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analyse_deconv.py
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analyse_deconv.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
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 global_config import *
import imageio
import deeplift
import innvestigate
import innvestigate.utils as iutils
import innvestigate.utils.visualizations as ivis
import eutils
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}
CLIP_MAX = {'mnist': 0.5, 'cifar': 0.5, 'svhn': 0.5, 'dr': 1.0, 'cxr': 1.0, 'derm': 1.0}
def balance_data(X, y, sub_sample=False): # assume that the positive samples is less than negative ones
idx_pos, = np.where(y>0)
idx_neg, = np.where(y<1)
if sub_sample:
idx_neg = np.random.choice(idx_neg, len(idx_pos))
else: # over sample
idx_pos = np.random.choice(idx_pos, len(idx_neg))
idx_resample = np.concatenate([idx_neg, idx_pos])
return X[idx_resample], y[idx_resample]
def bk_proj(X):
return ivis.graymap(X)
def heatmap(X):
return ivis.heatmap(X)
def graymap(X):
return ivis.graymap(np.abs(X)) #, input_is_postive_only=True)
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 detect(args):
assert args.dataset in ['mnist', 'cifar-10', 'svhn', 'dr', 'cxr', 'derm'], \
"Dataset parameter must be either 'mnist', 'cifar-10', 'svhn', 'dr', 'cxr', or 'derm'"
assert args.attack in ['fgsm', 'bim', 'jsma', 'deepfool', 'pgd', 'ead', 'cw-l2', 'cw-lid'], \
"Attack parameter must be either 'fgsm', 'bim', 'jsma', 'deepfool', " \
"'pgd', 'ead', 'cw-l2', 'cw-lid'"
# load feature/label data
cX_train, cy_train, cX_test, cy_test = get_data(args.dataset, onehot=False) # clean feat
_, _, aX_test, ay_test = get_data(args.dataset, onehot=False) # attack feat
correct_idx, train_idx, test_idx = np.load('data/' + ADV_PREFIX + 'split_%s.npy' % args.dataset, allow_pickle=True)
aX_all = np.load('data/' + ADV_PREFIX + 'Adv_%s_%s.npy' % (args.dataset, args.attack))
aX_test = aX_all[test_idx]
image_shape = cX_test.shape[1:]
model = get_model(args.dataset)
solve_name_controdiction(model)
input_range = [CLIP_MIN[args.dataset], CLIP_MAX[args.dataset]]
# Scale to [0, 1] range for plotting.
def input_postprocessing(X):
return (X - CLIP_MIN[args.dataset]) / (CLIP_MAX[args.dataset] - CLIP_MIN[args.dataset])
noise_scale = (input_range[1] - input_range[0]) * 0.1
ri = input_range[0] # reference input
methods = [
# NAME OPT.PARAMS POSTPROC FXN TITLE
# Show input
("input", {}, input_postprocessing, "Input"),
# Function
("gradient", {"postprocess": "abs"}, graymap, "Gradient"),
# ("smoothgrad", {"noise_scale": noise_scale,
# "postprocess": "square"},graymap, "SmoothGrad"),
# Signal
("deconvnet", {}, bk_proj, "Deconvnet"),
("guided_backprop", {}, bk_proj, "Guided Backprop",),
#("pattern.net", {"pattern_type": "relu"}, bk_proj, "PatternNet"),
# Interaction
#("pattern.attribution", {"pattern_type": "relu"}, heatmap, "PatternAttribution"),
("deep_taylor.bounded", {"low": input_range[0],
"high": input_range[1]}, heatmap, "DeepTaylor"),
("input_t_gradient", {}, heatmap, "Input * Gradient"),
# ("integrated_gradients", {"reference_inputs": ri}, heatmap, "Integrated Gradients"),
# ("deep_lift.wrapper", {"reference_inputs": ri}, heatmap, "DeepLIFT Wrapper - Rescale"),
# ("deep_lift.wrapper", {"reference_inputs": ri, "nonlinear_mode": "reveal_cancel"},
# heatmap, "DeepLIFT Wrapper - RevealCancel"),
("lrp.z", {}, heatmap, "LRP-Z"),
("lrp.epsilon", {"epsilon": 1}, heatmap, "LRP-Epsilon"),
]
model_wo_softmax = get_model(args.dataset, softmax=False)
analyzers = []
for method in methods:
analyzer = innvestigate.create_analyzer(method[0], # analysis method identifier
model_wo_softmax, # model without softmax output
**method[1]) # optional analysis parameters
# Some analyzers require training.
analyzer.fit(cX_train, batch_size=256, verbose=1)
analyzers.append(analyzer)
idx_pos, = np.where(cy_test>0)
idx_neg, = np.where(cy_test<1)
def analyse(test_images, output_path):
analysis = np.zeros([len(test_images), len(analyzers)] + list(image_shape))
text = []
for i, (x, y) in enumerate(test_images):
# Add batch axis.
x = x[None, :, :, :]
# Predict final activations, probabilites, and label.
presm = model_wo_softmax.predict_on_batch(x)[0]
prob = model.predict_on_batch(x)[0]
y_hat = prob.argmax()
# Save prediction info:
text.append(("%s" % str(y), # ground truth label
"%.2f" % presm.max(), # pre-softmax logits
"%.2f" % prob.max(), # probabilistic softmax output
"%s" % str(y_hat) # predicted label
))
for aidx, analyzer in enumerate(analyzers):
# Analyze.
print('analyzing', analyzer, '...')
a = analyzer.analyze(x)
# Apply common postprocessing, e.g., re-ordering the channels for plotting.
# a = postprocess(a)
# Apply analysis postprocessing, e.g., creating a heatmap.
a = methods[aidx][2](a)
# Store the analysis.
analysis[i, aidx] = a[0]
# analysis_c = np.concatenate(np.concatenate(analysis, axis=2), axis=0)
analysis_c = analysis[:, -2, ...] # lrp.z
analysis_c = np.reshape(analysis_c, [6, 6, 224, 224, 3])
analysis_c = np.concatenate(np.concatenate(analysis_c, axis=2), axis=0)
imageio.imsave(output_path, analysis_c)
return analysis
n = 36
ana1 = analyse(list(zip(cX_test[idx_pos][:n], cy_test[idx_pos][:n])), 'vis/ana_cpos.png')
# analyse(list(zip(cX_test[idx_neg][:n], cy_test[idx_neg][:n])), 'vis/ana_cneg.png')
# analyse(list(zip(aX_test[idx_pos][:n], cy_test[idx_pos][:n])), 'vis/ana_apos.png')
ana2 = analyse(list(zip(aX_test[idx_neg][:n], cy_test[idx_neg][:n])), 'vis/ana_aneg.png')
iutils.postprocess_images()
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=True, type=str
)
args = parser.parse_args()
detect(args)
# for ds in ['derm', 'dr', 'cxr']:
# for atk in ['fgsm', 'bim', 'pgd']:
# argv = ['-d', ds, '-a', atk]
# print('\n$> ', argv)
# args = parser.parse_args(argv)
# detect(args)