Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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TRAIN_DATASETS = {
'mnist': datasets.MNIST(
'./datasets/mnist', train=True, download=True,
transform=transforms.Compose(_MNIST_TRAIN_TRANSFORMS)
),
'cifar10': datasets.CIFAR10(
'./datasets/cifar10', train=True, download=True,
transform=transforms.Compose(_CIFAR_TRAIN_TRANSFORMS)
),
'cifar100': datasets.CIFAR100(
'./datasets/cifar100', train=True, download=True,
transform=transforms.Compose(_CIFAR_TRAIN_TRANSFORMS)
)
}
TEST_DATASETS = {
'mnist': datasets.MNIST(
'./datasets/mnist', train=False,
transform=transforms.Compose(_MNIST_TEST_TRANSFORMS)
),
'cifar10': datasets.CIFAR10(
'./datasets/cifar10', train=False,
transform=transforms.Compose(_CIFAR_TEST_TRANSFORMS)
),
'cifar100': datasets.CIFAR100(
'./datasets/cifar100', train=False,
transform=transforms.Compose(_CIFAR_TEST_TRANSFORMS)
)
}
DATASET_CONFIGS = {
'mnist': {'size': 32, 'channels': 1, 'classes': 10},
'cifar10': {'size': 32, 'channels': 3, 'classes': 10},
'cifar100': {'size': 32, 'channels': 3, 'classes': 100},
}
# <FILESEP>
import os, math, torch, pickle
from tqdm import tqdm
from datetime import datetime
from torch.nn.functional import cross_entropy
from config import ModelConfig
from utils import load_model_and_tokenizer, complete_input, extract_model_embedding
class Attacker:
def __init__(self, model_name, init_input, target, device='cuda:0', steps=768, topk=256, batch_size=1024, mini_batch_size=16, **kwargs):
try:
self.model_config = getattr(ModelConfig, model_name)[0]
except AttributeError:
raise NotImplementedError
self.model_name = model_name
self.init_input = init_input
self.target = target
self.device = device
self.steps = steps
self.topk = topk
self.batch_size = batch_size
self.mini_batch_size = mini_batch_size
self.mini_batches = math.ceil(self.batch_size/self.mini_batch_size)
self.kwargs = kwargs
self.model, self.tokenizer = load_model_and_tokenizer(
self.model_config['path'], self.device, False
)
self.temp_step = 0
self.temp_input = self.init_input
self.temp_output = ''
self.temp_loss = 1e+9
self.temp_grad = None
self.temp_input_ids = None
self.temp_sample_list = []
self.temp_sample_ids = None
self.input_slice = None
self.target_slice = None
self.input_list = []
self.output_list = []
self.loss_list = []
self.route_input = self.init_input
self.route_loss = 1e+9
self.route_step_list = []
self.route_input_list = []
self.route_output_list = []
self.route_loss_list = []
def test(self):
self.model.eval()
input_str = complete_input(self.model_config, self.temp_input)
input_ids = self.tokenizer(
input_str, truncation=True, return_tensors='pt'
).input_ids.to(self.device)
generate_ids = self.model.generate(input_ids, max_new_tokens=96)