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| """ | |
| 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): | |
| # move reshapes to the beginning | |
| # to make it fuser-friendly | |
| 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 | |