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cnn_bounds_full.py
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"""
cnn_bounds_full.py
Main CNN-Cert computation file for general networks
Copyright (C) 2018, Akhilan Boopathy <[email protected]>
Lily Weng <[email protected]>
Pin-Yu Chen <[email protected]>
Sijia Liu <[email protected]>
Luca Daniel <[email protected]>
"""
from numba import njit, jit
import numpy as np
from setup_mnist import MNIST
from setup_cifar import CIFAR
from setup_tinyimagenet import tinyImagenet
from cnn_bounds_full_core import pool, conv, conv_bound, conv_full, conv_bound_full, pool_linear_bounds
from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D, Lambda
from tensorflow.contrib.keras.api.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, InputLayer, BatchNormalization, Reshape
from tensorflow.contrib.keras.api.keras.models import load_model
from tensorflow.contrib.keras.api.keras import backend as K
from train_resnet import ResidualStart, ResidualStart2
import tensorflow as tf
from utils import generate_data
import time
from activations import relu_linear_bounds, ada_linear_bounds, atan_linear_bounds, sigmoid_linear_bounds, tanh_linear_bounds
linear_bounds = None
import random
def loss(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted)
#General model class
class Model:
def __init__(self, model, inp_shape = (28,28,1)):
temp_weights = [layer.get_weights() for layer in model.layers]
self.shapes = []
self.sizes = []
self.weights = []
self.biases = []
self.pads = []
self.strides = []
self.types = []
self.model = model
cur_shape = inp_shape
self.shapes.append(cur_shape)
i = 0
while i < len(model.layers):
layer = model.layers[i]
i += 1
print(cur_shape)
weights = layer.get_weights()
if type(layer) == Conv2D:
print('conv')
if len(weights) == 1:
W = weights[0].astype(np.float32)
b = np.zeros(W.shape[-1], dtype=np.float32)
else:
W, b = weights
W = W.astype(np.float32)
b = b.astype(np.float32)
padding = layer.get_config()['padding']
stride = layer.get_config()['strides']
pad = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride[0]))
desired_w = int(np.ceil(cur_shape[0]/stride[1]))
total_padding_h = stride[0]*(desired_h-1)+W.shape[0]-cur_shape[0]
total_padding_w = stride[1]*(desired_w-1)+W.shape[1]-cur_shape[1]
pad = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
cur_shape = (int((cur_shape[0]+pad[0]+pad[1]-W.shape[0])/stride[0])+1, int((cur_shape[1]+pad[2]+pad[3]-W.shape[1])/stride[1])+1, W.shape[-1])
W = np.ascontiguousarray(W.transpose((3,0,1,2)).astype(np.float32))
b = np.ascontiguousarray(b.astype(np.float32))
self.types.append('conv')
self.sizes.append(None)
self.strides.append(stride)
self.pads.append(pad)
self.shapes.append(cur_shape)
self.weights.append(W)
self.biases.append(b)
elif type(layer) == GlobalAveragePooling2D:
print('global avg pool')
b = np.zeros(cur_shape[-1], dtype=np.float32)
W = np.zeros((cur_shape[0],cur_shape[1],cur_shape[2],cur_shape[2]), dtype=np.float32)
for f in range(W.shape[2]):
W[:,:,f,f] = 1/(cur_shape[0]*cur_shape[1])
pad = (0,0,0,0)
stride = ((1,1))
cur_shape = (1,1,cur_shape[2])
W = np.ascontiguousarray(W.transpose((3,0,1,2)).astype(np.float32))
b = np.ascontiguousarray(b.astype(np.float32))
self.types.append('conv')
self.sizes.append(None)
self.strides.append(stride)
self.pads.append(pad)
self.shapes.append(cur_shape)
self.weights.append(W)
self.biases.append(b)
elif type(layer) == AveragePooling2D:
print('avg pool')
b = np.zeros(cur_shape[-1], dtype=np.float32)
padding = layer.get_config()['padding']
pool_size = layer.get_config()['pool_size']
stride = layer.get_config()['strides']
W = np.zeros((pool_size[0],pool_size[1],cur_shape[2],cur_shape[2]), dtype=np.float32)
for f in range(W.shape[2]):
W[:,:,f,f] = 1/(pool_size[0]*pool_size[1])
pad = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride[0]))
desired_w = int(np.ceil(cur_shape[0]/stride[1]))
total_padding_h = stride[0]*(desired_h-1)+pool_size[0]-cur_shape[0]
total_padding_w = stride[1]*(desired_w-1)+pool_size[1]-cur_shape[1]
pad = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
cur_shape = (int((cur_shape[0]+pad[0]+pad[1]-pool_size[0])/stride[0])+1, int((cur_shape[1]+pad[2]+pad[3]-pool_size[1])/stride[1])+1, cur_shape[2])
W = np.ascontiguousarray(W.transpose((3,0,1,2)).astype(np.float32))
b = np.ascontiguousarray(b.astype(np.float32))
self.types.append('conv')
self.sizes.append(None)
self.strides.append(stride)
self.pads.append(pad)
self.shapes.append(cur_shape)
self.weights.append(W)
self.biases.append(b)
elif type(layer) == Activation or type(layer) == Lambda:
print('activation')
self.types.append('relu')
self.sizes.append(None)
self.strides.append(None)
self.pads.append(None)
self.shapes.append(cur_shape)
self.weights.append(None)
self.biases.append(None)
elif type(layer) == InputLayer:
print('input')
elif type(layer) == BatchNormalization:
print('batch normalization')
gamma, beta, mean, std = weights
std = np.sqrt(std+0.001) #Avoids zero division
a = gamma/std
b = -gamma*mean/std+beta
self.weights[-1] = a*self.weights[-1]
self.biases[-1] = a*self.biases[-1]+b
elif type(layer) == Dense:
print('FC')
W, b = weights
b = b.astype(np.float32)
W = W.reshape(list(cur_shape)+[W.shape[-1]]).astype(np.float32)
cur_shape = (1,1,W.shape[-1])
W = np.ascontiguousarray(W.transpose((3,0,1,2)).astype(np.float32))
b = np.ascontiguousarray(b.astype(np.float32))
self.types.append('conv')
self.sizes.append(None)
self.strides.append((1,1))
self.pads.append((0,0,0,0))
self.shapes.append(cur_shape)
self.weights.append(W)
self.biases.append(b)
elif type(layer) == Dropout:
print('dropout')
elif type(layer) == MaxPooling2D:
print('pool')
pool_size = layer.get_config()['pool_size']
stride = layer.get_config()['strides']
padding = layer.get_config()['padding']
pad = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride[0]))
desired_w = int(np.ceil(cur_shape[0]/stride[1]))
total_padding_h = stride[0]*(desired_h-1)+pool_size[0]-cur_shape[0]
total_padding_w = stride[1]*(desired_w-1)+pool_size[1]-cur_shape[1]
pad = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
cur_shape = (int((cur_shape[0]+pad[0]+pad[1]-pool_size[0])/stride[0])+1, int((cur_shape[1]+pad[2]+pad[3]-pool_size[1])/stride[1])+1, cur_shape[2])
self.types.append('pool')
self.sizes.append(pool_size)
self.strides.append(stride)
self.pads.append(pad)
self.shapes.append(cur_shape)
self.weights.append(None)
self.biases.append(None)
elif type(layer) == Flatten:
print('flatten')
elif type(layer) == Reshape:
print('reshape')
elif type(layer) == ResidualStart2:
print('basic block 2')
conv1 = model.layers[i]
bn1 = model.layers[i+1]
conv2 = model.layers[i+3]
conv3 = model.layers[i+4]
bn2 = model.layers[i+5]
bn3 = model.layers[i+6]
i = i+8
W1, bias1 = conv1.get_weights()
W2, bias2 = conv2.get_weights()
W3, bias3 = conv3.get_weights()
gamma1, beta1, mean1, std1 = bn1.get_weights()
std1 = np.sqrt(std1+0.001) #Avoids zero division
a1 = gamma1/std1
b1 = gamma1*mean1/std1+beta1
W1 = a1*W1
bias1 = a1*bias1+b1
gamma2, beta2, mean2, std2 = bn2.get_weights()
std2 = np.sqrt(std2+0.001) #Avoids zero division
a2 = gamma2/std2
b2 = gamma2*mean2/std2+beta2
W2 = a2*W2
bias2 = a2*bias2+b2
gamma3, beta3, mean3, std3 = bn3.get_weights()
std3 = np.sqrt(std3+0.001) #Avoids zero division
a3 = gamma3/std3
b3 = gamma3*mean3/std3+beta3
W3 = a3*W3
bias3 = a3*bias3+b3
padding1 = conv1.get_config()['padding']
stride1 = conv1.get_config()['strides']
pad1 = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding1 == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride1[0]))
desired_w = int(np.ceil(cur_shape[0]/stride1[1]))
total_padding_h = stride1[0]*(desired_h-1)+W1.shape[0]-cur_shape[0]
total_padding_w = stride1[1]*(desired_w-1)+W1.shape[1]-cur_shape[1]
pad1 = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
cur_shape = (int((cur_shape[0]+pad1[0]+pad1[1]-W1.shape[0])/stride1[0])+1, int((cur_shape[1]+pad1[2]+pad1[3]-W1.shape[1])/stride1[1])+1, W1.shape[3])
padding2 = conv2.get_config()['padding']
stride2 = conv2.get_config()['strides']
pad2 = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding2 == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride2[0]))
desired_w = int(np.ceil(cur_shape[0]/stride2[1]))
total_padding_h = stride2[0]*(desired_h-1)+W2.shape[0]-cur_shape[0]
total_padding_w = stride2[1]*(desired_w-1)+W2.shape[1]-cur_shape[1]
pad2 = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
padding3 = conv3.get_config()['padding']
stride3 = conv3.get_config()['strides']
pad3 = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding3 == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride3[0]))
desired_w = int(np.ceil(cur_shape[0]/stride3[1]))
total_padding_h = stride3[0]*(desired_h-1)+W3.shape[0]-cur_shape[0]
total_padding_w = stride3[1]*(desired_w-1)+W3.shape[1]-cur_shape[1]
pad3 = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
W1 = np.ascontiguousarray(W1.transpose((3,0,1,2)).astype(np.float32))
bias1 = np.ascontiguousarray(bias1.astype(np.float32))
W2 = np.ascontiguousarray(W2.transpose((3,0,1,2)).astype(np.float32))
bias2 = np.ascontiguousarray(bias2.astype(np.float32))
W3 = np.ascontiguousarray(W3.transpose((3,0,1,2)).astype(np.float32))
bias3 = np.ascontiguousarray(bias3.astype(np.float32))
self.types.append('basic_block_2')
self.sizes.append(None)
self.strides.append((stride1, stride2, stride3))
self.pads.append((pad1, pad2, pad3))
self.shapes.append(cur_shape)
self.weights.append((W1, W2, W3))
self.biases.append((bias1, bias2, bias3))
elif type(layer) == ResidualStart:
print('basic block')
conv1 = model.layers[i]
bn1 = model.layers[i+1]
conv2 = model.layers[i+3]
bn2 = model.layers[i+4]
i = i+6
W1, bias1 = conv1.get_weights()
W2, bias2 = conv2.get_weights()
gamma1, beta1, mean1, std1 = bn1.get_weights()
std1 = np.sqrt(std1+0.001) #Avoids zero division
a1 = gamma1/std1
b1 = gamma1*mean1/std1+beta1
W1 = a1*W1
bias1 = a1*bias1+b1
gamma2, beta2, mean2, std2 = bn2.get_weights()
std2 = np.sqrt(std2+0.001) #Avoids zero division
a2 = gamma2/std2
b2 = gamma2*mean2/std2+beta2
W2 = a2*W2
bias2 = a2*bias2+b2
padding1 = conv1.get_config()['padding']
stride1 = conv1.get_config()['strides']
pad1 = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding1 == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride1[0]))
desired_w = int(np.ceil(cur_shape[0]/stride1[1]))
total_padding_h = stride1[0]*(desired_h-1)+W1.shape[0]-cur_shape[0]
total_padding_w = stride1[1]*(desired_w-1)+W1.shape[1]-cur_shape[1]
pad1 = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
cur_shape = (int((cur_shape[0]+pad1[0]+pad1[1]-W1.shape[0])/stride1[0])+1, int((cur_shape[1]+pad1[2]+pad1[3]-W1.shape[1])/stride1[1])+1, W1.shape[3])
padding2 = conv2.get_config()['padding']
stride2 = conv2.get_config()['strides']
pad2 = (0,0,0,0) #p_hl, p_hr, p_wl, p_wr
if padding2 == 'same':
desired_h = int(np.ceil(cur_shape[0]/stride2[0]))
desired_w = int(np.ceil(cur_shape[0]/stride2[1]))
total_padding_h = stride2[0]*(desired_h-1)+W2.shape[0]-cur_shape[0]
total_padding_w = stride2[1]*(desired_w-1)+W2.shape[1]-cur_shape[1]
pad2 = (int(np.floor(total_padding_h/2)),int(np.ceil(total_padding_h/2)),int(np.floor(total_padding_w/2)),int(np.ceil(total_padding_w/2)))
W1 = np.ascontiguousarray(W1.transpose((3,0,1,2)).astype(np.float32))
bias1 = np.ascontiguousarray(bias1.astype(np.float32))
W2 = np.ascontiguousarray(W2.transpose((3,0,1,2)).astype(np.float32))
bias2 = np.ascontiguousarray(bias2.astype(np.float32))
self.types.append('basic_block')
self.sizes.append(None)
self.strides.append((stride1, stride2))
self.pads.append((pad1, pad2))
self.shapes.append(cur_shape)
self.weights.append((W1, W2))
self.biases.append((bias1, bias2))
else:
print(str(type(layer)))
raise ValueError('Invalid Layer Type')
print(cur_shape)
def predict(self, data):
return self.model(data)
@njit
def UL_conv_bound(A, B, pad, stride, shape, W, b, inner_pad, inner_stride, inner_shape):
A_new = np.zeros((A.shape[0], A.shape[1], A.shape[2], inner_stride[0]*(A.shape[3]-1)+W.shape[1], inner_stride[1]*(A.shape[4]-1)+W.shape[2], W.shape[3]), dtype=np.float32)
B_new = B.copy()
assert A.shape[5] == W.shape[0]
for x in range(A_new.shape[0]):
p_start = np.maximum(0, pad[0]-stride[0]*x)
p_end = np.minimum(A.shape[3], shape[0]+pad[0]-stride[0]*x)
t_start = np.maximum(0, -stride[0]*inner_stride[0]*x+inner_stride[0]*pad[0]+inner_pad[0])
t_end = np.minimum(A_new.shape[3], inner_shape[0]-stride[0]*inner_stride[0]*x+inner_stride[0]*pad[0]+inner_pad[0])
for y in range(A_new.shape[1]):
q_start = np.maximum(0, pad[2]-stride[1]*y)
q_end = np.minimum(A.shape[4], shape[1]+pad[2]-stride[1]*y)
u_start = np.maximum(0, -stride[1]*inner_stride[1]*y+inner_stride[1]*pad[2]+inner_pad[2])
u_end = np.minimum(A_new.shape[4], inner_shape[1]-stride[1]*inner_stride[1]*y+inner_stride[1]*pad[2]+inner_pad[2])
for t in range(t_start, t_end):
for u in range(u_start, u_end):
for p in range(p_start, p_end):
for q in range(q_start, q_end):
if 0<=t-inner_stride[0]*p<W.shape[1] and 0<=u-inner_stride[1]*q<W.shape[2]:
A_new[x,y,:,t,u,:] += np.dot(A[x,y,:,p,q,:],W[:,t-inner_stride[0]*p,u-inner_stride[1]*q,:])
for p in range(p_start, p_end):
for q in range(q_start, q_end):
B_new[x,y,:] += np.dot(A[x,y,:,p,q,:],b)
return A_new, B_new
basic_block_2_cache = {}
def UL_basic_block_2_bound(A, B, pad, stride, W1, W2, W3, b1, b2, b3, pad1, pad2, pad3, stride1, stride2, stride3, upper=True):
LB, UB = basic_block_2_cache[np.sum(W1)]
A1, B1 = UL_conv_bound(A, B, np.asarray(pad), np.asarray(stride), np.asarray(UB.shape), W2, b2, np.asarray(pad2), np.asarray(stride2), np.asarray(UB.shape))
inter_pad = (stride2[0]*pad[0]+pad2[0], stride2[0]*pad[1]+pad2[1], stride2[1]*pad[2]+pad2[2], stride2[1]*pad[3]+pad2[3])
inter_stride = (stride2[0]*stride[0], stride2[1]*stride[1])
alpha_u, alpha_l, beta_u, beta_l = linear_bounds(LB, UB)
if upper:
A1, B1 = UL_relu_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), alpha_u, alpha_l, beta_u, beta_l)
else:
A1, B1 = UL_relu_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), alpha_l, alpha_u, beta_l, beta_u)
A1, B1 = UL_conv_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), np.asarray(UB.shape), W1, b1, np.asarray(pad1), np.asarray(stride1), np.asarray(UB.shape))
A2, B2 = UL_conv_bound(A, B, np.asarray(pad), np.asarray(stride), np.asarray(UB.shape), W3, b3, np.asarray(pad3), np.asarray(stride3), np.asarray(UB.shape))
height_diff = A1.shape[3]-A2.shape[3]
width_diff = A1.shape[4]-A2.shape[4]
assert height_diff % 2 == 0
assert width_diff % 2 == 0
d_h = height_diff//2
d_w = width_diff//2
A1[:,:,:,d_h:A1.shape[3]-d_h,d_w:A1.shape[4]-d_w,:] += A2
return A1, B1+B2-B
basic_block_cache = {}
def UL_basic_block_bound(A, B, pad, stride, W1, W2, b1, b2, pad1, pad2, stride1, stride2, upper=True):
LB, UB = basic_block_cache[np.sum(W1)]
A1, B1 = UL_conv_bound(A, B, np.asarray(pad), np.asarray(stride), np.asarray(UB.shape), W2, b2, np.asarray(pad2), np.asarray(stride2), np.asarray(UB.shape))
inter_pad = (stride2[0]*pad[0]+pad2[0], stride2[0]*pad[1]+pad2[1], stride2[1]*pad[2]+pad2[2], stride2[1]*pad[3]+pad2[3])
inter_stride = (stride2[0]*stride[0], stride2[1]*stride[1])
alpha_u, alpha_l, beta_u, beta_l = linear_bounds(LB, UB)
if upper:
A1, B1 = UL_relu_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), alpha_u, alpha_l, beta_u, beta_l)
else:
A1, B1 = UL_relu_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), alpha_l, alpha_u, beta_l, beta_u)
A1, B1 = UL_conv_bound(A1, B1, np.asarray(inter_pad), np.asarray(inter_stride), np.asarray(UB.shape), W1, b1, np.asarray(pad1), np.asarray(stride1), np.asarray(UB.shape))
height_diff = A1.shape[3]-A.shape[3]
width_diff = A1.shape[4]-A.shape[4]
assert height_diff % 2 == 0
assert width_diff % 2 == 0
d_h = height_diff//2
d_w = width_diff//2
A1[:,:,:,d_h:A1.shape[3]-d_h,d_w:A1.shape[4]-d_w,:] += A
return A1, B1
@njit
def UL_relu_bound(A, B, pad, stride, alpha_u, alpha_l, beta_u, beta_l):
A_new = np.zeros_like(A)
A_plus = np.maximum(A, 0)
A_minus = np.minimum(A, 0)
B_new = B.copy()
for x in range(A_new.shape[0]):
p_start = np.maximum(0, pad[0]-stride[0]*x)
p_end = np.minimum(A.shape[3], alpha_u.shape[0]+pad[0]-stride[0]*x)
for y in range(A_new.shape[1]):
q_start = np.maximum(0, pad[2]-stride[1]*y)
q_end = np.minimum(A.shape[4], alpha_u.shape[1]+pad[2]-stride[1]*y)
for z in range(A_new.shape[2]):
for p in range(p_start, p_end):
for q in range(q_start, q_end):
for r in range(A.shape[5]):
A_new[x,y,z,p,q,r] += A_plus[x,y,z,p,q,r]*alpha_u[p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],r]
A_new[x,y,z,p,q,r] += A_minus[x,y,z,p,q,r]*alpha_l[p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],r]
B_new[x,y,z] += A_plus[x,y,z,p,q,r]*beta_u[p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],r]
B_new[x,y,z] += A_minus[x,y,z,p,q,r]*beta_l[p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],r]
return A_new, B_new
@njit
def UL_pool_bound(A, B, pad, stride, pool_size, inner_pad, inner_stride, inner_shape, alpha_u, alpha_l, beta_u, beta_l):
A_new = np.zeros((A.shape[0], A.shape[1], A.shape[2], inner_stride[0]*(A.shape[3]-1)+pool_size[0], inner_stride[1]*(A.shape[4]-1)+pool_size[1], A.shape[5]), dtype=np.float32)
B_new = B.copy()
A_plus = np.maximum(A, 0)
A_minus = np.minimum(A, 0)
for x in range(A_new.shape[0]):
for y in range(A_new.shape[1]):
for t in range(A_new.shape[3]):
for u in range(A_new.shape[4]):
inner_index_x = t+stride[0]*inner_stride[0]*x-inner_stride[0]*pad[0]-inner_pad[0]
inner_index_y = u+stride[1]*inner_stride[1]*y-inner_stride[1]*pad[2]-inner_pad[2]
if 0<=inner_index_x<inner_shape[0] and 0<=inner_index_y<inner_shape[1]:
for p in range(A.shape[3]):
for q in range(A.shape[4]):
if 0<=t-inner_stride[0]*p<alpha_u.shape[0] and 0<=u-inner_stride[1]*q<alpha_u.shape[1] and 0<=p+stride[0]*x-pad[0]<alpha_u.shape[2] and 0<=q+stride[1]*y-pad[2]<alpha_u.shape[3]:
A_new[x,y,:,t,u,:] += A_plus[x,y,:,p,q,:]*alpha_u[t-inner_stride[0]*p,u-inner_stride[1]*q,p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],:]
A_new[x,y,:,t,u,:] += A_minus[x,y,:,p,q,:]*alpha_l[t-inner_stride[0]*p,u-inner_stride[1]*q,p+stride[0]*x-pad[0],q+stride[1]*y-pad[2],:]
B_new += conv_full(A_plus,beta_u,pad,stride) + conv_full(A_minus,beta_l,pad,stride)
return A_new, B_new
#Main function to find bounds at each layer
def compute_bounds(weights, biases, out_shape, nlayer, x0, eps, p_n, pads, strides, sizes, types, LBs, UBs):
if types[nlayer-1] == 'relu':
#print('relu')
return np.maximum(LBs[nlayer-1], 0), np.maximum(UBs[nlayer-1], 0)
elif types[nlayer-1] == 'conv':
#print('conv')
A_u = weights[nlayer-1].reshape((1, 1, weights[nlayer-1].shape[0], weights[nlayer-1].shape[1], weights[nlayer-1].shape[2], weights[nlayer-1].shape[3]))*np.ones((out_shape[0], out_shape[1], weights[nlayer-1].shape[0], weights[nlayer-1].shape[1], weights[nlayer-1].shape[2], weights[nlayer-1].shape[3]), dtype=np.float32)
B_u = biases[nlayer-1]*np.ones((out_shape[0], out_shape[1], out_shape[2]), dtype=np.float32)
A_l = A_u.copy()
B_l = B_u.copy()
pad = pads[nlayer-1]
stride = strides[nlayer-1]
elif types[nlayer-1] == 'pool':
#print('pool')
A_u = np.eye(out_shape[2]).astype(np.float32).reshape((1,1,out_shape[2],1,1,out_shape[2]))*np.ones((out_shape[0], out_shape[1], out_shape[2], 1,1,out_shape[2]), dtype=np.float32)
B_u = np.zeros(out_shape, dtype=np.float32)
A_l = A_u.copy()
B_l = B_u.copy()
pad = (0,0,0,0)
stride = (1,1)
alpha_u, alpha_l, beta_u, beta_l = pool_linear_bounds(LBs[nlayer-1], UBs[nlayer-1], np.asarray(pads[nlayer-1]), np.asarray(strides[nlayer-1]), np.asarray(sizes[nlayer-1]))
A_u, B_u = UL_pool_bound(A_u, B_u, np.asarray(pad), np.asarray(stride), np.asarray(sizes[nlayer-1]), np.asarray(pads[nlayer-1]), np.asarray(strides[nlayer-1]), np.asarray(LBs[nlayer-1].shape), alpha_u, alpha_l, beta_u, beta_l)
A_l, B_l = UL_pool_bound(A_l, B_l, np.asarray(pad), np.asarray(stride), np.asarray(sizes[nlayer-1]), np.asarray(pads[nlayer-1]), np.asarray(strides[nlayer-1]), np.asarray(LBs[nlayer-1].shape), alpha_l, alpha_u, beta_l, beta_u)
pad = pads[nlayer-1]
stride = strides[nlayer-1]
elif types[nlayer-1] == 'basic_block_2':
#print('basic block 2')
W1, W2, W3 = weights[nlayer-1]
b1, b2, b3 = biases[nlayer-1]
pad1, pad2, pad3 = pads[nlayer-1]
stride1, stride2, stride3 = strides[nlayer-1]
LB, UB = compute_bounds(weights[:nlayer-1]+[W1], biases[:nlayer-1]+[b1], out_shape, nlayer, x0, eps, p_n, pads[:nlayer-1]+[pad1], strides[:nlayer-1]+[stride1], sizes, types[:nlayer-1]+['conv'], LBs, UBs)
basic_block_2_cache[np.sum(W1)] = (LB, UB)
A_u = np.eye(out_shape[2]).astype(np.float32).reshape((1,1,out_shape[2],1,1,out_shape[2]))*np.ones((out_shape[0], out_shape[1], out_shape[2], 1,1,out_shape[2]), dtype=np.float32)
B_u = np.zeros(out_shape, dtype=np.float32)
A_l = A_u.copy()
B_l = B_u.copy()
pad = (0,0,0,0)
stride = (1,1)
A_u, B_u = UL_basic_block_2_bound(A_u, B_u, pad, stride, *weights[nlayer-1], *biases[nlayer-1], *pads[nlayer-1], *strides[nlayer-1], upper=True)
A_l, B_l = UL_basic_block_2_bound(A_l, B_l, pad, stride, *weights[nlayer-1], *biases[nlayer-1], *pads[nlayer-1], *strides[nlayer-1], upper=False)
inner_pad = pad3
inner_stride = stride3
pad = (inner_stride[0]*pad[0]+inner_pad[0], inner_stride[0]*pad[1]+inner_pad[1], inner_stride[1]*pad[2]+inner_pad[2], inner_stride[1]*pad[3]+inner_pad[3])
stride = (inner_stride[0]*stride[0], inner_stride[1]*stride[1])
elif types[nlayer-1] == 'basic_block':
#print('basic block')
W1, W2 = weights[nlayer-1]
b1, b2 = biases[nlayer-1]
pad1, pad2 = pads[nlayer-1]
stride1, stride2 = strides[nlayer-1]
LB, UB = compute_bounds(weights[:nlayer-1]+[W1], biases[:nlayer-1]+[b1], out_shape, nlayer, x0, eps, p_n, pads[:nlayer-1]+[pad1], strides[:nlayer-1]+[stride1], sizes, types[:nlayer-1]+['conv'], LBs, UBs)
basic_block_cache[np.sum(W1)] = (LB, UB)
A_u = np.eye(out_shape[2]).astype(np.float32).reshape((1,1,out_shape[2],1,1,out_shape[2]))*np.ones((out_shape[0], out_shape[1], out_shape[2], 1,1,out_shape[2]), dtype=np.float32)
B_u = np.zeros(out_shape, dtype=np.float32)
A_l = A_u.copy()
B_l = B_u.copy()
pad = (0,0,0,0)
stride = (1,1)
A_u, B_u = UL_basic_block_bound(A_u, B_u, pad, stride, *weights[nlayer-1], *biases[nlayer-1], *pads[nlayer-1], *strides[nlayer-1], upper=True)
A_l, B_l = UL_basic_block_bound(A_l, B_l, pad, stride, *weights[nlayer-1], *biases[nlayer-1], *pads[nlayer-1], *strides[nlayer-1], upper=False)
for i in range(nlayer-2, -1, -1):
if types[i] == 'conv':
#print('conv')
A_u, B_u = UL_conv_bound(A_u, B_u, np.asarray(pad), np.asarray(stride), np.asarray(UBs[i+1].shape), weights[i], biases[i], np.asarray(pads[i]), np.asarray(strides[i]), np.asarray(UBs[i].shape))
A_l, B_l = UL_conv_bound(A_l, B_l, np.asarray(pad), np.asarray(stride), np.asarray(LBs[i+1].shape), weights[i], biases[i], np.asarray(pads[i]), np.asarray(strides[i]), np.asarray(LBs[i].shape))
pad = (strides[i][0]*pad[0]+pads[i][0], strides[i][0]*pad[1]+pads[i][1], strides[i][1]*pad[2]+pads[i][2], strides[i][1]*pad[3]+pads[i][3])
stride = (strides[i][0]*stride[0], strides[i][1]*stride[1])
elif types[i] == 'pool':
#print('pool')
alpha_u, alpha_l, beta_u, beta_l = pool_linear_bounds(LBs[i], UBs[i], np.asarray(pads[i]), np.asarray(strides[i]), np.asarray(sizes[i]))
A_u, B_u = UL_pool_bound(A_u, B_u, np.asarray(pad), np.asarray(stride), np.asarray(sizes[i]), np.asarray(pads[i]), np.asarray(strides[i]), np.asarray(UBs[i].shape), alpha_u, alpha_l, beta_u, beta_l)
A_l, B_l = UL_pool_bound(A_l, B_l, np.asarray(pad), np.asarray(stride), np.asarray(sizes[i]), np.asarray(pads[i]), np.asarray(strides[i]), np.asarray(LBs[i].shape), alpha_l, alpha_u, beta_l, beta_u)
pad = (strides[i][0]*pad[0]+pads[i][0], strides[i][0]*pad[1]+pads[i][1], strides[i][1]*pad[2]+pads[i][2], strides[i][1]*pad[3]+pads[i][3])
stride = (strides[i][0]*stride[0], strides[i][1]*stride[1])
elif types[i] == 'relu':
#print('relu')
alpha_u, alpha_l, beta_u, beta_l = linear_bounds(LBs[i], UBs[i])
A_u, B_u = UL_relu_bound(A_u, B_u, np.asarray(pad), np.asarray(stride), alpha_u, alpha_l, beta_u, beta_l)
A_l, B_l = UL_relu_bound(A_l, B_l, np.asarray(pad), np.asarray(stride), alpha_l, alpha_u, beta_l, beta_u)
elif types[i] == 'basic_block_2':
#print('basic block 2')
A_u, B_u = UL_basic_block_2_bound(A_u, B_u, pad, stride, *weights[i], *biases[i], *pads[i], *strides[i], upper=True)
A_l, B_l = UL_basic_block_2_bound(A_l, B_l, pad, stride, *weights[i], *biases[i], *pads[i], *strides[i], upper=False)
inner_pad = pads[i][0]
inner_stride = strides[i][0]
pad = (inner_stride[0]*pad[0]+inner_pad[0], inner_stride[0]*pad[1]+inner_pad[1], inner_stride[1]*pad[2]+inner_pad[2], inner_stride[1]*pad[3]+inner_pad[3])
stride = (inner_stride[0]*stride[0], inner_stride[1]*stride[1])
inner_pad = pads[i][1]
inner_stride = strides[i][1]
pad = (inner_stride[0]*pad[0]+inner_pad[0], inner_stride[0]*pad[1]+inner_pad[1], inner_stride[1]*pad[2]+inner_pad[2], inner_stride[1]*pad[3]+inner_pad[3])
stride = (inner_stride[0]*stride[0], inner_stride[1]*stride[1])
elif types[i] == 'basic_block':
#print('basic block')
A_u, B_u = UL_basic_block_bound(A_u, B_u, pad, stride, *weights[i], *biases[i], *pads[i], *strides[i], upper=True)
A_l, B_l = UL_basic_block_bound(A_l, B_l, pad, stride, *weights[i], *biases[i], *pads[i], *strides[i], upper=False)
inner_pad = pads[i][0]
inner_stride = strides[i][0]
pad = (inner_stride[0]*pad[0]+inner_pad[0], inner_stride[0]*pad[1]+inner_pad[1], inner_stride[1]*pad[2]+inner_pad[2], inner_stride[1]*pad[3]+inner_pad[3])
stride = (inner_stride[0]*stride[0], inner_stride[1]*stride[1])
inner_pad = pads[i][1]
inner_stride = strides[i][1]
pad = (inner_stride[0]*pad[0]+inner_pad[0], inner_stride[0]*pad[1]+inner_pad[1], inner_stride[1]*pad[2]+inner_pad[2], inner_stride[1]*pad[3]+inner_pad[3])
stride = (inner_stride[0]*stride[0], inner_stride[1]*stride[1])
LUB, UUB = conv_bound_full(A_u, B_u, np.asarray(pad), np.asarray(stride), x0, eps, p_n)
LLB, ULB = conv_bound_full(A_l, B_l, np.asarray(pad), np.asarray(stride), x0, eps, p_n)
return LLB, UUB
#Main function to find output bounds
def find_output_bounds(weights, biases, shapes, pads, strides, sizes, types, x0, eps, p_n):
LBs = [x0-eps]
UBs = [x0+eps]
for i in range(1,len(weights)+1):
print('Layer ' + str(i))
LB, UB = compute_bounds(weights, biases, shapes[i], i, x0, eps, p_n, pads, strides, sizes, types, LBs, UBs)
UBs.append(UB)
LBs.append(LB)
return LBs[-1], UBs[-1]
#Warms up numba functions
def warmup(model, x, eps_0, p_n, func):
print('Warming up...')
weights = model.weights[:-1]
biases = model.biases[:-1]
shapes = model.shapes[:-1]
W, b, s = model.weights[-1], model.biases[-1], model.shapes[-1]
last_weight = np.ascontiguousarray((W[0,:,:,:]).reshape([1]+list(W.shape[1:])),dtype=np.float32)
weights.append(last_weight)
biases.append(np.asarray([b[0]]))
shapes.append((1,1,1))
func(weights, biases, shapes, model.pads, model.strides, model.sizes, model.types, x, eps_0, p_n)
#Main function to compute CNN-Cert bound
def run(file_name, n_samples, p_n, q_n, activation = 'relu', cifar=False, tinyimagenet=False):
np.random.seed(1215)
tf.set_random_seed(1215)
random.seed(1215)
keras_model = load_model(file_name, custom_objects={'fn':loss, 'ResidualStart':ResidualStart, 'ResidualStart2':ResidualStart2, 'tf':tf})
if tinyimagenet:
model = Model(keras_model, inp_shape = (64,64,3))
elif cifar:
model = Model(keras_model, inp_shape = (32,32,3))
else:
model = Model(keras_model)
#Set correct linear_bounds function
global linear_bounds
if activation == 'relu':
linear_bounds = relu_linear_bounds
elif activation == 'ada':
linear_bounds = ada_linear_bounds
elif activation == 'sigmoid':
linear_bounds = sigmoid_linear_bounds
elif activation == 'tanh':
linear_bounds = tanh_linear_bounds
elif activation == 'arctan':
linear_bounds = atan_linear_bounds
if cifar:
inputs, targets, true_labels, true_ids, img_info = generate_data(CIFAR(), samples=n_samples, targeted=True, random_and_least_likely = True, target_type = 0b0010, predictor=model.model.predict, start=0)
elif tinyimagenet:
inputs, targets, true_labels, true_ids, img_info = generate_data(tinyImagenet(), samples=n_samples, targeted=True, random_and_least_likely = True, target_type = 0b0010, predictor=model.model.predict, start=0)
else:
inputs, targets, true_labels, true_ids, img_info = generate_data(MNIST(), samples=n_samples, targeted=True, random_and_least_likely = True, target_type = 0b0010, predictor=model.model.predict, start=0)
if len(inputs) == 0:
return 0, 0
#0b01111 <- all
#0b0010 <- random
#0b0001 <- top2
#0b0100 <- least
preds = model.model.predict(inputs[0][np.newaxis,:]).flatten()
steps = 15
eps_0 = 0.05
summation = 0
warmup(model, inputs[0].astype(np.float32), eps_0, p_n, find_output_bounds)
start_time = time.time()
for i in range(len(inputs)):
print('--- CNN-Cert: Computing eps for input image ' + str(i)+ '---')
predict_label = np.argmax(true_labels[i])
target_label = np.argmax(targets[i])
weights = model.weights[:-1]
biases = model.biases[:-1]
shapes = model.shapes[:-1]
W, b, s = model.weights[-1], model.biases[-1], model.shapes[-1]
last_weight = (W[predict_label,:,:,:]-W[target_label,:,:,:]).reshape([1]+list(W.shape[1:]))
weights.append(last_weight)
biases.append(np.asarray([b[predict_label]-b[target_label]]))
shapes.append((1,1,1))
#Perform binary searchcc
log_eps = np.log(eps_0)
log_eps_min = -np.inf
log_eps_max = np.inf
for j in range(steps):
#print('Step ' + str(j))
LB, UB = find_output_bounds(weights, biases, shapes, model.pads, model.strides, model.sizes, model.types, inputs[i].astype(np.float32), np.exp(log_eps), p_n)
print("Step {}, eps = {:.5f}, {:.6s} <= f_c - f_t <= {:.6s}".format(j,np.exp(log_eps),str(np.squeeze(LB)),str(np.squeeze(UB))))
if LB > 0: #Increase eps
log_eps_min = log_eps
log_eps = np.minimum(log_eps+1, (log_eps_max+log_eps_min)/2)
else: #Decrease eps
log_eps_max = log_eps
log_eps = np.maximum(log_eps-1, (log_eps_max+log_eps_min)/2)
if p_n == 105:
str_p_n = 'i'
else:
str_p_n = str(p_n)
print("[L1] method = CNN-Cert-{}, model = {}, image no = {}, true_id = {}, target_label = {}, true_label = {}, norm = {}, robustness = {:.5f}".format(activation,file_name, i, true_ids[i],target_label,predict_label,str_p_n,np.exp(log_eps_min)))
summation += np.exp(log_eps_min)
K.clear_session()
eps_avg = summation/len(inputs)
total_time = (time.time()-start_time)/len(inputs)
print("[L0] method = CNN-Cert-{}, model = {}, total images = {}, norm = {}, avg robustness = {:.5f}, avg runtime = {:.2f}".format(activation,file_name,len(inputs),str_p_n,eps_avg,total_time))
return eps_avg, total_time