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from functools import reduce
from operator import add
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet
class Backbone(nn.Module):
def __init__(self, typestr):
super(Backbone, self).__init__()
self.backbone = typestr
# feature extractor initialization
if typestr == 'resnet50':
self.feature_extractor = resnet.resnet50(weights=resnet.ResNet50_Weights.DEFAULT)
self.feat_channels = [256, 512, 1024, 2048]
self.nlayers = [3, 4, 6, 3]
self.feat_ids = list(range(0, 17))
else:
raise Exception('Unavailable backbone: %s' % typestr)
self.feature_extractor.eval()
# define model
self.lids = reduce(add, [[i + 1] * x for i, x in enumerate(self.nlayers)])
self.stack_ids = torch.tensor(self.lids).bincount()[-4:].cumsum(dim=0)
self.cross_entropy_loss = nn.CrossEntropyLoss()
def extract_feats(self, img):
r""" Extract input image features """
feats = []
bottleneck_ids = reduce(add, list(map(lambda x: list(range(x)), self.nlayers)))
# Layer 0
feat = self.feature_extractor.conv1.forward(img)
feat = self.feature_extractor.bn1.forward(feat)
feat = self.feature_extractor.relu.forward(feat)
feat = self.feature_extractor.maxpool.forward(feat)
# Layer 1-4
for hid, (bid, lid) in enumerate(zip(bottleneck_ids, self.lids)):
res = feat
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].conv1.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].bn1.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].relu.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].conv2.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].bn2.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].relu.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].conv3.forward(feat)
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].bn3.forward(feat)
if bid == 0:
res = self.feature_extractor.__getattr__('layer%d' % lid)[bid].downsample.forward(res)
feat += res
if hid + 1 in self.feat_ids:
feats.append(feat.clone())
feat = self.feature_extractor.__getattr__('layer%d' % lid)[bid].relu.forward(feat)
return feats
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