87a0ba3db3
* export clip models * export to hf refactored export code * export mclip, general refactoring cleanup * updated conda deps * do transforms with pillow and numpy, add tokenization config to export, general refactoring * moved conda dockerfile, re-added poetry * minor fixes * updated link * updated tests * removed `requirements.txt` from workflow * fixed mimalloc path * removed torchvision * cleaner np typing * review suggestions * update default model name * update test
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import json
|
|
from argparse import ArgumentParser
|
|
from io import BytesIO
|
|
from typing import Any
|
|
|
|
from locust import HttpUser, events, task
|
|
from locust.env import Environment
|
|
from PIL import Image
|
|
|
|
byte_image = BytesIO()
|
|
|
|
|
|
@events.init_command_line_parser.add_listener
|
|
def _(parser: ArgumentParser) -> None:
|
|
parser.add_argument("--tag-model", type=str, default="microsoft/resnet-50")
|
|
parser.add_argument("--clip-model", type=str, default="ViT-B-32::openai")
|
|
parser.add_argument("--face-model", type=str, default="buffalo_l")
|
|
parser.add_argument(
|
|
"--tag-min-score",
|
|
type=int,
|
|
default=0.0,
|
|
help="Returns all tags at or above this score. The default returns all tags.",
|
|
)
|
|
parser.add_argument(
|
|
"--face-min-score",
|
|
type=int,
|
|
default=0.034,
|
|
help=(
|
|
"Returns all faces at or above this score. The default returns 1 face per request; "
|
|
"setting this to 0 blows up the number of faces to the thousands."
|
|
),
|
|
)
|
|
parser.add_argument("--image-size", type=int, default=1000)
|
|
|
|
|
|
@events.test_start.add_listener
|
|
def on_test_start(environment: Environment, **kwargs: Any) -> None:
|
|
global byte_image
|
|
assert environment.parsed_options is not None
|
|
image = Image.new("RGB", (environment.parsed_options.image_size, environment.parsed_options.image_size))
|
|
byte_image = BytesIO()
|
|
image.save(byte_image, format="jpeg")
|
|
|
|
|
|
class InferenceLoadTest(HttpUser):
|
|
abstract: bool = True
|
|
host = "http://127.0.0.1:3003"
|
|
data: bytes
|
|
headers: dict[str, str] = {"Content-Type": "image/jpg"}
|
|
|
|
# re-use the image across all instances in a process
|
|
def on_start(self) -> None:
|
|
global byte_image
|
|
self.data = byte_image.getvalue()
|
|
|
|
|
|
class ClassificationFormDataLoadTest(InferenceLoadTest):
|
|
@task
|
|
def classify(self) -> None:
|
|
data = [
|
|
("modelName", self.environment.parsed_options.clip_model),
|
|
("modelType", "clip"),
|
|
("options", json.dumps({"minScore": self.environment.parsed_options.tag_min_score})),
|
|
]
|
|
files = {"image": self.data}
|
|
self.client.post("/predict", data=data, files=files)
|
|
|
|
|
|
class CLIPTextFormDataLoadTest(InferenceLoadTest):
|
|
@task
|
|
def encode_text(self) -> None:
|
|
data = [
|
|
("modelName", self.environment.parsed_options.clip_model),
|
|
("modelType", "clip"),
|
|
("options", json.dumps({"mode": "text"})),
|
|
("text", "test search query"),
|
|
]
|
|
self.client.post("/predict", data=data)
|
|
|
|
|
|
class CLIPVisionFormDataLoadTest(InferenceLoadTest):
|
|
@task
|
|
def encode_image(self) -> None:
|
|
data = [
|
|
("modelName", self.environment.parsed_options.clip_model),
|
|
("modelType", "clip"),
|
|
("options", json.dumps({"mode": "vision"})),
|
|
]
|
|
files = {"image": self.data}
|
|
self.client.post("/predict", data=data, files=files)
|
|
|
|
|
|
class RecognitionFormDataLoadTest(InferenceLoadTest):
|
|
@task
|
|
def recognize(self) -> None:
|
|
data = [
|
|
("modelName", self.environment.parsed_options.face_model),
|
|
("modelType", "facial-recognition"),
|
|
("options", json.dumps({"minScore": self.environment.parsed_options.face_min_score})),
|
|
]
|
|
files = {"image": self.data}
|
|
|
|
self.client.post("/predict", data=data, files=files)
|