python常用工具库：coco数据集、labelme数据集、segmentation数据集、classification数据集制作和转换脚本；文件操作、表格操作、数据结构、web服务封装等工具集
import fastdeploy
from pathlib import Path
from zhousflib.infer_framework.fast_infer import FastInfer
def demo_classification():
# 图像分类
model_dir = Path(r"D:\workspace\ZhousfLib\model\PPLCNet_x1_0_infer-v9")
fast = FastInfer(model_dir=model_dir, device_id=0)
runtime_option = fastdeploy.RuntimeOption()
# 采用onnx推理引擎
runtime_option.use_ort_backend()
"""
推理后端采用fastdeploy
模型组件采用PaddleClasModel
"""
fast.use_fastdeploy_backend(plugin="fd.vision.classification.PaddleClasModel", runtime_option=runtime_option)
result = fast.infer(input_data=model_dir.joinpath("test.png"))
print(result)
def demo_detection():
# 目标检测
model_dir = Path(r"D:\workspace\ZhousfLib\model\global_ppyoloe_plus_crn_l_80e_coco-v10")
fast = FastInfer(model_dir=model_dir, device_id=0)
runtime_option = fastdeploy.RuntimeOption()
# 采用tensorrt推理引擎
runtime_option.use_trt_backend()
# 保存trt文件
runtime_option.trt_option.serialize_file = str(model_dir.joinpath("model.trt"))
"""
推理后端采用fastdeploy
模型组件采用PPYOLOE
"""
fast.use_fastdeploy_backend(plugin="fd.vision.detection.PPYOLOE", runtime_option=runtime_option)
result = fast.infer(input_data=model_dir.joinpath("test.jpg"))
print(result)
def demo_segmentation():
# 图像分割
model_dir = Path(r"D:\workspace\ZhousfLib\model\local_pp_liteseg_512x512_1k-v5")
fast = FastInfer(model_dir=model_dir, device_id=-1)
"""
推理后端采用fastdeploy
模型组件采用PaddleSegModel
"""
fast.use_fastdeploy_backend(plugin="fd.vision.segmentation.PaddleSegModel")
image_file = model_dir.joinpath("test.jpg")
vis_image_file = image_file.with_name("{0}_vis{1}".format(image_file.stem, image_file.suffix))
# 保存并显示可视化文件vis_image_file
result = fast.infer(input_data=image_file,
vis_image_file=vis_image_file,
vis_show=True)
print(result.contain_score_map)
def demo_bert():
import torch
from zhousflib.infer_framework.ann.torch import torch_to_onnx
fast_infer = FastInfer(model_dir=Path(r"F:\torch\test"), device_id=0)
"""
推理后端采用tensorrt
推理平台采用torch
"""
fast_infer.use_tensorrt_backend(from_platform="torch",
module=torch.nn.Module(),
example_inputs=(torch_to_onnx.example_inputs_demo(device_id=0),),
dynamic_axes={'input_ids': {0: 'batch_size'},
'token_type_ids': {0: 'batch_size'},
'attention_mask': {0: 'batch_size'},
'output': {0: 'batch_size'}},
shape={"input_ids": [(10, 128), (10, 128), (10, 128)],
"token_type_ids": [(10, 128), (10, 128), (10, 128)],
"attention_mask": [(10, 128), (10, 128), (10, 128)]})
return fast_infer.infer(torch_to_onnx.example_inputs_demo()).tolist()
def demo():
import numpy as np
from zhousflib.infer_framework.ann import to_numpy
from zhousflib.infer_framework.ann.torch.torch_to_onnx import example_inputs_demo
from zhousflib.infer_framework.triton.client_http import ClientHttp
args = example_inputs_demo(input_size=1)
data_arr = np.asarray([to_numpy(args[0].int()), to_numpy(args[1].int()), to_numpy(args[2].int())], dtype=np.int64)
client = ClientHttp(url="127.0.0.1:5005", concurrency=100)
"""
同步请求demo
"""
client.infer_sync_demo(data=data_arr, infer_count=500)
"""
异步请求demo
"""
# client.infer_async_demo(data=data_arr, infer_count=10)
def demo_onnx_to_trt():
from pathlib import Path
from zhousflib.infer_framework.ann.onnx.onnx_to_trt import convert_trt
convert_trt(onnx_file_path=Path(r"F:\torch\onnx\model.onnx"),
save_trt_path=Path(r"F:\torch\onnx\model_32.trt"),
use_fp16=True,
shape={"input_ids": [(10, 128), (10, 128), (50, 128)],
"token_type_ids": [(10, 128), (10, 128), (50, 128)],
"attention_mask": [(10, 128), (10, 128), (50, 128)]})
def convert_bert_demo():
"""
转换示例：以bert转onnx为例
:return:
"""
import torch
from pathlib import Path
from zhousflib.infer_framework.ann.torch.torch_to_onnx import convert_onnx, example_inputs_demo
convert_onnx(module=torch.nn.Module(),
model_dir=Path(r"F:\torch\train_model"),
export_dir=Path(r"F:\torch\onnx2"),
device="cpu", example_inputs=(example_inputs_demo(device_id=-1), ),
verbose=True,
export_params=True,
opset_version=10,
input_names=['input_ids', 'token_type_ids', 'attention_mask'],
output_names=['output'],
dynamic_axes={'input_ids': {0: 'batch_size'},
'token_type_ids': {0: 'batch_size'},
'attention_mask': {0: 'batch_size'},
'output': {0: 'batch_size'}})
def convert_bert_demo():
"""
转换示例：以bert转torchScript为例
:return:
"""
import torch
from pathlib import Path
from zhousflib.infer_framework.ann.torch.torch_to_script import convert_script_model, example_inputs_demo
convert_script_model(module=torch.nn.Module(),
model_dir=Path(r"F:\torch\train_model"),
export_dir=Path(r"F:\torch\script"),
device="cpu", example_inputs=(example_inputs_demo(), ))
def demo_trt_infer():
import numpy as np
from pathlib import Path
from zhousflib.infer_framework.ann import to_numpy
from zhousflib.infer_framework.ann.torch.torch_to_onnx import example_inputs_demo
from zhousflib.infer_framework.ann.tensorrt.tensorrt_infer import RTInfer
args = example_inputs_demo(input_size=1)
batch = np.asarray([to_numpy(args[0].int()), to_numpy(args[1].int()), to_numpy(args[2].int())])
rt_engine = RTInfer(trt_file_path=Path(r"F:\torch\onnx\model_32.trt"), device_id=0, use_stack=True)
data = rt_engine.infer(input_arr=batch)
print(data)
util
[util/cv_util]：opencv读写中文路径图片，图像相关处理
[util/char_util]：字符相关处理，全角、半角
[util/encrypt_util]：AES加密
[util/iou_util]：IoU计算
[util/json_util]：json读写
[util/poly_util]：按照宽高对图片进行网格划分/切图
[util/re_util]：re提取数字、字母、中文
[util/singleton]：单例
[util/string_util]：非空、包含、补齐
[util/time_util]：日期、毫秒级时间戳、微秒级时间戳、日期比较
FAQs
a python library of zhousf
We found that zhousf-lib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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