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ShrinkPad.py
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ShrinkPad.py
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'''
This is the implement of pre-processing-based backdoor defense with ShrinkPad proposed in [1].
Reference:
[1] Backdoor Attack in the Physical World. ICLR Workshop, 2021.
'''
import os
from copy import deepcopy
import torch
import torchvision.transforms as transforms
from .base import Base
from ..utils import test
def RandomPad(sum_w, sum_h, fill=0):
transforms_bag=[]
for i in range(sum_w+1):
for j in range(sum_h+1):
transforms_bag.append(transforms.Pad(padding=(i,j,sum_w-i,sum_h-j)))
return transforms_bag
def build_ShrinkPad(size_map, pad):
return transforms.Compose([
transforms.Resize((size_map - pad, size_map - pad)),
transforms.RandomChoice(RandomPad(sum_w=pad, sum_h=pad))
])
class ShrinkPad(Base):
"""Construct defense datasets with ShrinkPad method.
Args:
size_map (int): Size of image.
pad (int): Size of pad.
seed (int): Global seed for random numbers. Default: 0.
deterministic (bool): Sets whether PyTorch operations must use "deterministic" algorithms.
That is, algorithms which, given the same input, and when run on the same software and hardware,
always produce the same output. When enabled, operations will use deterministic algorithms when available,
and if only nondeterministic algorithms are available they will throw a RuntimeError when called. Default: False.
"""
def __init__(self,
size_map,
pad,
seed=0,
deterministic=False):
super(ShrinkPad, self).__init__(seed=seed, deterministic=deterministic)
self.global_size_map = size_map
self.current_size_map = None
self.global_pad = pad
self.current_pad = None
def preprocess(self, data, size_map=None, pad=None):
"""Perform ShrinkPad defense method on data and return the preprocessed data.
Args:
data (torch.Tensor): Input data.
size_map (int): Size of image. Default: None.
pad (int): Size of pad. Default: None.
Returns:
torch.Tensor: The preprocessed data.
"""
if size_map is None:
self.current_size_map = self.global_size_map
else:
self.current_size_map = size_map
if pad is None:
self.current_pad = self.global_pad
else:
self.current_pad = pad
shrinkpad = build_ShrinkPad(self.current_size_map, self.current_pad)
return shrinkpad(data)
def _predict(self, model, data, device, batch_size, num_workers):
with torch.no_grad():
model = model.to(device)
model.eval()
predict_digits = []
for i in range(data.shape[0] // batch_size):
# breakpoint()
batch_img = data[i*batch_size:(i+1)*batch_size, ...]
batch_img = batch_img.to(device)
batch_img = model(batch_img)
batch_img = batch_img.cpu()
predict_digits.append(batch_img)
if data.shape[0] % batch_size != 0:
batch_img = data[(data.shape[0] // batch_size) * batch_size:, ...]
batch_img = batch_img.to(device)
batch_img = model(batch_img)
batch_img = batch_img.cpu()
predict_digits.append(batch_img)
predict_digits = torch.cat(predict_digits, dim=0)
return predict_digits
def predict(self, model, data, schedule, size_map=None, pad=None):
"""Apply ShrinkPad defense method to input data and get the predicts.
Args:
model (torch.nn.Module): Network.
data (torch.Tensor): Input data.
schedule (dict): Schedule for predicting.
size_map (int): Size of image. Default: None.
pad (int): Size of pad. Default: None.
Returns:
torch.Tensor: The predicts.
"""
if size_map is None:
self.current_size_map = self.global_size_map
else:
self.current_size_map = size_map
if pad is None:
self.current_pad = self.global_pad
else:
self.current_pad = pad
shrinkpad = build_ShrinkPad(self.current_size_map, self.current_pad)
preprocessed_data = self.preprocess(data)
if 'test_model' in schedule:
model.load_state_dict(torch.load(schedule['test_model']), strict=False)
# Use GPU
if 'device' in schedule and schedule['device'] == 'GPU':
if 'CUDA_VISIBLE_DEVICES' in schedule:
os.environ['CUDA_VISIBLE_DEVICES'] = schedule['CUDA_VISIBLE_DEVICES']
assert torch.cuda.device_count() > 0, 'This machine has no cuda devices!'
assert schedule['GPU_num'] >0, 'GPU_num should be a positive integer'
print(f"This machine has {torch.cuda.device_count()} cuda devices, and use {schedule['GPU_num']} of them to train.")
if schedule['GPU_num'] == 1:
device = torch.device("cuda:0")
else:
gpus = list(range(schedule['GPU_num']))
model = nn.DataParallel(model.cuda(), device_ids=gpus, output_device=gpus[0])
# TODO: DDP training
pass
# Use CPU
else:
device = torch.device("cpu")
return self._predict(model, preprocessed_data, device, schedule['batch_size'], schedule['num_workers'])
def test(self, model, dataset, schedule, size_map=None, pad=None):
"""Test ShrinkPad on dataset.
Args:
model (torch.nn.Module): Network.
dataset (types in support_list): Dataset.
schedule (dict): Schedule for testing.
size_map (int): Size of image. Default: None.
pad (int): Size of pad. Default: None.
"""
if size_map is None:
self.current_size_map = self.global_size_map
else:
self.current_size_map = size_map
if pad is None:
self.current_pad = self.global_pad
else:
self.current_pad = pad
defense_dataset = deepcopy(dataset)
# defense_dataset.transform.transforms.append(transforms.ToPILImage())
defense_dataset.transform.transforms.append(build_ShrinkPad(self.current_size_map, self.current_pad))
# defense_dataset.transform.transforms.append(transforms.ToTensor())
if hasattr(defense_dataset, 'poisoned_transform'):
# defense_dataset.poisoned_transform.transforms.append(transforms.ToPILImage())
defense_dataset.poisoned_transform.transforms.append(build_ShrinkPad(self.current_size_map, self.current_pad))
# defense_dataset.poisoned_transform.transforms.append(transforms.ToTensor())
test(model, defense_dataset, schedule)