defmodule Evision.Zoo.ImageClassification.PPResNet do
@moduledoc """
Deep Residual Learning for Image Recognition.
"""
@doc """
Default configuration.
"""
@spec default_config :: map()
def default_config do
%{
backend: Evision.Constant.cv_DNN_BACKEND_OPENCV(),
target: Evision.Constant.cv_DNN_TARGET_CPU(),
top_k: 5
}
end
@doc """
Customizable parameters from smart cell.
"""
@spec smartcell_params() :: Evision.Zoo.smartcell_params()
def smartcell_params() do
config = default_config()
[
%{
name: "Image Classifier",
params: [
%{field: "top_k", label: "Top-k", type: :number, default: config[:top_k]},
]
}
]
end
@doc """
Initialize model.
##### Positional arguments
- **model**: `String.t()` | `:default_model` | `:quant_model`.
- When `model` is a string, it will be treat as the path to a weight file
and `init/2` will load the model from it.
- When `model` is either `:default_model` or `:quant_model`, `init/2` will
download and load the predefined model.
##### Keyword arguments
- **cache_dir**: `String.t()`.
Path to the cache directory.
Optional. Defaults to `:filename.basedir(:user_cache, "", ...)`
- **backend**: `integer()`.
Specify the backend.
Optional. Defaults to `Evision.Constant.cv_DNN_BACKEND_OPENCV()`.
- **target**: `integer()`.
Specify the target.
Optional. Defaults to `Evision.Constant.cv_DNN_TARGET_CPU()`.
"""
@spec init(binary | :default_model | :quant_model, nil | Keyword.t()) :: {:error, String.t()} | Evision.DNN.Net.t()
def init(model_path, opts \\ [])
def init(model_type, opts) when model_type in [:default_model, :quant_model] do
{model_url, filename} = model_info(model_type)
{labels_url, labels_filename} = labels()
cache_dir = opts[:cache_dir]
with {:ok, local_path} <- Evision.Zoo.download(model_url, filename, cache_dir: cache_dir),
{:ok, _labels_path} <- Evision.Zoo.download(labels_url, labels_filename, cache_dir: cache_dir) do
init(local_path, opts)
else
{:error, msg} ->
raise msg
end
end
def init(model_path, opts) when is_binary(model_path) do
config = default_config()
backend = opts[:backend] || config[:backend]
target = opts[:target] || config[:target]
net = Evision.DNN.readNet(model_path)
Evision.DNN.Net.setPreferableBackend(net, backend)
Evision.DNN.Net.setPreferableTarget(net, target)
net
end
@doc """
Inference.
##### Positional arguments
- **self**: `Evision.DNN.Net.t()`.
An initialized PPResNet model.
- **image**: `Evision.Mat.maybe_mat_in()`.
Input image.
##### Keyword arguments
- **top_k**: `pos_integer()`.
Get top k results.
Optional. Defaults to `5`.
"""
@spec infer(Evision.DNN.Net.t(), Evision.Mat.maybe_mat_in(), Keyword.t()) :: [number()]
def infer(self=%Evision.DNN.Net{}, image, opts \\ []) do
top_k = opts[:top_k] || 5
inputBlob = preprocess(image)
Evision.DNN.Net.setInput(self, inputBlob)
outputBlob = Evision.DNN.Net.forward(self, outputName: "save_infer_model/scale_0.tmp_0")
outputBlob =
if is_list(outputBlob) do
[outputBlob] = outputBlob
outputBlob
else
outputBlob
end
result = Nx.squeeze(Evision.Mat.to_nx(outputBlob, Nx.BinaryBackend))
# todo: use Evision.Backend when Nx.slice is implemented
Nx.to_flat_list(Nx.argsort(result, direction: :desc)[[0..top_k-1]])
end
@doc """
Get labels.
##### Keyword arguments
- **labels_path**: `String.t()`.
Path to the label file. Defaults to `nil`.
When `labels_path` is `nil`, `get_labels/1` will try to
download the default label file.
- **cache_dir**: `String.t()`.
Path to the cache directory.
Optional. Defaults to `:filename.basedir(:user_cache, "", ...)`
##### Returns
A list of labels.
"""
@spec get_labels(Keyword.t()) :: [binary]
def get_labels(opts \\ []) do
labels_path = opts[:labels_path]
labels_path =
if labels_path == nil do
cache_dir = opts[:cache_dir]
{labels_url, labels_filename} = labels()
with {:ok, labels_path} <- Evision.Zoo.download(labels_url, labels_filename, cache_dir: cache_dir) do
labels_path
else
{:error, msg} ->
raise "Cannot download label file: #{inspect(msg)}"
end
else
labels_path
end
with {:ok, content} <- File.read(labels_path) do
String.split(content, "\n")
else
{:error, msg} ->
raise "Cannot load label file: #{inspect(msg)}"
end
end
@doc """
Preprocessing the input image.
`infer/3` will call this function automatically.
##### Positional arguments
- **image**: `Evision.Mat.maybe_mat_in()`.
Input image.
"""
@spec preprocess(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t()
def preprocess(image) do
image
|> Evision.Mat.as_type(:f32)
|> Evision.resize({224, 224})
|> Evision.Mat.to_nx()
|> Nx.divide(Nx.broadcast(Nx.tensor(255.0, backend: Evision.Backend), {224, 224, 3}))
|> Nx.subtract(mean())
|> Nx.divide(Nx.broadcast(std(), {224, 224, 3}))
|> Evision.Mat.from_nx_2d()
|> Evision.DNN.blobFromImage()
end
defp mean do
Evision.Mat.to_nx(Evision.Mat.literal([[[0.485, 0.456, 0.406]]], :f32))
end
defp std do
Evision.Mat.to_nx(Evision.Mat.literal([[[0.229, 0.224, 0.225]]], :f32))
end
@doc """
Model URL and filename of predefined model.
"""
@spec model_info(:default_model | :quant_model) :: {String.t(), String.t()}
def model_info(:default_model) do
{
"https://github.com/opencv/opencv_zoo/blob/main/models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx?raw=true",
"image_classification_ppresnet50_2022jan.onnx"
}
end
def model_info(:quant_model) do
{
"https://github.com/opencv/opencv_zoo/blob/main/models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8.onnx?raw=true",
"image_classification_ppresnet50_2022jan_int8.onnx"
}
end
@doc """
Default label file URL and filename.
"""
@spec labels :: {String.t(), String.t()}
def labels do
{
"https://raw.githubusercontent.com/opencv/opencv_zoo/1147b6cd24c36c6ba849812a6f4c22a4ec1c5135/models/image_classification_ppresnet/imagenet_labels.txt",
"image_classification_ppresnet50_imagenet_labels.txt"
}
end
@doc """
Docs in smart cell.
"""
@spec docs() :: String.t()
def docs do
@moduledoc
end
@doc """
Smart cell tasks.
A list of variants of the current model.
"""
@spec smartcell_tasks() :: Evision.Zoo.smartcell_tasks()
def smartcell_tasks do
[
%{
id: "pp_resnet",
label: "PP-ResNet",
docs_url: "https://github.com/opencv/opencv_zoo/tree/master/models/image_classification_ppresnet",
params: smartcell_params(),
docs: docs(),
},
%{
id: "pp_resnet_quant",
label: "PP-ResNet (quant)",
docs_url: "https://github.com/opencv/opencv_zoo/tree/master/models/image_classification_ppresnet",
params: smartcell_params(),
docs: docs(),
},
]
end
@doc """
Generate quoted code from smart cell attrs.
"""
@spec to_quoted(map()) :: list()
def to_quoted(attrs) do
{backend, target} = Evision.Zoo.to_quoted_backend_and_target(attrs)
opts = [
backend: backend,
target: target
]
top_k = attrs["top_k"]
model =
case attrs["variant_id"] do
"pp_resnet_quant" ->
:quant_model
_ ->
:default_model
end
[
quote do
model = Evision.Zoo.ImageClassification.PPResNet.init(unquote(model), unquote(opts))
end,
quote do
image_input = Kino.Input.image("Image")
form = Kino.Control.form([image: image_input], submit: "Run")
frame = Kino.Frame.new()
form
|> Kino.Control.stream()
|> Stream.filter(& &1.data.image)
|> Kino.listen(fn %{data: %{image: image}} ->
Kino.Frame.render(frame, Kino.Markdown.new("Running..."))
{height, width} = {image.height, image.width}
image =
image.file_ref
|> Kino.Input.file_path()
|> File.read!()
|> Evision.Mat.from_binary({:u, 8}, height, width, 3)
image_ = Evision.resize(image, {256, 256})[[16..239, 16..239]]
results = Evision.Zoo.ImageClassification.PPResNet.infer(model, image_, top_k: unquote(top_k))
labels = Evision.Zoo.ImageClassification.PPResNet.get_labels()
top_classes = Enum.map(results, &Enum.at(labels, &1))
Kino.Frame.render(frame, Evision.SmartCell.SimpleList.new(top_classes))
end)
Kino.Layout.grid([form, frame], boxed: true, gap: 16)
end
]
end
end
