# imports
import model_io
import data_io
import render
import importlib.util as imp
import numpy
import numpy as np
if imp.find_spec("cupy"): #use cupy for GPU support if available
import cupy
import cupy as np
na = np.newaxis
# end of imports
nn = model_io.read('../models/MNIST/LeNet-5.nn') # read model
X = data_io.read('../data/MNIST/test_images.npy')[
na, 0, :] # load first MNIST test image
X = X / 127.5 - 1 # normalized data to range [-1 1]
Ypred = nn.forward(X) # forward pass through network
R = nn.lrp(Ypred) # lrp to explain prediction of X
if not np == numpy: # np=cupy
X = np.asnumpy(X)
R = np.asnumpy(R)
# render rgb images and save as image
digit = render.digit_to_rgb(X)
hm = render.hm_to_rgb(R, X) # render heatmap R, use X as outline
render.save_image([digit, hm], '../2nd_py.png')
\begin{Verbatim}[frame=single, fontsize=\small]
# imports
import model_io
import data_io
import render
import numpy as np
na = np.newaxis
# end of imports
# read model and first MNIST test image
nn = model_io.read(<model_path>)
X = data_io.read(<data_path>)[na,0,:]
# normalized data to range [-1 1]
X = X / 127.5 - 1
# forward pass through network
Ypred = nn.forward(X)
# lrp to explain prediction of X
R = nn.lrp(Ypred)
# render rgb images and save as image
digit = render.digit_to_rgb(X)
# render heatmap R, use X as outline
hm = render.hm_to_rgb(R,X)
render.save_image([digit,hm],<i_path>)
\end{Verbatim}
yselect = 3
yselect = (np.arange(Y.shape[1])[na,:] == yselect)*1.
R = nn.lrp(ypred*yselect) #compute first layer relvance for an arbitrarily selected class
'''
#undo input normalization for digit drawing. get it back to range [0,1] per pixel
x = (x + 1.) / 2.
if not np == numpy: # np=cupy
x = np.asnumpy(x)
R = np.asnumpy(R)
#render input and heatmap as rgb images
digit = render.digit_to_rgb(x, scaling=3)
hm = render.hm_to_rgb(R, X=x, scaling=3, sigma=2)
digit_hm = render.save_image([digit, hm], '../heatmap.png')
data_io.write(R, '../heatmap.npy')
#display the image as written to file
plt.imshow(digit_hm, interpolation='none')
plt.axis('off')
plt.show()
#note that modules.Sequential allows for batch processing inputs
if True:
N = 256
t_start = time.time()
x = X[:N, ...]
y = nn.forward(x)
R = nn.lrp(y)
data_io.write(R, '../Rbatch.npy')
#R = nn.lrp(ypred,'alphabeta',2) #as Eq(60) from DOI: 10.1371/journal.pone.0130140
'''
R = nn.lrp(Y[na,i]) #compute first layer relevance according to the true class label
'''
'''
yselect = 3
yselect = (np.arange(Y.shape[1])[na,:] == yselect)*1.
R = nn.lrp(yselect) #compute first layer relvance for an arbitrarily selected class
'''
#render input and heatmap as rgb images
digit = render.digit_to_rgb(x, scaling = 3)
hm = render.hm_to_rgb(R, X = x, scaling = 3, sigma = 2)
digit_hm = render.save_image([digit,hm],'../heatmap.png')
data_io.write(R,'../heatmap.npy')
#display the image as written to file
plt.imshow(digit_hm, interpolation = 'none')
plt.axis('off')
plt.show()
#note that modules.Sequential allows for batch processing inputs
'''
x = X[:10,:]
y = nn.forward(x)
R = nn.lrp(y)
data_io.write(R,'../Rbatch.npy')
'''
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# types = ('*.jpg', '*.png')
# image_path_list= []
# for files in types:
# image_path_list.extend(glob(os.path.join(image_folder, files)))
# total_num = len(image_path_list)
for dir, dirs, files in sorted(os.walk(image_folder)):
for file in files:
image_path = os.path.join(dir, file)
dir = dir.replace("\\", "/")
new_dir = dir.replace(image_folder, save_folder)
if not os.path.isdir(new_dir):
os.mkdir(new_dir)
name = image_path.replace(image_folder, save_folder)
print('data path:', name)
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c>3:
image = image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size> 1000:
image = rescale(image, 1000./max_size)
image = (image*255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
else:
# if image.shape[0] == image.shape[1]:
# image = resize(image, (256,256))
# pos = prn.net_forward(image/255.) # input image has been cropped to 256x256
# else:
box = np.array([0, image.shape[1]-1, 0, image.shape[0]-1]) # cropped with bounding box
pos = prn.process(image, box)
image = image/255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:,1] = h - 1 - save_vertices[:,1]
if args.isImage:
imsave(name, image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(pos, (args.texture_size, args.texture_size), preserve_range = True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image, pos_interpolated[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w, prn.resolution_op)
uv_mask = resize(uv_mask, (args.texture_size, args.texture_size), preserve_range = True)
texture = texture*uv_mask[:,:,np.newaxis]
write_obj_with_texture(name.replace('.jpg', '.obj'), save_vertices, prn.triangles, texture, prn.uv_coords/prn.resolution_op)#save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(name.replace('.jpg', '.obj'), save_vertices, prn.triangles, colors) #save 3d face(can open with meshlab)
filepath = name.replace('.jpg', '.obj')
filepath = filepath.replace("\\", "/")
print('filepath:', filepath)
new_dir = dir.replace(args.inputDir, args.renderDir)
# print(new_dir + '/' + file)
if not os.path.isdir(new_dir):
os.mkdir(new_dir)
color_image1, _ = render_scene(filepath, 4.0, 0.0, 3.0)
color_image2, _ = render_scene(filepath, 4.0, np.pi / 18.0, 3.0)
color_image3, _ = render_scene(filepath, 4.0, np.pi / 9.0, 3.0)
if color_image1 is None or color_image2 is None:
continue
new_path = filepath.replace(args.outputDir, args.renderDir)
# print('new_path:', new_path)
save_image(new_path, '_40_', color_image1)
save_image(new_path, '_50_', color_image2)
save_image(new_path, '_60_', color_image3)
os.remove(name.replace('.jpg', '.obj'))
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(name.replace('.jpg', '_depth.jpg')), depth_image)
sio.savemat(name.replace('.jpg', '_depth.mat'), {'depth': depth})
if args.isMat:
sio.savemat(name.replace('.jpg', '_mesh.mat'),
{'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(name.replace('.jpg', '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(name.replace('.jpg', '_pose.txt'), pose)
np.savetxt(name.replace('.jpg', '_camera_matrix.txt'), camera_matrix)
np.savetxt(name.replace('.jpg', '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
data_train = unpickle(occdir.format('train'))
Xtrain = np.array(data_train)
Ytrain = unpickle(occdir_y.format('train'))
Ytrain = np.array(Ytrain)
X = Xtrain
Y = Ytrain
occdir = get_savedir() + '{}_{}_{}.pickle'.format('{}', 'LRP', '0.1')
# occdir_y = get_savedir() + '{}_{}_{}_{}.pickle'.format('{}', 'normal', '0.1','label')
data_train = unpickle(occdir.format('train'))
XR = np.array(data_train)
# Ytrain = unpickle(occdir_y.format('train'))
I = Y[:,0].astype(int)
Y = np.zeros([X.shape[0],np.unique(Y).size])
Y[np.arange(Y.shape[0]),I] = 1
nn = model_io.read('../models/MNIST/LeNet-5.nn')
for i in I[:10]:
x = X[i:i+1,...]
r = XR[i:i+1,...]
ypred = nn.forward(x)
print('True Class: ', np.argmax(Y[i]))
print('Predicted Class:', np.argmax(ypred),'\n')
digit = render.digit_to_rgb(x, scaling = 3)
Rdigit = render.digit_to_rgb(r, scaling = 3)
hm = render.hm_to_rgb(r, X = x, scaling = 3, sigma = 2)
digit_hm = render.save_image([digit,Rdigit],'../re_heatmap.png')
plt.imshow(digit_hm, interpolation = 'none')
plt.axis('off')
plt.show()
Rinit = ypred*mask
print("Lrp R shape {} : ".format(Rinit.shape))
#compute first layer relevance according to prediction
#R = nn.lrp(Rinit) #as Eq(56) from DOI: 10.1371/journal.pone.0130140
R = nn.lrp(Rinit,'epsilon',1.)
R = R.sum(axis=3)
xs = ((x+1.)/2.).sum(axis=3)
if not np == numpy:
xs = np.asnumpy(xs)
R = np.asnumpy(R)
digit = render.digit_to_rgb(xs, scaling = 3)
hm = render.hm_to_rgb(R, X = xs, scaling = 3, sigma = 2)
digit_hm = render.save_image([digit,hm],'../heatmap.png')
data_io.write(R,'../heatmap.npy')
data_io.write(xs,'../xs.npy')
print(xs.shape)
y = xs
a = np.load('../r_array/convolution.npy')
a = np.reshape(a,[a.shape[1]*a.shape[2],1])
b = np.load('../r_array/rect.npy')
b = np.pad(b,((0,0),(2,2),(2,2),(0,0)))
b = np.reshape(b,[b.shape[1]*b.shape[2],b.shape[0]*b.shape[3]])
c = np.load('../r_array/sumpoll.npy')
c = np.pad(c,((0,0),(2,2),(2,2),(0,0)))
c = np.reshape(c,[c.shape[1]*c.shape[2],c.shape[3]])
new_b = np.hstack((b, c))
new = np.hstack((a, new_b))
@author: Sebastian Bach @maintainer: Sebastian Bach @contact: [email protected] @date: 21.09.2015 @version: 1.0 @copyright: Copyright (c) 2015, Sebastian Bach, Alexander Binder, Gregoire Montavon, Klaus-Robert Mueller @license : BSD-2-Clause ''' # imports import model_io import data_io import render import numpy as np na = np.newaxis # end of imports nn = model_io.read('../models/MNIST/long-rect.nn') # read model X = data_io.read('../data/MNIST/test_images.npy')[na,0,:] # load first MNIST test image X = X / 127.5 - 1 # normalized data to range [-1 1] Ypred = nn.forward(X) # forward pass through network R = nn.lrp(Ypred) # lrp to explain prediction of X # render rgb images and save as image digit = render.digit_to_rgb(X) hm = render.hm_to_rgb(R, X) # render heatmap R, use X as outline render.save_image([digit, hm], './hm_py.png')
def occlude_dataset(DNN, attribution, percentiles, test=False, keep=False, random=False, batch_size= 64, savedir=''):
print("Condition of test : {}".format(test))
if test:
Xs = Xtest
ys = Ytest
else:
Xs = Xtrain
ys = Ytrain
print("initial batch_size is : {}".format(batch_size))
total_batch = math.ceil(len(Xs) / batch_size)
print("batch size is :{}".format(total_batch))
hmaps = []
data = []
label = []
hmaps_00 = []
hmaps_01 = []
hmaps_02 = []
hmaps_03 = []
hmaps_04 = []
hmaps_05 = []
hmaps_06 = []
hmaps_07 = []
hmaps_08 = []
hmaps_09 = []
hmaps_10 = []
data_00 = []
data_01 = []
data_02 = []
data_03 = []
data_04 = []
data_05 = []
data_06 = []
data_07 = []
data_08 = []
data_09 = []
data_10 = []
label_00 = []
label_01 = []
label_02 = []
label_03 = []
label_04 = []
label_05 = []
label_06 = []
label_07 = []
label_08 = []
label_09 = []
label_10 = []
for i in tqdm(range(total_batch)):
if 'LRP' in attribution:
x = Xs[i:i+1,...]
y = ys[i:i+1,...]
ypred = DNN.forward(x)
m = np.zeros_like(ypred)
m[:,np.argmax(ypred)] = 1
Rinit = ypred*m
Rinit.astype(np.float)
R = DNN.lrp(Rinit,'epsilon',1.)
R = R.sum(axis=3)
if not np == numpy:
R = np.asnumpy(R)
attrs = R
data = x
attrs = scaling(attrs)
attrs *= 255
attrs = attrs.astype(np.uint8)
attrs = scale(attrs)
elif 'proposed_method' in attribution:
x = Xs[i:i+1,...]
y = ys[i:i+1,...]
ypred = DNN.forward(x)
m = np.zeros_like(ypred)
m[:,np.argmax(ypred)] = 1
Rinit = ypred*m
Rinit.astype(np.float)
R = DNN.lrp(Rinit,'epsilon',1.)
R = R.sum(axis=3)
if not np == numpy:
xs = np.asnumpy(x)
R = np.asnumpy(R)
xs = x
tar = xs
a = np.load('../r_array/convolution.npy')
a = np.reshape(a,[a.shape[1]*a.shape[2],1])
b = np.load('../r_array/rect.npy')
b = np.pad(b,((0,0),(2,2),(2,2),(0,0)))
b = np.reshape(b,[b.shape[1]*b.shape[2],b.shape[0]*b.shape[3]])
c = np.load('../r_array/sumpoll.npy')
c = np.pad(c,((0,0),(2,2),(2,2),(0,0)))
c = np.reshape(c,[c.shape[1]*c.shape[2],c.shape[3]])
new_b = np.hstack((b, c))
new = np.hstack((a, new_b))
tar = np.reshape(tar, [tar.shape[0]*tar.shape[1]*tar.shape[2]])
y_tran = tar.transpose()
new = sm.add_constant(new)
model = sm.GLSAR(y_tran, new, rho = 2)
result = model.iterative_fit()
find = result.resid
check = np.reshape(find,[1,32,32])
attrs = check
data = x
attrs = scaling(attrs)
attrs *= 255
attrs = attrs.astype(np.uint8)
attrs = scale(attrs)
else:
x = Xs[i:i+1,...]
y = ys[i:i+1,...]
if not np == numpy:
xs = np.asnumpy(x)
xs = x
attrs = xs
data = x
# attrs = scaling(attrs)
attrs *= 255
attrs = attrs.astype(np.uint8)
attrs = scale(attrs)
for percent in tqdm(percentiles):
batch_attrs = attrs
data = data
if attribution == 'normal':
if percent == 0.0:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_00.append(occluded_images)
data_00.append(data)
label_00.append(y)
elif percent == 0.1:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_01.append(occluded_images)
data_01.append(data)
label_01.append(y)
elif percent == 0.2:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_02.append(occluded_images)
data_02.append(data)
label_02.append(y)
elif percent == 0.3:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_03.append(occluded_images)
data_03.append(data)
label_03.append(y)
elif percent == 0.4:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_04.append(occluded_images)
data_04.append(data)
label_04.append(y)
elif percent == 0.5:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_05.append(occluded_images)
data_05.append(data)
label_05.append(y)
elif percent == 0.6:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_06.append(occluded_images)
data_06.append(data)
label_06.append(y)
elif percent == 0.7:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_07.append(occluded_images)
data_07.append(data)
label_07.append(y)
elif percent == 0.8:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_08.append(occluded_images)
data_08.append(data)
label_08.append(y)
elif percent == 0.9:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_09.append(occluded_images)
data_09.append(data)
label_09.append(y)
elif percent == 1:
occluded_images = random_remove(data, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_10.append(occluded_images)
data_10.append(data)
label_10.append(y)
else:
raise ValueError("attribution?")
else:
if percent == 0.0:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_00.append(occluded_images)
data_00.append(data)
label_00.append(y)
elif percent == 0.1:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_01.append(occluded_images)
data_01.append(data)
label_01.append(y)
elif percent == 0.2:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_02.append(occluded_images)
data_02.append(data)
label_02.append(y)
elif percent == 0.3:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_03.append(occluded_images)
data_03.append(data)
label_03.append(y)
elif percent == 0.4:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_04.append(occluded_images)
data_04.append(x)
label_04.append(y)
elif percent == 0.5:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_05.append(occluded_images)
data_05.append(x)
label_05.append(y)
elif percent == 0.6:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_06.append(occluded_images)
data_06.append(x)
label_06.append(y)
elif percent == 0.7:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_07.append(occluded_images)
data_07.append(x)
label_07.append(y)
elif percent == 0.8:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_08.append(occluded_images)
data_08.append(x)
label_08.append(y)
elif percent == 0.9:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_09.append(occluded_images)
data_09.append(x)
label_09.append(y)
elif percent == 1:
print(" percent : {}".format(percent))
occluded_images = remove(data, batch_attrs, percent, keep)
raw_image = render.digit_to_rgb(data, scaling = 1)
prdigit = render.digit_to_rgb(occluded_images, scaling = 1)
test_image = render.save_image([raw_image, prdigit],'../{}_KAR_check_point_{}.png'.format(attribution, percent))
hmaps_10.append(occluded_images)
data_10.append(x)
label_10.append(y)
else:
raise ValueError("Error : {}".format(percent))
# hmaps.append(attrs)
# data.append(x)
# label.append(y)
# print("print final : {}".format(len(hmaps)))
print("Interpretation is done, concatenate...")
hmaps_00 = np.concatenate(hmaps_00, axis=0)
hmaps_01 = np.concatenate(hmaps_01, axis=0)
hmaps_02 = np.concatenate(hmaps_02, axis=0)
hmaps_03 = np.concatenate(hmaps_03, axis=0)
hmaps_04 = np.concatenate(hmaps_04, axis=0)
hmaps_05 = np.concatenate(hmaps_05, axis=0)
hmaps_06 = np.concatenate(hmaps_06, axis=0)
hmaps_07 = np.concatenate(hmaps_07, axis=0)
hmaps_08 = np.concatenate(hmaps_08, axis=0)
hmaps_09 = np.concatenate(hmaps_09, axis=0)
hmaps_10 = np.concatenate(hmaps_10, axis=0)
data_00 = np.concatenate(data_00, axis = 0)
data_01 = np.concatenate(data_01, axis = 0)
data_02 = np.concatenate(data_02, axis = 0)
data_03 = np.concatenate(data_03, axis = 0)
data_04 = np.concatenate(data_04, axis = 0)
data_05 = np.concatenate(data_05, axis = 0)
data_06 = np.concatenate(data_06, axis = 0)
data_07 = np.concatenate(data_07, axis = 0)
data_08 = np.concatenate(data_08, axis = 0)
data_09 = np.concatenate(data_09, axis = 0)
data_10 = np.concatenate(data_10, axis = 0)
label_00 = np.concatenate(label_00, axis = 0)
label_01 = np.concatenate(label_01, axis = 0)
label_02 = np.concatenate(label_02, axis = 0)
label_03 = np.concatenate(label_03, axis = 0)
label_04 = np.concatenate(label_04, axis = 0)
label_05 = np.concatenate(label_05, axis = 0)
label_06 = np.concatenate(label_06, axis = 0)
label_07 = np.concatenate(label_07, axis = 0)
label_08 = np.concatenate(label_08, axis = 0)
label_09 = np.concatenate(label_09, axis = 0)
label_10 = np.concatenate(label_10, axis = 0)
# hmaps = np.concatenate(hmaps, axis=0)
# data = np.concatenate(data, axis = 0)
print("concatenate is done...")
# print("print final : {}".format(hmaps.shape))
# percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
for percent in tqdm(percentiles):
if percent == 0.0:
save(hmaps_00, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_00, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.1:
save(hmaps_01, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_01, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.2:
save(hmaps_02, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_02, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.3:
save(hmaps_03, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_03, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.4:
save(hmaps_04, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_04, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.5:
save(hmaps_05, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_05, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.6:
save(hmaps_06, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_06, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.7:
save(hmaps_07, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_07, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.8:
save(hmaps_08, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_08, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 0.9:
save(hmaps_09, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_09, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
elif percent == 1:
save(hmaps_10, savedir + '{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent))
save(label_10, savedir + '{}_{}_{}_{}.pickle'.format('test' if test else 'train', attribution, percent, 'label'))
print("Occlude image {} percentile...".format(percent))
else:
print("error")
del hmaps_00,hmaps_01,hmaps_02,hmaps_03,hmaps_04,hmaps_05,hmaps_06,hmaps_07,hmaps_08,hmaps_09,hmaps_10,data_00,data_01,data_02,data_03,data_04,data_05,data_06,data_07,data_08,data_09,data_10,label_00,label_01,label_02,label_03,label_04,label_05,label_06,label_07,label_08,label_09,label_10