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Merge branch 'feature/add-another-model-of-the-same-kind' into 'main'
Add FasterRCNN Detector See merge request swm-ai/ex_vision!4
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Original file line number | Diff line number | Diff line change |
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defmodule ExVision.Detection.FasterRCNN_ResNet50_FPN do | ||
@moduledoc """ | ||
FasterRCNN object detector with ResNet50 backbone and FPN detection head, exported from torchvision. | ||
""" | ||
use ExVision.Model.Definition.Ortex, base_dir: "detection/fasterrcnn_resnet50_fpn" | ||
use ExVision.Detection.GenericDetector | ||
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require Logger | ||
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@impl true | ||
def load(options \\ []) do | ||
if Keyword.has_key?(options, :batch_size) do | ||
Logger.warning( | ||
"`:max_batch_size` was given, but this model can only process batch of size 1. Overriding" | ||
) | ||
end | ||
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options | ||
|> Keyword.put(:batch_size, 1) | ||
|> default_model_load() | ||
end | ||
end |
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defmodule ExVision.Detection.GenericDetector do | ||
@moduledoc false | ||
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# Contains a default implementation of pre and post processing for TorchVision detectors | ||
# To use: `use ExVision.Detection.GenericDetector` | ||
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require Logger | ||
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alias ExVision.Types.{BBox, ImageMetadata} | ||
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@typep output_t() :: [BBox.t()] | ||
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@spec preprocessing(Nx.Tensor.t(), ImageMetadata.t()) :: Nx.Tensor.t() | ||
def preprocessing(img, _metadata) do | ||
ExVision.Utils.resize(img, {224, 224}) | ||
end | ||
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@spec postprocessing({Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t()}, ImageMetadata.t(), [atom()]) :: | ||
output_t() | ||
def postprocessing( | ||
%{"boxes" => bboxes, "scores" => scores, "labels" => labels}, | ||
metadata, | ||
categories | ||
) do | ||
{h, w} = metadata.original_size | ||
scale_x = w / 224 | ||
scale_y = h / 224 | ||
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bboxes = | ||
bboxes | ||
|> Nx.multiply(Nx.tensor([scale_x, scale_y, scale_x, scale_y])) | ||
|> Nx.round() | ||
|> Nx.as_type(:s64) | ||
|> Nx.to_list() | ||
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scores = scores |> Nx.to_list() | ||
labels = labels |> Nx.to_list() | ||
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[bboxes, scores, labels] | ||
|> Enum.zip() | ||
|> Enum.filter(fn {_bbox, score, _label} -> score > 0.1 end) | ||
|> Enum.map(fn {[x1, y1, x2, y2], score, label} -> | ||
%BBox{ | ||
x1: x1, | ||
x2: x2, | ||
y1: y1, | ||
y2: y2, | ||
score: score, | ||
label: Enum.at(categories, label) | ||
} | ||
end) | ||
end | ||
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defmacro __using__(_opts) do | ||
quote do | ||
@typedoc """ | ||
A type describing output of `run/2` as a list of a bounding boxes. | ||
Each bounding box describes the location of the object indicated by the `label`. | ||
It also provides the `score` field marking the probability of the prediction. | ||
Bounding boxes with very low scores should most likely be ignored. | ||
""" | ||
@type output_t() :: [BBox.t()] | ||
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@impl true | ||
defdelegate preprocessing(image, metadata), to: ExVision.Detection.GenericDetector | ||
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@impl true | ||
@spec postprocessing(tuple(), ExVision.Types.ImageMetadata.t()) :: output_t() | ||
def postprocessing(output, metadata) do | ||
ExVision.Detection.GenericDetector.postprocessing(output, metadata, categories()) | ||
end | ||
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defoverridable preprocessing: 2, postprocessing: 2 | ||
end | ||
end | ||
end |
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