rmaphoh
211 lines
6.3 KiB
Plaintext
211 lines
6.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1eae7403-f458-4f55-a557-4e045bd6f679",
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"metadata": {
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"id": "1eae7403-f458-4f55-a557-4e045bd6f679"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from PIL import Image\n",
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"import models_vit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4573e6be-935a-4106-8c06-e467552b0e3d",
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"metadata": {
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"id": "4573e6be-935a-4106-8c06-e467552b0e3d"
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},
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"outputs": [],
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"source": [
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"\n",
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"imagenet_mean = np.array([0.485, 0.456, 0.406])\n",
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"imagenet_std = np.array([0.229, 0.224, 0.225])\n",
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"\n",
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"\n",
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"def prepare_model(chkpt_dir, arch='vit_large_patch16'):\n",
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" # build model\n",
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" model = models_vit.__dict__[arch](\n",
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" img_size=224,\n",
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" num_classes=5,\n",
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" drop_path_rate=0,\n",
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" global_pool=True,\n",
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" )\n",
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" # load model\n",
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" checkpoint = torch.load(chkpt_dir, map_location='cpu')\n",
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" msg = model.load_state_dict(checkpoint['model'], strict=False)\n",
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" return model\n",
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"\n",
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"def run_one_image(img, model):\n",
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" \n",
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" x = torch.tensor(img)\n",
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" x = x.unsqueeze(dim=0)\n",
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" x = torch.einsum('nhwc->nchw', x)\n",
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" \n",
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" x = x.to(device, non_blocking=True)\n",
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" latent = model.forward_features(x.float())\n",
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" latent = torch.squeeze(latent)\n",
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" \n",
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" return latent\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b7e691d-93d2-439f-91d6-c22716a897b5",
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"metadata": {
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"id": "8b7e691d-93d2-439f-91d6-c22716a897b5"
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},
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"source": [
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"### Load a pre-trained model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "fd2d7da9-f75c-4b27-a84b-6d1247f73a7d",
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"metadata": {
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"id": "fd2d7da9-f75c-4b27-a84b-6d1247f73a7d",
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"outputId": "a1f0dba1-2cae-484b-ad84-8b00bc7628aa"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model loaded.\n"
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]
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}
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],
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"source": [
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"# download pre-trained RETFound \n",
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"\n",
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"chkpt_dir = './RETFound_cfp.pth'\n",
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"model_ = prepare_model(chkpt_dir, 'vit_large_patch16')\n",
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"\n",
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"device = torch.device('cuda')\n",
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"model_.to(device)\n",
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"print('Model loaded.')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7d15a0a7-c093-439a-9a4d-c37ce0c0eaa6",
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"metadata": {
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"id": "7d15a0a7-c093-439a-9a4d-c37ce0c0eaa6"
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},
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"source": [
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"### Load images and save latent feature"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "27755296-05cc-4344-90de-a8ab3878f485",
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"metadata": {
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"id": "27755296-05cc-4344-90de-a8ab3878f485",
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"outputId": "34c3c12a-0a17-44fe-b72a-cef6eecabc70",
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"tags": []
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},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'Your data path'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/ipykernel_16866/3238108902.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# get image list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Your data path'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mimg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mname_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Your data path'"
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]
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}
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],
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"source": [
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"# get image list\n",
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"data_path = 'Your data path'\n",
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"img_list = os.listdir(data_path)\n",
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"\n",
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"name_list = []\n",
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"feature_list = []\n",
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"model_.eval()\n",
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"\n",
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"for i in img_list:\n",
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" img = Image.open(os.path.join(data_path, i))\n",
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" img = img.resize((224, 224))\n",
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" img = np.array(img) / 255.\n",
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"\n",
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" assert img.shape == (224, 224, 3)\n",
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"\n",
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" # normalize by mean and sd\n",
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" # can use customised mean and sd for your data\n",
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" img = img - imagenet_mean\n",
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" img = img / imagenet_std\n",
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" \n",
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" latent_feature = run_one_image(img, model_)\n",
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" \n",
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" name_list.append(i)\n",
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" feature_list.append(latent_feature.detach().cpu().numpy())\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a365ec24-8e29-485e-83b5-5ac1d02945bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"latent_csv = pd.DataFrame({'Name':name_list, 'Latent_feature':feature_list})\n",
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"latent_csv.to_csv('Feature_latent.csv', index = False, encoding='utf8')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2e8bd5e6-5780-420d-9d4c-96025b265668",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"environment": {
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"kernel": "python3",
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"name": "common-cu110.m91",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/base-cu110:m91"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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