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..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 ``` @@ -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..a4e86db --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_l.ex @@ -0,0 +1,23 @@ +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" + + use ExVision.Classification.GenericClassifier + + @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 +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..83ccafd --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_m.ex @@ -0,0 +1,23 @@ +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" + + use ExVision.Classification.GenericClassifier + + @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 +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..277f9ce --- /dev/null +++ b/lib/ex_vision/classification/efficientnet_v2_s.ex @@ -0,0 +1,23 @@ +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" + + use ExVision.Classification.GenericClassifier + + @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 +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..5e7b9d1 --- /dev/null +++ b/lib/ex_vision/classification/generic_classifier.ex @@ -0,0 +1,40 @@ +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() + |> 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 new file mode 100644 index 0000000..6865847 --- /dev/null +++ b/lib/ex_vision/classification/squeezenet1_1.ex @@ -0,0 +1,23 @@ +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" + + use ExVision.Classification.GenericClassifier + + @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 +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.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/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/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": 0.00012398134276736528, "hammerhead": 3.4349955967627466e-05, "electric_ray": 4.661731873056851e-05, "stingray": 3.0337419957504608e-05, "cock": 5.004939157515764e-05, "hen": 5.688825331162661e-05, "ostrich": 6.585201481357217e-05, "brambling": 0.00010540689982008189, "goldfinch": 3.4171953302575275e-05, "house_finch": 0.00019901013001799583, "junco": 8.97049976629205e-05, "indigo_bunting": 0.00011461938265711069, "robin": 8.638700819574296e-05, "bulbul": 3.824595842161216e-05, "jay": 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3.0425155728153186e-07} \ No newline at end of file diff --git a/test/assets/results/classification/mobilenetv3.json b/test/assets/results/classification/mobilenetv3.json deleted file mode 100644 index 48caceb..0000000 --- a/test/assets/results/classification/mobilenetv3.json +++ /dev/null @@ -1 +0,0 @@ -{"tench": 5.86646322631168e-08, "goldfish": 4.816728704781781e-08, "great_white_shark": 1.208600663993309e-09, "tiger_shark": 2.3474816757129702e-08, "hammerhead": 1.1246144460130836e-08, "electric_ray": 1.8671907753287087e-07, "stingray": 1.6774871269831237e-08, "cock": 3.228852740733146e-10, "hen": 1.8770585086258507e-09, "ostrich": 2.9730580664022455e-08, "brambling": 8.380954596987067e-09, "goldfinch": 2.8723283751475037e-09, "house_finch": 5.591252794090451e-09, "junco": 7.022930570421693e-10, "indigo_bunting": 5.539247949215564e-10, "robin": 5.7740471959277784e-08, "bulbul": 1.4611278409404349e-08, "jay": 8.135266904574223e-10, "magpie": 1.1978282810076735e-09, "chickadee": 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"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/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) 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