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generate_importance_score.py
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import torch
import numpy as np
import torchvision
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
import os, sys
import argparse
import pickle
from core.model_generator import wideresnet, preact_resnet, resnet
from core.training import Trainer, TrainingDynamicsLogger
from core.data import IndexDataset, CIFARDataset, SVHNDataset, CINIC10Dataset
from core.utils import print_training_info, StdRedirect
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
######################### Data Setting #########################
parser.add_argument('--batch-size', type=int, default=256, metavar='N',
help='input batch size for training.')
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'cifar100', 'tiny', 'svhn', 'cinic10'])
######################### Path Setting #########################
parser.add_argument('--data-dir', type=str, default='../data/',
help='The dir path of the data.')
parser.add_argument('--base-dir', type=str,
help='The base dir of this project.')
parser.add_argument('--task-name', type=str, default='tmp',
help='The name of the training task.')
######################### GPU Setting #########################
parser.add_argument('--gpuid', type=str, default='0',
help='The ID of GPU.')
args = parser.parse_args()
######################### Set path variable #########################
task_dir = os.path.join(args.base_dir, args.task_name)
ckpt_path = os.path.join(task_dir, f'ckpt-last.pt')
td_path = os.path.join(task_dir, f'td-{args.task_name}.pickle')
data_score_path = os.path.join(task_dir, f'data-score-{args.task_name}.pickle')
######################### Print setting #########################
print_training_info(args, all=True)
#########################
dataset = args.dataset
if dataset in ['cifar10', 'svhn', 'cinic10']:
num_classes=10
elif dataset == 'cifar100':
num_classes=100
######################### Ftn definition #########################
"""Calculate loss and entropy"""
def post_training_metrics(model, dataloader, data_importance, device):
model.eval()
data_importance['entropy'] = torch.zeros(len(dataloader.dataset))
data_importance['loss'] = torch.zeros(len(dataloader.dataset))
for batch_idx, (idx, (inputs, targets)) in enumerate(dataloader):
inputs, targets = inputs.to(device), targets.to(device)
logits = model(inputs)
prob = nn.Softmax(dim=1)(logits)
entropy = -1 * prob * torch.log(prob + 1e-10)
entropy = torch.sum(entropy, dim=1).detach().cpu()
loss = nn.CrossEntropyLoss(reduction='none')(logits, targets).detach().cpu()
data_importance['entropy'][idx] = entropy
data_importance['loss'][idx] = loss
"""Calculate td metrics"""
def training_dynamics_metrics(td_log, dataset, data_importance):
targets = []
data_size = len(dataset)
for i in range(data_size):
_, (_, y) = dataset[i]
targets.append(y)
targets = torch.tensor(targets)
data_importance['targets'] = targets.type(torch.int32)
data_importance['correctness'] = torch.zeros(data_size).type(torch.int32)
data_importance['forgetting'] = torch.zeros(data_size).type(torch.int32)
data_importance['last_correctness'] = torch.zeros(data_size).type(torch.int32)
data_importance['accumulated_margin'] = torch.zeros(data_size).type(torch.float32)
def record_training_dynamics(td_log):
output = torch.exp(td_log['output'].type(torch.float))
predicted = output.argmax(dim=1)
index = td_log['idx'].type(torch.long)
label = targets[index]
correctness = (predicted == label).type(torch.int)
data_importance['forgetting'][index] += torch.logical_and(data_importance['last_correctness'][index] == 1, correctness == 0)
data_importance['last_correctness'][index] = correctness
data_importance['correctness'][index] += data_importance['last_correctness'][index]
batch_idx = range(output.shape[0])
target_prob = output[batch_idx, label]
output[batch_idx, label] = 0
other_highest_prob = torch.max(output, dim=1)[0]
margin = target_prob - other_highest_prob
data_importance['accumulated_margin'][index] += margin
for i, item in enumerate(td_log):
if i % 10000 == 0:
print(i)
record_training_dynamics(item)
"""Calculate td metrics"""
def EL2N(td_log, dataset, data_importance, max_epoch=10):
targets = []
data_size = len(dataset)
for i in range(data_size):
_, (_, y) = dataset[i]
targets.append(y)
targets = torch.tensor(targets)
data_importance['targets'] = targets.type(torch.int32)
data_importance['el2n'] = torch.zeros(data_size).type(torch.float32)
l2_loss = torch.nn.MSELoss(reduction='none')
def record_training_dynamics(td_log):
output = torch.exp(td_log['output'].type(torch.float))
predicted = output.argmax(dim=1)
index = td_log['idx'].type(torch.long)
label = targets[index]
label_onehot = torch.nn.functional.one_hot(label, num_classes=num_classes)
el2n_score = torch.sqrt(l2_loss(label_onehot,output).sum(dim=1))
data_importance['el2n'][index] += el2n_score
for i, item in enumerate(td_log):
if i % 10000 == 0:
print(i)
if item['epoch'] == max_epoch:
return
record_training_dynamics(item)
#########################
GPUID = args.gpuid
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPUID)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform_identical = transforms.Compose([
transforms.ToTensor(),
])
data_dir = os.path.join(args.data_dir, dataset)
print(f'dataset: {dataset}')
if dataset == 'cifar10':
trainset = CIFARDataset.get_cifar10_train(data_dir, transform = transform_identical)
elif dataset == 'cifar100':
trainset = CIFARDataset.get_cifar100_train(data_dir, transform = transform_identical)
elif dataset == 'svhn':
trainset = SVHNDataset.get_svhn_train(data_dir, transform = transform_identical)
elif args.dataset == 'cinic10':
trainset = CINIC10Dataset.get_cinic10_train(data_dir, transform = transform_identical)
trainset = IndexDataset(trainset)
print(len(trainset))
data_importance = {}
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=args.batch_size, shuffle=False, num_workers=16)
model = resnet('resnet18', num_classes=num_classes, device=device)
model = model.to(device)
print(f'Ckpt path: {ckpt_path}.')
checkpoint = torch.load(ckpt_path)['model_state_dict']
model.load_state_dict(checkpoint)
model.eval()
with open(td_path, 'rb') as f:
pickled_data = pickle.load(f)
training_dynamics = pickled_data['training_dynamics']
post_training_metrics(model, trainloader, data_importance, device)
training_dynamics_metrics(training_dynamics, trainset, data_importance)
EL2N(training_dynamics, trainset, data_importance, max_epoch=10)
print(f'Saving data score at {data_score_path}')
with open(data_score_path, 'wb') as handle:
pickle.dump(data_importance, handle)