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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from torchvision.models import densenet121, DenseNet121_Weights\n",
"from torchvision.models import resnet50, ResNet50_Weights\n",
"from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights\n",
"from torchvision.models import alexnet, AlexNet_Weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def changedClassifierLayer(model, modelName, N_CLASSES=10):\n",
" print(modelName)\n",
" for param in model.parameters():\n",
" param.requires_grad = True\n",
"\n",
" if modelName == \"DenseNet121\":\n",
" num_input = model.classifier.in_features\n",
"\n",
" elif modelName == \"ResNet50\":\n",
" num_input = model.fc.in_features\n",
"\n",
" elif modelName == \"EfficientNet-V2-M\" or modelName == \"AlexNet\":\n",
" num_input = model.classifier[1].in_features\n",
"\n",
" classifier = nn.Sequential(\n",
" nn.Linear(num_input, 256),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.2),\n",
" nn.Linear(256, 128),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.2),\n",
" nn.Linear(128, N_CLASSES),\n",
" nn.LogSoftmax(dim=1)\n",
" )\n",
"\n",
" if modelName == \"ResNet50\":\n",
" model.fc = classifier\n",
" else:\n",
" model.classifier = classifier"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"efficientnet_weights_path = 'models-2/EfficientNet-V2-M.pth'\n",
"densenet_weights_path = 'models-2/DenseNet121.pth'\n",
"resnet_weights_path = 'models-2/ResNet50.pth'\n",
"alexnet_weights_path = 'models-2/AlexNet.pth'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"efficientnetV2M_model = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1)\n",
"densenet_model = densenet121(weights=DenseNet121_Weights.IMAGENET1K_V1)\n",
"resnet_model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)\n",
"alexnet_model = alexnet(weights=AlexNet_Weights.IMAGENET1K_V1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EfficientNet-V2-M\n",
"DenseNet121\n",
"ResNet50\n",
"AlexNet\n"
]
}
],
"source": [
"changedClassifierLayer(efficientnetV2M_model, \"EfficientNet-V2-M\")\n",
"changedClassifierLayer(densenet_model, \"DenseNet121\")\n",
"changedClassifierLayer(resnet_model, \"ResNet50\")\n",
"changedClassifierLayer(alexnet_model, \"AlexNet\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"efficientnetV2M_model.load_state_dict(torch.load(efficientnet_weights_path))\n",
"densenet_model.load_state_dict(torch.load(densenet_weights_path))\n",
"resnet_model.load_state_dict(torch.load(resnet_weights_path))\n",
"alexnet_model.load_state_dict(torch.load(alexnet_weights_path))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class EnsembleModel(nn.Module):\n",
" def __init__(self, model_list, weights=None):\n",
" super(EnsembleModel, self).__init__()\n",
" self.models = nn.ModuleList(model_list)\n",
" self.weights = weights\n",
"\n",
" def forward(self, x):\n",
" outputs = [model(x.to(next(model.parameters()).device)) for model in self.models]\n",
"\n",
" if self.weights is None:\n",
" # ensemble_output = torch.mean(torch.stack(outputs), dim=0)\n",
"\n",
" ensemble_output, _ = torch.max(torch.stack(outputs), dim=0)\n",
" else:\n",
" weighted_outputs = torch.stack([w * output for w, output in zip(self.weights, outputs)])\n",
" ensemble_output = torch.sum(weighted_outputs, dim=0)\n",
"\n",
" return ensemble_output"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"models_list = [\n",
" efficientnetV2M_model,\n",
" densenet_model,\n",
" resnet_model,\n",
" alexnet_model\n",
"]\n",
"\n",
"ensemble_model = EnsembleModel(models_list)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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