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1. ps -ef | grep python 로 돌고있는 task PID 확인 ( sudo fuser -v /dev/nvidia* 도 확인 가능) PID : 두번쨰 cloumn 457170 2. sudo kill -9 457170

student에 키가 존재하고 size 같을 때만 pretrained weight load하고 싶을 때 if cfg.MODEL.student_dir is not None: pretrained = torch.load(cfg.MODEL.student_dir) #pretrained=pretrained['teacher'].state_dict() student_dict = student.state_dict() pretrained_dict = {k: v for k, v in pretrained['teacher'].items() if (k in student_dict) and (student_dict[k].shape==pretrained['teacher'][k].shape)} student_dict.update(pr..

한장씩 저장 -> torchvision.save_image가 더 편함 from torchvision import transforms import torchvision from torchvision.utils import save_image for index, (data, label) in tqdm(enumerate(dataloader), total=len(dataloader)): #cuda 올리기 data = data.to('cuda') ################ #case 1. 좌상단 ori = data.detach() parted = data.detach()[:, :, :128, :128] ori[:, :, :128, 128:] = parted ################# for i in ra..

import os import random import shutil source = '../../dataset/dongkyun/dk_dataset/afhq_dataset/afhq/train/cat/cat_all' dest1 = '../../dataset/dongkyun/dk_dataset/afhq_dataset/afhq/train/cat/cat_4739' files = os.listdir(source) no_of_files = 4739 print(no_of_files) if not os.path.isdir(dest1): os.makedirs(dest1) #import pdb;pdb.set_trace() for file_name in random.sample(files, no_of_files): #move..

from sklearn.manifold import TSNE import numpy as np import matplotlib.pyplot as plt data = np.load("_for_tsne_multi_gc.npy", allow_pickle=True) tsne_exp_name = "multi_(gc)" tsne_data = TSNE(n_components = 2).fit_transform(data.item().get('data')) plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c = data.item().get('gt_label')[:], s = 3) plt.xlim(tsne_data[:, 0].min(), tsne_data[:, 0].max()) # 최소, ..

https://hmkim312.github.io/posts/%EC%A0%95%EB%B0%80%EB%8F%84(Precision)%EC%99%80_%EC%9E%AC%ED%98%84%EC%9C%A8(recall)/ 정밀도(Precision)와 재현율(Recall) 1. 정밀도와 재현율의 트레이드 오프 hmkim312.github.io import matplotlib.pyplot as plt from sklearn.metrics import precision_recall_curve import numpy as np import os def PR_curve(target, pred, iter, cfg): out_dir = os.path.join("./PR", cfg.TRAIN.exp_name.split('/')[..

for문 내에서 배치 내 이미지 잘 가져왔나 확인 -> tensor 받아서 make_grid로 그리기 for i, (examples) in tqdm(enumerate(loader), total=len(loader)): examples = examples.cuda(device) plt.close() grid = torchvision.utils.make_grid(examples, nrow = 4) plt.imshow(grid.permute(1, 2, 0).detach().cpu().numpy()) plt.show() plt.savefig('./dfddfdf.png', dpi = 600) output = model(examples) correlation 구하고 그리기 import numpy as np impo..

1. dataset 정의하고 2. loader 쓰는게 일반적인 사용법임. 1) Image folder 쓰는 경우 특징 : 이미지 한장씩 차곡차곡 불러오는 경우 각 폴더가 label이름임 예시 ####tuils.py 내 내용 def imagenet_transform(): return transforms.Compose([ transforms.Resize((256,256)), transforms.RandomCrop((240,240), padding=4), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]) ###main.py 내..