@compatibility(is_backward_compatible=True)defsymbolic_trace(root:Union[torch.nn.Module,Callable[...,Any]],concrete_args:Optional[Dict[str,Any]]=None,enable_cpatching:bool=False)->GraphModule:"""Symbolic tracing APIGiven an ``nn.Module`` or function instance ``root``, this function will re...
fx在pytorch-1.10中已经处于stable状态,大部分API已经稳定了,我也拿torch.fx量化了几个模型,最终搞到TensorRT上,涉及到卷积、BN、反卷积、add、concat等基本操作,使用的版本是Pytorch-1.10和TensorRT-8.2。 其中fx部分自己修改了下源码,补充了一些op。这里我是直接把最新release的pytorch中的fx部分摘出来,然后pip安装to...
fx在pytorch-1.10中已经处于stable状态,大部分API已经稳定了,我也拿torch.fx量化了几个模型,最终搞到TensorRT上,涉及到卷积、BN、反卷积、add、concat等基本操作,使用的版本是Pytorch-1.10和TensorRT-8.2。 其中fx部分自己修改了下源码,补充了一些op。这里我是直接把最新release的pytorch中的fx部分摘出来,然后pip安装to...
其中,Conv+bn+relu的融合细节会调用pytorch/torch/ao/quantization/fuser_method_mappings.py中的fuse_conv_bn_relu函数: def fuse_conv_bn_relu(is_qat, conv, bn, relu): assert(conv.training == bn.training == relu.training),\ "Conv and BN both must be in the same mode (train or eval)."...
很简单地继承于torch.nn.Module的Module(熟悉pytorch的应该都懂)。其中前向forward函数也记录了这个module的具体操作逻辑。 如果我们想把这个Module中forward中的一部分操作逻辑self.linear(x + self.param).clamp(min=0.0, max=1.0)的clamp部分替换为si...
torch.nn.intrinsic.ConvBn2d Supported 282 torch.nn.intrinsic.ConvBnReLU2d Supported 283 torch.nn.intrinsic.ConvReLU2d Supported 284 torch.nn.intrinsic.ConvReLU3d Unsupported. 285 torch.nn.intrinsic.LinearReLU Supported 286 torch.nn.intrinsic.qat.ConvBn2d Unsupported. 287 torch.nn...
model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model File "/home/gyhd/Desktop/yolov5/models/yolo.py", line 151, in fuse m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn) # update conv ...
Source File: torch_utils.py From pruning_yolov3 with GNU General Public License v3.0 6 votes def fuse_conv_and_bn(conv, bn): # https://tehnokv.com/posts/fusing-batchnorm-and-conv/ with torch.no_grad(): # init fusedconv = torch.nn.Conv2d(conv.in_channels, conv.out_channels, ...
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benchmark = False net_conv = self._image_to_head() ''' (2) do net_conv -> faster-branch ''' cls_prob_fast, bbox_pred_fast = self._predict_fast(net_conv) if mode == 'TEST': if 1: ''' (3) do net_conv -> wsddn-branch ''' fuse_prob = self._predict(net_conv) stds...