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In [1]:



    


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



    


















    






Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)
/home/d/nolearnNEW/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.")

















In [2]:



    


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
import numpy as np
import time



    











In [3]:



    


def r_d(cnn):
    # load dojo data
    input_image, input_prob, input_gold, input_rhoana, dojo_bbox = gp.Legacy.read_dojo_data()


    original_mean_VI, original_median_VI, original_VI_s = gp.Legacy.VI(input_gold, input_rhoana)

    # output folder for anything to store
    output_folder = '/home/d/netstatsCVPR2018/'+cnn.uuid+'/'
    if not os.path.exists(output_folder):
      os.makedirs(output_folder)

#     # find merge errors, if we did not generate them before
#     merge_error_file = output_folder+'/merges_new_cnn.p'
#     if os.path.exists(merge_error_file):
#       print 'Loading merge errors from file..'
#       with open(merge_error_file, 'rb') as f:
#         merge_errors = pickle.load(f)
#     else:
#       print 'Finding Top 5 merge errors..'
#       merge_errors = gp.Legacy.get_top5_merge_errors(cnn, input_image, input_prob, input_rhoana)
#       with open(merge_error_file, 'wb') as f:
#         pickle.dump(merge_errors, f)

#     print len(merge_errors), ' merge errors found.'

    # we need to create a bigM for the dojo volume
    bigM_dojo_file = output_folder + '/bigM_dojo_test2.p'
    if os.path.exists(bigM_dojo_file):
      print 'Loading dojo bigM from file..'
      with open(bigM_dojo_file, 'rb') as f:
        bigM_dojo = pickle.load(f)
    else:
      print 'Creating dojo bigM..'
      bigM_dojo = gp.Legacy.create_bigM_without_mask(cnn, input_image, input_prob, input_rhoana, verbose=False)
      with open(bigM_dojo_file, 'wb') as f:
        pickle.dump(bigM_dojo, f)    



    print
    dojo_vi_95_file = output_folder + '/dojo_vi_95_t6.p'

    dojo_merge_vis = output_folder + '/dojo_merge_auto95_vis.p'
    dojo_split_vis = output_folder + '/dojo_split_auto95_vis.p'

    dojo_merge_fixes = output_folder + '/dojo_merge_auto95_fixes.p'
    dojo_split_fixes = output_folder + '/dojo_split_auto95_fixes.p'

    dojo_output_95 = output_folder + '/dojo_auto95_output.p'

    if os.path.exists(dojo_vi_95_file):
      print 'Loading merge errors p < .05 and split errors p > .95 from file..'
      with open(dojo_vi_95_file, 'rb') as f:
        dojo_vi_95 = pickle.load(f)
    else:      
#       #
#       # perform merge correction with p < .05
#       #
#       print 'Correcting merge errors with p < .05'
#       bigM_dojo_05, corrected_rhoana_05, dojo_auto_merge_fixes, vi_s_per_step = gp.Legacy.perform_auto_merge_correction(cnn, bigM_dojo, input_image, input_prob, input_rhoana, merge_errors, .05, input_gold=input_gold)

#       print '   Mean VI improvement', original_mean_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[0]
#       print '   Median VI improvement', original_median_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[1]

#       with open(dojo_merge_vis, 'wb') as f:
#         pickle.dump(vi_s_per_step, f)


#       with open(dojo_merge_fixes, 'wb') as f:
#         pickle.dump(dojo_auto_merge_fixes, f) 

      #
      # perform split correction with p > .95
      #
      print 'Correcting split errors with p > .95'
      bigM_dojo_after_95, out_dojo_volume_after_auto_95, dojo_auto_fixes_95, dojo_auto_vi_s_95, vi_s_per_step2 = gp.Legacy.splits_global_from_M_automatic(cnn, bigM_dojo, input_image, input_prob, input_rhoana, input_gold, sureness_threshold=.95)

      dojo_vi_95 = gp.Legacy.VI(input_gold, out_dojo_volume_after_auto_95)

      with open(dojo_vi_95_file, 'wb') as f:
        pickle.dump(dojo_vi_95, f)

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

      with open(dojo_split_fixes, 'wb') as f:
        pickle.dump(dojo_auto_fixes_95, f)       

      with open(dojo_output_95, 'wb') as f:
        pickle.dump(out_dojo_volume_after_auto_95, f) 

    print '   Mean VI improvement', original_mean_VI-dojo_vi_95[0]
    print '   Median VI improvement', original_median_VI-dojo_vi_95[1]



    











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In [4]:



    


NETS = []
NETS.append('../../nets/PLB_FULL.p')
print NETS



    


















    






['../../nets/PLB_FULL.p']

















In [5]:



    


roc1 = []
roc2 = []



    











In [6]:



    


#
# binary + large_border
#
network_path = NETS[0]

with open(network_path, 'rb') as f:
    net = pickle.load(f)
X_test, y_test = gp.Patch.load_rgba_test_only('ipmlb')
X_test = np.delete(X_test, [0,1] , axis=1)
test_prediction = net.predict(X_test)
test_prediction_prob = net.predict_proba(X_test)
print
print 'Precision/Recall:'
print classification_report(y_test, test_prediction)
test_acc = net.score(X_test, y_test)
acc_score = accuracy_score(y_test, test_prediction)
print 'Test Accuracy:', test_acc
print 'Accuracy Score:', acc_score

fpr, tpr, thresholds = roc_curve(y_test, test_prediction)
area = auc(fpr, tpr)
print 'AUC', area

fpr2, tpr2, thresholds = roc_curve(y_test, test_prediction_prob[:,1])
area2 = auc(fpr2, tpr2)
print 'AUC2', area2

roc1.append({'ip':[fpr,tpr,area]})
roc2.append({'ip':[fpr2,tpr2,area2]})

t0 = time.time()
net.uuid = 'PLB'

r_d(net)
print time.time()-t0, 'seconds'



    


















    






Loaded /home/d/patches//ipmlb/ in 0.000532865524292 seconds.

Precision/Recall:
             precision    recall  f1-score   support

          0       0.45      0.51      0.48      8780
          1       0.44      0.38      0.41      8780

avg / total       0.44      0.44      0.44     17560

Test Accuracy: 0.444646924829
Accuracy Score: 0.444646924829
AUC 0.444646924829
AUC2 0.4241103201
Creating dojo bigM..
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[ 9 11] (474, 474) (474, 474) (474, 474)
[ 9 10 12 17 19 22] (474, 474) (474, 474) (474, 474)
[11] (474, 474) (474, 474) (474, 474)
[ 2  3  4 15 16] (474, 474) (474, 474) (474, 474)
[ 6  7  8  9 18 20 29 30] (474, 474) (474, 474) (474, 474)
[ 2 13 16 21 24 27] (474, 474) (474, 474) (474, 474)
[ 4 13 15 20 27 28 33 37 38] (474, 474) (474, 474) (474, 474)
[ 9 11 18 19 23] (474, 474) (474, 474) (474, 474)
[ 9 14 17 23 25 30 36] (474, 474) (474, 474) (474, 474)
[11 17 22 23 25 26] (474, 474) (474, 474) (474, 474)
[ 4  6 14 16 28 29 31] (474, 474) (474, 474) (474, 474)
[ 2 15 24] (474, 474) (474, 474) (474, 474)
[11 19 25 26] (474, 474) (474, 474) (474, 474)
[17 18 19 25] (474, 474) (474, 474) (474, 474)
[15 21 27] (474, 474) (474, 474) (474, 474)
[18 19 22 23 26 34 36 42 44] (474, 474) (474, 474) (474, 474)
[19 22 25] (474, 474) (474, 474) (474, 474)
[15 16 24 32 37 41] (474, 474) (474, 474) (474, 474)
[16 20 30 31 33 35 39 40] (474, 474) (474, 474) (474, 474)
[14 20 30 31] (474, 474) (474, 474) (474, 474)
[14 18 28 29 31 36 39 42 43 44 45 46 47 49 50 51 55] (474, 474) (474, 474) (474, 474)
[20 28 29 30] (474, 474) (474, 474) (474, 474)
[27] (474, 474) (474, 474) (474, 474)
[16 28 35 38 40] (474, 474) (474, 474) (474, 474)
[25 44 48 50] (474, 474) (474, 474) (474, 474)
[28 33] (474, 474) (474, 474) (474, 474)
[18 25 30 42] (474, 474) (474, 474) (474, 474)
[16 27 38 41] (474, 474) (474, 474) (474, 474)
[16 33 37 40 46 52 54 56] (474, 474) (474, 474) (474, 474)
[28 30 40 43 46] (474, 474) (474, 474) (474, 474)
[28 33 38 39 46] (474, 474) (474, 474) (474, 474)
[27 37] (474, 474) (474, 474) (474, 474)
[25 30 36 45 47] (474, 474) (474, 474) (474, 474)
[30 39 46] (474, 474) (474, 474) (474, 474)
[25 30 34 45 50] (474, 474) (474, 474) (474, 474)
[30 42 44 47] (474, 474) (474, 474) (474, 474)
[30 38 39 40 43 49 51 56] (474, 474) (474, 474) (474, 474)
[30 42 45] (474, 474) (474, 474) (474, 474)
[34 50] (474, 474) (474, 474) (474, 474)
[30 46 51] (474, 474) (474, 474) (474, 474)
[30 34 44 48 53 55] (474, 474) (474, 474) (474, 474)
[30 46 49 55 56 58 59 60 61] (474, 474) (474, 474) (474, 474)
[38 54 57] (474, 474) (474, 474) (474, 474)
[50 55] (474, 474) (474, 474) (474, 474)
[38 52 56 57 62] (474, 474) (474, 474) (474, 474)
[30 50 51 53 58 64] (474, 474) (474, 474) (474, 474)
[38 46 51 54 59 62] (474, 474) (474, 474) (474, 474)
[52 54 62 63 65] (474, 474) (474, 474) (474, 474)
[51 55 61 64 66] (474, 474) (474, 474) (474, 474)
[51 56 60 61 62 66 69 70 71] (474, 474) (474, 474) (474, 474)
[51 59 61] (474, 474) (474, 474) (474, 474)
[51 58 59 60 66 69] (474, 474) (474, 474) (474, 474)
[54 56 57 59 65 67 70] (474, 474) (474, 474) (474, 474)
[57 65 67 68] (474, 474) (474, 474) (474, 474)
[55 58 66] (474, 474) (474, 474) (474, 474)
[57 62 63 67] (474, 474) (474, 474) (474, 474)
[58 59 61 64 69] (474, 474) (474, 474) (474, 474)
[62 63 65 68 70] (474, 474) (474, 474) (474, 474)
[63 67 70 71] (474, 474) (474, 474) (474, 474)
[59 61 66] (474, 474) (474, 474) (474, 474)
[59 62 67 68 71] (474, 474) (474, 474) (474, 474)
[59 68 70] (474, 474) (474, 474) (474, 474)
[ 2 16] (474, 474) (474, 474) (474, 474)
[ 1  3 16 17 18] (474, 474) (474, 474) (474, 474)
[ 2  4  5  6 18 24 26] (474, 474) (474, 474) (474, 474)
[3] (474, 474) (474, 474) (474, 474)
[3 6] (474, 474) (474, 474) (474, 474)
[ 3  5  7  8 19 20 26] (474, 474) (474, 474) (474, 474)
[6 8] (474, 474) (474, 474) (474, 474)
[ 6  7  9 10 11 12 19 20] (474, 474) (474, 474) (474, 474)
[8] (474, 474) (474, 474) (474, 474)
[8] (474, 474) (474, 474) (474, 474)
[ 8 12] (474, 474) (474, 474) (474, 474)
[ 8 11 13 15 20 22 25] (474, 474) (474, 474) (474, 474)
[12 14 15] (474, 474) (474, 474) (474, 474)
[13 15 21] (474, 474) (474, 474) (474, 474)
[12 13 14 21 25 27] (474, 474) (474, 474) (474, 474)
[ 1  2 17 18 23 28] (474, 474) (474, 474) (474, 474)
[ 2 16 18] (474, 474) (474, 474) (474, 474)
[ 2  3 16 17 23 24] (474, 474) (474, 474) (474, 474)
[ 6  8 20] (474, 474) (474, 474) (474, 474)
[ 6  8 12 19 22 26 31 36] (474, 474) (474, 474) (474, 474)
[14 15 27 32 40] (474, 474) (474, 474) (474, 474)
[12 20 25 29 32 36 42] (474, 474) (474, 474) (474, 474)
[16 18 24 28 30 35] (474, 474) (474, 474) (474, 474)
[ 3 18 23 26 33 34 35 39 43 44 47] (474, 474) (474, 474) (474, 474)
[12 15 22 27 29] (474, 474) (474, 474) (474, 474)
[ 3  6 20 24 31 33 36 38] (474, 474) (474, 474) (474, 474)
[15 21 25 29 32] (474, 474) (474, 474) (474, 474)
[16 23 30] (474, 474) (474, 474) (474, 474)
[22 25 27 32] (474, 474) (474, 474) (474, 474)
[23 28 35] (474, 474) (474, 474) (474, 474)
[20 26 36] (474, 474) (474, 474) (474, 474)
[21 22 27 29 40 42 49 54 55] (474, 474) (474, 474) (474, 474)
[24 26 34 36 38 39 41 45 48 50] (474, 474) (474, 474) (474, 474)
[24 33] (474, 474) (474, 474) (474, 474)
[23 24 30 37 44 46] (474, 474) (474, 474) (474, 474)
[20 22 26 31 33 38 42 48 49 51 52 56 58 59] (474, 474) (474, 474) (474, 474)
[35] (474, 474) (474, 474) (474, 474)
[26 33 36] (474, 474) (474, 474) (474, 474)
[24 33 41 43 45] (474, 474) (474, 474) (474, 474)
[21 32 53 55 57] (474, 474) (474, 474) (474, 474)
[33 39 45] (474, 474) (474, 474) (474, 474)
[22 32 36 49] (474, 474) (474, 474) (474, 474)
[24 39 45 47] (474, 474) (474, 474) (474, 474)
[24 35 46 47] (474, 474) (474, 474) (474, 474)
[33 39 41 43 47 50 56] (474, 474) (474, 474) (474, 474)
[35 44] (474, 474) (474, 474) (474, 474)
[24 43 44 45 56 58 60 62] (474, 474) (474, 474) (474, 474)
[33 36 50 51] (474, 474) (474, 474) (474, 474)
[32 36 42 52 54] (474, 474) (474, 474) (474, 474)
[33 45 48 51 56] (474, 474) (474, 474) (474, 474)
[36 48 50 56] (474, 474) (474, 474) (474, 474)
[36 49 54 59] (474, 474) (474, 474) (474, 474)
[40 57] (474, 474) (474, 474) (474, 474)
[32 49 52 55 57 59] (474, 474) (474, 474) (474, 474)
[32 40 54 57] (474, 474) (474, 474) (474, 474)
[36 45 47 50 51 58] (474, 474) (474, 474) (474, 474)
[40 53 54 55 59 61 63] (474, 474) (474, 474) (474, 474)
[36 47 56 59 62 63 64 66 67 68 69 70 72] (474, 474) (474, 474) (474, 474)
[36 52 54 57 58 63] (474, 474) (474, 474) (474, 474)
[47 62 64 65] (474, 474) (474, 474) (474, 474)
[57 63] (474, 474) (474, 474) (474, 474)
[47 58 60 64] (474, 474) (474, 474) (474, 474)
[57 58 59 61 66 71] (474, 474) (474, 474) (474, 474)
[58 60 62 65 72 73] (474, 474) (474, 474) (474, 474)
[60 64] (474, 474) (474, 474) (474, 474)
[58 63 69 71 74] (474, 474) (474, 474) (474, 474)
[58 68 69] (474, 474) (474, 474) (474, 474)
[58 67 69 70 74 77 78 80] (474, 474) (474, 474) (474, 474)
[58 66 67 68 74 77] (474, 474) (474, 474) (474, 474)
[58 68 72 75 78] (474, 474) (474, 474) (474, 474)
[63 66 74 79] (474, 474) (474, 474) (474, 474)
[58 64 70 73 75] (474, 474) (474, 474) (474, 474)
[64 72 75 76] (474, 474) (474, 474) (474, 474)
[66 68 69 71 77 79] (474, 474) (474, 474) (474, 474)
[70 72 73 76 78] (474, 474) (474, 474) (474, 474)
[73 75 78 80] (474, 474) (474, 474) (474, 474)
[68 69 74] (474, 474) (474, 474) (474, 474)
[68 70 75 76 80] (474, 474) (474, 474) (474, 474)
[71 74] (474, 474) (474, 474) (474, 474)
[68 76 78] (474, 474) (474, 474) (474, 474)
[ 2 14 16 17] (474, 474) (474, 474) (474, 474)
[ 1  3 12 14] (474, 474) (474, 474) (474, 474)
[ 2  4  5  6 12 15 18] (474, 474) (474, 474) (474, 474)
[3] (474, 474) (474, 474) (474, 474)
[3 6] (474, 474) (474, 474) (474, 474)
[ 3  5  7  8  9 15 21] (474, 474) (474, 474) (474, 474)
[6] (474, 474) (474, 474) (474, 474)
[6 9] (474, 474) (474, 474) (474, 474)
[ 6  8 10 13 21 23] (474, 474) (474, 474) (474, 474)
[ 9 11 13] (474, 474) (474, 474) (474, 474)
[10 13 19] (474, 474) (474, 474) (474, 474)
[ 2  3 14 18] (474, 474) (474, 474) (474, 474)
[ 9 10 11 19 23 25] (474, 474) (474, 474) (474, 474)
[ 1  2 12 17 18 20 22 24] (474, 474) (474, 474) (474, 474)
[ 3  6 18 21 32 35] (474, 474) (474, 474) (474, 474)
[ 1 17] (474, 474) (474, 474) (474, 474)
[ 1 14 16 22 26] (474, 474) (474, 474) (474, 474)
[ 3 12 14 15 20 24 28 32 35 38] (474, 474) (474, 474) (474, 474)
[11 13 25 30 40] (474, 474) (474, 474) (474, 474)
[14 18 24] (474, 474) (474, 474) (474, 474)
[ 6  9 15 23 27 30 35 36 39] (474, 474) (474, 474) (474, 474)
[14 17 24 26 29 33 34] (474, 474) (474, 474) (474, 474)
[ 9 13 21 25 27] (474, 474) (474, 474) (474, 474)
[14 18 20 22 28 31 33 34 41 42 43 44] (474, 474) (474, 474) (474, 474)
[13 19 23 27 30] (474, 474) (474, 474) (474, 474)
[17 22 29] (474, 474) (474, 474) (474, 474)
[21 23 25 30] (474, 474) (474, 474) (474, 474)
[18 24 31 35 38 41 42 49 51 52] (474, 474) (474, 474) (474, 474)
[22 26 33] (474, 474) (474, 474) (474, 474)
[19 21 25 27 36 39 40 50 55] (474, 474) (474, 474) (474, 474)
[24 28] (474, 474) (474, 474) (474, 474)
[15 18 35] (474, 474) (474, 474) (474, 474)
[22 24 29 34 37 44 45 46 47 48] (474, 474) (474, 474) (474, 474)
[22 24 33] (474, 474) (474, 474) (474, 474)
[15 18 21 28 32 38 39 50 52 54 57 60] (474, 474) (474, 474) (474, 474)
[21 30 39] (474, 474) (474, 474) (474, 474)
[33] (474, 474) (474, 474) (474, 474)
[18 28 35] (474, 474) (474, 474) (474, 474)
[21 30 35 36 50] (474, 474) (474, 474) (474, 474)
[19 30 53 55 58] (474, 474) (474, 474) (474, 474)
[24 28 42] (474, 474) (474, 474) (474, 474)
[24 28 41 43 49] (474, 474) (474, 474) (474, 474)
[24 42 44 49] (474, 474) (474, 474) (474, 474)
[24 33 43 46 49 57 62 64 66] (474, 474) (474, 474) (474, 474)
[33 46] (474, 474) (474, 474) (474, 474)
[33 44 45 47 48] (474, 474) (474, 474) (474, 474)
[33 46 48] (474, 474) (474, 474) (474, 474)
[33 46 47] (474, 474) (474, 474) (474, 474)
[28 42 43 44 51 57] (474, 474) (474, 474) (474, 474)
[30 35 39 54 55 56] (474, 474) (474, 474) (474, 474)
[28 49 52 57] (474, 474) (474, 474) (474, 474)
[28 35 51 57] (474, 474) (474, 474) (474, 474)
[40 58] (474, 474) (474, 474) (474, 474)
[35 50 56 60] (474, 474) (474, 474) (474, 474)
[30 40 50 56 58 59] (474, 474) (474, 474) (474, 474)
[50 54 55 59 60] (474, 474) (474, 474) (474, 474)
[35 44 49 51 52 60 61 66 67] (474, 474) (474, 474) (474, 474)
[40 53 55 59 60 63 65] (474, 474) (474, 474) (474, 474)
[55 56 58 60] (474, 474) (474, 474) (474, 474)
[35 54 56 57 58 59 61 65 68 74] (474, 474) (474, 474) (474, 474)
[57 60 67 68 69] (474, 474) (474, 474) (474, 474)
[44 64 66 70] (474, 474) (474, 474) (474, 474)
[58 65] (474, 474) (474, 474) (474, 474)
[44 62 66] (474, 474) (474, 474) (474, 474)
[58 60 63 74] (474, 474) (474, 474) (474, 474)
[44 57 62 64 67 69 70 71 72 73 75 79] (474, 474) (474, 474) (474, 474)
[57 61 66 69] (474, 474) (474, 474) (474, 474)
[60 61 69 74] (474, 474) (474, 474) (474, 474)
[61 66 67 68 73 74 76 78] (474, 474) (474, 474) (474, 474)
[62 66] (474, 474) (474, 474) (474, 474)
[66 72 77 79 86 87] (474, 474) (474, 474) (474, 474)
[66 71 73 77] (474, 474) (474, 474) (474, 474)
[66 69 72 76 77 80] (474, 474) (474, 474) (474, 474)
[60 65 68 69 76 78 82 89] (474, 474) (474, 474) (474, 474)
[66 79 81 83 85] (474, 474) (474, 474) (474, 474)
[69 73 74 77 78 82 84] (474, 474) (474, 474) (474, 474)
[71 72 73 76 80 82 84 86 87 88 90] (474, 474) (474, 474) (474, 474)
[69 74 76] (474, 474) (474, 474) (474, 474)
[66 71 75 85 86] (474, 474) (474, 474) (474, 474)
[73 77] (474, 474) (474, 474) (474, 474)
[75] (474, 474) (474, 474) (474, 474)
[74 76 77 84 88 89] (474, 474) (474, 474) (474, 474)
[75 85 86 90] (474, 474) (474, 474) (474, 474)
[76 77 82] (474, 474) (474, 474) (474, 474)
[75 79 83 86] (474, 474) (474, 474) (474, 474)
[71 77 79 83 85 87 90] (474, 474) (474, 474) (474, 474)
[71 77 86] (474, 474) (474, 474) (474, 474)
[77 82] (474, 474) (474, 474) (474, 474)
[74 82] (474, 474) (474, 474) (474, 474)
[77 83 86] (474, 474) (474, 474) (474, 474)

Correcting split errors with p > .95
0.949454724789
   Mean VI improvement -0.0585994110693
   Median VI improvement -0.079610244225
84.4330050945 seconds

















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Content source: VCG/gp

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