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In [1]:



    


%load_ext autoreload
%autoreload 2

import cPickle as pickle
import os; import sys; sys.path.append('..'); sys.path.append('../gp/')
import gp
import gp.nets as nets

from nolearn.lasagne.visualize import plot_loss
from nolearn.lasagne.visualize import plot_conv_weights
from nolearn.lasagne.visualize import plot_conv_activity
from nolearn.lasagne.visualize import plot_occlusion

from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, precision_recall_fscore_support, f1_score, precision_recall_curve, average_precision_score, zero_one_loss


from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt

%matplotlib inline



    


















    






/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)
/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
  "downsample module has been moved to the theano.tensor.signal.pool module.")

















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In [2]:



    


input_image = []
input_prob = []
input_gold = []
input_rhoana = []

# for z in range(0,50):
test_slices = range(15,25) + range(40,50) + range(65,75)
for z in test_slices:
    image, prob, gold, rhoana = gp.Util.read_cremi_section(os.path.expanduser('/home/d/data/CREMIGP/TEST/'), z)
    input_image.append(image[0:500,0:500])
    input_prob.append(255.-prob[0:500,0:500])
    input_gold.append(gold[0:500,0:500])
    input_rhoana.append(rhoana[0:500,0:500])



    











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In [3]:



    


with open('/home/d/FP/CREMITEST/bigM_fp_2D.p', 'rb') as f:
    bigM = pickle.load(f)



    











In [7]:



    


bigM_cA_after_95, out_cA_volume_after_auto_95, cA_auto_fixes_95, cA_auto_vi_s_95, visperstep = gp.Legacy.splits_global_from_M_automatic(None, 
                                                                                                                            bigM, 
                                                                                                                            input_image, 
                                                                                                                            input_prob, 
                                                                                                                            input_rhoana,
                                                                                                                            input_gold, 
                                                                                                                            sureness_threshold=.3,FP=True)



    


















    






0.298039

















In [18]:



    


OUTDIR = '/home/d/FP/CREMITEST'
split_vis = OUTDIR + '/cylinder_split_auto95_vis.p'

split_fixes = OUTDIR + '/cylinder_split_auto95_fixes.p'

output_95 = OUTDIR + '/cylinder_auto95_output.p'

with open(split_vis, 'wb') as f:
    pickle.dump(visperstep, f)

with open(split_fixes, 'wb') as f:
    pickle.dump(cA_auto_fixes_95, f)       

with open(output_95, 'wb') as f:
    pickle.dump(out_cA_volume_after_auto_95, f)



    











In [19]:



    


split_vis = OUTDIR + '/cylinder_split_simuser_vis.p'

split_fixes = OUTDIR + '/cylinder_split_simuser_fixes.p'

output_95 = OUTDIR + '/cylinder_simuser_output.p'
bigM_cylinder_after_95, out_cylinder_volume_after_auto_95, cylinder_auto_fixes_95, cylinder_auto_vi_s_95, vi_s_per_step2 = gp.Legacy.splits_global_from_M(None, bigM, input_image, input_prob, input_rhoana, input_gold, hours=-1, FP=True)



    


















    






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0.172549 0.188235 0.188235
0.215686 0.0 0.215686
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0.188235 0.227451 0.227451
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0.192157 0.14902 0.192157
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0.176471 0.0 0.176471
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0.113725 0.117647 0.117647
0.113725 0.0 0.113725
0.0 0.0 0.0
0.137255 0.0 0.137255
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0.164706 0.0 0.164706
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0.113725 0.0 0.113725
0.121569 0.0 0.121569
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0.164706 0.152941 0.164706
0.137255 0.0 0.137255
0.133333 0.0 0.133333
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0.113725 0.113725 0.113725
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0.113725 0.0 0.113725
0.0 0.0 0.0
0.109804 0.0 0.109804
0.0 0.0 0.0
done

















In [20]:



    


with open(split_vis, 'wb') as f:
    pickle.dump(vi_s_per_step2, f)

with open(split_fixes, 'wb') as f:
    pickle.dump(cylinder_auto_fixes_95, f)       

with open(output_95, 'wb') as f:
    pickle.dump(out_cylinder_volume_after_auto_95, f)



    











In [ ]:



    






    











In [14]:



    


len(visperstep)



    


















    
Out[14]:








201

















In [38]:



    


bigM_cA_after_95, out_cA_volume_after_auto_95, cA_auto_fixes_95, cA_auto_vi_s_95, visperstep = gp.Legacy.splits_global_from_M_automatic(net, 
                                                                                                                            bigM_cremiA, 
                                                                                                                            input_image, 
                                                                                                                            input_prob, 
                                                                                                                            input_rhoana,
                                                                                                                            input_gold, 
                                                                                                                            sureness_threshold=.95)



    


















    






here

















In [135]:



    


gp.Legacy.VI(out_cA_volume_after_auto_95, input_gold)



    


















    
Out[135]:








(1.2772683303625527,
 1.4227682148970084,
 [0.9914972295290623,
  1.0065468511430007,
  0.9386433274922785,
  0.8513864951451726,
  0.9020146231854129,
  0.9028561846963399,
  0.9029402917052867,
  0.8343376887584597,
  0.8900334300076347,
  0.7640631368301207,
  1.5897406283580051,
  1.4259567455799158,
  1.4067934875872496,
  1.50799981769243,
  1.4216826061807275,
  1.5739188222653064,
  1.49120944768171,
  1.524625720524651,
  1.4673106923648627,
  1.4389061338649043,
  1.4238538236132894,
  1.2980002460822213,
  1.5434885895724069,
  1.5995917290199704,
  1.477708348120366,
  1.4370088289571,
  1.3529149509418676,
  1.3095287650616445,
  1.5158051045850938,
  1.527686164330098])

















In [27]:



    


visperstep



    


















    
Out[27]:








[(1.1205187243522197,
  1.1205187243522197,
  [1.0903680301647931, 1.1506694185396462]),
 (1.1205750199211311,
  1.1205750199211311,
  [1.090480621302616, 1.1506694185396462]),
 (1.1205373537420451,
  1.1205373537420451,
  [1.090405288944444, 1.1506694185396462]),
 (1.1204855308356558,
  1.1204855308356558,
  [1.090405288944444, 1.1505657727268677]),
 (1.1204578291693812,
  1.1204578291693812,
  [1.090405288944444, 1.1505103693943184]),
 (1.1204093220969673,
  1.1204093220969673,
  [1.0903082747996162, 1.1505103693943184]),
 (1.1204352670924926,
  1.1204352670924926,
  [1.0903601647906669, 1.1505103693943184]),
 (1.120432408075092,
  1.120432408075092,
  [1.0903601647906669, 1.1505046513595172]),
 (1.1204205447144391,
  1.1204205447144391,
  [1.090336438069361, 1.1505046513595172]),
 (1.1203517795432445,
  1.1203517795432445,
  [1.0901989077269718, 1.1505046513595172]),
 (1.1203197728565311,
  1.1203197728565311,
  [1.090134894353545, 1.1505046513595172]),
 (1.1202874124069959,
  1.1202874124069959,
  [1.090134894353545, 1.150439930460447]),
 (1.1202405820828272,
  1.1202405820828272,
  [1.090134894353545, 1.1503462698121094]),
 (1.1202129273125037,
  1.1202129273125037,
  [1.090079584812898, 1.1503462698121094]),
 (1.1202013194538223,
  1.1202013194538223,
  [1.090079584812898, 1.1503230540947467]),
 (1.1201329366255122,
  1.1201329366255122,
  [1.090079584812898, 1.1501862884381264]),
 (1.1201146017321566,
  1.1201146017321566,
  [1.0900429150261868, 1.1501862884381264]),
 (1.1201505308568995,
  1.1201505308568995,
  [1.0901147732756726, 1.1501862884381264]),
 (1.1199677432258035,
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In [56]:



    


gp.metrics.adapted_rand(out_cA_volume_after_auto_95, input_gold)



    


















    
Out[56]:








0.51512425659267813

















In [57]:



    


cA_auto_fixes_95



    


















    
Out[57]:








[(0, 0.99999964237213135),
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In [64]:



    


import numpy as np



    











In [70]:



    


p = np.load('/tmp/trainB.npz.npy', mmap_mode='r')
t = np.load('/tmp/trainB_targets.npz.npy', mmap_mode='r')



    











In [80]:



    


gp.Util.view(p[2][0], color=False)



    


















    
























In [81]:



    


gp.Util.view(p[2][1], color=False)



    


















    
























In [83]:



    


gp.Util.view(p[2][3], color=False)



    


















    
























In [79]:



    


t[2]



    


















    
Out[79]:








1.0

















In [ ]:

Content source: VCG/gp

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