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helloproject-ai
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This commit is contained in:
yayoimizuha 2024-12-07 23:23:36 +09:00
parent f3bc7e5529
commit 707f6fdf91
2 changed files with 67 additions and 28 deletions
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11
test_script/qsv_decode_test.py
View File

@ -46,15 +46,14 @@ pyplot.figure(figsize=(20, 20), dpi=150)
pyplot.imshow(yuv_plane[2, :, :])
pyplot.show()
pyplot.close("all")
ycbcr_mat = yuv_plane.transpose((1, 2, 0)).reshape((-1, 3)) - [0, 128, 128]
ycbcr_mat = yuv_plane.transpose((1, 2, 0)) - [0, 128, 128]
# print(ycbcr_mat)
transform_matrix = numpy.array([
[1.0, 0.0, 1.5748],
[1.0, -0.1873, -0.4681],
[1.0, 1.8556, 0.0]
[1, 0, 1.402],
[1, -0.344136, -0.714136],
[1, 1.772, 0]
])
rgb_plane = (numpy.clip(numpy.dot(ycbcr_mat, transform_matrix.T), 0, 255)
.reshape(pitch_h, pitch_w, 3).astype(numpy.uint8))
rgb_plane = (numpy.clip(numpy.dot(ycbcr_mat, transform_matrix.T), 0, 255).astype(numpy.uint8))
pyplot.figure(figsize=(20, 20), dpi=150)
pyplot.imshow(rgb_plane)
pyplot.show()

84
test_script/qsv_jpeg_decode.py
View File

@ -1,7 +1,12 @@
import ctypes
import inspect
import json
import math
import os
import warnings
from numpy.f2py.auxfuncs import throw_error
warnings.filterwarnings("ignore", lineno=6, category=UserWarning)
from concurrent.futures.process import ProcessPoolExecutor
from itertools import chain
@ -16,7 +21,7 @@ import tqdm
from PIL import Image
from uuid import uuid4
from onnxruntime import InferenceSession, SessionOptions, GraphOptimizationLevel
from torch import tensor
from torch import tensor, as_strided
import aiofiles
import numpy
import torch
@ -24,6 +29,7 @@ from torchvision.io import decode_jpeg
from asyncio import run, gather, Semaphore
from site import getsitepackages
from rust_retinaface_post_processor import resnet_post_process
from test_ext import decode as qsv_decode
USE_OPENVINO = True
if USE_OPENVINO:
@ -39,7 +45,7 @@ files = []
files_data: dict[str, numpy.ndarray | None] = {}
chunk_size = 16
image_size = 640
device = torch.device("cpu") if torch.xpu.is_available() else exit(-1)
device = torch.device("xpu") if torch.xpu.is_available() else exit(-1)
async def async_read(path: str, semaphore: Semaphore):
@ -78,6 +84,7 @@ def post_processor_shm(shm_name, sizes, batch_size, image_size):
def dec_jpg(f, fn):
# print("USE PILLOW")
_decoded_image = tensor(numpy.array(Image.open(BytesIO(f.tobytes()))).transpose([2, 0, 1]))
_decoded_image = _decoded_image.to(device, torch.float16) / 255
_decoded_image = fn[2](_decoded_image)
@ -85,6 +92,31 @@ def dec_jpg(f, fn):
return fn[1](_decoded_image_resized)
def dec_jpg_qsv(f, fn):
ptr, height, width, pitch = qsv_decode(f.tobytes())
pitch_h = math.ceil(height / 2) * 2
pitch_w = math.ceil(width / 2) * 2
y_arr = torch.frombuffer((ctypes.c_uint8 * (pitch_h * pitch)).from_address(ptr), dtype=torch.uint8,
count=pitch_h * pitch).to(device)
uv_arr = torch.frombuffer((ctypes.c_uint8 * (int(pitch_h * 1.5) * pitch)).from_address(ptr),
dtype=torch.uint8, count=int(pitch_h / 2) * pitch, offset=pitch_h * pitch).to(device)
y_plane = as_strided(y_arr, (pitch_h, pitch_w), (pitch, 1))
uv_plane = as_strided(uv_arr, (int(pitch_h / 2), int(pitch_w / 2), 2), (pitch, 2, 1))
yuv_plane = torch.stack([y_plane,
uv_plane[:, :, 0].repeat_interleave(2, dim=0).repeat_interleave(2, dim=1),
uv_plane[:, :, 1].repeat_interleave(2, dim=0).repeat_interleave(2, dim=1)])
ycbcr_mat = yuv_plane.permute((1, 2, 0)) - torch.Tensor([0, 128, 128]).to(device)
transform_matrix = torch.Tensor([
[1, 0, 1.402],
[1, -0.344136, -0.714136],
[1, 1.772, 0]
]).to(device)
rgb_plane = torch.clip(torch.matmul(ycbcr_mat, transform_matrix.T), 0, 255).to(device, torch.uint8) / 255
_decoded_image = fn[2](rgb_plane.permute((2, 0, 1)))
_decoded_image_resized = fn[0](_decoded_image)
return fn[1](_decoded_image_resized)
if __name__ == '__main__':
from kornia.augmentation import LongestMaxSize, PadTo, Normalize
from kornia.constants import Resample
@ -93,10 +125,14 @@ if __name__ == '__main__':
pad_to = PadTo(size=(640, 640), pad_value=1.)
normalize = Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
if USE_OPENVINO:
for ov_device in ov_core.get_available_devices():
device_name = ov_core.get_property(ov_device, "FULL_DEVICE_NAME")
print(f"{ov_device}: {device_name}")
onnx_model = ov_core.read_model(model_path)
onnx_model.reshape([chunk_size, 3, image_size, image_size])
onnx_model = ov_core.compile_model(onnx_model, device_name='GPU')
# print(onnx_model)
else:
session_options = SessionOptions()
session_options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
@ -104,26 +140,17 @@ if __name__ == '__main__':
session = InferenceSession(
path_or_bytes=model_path,
providers=[
('TensorrtExecutionProvider', {
'trt_engine_cache_enable': True,
'trt_engine_cache_path': 'trt_cache',
'trt_fp16_enable': True,
'trt_profile_min_shapes': f'input:1x3x{image_size}x{image_size}',
'trt_profile_max_shapes': f'input:{chunk_size}x3x{image_size}x{image_size}',
'trt_profile_opt_shapes': f'input:{chunk_size}x3x{image_size}x{image_size}',
}),
('OpenVINOExecutionProvider', {
'device_type': 'GPU.0',
'precision': 'FP16',
'cache_dir': 'openvino_cache'
}),
'CUDAExecutionProvider',
'CPUExecutionProvider'
],
sess_options=session_options
)
if os.path.exists("faces.jsonl"):
with open(file="faces.jsonl", mode="r", encoding="utf-8") as fp:
if os.path.exists("faces_qsv.jsonl"):
with open(file="faces_qsv.jsonl", mode="r", encoding="utf-8") as fp:
already = {list(msgspec.json.decode(line).keys())[0] for line in fp.read().removesuffix("\n").split("\n")}
else:
already = set()
@ -134,7 +161,7 @@ if __name__ == '__main__':
# exit(0)
for name in listdir(root_dir):
with (ProcessPoolExecutor(max_workers=16) as executor):
with (ProcessPoolExecutor(max_workers=12) as executor):
pbar.set_description_str(desc=name, refresh=True)
if name != "ブログ":
# continue
@ -157,12 +184,26 @@ if __name__ == '__main__':
if USE_OPENVINO:
fn_pack = [longest_max_size, pad_to, normalize]
submits = []
# for file, dat in cnk:
# submits.append(executor.submit(dec_jpg_qsv, dat, fn_pack))
# names.append(file)
# for submit in submits:
# try:
# stack.append(submit.result().to(device).squeeze())
# except Exception as e:
# print(e)
# stack.append(dec_jpg(dat, fn_pack).squeeze())
for file, dat in cnk:
submits.append(executor.submit(dec_jpg, dat, fn_pack))
try:
raise Exception
stack.append(dec_jpg_qsv(dat, fn_pack).squeeze())
except Exception as e:
# print(e)
stack.append(dec_jpg(dat, fn_pack).squeeze())
names.append(file)
for submit in submits:
stack.append(submit.result().squeeze())
else:
print("fallback", inspect.currentframe().f_lineno)
for file, dat in cnk:
try:
decoded_image = decode_jpeg(tensor(dat), device=device)
@ -181,10 +222,9 @@ if __name__ == '__main__':
stacked = torch.stack(stack).contiguous()
# print(stacked.shape)
if USE_OPENVINO:
_outputs = onnx_model([stacked])
_outputs = onnx_model([stacked.cpu()])
# print(_outputs[onnx_model.output(0)])
outputs = [_outputs[onnx_model.output(i)] for i in range(2, -1, -1)]
# print(outputs)
else:
io_binding = session.io_binding()
io_binding.bind_input(
@ -200,7 +240,7 @@ if __name__ == '__main__':
io_binding.bind_output("bbox")
session.run_with_iobinding(iobinding=io_binding)
outputs: list[numpy.ndarray] = io_binding.copy_outputs_to_cpu()
print("fallback", inspect.currentframe().f_lineno)
# [numpy.memmap(filename=path.join("memmap", tmp_file_name + str(order)), dtype=numpy.float16,
# mode="w+", shape=output.shape) for order, output in enumerate(outputs)]
uuid = uuid4().__str__()
@ -218,7 +258,7 @@ if __name__ == '__main__':
# exit(0)
pbar.update(n=cnk.__len__())
# result_dict = dict()
with open("faces.jsonl", mode="a", encoding="utf-8") as fp:
with open("faces_qsv.jsonl", mode="a", encoding="utf-8") as fp:
futures_results = [future.result() for future in futures]
# pprint(futures_results)
for names, futures_result in zip(namess, futures_results):