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qualitative_coco.py
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import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import random
from captum.attr import IntegratedGradients, Saliency, GradientShap
from captum.attr import GuidedBackprop, Deconvolution, LRP, InputXGradient, Lime
from captum._utils.models.linear_model import SkLearnLasso
from zennit.composites import EpsilonAlpha2Beta1
from torch.utils.data import DataLoader, TensorDataset
# device='cuda:6'
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
SEEDS = [12031212,1234,5845389,23423,343495,2024,3842834,23402304,482347247,1029237127]
SEED=SEEDS[1]
np.random.seed(SEED)
torch.manual_seed(SEED)
os.environ['PYTHONHASHSEED']=str(SEED)
random.seed(SEED)
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
def load_model(path):
model = Net()
model.load_state_dict(torch.load(path,map_location=DEVICE))
model.eval()
model.zero_grad()
model.relu=nn.ReLU(inplace=False)
return model
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=5, padding='same')
self.conv2=nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3, padding='same')
self.conv3=nn.Conv2d(in_channels=128,out_channels=256,kernel_size=5, padding='same')
self.maxPooling4_0=nn.MaxPool2d(kernel_size=4)
self.maxPooling4_1=nn.MaxPool2d(kernel_size=4)
self.maxPooling2=nn.MaxPool2d(kernel_size=2)
# self.adPooling=nn.AdaptiveAvgPool1d(256)
self.fc1=nn.Linear(in_features=12544,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 lrp(data,model,target):
# 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).to(DEVICE)[[target]].to(DEVICE)
# gradient/ relevance wrt. class/output 0
output.backward(gradient=grad.reshape((1,2)))
# 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=data.grad.detach().cpu().squeeze().numpy()
# rgb_weights = [0.2989, 0.5870, 0.1140]
# grayscale_att_lrp = np.dot(att[...,:3], rgb_weights)
return att
def plot_atts(data,model,target):
# DEVICE = 'cpu'
# data is a tensor of shape torch.Size([1, 3, 128, 128])
# model is
# target is an integer
ig_att = IntegratedGradients(model).attribute(data, target=target).cpu().detach().numpy().squeeze()
gradshap_att = GradientShap(model).attribute(data,target=target, baselines=torch.zeros(data.shape).to(DEVICE)).cpu().detach().numpy().squeeze()
deconv_att = Deconvolution(model).attribute(data,target=target).cpu().detach().numpy().squeeze()
lrp_att=LRP(model).attribute(data,target=target).cpu().detach().numpy().squeeze()
lrp_ab = lrp(data,model,target)
out=model(data)
Y_probs = F.softmax(out[0], dim=-1)
target = int(target)
# with torch.no_grad():
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,interpretable_model=SkLearnLasso(alpha=0.01)).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),'gradients':abs(grayscale_att_ig),
# 'shap':abs(grayscale_att_shap),'backprop':abs(grayscale_att_backprp),'ix':abs(grayscale_att_ix),
# 'lrp':abs(grayscale_att_lrp), 'lime': abs(grayscale_att_lime), 'lrp-ab': abs(lrp_ab)}
atts={'deconv':abs(grayscale_att_deconv),'ig':abs(grayscale_att_ig), 'shap':abs(grayscale_att_shap),
'lrp':abs(grayscale_att_lrp), 'lrp-ab': abs(lrp_ab)}
return atts,Y_probs
split = 0
model_ind = 0
with open(f'./artifacts/split_{split}_coco_data_norm_test.pkl', 'rb') as f:
[x_test_norm, y_test_norm, masks_test_norm, _] = pkl.load(f)
norm_loader = DataLoader(TensorDataset(torch.tensor(x_test_norm.transpose(0,3,1,2)), torch.tensor(y_test_norm)), batch_size=batch_size, shuffle=False, num_workers=4)
with open(f'./artifacts/split_{split}_coco_data_conf_test.pkl', 'rb') as f:
[x_test_conf, y_test_conf, masks_test_conf, _] = pkl.load(f)
# with open(f'coco_data_conf.pkl', 'rb') as f:
# [(_, _, _), (_, _, _), (x_test, y_test, masks_test_conf)] = pkl.load(f)
conf_loader = DataLoader(TensorDataset(torch.tensor(x_test_conf.transpose(0,3,1,2)), torch.tensor(y_test_conf)), batch_size=batch_size, shuffle=False, num_workers=4)
with open(f'./artifacts/split_{split}_coco_data_sup_test.pkl', 'rb') as f:
[x_test_sup, y_test_sup, masks_test_sup, _] = pkl.load(f)
sup_loader = DataLoader(TensorDataset(torch.tensor(x_test_sup.transpose(0,3,1,2)), torch.tensor(y_test_sup)), batch_size=batch_size, shuffle=False, num_workers=4)
model_conf=load_model(f'./models/cnn_confounder_{split}_{model_ind}.pt').to(DEVICE).eval()
model_sup=load_model(f'./models/cnn_suppressor_{split}_{model_ind}.pt').to(DEVICE).eval()
model_no=load_model(f'./models/cnn_no_watermark_{split}_{model_ind}.pt').to(DEVICE).eval()
def get_atts(data_watermark, data_no_watermark, ind):
#target (same for both images)
target=watermark_dataset[ind][1]
return {'watermark_conf':plot_atts(data_watermark,model_conf,target),
'watermark_sup':plot_atts(data_watermark,model_sup,target),
'watermark_no':plot_atts(data_watermark,model_no,target),
'no_watermark_conf':plot_atts(data_no_watermark,model_conf,target),
'no_watermark_sup':plot_atts(data_no_watermark,model_sup,target),
'no_watermark_no':plot_atts(data_no_watermark,model_no,target)}
def rgb2gray(rgb):
rgb = rgb.transpose(1,2,0)
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
def gray2rgb(gray):
rgb = np.stack((gray,)*3, axis=-1)
return rgb
def plots(n,atts, watermark_image, no_watermark_image):
#plots
fig, axs = plt.subplots(6, 9,figsize=(28, 21))
plt.subplots_adjust(wspace=0.05, hspace=0.05)
font_size=25
plt.rcParams['font.size'] = font_size
plt.rc('axes', titlesize=font_size) #title
original_imgs=[watermark_image,no_watermark_image, abs(rgb2gray(watermark_image) - rgb2gray(no_watermark_image)),
watermark_image,no_watermark_image, abs(rgb2gray(watermark_image) - rgb2gray(no_watermark_image)),
watermark_image,no_watermark_image, abs(rgb2gray(watermark_image) - rgb2gray(no_watermark_image))]
images=[atts['watermark_conf'][0],atts['no_watermark_conf'][0], atts['diff_conf'],
atts['watermark_sup'][0],atts['no_watermark_sup'][0], atts['diff_conf'],
atts['watermark_no'][0],atts['no_watermark_no'][0], atts['diff_conf'] ]
labels=['Original', 'Deconv','Int. Grad.','Grad SHAP',r'LRP-$\epsilon$', r'LRP-$\alpha \beta$']
cmap = 'magma'
for i in range(9): #images watermark/no watermark
if i == 2 or i == 5 or i == 8:
axs[0,i].imshow(original_imgs[i], cmap=cmap)
else:
axs[0,i].imshow(original_imgs[i].transpose(1,2,0))
axs[1,i].imshow(images[i]['deconv'],cmap=cmap)
axs[2,i].imshow(images[i]['ig'],cmap=cmap)
axs[3,i].imshow(images[i]['shap'],cmap=cmap)
axs[4,i].imshow(images[i]['lrp'],cmap=cmap)
axs[5,i].imshow(images[i]['lrp-ab'],cmap=cmap)
for j in range(6): #original image + XAI methods
axs[j,i].tick_params(left=False,bottom=False,labelleft=False,labelbottom=False)
if i==0:
axs[j,0].set_ylabel(labels[j])
#titles model type
plt.figtext(0.25,0.9,"Model trained with \n Confounder dataset", va="center", ha="center", size=font_size)
plt.figtext(0.53,0.9,"Model trained with \n Suppressor dataset", va="center", ha="center", size=font_size)
plt.figtext(0.78,0.9,"Model trained with \n No Watermark dataset", va="center", ha="center", size=font_size)
plt.savefig(f'./figures/qualitative_{n}.png', bbox_inches='tight')
plt.savefig(f'./figures/qualitative_{n}_hires.png', bbox_inches='tight', dpi=300)
for ind in np.random.choice(len(watermark_dataset), size=10, replace=False):
watermark_image=watermark_dataset[ind][0]
data_watermark=torch.tensor(watermark_image).unsqueeze(0).to(DEVICE,dtype=torch.float)
no_watermark_image=no_watermark_dataset[ind][0]
data_no_watermark=torch.tensor(no_watermark_image).unsqueeze(0).to(DEVICE,dtype=torch.float)
atts=get_atts(data_watermark, data_no_watermark, ind)
plots(ind,atts,DEVICE,0, data_watermark, data_no_watermark)