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e4a6421 | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | import os
import sys
import numpy as np
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import torchvision.utils
import torchvision
from torchvision import models
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import copy
def get_model(arch, class_num, pretrained=False):
# pytorch resnet
if arch == 'resnet18':
model = models.resnet18(pretrained=pretrained, num_classes=class_num)
if arch == 'resnet34':
model = models.resnet34(pretrained=pretrained, num_classes=class_num)
elif arch == 'resnet50':
model = models.resnet50(pretrained=pretrained, num_classes=class_num)
elif arch == 'resnet152':
model = models.resnet152(pretrained=pretrained, num_classes=class_num)
elif arch == 'resnext101':
model = models.resnext101(pretrained=pretrained, num_classes=class_num)
elif arch == 'resnet152':
model = models.resnet152(pretrained=pretrained, num_classes=class_num)
# pytorch vgg
elif arch == 'vgg11':
model = models.vgg11(pretrained=pretrained, num_classes=class_num)
elif arch == 'vgg13':
model = models.vgg13(pretrained=pretrained, num_classes=class_num)
elif arch == 'vgg16':
model = models.vgg16(pretrained=pretrained, num_classes=class_num)
elif arch == 'vgg16_bn':
model = models.vgg16(pretrained=pretrained, num_classes=class_num)
elif arch == 'vgg19':
model = models.vgg19(pretrained=pretrained, num_classes=class_num)
elif arch == 'vgg19_bn':
model = models.vgg19_bn(pretrained=pretrained, num_classes=class_num)
# pytorch densenet
elif arch == "densenet121":
model = models.densenet121(pretrained=pretrained, num_classes=class_num)
elif arch == "densenet169":
model = models.densenet169(pretrained=pretrained, num_classes=class_num)
# https://github.com/kuangliu/pytorch-cifar
elif arch == "cifar10_resnet18":
sys.path.append("../pytorch-cifar/models")
from resnet import ResNet18
model = ResNet18()
elif arch == "cifar10_vgg16_bn":
sys.path.append("../pytorch-cifar/models")
from vgg import VGG
model = VGG("VGG16")
elif arch == "cifar10_densenet121":
sys.path.append("../pytorch-cifar/models")
from densenet import densenet_cifar
model = densenet_cifar()
# https://github.com/weiaicunzai/pytorch-cifar100
elif arch == "cifar100_resnet18":
sys.path.append("pytorch-cifar100/models")
from resnet import resnet18
model = resnet18()
elif arch == "cifar100_vgg16_bn":
sys.path.append("pytorch-cifar100/models")
from vgg import vgg16_bn
model = vgg16_bn()
elif arch == "cifar100_densenet121":
sys.path.append("pytorch-cifar100/models")
from densenet import densenet121
model = densenet121()
# timm models
elif arch == "xception":
model = timm.create_model(
"xception", pretrained=pretrained, num_classes=class_num
)
elif arch == "vit_base_patch16_224":
print(f"{arch} is pre-trained on ImageNet-21k.")
model = timm.create_model(
"vit_base_patch16_224", pretrained=pretrained, num_classes=class_num
)
elif arch == "BiT_M":
print(f"{arch} is pre-trained on ImageNet-21k.")
model = timm.create_model(
"resnetv2_101x1_bitm",
pretrained=pretrained,
num_classes=class_num,
)
elif arch == "resnext101_32x8d_wsl":
model = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
return model
def load_model(model, file_name):
assert os.path.exists(file_name), "No exps found. {}".format(file_name)
checkpoint = torch.load(file_name)
model.load_state_dict(checkpoint["state_dict"])
print("Loaded ... ", file_name)
best_acc, best_epoch = checkpoint["acc"], checkpoint["epoch"]
print("best_acc:{} at epoch {}".format(best_acc, best_epoch))
return model, best_acc, best_epoch
class FeatureExtractor(nn.Module):
def __init__(
self,
model: nn.Module,
hook_layers,
return_dict=True,
):
super(FeatureExtractor, self).__init__()
self.return_dict = return_dict
self.model = copy.deepcopy(model)
if isinstance(hook_layers, list):
self.hook_layers = hook_layers
self.hook_layers_dict = {k: k for k in hook_layers}
elif isinstance(hook_layers, dict):
self.hook_layers = [k for k, v in hook_layers.items()]
self.hook_layers_dict = hook_layers
# hook_layers_dict = {original_name: return_name}
print("hook_layers:", hook_layers)
added_layer_names = []
for name, module in self.model.named_modules():
if name in self.hook_layers:
module.register_forward_hook(self.extract())
added_layer_names += [name]
assert len(added_layer_names) == len(
hook_layers
), f"Some layer did not exist. {set(added_layer_names) - set(hook_layers)},{set(hook_layers) - set(added_layer_names)}"
self.features = []
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
def avg_pool_feature(self, o):
"""o: cpu feature map"""
if len(o.shape) == 4:
feat = self.avgpool(o).reshape(o.shape[0], -1).data
elif len(o.shape) == 3:
feat = torch.mean(o, 1).data
elif len(o.shape) == 2:
feat = o.data
else:
print(k, o.shape)
raise ValueError
return feat
def extract(self):
def _extract(module, f_in, f_out):
f_out = self.avg_pool_feature(f_out)
self.features.append(f_out)
return _extract
def forward(self, input):
_ = self.model(input)
assert len(self.features) == len(self.hook_layers), (
"Something's wrong.",
len(self.features),
len(self.hook_layers),
)
if self.return_dict:
d = {
self.hook_layers_dict[k]: feat
for k, feat in zip(self.hook_layers, self.features)
}
self.features = []
return d
else:
features = self.features
self.features = []
return features
if __name__=="__main__":
arch = "resnet18"
class_num = 10
model = get_model(arch, class_num, pretrained=False)
print("Model arch:")
print(model)
# check available layers
for name, module in model.named_modules():
print("- ", name) |