| """ |
| Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) |
| Copyright(c) 2023 lyuwenyu. All Rights Reserved. |
| """ |
|
|
| import torch |
| import torch.nn as nn |
|
|
|
|
| class ConvNormLayer(nn.Module): |
| def __init__(self, ch_in, ch_out, kernel_size, stride, padding=None, bias=False, act=None): |
| super().__init__() |
| self.conv = nn.Conv2d( |
| ch_in, |
| ch_out, |
| kernel_size, |
| stride, |
| padding=(kernel_size-1)//2 if padding is None else padding, |
| bias=bias) |
| self.norm = nn.BatchNorm2d(ch_out) |
| self.act = nn.Identity() if act is None else get_activation(act) |
|
|
| def forward(self, x): |
| return self.act(self.norm(self.conv(x))) |
|
|
|
|
| class FrozenBatchNorm2d(nn.Module): |
| """copy and modified from https://github.com/facebookresearch/detr/blob/master/models/backbone.py |
| BatchNorm2d where the batch statistics and the affine parameters are fixed. |
| Copy-paste from torchvision.misc.ops with added eps before rqsrt, |
| without which any other models than torchvision.models.resnet[18,34,50,101] |
| produce nans. |
| """ |
| def __init__(self, num_features, eps=1e-5): |
| super(FrozenBatchNorm2d, self).__init__() |
| n = num_features |
| self.register_buffer("weight", torch.ones(n)) |
| self.register_buffer("bias", torch.zeros(n)) |
| self.register_buffer("running_mean", torch.zeros(n)) |
| self.register_buffer("running_var", torch.ones(n)) |
| self.eps = eps |
| self.num_features = n |
|
|
| def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, |
| missing_keys, unexpected_keys, error_msgs): |
| num_batches_tracked_key = prefix + 'num_batches_tracked' |
| if num_batches_tracked_key in state_dict: |
| del state_dict[num_batches_tracked_key] |
|
|
| super(FrozenBatchNorm2d, self)._load_from_state_dict( |
| state_dict, prefix, local_metadata, strict, |
| missing_keys, unexpected_keys, error_msgs) |
|
|
| def forward(self, x): |
| |
| |
| w = self.weight.reshape(1, -1, 1, 1) |
| b = self.bias.reshape(1, -1, 1, 1) |
| rv = self.running_var.reshape(1, -1, 1, 1) |
| rm = self.running_mean.reshape(1, -1, 1, 1) |
| scale = w * (rv + self.eps).rsqrt() |
| bias = b - rm * scale |
| return x * scale + bias |
|
|
| def extra_repr(self): |
| return ( |
| "{num_features}, eps={eps}".format(**self.__dict__) |
| ) |
|
|
| def freeze_batch_norm2d(module: nn.Module) -> nn.Module: |
| if isinstance(module, nn.BatchNorm2d): |
| module = FrozenBatchNorm2d(module.num_features) |
| else: |
| for name, child in module.named_children(): |
| _child = freeze_batch_norm2d(child) |
| if _child is not child: |
| setattr(module, name, _child) |
| return module |
|
|
|
|
| def get_activation(act: str, inplace: bool=True): |
| """get activation |
| """ |
| if act is None: |
| return nn.Identity() |
|
|
| elif isinstance(act, nn.Module): |
| return act |
|
|
| act = act.lower() |
|
|
| if act == 'silu' or act == 'swish': |
| m = nn.SiLU() |
|
|
| elif act == 'relu': |
| m = nn.ReLU() |
|
|
| elif act == 'leaky_relu': |
| m = nn.LeakyReLU() |
|
|
| elif act == 'silu': |
| m = nn.SiLU() |
|
|
| elif act == 'gelu': |
| m = nn.GELU() |
|
|
| elif act == 'hardsigmoid': |
| m = nn.Hardsigmoid() |
|
|
| else: |
| raise RuntimeError('') |
|
|
| if hasattr(m, 'inplace'): |
| m.inplace = inplace |
|
|
| return m |
|
|
