| |
| |
| import copy |
| import torch |
| |
| def fuse_conv_bn_eval(conv, bn): |
| 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) |
| |
| return fused_conv |
| |
| def fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b): |
| if conv_b is None: |
| conv_b = torch.zeros_like(bn_rm) |
| if bn_w is None: |
| bn_w = torch.ones_like(bn_rm) |
| if bn_b is None: |
| bn_b = torch.zeros_like(bn_rm) |
| bn_var_rsqrt = torch.rsqrt(bn_rv + bn_eps) |
| |
| conv_w = conv_w * (bn_w * bn_var_rsqrt).reshape([-1] + [1] * (len(conv_w.shape) - 1)) |
| conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b |
| |
| return torch.nn.Parameter(conv_w), torch.nn.Parameter(conv_b) |
