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calculate_energy_variable.py
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import os
import sys
import numpy as np
import time
import pickle
from torch import manual_seed
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
from PIL import Image
from scipy.ndimage import sobel, laplace
SEEDS = [12031212,1234,5845389,23423,343495,2024,3842834,23402304,482347247,1029237127]
SEED=SEEDS[int(sys.argv[1])]
manual_seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
os.environ['PYTHONHASHSEED']=str(SEED)
random.seed(SEED)
# if sys.argv[2] is not None:
# DEVICE = torch.device("cuda",int(sys.argv[2]))
# else:
# DEVICE = torch.device("cuda",2)
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# DEVICE = 'cpu'
from captum.attr import IntegratedGradients, GradientShap, Deconvolution, LRP, Lime
from zennit.composites import EpsilonAlpha2Beta1
split = sys.argv[1]
model_ind = sys.argv[2]
print(f'SPLIT: {split}')
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=5)
self.conv2=nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3)
self.conv3=nn.Conv2d(in_channels=128,out_channels=256,kernel_size=5)
self.maxPooling2=nn.MaxPool2d(kernel_size=2)
self.maxPooling4_0=nn.MaxPool2d(kernel_size=4)
self.maxPooling4_1=nn.MaxPool2d(kernel_size=4)
# self.adPooling=nn.AdaptiveAvgPool1d(256)
self.fc1=nn.Linear(in_features=256,out_features=128)
self.fc2=nn.Linear(in_features=128,out_features=64)
self.out=nn.Linear(in_features=64,out_features=2)
def forward(self,x):
x=self.conv1(x)
x=self.maxPooling4_0(x)
x=F.relu(x)
x=self.conv2(x)
x=self.maxPooling4_1(x)
x=F.relu(x)
x=self.conv3(x)
x=self.maxPooling2(x)
x=F.relu(x)
x=F.dropout(x)
x=x.view(1,x.size()[0],-1) #stretch to 1d data
#x=self.adPooling(x).squeeze()
x=self.fc1(x)
x=F.relu(x)
x=self.fc2(x)
x=F.relu(x)
x=self.out(x)
return x[0]
def rescale_values(image,max_val,min_val):
'''
image - numpy array
max_val/min_val - float
'''
return (image-image.min())/(image.max()-image.min())*(max_val-min_val)+min_val
with open(f'./artifacts/split_{split}_no_watermark_variable_test.pkl', 'rb') as f:
no_watermark_dataset, labels_test_no, _, _ = pickle.load(f)
no_watermark_dataset = [[rescale_values(x,1,0).transpose(2,0,1),labels_test_no.flatten()[i]] for i,x in enumerate(no_watermark_dataset)]
with open(f'./artifacts/split_{split}_all_watermark_variable_test.pkl', 'rb') as f:
watermark_dataset, labels_test, _, masks_wm = pickle.load(f)
watermark_dataset = [[rescale_values(x,1,0).transpose(2,0,1),labels_test.flatten()[i]] for i,x in enumerate(watermark_dataset)]
# folder=os.getcwd()
# watermark_path=folder+'/watermark banner.jpg'
# watermark = Image.open(watermark_path)
# w=int(watermark_dataset[0][0].shape[1])
# h=int(watermark.size[1]*w/watermark.size[0])
# watermark = watermark.resize((w,h))
# watermark=np.array(watermark)
# rgb=rescale_values(watermark,1,0)
# r, g, blue = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
# gray = 1-(0.2989 * r + 0.5870 * g + 0.1140 * blue)
# white=np.ones((w,w))
# white[0:gray.shape[0],0:gray.shape[1]]=gray
# bin_water=1.0*(white<1)
def lrp(data,model,target,device):
# create a composite instance
#composite = EpsilonPlusFlat()
composite = EpsilonAlpha2Beta1()
# use the following instead to ignore bias for the relevance
# composite = EpsilonPlusFlat(zero_params='bias')
# make sure the input requires a gradient
data.requires_grad = True
# compute the output and gradient within the composite's context
with composite.context(model) as modified_model:
modified_model=modified_model.to(device)
output = modified_model(data.to(device)).to(device)
grad = torch.eye(2, device=device)[[target]].to(device)
# gradient/ relevance wrt. class/output 0
output.backward(gradient=grad)
# relevance is not accumulated in .grad if using torch.autograd.grad
# relevance, = torch.autograd.grad(output, input, torch.eye(10)[[0])
# gradient is accumulated in input.grad
att=abs(data.grad.detach().cpu().squeeze().numpy().transpose(1,2,0))
rgb_weights = [0.2989, 0.5870, 0.1140]
grayscale_att_lrp = np.dot(att[...,:3], rgb_weights)
return grayscale_att_lrp
def plot_atts(data,model,target):
# data is a tensor of shape torch.Size([1, 3, 128, 128])
# model is
# target is an integer
torch.manual_seed(SEED)
out=model(data)
Y_probs = F.softmax(out[0], dim=-1)
target = int(target)
ig_att = np.transpose(IntegratedGradients(model).attribute(data, target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
# sal_att = np.transpose(Saliency(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
gradshap_att = np.transpose(GradientShap(model).attribute(data,target=target, baselines=torch.zeros(data.shape).to(DEVICE)).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
# backprop_att = np.transpose(GuidedBackprop(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
# ix_att = np.transpose(InputXGradient(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
deconv_att = np.transpose(Deconvolution(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
lrp_att=np.transpose(LRP(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
lime_att=np.transpose(Lime(model).attribute(data,target=target).squeeze().cpu().detach().numpy(), (1, 2, 0)).squeeze()
lrp_ab = lrp(data,model,target, DEVICE)
rgb_weights = [0.2989, 0.5870, 0.1140]
grayscale_att_deconv = np.dot(deconv_att[...,:3], rgb_weights)
# grayscale_att_ix = np.dot(ix_att[...,:3], rgb_weights)
# grayscale_att_backprp = np.dot(backprop_att[...,:3], rgb_weights)
grayscale_att_shap = np.dot(gradshap_att[...,:3], rgb_weights)
# grayscale_att_sal = np.dot(sal_att[...,:3], rgb_weights)
grayscale_att_ig = np.dot(ig_att[...,:3], rgb_weights)
grayscale_att_lrp = np.dot(lrp_att[...,:3], rgb_weights)
grayscale_att_lime = np.dot(lime_att[...,:3], rgb_weights)
atts={
'deconv':abs(grayscale_att_deconv),
# 'saliency':abs(grayscale_att_sal),
'int_grads':abs(grayscale_att_ig),
'shap':abs(grayscale_att_shap),
# 'backprop':abs(grayscale_att_backprp),
# 'ix':abs(grayscale_att_ix),
'lrp':abs(grayscale_att_lrp),
'lrp_ab':abs(lrp_ab),
'lime': abs(grayscale_att_lime)
}
return atts,Y_probs
# def energy(att):
# image_size=att.shape[0]*att.shape[1]
# watermark_size=np.sum(bin_water)
# watermark_att=att*bin_water
# watermark_energy=np.sum(watermark_att)
# image_energy=np.sum(att)
# energy=(watermark_energy/watermark_size)/(image_energy/image_size)
# return energy
def energy(att, mask):
mask[mask>0]=1
image_size=att.shape[0]*att.shape[1]
watermark_size=np.sum(mask)
watermark_att=att*mask
watermark_energy=np.sum(watermark_att)
image_energy=np.sum(att)
# Gives energy(watermark) = 7.858
energy=(watermark_energy/watermark_size)/(image_energy/image_size)
# Gives energy(watermark) = 1.0
# energy = (np.sum(att*bin_water)/att.shape[0]*att.shape[1]) / (np.sum(att)/att.shape[0]*att.shape[1])
return energy
def load_trained(path):
model = Net()
model.load_state_dict(torch.load(path, map_location=DEVICE))
return model
energy_water_conf={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
energy_water_sup={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
energy_water_no={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
energy_no_water_conf={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': [],}
energy_no_water_sup={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
energy_no_water_no={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
explanations_water_conf={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
explanations_water_sup={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
explanations_water_no={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
explanations_no_water_conf={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': [],}
explanations_no_water_sup={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
explanations_no_water_no={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
# energy_water_conf_gt={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
# energy_water_sup_gt={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
# energy_water_no_gt={'deconv':[],'int_grads':[],'shap':[],'lrp':[], 'lrp_ab': []}
# energy_no_water_conf_gt={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
# energy_no_water_sup_gt={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
# energy_no_water_no_gt={'deconv':[], 'int_grads':[], 'shap':[], 'lrp':[], 'lrp_ab': []}
folder=os.getcwd()+'/images'
print(folder)
t0=time.time()
N_test = 1800
wm_avg = np.zeros((N_test,3,128,128))
no_wm_avg = np.zeros((N_test,3,128,128))
for i in range(N_test):
wm_avg[i] = watermark_dataset[i][0]
no_wm_avg[i] = no_watermark_dataset[i][0]
wm_avg = np.mean(wm_avg, axis=0)
no_wm_avg = np.mean(no_wm_avg, axis=0)
rgb_weights = [0.2989, 0.5870, 0.1140]
res = {
'laplace': [[], []],
'sobel': [[], []],
'x': [[], []]
}
for model_ind in [int(model_ind)]:
model_conf=load_trained(f'./models/cnn_confounder_variable_{split}_{model_ind}.pt').eval().to(DEVICE)
model_sup=load_trained(f'./models/cnn_suppressor_variable_{split}_{model_ind}.pt').eval().to(DEVICE)
model_no=load_trained(f'./models/cnn_no_watermark_variable_{split}_{model_ind}.pt').eval().to(DEVICE)
for i in range(len(watermark_dataset)):
w_image = watermark_dataset[i]
w_target=w_image[1]
w_image=w_image[0]
w_mask = masks_wm[i]
w_example=torch.tensor(w_image).unsqueeze(0).to(DEVICE,dtype=torch.float)
w_target_conf = torch.max(model_conf(w_example), 1)[1].to(int)
w_target_sup = torch.max(model_sup(w_example), 1)[1].to(int)
w_target_no = torch.max(model_no(w_example), 1)[1].to(int)
nw_image = no_watermark_dataset[i]
nw_target=nw_image[1]
nw_image=nw_image[0]
# nw_mask = masks_no[i]
nw_example=torch.tensor(nw_image).unsqueeze(0).to(DEVICE,dtype=torch.float)
nw_target_conf = torch.max(model_conf(nw_example), 1)[1].to(int)
nw_target_sup = torch.max(model_sup(nw_example), 1)[1].to(int)
nw_target_no = torch.max(model_no(nw_example), 1)[1].to(int)
# explanations for predicted class (n)w_target_{MODEL}
a_conf_w,_=plot_atts(w_example,model_conf,w_target_conf)
a_sup_w,_=plot_atts(w_example,model_sup,w_target_sup)
a_no_w,_=plot_atts(w_example,model_no,w_target_no)
a_conf_nw,_=plot_atts(nw_example,model_conf,nw_target_conf)
a_sup_nw,_=plot_atts(nw_example,model_sup,nw_target_sup)
a_no_nw,_=plot_atts(nw_example,model_no,nw_target_no)
# # explanations for ground truth class (n)w_target
# a_conf_w_gt,_=plot_atts(w_example,model_conf,w_target)
# a_sup_w_gt,_=plot_atts(w_example,model_sup,w_target)
# a_no_w_gt,_=plot_atts(w_example,model_no,w_target)
# a_conf_nw_gt,_=plot_atts(nw_example,model_conf,nw_target)
# a_sup_nw_gt,_=plot_atts(nw_example,model_sup,nw_target)
# a_no_nw_gt,_=plot_atts(nw_example,model_no,nw_target)
for method in list(energy_water_conf.keys()):
energy_water_conf[method].append(energy(a_conf_w[method], w_mask))
energy_water_sup[method].append(energy(a_sup_w[method], w_mask))
energy_water_no[method].append(energy(a_no_w[method], w_mask))
energy_no_water_conf[method].append(energy(a_conf_nw[method], w_mask))
energy_no_water_sup[method].append(energy(a_sup_nw[method], w_mask))
energy_no_water_no[method].append(energy(a_no_nw[method], w_mask))
explanations_water_conf[method].append(a_conf_w[method])
explanations_water_sup[method].append(a_sup_w[method])
explanations_water_no[method].append(a_no_w[method])
explanations_no_water_conf[method].append(a_conf_nw[method])
explanations_no_water_sup[method].append(a_sup_nw[method])
explanations_no_water_no[method].append(a_no_nw[method])
# energy_water_conf_gt[method].append(energy(a_conf_w_gt[method]))
# energy_water_sup_gt[method].append(energy(a_sup_w_gt[method]))
# energy_water_no_gt[method].append(energy(a_no_w_gt[method]))
# energy_no_water_conf_gt[method].append(energy(a_conf_nw_gt[method]))
# energy_no_water_sup_gt[method].append(energy(a_sup_nw_gt[method]))
# energy_no_water_no_gt[method].append(energy(a_no_nw_gt[method]))
x_wm = energy(np.dot(w_image.copy().transpose(1,2,0)[...,:3], rgb_weights), w_mask)
x_no = energy(np.dot(nw_image.copy().transpose(1,2,0)[...,:3], rgb_weights), w_mask)
sample = w_image.copy().transpose(1,2,0) - wm_avg.transpose(1,2,0)
img_r = sample[:,:,0]
img_g = sample[:,:,1]
img_b = sample[:,:,2]
lapl_wm = energy(np.abs(laplace(img_r)) + np.abs(laplace(img_g)) + np.abs(laplace(img_b)), w_mask)
sob_wm = energy(np.abs(sobel(img_r)) + np.abs(sobel(img_g)) + np.abs(sobel(img_b)), w_mask)
# nw_image.append(energy(np.dot(nw_image.copy().transpose(1,2,0)[...,:3], rgb_weights)))
sample = nw_image.copy().transpose(1,2,0) - no_wm_avg.transpose(1,2,0)
img_r = sample[:,:,0]
img_g = sample[:,:,1]
img_b = sample[:,:,2]
lapl_no = energy(np.abs(laplace(img_r)) + np.abs(laplace(img_g)) + np.abs(laplace(img_b)), w_mask)
sob_no = energy(np.abs(sobel(img_r)) + np.abs(sobel(img_g)) + np.abs(sobel(img_b)), w_mask)
res['laplace'][0].append(lapl_wm)
res['laplace'][1].append(lapl_no)
res['sobel'][0].append(sob_wm)
res['sobel'][1].append(sob_no)
res['x'][0].append(x_wm)
res['x'][1].append(x_no)
if (i%100)==0:
print(i, 'out of', len(watermark_dataset))
print(time.time()-t0)
# t0=time.time()
for baseline, results in res.items():
energy_water_conf[baseline] = results[0]
energy_no_water_conf[baseline] = results[1]
energy_water_sup[baseline] = results[0]
energy_no_water_sup[baseline] = results[1]
energy_no_water_conf[baseline] = results[0]
energy_no_water_no[baseline] = results[1]
with open(f'./energies/energy_water_conf_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_water_conf, f)
with open(f'./energies/energy_water_sup_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_water_sup, f)
with open(f'./energies/energy_water_no_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_water_no, f)
with open(f'./energies/energy_no_water_conf_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_no_water_conf, f)
with open(f'./energies/energy_no_water_sup_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_no_water_sup, f)
with open(f'./energies/energy_no_water_no_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(energy_no_water_no, f)
# Explanation files are ~1.1GB each, need to save server disk space by only using the first split data.
if str(split) == "0":
with open(f'./explanations/explanations_water_conf_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_water_conf, f)
with open(f'./explanations/explanations_water_sup_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_water_sup, f)
with open(f'./explanations/explanations_water_no_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_water_no, f)
with open(f'./explanations/explanations_no_water_conf_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_no_water_conf, f)
with open(f'./explanations/explanations_no_water_sup_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_no_water_sup, f)
with open(f'./explanations/explanations_no_water_no_pred_variable_{split}_{model_ind}.pickle', 'wb') as f:
pickle.dump(explanations_no_water_no, f)
# with open(f'./energies/energy_water_conf_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_water_conf_gt, f)
# with open(f'./energies/energy_water_sup_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_water_sup_gt, f)
# with open(f'./energies/energy_water_no_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_water_no_gt, f)
# with open(f'./energies/energy_no_water_conf_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_no_water_conf_gt, f)
# with open(f'./energies/energy_no_water_sup_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_no_water_sup_gt, f)
# with open(f'./energies/energy_no_water_no_gt_{split}_{model_ind}.pickle', 'wb') as f:
# pickle.dump(energy_no_water_no_gt, f)