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from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torchvision
from .normalizer import Normalizer
class RGBResNet34(nn.Sequential):
def __init__(self):
super(RGBResNet34, self).__init__()
self.resnet = torchvision.models.resnet34(pretrained=True)
self.normalizer = Normalizer()
super(RGBResNet34, self).__init__(self.normalizer, self.resnet)
class RGBResNet50(nn.Sequential):
def __init__(self):
super(RGBResNet50, self).__init__()
self.resnet = torchvision.models.resnet50(pretrained=True)
self.normalizer = Normalizer()
super(RGBResNet50, self).__init__(self.normalizer, self.resnet)
class RGBResNet50_alt(nn.Sequential):
def __init__(self):
super(RGBResNet50, self).__init__()
self.resnet = torchvision.models.resnet50(pretrained=True)
self.normalizer = Normalizer()
state_dict = torch.load("Resnet-AlternativePreTrain.pth")
model.load_state_dict(state_dict)
super(RGBResNet50, self).__init__(self.normalizer, self.resnet)
class RGBResNet101(nn.Sequential):
def __init__(self):
super(RGBResNet101, self).__init__()
self.resnet = torchvision.models.resnet101(pretrained=True)
self.normalizer = Normalizer()
super(RGBResNet101, self).__init__(self.normalizer, self.resnet)
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