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analyse_lossplot.py
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analyse_lossplot.py
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"""Detect adv/clean from the hidden feature"""
from __future__ import absolute_import
from __future__ import print_function
import os
import argparse
from datasets import get_data
from models import get_model
import numpy as np
import sklearn.metrics
import keras.backend as K
import keras
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold, train_test_split
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from global_config import *
import glob
from tqdm import tqdm
import imageio
import cv2
from vis.visualization import visualize_saliency
from vis.utils import utils
from cleverhans.evaluation import batch_eval
DATASETS = ['dr', 'cxr', 'derm']
ATTACKS = ['fgsm', 'bim', 'jsma', 'cw-l2', 'clean']
TEST_SIZE = {'dr': 0.7, 'cxr': 0.7, 'derm': 0.5}
CLIP_MIN = {'mnist': -0.5, 'cifar': -0.5, 'svhn': -0.5, 'dr': -1.0, 'cxr': -1.0, 'derm': -1.0, 'imagenet':-128}
CLIP_MAX = {'mnist': 0.5, 'cifar': 0.5, 'svhn': 0.5, 'dr': 1.0, 'cxr': 1.0, 'derm': 1.0, 'imagenet':128}
def solve_name_controdiction(model):
for name in map(lambda x: x.__class__.__name__, model.layers):
K.get_uid(name)
K.get_uid('input')
K.get_uid('input')
def analyze(args):
assert args.dataset in ['mnist', 'cifar-10', 'svhn', 'dr', 'cxr', 'derm', 'imagenet'], \
"Dataset parameter must be either 'mnist', 'cifar-10', 'svhn', 'dr', 'cxr', or 'derm'"
# load feature/label data
if args.dataset == 'imagenet':
flist = glob.glob('data/imagenet/*.jpg')
X = map(lambda path: cv2.resize(imageio.imread(path), (224, 224)), flist)
X = list(X)
X = np.stack(X)
X = keras.applications.resnet50.preprocess_input(X)
model = keras.applications.resnet50.ResNet50(include_top=True)
layer_idx = utils.find_layer_idx(model, 'fc1000')
y = model.predict(X).argmax(-1) #[248, 281, 281, 248, 281]
elif args.dataset == 'mnist':
_, _, X, y = get_data(args.dataset, onehot=False, split_traintest=False) # clean image
model = get_model(args.dataset, softmax=True)
layer_idx = utils.find_layer_idx(model, 'dense_2')
solve_name_controdiction(model)
else:
_, _, X, y = get_data(args.dataset, onehot=False, split_traintest=False) # clean image
model = get_model(args.dataset, softmax=True)
layer_idx = utils.find_layer_idx(model, 'dense_2') # 'dense_nosoftmax')
solve_name_controdiction(model)
x_in = model.input
y_pred = model.output
y_true = keras.backend.placeholder(shape=y_pred.shape, dtype='float32')
loss = keras.losses.categorical_crossentropy(y_true, y_pred)
x_grad, = keras.backend.gradients(loss, x_in)
get_grad = keras.backend.function([x_in, y_true], [x_grad])
get_loss = keras.backend.function([x_in, y_true], [loss])
def reg(x):
if x.shape[-1] == 1:
x = np.tile(x, [1, 1, 3])
return (x - x.min()) / (x.max() - x.min())
plot_range = {
'imagenet': slice(0, None), # 2
'derm': np.concatenate([ np.where(y>0)[0][-5:], np.where(y<1)[0][:5] ]), # -1
'dr': np.concatenate([ np.where(y>0)[0][-5:], np.where(y<1)[0][:5] ]), # -6
'cxr': np.concatenate([ np.where(y>0)[0][-5:], np.where(y<1)[0][:5] ]), #-
'mnist': np.where(y == 6)[0][:10],
}
for i, (img, label) in enumerate(zip(X[plot_range[args.dataset]], y[plot_range[args.dataset]])):
grads, = get_grad([img[None, ...], keras.utils.to_categorical(label, int(y_true.shape[1]))])
grads = np.abs(grads)
sort = np.argsort(grads.reshape([-1]))
ind = np.unravel_index(np.argsort(grads, axis=None)[-2:], grads.shape)
_, (h1, h2), (w1, w2), (c1, c2) = ind
delta = 0.3 / 2 * (CLIP_MAX[args.dataset] - CLIP_MIN[args.dataset])
# assert img[h1, w1, c1] - delta >= CLIP_MIN[args.dataset] and img[h1, w1, c1] + delta <= CLIP_MAX[args.dataset]
# assert img[h2, w2, c2] - delta >= CLIP_MIN[args.dataset] and img[h2, w2, c2] + delta <= CLIP_MAX[args.dataset]
n_step = 100 + 1
tick = np.linspace(-delta, delta, n_step)
losses = np.zeros([n_step, n_step])
for j, d1 in enumerate(tqdm(tick)):
x = np.tile(img, [n_step, 1, 1, 1])
x[:, h1, w1, c1] += d1
x[:, h2, w2, c2] += tick
loss_line, = get_loss([x, keras.utils.to_categorical([label]*n_step, int(y_true.shape[1]))])
losses[j, :] = loss_line
X_grid, Y_grid = np.meshgrid(tick, tick)
X_grid *= 2 / (CLIP_MAX[args.dataset] - CLIP_MIN[args.dataset])
Y_grid *= 2 / (CLIP_MAX[args.dataset] - CLIP_MIN[args.dataset])
surf = np.stack([X_grid, Y_grid, losses])
np.save('vis/lossplot/surf_%s_%d.npy' % (args.dataset, i), surf)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X_grid, Y_grid, losses)
plt.savefig('vis/lossplot/%s_%d_plot.png' % (args.dataset, i))
plt.show()
# X, Y, Z = surf.reshape([3, -1])
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot_trisurf(X, Y, Z)
# plt.show()
if args.dataset == 'imagenet':
img = img[..., ::-1]
imageio.imwrite('vis/lossplot/%s_%d_original.png' % (args.dataset, i), img)
# return
def restore_surf():
fl = glob.glob('vis/lossplot/surf_*.npy')
for f in fl:
surf = np.load(f)
_, ds, id = f.split('_') # 'cxr', '2.npy'
id = id.split('.')[0] # 2
X_grid, Y_grid, losses = surf
losses -= losses[len(losses)//2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X_grid, Y_grid, losses, cmap='jet', vmin=-0.02, vmax=0.02)
ax.set_zlim(-0.02, 0.02)
plt.savefig('vis/lossplot/%s_%s_plot.png' % (ds, id))
plt.show()
return
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'-d', '--dataset',
help="Dataset to use",
required=True, type=str
)
parser.add_argument(
'-a', '--attack',
help="Attack to use train the discriminator; either 'fgsm', 'bim-a', 'bim-b', 'jsma', 'cw-l2'",
required=False, type=str
)
# args = parser.parse_args()
# analyze(args)
for ds in ['imagenet', 'dr', 'cxr', 'derm']:
# for atk in ['fgsm', 'bim', 'pgd']:
argv = ['-d', ds]
print('\n$> ', argv)
args = parser.parse_args(argv)
analyze(args)