def script_writeHTMLS():
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])
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)
to_exclude = imagenet.removeClassesWithOverlap(val_just_ids,
pascal_just_ids,
keepMapping=True)
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])
file_text_labels = '../../data/ilsvrc12/synset_words.txt'
text_labels = np.loadtxt(file_text_labels, str, delimiter='\t')
trained_file = '/disk2/octoberExperiments/nn_performance_without_pascal/trained/20151023153522'
notrained_file = '/disk2/octoberExperiments/nn_performance_without_pascal/notrained/20151026132705'
file_pres = [trained_file, notrained_file]
layers = ['pool5', 'fc6', 'fc7']
file_names = [
file_pre + '_' + layer for file_pre in file_pres for layer in layers
]
for file_name in file_names:
[img_paths, gt_labels, indices,
_] = pickle.load(open(file_name + '.p', 'rb'))
gt_labels_pascal = [
list_of_ids_pascal[list_of_ids.index(gt_label)]
for gt_label in gt_labels
]
file_name_l = file_name + '_' + layer + '_pascal'
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_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