I’m converting a model into jit tracer and keep getting a tracer warning for the code below.
Can someone give me some advise？
class YOLOLayer(nn.Module):
    """Detection layer"""

    def __init__(self, anchors, num_classes, img_dim=512):
        super(YOLOLayer, self).__init__()
        self.anchors = anchors
        self.num_anchors = len(anchors)
        self.num_classes = num_classes
        self.bbox_attrs = 5 + num_classes
        self.ignore_thres = 0.5
        self.mse_loss = nn.MSELoss()
        self.bce_loss = nn.BCELoss()
        self.ce_loss = nn.CrossEntropyLoss()
        self.l1_loss = nn.L1Loss()
        self.obj_scale = 1
        self.noobj_scale = 10
        self.metrics = {}
        self.img_dim = img_dim


    def forward(self, x, targets=None):
        nA = self.num_anchors
        nB = x.size(0)
        nG = x.size(2)
        stride = self.img_dim / nG

        # Tensors for cuda support
        FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
        LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
        ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor

        prediction = x.view(nB, nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous()

        # Get outputs
        x = torch.sigmoid(prediction[..., 0])  # Center x
        y = torch.sigmoid(prediction[..., 1])  # Center y
        w = prediction[..., 2]  # Width
        h = prediction[..., 3]  # Height
        pred_conf = torch.sigmoid(prediction[..., 4])  # Conf
        pred_cls = torch.sigmoid(prediction[..., 5:])  # Cls pred.

        # Calculate offsets for each grid
        grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).type(FloatTensor)
        grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).type(FloatTensor)
        scaled_anchors = FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in self.anchors])
        anchor_w = scaled_anchors[:, 0:1].view((1, nA, 1, 1))
        anchor_h = scaled_anchors[:, 1:2].view((1, nA, 1, 1))

        # Add offset and scale with anchors
        pred_boxes = FloatTensor(prediction[..., :4].shape)
        pred_boxes[..., 0] = x.data + grid_x
        pred_boxes[..., 1] = y.data + grid_y
        pred_boxes[..., 2] = torch.exp(w.data) * anchor_w
        pred_boxes[..., 3] = torch.exp(h.data) * anchor_h 
        

        output = torch.cat(
            (pred_boxes.view(nB, -1, 4) * stride, pred_conf.view(nB, -1, 1), pred_cls.view(nB, -1, self.num_classes)),
            -1,
        )

The TracerWarning infomatiom:
E:\DetectionYoloV3\models.py:157: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  scaled_anchors = FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in self.anchors])
E:\DetectionYoloV3\models.py:162: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  pred_boxes = FloatTensor(prediction[..., :4].shape)
D:\Anoconda\envs\pytorch_gpu_1.1_CUDA10.1\lib\site-packages\torch\jit\__init__.py:702: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 3754, 2] (2.9778142945768974e+38 vs. inf) and 99135 other locations (21.00%)
  _check_trace([example_inputs], func, executor_options, traced, check_tolerance, _force_outplace)