Classification.MobileNetV3Small do model: "mobilenetv3small-classifier.onnx", categories: "priv/categories/imagenet_v2_categories.json" - require Bunch.Typespec - alias ExVision.Utils - - @typedoc """ - A type describing the output of MobileNetV3 classifier as a mapping of category to probability. - """ - @type output_t() :: %{category_t() => number()} + use ExVision.Classification.GenericClassifier @impl true def preprocessing(image, _metadata) do @@ -26,15 +20,4 @@ defmodule ExVision.Classification.MobileNetV3Small do channels: :first ) end - - @impl true - def postprocessing(%{"output" => scores}, _metadata) do - scores - |> Nx.backend_transfer() - |> Nx.flatten() - |> Utils.softmax() - |> Nx.to_flat_list() - |> then(&Enum.zip(categories(), &1)) - |> Map.new() - end end diff --git a/lib/ex_vision/classification/squeezenet1_1.ex b/lib/ex_vision/classification/squeezenet1_1.ex index af43a61..6865847 100644 --- a/lib/ex_vision/classification/squeezenet1_1.ex +++ b/lib/ex_vision/classification/squeezenet1_1.ex @@ -8,13 +8,7 @@ defmodule ExVision.Classification.SqueezeNet1_1 do model: "squeezenet1_1_classifier.onnx", categories: "priv/categories/imagenet_v2_categories.json" - require Bunch.Typespec - alias ExVision.Utils - - @typedoc """ - A type describing the output of SqueezeNet1_1 classifier as a mapping of category to probability. - """ - @type output_t() :: %{category_t() => number()} + use ExVision.Classification.GenericClassifier @impl true def preprocessing(image, _metadata) do @@ -26,15 +20,4 @@ defmodule ExVision.Classification.SqueezeNet1_1 do channels: :first ) end - - @impl true - def postprocessing(%{"output" => scores}, _metadata) do - scores - |> Nx.backend_transfer() - |> Nx.flatten() - |> Utils.softmax() - |> Nx.to_flat_list() - |> then(&Enum.zip(categories(), &1)) - |> Map.new() - end end diff --git a/python/exports/classification.py b/python/exports/classification.py new file mode 100644 index 0000000..f392c24 --- /dev/null +++ b/python/exports/classification.py @@ -0,0 +1,80 @@ +import argparse +from torchvision.transforms.functional import to_tensor, resize +import torch +import json +from pathlib import Path +from PIL import Image + + +def export(model_builder, Model_Weights, input_shape): + base_dir = Path(f"models/classification/{model_builder.__name__}") + base_dir.mkdir(parents=True, exist_ok=True) + + model_file = base_dir / "model.onnx" + categories_file = base_dir / "categories.json" + + weights = Model_Weights.DEFAULT + model = model_builder(weights=weights) + model.eval() + + categories = [x.lower().replace(" ", "_") + for x in weights.meta["categories"]] + transforms = weights.transforms() + + with open(categories_file, "w") as f: + json.dump(categories, f) + + onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) + onnx_input = resize(onnx_input, input_shape) + onnx_input = transforms(onnx_input) + + torch.onnx.export( + model, + onnx_input, + str(model_file), + verbose=False, + input_names=["input"], + output_names=["output"], + dynamic_axes={ + "input": {0: "batch_size"}, + "output": {0: "batch_size"} + }, + export_params=True, + ) + + expected_output: torch.Tensor = model(onnx_input) + expected_output = expected_output.softmax(dim=1) + + result = dict(zip(categories, expected_output[0].tolist())) + + file = Path( + f"test/assets/results/classification/{model_builder.__name__}.json" + ) + file.parent.mkdir(exist_ok=True, parents=True) + + with file.open("w") as f: + json.dump(result, f) + + +parser = argparse.ArgumentParser() +parser.add_argument("model") +args = parser.parse_args() + +match(args.model): + case "mobilenet_v3_small": + from torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights + export(mobilenet_v3_small, MobileNet_V3_Small_Weights, [224, 224]) + case "efficientnet_v2_s": + from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights + export(efficientnet_v2_s, EfficientNet_V2_S_Weights, [384, 384]) + case "efficientnet_v2_m": + from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights + export(efficientnet_v2_m, EfficientNet_V2_M_Weights, [480, 480]) + case "efficientnet_v2_l": + from torchvision.models import efficientnet_v2_l, EfficientNet_V2_L_Weights + export(efficientnet_v2_l, EfficientNet_V2_L_Weights, [480, 480]) + case "squeezenet1_1": + from torchvision.models import squeezenet1_1, SqueezeNet1_1_Weights + export(squeezenet1_1, SqueezeNet1_1_Weights, [224, 224]) + case _: + print("Model not found") diff --git a/python/exports/classification/efficientnet_v2_l.py b/python/exports/classification/efficientnet_v2_l.py deleted file mode 100644 index 80ab3cb..0000000 --- a/python/exports/classification/efficientnet_v2_l.py +++ /dev/null @@ -1,48 +0,0 @@ -from torchvision.models import efficientnet_v2_l, EfficientNet_V2_L_Weights -from torchvision.transforms.functional import to_tensor, resize -import torch -import json -from pathlib import Path -from PIL import Image - -base_dir = Path("models/classification/efficientnet_v2_l") -base_dir.mkdir(parents=True, exist_ok=True) - -model_file = base_dir / "model.onnx" -categories_file = base_dir / "categories.json" - -weights = EfficientNet_V2_L_Weights.DEFAULT -model = efficientnet_v2_l(weights=weights) -model.eval() - -categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] -transforms = weights.transforms() - -with open(categories_file, "w") as f: - json.dump(categories, f) - -onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) -onnx_input = resize(onnx_input, [480, 480]) -onnx_input = transforms(onnx_input) - -torch.onnx.export( - model, - onnx_input, - str(model_file), - verbose=False, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - export_params=True, -) - -expected_output: torch.Tensor = model(onnx_input) -expected_output = expected_output.softmax(dim=1) - -result = dict(zip(categories, expected_output[0].tolist())) - -file = Path("test/assets/results/classification/efficientnet_v2_l.json") -file.parent.mkdir(exist_ok=True, parents=True) - -with file.open("w") as f: - json.dump(result, f) diff --git a/python/exports/classification/efficientnet_v2_m.py b/python/exports/classification/efficientnet_v2_m.py deleted file mode 100644 index 0912fe5..0000000 --- a/python/exports/classification/efficientnet_v2_m.py +++ /dev/null @@ -1,48 +0,0 @@ -from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights -from torchvision.transforms.functional import to_tensor, resize -import torch -import json -from pathlib import Path -from PIL import Image - -base_dir = Path("models/classification/efficientnet_v2_m") -base_dir.mkdir(parents=True, exist_ok=True) - -model_file = base_dir / "model.onnx" -categories_file = base_dir / "categories.json" - -weights = EfficientNet_V2_M_Weights.DEFAULT -model = efficientnet_v2_m(weights=weights) -model.eval() - -categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] -transforms = weights.transforms() - -with open(categories_file, "w") as f: - json.dump(categories, f) - -onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) -onnx_input = resize(onnx_input, [480, 480]) -onnx_input = transforms(onnx_input) - -torch.onnx.export( - model, - onnx_input, - str(model_file), - verbose=False, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - export_params=True, -) - -expected_output: torch.Tensor = model(onnx_input) -expected_output = expected_output.softmax(dim=1) - -result = dict(zip(categories, expected_output[0].tolist())) - -file = Path("test/assets/results/classification/efficientnet_v2_m.json") -file.parent.mkdir(exist_ok=True, parents=True) - -with file.open("w") as f: - json.dump(result, f) diff --git a/python/exports/classification/efficientnet_v2_s.py b/python/exports/classification/efficientnet_v2_s.py deleted file mode 100644 index 6d9fbd2..0000000 --- a/python/exports/classification/efficientnet_v2_s.py +++ /dev/null @@ -1,48 +0,0 @@ -from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights -from torchvision.transforms.functional import to_tensor, resize -import torch -import json -from pathlib import Path -from PIL import Image - -base_dir = Path("models/classification/efficientnet_v2_s") -base_dir.mkdir(parents=True, exist_ok=True) - -model_file = base_dir / "model.onnx" -categories_file = base_dir / "categories.json" - -weights = EfficientNet_V2_S_Weights.DEFAULT -model = efficientnet_v2_s(weights=weights) -model.eval() - -categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] -transforms = weights.transforms() - -with open(categories_file, "w") as f: - json.dump(categories, f) - -onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) -onnx_input = resize(onnx_input, [384, 384]) -onnx_input = transforms(onnx_input) - -torch.onnx.export( - model, - onnx_input, - str(model_file), - verbose=False, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - export_params=True, -) - -expected_output: torch.Tensor = model(onnx_input) -expected_output = expected_output.softmax(dim=1) - -result = dict(zip(categories, expected_output[0].tolist())) - -file = Path("test/assets/results/classification/efficientnet_v2_s.json") -file.parent.mkdir(exist_ok=True, parents=True) - -with file.open("w") as f: - json.dump(result, f) diff --git a/python/exports/classification/squeezenet1_1.py b/python/exports/classification/squeezenet1_1.py deleted file mode 100644 index 54b507f..0000000 --- a/python/exports/classification/squeezenet1_1.py +++ /dev/null @@ -1,48 +0,0 @@ -from torchvision.models import squeezenet1_1, SqueezeNet1_1_Weights -from torchvision.transforms.functional import to_tensor, resize -import torch -import json -from pathlib import Path -from PIL import Image - -base_dir = Path("models/classification/squeezenet1_1") -base_dir.mkdir(parents=True, exist_ok=True) - -model_file = base_dir / "model.onnx" -categories_file = base_dir / "categories.json" - -weights = SqueezeNet1_1_Weights.DEFAULT -model = squeezenet1_1(weights=weights) -model.eval() - -categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] -transforms = weights.transforms() - -with open(categories_file, "w") as f: - json.dump(categories, f) - -onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) -onnx_input = resize(onnx_input, [224, 224]) -onnx_input = transforms(onnx_input) - -torch.onnx.export( - model, - onnx_input, - str(model_file), - verbose=False, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - export_params=True, -) - -expected_output: torch.Tensor = model(onnx_input) -expected_output = expected_output.softmax(dim=1) - -result = dict(zip(categories, expected_output[0].tolist())) - -file = Path("test/assets/results/classification/squeezenet1_1.json") -file.parent.mkdir(exist_ok=True, parents=True) - -with file.open("w") as f: - json.dump(result, f) diff --git a/python/exports/mobilenetv3.py b/python/exports/mobilenetv3.py deleted file mode 100644 index 01cebfe..0000000 --- a/python/exports/mobilenetv3.py +++ /dev/null @@ -1,53 +0,0 @@ -from torchvision.models import mobilenet_v3_small, MobileNet_V3_Small_Weights -from torchvision.io import read_file -from torchvision.transforms.functional import to_tensor, resize -import torch -import json -from pathlib import Path -from PIL import Image - -base_dir = Path("models/classification/mobilenet_v3_small") -base_dir.mkdir(parents=True, exist_ok=True) - -model_file = base_dir / "model.onnx" -categories_file = base_dir / "categories.json" - -weights = MobileNet_V3_Small_Weights.DEFAULT -model = mobilenet_v3_small(weights=weights) -model.eval() - -categories = [x.lower().replace(" ", "_") for x in weights.meta["categories"]] -transforms = weights.transforms() - -print(transforms) - -with open(categories_file, "w") as f: - json.dump(categories, f) - -onnx_input = to_tensor(Image.open("test/assets/cat.jpg")).unsqueeze(0) -onnx_input = resize(onnx_input, [224, 224]) -onnx_input = transforms(onnx_input) - -torch.onnx.export( - model, - onnx_input, - str(model_file), - verbose=False, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - export_params=True, -) - -expected_output: torch.Tensor = model(onnx_input) -expected_output = expected_output.softmax(dim=1) -print(expected_output.shape) -print(expected_output) - -result = dict(zip(categories, expected_output[0].tolist())) - -file = Path("test/assets/results/classification/mobilenetv3.json") -file.parent.mkdir(exist_ok=True, parents=True) - -with file.open("w") as f: - json.dump(result, f) diff --git a/test/assets/results/classification/mobilenet_v3_small.json b/test/assets/results/classification/mobilenet_v3_small.json new file mode 100644 index 0000000..d111007 --- /dev/null +++ b/test/assets/results/classification/mobilenet_v3_small.json @@ -0,0 +1 @@ +{"tench": 5.8664504365424364e-08, "goldfish": 4.816718401912112e-08, "great_white_shark": 1.2086026623947532e-09, "tiger_shark": 2.34747670191382e-08, "hammerhead": 1.1246121367491924e-08, "electric_ray": 1.8671850909868226e-07, "stingray": 1.677486594076072e-08, "cock": 3.2288394180568503e-10, "hen": 1.8770294207826055e-09, "ostrich": 2.9730347961276493e-08, "brambling": 8.380984795053337e-09, "goldfinch": 2.872327486969084e-09, "house_finch": 5.591240803681785e-09, "junco": 7.022888937058269e-10, "indigo_bunting": 5.53919410339887e-10, "robin": 5.774040090500421e-08, "bulbul": 1.461124732315966e-08, "jay": 8.135250251228854e-10, "magpie": 1.1978233960263651e-09, "chickadee": 6.819012368652899e-12, "water_ouzel": 7.226703124807443e-10, "kite": 4.854070567716917e-08, "bald_eagle": 1.5199595748693895e-10, "vulture": 3.4006686355780857e-10, "great_grey_owl": 1.8509696531054942e-07, "european_fire_salamander": 8.397056661113567e-11, "common_newt": 4.3407220040592165e-09, "eft": 2.9866911166465115e-09, "spotted_salamander": 2.071193438979435e-09, "axolotl": 7.693317982671033e-09, "bullfrog": 2.3859578845986107e-07, "tree_frog": 5.78727906486165e-07, "tailed_frog": 5.704077210566538e-08, "loggerhead": 4.678123488588426e-08, "leatherback_turtle": 2.6775143879831376e-08, "mud_turtle": 4.18702317261932e-09, "terrapin": 1.0537851125036468e-07, "box_turtle": 2.1065562627597956e-09, "banded_gecko": 1.8970367818837985e-06, "common_iguana": 3.767977485580332e-09, "american_chameleon": 6.221404280637444e-09, "whiptail": 2.7335072871892407e-08, "agama": 4.291303146164438e-10, "frilled_lizard": 3.742236298620583e-09, "alligator_lizard": 1.1979610192724977e-08, "gila_monster": 3.566547751576721e-11, "green_lizard": 1.0963317720324994e-07, "african_chameleon": 1.9250967397965724e-07, "komodo_dragon": 3.8389810774752675e-10, "african_crocodile": 4.38807123970264e-09, "american_alligator": 1.7080673542224645e-09, "triceratops": 2.30581509441663e-09, "thunder_snake": 6.724206258468257e-08, "ringneck_snake": 2.5971400585689253e-09, "hognose_snake": 7.947225100224387e-09, "green_snake": 1.6987291928671766e-06, "king_snake": 3.2655809167891903e-09, "garter_snake": 7.162363147017459e-09, "water_snake": 1.5547325649123422e-09, "vine_snake": 1.049719958245987e-05, "night_snake": 5.935579849847272e-08, "boa_constrictor": 6.858568712964086e-10, "rock_python": 1.8641078014525192e-08, "indian_cobra": 4.9631950105322176e-08, "green_mamba": 1.8793448361975607e-06, "sea_snake": 1.9920646465720893e-08, "horned_viper": 1.2203464621052262e-06, "diamondback": 6.457066348275475e-08, "sidewinder": 3.0965023256612767e-07, "trilobite": 2.1532031713178412e-09, "harvestman": 2.566583834351377e-09, "scorpion": 7.766622900362563e-09, "black_and_gold_garden_spider": 3.6358895894039733e-09, "barn_spider": 5.919858381275844e-08, "garden_spider": 4.358642335944296e-09, "black_widow": 4.748780257912699e-10, "tarantula": 2.8233970716939893e-10, "wolf_spider": 5.318881779459161e-09, "tick": 1.3652106112260753e-08, "centipede": 1.26866950722615e-07, "black_grouse": 4.572196132812678e-08, "ptarmigan": 2.927035502864328e-09, "ruffed_grouse": 2.729327661654679e-07, "prairie_chicken": 1.2752625480061397e-05, "peacock": 2.2107850838892773e-07, "quail": 2.9069948670468193e-09, "partridge": 2.2212843475699628e-07, "african_grey": 3.2174171660237505e-10, "macaw": 2.7431582338977023e-07, "sulphur-crested_cockatoo": 2.5370922029921417e-10, "lorikeet": 7.535473400821502e-08, "coucal": 5.790122159510247e-09, "bee_eater": 1.0890321533452152e-07, "hornbill": 2.069628357581621e-10, "hummingbird": 2.876532789741759e-07, "jacamar": 4.635808181774337e-06, "toucan": 5.045307460704862e-08, "drake": 3.8500061805279984e-08, "red-breasted_merganser": 7.805179080833113e-08, "goose": 1.5095306116208462e-09, "black_swan": 1.1478532013775578e-10, "tusker": 4.971138500842187e-10, "echidna": 4.6128881270313116e-10, "platypus": 1.6490369958432893e-08, "wallaby": 1.4196412223554944e-07, "koala": 1.3247952734829482e-09, "wombat": 8.551168662052078e-07, "jellyfish": 3.812409588022092e-08, "sea_anemone": 1.651872736374571e-07, "brain_coral": 1.8144885416404577e-07, "flatworm": 1.076167932545502e-09, "nematode": 4.827894372283481e-06, "conch": 8.792453343176021e-08, "snail": 1.295608687001959e-08, "slug": 3.020769412387381e-08, "sea_slug": 4.659049679389682e-08, "chiton": 4.0333847373119625e-09, "chambered_nautilus": 1.3748765681498298e-08, "dungeness_crab": 4.155857880050462e-09, "rock_crab": 8.390593220219955e-10, "fiddler_crab": 2.0341353601516232e-11, "king_crab": 6.698360754953114e-10, "american_lobster": 1.236360347434129e-09, "spiny_lobster": 4.075567383576839e-10, "crayfish": 8.107650550925882e-09, "hermit_crab": 6.089898363370594e-09, "isopod": 2.3561025130902635e-08, "white_stork": 9.152526826705643e-12, "black_stork": 4.5905504397758534e-10, "spoonbill": 7.849327438025355e-11, "flamingo": 5.78270265005898e-10, "little_blue_heron": 1.3132270271221103e-10, "american_egret": 8.735859813313596e-10, "bittern": 6.837138410986654e-09, "crane_bird": 8.288202762996022e-11, "limpkin": 3.3729305459750947e-10, "european_gallinule": 4.650710483389275e-09, "american_coot": 1.3330483383811043e-09, "bustard": 8.087479130836073e-09, "ruddy_turnstone": 8.537156537258284e-10, "red-backed_sandpiper": 3.0435018949503956e-09, "redshank": 4.485579407909057e-10, "dowitcher": 8.040083876448278e-11, "oystercatcher": 1.7321012679705206e-10, "pelican": 4.452376314856288e-11, "king_penguin": 4.366313977044456e-09, "albatross": 2.785807695637743e-10, "grey_whale": 5.385798917956208e-09, "killer_whale": 1.0894481761170027e-08, "dugong": 1.5154366650449447e-08, "sea_lion": 8.609442403439971e-08, "chihuahua": 4.360981620266102e-06, "japanese_spaniel": 1.1778311659327301e-07, "maltese_dog": 2.4057777991970397e-08, "pekinese": 5.568701766378581e-08, "shih-tzu": 2.90366042321466e-08, "blenheim_spaniel": 3.247635715908359e-09, "papillon": 5.117540879950866e-08, "toy_terrier": 2.0206746853546065e-07, "rhodesian_ridgeback": 4.669349351615892e-09, "afghan_hound": 1.9614479485063896e-10, "basset": 8.855557953602045e-10, "beagle": 7.528286438684972e-09, "bloodhound": 9.535173761676674e-10, "bluetick": 4.224218752568731e-09, "black-and-tan_coonhound": 1.3428578249374823e-09, "walker_hound": 1.1591381188225114e-09, "english_foxhound": 2.950380828536936e-09, "redbone": 1.989983644534732e-08, "borzoi": 1.468073440591411e-10, "irish_wolfhound": 4.187815960676744e-08, "italian_greyhound": 3.242135448999761e-08, "whippet": 1.2446967012635923e-08, "ibizan_hound": 1.6466712793317129e-07, "norwegian_elkhound": 1.5802104069706502e-09, "otterhound": 7.423229142489163e-09, "saluki": 9.96968396727027e-10, "scottish_deerhound": 6.390064033467979e-09, "weimaraner": 2.1911750636149918e-08, "staffordshire_bullterrier": 7.664636147985959e-10, "american_staffordshire_terrier": 5.610445441561751e-10, "bedlington_terrier": 2.285982292349331e-09, "border_terrier": 2.3643983126930834e-07, "kerry_blue_terrier": 3.246529090006334e-08, "irish_terrier": 3.8040855798726625e-08, "norfolk_terrier": 4.479188930872624e-08, "norwich_terrier": 8.40985237005043e-08, "yorkshire_terrier": 1.8027837711542816e-07, "wire-haired_fox_terrier": 3.045630592168891e-07, "lakeland_terrier": 1.7492977733013504e-08, "sealyham_terrier": 1.1906329255850778e-08, "airedale": 2.2749919281750408e-08, "cairn": 2.7017074799573493e-08, "australian_terrier": 9.962609510694165e-07, "dandie_dinmont": 3.582773189236832e-08, "boston_bull": 1.0076656309365717e-07, "miniature_schnauzer": 9.141083268104921e-08, "giant_schnauzer": 1.1858078963200569e-08, "standard_schnauzer": 2.0414601920037967e-07, "scotch_terrier": 4.81116337880394e-08, "tibetan_terrier": 3.034304896232243e-08, "silky_terrier": 6.302587962636608e-08, "soft-coated_wheaten_terrier": 6.165755195297606e-08, "west_highland_white_terrier": 2.4358827843684594e-08, "lhasa": 1.1044894776546244e-08, "flat-coated_retriever": 1.1428511470512603e-09, "curly-coated_retriever": 3.093913569784945e-09, "golden_retriever": 1.2736550480951792e-08, "labrador_retriever": 2.988037195450488e-08, "chesapeake_bay_retriever": 1.929934656530463e-09, "german_short-haired_pointer": 3.600207065801442e-08, "vizsla": 9.786881349782561e-08, "english_setter": 1.514174030603499e-08, "irish_setter": 2.8578133193235544e-09, "gordon_setter": 8.975441501135606e-10, "brittany_spaniel": 6.71085809145211e-09, "clumber": 2.091671547077567e-08, "english_springer": 4.3542694450060537e-10, "welsh_springer_spaniel": 1.6195362828597126e-09, "cocker_spaniel": 7.268371238211557e-09, "sussex_spaniel": 5.470132791174365e-09, "irish_water_spaniel": 8.012287500136495e-10, "kuvasz": 3.3159814893934936e-09, "schipperke": 1.8302567239558698e-09, "groenendael": 3.4937255866118733e-10, "malinois": 3.6637823330742947e-10, "briard": 1.6883159315028706e-09, "kelpie": 1.902179747048649e-08, "komondor": 3.1932758659536375e-09, "old_english_sheepdog": 5.147739567945564e-09, "shetland_sheepdog": 6.070461466833876e-09, "collie": 2.642566387933698e-09, "border_collie": 3.683599869575005e-09, "bouvier_des_flandres": 1.5062671110399606e-08, "rottweiler": 4.71466587992353e-10, "german_shepherd": 5.486161747114693e-09, "doberman": 3.987786101333768e-09, "miniature_pinscher": 1.1547760969676801e-08, "greater_swiss_mountain_dog": 6.381826733736773e-10, "bernese_mountain_dog": 2.139472599083092e-09, "appenzeller": 1.040502883853378e-08, "entlebucher": 3.728044983830614e-09, "boxer": 2.9578521854034534e-09, "bull_mastiff": 1.5907886119492787e-09, "tibetan_mastiff": 4.017290389235484e-10, "french_bulldog": 2.6268736519341473e-08, "great_dane": 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1.1944545796893635e-08, "promontory": 1.6116696865964286e-09, "sandbar": 1.8916262334300882e-08, "seashore": 1.593580734038369e-08, "valley": 1.3737917470280081e-08, "volcano": 1.1469043492695619e-09, "ballplayer": 1.7534361740345616e-09, "groom": 6.494632387443744e-09, "scuba_diver": 1.504477893377043e-07, "rapeseed": 2.4387364305766823e-07, "daisy": 2.839889567951559e-08, "yellow_lady's_slipper": 6.645288763706958e-09, "corn": 1.8926126358564943e-05, "acorn": 2.75540031680066e-07, "hip": 2.4615102844904868e-08, "buckeye": 1.6971775096408237e-07, "coral_fungus": 9.283969149009863e-09, "agaric": 8.453553412834935e-09, "gyromitra": 1.1945616051889374e-08, "stinkhorn": 1.547136418977857e-09, "earthstar": 9.364189423877178e-10, "hen-of-the-woods": 6.841014421610225e-09, "bolete": 5.623681076372122e-09, "ear": 6.835025033069542e-06, "toilet_tissue": 3.042516141249507e-07} \ No newline at end of file diff --git a/test/ex_vision/classification/mobilenet_v3_small_test.exs b/test/ex_vision/classification/mobilenet_v3_small_test.exs index 8617cff..a04c335 100644 --- a/test/ex_vision/classification/mobilenet_v3_small_test.exs +++ b/test/ex_vision/classification/mobilenet_v3_small_test.exs @@ -3,7 +3,7 @@ defmodule ExVision.Classification.MobileNetV3Test do use ExVision.Model.Case, module: ExVision.Classification.MobileNetV3Small use ExVision.TestUtils - @expected_result "test/assets/results/classification/mobilenetv3.json" + @expected_result "test/assets/results/classification/mobilenet_v3_small.json" |> File.read!() |> Jason.decode!() |> Map.new(fn {k, v} -> {ExVision.Utils.normalize_category_name(k), v} end) From 375ffb6f99640333e9b15e44f248c2ce87de369d Mon Sep 17 00:00:00 2001 From: jakmro Date: Thu, 20 Jun 2024 18:52:07 +0200 Subject: [PATCH 4/4] Improve code readability --- lib/ex_vision/classification/generic_classifier.ex | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/lib/ex_vision/classification/generic_classifier.ex b/lib/ex_vision/classification/generic_classifier.ex index 64b8b0a..5e7b9d1 100644 --- a/lib/ex_vision/classification/generic_classifier.ex +++ b/lib/ex_vision/classification/generic_classifier.ex @@ -1,6 +1,16 @@ defmodule ExVision.Classification.GenericClassifier do + @moduledoc false + + # Contains a default implementation of post processing for TorchVision classifiers + # To use: `use ExVision.Classification.GenericClassifier` + alias ExVision.Utils + alias ExVision.Types.ImageMetadata + + @typep output_t() :: %{atom() => number()} + + @spec postprocessing(map(), ImageMetadata.t(), [atom()]) :: output_t() def postprocessing(%{"output" => scores}, _metadata, categories) do scores |> Nx.backend_transfer()