File size: 7,427 Bytes
0293aec | 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 | import torch
from tqdm import tqdm
import torchvision
# Import own models
from models.resnet_imagenet import ResNet_ImageNet as own_resnet50, ResNet101_ImageNet as own_resnet101, ResNet152_ImageNet as own_resnet152
from models.densenet_imagenet import DenseNet121_ImageNet as own_densenet121, DenseNet169_ImageNet as own_densenet169
from models.wide_resnet_imagenet import WideResNet_ImageNet as own_wide_resnet
from models.mobilenet_v2_imagenet import MobileNet_V2_ImageNet as own_mobilenet_v2
from models.vit_imagenet import ViT_B_16_ImageNet as own_vit_b_16, ViT_B_32_ImageNet as own_vit_b_32, ViT_L_16_ImageNet as own_vit_l_16, ViT_L_32_ImageNet as own_vit_l_32
from models.swin_imagenet import Swin_B_ImageNet as own_swin_b
from models.beit_imagenet import BEiT_Base_ImageNet as own_beit_base, BEiT_Large_ImageNet as own_beit_large, BEiTv2_Base_ImageNet as own_beitv2_base
from models.convnext_imagenet import ConvNext_Tiny_ImageNet as own_convnext_tiny, ConvNext_Base_ImageNet as own_convnext_base, ConvNext_Large_ImageNet as own_convnext_large
# EVA02 with ImageNet-1K fine-tuned classification heads (uses 448x448 input)
from models.eva_imagenet import EVA02_Base_ImageNet as own_eva02_base, EVA02_Large_ImageNet as own_eva02_large, EVA02_Small_ImageNet as own_eva02_small
# Import CIFAR-compatible versions of the same models
from models.resnet_cifar import ResNet50_CIFAR as own_resnet50_cifar, ResNet110_CIFAR as own_resnet110_cifar
# For WideResNet and DenseNet, use the original implementations that match saved weights
from Net.wide_resnet import wide_resnet_cifar as own_wide_resnet_cifar
from Net.densenet import densenet121 as own_densenet121_cifar
# Import dataloaders
import Datasets.cifar10 as cifar10
import Datasets.cifar100 as cifar100
import Datasets.tiny_imagenet as tiny_imagenet
import Datasets.imagenet as imagenet
import Datasets.imagenet_original_val as imagenet_original_val
import Datasets.imagenet_lt as imagenet_lt
import Datasets.imagenet_c as imagenet_c
import Datasets.imagenet_sketch as imagenet_sketch
import Datasets.iwildcam as iwildcam
# Dataset params
dataset_num_classes = {
'cifar10': 10,
'cifar100': 100,
'tiny_imagenet': 200,
'imagenet': 1000,
'imagenet_lt': 1000,
'imagenet_c': 1000,
'imagenet_sketch': 1000,
'imagenet_original_val': 1000,
'iwildcam': 206
}
dataset_loader = {
'cifar10': cifar10,
'cifar100': cifar100,
'tiny_imagenet': tiny_imagenet,
'imagenet': imagenet,
'imagenet_lt': imagenet_lt,
'imagenet_c': imagenet_c,
'imagenet_sketch': imagenet_sketch,
'imagenet_original_val': imagenet_original_val,
'iwildcam': iwildcam
}
# Mapping model name to model function
models_dict = {
"cifar10":{
'resnet50': own_resnet50_cifar,
'resnet110': own_resnet110_cifar,
'wide_resnet': own_wide_resnet_cifar,
'densenet121': own_densenet121_cifar
},
"cifar100":{
'resnet50': own_resnet50_cifar,
'resnet110': own_resnet110_cifar,
'wide_resnet': own_wide_resnet_cifar,
'densenet121': own_densenet121_cifar
},
"imagenet":{
'resnet50': own_resnet50,
'resnet101': own_resnet101,
'resnet152': own_resnet152,
'densenet121': own_densenet121,
'densenet169': own_densenet169,
'wide_resnet': own_wide_resnet,
'mobilenet_v2': own_mobilenet_v2,
'vit_l_16': own_vit_l_16,
'vit_b_16': own_vit_b_16,
'vit_b_32': own_vit_b_32,
'vit_l_32': own_vit_l_32,
'swin_b': own_swin_b,
'beit_base': own_beit_base,
'beit_large': own_beit_large,
'beitv2_base': own_beitv2_base,
'convnext_tiny': own_convnext_tiny,
'convnext_base': own_convnext_base,
'convnext_large': own_convnext_large,
'eva02_small': own_eva02_small,
'eva02_base': own_eva02_base,
'eva02_large': own_eva02_large
},
"imagenet_lt": {
'resnet50': own_resnet50,
'resnet101': own_resnet101,
'resnet152': own_resnet152,
'densenet121': own_densenet121,
'densenet169': own_densenet169,
'wide_resnet': own_wide_resnet,
'mobilenet_v2': own_mobilenet_v2,
'vit_l_16': own_vit_l_16,
'vit_b_16': own_vit_b_16,
'vit_b_32': own_vit_b_32,
'vit_l_32': own_vit_l_32,
'swin_b': own_swin_b,
'beit_base': own_beit_base,
'beit_large': own_beit_large,
'beitv2_base': own_beitv2_base,
'convnext_tiny': own_convnext_tiny,
'convnext_base': own_convnext_base,
'convnext_large': own_convnext_large,
'eva02_small': own_eva02_small,
'eva02_base': own_eva02_base,
'eva02_large': own_eva02_large
},
"imagenet_c": {
'resnet50': own_resnet50,
'resnet101': own_resnet101,
'resnet152': own_resnet152,
'densenet121': own_densenet121,
'densenet169': own_densenet169,
'wide_resnet': own_wide_resnet,
'mobilenet_v2': own_mobilenet_v2,
'vit_l_16': own_vit_l_16,
'vit_b_16': own_vit_b_16,
'vit_b_32': own_vit_b_32,
'vit_l_32': own_vit_l_32,
'swin_b': own_swin_b,
'beit_base': own_beit_base,
'beit_large': own_beit_large,
'beitv2_base': own_beitv2_base,
'convnext_tiny': own_convnext_tiny,
'convnext_base': own_convnext_base,
'convnext_large': own_convnext_large,
'eva02_small': own_eva02_small,
'eva02_base': own_eva02_base,
'eva02_large': own_eva02_large
},
"imagenet_sketch": {
'resnet50': own_resnet50,
'resnet101': own_resnet101,
'resnet152': own_resnet152,
'densenet121': own_densenet121,
'densenet169': own_densenet169,
'wide_resnet': own_wide_resnet,
'mobilenet_v2': own_mobilenet_v2,
'vit_l_16': own_vit_l_16,
'vit_b_16': own_vit_b_16,
'vit_b_32': own_vit_b_32,
'vit_l_32': own_vit_l_32,
'swin_b': own_swin_b,
'beit_base': own_beit_base,
'beit_large': own_beit_large,
'beitv2_base': own_beitv2_base,
'convnext_tiny': own_convnext_tiny,
'convnext_base': own_convnext_base,
'convnext_large': own_convnext_large,
'eva02_small': own_eva02_small,
'eva02_base': own_eva02_base,
'eva02_large': own_eva02_large
},
"imagenet_original_val": {
'resnet50': own_resnet50,
'own_resnet50': own_resnet50,
'resnet101': own_resnet101,
'resnet152': own_resnet152,
'densenet121': own_densenet121,
'densenet169': own_densenet169,
'wide_resnet': own_wide_resnet,
'mobilenet_v2': own_mobilenet_v2,
'vit_l_16': own_vit_l_16,
'vit_b_16': own_vit_b_16,
'vit_b_32': own_vit_b_32,
'vit_l_32': own_vit_l_32,
'swin_b': own_swin_b,
'beit_base': own_beit_base,
'beit_large': own_beit_large,
'beitv2_base': own_beitv2_base,
'convnext_tiny': own_convnext_tiny,
'convnext_base': own_convnext_base,
'convnext_large': own_convnext_large,
'eva02_small': own_eva02_small,
'eva02_base': own_eva02_base,
'eva02_large': own_eva02_large
},
"iwildcam": {
'resnet50': own_resnet50,
'vit_b_16': own_vit_b_16,
'eva02_large': own_eva02_large
}
}
|