In the following I am updating the wights in the resnet101 without a problem
base_ = ResNet(Bottleneck, [3, 4, 23, 3])
resnet_weights = torch.load('resnet101_caffe.pth')
base_.load_state_dict(resnet_weights, strict=False)

if i do
print(resnet_weights.keys())
i will have

odict_keys([‘conv1.weight’, ‘bn1.weight’, ‘bn1.bias’, ‘bn1.running_mean’, ‘bn1.running_var’, ‘layer1.0.conv1.weight’, ‘layer1.0.bn1.weight’, ‘layer1.0.bn1.bias’, ‘layer1.0.bn1.running_mean’, ‘layer1.0.bn1.running_var’, ‘layer1.0.conv2.weight’, ‘layer1.0.bn2.weight’, ‘layer1.0.bn2.bias’, ‘layer1.0.bn2.running_mean’, ‘layer1.0.bn2.running_var’, ‘layer1.0.conv3.weight’, ‘layer1.0.bn3.weight’,
.
.
.

my question is:
if i use
model = models.resnet101(pretrained = True)
which is from pytorch, how i can use the weights in this model to update my model in base_.load_state_dict(resnet_weights, strict=False)
like i did above? is it even possible?