322 lines
8.2 KiB
Plaintext
322 lines
8.2 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": "b7f09d7e-dc1e-4962-a031-0a55e5b67a90",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:10:57.446546Z",
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"start_time": "2024-06-22T13:10:56.276676Z"
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}
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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 cv2\n",
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"import glob\n",
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"import json\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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"\n",
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"from tqdm import tqdm\n",
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"from PIL import Image\n",
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"from utils.e2e_metrics import get_metrics\n",
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"from core.data import get_y_3\n",
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"from core.data import load_data\n",
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"from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bde4590a-e868-4668-8b88-9b7ae6741c02",
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"metadata": {},
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"source": [
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"# Update Class"
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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": 2,
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"id": "7aaf0964-d720-4d61-bf66-4e21d58d8c9c",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:10:57.449661Z",
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"start_time": "2024-06-22T13:10:57.447666Z"
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}
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},
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"outputs": [],
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"source": [
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"class_dict = {\n",
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" 1: 'Norm', \n",
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" 2: 'SV',\n",
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" 3: 'LineSV',\n",
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"}\n",
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"\n",
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"class_dict_rev = {\n",
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" 'Norm': 1, \n",
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" 'SV': 2,\n",
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" 'LineSV': 3,\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": 3,
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"id": "41873800-6b74-4fa4-8c8b-3cecd7089518",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:10:57.977015Z",
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"start_time": "2024-06-22T13:10:57.450135Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "41"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"with open('/home/gao/mouclear/cc/code/egnn/logs/0/version_4/test-on-e2e.json') as f:\n",
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" data = json.load(f)\n",
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"\n",
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"name = np.array(data['name'])\n",
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"pred = np.argmax(np.array(data['pred']), axis=1)\n",
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"pred_dict = dict(zip(name, pred))\n",
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"\n",
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"json_lst = glob.glob('/home/gao/mouclear/cc/data/all_sv_e2e/sv/raw/raw/*.json', recursive=True); len(json_lst)"
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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": "b002854a-b36e-4423-a016-dd088a344681",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.565227Z",
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"start_time": "2024-06-22T13:10:57.977878Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 41/41 [01:11<00:00, 1.75s/it]\n"
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]
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}
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],
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"source": [
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"for json_path in tqdm(json_lst):\n",
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" base_name = json_path.split('/')[-1].split('.')[0]\n",
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" points, edge_index, _, _ = load_data(json_path)\n",
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" labels = np.array([pred_dict['{}_{}'.format(base_name, '_'.join(np.array(point, np.str_)))] for point in points])\n",
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" \n",
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" with open(json_path) as f:\n",
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" data = json.load(f)\n",
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"\n",
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" for i in range(len(labels)):\n",
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" data['shapes'][i]['label'] = class_dict[labels[i] + 1]\n",
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" \n",
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" with open(json_path, 'w') as f:\n",
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" json.dump(data, f)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac107f45-f949-4fcd-8950-9235b23d07ba",
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"metadata": {},
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"source": [
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"# Metrics"
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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": "139d5824-cf33-45d4-8fce-e525245295ea",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.567187Z",
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"start_time": "2024-06-22T13:12:09.565874Z"
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}
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},
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"outputs": [],
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"source": []
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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": "8ccaf1cb-0202-4f92-9757-ba2eaf6be30a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.571148Z",
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"start_time": "2024-06-22T13:12:09.567671Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"0it [00:00, ?it/s]\n"
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]
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}
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],
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"source": [
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"json_lst = glob.glob('/home/gao/mouclear/cc/data/end-to-end-result/gnn_data/test_on_truth/raw/raw/*.json', recursive=True); len(json_lst)\n",
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"res = []\n",
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"\n",
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"for json_path in tqdm(json_lst):\n",
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" base_name = json_path.split('/')[-1].split('.')[0]\n",
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" points, edge_index, labels, _ = load_data(json_path)\n",
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"\n",
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" mask_pd = np.zeros((2048, 2048))\n",
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" mask_pd[points[:, 0], points[:, 1]] = labels + 1\n",
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" mask_pd = np.array(mask_pd, np.uint8)\n",
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"\n",
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" mask_gt = np.array(Image.open(json_path.replace('.json', '.png')), np.uint8)\n",
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"\n",
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" for i in range(1, 4):\n",
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" res += [get_metrics(mask_gt == i, mask_pd == i)]"
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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": 6,
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"id": "cca8debf-9de3-4473-a7eb-bd3ca8884a2e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.580502Z",
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"start_time": "2024-06-22T13:12:09.571614Z"
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}
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},
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"outputs": [],
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"source": [
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"res = np.array(res)"
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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": 7,
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"id": "5d92bb3b-e3c2-4373-8ae1-2bbdc901476b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.730432Z",
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"start_time": "2024-06-22T13:12:09.581013Z"
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}
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},
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"outputs": [
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{
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"ename": "IndexError",
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"evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
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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;31mIndexError\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[7], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;66;03m# Norm\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(np\u001B[38;5;241m.\u001B[39mmean(\u001B[43mres\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m:\u001B[49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m))\n",
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"\u001B[0;31mIndexError\u001B[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
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]
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}
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],
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"source": [
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"# Norm\n",
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"print(np.mean(res[::3, :], axis=0))"
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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": "995b9bab-768c-47e2-9199-91fa3572380b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.731220Z",
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"start_time": "2024-06-22T13:12:09.731147Z"
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}
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},
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"outputs": [],
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"source": [
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"# SV\n",
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"print(np.mean(res[1::3, :], axis=0))"
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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": "34a00da7-8576-4102-a3a8-e78e15db0d45",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-22T13:12:09.731842Z",
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"start_time": "2024-06-22T13:12:09.731701Z"
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}
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},
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"outputs": [],
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"source": [
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"# LineSV\n",
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"print(np.mean(res[2::3, :], axis=0))"
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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": "4926aea8-b9d2-4878-b8db-5f676d067bc1",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(np.mean(res, axis=0))"
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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": "24043672-21c7-40ef-91a1-5c612cfd6c78",
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"metadata": {},
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"outputs": [],
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"source": [
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"# train+test with vor:\n",
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"0.994757\n",
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"0.995859\n",
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"0.995307\n",
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"\n",
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"0.930622\n",
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"0.869687\n",
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"0.898882\n",
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"\n",
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"0.911436\n",
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"0.933655\n",
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"0.922190\n",
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"\n",
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"0.945605\n",
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"0.933067\n",
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"0.938793\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.8.18"
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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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