mirror of
https://github.com/immich-app/immich.git
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b7fd5dcb4a
* fixed dev docker compose * updated locustfile * deleted old script, moved comments to locustfile
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
from io import BytesIO
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import json
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from typing import Any
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from locust import HttpUser, events, task
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from locust.env import Environment
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from PIL import Image
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from argparse import ArgumentParser
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byte_image = BytesIO()
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@events.init_command_line_parser.add_listener
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def _(parser: ArgumentParser) -> None:
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parser.add_argument("--tag-model", type=str, default="microsoft/resnet-50")
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parser.add_argument("--clip-model", type=str, default="ViT-B-32::openai")
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parser.add_argument("--face-model", type=str, default="buffalo_l")
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parser.add_argument("--tag-min-score", type=int, default=0.0,
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help="Returns all tags at or above this score. The default returns all tags.")
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parser.add_argument("--face-min-score", type=int, default=0.034,
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help=("Returns all faces at or above this score. The default returns 1 face per request; "
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"setting this to 0 blows up the number of faces to the thousands."))
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parser.add_argument("--image-size", type=int, default=1000)
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@events.test_start.add_listener
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def on_test_start(environment: Environment, **kwargs: Any) -> None:
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global byte_image
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assert environment.parsed_options is not None
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image = Image.new("RGB", (environment.parsed_options.image_size, environment.parsed_options.image_size))
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byte_image = BytesIO()
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image.save(byte_image, format="jpeg")
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class InferenceLoadTest(HttpUser):
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abstract: bool = True
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host = "http://127.0.0.1:3003"
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data: bytes
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headers: dict[str, str] = {"Content-Type": "image/jpg"}
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# re-use the image across all instances in a process
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def on_start(self) -> None:
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global byte_image
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self.data = byte_image.getvalue()
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class ClassificationFormDataLoadTest(InferenceLoadTest):
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@task
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def classify(self) -> None:
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data = [
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("modelName", self.environment.parsed_options.clip_model),
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("modelType", "clip"),
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("options", json.dumps({"minScore": self.environment.parsed_options.tag_min_score})),
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]
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files = {"image": self.data}
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self.client.post("/predict", data=data, files=files)
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class CLIPTextFormDataLoadTest(InferenceLoadTest):
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@task
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def encode_text(self) -> None:
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data = [
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("modelName", self.environment.parsed_options.clip_model),
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("modelType", "clip"),
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("options", json.dumps({"mode": "text"})),
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("text", "test search query")
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]
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self.client.post("/predict", data=data)
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class CLIPVisionFormDataLoadTest(InferenceLoadTest):
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@task
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def encode_image(self) -> None:
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data = [
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("modelName", self.environment.parsed_options.clip_model),
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("modelType", "clip"),
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("options", json.dumps({"mode": "vision"})),
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]
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files = {"image": self.data}
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self.client.post("/predict", data=data, files=files)
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class RecognitionFormDataLoadTest(InferenceLoadTest):
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@task
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def recognize(self) -> None:
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data = [
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("modelName", self.environment.parsed_options.face_model),
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("modelType", "facial-recognition"),
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("options", json.dumps({"minScore": self.environment.parsed_options.face_min_score})),
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]
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files = {"image": self.data}
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self.client.post("/predict", data=data, files=files)
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