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convert_trt.py
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'''
Description:
Author: Egrt
Date: 2023-02-18 16:44:58
LastEditors: Egrt
LastEditTime: 2023-02-18 17:25:24
'''
import torch
from nets.yolo import YoloBody
from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input,
resize_image, show_config)
from torch2trt import torch2trt
class YOLO(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/yolov7_tiny_obb_uav.pth',
"classes_path" : 'model_data/uav_classes.txt',
#---------------------------------------------------------------------#
# anchors_path代表先验框对应的txt文件，一般不修改。
# anchors_mask用于帮助代码找到对应的先验框，一般不修改。
#---------------------------------------------------------------------#
"anchors_path" : 'model_data/yolo_anchors.txt',
"anchors_mask" : [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
#---------------------------------------------------------------------#
# 输入图片的大小，必须为32的倍数。
#---------------------------------------------------------------------#
"input_shape" : [640, 640],
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence" : 0.5,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou" : 0.3,
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : True,
#-------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#-------------------------------#
"cuda" : True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化YOLO
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
self._defaults[name] = value
#---------------------------------------------------#
# 获得种类和先验框的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.anchors, self.num_anchors = get_anchors(self.anchors_path)
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# 生成模型
#---------------------------------------------------#
def generate(self, onnx=False):
#---------------------------------------------------#
# 建立yolo模型，载入yolo模型的权重
#---------------------------------------------------#
self.net = YoloBody(self.anchors_mask, self.num_classes)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=device))
self.net = self.net.fuse().eval()
print('{} model, and classes loaded.'.format(self.model_path))
if self.cuda:
self.net = self.net.cuda()
# create some regular pytorch model...
model = YOLO().net
# create example data
x = torch.ones((1, 3, 640, 640)).cuda()
# convert to TensorRT feeding sample data as input
model_trt = torch2trt(model, [x], fp16_mode=True)
y = model(x)
y_trt = model_trt(x)
# check the output against PyTorch
# print(torch.max(torch.abs(y - y_trt)))
# save the tensorrt model
torch.save(model_trt.state_dict(), 'model_data/yolov7_tiny_trt.pth')