Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

[util] Add generic torch device class (#6174)

Browse Source 
* introduce new abstraction layer for GPU devices

* add unit test for device abstraction

* fix ruff

* convert TorchDeviceSelect into a stateless class

* move logic to select context-specific execution device into context API

* add mock hardware environments to pytest

* remove dangling mocker fixture

* fix unit test for running on non-CUDA systems

* remove unimplemented get_execution_device() call

* remove autocast precision

* Multiple changes:

1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to
   context.models.get_execution_device().
2. Rename TorchDeviceSelect to TorchDevice
3. Added back the legacy public API defined in `invocation_api`, including
   choose_precision().
4. Added a config file migration script to accommodate removal of precision=autocast.

* add deprecation warnings to choose_torch_device() and choose_precision()

* fix test crash

* remove app_config argument from choose_torch_device() and choose_torch_dtype()

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
This commit is contained in:
Lincoln Stein
2024-04-15 09:12:49 -04:00
committed by GitHub
parent 5a8489bbfc
commit e93f4d632d
20 changed files with 327 additions and 176 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
invokeai/backend/image_util/dw_openpose/wholebody.py
View File

@ -7,7 +7,7 @@ import onnxruntime as ort
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.devices import TorchDevice
from .onnxdet import inference_detector
from .onnxpose import inference_pose
@ -28,9 +28,9 @@ config = get_config()
class Wholebody:
def __init__(self):
device = choose_torch_device()
device = TorchDevice.choose_torch_device()
providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
DET_MODEL_PATH = config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"]
download_with_progress_bar("yolox_l.onnx", DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)