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visual_gradcam.py
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visual_gradcam.py
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import sys
import os
sys.path.append("../")
sys.path.append("../../")
sys.path.append(os.getcwd())
from utils.defense_utils.dbd.model.model import SelfModel, LinearModel
from utils.defense_utils.dbd.model.utils import (
get_network_dbd,
load_state,
get_criterion,
get_optimizer,
get_scheduler,
)
from utils.save_load_attack import load_attack_result
from utils.aggregate_block.model_trainer_generate import generate_cls_model
from utils.aggregate_block.fix_random import fix_random
from utils.aggregate_block.dataset_and_transform_generate import (
get_transform,
get_dataset_denormalization,
)
from visual_utils import *
import yaml
import torch
import numpy as np
import torchvision.transforms as transforms
from pytorch_grad_cam import (
GradCAM,
ScoreCAM,
GradCAMPlusPlus,
AblationCAM,
XGradCAM,
EigenCAM,
FullGrad,
)
from pytorch_grad_cam.utils.image import show_cam_on_image
# Basic setting: args
args = get_args()
with open(args.yaml_path, "r") as stream:
config = yaml.safe_load(stream)
config.update({k: v for k, v in args.__dict__.items() if v is not None})
args.__dict__ = config
args = preprocess_args(args)
fix_random(int(args.random_seed))
save_path_attack = "./record/" + args.result_file_attack
visual_save_path = save_path_attack + "/visual"
# Load result
if args.prototype:
result_attack = load_prototype_result(args, save_path_attack)
else:
result_attack = load_attack_result(save_path_attack + "/attack_result.pt")
selected_classes = np.arange(args.num_classes)
# Select classes to visualize
if args.num_classes > args.c_sub:
selected_classes = np.delete(selected_classes, args.target_class)
selected_classes = np.random.choice(
selected_classes, args.c_sub-1, replace=False)
selected_classes = np.append(selected_classes, args.target_class)
# keep the same transforms for train and test dataset for better visualization
result_attack["clean_train"].wrap_img_transform = result_attack["clean_test"].wrap_img_transform
result_attack["bd_train"].wrap_img_transform = result_attack["bd_test"].wrap_img_transform
# Create dataset
if args.visual_dataset == 'mixed':
bd_test_with_trans = result_attack["bd_test"]
visual_dataset = generate_mix_dataset(
bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)
elif args.visual_dataset == 'clean_train':
clean_train_with_trans = result_attack["clean_train"]
visual_dataset = generate_clean_dataset(
clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)
elif args.visual_dataset == 'clean_test':
clean_test_with_trans = result_attack["clean_test"]
visual_dataset = generate_clean_dataset(
clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)
elif args.visual_dataset == 'bd_train':
bd_train_with_trans = result_attack["bd_train"]
visual_dataset = generate_bd_dataset(
bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)
elif args.visual_dataset == 'bd_test':
bd_test_with_trans = result_attack["bd_test"]
visual_dataset = generate_bd_dataset(
bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)
else:
assert False, "Illegal vis_class"
print(
f'Create visualization dataset with \n \t Dataset: {args.visual_dataset} \n \t Number of samples: {len(visual_dataset)} \n \t Selected classes: {selected_classes}')
# Create data loader
data_loader = torch.utils.data.DataLoader(
visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False
)
# Create denormalization function
for trans_t in data_loader.dataset.wrap_img_transform.transforms:
if isinstance(trans_t, transforms.Normalize):
denormalizer = get_dataset_denormalization(trans_t)
# Load model
model_visual = generate_cls_model(args.model, args.num_classes)
if args.result_file_defense != "None":
save_path_defense = "./record/" + args.result_file_defense
visual_save_path = save_path_defense + "/visual"
result_defense = load_attack_result(
save_path_defense + "/defense_result.pt")
defense_method = args.result_file_defense.split('/')[-1]
if defense_method == 'fp':
model_visual.layer4[1].conv2 = torch.nn.Conv2d(
512, 512 - result_defense['index'], (3, 3), stride=1, padding=1, bias=False)
model_visual.linear = torch.nn.Linear(
(512 - result_defense['index'])*1, args.num_classes)
if defense_method == 'dbd':
backbone = get_network_dbd(args)
model_visual = LinearModel(
backbone, backbone.feature_dim, args.num_classes)
model_visual.load_state_dict(result_defense["model"])
print(f"Load model {args.model} from {args.result_file_defense}")
else:
model_visual.load_state_dict(result_attack["model"])
print(f"Load model {args.model} from {args.result_file_attack}")
model_visual.to(args.device)
# !!! Important to set eval mode !!!
model_visual.eval()
# make visual_save_path if not exist
os.mkdir(visual_save_path) if not os.path.exists(visual_save_path) else None
# Choose samples to show Grad-CAM values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM
total_num = 4
bd_num = 0
visual_samples = []
visual_labels = []
visual_poison_indicator = np.array(
get_poison_indicator_from_bd_dataset(visual_dataset))
if visual_poison_indicator.sum() > 0:
print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')
# random choose two poisoned samples
selected_bd_idx = np.random.choice(
np.where(visual_poison_indicator == 1)[0], 2, replace=False)
for i in selected_bd_idx:
visual_samples.append(visual_dataset[i][0].unsqueeze(0))
visual_labels.append(visual_dataset[i][4])
bd_num = len(selected_bd_idx)
print(f'Select {bd_num} poisoned samples')
# Trun all samples to clean
with temporary_all_clean(visual_dataset):
# you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples
selected_clean_idx = np.random.choice(
len(visual_dataset), total_num-bd_num, replace=False)
for i in selected_clean_idx:
visual_samples.append(visual_dataset[i][0].unsqueeze(0))
visual_labels.append(visual_dataset[i][1])
print(f'Select {len(selected_clean_idx)} clean samples')
# Clean sample first
visual_samples = visual_samples[::-1]
visual_labels = visual_labels[::-1]
visual_samples = torch.cat(visual_samples, axis=0).to(args.device)
############## Grad-CAM ##################
print('Plotting Grad-CAM')
module_dict = dict(model_visual.named_modules())
target_layer = module_dict[args.target_layer_name]
print(f'Choose layer {args.target_layer_name} from model {args.model}')
sfm = torch.nn.Softmax(dim=1)
outputs = model_visual(visual_samples)
pre_p, pre_label = torch.max(sfm(outputs), dim=1)
cam = FullGrad(model=model_visual, target_layers=[
target_layer], use_cuda=True if args.device == 'cuda' else False)
targets = None
# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
grayscale_cam_full = cam(input_tensor=visual_samples, targets=targets)
grayscale_cam = grayscale_cam_full[0, :]
rgb_image = np.swapaxes(
np.swapaxes(denormalizer(visual_samples[0]).cpu().numpy(), 0, 1), 1, 2
)
visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)
# get the names for the classes
class_names = np.array(args.class_names).reshape([-1])
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))
for im in range(4):
grayscale_cam = grayscale_cam_full[im, :]
rgb_image = np.swapaxes(
np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2
)
rgb_image[rgb_image < 1e-12] = 1e-12
visual_cam = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)
axes[im // 2, im % 2 * 2].imshow(rgb_image)
axes[im // 2, im % 2 * 2].axis("off")
axes[im // 2, im % 2 * 2].set_title(
"Original Image: %s" % (class_names[visual_labels[im]].capitalize())
)
axes[im // 2, im % 2 * 2 + 1].imshow(visual_cam)
axes[im // 2, im % 2 * 2 + 1].axis("off")
axes[im // 2, im % 2 * 2 + 1].set_title(
"Predicted: %s, %.2f%%" % (
class_names[pre_label[im]].capitalize(), pre_p[im] * 100)
)
plt.tight_layout()
plt.savefig(visual_save_path + f"/gradcam_{args.visual_dataset}.png")
print(f'Save to {visual_save_path + f"/gradcam_{args.visual_dataset}"}.png')