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import torch
from torch import nn
from torch.nn import functional as F
from torchvision import models
from torchvision.ops.misc import FrozenBatchNorm2d
class Backbone(nn.Module):
def __init__(
self,
name: str,
pretrained: bool,
dilation: bool,
reduction: int,
swav: bool,
requires_grad: bool
):
super(Backbone, self).__init__()
resnet = getattr(models, name)(
replace_stride_with_dilation=[False, False, dilation],
pretrained=pretrained, norm_layer=FrozenBatchNorm2d
)
self.backbone = resnet
self.reduction = reduction
if name == 'resnet50' and swav:
checkpoint = torch.hub.load_state_dict_from_url(
'https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar',
map_location="cpu"
)
state_dict = {k.replace("module.", ""): v for k, v in checkpoint.items()}
self.backbone.load_state_dict(state_dict, strict=False)
# concatenation of layers 2, 3 and 4
self.num_channels = 896 if name in ['resnet18', 'resnet34'] else 3584
for n, param in self.backbone.named_parameters():
if 'layer2' not in n and 'layer3' not in n and 'layer4' not in n:
param.requires_grad_(False)
else:
param.requires_grad_(requires_grad)
def forward(self, x):
size = x.size(-2) // self.reduction, x.size(-1) // self.reduction
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.relu(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
x = layer2 = self.backbone.layer2(x)
x = layer3 = self.backbone.layer3(x)
x = layer4 = self.backbone.layer4(x)
x = torch.cat([
F.interpolate(f, size=size, mode='bilinear', align_corners=True)
for f in [layer2, layer3, layer4]
], dim=1)
return x
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