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Diffstat (limited to 'cv/img2vec/image_feature_extractor_similarity.ipynb')
| -rw-r--r-- | cv/img2vec/image_feature_extractor_similarity.ipynb | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/cv/img2vec/image_feature_extractor_similarity.ipynb b/cv/img2vec/image_feature_extractor_similarity.ipynb new file mode 100644 index 0000000..a9fc867 --- /dev/null +++ b/cv/img2vec/image_feature_extractor_similarity.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. 图像数据处理" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:20:04.751148Z", + "start_time": "2022-06-22T15:20:03.148807Z" + } + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "from torchvision import transforms as T\n", + "import timm" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:17:08.148080Z", + "start_time": "2022-06-22T15:17:08.142540Z" + } + }, + "outputs": [], + "source": [ + "img1 = Image.open('./imgs/cat.jpg')\n", + "img2 = Image.open('./imgs/cat2.jpg')\n", + "img3 = Image.open('./imgs/face.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:20:48.808680Z", + "start_time": "2022-06-22T15:20:48.804760Z" + } + }, + "outputs": [], + "source": [ + "trans = T.Compose([\n", + " T.Resize((256, 256)),\n", + " T.ToTensor(),\n", + " T.Normalize(mean=timm.data.IMAGENET_DEFAULT_MEAN, std=timm.data.IMAGENET_DEFAULT_STD)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:21:36.222222Z", + "start_time": "2022-06-22T15:21:36.112742Z" + } + }, + "outputs": [], + "source": [ + "t_img1 = trans(img1).unsqueeze(0)\n", + "t_img2 = trans(img2).unsqueeze(0)\n", + "t_img3 = trans(img3).unsqueeze(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 加载模型" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:25:37.025421Z", + "start_time": "2022-06-22T15:25:37.021239Z" + } + }, + "outputs": [], + "source": [ + "from torchvision import models\n", + "import torch\n", + "from torch import nn" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:18:02.323997Z", + "start_time": "2022-06-22T15:18:00.711753Z" + } + }, + "outputs": [], + "source": [ + "model = models.resnet152(pretrained=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:19:05.325328Z", + "start_time": "2022-06-22T15:19:05.321574Z" + } + }, + "outputs": [], + "source": [ + "layer = model._modules['avgpool']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:25:15.871839Z", + "start_time": "2022-06-22T15:25:15.110680Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 2048, 1, 1])\n" + ] + } + ], + "source": [ + "model.eval()\n", + "with torch.no_grad():\n", + " feature_vec3 = torch.zeros(2048)\n", + " def copy(m, i, o):\n", + " print(o.shape)\n", + " feature_vec3.copy_(o.reshape(o.shape[1]))\n", + " h = layer.register_forward_hook(copy)\n", + " model(t_img3) # forward\n", + " h.remove()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:25:27.967597Z", + "start_time": "2022-06-22T15:25:27.962756Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.1978, 0.4018, 0.0321, ..., 0.0981, 0.2331, 0.2016])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_vec3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 输出处理" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:25:48.350136Z", + "start_time": "2022-06-22T15:25:48.347187Z" + } + }, + "outputs": [], + "source": [ + "cos_sim = nn.CosineSimilarity(dim=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:26:16.628814Z", + "start_time": "2022-06-22T15:26:16.623559Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.6978])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cos_sim(feature_vec.unsqueeze(0), feature_vec2.unsqueeze(0))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-22T15:26:43.703193Z", + "start_time": "2022-06-22T15:26:43.698088Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.5045])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cos_sim(feature_vec.unsqueeze(0), feature_vec3.unsqueeze(0))" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |