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ood_detection.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Code for classifying in-distribution and out-of-distribution images
based on the spectral signatures of CNN feature maps.
@author: davood
"""
#### Inspect
from __future__ import division
import numpy as np
import os
import tensorflow as tf
# import tensorlayer as tl
#from os import listdir
#from os.path import isfile, join, isdir
#import scipy.io as sio
#from skimage import io
#import skimage
#import SimpleITK as sitk
#import matplotlib.pyplot as plt
import h5py
#import scipy
#from scipy.spatial import ConvexHull
#from mpl_toolkits.mplot3d import Axes3D
#from scipy.spatial import Delaunay
import os.path
#import pandas as pd
#import sys
#import pickle
#from scipy.spatial.distance import directed_hausdorff
from tqdm import tqdm
#import dk_seg
import dk_model
import dk_aux
#from PIL import Image
#from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
import matplotlib.pyplot as plt
#from medpy.metric import hd95
from scipy import linalg
from scipy.ndimage import zoom
### initialize the network model and restore the trained model weights
n_channel = 1
n_class = 2
gpu_ind = 1
n_feat_0 = 14
depth = 4
SX, SY, SZ= 135, 189, 155
LX, LY, LZ = 96, 96, 96
ks_0 = 3
X = tf.placeholder("float32", [None, LX, LY, LZ, n_channel])
Y = tf.placeholder("float32", [None, LX, LY, LZ, n_class])
p_keep_conv = tf.placeholder("float")
learning_rate = tf.placeholder("float")
#logit_f, _ = dk_model.davood_net(X, ks_0, depth, n_feat_0, n_channel, n_class, p_keep_conv, bias_init=0.001)
logit_f, _ , f_1, f_2= dk_model.davood_net_return_fmaps(X, ks_0, depth, n_feat_0, n_channel, n_class, p_keep_conv, bias_init=0.001)
predicter = tf.nn.softmax(logit_f)
cost= dk_model.cost_dice_forground(Y, predicter, loss_type= 'sorensen', smooth = 1e-5)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_ind)
saver = tf.train.Saver(max_to_keep=50)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# addressed of the saved checkpoint
restore_model_path= '/media/nerossd2/segment_everything/One_organ_all_1/models/model_saved_24_8963.ckpt'
saver.restore(sess, restore_model_path)
## load the data to be tested. This includes in-distribution and out-of distribution
# datd
####################### SPLEEN ##########################################
spleen_dir = '/media/nerossd2/segment_everything/spleen/'
data_file_name= 'spleen_data.h5'
if os.path.exists(spleen_dir + data_file_name):
print('Reading data')
h5f = h5py.File(spleen_dir + data_file_name, 'r')
X_vol_s = h5f['X_vol'][:]
Y_vol_s = h5f['Y_vol'][:]
info_vol_s = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### HEART ##########################################
heart_dir = '/media/nerossd2/segment_everything/heart/'
data_file_name= 'heart_data.h5'
if os.path.exists(heart_dir + data_file_name):
print('Reading data')
h5f = h5py.File(heart_dir + data_file_name, 'r')
X_vol_h = h5f['X_vol'][:]
Y_vol_h = h5f['Y_vol'][:]
info_vol_h = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### PROSTATE #######################################
prostate_dir = '/media/nerossd2/segment_everything/prostate/'
data_file_name= 'prostate_data.h5'
if os.path.exists(prostate_dir + data_file_name):
print('Reading data')
h5f = h5py.File(prostate_dir + data_file_name, 'r')
X_vol_p = h5f['X_vol'][:]
Y_vol_p = h5f['Y_vol'][:]
info_vol_p = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### HIPPOCAMPUS #####################################
hippocampus_dir = '/media/nerossd2/segment_everything/hippocampus/'
data_file_name= 'hippocampus_data.h5'
if os.path.exists(hippocampus_dir + data_file_name):
print('Reading data')
h5f = h5py.File(hippocampus_dir + data_file_name, 'r')
X_vol_c = h5f['X_vol'][:]
Y_vol_c = h5f['Y_vol'][:]
info_vol_c = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### pancreas ##################################
pancreas_dir = '/media/nerossd2/segment_everything/Pancreas/'
data_file_name= 'pancreas_data_decath.h5'
if os.path.exists(pancreas_dir + data_file_name):
print('Reading data')
h5f = h5py.File(pancreas_dir + data_file_name, 'r')
X_vol_r = h5f['X_vol'][:]
Y_vol_r = h5f['Y_vol'][:]
info_vol_r = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### Liver CT #####################################
Silver_dir = '/media/nerossd2/segment_everything/Silver/'
data_file_name= 'Silver_data.h5'
if os.path.exists(Silver_dir + data_file_name):
print('Reading data')
h5f = h5py.File(Silver_dir + data_file_name, 'r')
X_vol_sil = h5f['X_vol'][:]
Y_vol_sil = h5f['Y_vol'][:]
info_vol_sil = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
####################### Liver-MRI-SPIR ##################################
CHAOS_dir = '/media/nerossd2/segment_everything/CHAOS/CHAOS_Train_Sets/Train_Sets/MR/'
data_file_name= 'CHAOS_data.h5'
if os.path.exists(CHAOS_dir + data_file_name):
print('Reading data')
h5f = h5py.File(CHAOS_dir + data_file_name, 'r')
X_vol_chaos = h5f['X_vol'][:]
Y_vol_chaos = h5f['Y_vol'][:]
info_vol_chaos = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
########## Liver-MRI-DUAL-in and Liver-MRI-DUAL-out ######################
CHAOS_dir = '/media/nerossd2/segment_everything/CHAOS/CHAOS_Train_Sets/Train_Sets/MR/'
data_file_name= 'CHAOS_data_dual.h5'
if os.path.exists(CHAOS_dir + data_file_name):
print('Reading data')
h5f = h5py.File(CHAOS_dir + data_file_name, 'r')
X_vol_chaos_in = h5f['X_vol_in'][:]
X_vol_chaos_out = h5f['X_vol_out'][:]
Y_vol_chaos_in = h5f['Y_vol_in'][:]
Y_vol_chaos_out = h5f['Y_vol_out'][:]
info_vol_chaos_dual = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
##### CP- newborn
DHCP_dir = '/media/nerossd2/segment_everything/DHCP/'
data_file_name= 'DHCP_data.h5'
if os.path.exists(DHCP_dir + data_file_name):
print('Reading data')
h5f = h5py.File(DHCP_dir + data_file_name, 'r')
X_vol_dhcp = h5f['X_vol'][:]
Y_vol_dhcp = h5f['Y_vol'][:]
info_vol_dhcp = h5f['info_vol'][:]
h5f.close()
print('Finished reading data')
###############################################################################
## divide the data into train, validation, and test sets
# we do it randomly but setting the seed to get the same results.
###############################################################################
test_frac= 0.30
valid_frac= 0.40
######### CP- fetus
data_file_name= 'CPSP_seg_data_' + str(SX) + '_' + str(SY) + '_' + str(SZ)+'.h5'
h5f = h5py.File('/media/davood/bd794557-342d-4d50-8b7f-f6c209248f89/davood/CpSpS/data/' + data_file_name, 'r')
X_vol_CP = h5f['X_vol'][:]
Y_vol_CP = h5f['Y_vol'][:]
info_vol_CP = h5f['info_vol'][:]
h5f.close()
Y_vol_CP[:,:,:,:,0]= 1- Y_vol_CP[:,:,:,:,1]
Y_vol_CP = Y_vol_CP [:, :, :, :, :2]
p_young = np.where(info_vol_CP[:, 19] > 0)[0]
p_old = np.where(info_vol_CP[:, 19] == 0)[0]
np.random.seed(0)
np.random.shuffle(p_young)
np.random.shuffle(p_old)
p_test_yng = p_young[:int(test_frac * len(p_young))]
p_valid_yng= p_young[int(test_frac * len(p_young)):int(valid_frac * len(p_young))]
p_train_yng= p_young[int(valid_frac * len(p_young)):]
p_test_old = p_old[:int(test_frac * len(p_old))]
p_valid_old= p_old[int(test_frac * len(p_old)):int(valid_frac * len(p_old))]
p_train_old= p_old[int(valid_frac * len(p_old)):]
X_test_CP_yng = X_vol_CP[p_test_yng].copy()
X_train_CP_yng= X_vol_CP[p_train_yng].copy()
X_valid_CP_yng= X_vol_CP[p_valid_yng].copy()
Y_test_CP_yng = Y_vol_CP[p_test_yng].copy()
Y_train_CP_yng= Y_vol_CP[p_train_yng].copy()
Y_valid_CP_yng= Y_vol_CP[p_valid_yng].copy()
X_test_CP_old = X_vol_CP[p_test_old].copy()
X_train_CP_old= X_vol_CP[p_train_old].copy()
X_valid_CP_old= X_vol_CP[p_valid_old].copy()
Y_test_CP_old = Y_vol_CP[p_test_old].copy()
Y_train_CP_old= Y_vol_CP[p_train_old].copy()
Y_valid_CP_old= Y_vol_CP[p_valid_old].copy()
X_vol_CP= Y_vol_CP= 0
######### SPLEEN
n_cases= X_vol_s.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_s= X_vol_s[p_train,:,:,:,:].copy()
Y_train_s= Y_vol_s[p_train,:,:,:,:].copy()
X_test_s= X_vol_s[p_test,:,:,:,:].copy()
Y_test_s= Y_vol_s[p_test,:,:,:,:].copy()
X_valid_s= X_vol_s[p_valid,:,:,:,:].copy()
Y_valid_s= Y_vol_s[p_valid,:,:,:,:].copy()
X_vol_s= Y_vol_s= 0
######### HEART
n_cases= X_vol_h.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_h= X_vol_h[p_train,:,:,:,:].copy()
Y_train_h= Y_vol_h[p_train,:,:,:,:].copy()
X_test_h= X_vol_h[p_test,:,:,:,:].copy()
Y_test_h= Y_vol_h[p_test,:,:,:,:].copy()
X_valid_h= X_vol_h[p_valid,:,:,:,:].copy()
Y_valid_h= Y_vol_h[p_valid,:,:,:,:].copy()
X_vol_h= Y_vol_h= 0
######### PROSTATE
n_cases= X_vol_p.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_p= X_vol_p[p_train,:,:,:,0:1].copy()
Y_train_p= Y_vol_p[p_train,:,:,:,:].copy()
X_test_p= X_vol_p[p_test,:,:,:,0:1].copy()
Y_test_p= Y_vol_p[p_test,:,:,:,:].copy()
X_valid_p= X_vol_p[p_valid,:,:,:,0:1].copy()
Y_valid_p= Y_vol_p[p_valid,:,:,:,:].copy()
X_vol_p= Y_vol_p= 0
######### HIPPOCAMPUS
n_cases= X_vol_c.shape[0]
Y_vol_c[:,:,:,:,1:2]= Y_vol_c[:,:,:,:,1:2] + Y_vol_c[:,:,:,:,2:]
Y_vol_c= Y_vol_c[:,:,:,:,:2]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_c= X_vol_c[p_train,:,:,:,0:1].copy()
Y_train_c= Y_vol_c[p_train,:,:,:,:].copy()
X_test_c= X_vol_c[p_test,:,:,:,0:1].copy()
Y_test_c= Y_vol_c[p_test,:,:,:,:].copy()
X_valid_c= X_vol_c[p_valid,:,:,:,0:1].copy()
Y_valid_c= Y_vol_c[p_valid,:,:,:,:].copy()
X_vol_c= Y_vol_c= 0
########## Pancreas
n_cases= X_vol_r.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_r= X_vol_r[p_train,:,:,:,:].copy()
Y_train_r= Y_vol_r[p_train,:,:,:,:].copy()
X_test_r= X_vol_r[p_test,:,:,:,:].copy()
Y_test_r= Y_vol_r[p_test,:,:,:,:].copy()
X_valid_r= X_vol_r[p_valid,:,:,:,:].copy()
Y_valid_r= Y_vol_r[p_valid,:,:,:,:].copy()
X_vol_r= Y_vol_r= 0
######### Liver CT
n_cases= X_vol_sil.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_sil= X_vol_sil[p_train,:,:,:,0:1].copy()
Y_train_sil= Y_vol_sil[p_train,:,:,:,:].copy()
X_test_sil= X_vol_sil[p_test,:,:,:,0:1].copy()
Y_test_sil= Y_vol_sil[p_test,:,:,:,:].copy()
X_valid_sil= X_vol_sil[p_valid,:,:,:,0:1].copy()
Y_valid_sil= Y_vol_sil[p_valid,:,:,:,:].copy()
X_vol_sil= Y_vol_sil= 0
######### Liver-MRI-SPIR
n_cases= X_vol_chaos.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_chaos= X_vol_chaos[p_train,:,:,:,0:1].copy()
Y_train_chaos= Y_vol_chaos[p_train,:,:,:,:].copy()
X_test_chaos= X_vol_chaos[p_test,:,:,:,0:1].copy()
Y_test_chaos= Y_vol_chaos[p_test,:,:,:,:].copy()
X_valid_chaos= X_vol_chaos[p_valid,:,:,:,0:1].copy()
Y_valid_chaos= Y_vol_chaos[p_valid,:,:,:,:].copy()
X_vol_chaos= Y_vol_chaos= 0
######### Liver-MRI-DUAL-in and Liver-MRI-DUAL-out
n_cases= X_vol_chaos_in.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_chaos_in= X_vol_chaos_in[p_train,:,:,:,0:1].copy()
Y_train_chaos_in= Y_vol_chaos_in[p_train,:,:,:,:].copy()
X_test_chaos_in= X_vol_chaos_in[p_test,:,:,:,0:1].copy()
Y_test_chaos_in= Y_vol_chaos_in[p_test,:,:,:,:].copy()
X_valid_chaos_in= X_vol_chaos_in[p_valid,:,:,:,0:1].copy()
Y_valid_chaos_in= Y_vol_chaos_in[p_valid,:,:,:,:].copy()
X_vol_chaos_in= Y_vol_chaos_in= 0
n_cases= X_vol_chaos_out.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_chaos_out= X_vol_chaos_out[p_train,:,:,:,0:1].copy()
Y_train_chaos_out= Y_vol_chaos_out[p_train,:,:,:,:].copy()
X_test_chaos_out= X_vol_chaos_out[p_test,:,:,:,0:1].copy()
Y_test_chaos_out= Y_vol_chaos_out[p_test,:,:,:,:].copy()
X_valid_chaos_out= X_vol_chaos_out[p_valid,:,:,:,0:1].copy()
Y_valid_chaos_out= Y_vol_chaos_out[p_valid,:,:,:,:].copy()
X_vol_chaos_out= Y_vol_chaos_out= 0
######### DHCP
X_vol_dhcp_copy= X_vol_dhcp.copy()
Y_vol_dhcp_copy= Y_vol_dhcp.copy()
n_cases= X_vol_dhcp.shape[0]
np.random.seed(0)
p= np.arange(n_cases)
np.random.shuffle(p)
p_test = p[:int(test_frac * n_cases)]
p_valid = p[int(test_frac * n_cases):int(valid_frac * n_cases)]
p_train= p[int(valid_frac * n_cases):]
X_train_dhcp= X_vol_dhcp[p_train,:,:,:,:].copy()
Y_train_dhcp= Y_vol_dhcp[p_train,:,:,:,:].copy()
X_test_dhcp= X_vol_dhcp[p_test,:,:,:,:].copy()
Y_test_dhcp= Y_vol_dhcp[p_test,:,:,:,:].copy()
X_valid_dhcp= X_vol_dhcp[p_valid,:,:,:,:].copy()
Y_valid_dhcp= Y_vol_dhcp[p_valid,:,:,:,:].copy()
# Specify training and test sets
SXX, SYY, SZZ= 128, 160, 144
X_train= [X_train_CP_yng[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_train_CP_old[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_train_h[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_train_p[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_train_sil[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_train_chaos[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_train_chaos_in[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy()]
Y_train= [Y_train_CP_yng[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_train_CP_old[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_train_h[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_train_p[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_train_sil[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_train_chaos[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_train_chaos_in[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy()]
X_test= [X_test_CP_yng[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_test_CP_old[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_test_h[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_test_p[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_test_sil[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_test_chaos[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_test_chaos_in[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), X_test_c[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
X_test_r[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy()]
Y_test= [Y_test_CP_yng[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_test_CP_old[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_test_h[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_test_p[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_test_sil[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_test_chaos[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_test_chaos_in[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(), Y_test_c[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy(),
Y_test_r[:, 7:7+SXX, 10:10+SYY, 7:7+SZZ, :].copy()]
SX, SY, SZ= 128, 160, 144
LX, LY, LZ = 96, 96, 96
test_shift= LX//4
lx_list= np.squeeze( np.concatenate( (np.arange(0, SX-LX, test_shift)[:,np.newaxis] , np.array([SX-LX])[:,np.newaxis] ) ) .astype(np.int) )
ly_list= np.squeeze( np.concatenate( (np.arange(0, SY-LY, test_shift)[:,np.newaxis] , np.array([SY-LY])[:,np.newaxis] ) ) .astype(np.int) )
lz_list= np.squeeze( np.concatenate( (np.arange(0, SZ-LZ, test_shift)[:,np.newaxis] , np.array([SZ-LZ])[:,np.newaxis] ) ) .astype(np.int) )
LXc, LYc, LZc= LX//4, LY//4, LZ//4
keep_test= 1.0
# Compute SVD of feature maps -
# - we actually only work with the feature number "3", which is the
# last (deepest) layer.
F_train_1= list()
F_train_2= list()
F_train_3= list()
F_train_4= list()
F_train_5= list()
F_train_6= list()
for i in range(len(X_train)):
X_temp= X_train[i]
Y_temp= Y_train[i]
Sv_1= np.zeros( (X_temp.shape[0],n_feat_0) )
Sv_2= np.zeros( (X_temp.shape[0],n_feat_0*2) )
Sv_3= np.zeros( (X_temp.shape[0],n_feat_0*4) )
Sv_4= np.zeros( (X_temp.shape[0],n_feat_0) )
Sv_5= np.zeros( (X_temp.shape[0],n_feat_0*2) )
Sv_6= np.zeros( (X_temp.shape[0],n_feat_0*4) )
for i_c in tqdm(range(X_temp.shape[0]), ascii=True):
y_sum = np.zeros((SX, SY, SZ, n_class))
f_sum_1 = np.zeros((SX, SY, SZ, n_feat_0))
f_sum_2 = np.zeros((SX//2, SY//2, SZ//2, n_feat_0*2))
f_sum_3 = np.zeros((SX//4, SY//4, SZ//4, n_feat_0*4))
f_sum_4 = np.zeros((SX, SY, SZ, n_feat_0))
f_sum_5 = np.zeros((SX//2, SY//2, SZ//2, n_feat_0*2))
f_sum_6 = np.zeros((SX//4, SY//4, SZ//4, n_feat_0*4))
f_cnt = np.zeros((SX, SY, SZ))
for lx in lx_list:
for ly in ly_list:
for lz in lz_list:
if np.max(Y_temp[i_c:i_c + 1, lx + LXc:lx + LX - LXc, ly + LYc:ly + LY - LYc, lz + LZc:lz + LZ - LZc, 1:]) > 0:
batch_x = X_temp[i_c:i_c + 1, lx:lx + LX, ly:ly + LY, lz:lz + LZ, :].copy()
y_prd, f_1c, f_2c= sess.run([predicter, f_1, f_2], feed_dict={X: batch_x, p_keep_conv: 1.0})
y_sum[lx:lx + LX, ly:ly + LY, lz:lz + LZ, :] += y_prd[0, :, :, :, :]
z= f_1c[0]
f_sum_1[lx:lx + LX, ly:ly + LY, lz:lz + LZ,:] += z[0, :, :, :, :].copy()
z= f_1c[1]
f_sum_2[lx//2:lx//2 + LX//2, ly//2:ly//2 + LY//2, lz//2:lz//2 + LZ//2,:] += z[0, :, :, :, :].copy()
z= f_1c[2]
f_sum_3[lx//4:lx//4 + LX//4, ly//4:ly//4 + LY//4, lz//4:lz//4 + LZ//4,:] += z[0, :, :, :, :].copy()
z= f_2c[0]
f_sum_4[lx:lx + LX, ly:ly + LY, lz:lz + LZ,:] += z[0, :, :, :, n_feat_0:].copy()
z= f_2c[1]
f_sum_5[lx//2:lx//2 + LX//2, ly//2:ly//2 + LY//2, lz//2:lz//2 + LZ//2,:] += z[0, :, :, :, 2*n_feat_0:].copy()
z= f_2c[2]
f_sum_6[lx//4:lx//4 + LX//4, ly//4:ly//4 + LY//4, lz//4:lz//4 + LZ//4,:] += z[0, :, :, :, 4*n_feat_0:].copy()
f_cnt[lx:lx + LX, ly:ly + LY, lz:lz + LZ] += 1
y_sum = np.argmax(y_sum, axis=-1)
y_sum[f_cnt == 0] = 0
batch_x = X_temp[i_c:i_c + 1, :, :, :, :].copy()
y_t_c= Y_temp[i_c, :, :, :, 1].copy()
#dk_aux.save_pred_thumbs(batch_x[0,:,:,:,0], y_t_c, y_sum, False, i, i_c, thumbs_dir )
f_cnt_d = zoom( f_cnt , (0.5, 0.5, 0.5))
f_cnt_dd= zoom( f_cnt , (0.25, 0.25, 0.25))
#### SVDs
f_mean_1 = np.zeros((SXX//4, SYY//4, SZZ//4, n_feat_0))
for i_f in range(n_feat_0):
temp= f_sum_1[:,:,:,i_f]/(f_cnt+1e-10)
temp= zoom( temp , (0.25, 0.25, 0.25))
f_mean_1[:,:,:,i_f]= temp
f_mean_1= f_mean_1.reshape(( (SX//4)*(SY//4)*(SZ//4), n_feat_0))
U, s, Vh = linalg.svd(f_mean_1)
Sv_1[i_c,:]= s
f_mean_2 = np.zeros((SXX//4, SYY//4, SZZ//4, 2*n_feat_0))
for i_f in range(2*n_feat_0):
temp= f_sum_2[:,:,:,i_f]/(f_cnt_d+1e-10)
temp= zoom( temp , (0.5, 0.5, 0.5))
f_mean_2[:,:,:,i_f]= temp
f_mean_2= f_mean_2.reshape(( (SX//4)*(SY//4)*(SZ//4), 2*n_feat_0))
U, s, Vh = linalg.svd(f_mean_2)
Sv_2[i_c,:]= s
f_mean_3 = np.zeros((SXX//4, SYY//4, SZZ//4, 4*n_feat_0))
for i_f in range(4*n_feat_0):
temp= f_sum_3[:,:,:,i_f]/(f_cnt_dd+1e-10)
f_mean_3[:,:,:,i_f]= temp
f_mean_3= f_mean_3.reshape(( (SX//4)*(SY//4)*(SZ//4), 4*n_feat_0))
U, s, Vh = linalg.svd(f_mean_3)
Sv_3[i_c,:]= s
f_mean_4 = np.zeros((SXX//4, SYY//4, SZZ//4, n_feat_0))
for i_f in range(n_feat_0):
temp= f_sum_4[:,:,:,i_f]/(f_cnt+1e-10)
temp= zoom( temp , (0.25, 0.25, 0.25))
f_mean_4[:,:,:,i_f]= temp
f_mean_4= f_mean_4.reshape(( (SX//4)*(SY//4)*(SZ//4), n_feat_0))
U, s, Vh = linalg.svd(f_mean_4)
Sv_4[i_c,:]= s
f_mean_5 = np.zeros((SXX//4, SYY//4, SZZ//4, 2*n_feat_0))
for i_f in range(2*n_feat_0):
temp= f_sum_5[:,:,:,i_f]/(f_cnt_d+1e-10)
temp= zoom( temp , (0.5, 0.5, 0.5))
f_mean_5[:,:,:,i_f]= temp
f_mean_5= f_mean_5.reshape(( (SX//4)*(SY//4)*(SZ//4), 2*n_feat_0))
U, s, Vh = linalg.svd(f_mean_5)
Sv_5[i_c,:]= s
f_mean_6 = np.zeros((SXX//4, SYY//4, SZZ//4, 4*n_feat_0))
for i_f in range(4*n_feat_0):
temp= f_sum_6[:,:,:,i_f]/(f_cnt_dd+1e-10)
f_mean_6[:,:,:,i_f]= temp
f_mean_6= f_mean_6.reshape(( (SX//4)*(SY//4)*(SZ//4), 4*n_feat_0))
U, s, Vh = linalg.svd(f_mean_6)
Sv_6[i_c,:]= s
F_train_1.append(Sv_1)
F_train_2.append(Sv_2)
F_train_3.append(Sv_3)
F_train_4.append(Sv_4)
F_train_5.append(Sv_5)
F_train_6.append(Sv_6)
np.save('/media/nerossd2/segment_everything/F_train_1.npy', F_train_1)
np.save('/media/nerossd2/segment_everything/F_train_2.npy', F_train_2)
np.save('/media/nerossd2/segment_everything/F_train_3.npy', F_train_3)
np.save('/media/nerossd2/segment_everything/F_train_4.npy', F_train_4)
np.save('/media/nerossd2/segment_everything/F_train_5.npy', F_train_5)
np.save('/media/nerossd2/segment_everything/F_train_6.npy', F_train_6)
F_test_1= list()
F_test_2= list()
F_test_3= list()
F_test_4= list()
F_test_5= list()
F_test_6= list()
for i in range(len(X_test)):
X_temp= X_test[i]
Y_temp= Y_test[i]
Sv_1= np.zeros( (X_temp.shape[0],n_feat_0) )
Sv_2= np.zeros( (X_temp.shape[0],n_feat_0*2) )
Sv_3= np.zeros( (X_temp.shape[0],n_feat_0*4) )
Sv_4= np.zeros( (X_temp.shape[0],n_feat_0) )
Sv_5= np.zeros( (X_temp.shape[0],n_feat_0*2) )
Sv_6= np.zeros( (X_temp.shape[0],n_feat_0*4) )
for i_c in tqdm(range(X_temp.shape[0]), ascii=True):
y_sum = np.zeros((SX, SY, SZ, n_class))
f_sum_1 = np.zeros((SX, SY, SZ, n_feat_0))
f_sum_2 = np.zeros((SX//2, SY//2, SZ//2, n_feat_0*2))
f_sum_3 = np.zeros((SX//4, SY//4, SZ//4, n_feat_0*4))
f_sum_4 = np.zeros((SX, SY, SZ, n_feat_0))
f_sum_5 = np.zeros((SX//2, SY//2, SZ//2, n_feat_0*2))
f_sum_6 = np.zeros((SX//4, SY//4, SZ//4, n_feat_0*4))
f_cnt = np.zeros((SX, SY, SZ))
for lx in lx_list:
for ly in ly_list:
for lz in lz_list:
if np.max(Y_temp[i_c:i_c + 1, lx + LXc:lx + LX - LXc, ly + LYc:ly + LY - LYc, lz + LZc:lz + LZ - LZc, 1:]) > 0:
batch_x = X_temp[i_c:i_c + 1, lx:lx + LX, ly:ly + LY, lz:lz + LZ, :].copy()
y_prd, f_1c, f_2c= sess.run([predicter, f_1, f_2], feed_dict={X: batch_x, p_keep_conv: 1.0})
y_sum[lx:lx + LX, ly:ly + LY, lz:lz + LZ, :] += y_prd[0, :, :, :, :]
z= f_1c[0]
f_sum_1[lx:lx + LX, ly:ly + LY, lz:lz + LZ,:] += z[0, :, :, :, :].copy()
z= f_1c[1]
f_sum_2[lx//2:lx//2 + LX//2, ly//2:ly//2 + LY//2, lz//2:lz//2 + LZ//2,:] += z[0, :, :, :, :].copy()
z= f_1c[2]
f_sum_3[lx//4:lx//4 + LX//4, ly//4:ly//4 + LY//4, lz//4:lz//4 + LZ//4,:] += z[0, :, :, :, :].copy()
z= f_2c[0]
f_sum_4[lx:lx + LX, ly:ly + LY, lz:lz + LZ,:] += z[0, :, :, :, n_feat_0:].copy()
z= f_2c[1]
f_sum_5[lx//2:lx//2 + LX//2, ly//2:ly//2 + LY//2, lz//2:lz//2 + LZ//2,:] += z[0, :, :, :, 2*n_feat_0:].copy()
z= f_2c[2]
f_sum_6[lx//4:lx//4 + LX//4, ly//4:ly//4 + LY//4, lz//4:lz//4 + LZ//4,:] += z[0, :, :, :, 4*n_feat_0:].copy()
f_cnt[lx:lx + LX, ly:ly + LY, lz:lz + LZ] += 1
y_sum = np.argmax(y_sum, axis=-1)
y_sum[f_cnt == 0] = 0
batch_x = X_temp[i_c:i_c + 1, :, :, :, :].copy()
y_t_c= Y_temp[i_c, :, :, :, 1].copy()
#dk_aux.save_pred_thumbs(batch_x[0,:,:,:,0], y_t_c, y_sum, False, i, i_c, thumbs_dir )
f_cnt_d = zoom( f_cnt , (0.5, 0.5, 0.5))
f_cnt_dd= zoom( f_cnt , (0.25, 0.25, 0.25))
#### SVDs
f_mean_1 = np.zeros((SXX//4, SYY//4, SZZ//4, n_feat_0))
for i_f in range(n_feat_0):
temp= f_sum_1[:,:,:,i_f]/(f_cnt+1e-10)
temp= zoom( temp , (0.25, 0.25, 0.25))
f_mean_1[:,:,:,i_f]= temp
f_mean_1= f_mean_1.reshape(( (SX//4)*(SY//4)*(SZ//4), n_feat_0))
U, s, Vh = linalg.svd(f_mean_1)
Sv_1[i_c,:]= s
f_mean_2 = np.zeros((SXX//4, SYY//4, SZZ//4, 2*n_feat_0))
for i_f in range(2*n_feat_0):
temp= f_sum_2[:,:,:,i_f]/(f_cnt_d+1e-10)
temp= zoom( temp , (0.5, 0.5, 0.5))
f_mean_2[:,:,:,i_f]= temp
f_mean_2= f_mean_2.reshape(( (SX//4)*(SY//4)*(SZ//4), 2*n_feat_0))
U, s, Vh = linalg.svd(f_mean_2)
Sv_2[i_c,:]= s
f_mean_3 = np.zeros((SXX//4, SYY//4, SZZ//4, 4*n_feat_0))
for i_f in range(4*n_feat_0):
temp= f_sum_3[:,:,:,i_f]/(f_cnt_dd+1e-10)
f_mean_3[:,:,:,i_f]= temp
f_mean_3= f_mean_3.reshape(( (SX//4)*(SY//4)*(SZ//4), 4*n_feat_0))
U, s, Vh = linalg.svd(f_mean_3)
Sv_3[i_c,:]= s
f_mean_4 = np.zeros((SXX//4, SYY//4, SZZ//4, n_feat_0))
for i_f in range(n_feat_0):
temp= f_sum_4[:,:,:,i_f]/(f_cnt+1e-10)
temp= zoom( temp , (0.25, 0.25, 0.25))
f_mean_4[:,:,:,i_f]= temp
f_mean_4= f_mean_4.reshape(( (SX//4)*(SY//4)*(SZ//4), n_feat_0))
U, s, Vh = linalg.svd(f_mean_4)
Sv_4[i_c,:]= s
f_mean_5 = np.zeros((SXX//4, SYY//4, SZZ//4, 2*n_feat_0))
for i_f in range(2*n_feat_0):
temp= f_sum_5[:,:,:,i_f]/(f_cnt_d+1e-10)
temp= zoom( temp , (0.5, 0.5, 0.5))
f_mean_5[:,:,:,i_f]= temp
f_mean_5= f_mean_5.reshape(( (SX//4)*(SY//4)*(SZ//4), 2*n_feat_0))
U, s, Vh = linalg.svd(f_mean_5)
Sv_5[i_c,:]= s
f_mean_6 = np.zeros((SXX//4, SYY//4, SZZ//4, 4*n_feat_0))
for i_f in range(4*n_feat_0):
temp= f_sum_6[:,:,:,i_f]/(f_cnt_dd+1e-10)
f_mean_6[:,:,:,i_f]= temp
f_mean_6= f_mean_6.reshape(( (SX//4)*(SY//4)*(SZ//4), 4*n_feat_0))
U, s, Vh = linalg.svd(f_mean_6)
Sv_6[i_c,:]= s
F_test_1.append(Sv_1)
F_test_2.append(Sv_2)
F_test_3.append(Sv_3)
F_test_4.append(Sv_4)
F_test_5.append(Sv_5)
F_test_6.append(Sv_6)
np.save('/media/nerossd2/segment_everything/F_test_1.npy', F_test_1)
np.save('/media/nerossd2/segment_everything/F_test_2.npy', F_test_2)
np.save('/media/nerossd2/segment_everything/F_test_3.npy', F_test_3)
np.save('/media/nerossd2/segment_everything/F_test_4.npy', F_test_4)
np.save('/media/nerossd2/segment_everything/F_test_5.npy', F_test_5)
np.save('/media/nerossd2/segment_everything/F_test_6.npy', F_test_6)
###############################################################################
### Load the saved spectral signatures for analysis
# This is the index of the features maps used. We only use number "3",
# which is the deepest layer.
i_F= 3
features_dir= 'C:\\Users\\davoo\\Dropbox\\scratch\\thumbs_svd_mixed\\'
F_train_1= list(np.load(features_dir+'F_train_1.npy'))
F_train_2= list(np.load(features_dir+'F_train_2.npy'))
F_train_3= list(np.load(features_dir+'F_train_3.npy'))
F_train_4= list(np.load(features_dir+'F_train_4.npy'))
F_train_5= list(np.load(features_dir+'F_train_5.npy'))
F_train_6= list(np.load(features_dir+'F_train_6.npy'))
F_test_1= list(np.load(features_dir+'F_test_1.npy'))
F_test_2= list(np.load(features_dir+'F_test_2.npy'))
F_test_3= list(np.load(features_dir+'F_test_3.npy'))
F_test_4= list(np.load(features_dir+'F_test_4.npy'))
F_test_5= list(np.load(features_dir+'F_test_5.npy'))
F_test_6= list(np.load(features_dir+'F_test_6.npy'))
F_train= [F_train_1, F_train_2, F_train_3, F_train_4, F_train_5, F_train_6]
F_test = [F_test_1, F_test_2, F_test_3, F_test_4, F_test_5, F_test_6]
for i in range(len(F_train)):
for j in range(len(F_train[i])):
temp= F_train[i][j]
temp= np.log(temp)
temp= temp/np.linalg.norm(temp, axis=1)[:,np.newaxis]
F_train[i][j]= temp
for i in range(len(F_test)):
for j in range(len(F_test[i])):
temp= F_test[i][j]
temp= np.log(temp)
temp= temp/np.linalg.norm(temp, axis=1)[:,np.newaxis]
F_test[i][j]= temp
colors= ['b', 'g', 'r', 'c', 'm', 'y', 'k']
plt.figure()
for i in range(7):
plt.plot(F_train[i_F][i].T, colors[i])
for i in range(7,10):
plt.plot(F_test[i_F][i].T, colors[i-7], marker='*')
# Here, you must specify the indices of in-distribution training data (for F_tr),
# in-distribution test data (for F_te), and out-of-distribution data (for F_od)
F_tr= F_train[i_F][0]
for i in range(1,7):
F_tr= np.concatenate((F_tr, F_train[i_F][i]), axis=0)
F_te= F_test[i_F][0]
for i in range(1,7):
F_te= np.concatenate((F_te, F_test[i_F][i]), axis=0)