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Thalles 2020-03-15 21:55:09 -03:00
parent 68d57a13c7 eee0ff6731
commit 6597622376
2 changed files with 25 additions and 153 deletions
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3
README.md
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@ -83,7 +83,6 @@ Check the [![Open In Colab](https://colab.research.google.com/assets/colab-badge
| KNN | SimCLR | ResNet-18 | 512 | 256 | 40 | 66.2% |
| Logistic Regression (LBFGS) | SimCLR | [ResNet-18](https://drive.google.com/open?id=1L0yoeY9i2mzDcj69P4slTWb-cfr3PyoT) | 512 | 256 | 80 | 72.9% |
| KNN | SimCLR | ResNet-18 | 512 | 256 | 80 | 69.8% |
| Logistic Regression (Adam) | SimCLR | [ResNet-18](https://drive.google.com/open?id=1SgMCbzp1fXoqUFDJcnlb7hmwqjUvGusd) | 512 | 512 | 80 | 75.4% |
| Logistic Regression (Adam) | SimCLR | [ResNet-50](https://drive.google.com/open?id=1TZqBNTFCsO-mxAiR-zJeyupY-J2gA27Q) | 2048 | 128 | 40 | 74.6% |
| Logistic Regression (Adam) | SimCLR | [ResNet-50](https://drive.google.com/open?id=1is1wkBRccHdhSKQnPUTQoaFkVNSaCb35) | 2048 | 128 | 80 | 77.3% |

175
feature_eval/mini_batch_logistic_regression_evaluator.ipynb
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@ -65,32 +65,13 @@
"metadata": {
"id": "WSgRE1CcLqdS",
"colab_type": "code",
"outputId": "3bd80a41-005c-416d-9476-a0dc14921ab0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 163
}
"colab": {}
},
"source": [
"!pip install gdown"
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from gdown) (4.28.1)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.21.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.12.0)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2019.11.28)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.8)\n",
"Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
@ -115,38 +96,22 @@
"metadata": {
"id": "G7YMxsvEZMrX",
"colab_type": "code",
"outputId": "430bc8d7-6e3c-44c5-eb8f-7f3ca24c4172",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
"colab": {}
},
"source": [
"folder_name = 'resnet-50_40-epochs'\n",
"file_id = get_file_id_by_model(folder_name)\n",
"print(folder_name, file_id)"
],
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"text": [
"resnet-50_40-epochs 1TZqBNTFCsO-mxAiR-zJeyupY-J2gA27Q\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PWZ8fet_YoJm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"outputId": "2871e598-a429-4cfa-cd96-40850003e638"
"colab": {}
},
"source": [
"# download and extract model files\n",
@ -154,18 +119,8 @@
"os.system('unzip {}'.format(folder_name))\n",
"!ls"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"data\t\t resnet-50_40-epochs.zip sample_data\n",
"log_regression.pth resnet-50_80-epochs\n",
"resnet-50_40-epochs resnet-50_80-epochs.zip\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
@ -187,68 +142,29 @@
"metadata": {
"id": "lDfbL3w_Z0Od",
"colab_type": "code",
"outputId": "d148eb48-8e56-4af5-8c2b-c7821e2c7149",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
"colab": {}
},
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"print(\"Using device:\", device)"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"Using device: cuda\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IQMIryc6LjQd",
"colab_type": "code",
"outputId": "9020c91b-d9ad-4d46-c181-cd394061df0d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 217
}
"colab": {}
},
"source": [
"checkpoints_folder = os.path.join(folder_name, 'checkpoints')\n",
"config = yaml.load(open(os.path.join(checkpoints_folder, \"config.yaml\"), \"r\"))\n",
"config"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'batch_size': 256,\n",
" 'dataset': {'input_shape': '(96,96,3)',\n",
" 'num_workers': 0,\n",
" 's': 1,\n",
" 'valid_size': 0.05},\n",
" 'epochs': 40,\n",
" 'eval_every_n_epochs': 1,\n",
" 'fine_tune_from': 'None',\n",
" 'log_every_n_steps': 50,\n",
" 'loss': {'temperature': 0.5, 'use_cosine_similarity': True},\n",
" 'model': {'base_model': 'resnet50', 'out_dim': 128}}"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
@ -355,53 +271,26 @@
"metadata": {
"id": "kghx1govJq5_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "36040306-9730-4781-eaef-dc9018e75176"
"colab": {}
},
"source": [
"resnet_feature_extractor = ResNetFeatureExtractor(checkpoints_folder)"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": [
"Feature extractor: resnet50\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "S_JcznxVJ1Xj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 90
},
"outputId": "aea5aa1c-d78c-4df2-86da-97efa484e093"
"colab": {}
},
"source": [
"X_train_feature, y_train, X_test_feature, y_test = resnet_feature_extractor.get_resnet_features()"
],
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n",
"Features shape (5000, 2048)\n",
"Features shape (8000, 2048)\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
@ -527,31 +416,15 @@
"metadata": {
"id": "NE716m7SOkaK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 108
},
"outputId": "87de8f71-4312-4d76-a1f3-7af5bbe0e9ba"
"colab": {}
},
"source": [
"log_regressor_evaluator = LogiticRegressionEvaluator(n_features=X_train_feature.shape[1], n_classes=10)\n",
"\n",
"log_regressor_evaluator.train(X_train_feature, y_train, X_test_feature, y_test)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"Standard Scaling Normalizer\n",
"Sampled weight decay: 0.00017782794100389227\n",
"--------------\n",
"Done training\n",
"Best accuracy: 73.6625\n"
],
"name": "stdout"
}
]
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",