File size: 2,152 Bytes
646f45c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import torch
from torch import nn
from typing import Optional, Union, Tuple
class ConvLayer2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = False,
use_norm: bool = True,
use_act: bool = True,
norm_layer: Optional[nn.Module] = None,
act_layer: Optional[nn.Module] = None,
):
super().__init__()
layers = []
layers.append(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias
)
)
if use_norm:
if norm_layer is None:
norm_layer = nn.BatchNorm2d(out_channels)
layers.append(norm_layer)
if use_act:
if act_layer is None:
act_layer = nn.ReLU(inplace=True)
layers.append(act_layer)
self.block = nn.Sequential(*layers)
def forward(self, x):
return self.block(x)
# PEG from https://arxiv.org/abs/2102.10882
class PosCNN(nn.Module):
def __init__(self, in_chans, embed_dim=None, s=1):
super(PosCNN, self).__init__()
self.proj = nn.Sequential(
nn.Conv2d(in_chans, embed_dim, 3, s, 1,
bias=True, groups=embed_dim),
)
self.s = s
def forward(self, x, H, W):
B, N, C = x.shape
feat_token = x
cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
if self.s == 1:
x = self.proj(cnn_feat) + cnn_feat
else:
x = self.proj(cnn_feat)
x = x.flatten(2).transpose(1, 2)
return x
def no_weight_decay(self):
return ["proj.%d.weight" % i for i in range(4)]
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