import torch from torch.utils.data import Dataset import torchvision import numpy as np import cfg import os from utils import * from PIL import Image,ImageDraw import math LABEL_FILE_PATH = "data/data.txt" IMG_BASE_DIR = "data/images" transforms = torchvision.transforms.Compose([ torchvision...
def kmean_anchors(path='./2007_train.txt', n=5, img_size=(416, 416)): # from utils.utils import *; _ = kmean_anchors() # Produces a list of target kmeans suitable for use in *.cfg files from utils.datasets import LoadImagesAndLabels thr = 0.20 # IoU threshold def print_result...
# This script needs these libraries to be installed: # torch, torchvision, pytorch_lightning import wandb import os from torch import optim, nn, utils from torchvision.datasets import MNIST from torchvision.transforms import ToTensor import pytorch_lightning as pl from pytorch_lightning.loggers import...
# 需要導入模塊: from keras import models [as 別名]# 或者: from keras.models importmodel_from_yaml[as 別名]defload_model(self):print('Loading model ...')# check existence of paramsassertos.path.exists(self.model_folder),'model_folder is not found: {}'.format(self.model_folder)assertsel...
from torch.utils.data import DataLoader num_workers = 0 batch_size = 8 torch.manual_seed(123) train_loader = DataLoader( dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True, ) val_loader = DataLoader( dataset=val_dataset, batch_size=batch_...
Dropout from keras.layers import Dense, Activation, Flatten from keras.utils import to_categorical from keras import backend as K from sklearn.model_selection import train_test_split from Model import model from keras import callbacks # Path for face image database path = 'dataset' recognizer =...
pythonCopy codefrom sklearn.preprocessing import StandardScalerscaler = StandardScaler()3...pythonCopy codefrom sklearn.linear_model import Ridgemodel = Ridge(alpha=0.1)4...pythonCopy codefrom sklearn.utils import resampleresample(X_train, y_train, n_samples=1000)5...pythonCopy codefrom tensorflow...
If you were previously relying on _() always being present, you should now explicitly import ugettext or ugettext_lazy, if appropriate, and alias it to _ yourself: from django.utils.translation import ugettext as _ HTTP request/response objects¶ Dictionary access to HttpRequest¶ HttpRequest...
Keras utilities.Classesclass CustomObjectScope: Provides a scope that changes to _GLOBAL_CUSTOM_OBJECTS cannot escape.class GeneratorEnqueuer: Builds a queue out of a data generator.class HDF5Matrix: Representation of HDF5 dataset to be used instead of a N ...
I was able to embed the text and save it as a CSV file. When the input comes, I would like to get do the cosine similarities matching in vector text data. I have been using OpenAI documentation for this POC, where i found the below code sample. from openai.embeddings_utils import ...