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