def script_visualizePerformanceDifference():
trained_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/trained/20151027204114'
notrained_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/no_trained/20151027203547'
out_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/nn_performance_comparison_trained_notrained.png'
out_file_diff = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/nn_performance_comparison_trained_notrained_diff.png'
file_pres = [trained_file, notrained_file]
layers = ['pool5', 'fc6', 'fc7']
legend_pres = ['Trained', 'Unfamiliar']
legend_entries = []
for leg in legend_pres:
legend_entries.extend([leg + ' ' + layer for layer in layers])
vecs_to_plot = []
file_names = [
file_pre + '_' + layer for file_pre in file_pres for layer in layers
]
for idx, file_name in enumerate(file_names):
print file_name, legend_entries[idx]
[img_paths, gt_labels, indices,
_] = pickle.load(open(file_name + '.p', 'rb'))
no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
indices, gt_labels)
with open(file_name + '.txt', 'wb') as f:
for num in no_correct:
f.write(str(num) + ' ')
f.write('\n')
# with open(file_name+'.txt','rb') as f:
# no_correct=f.readline();
# no_correct=[float(no_correct_curr) for no_correct_curr in no_correct.strip('\n').split()];
vecs_to_plot.append(no_correct)
# print legend_entries;
print vecs_to_plot
# print len(vecs_to_plot);
# print len(legend_entries);
plt.figure()
plt.xlabel('Number of Nearest Neighbours K')
plt.ylabel('Accuracy')
plt.title('NN Accuracy DNN Features for Pascal Classes')
plt.xlim(0, 6)
plt.ylim(
min([min(vec) for vec in vecs_to_plot]) - 0.05,
max([max(vec) for vec in vecs_to_plot]) + 0.05)
handles = []
for vec in vecs_to_plot:
handle, = plt.plot(range(1,
len(vec) + 1), vec)
handles.append(handle)
plt.legend(handles, legend_entries, loc=2, prop={'size': 10})
plt.savefig(out_file)
legend_entries = ['Trained-Untrained ' + layer for layer in layers]
diffs = []
for idx in range(3):
a = vecs_to_plot[idx]
b = vecs_to_plot[idx + 3]
diff = [a[idx_curr] - b[idx_curr] for idx_curr in range(len(a))]
diffs.append(diff)
vecs_to_plot = diffs
plt.figure()
plt.xlabel('Number of Nearest Neighbours K')
plt.ylabel('Accuracy Difference')
plt.title('NN Accuracy DNN Features for Pascal Classes')
plt.xlim(0, 6)
plt.ylim(
min([min(vec) for vec in vecs_to_plot]) - 0.01,
max([max(vec) for vec in vecs_to_plot]) + 0.01)
handles = []
for vec in vecs_to_plot:
handle, = plt.plot(range(1,
len(vec) + 1), vec)
handles.append(handle)
plt.legend(handles, legend_entries, loc=2, prop={'size': 10})
plt.savefig(out_file_diff)
def script_runNNOnPascalExcludedInTraining():
path_to_file = '../../data/ilsvrc12/synset_words.txt'
val_ids = imagenet.readLabelsFile(path_to_file)
val_just_ids = list(zip(*val_ids)[0])
val_just_labels = list(zip(*val_ids)[1])
pascal_ids_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
pascal_ids = imagenet.readLabelsFile(pascal_ids_file)
pascal_just_ids = list(zip(*pascal_ids)[0])
to_exclude = imagenet.removeClassesWithOverlap(val_just_ids,
pascal_just_ids,
keepMapping=True)
val_gt_file = '../../data/ilsvrc12/val.txt'
list_of_ids_im = [id for id_list in to_exclude for id in id_list]
mapping_file = '../../data/ilsvrc12/synsets.txt'
print len(list_of_ids_im)
list_of_ids, _ = imagenet.getImagenetIdToTrainingIdMapping(
mapping_file, list_of_ids_im)
print len(list_of_ids)
# print list_of_ids[0]
list_of_ids_pascal = []
for id_no in range(len(to_exclude)):
list_of_ids_pascal = list_of_ids_pascal + [id_no] * len(
to_exclude[id_no])
path_to_val = '/disk2/imagenet/val'
test_set = imagenet.selectTestSetByID(val_gt_file, list_of_ids,
path_to_val)
# out_dir='/disk2/octoberExperiments/nn_performance_without_pascal/notrained'
out_dir = '/disk2/novemberExperiments/nn_imagenet_top5/trained'
if not os.path.exists(out_dir):
os.mkdir(out_dir)
layers = ['pool5', 'fc6', 'fc7']
gpu_no = 1
path_to_classify = '..'
numberOfN = 5
relativePaths = ['/disk2', '../../../..']
# deployFile='/disk2/octoberExperiments/nn_performance_without_pascal/deploy.prototxt'
# meanFile='/disk2/octoberExperiments/nn_performance_without_pascal/mean.npy'
# modelFile='/disk2/octoberExperiments/nn_performance_without_pascal/snapshot_iter_450000.caffemodel'
modelFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
deployFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/models/bvlc_reference_caffenet/deploy.prototxt'
meanFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/python/caffe/imagenet/ilsvrc_2012_mean.npy'
# modelFile='/disk2/novemberExperiments/network_no_pascal/snapshots/snapshot_iter_450000.caffemodel';
# deployFile='/disk2/novemberExperiments/network_no_pascal/deploy.prototxt';
# meanFile='/disk2/novemberExperiments/network_no_pascal/mean.npy';
# out_file=script_nearestNeigbourExperiment.runClassificationTestSet(test_set,out_dir,path_to_classify,gpu_no,layers,deployFile=deployFile,meanFile=meanFile,modelFile=modelFile)
# return
# file_name='/disk2/octoberExperiments/nn_performance_without_pascal/notrained/20151026132705'
# file_name='/disk2/novemberExperiments/nn_imagenet_top5/notrained/20151130193757';
file_name = '/disk2/novemberExperiments/nn_imagenet_top5/trained/20151130230243'
file_text_labels = '../../data/ilsvrc12/synset_words.txt'
text_labels = np.loadtxt(file_text_labels, str, delimiter='\t')
vals = np.load(file_name + '.npz')
test_set = sorted(test_set, key=lambda x: x[0])
test_set = zip(*test_set)
img_paths = list(test_set[0])
gt_labels = list(test_set[1])
gt_labels_pascal = [
list_of_ids_pascal[list_of_ids.index(gt_label)]
for gt_label in gt_labels
]
for layer in layers:
print layer
file_name_l = file_name + '_' + layer
indices = script_nearestNeigbourExperiment.doNN(img_paths,
gt_labels,
vals[layer],
numberOfN=numberOfN,
distance='cosine',
algo='brute')
conf_matrix = 0
pickle.dump([img_paths, gt_labels, indices, conf_matrix],
open(file_name_l + '.p', 'wb'))
file_text_labels_pascal = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
text_labels_pascal = np.loadtxt(file_text_labels_pascal,
str,
delimiter='\t')
for layer in layers:
print layer
file_name_l = file_name + '_' + layer
[img_paths, gt_labels, indices,
_] = pickle.load(open(file_name_l + '.p', 'rb'))
img_paths_curr = [
x.replace(relativePaths[0], relativePaths[1]) for x in img_paths
]
im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
img_paths_curr, gt_labels, indices, text_labels)
script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
im_paths, captions)
no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
indices, gt_labels)
print no_correct
with open(file_name_l + '.txt', 'wb') as f:
for no_correct_curr in no_correct:
f.write(str(no_correct_curr) + ' ')
file_name_l = file_name + '_' + layer + '_pascal'
im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
img_paths_curr, gt_labels_pascal, indices, text_labels_pascal)
script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
im_paths, captions)
no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
indices, gt_labels_pascal)
with open(file_name_l + '.txt', 'wb') as f:
for no_correct_curr in no_correct:
f.write(str(no_correct_curr) + ' ')
print no_correct
def script_visualizePerformanceDifference():
trained_file='/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/trained/20151027204114'
notrained_file='/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/no_trained/20151027203547'
out_file='/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/nn_performance_comparison_trained_notrained.png';
out_file_diff='/disk2/octoberExperiments/nn_performance_without_pascal/pascal_3d/nn_performance_comparison_trained_notrained_diff.png';
file_pres=[trained_file,notrained_file]
layers=['pool5','fc6','fc7'];
legend_pres=['Trained','Unfamiliar'];
legend_entries=[];
for leg in legend_pres:
legend_entries.extend([leg+' '+layer for layer in layers]);
vecs_to_plot=[];
file_names=[file_pre+'_'+layer for file_pre in file_pres for layer in layers];
for idx,file_name in enumerate(file_names):
print file_name,legend_entries[idx];
[img_paths,gt_labels,indices,_]=pickle.load(open(file_name+'.p','rb'));
no_correct,_=script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(indices,gt_labels)
with open(file_name+'.txt','wb') as f:
for num in no_correct:
f.write(str(num)+' ');
f.write('\n');
# with open(file_name+'.txt','rb') as f:
# no_correct=f.readline();
# no_correct=[float(no_correct_curr) for no_correct_curr in no_correct.strip('\n').split()];
vecs_to_plot.append(no_correct);
# print legend_entries;
print vecs_to_plot;
# print len(vecs_to_plot);
# print len(legend_entries);
plt.figure();
plt.xlabel('Number of Nearest Neighbours K');
plt.ylabel('Accuracy');
plt.title('NN Accuracy DNN Features for Pascal Classes');
plt.xlim(0,6);
plt.ylim(min([min(vec) for vec in vecs_to_plot])-0.05,max([max(vec) for vec in vecs_to_plot])+0.05);
handles=[];
for vec in vecs_to_plot:
handle,=plt.plot(range(1,len(vec)+1),vec);
handles.append(handle);
plt.legend(handles, legend_entries,loc=2,prop={'size':10});
plt.savefig(out_file);
legend_entries=['Trained-Untrained '+layer for layer in layers];
diffs=[];
for idx in range(3):
a=vecs_to_plot[idx];
b=vecs_to_plot[idx+3];
diff=[a[idx_curr]-b[idx_curr] for idx_curr in range(len(a))];
diffs.append(diff);
vecs_to_plot=diffs;
plt.figure();
plt.xlabel('Number of Nearest Neighbours K');
plt.ylabel('Accuracy Difference');
plt.title('NN Accuracy DNN Features for Pascal Classes');
plt.xlim(0,6);
plt.ylim(min([min(vec) for vec in vecs_to_plot])-0.01,max([max(vec) for vec in vecs_to_plot])+0.01);
handles=[];
for vec in vecs_to_plot:
handle,=plt.plot(range(1,len(vec)+1),vec);
handles.append(handle);
plt.legend(handles, legend_entries,loc=2,prop={'size':10});
plt.savefig(out_file_diff);
def script_runNNOnPascalIncludedInTraining():
path_to_file = '../../data/ilsvrc12/synset_words.txt'
val_ids = imagenet.readLabelsFile(path_to_file)
val_just_ids = list(zip(*val_ids)[0])
val_just_labels = list(zip(*val_ids)[1])
pascal_ids_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
pascal_ids = imagenet.readLabelsFile(pascal_ids_file)
pascal_just_ids = list(zip(*pascal_ids)[0])
to_exclude = imagenet.removeClassesWithOverlap(val_just_ids,
pascal_just_ids,
keepMapping=True)
val_gt_file = '../../data/ilsvrc12/val.txt'
list_of_ids_im = [id for id_list in to_exclude for id in id_list]
mapping_file = '../../data/ilsvrc12/synsets.txt'
list_of_ids = imagenet.getImagenetIdToTrainingIdMapping(
mapping_file, list_of_ids_im)
print len(list_of_ids)
list_of_ids_pascal = []
for id_no in range(len(to_exclude)):
list_of_ids_pascal = list_of_ids_pascal + [id_no] * len(
to_exclude[id_no])
path_to_val = '/disk2/imagenet/val'
test_set = imagenet.selectTestSetByID(val_gt_file, list_of_ids,
path_to_val)
out_dir = '/disk2/octoberExperiments/nn_performance_without_pascal/trained'
layers = ['pool5', 'fc6', 'fc7']
gpu_no = 0
path_to_classify = '..'
numberOfN = 5
relativePaths = ['/disk2', '../../../..']
# out_file=script_nearestNeigbourExperiment.runClassificationTestSet(test_set,out_dir,path_to_classify,gpu_no,layers)
file_name = '/disk2/octoberExperiments/nn_performance_without_pascal/trained/20151023153522'
file_text_labels = '../../data/ilsvrc12/synset_words.txt'
text_labels = np.loadtxt(file_text_labels, str, delimiter='\t')
vals = np.load(file_name + '.npz')
test_set = sorted(test_set, key=lambda x: x[0])
test_set = zip(*test_set)
img_paths = list(test_set[0])
gt_labels = list(test_set[1])
gt_labels_pascal = [
list_of_ids_pascal[list_of_ids.index(gt_label)]
for gt_label in gt_labels
]
# for layer in layers:
# file_name_l=file_name+'_'+layer;
# indices,conf_matrix=doNN(img_paths,gt_labels,vals[layer],numberOfN=numberOfN,distance='cosine',algo='brute')
# pickle.dump([img_paths,gt_labels,indices,conf_matrix],open(file_name_l+'.p','wb'));
file_text_labels_pascal = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
text_labels_pascal = np.loadtxt(file_text_labels_pascal,
str,
delimiter='\t')
for layer in layers:
print layer
file_name_l = file_name + '_' + layer
[img_paths, gt_labels, indices,
_] = pickle.load(open(file_name_l + '.p', 'rb'))
img_paths_curr = [
x.replace(relativePaths[0], relativePaths[1]) for x in img_paths
]
im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
img_paths_curr, gt_labels, indices, text_labels)
script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
im_paths, captions)
no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
indices, gt_labels)
print no_correct
with open(file_name_l + '.txt', 'wb') as f:
for no_correct_curr in no_correct:
f.write(str(no_correct_curr) + ' ')
file_name_l = file_name + '_' + layer + '_pascal'
im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
img_paths_curr, gt_labels_pascal, indices, text_labels_pascal)
script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
im_paths, captions)
no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
indices, gt_labels_pascal)
with open(file_name_l + '.txt', 'wb') as f:
for no_correct_curr in no_correct:
f.write(str(no_correct_curr) + ' ')
print no_correct
