2 篇文章带有标签 “optimisation”

在 PyTorch 中融合卷积和批量标准化

融合卷积和批量标准化的原理

PyTorch 的实现 def fuse_conv_bn_eval(conv, bn, transpose=False): assert(not (conv.training or bn.training)), "Fusion only for eval!" fused_conv = copy.deepcopy(conv) fused_conv.weight, fused_conv.bias = \ fuse_conv_bn_weights(fused_conv.weight, fused_conv.bias, bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias, transpose) return fused_conv def fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b, transpose=False): if conv_b is None: conv_b = torch.zeros_like(bn_rm) if bn_w is None: bn_w = torch.

pytorchconvolutionbatchnormmodel-fusionoptimisationperformancedeep-learning

OpenVINO Benchmark Python Tool

性能指标评测工具

该工具使用卷积网络执行推理。性能可以测量两种推理模式：

  • 同步（面向延迟 Latency）
  • 异步（面向吞吐量 Throughput）

帮助信息 -i PATHS_TO_INPUT [PATHS_TO_INPUT ...], --paths_to_input PATHS_TO_INPUT [PATHS_TO_INPUT ...] Optional. Path to a folder with images and/or binaries or to specific image or binary file.It is also allowed to map files to network inputs: input_1:file_1/dir1,file_2/dir2,input_4:file_4/dir4 input_2:file_3/dir3 -m PATH_TO_MODEL, --path_to_model PATH_TO_MODEL Required. Path to an .xml/.onnx file with a trained model or to a .blob file with a trained compiled model. -d TARGET_DEVICE, --target_device TARGET_DEVICE Optional.

openvinobenchmark_appperformancepythoninferencebenchmarksoptimisation