from models.experimental import End2End

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

Susan19900316 / yolov5_tensorrt_int8

from models.experimental import End2End #2