Closed pguanhai closed 9 months ago
1End2End是我将NMS加入到yolov5模型中,以加速导出的模型转为tensorrt后的运行速度,你可以直接将此处换为加载yolov5的模型,或者添加nms到yolov5模型,添加方式如下
class End2End(nn.Module):
'''export onnx or tensorrt model with NMS operation.'''
def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, device=None, ort=False, trt_version=8, with_preprocess=False):
super().__init__()
device = device if device else torch.device('cpu')
self.with_preprocess = with_preprocess
self.model = model.to(device)
TRT = ONNX_TRT8
self.patch_model = TRT
self.nms = self.patch_model(max_obj, iou_thres, score_thres, device)
self.nms.eval()
# def forward(self, x, cord):
def forward(self, x):
x = self.model(x)[0]
num_det_whole, det_boxes_whole, det_scores_whole, det_classes_whole = self.nms(x)
det_result = torch.cat((det_boxes_whole, det_scores_whole.unsqueeze(2), det_classes_whole.float().unsqueeze(2)), dim=2)
return num_det_whole, det_result
class ONNX_TRT8(nn.Module):
'''onnx module with TensorRT NMS operation.'''
def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, device=None):
super().__init__()
self.device = device if device else torch.device('cpu')
self.background_class = -1,
self.box_coding = 1,
self.iou_threshold = iou_thres
self.max_obj = max_obj
self.plugin_version = '1'
self.score_activation = 0
self.score_threshold = score_thres
def forward(self, x):
box = x[:, :, :4]
conf = x[:, :, 4:5]
score = x[:, :, 5:]
score *= conf
num_det, det_boxes, det_scores, det_classes = TRT8_NMS.apply(box, score, self.background_class, self.box_coding,
self.iou_threshold, self.max_obj,
self.plugin_version, self.score_activation,
self.score_threshold)
return num_det, det_boxes, det_scores, det_classes```
class TRT8_NMS(torch.autograd.Function):
'''TensorRT NMS operation'''
@staticmethod
def forward(
ctx,
boxes,
scores,
background_class=-1,
box_coding=1,
iou_threshold=0.45,
max_output_boxes=100,
plugin_version="1",
score_activation=0,
score_threshold=0.25,
):
batch_size, num_boxes, num_classes = scores.shape
num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
det_boxes = torch.randn(batch_size, max_output_boxes, 4)
det_scores = torch.randn(batch_size, max_output_boxes)
det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
return num_det, det_boxes, det_scores, det_classes
@staticmethod
def symbolic(g,
boxes,
scores,
background_class=-1,
box_coding=1,
iou_threshold=0.45,
max_output_boxes=100,
plugin_version="1",
score_activation=0,
score_threshold=0.25):
out = g.op("TRT::EfficientNMS_TRT",
boxes,
scores,
background_class_i=background_class,
box_coding_i=box_coding,
iou_threshold_f=iou_threshold,
max_output_boxes_i=max_output_boxes,
plugin_version_s=plugin_version,
score_activation_i=score_activation,
score_threshold_f=score_threshold,
outputs=4)
nums, boxes, scores, classes = out
return nums, boxes, scores, classes
在yolov5缺少End2End库,导致pytorch_yolov5_ptq.py 文件中from models.experimental import End2End失败