Add EfficientNetV2 and SqueezeNet

.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

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

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

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

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

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

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

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

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

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"},

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")