from Embedding import *
from Annotation import JHMDBAnnotator as JA, Evaluator as EVAL
import numpy as np
import random
import time
from configs import getConfigs
conf = getConfigs(43)
snapshot_path = conf.solver['snapshot_prefix']
db = conf.db_settings[conf.db]
action = 'pour'
video = db['video_name'][action][0]
level = db['level']
dataset_path = db['pickle_path'].format(action_name='pour',
video_name=video,
level=level)
#dataset_path = '/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/dataset/{name}'
net_path = conf.model['model_prototxt_path']
#net_path = {name}'
annotation_path = db['annotation_path'].format(action_name=action,
video_name=video)
#annotation_path = '/cs/vml3/mkhodaba/cvpr16/dataset/{name}'
#snapshot_name = '_iter_870000.caffemodel'
snapshot_name = '_iter_25000.caffemodel'
#model_name = 'model.prototxt'
test_model_path = '/cs/vml2/mkhodaba/cvpr16/expriments/8/test.prototxt'
snapshot_path = conf.solver[
'snapshot_prefix'] #'/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/snapshot/vml_gpu/256bins/'
print net_path
import caffe from numpy import zeros import numpy as np from configs import getConfigs conf = getConfigs(8) model_prototxt_path = conf.model['model_prototxt_path'] solver_prototxt_path = conf.solver['_solver_prototxt_path'] #root = '/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/model/' #caffe.set_decive(2) caffe.set_mode_gpu() #caffe.set_device(2) test_interval = conf.solver['test_interval'] #10000 niter = conf.solver['max_iter'] #500000 train_interval = conf.solver['_train_interval'] #1000 termination_threshold = conf.solver['_termination_threshold'] net = caffe.Net(model_prototxt_path, caffe.TRAIN) #solver = caffe.SGDSolver(root+'solver.prototxt') solver = caffe.SGDSolver(solver_prototxt_path) # losses will also be stored in the log #train_loss = zeros(niter)
return reps
def computeDistanceMatrix(representations):
return -1*(representations.dot(representations.T))
def getBaselineRepresentations(segment, k, feature_type):
segment.__class__ = MySegmentation
features = MySegmentation.getFeatures(segment,k, feature_type=feature_type)
return features
if __name__ == '__main__':
print "START"
caffe.set_mode_gpu()
#caffe.set_decive(2)
if len(sys.argv) == 1:
conf = getConfigs(-1)
else:
conf = getConfigs(int(sys.argv[1]))
logger = Logger(LogType.FILE, conf.solver['_solver_log_path'])
threshold = 0.6
db = conf.db_settings
model_prototxt_path = conf.model['model_prototxt_path']
solver_prototxt_path = conf.solver['_solver_prototxt_path']
test_interval = conf.solver['test_interval'] #10000
niter = conf.solver['max_iter'] #max(100000, conf.solver['max_iter']) #500000
train_interval = conf.solver['_train_interval'] #1000
termination_threshold = conf.solver['_termination_threshold']
print "net"
net = caffe.Net(model_prototxt_path, caffe.TRAIN)
def create_dbs():
configs = getConfigs()
frame_format = configs.frame_format
seg_path = configs.seg_path
orig_path = configs.orig_path
first_output = configs.first_output
output_path = configs.output_path
dataset_path = configs.dataset_path
annotation_path = configs.annotation_path
n_neg = configs.number_of_negatives
print 'n_neg \t= \t' + str(n_neg)
feature_name = '256bin'
level = 2
segmentors = []
vid_num = 2
frames_per_video = 31
if 1 == 1:
for dd in range(vid_num):
d = dd+1
print 'b{0}'.format(d)
annotator = JA(annotation_path.format(name='b'+str(d)))
segmentor = MySegmentation(orig_path.format(d)+frame_format, seg_path.format(d,level)+frame_format, annotator, negative_neighbors=n_neg)
for i in range(1, frames_per_video):
print "processing frame {i}".format(i=i)
segmentor.processNewFrame()
segmentor.doneProcessing()
segmentors.append(segmentor)
print "Total number of supervoxels: {0}".format(len(segmentor.supervoxels))
print
try:
mkdirs(dataset_path)
except:
pass
print 'Piclking ...'
t = time.time()
for i in range(vid_num):
pickle.dump(segmentors[i], open(dataset_path.format(name='segment_{0}.p'.format(i+1)), 'w'))
print '{0}-th done. time elapsed: {1}'.format(i+1, time.time()-t)
t = time.time()
#TODO create database
else:
for i in range(vid_num):
segmentors.append(pickle.load(open(dataset_path.format(name='segment_{0}.p'.format(i+1)), 'r')))
database = DB(dataset_path.format(name='videos{v}_feature{f}_lvl{l}.h5'.format(\
v='_'.join(map(str,range(1,vid_num))),
f=feature_name,
l=level)))
print 'Collecting features ...'
neighbor_num = 6
keys = ['target', 'negative'] + [ 'neighbor{0}'.format(i) for i in range(neighbor_num)]
features = segmentors[0].getFeatures(neighbor_num)
print 'shape features', features['target'].shape
feats = [features]
print 'video 1 done!'
for i in range(1, len(segmentors)):
tmp = segmentors[i].getFeatures(neighbor_num)
#feats.append(tmp)
for key in keys:
features[key] = np.append(features[key], tmp[key], axis=0)
print 'video {0} done!'.format(i+1)
#print data
#database_path = '
print 'saving to database ...'
for name, data in features.iteritems():
database.save(data, name)
#database.save(dataset)
database.close()
print 'done!'
def create_dbs():
configs = getConfigs()
frame_format = configs.frame_format
seg_path = configs.seg_path
orig_path = configs.orig_path
first_output = configs.first_output
output_path = configs.output_path
dataset_path = configs.dataset_path
annotation_path = configs.annotation_path
action
feature_name = '256bin'
level = 2
segmentors = []
vid_num = 2
frames_per_video = 31
if 1 == 1:
for dd in range(vid_num):
d = dd + 1
print 'b{0}'.format(d)
annotator = JA(annotation_path.format(name='b' + str(d)))
segmentor = MySegmentation(
orig_path.format(d) + frame_format,
seg_path.format(d, level) + frame_format, annotator)
for i in range(1, frames_per_video):
print "processing frame {i}".format(i=i)
segmentor.processNewFrame()
segmentor.doneProcessing()
segmentors.append(segmentor)
print "Total number of supervoxels: {0}".format(
len(segmentor.supervoxels))
print
try:
mkdirs(dataset_path)
except:
pass
print 'Piclking ...'
t = time.time()
for i in range(vid_num):
pickle.dump(
segmentors[i],
open(dataset_path.format(name='segment_{0}.p'.format(i + 1)),
'w'))
print '{0}-th done. time elapsed: {1}'.format(
i + 1,
time.time() - t)
t = time.time()
#TODO create database
else:
for i in range(vid_num):
segmentors.append(
pickle.load(
open(
dataset_path.format(name='segment_{0}.p'.format(i +
1)),
'r')))
database = DB(dataset_path.format(name='videos{v}_feature{f}_lvl{l}.h5'.format(\
v='_'.join(map(str,range(1,vid_num))),
f=feature_name,
l=level)))
print 'Collecting features ...'
neighbor_num = 6
keys = ['target', 'negative'
] + ['neighbor{0}'.format(i) for i in range(neighbor_num)]
features = segmentors[0].getFeatures(neighbor_num)
print 'shape features', features['target'].shape
feats = [features]
print 'video 1 done!'
for i in range(1, len(segmentors)):
tmp = segmentors[i].getFeatures(neighbor_num)
#feats.append(tmp)
for key in keys:
features[key] = np.append(features[key], tmp[key], axis=0)
print 'video {0} done!'.format(i + 1)
#print data
#database_path = '
print 'saving to database ...'
for name, data in features.iteritems():
database.save(data, name)
#database.save(dataset)
database.close()
print 'done!'
if x[1]:
print 'x[1]:', ['-' + x[0]]
return ['-'+x[0]]
else:
print 'x[0]:', ['']
return ['']
[in_args.extend(select(x)) for x in options.__dict__.items() if x[1] is not None]
result = subprocess.Popen(in_args, stdout=subprocess.PIPE)
result = result.stdout.read().split('\n')
pprint.pprint(result)
exp_id = [x for x in result if x.startswith('Experiment number: ')][0]
args = exp_id.split(': ')[-1]
os.system('python new_solver.py ' + args)
conf = getConfigs(int(args))
snapshot_path = conf.solver['snapshot_prefix']
exp_root = os.path.basename(os.path.dirname(snapshot_path[:-1]))
exp_root = conf.experiment_folder_name
os.system('python compute_similarities.py -e ' + args)
#os.system('python compute_similarities_vox2pix.py ' + args)
#os.system('mv -f ' + conf.experiments_path + '/similarities.mat '+ conf.experiments_path + '/similarities_1.mat')
#result = subprocess.Popen(in_args.append['-F', 'FCN', '-E', exp_root], stdout=subprocess.PIPE)
#result = result.stdout.read().split('\n')
#os.system('python new_solver.py ' + args)
#os.system('python compute_similarities.py -e ' + args)
#x = np.add(loadmat(conf.experiments_path + 'similarities_1.mat')['similarities'], loadmat(conf.experiments_path + 'similarities.mat')['similarities'])
#np.savemat(conf.experiments_path + 'similarities.mat', similarities=x)
from scipy.io import loadmat
try:
loadmat('/local-scratch/experiments/' + exp_root + '/similarities.mat')
import caffe from numpy import zeros import numpy as np from configs import getConfigs conf = getConfigs(8) model_prototxt_path = conf.model['model_prototxt_path'] solver_prototxt_path = conf.solver['_solver_prototxt_path'] #root = '/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/model/' #caffe.set_decive(2) caffe.set_mode_gpu() #caffe.set_device(2) test_interval = conf.solver['test_interval'] #10000 niter = conf.solver['max_iter'] #500000 train_interval = conf.solver['_train_interval'] #1000 termination_threshold = conf.solver['_termination_threshold'] net = caffe.Net(model_prototxt_path, caffe.TRAIN) #solver = caffe.SGDSolver(root+'solver.prototxt') solver = caffe.SGDSolver(solver_prototxt_path) # losses will also be stored in the log #train_loss = zeros(niter) train_loss = np.array([]) test_acc = zeros(int(np.ceil(niter / test_interval))) #output = zeros((niter, 8, 10)) test_loss = 0
def getBaselineRepresentations(segment, k, feature_type):
segment.__class__ = MySegmentation
features = MySegmentation.getFeatures(segment,
k,
feature_type=feature_type)
return features
if __name__ == '__main__':
print "START"
caffe.set_mode_gpu()
#caffe.set_decive(2)
if len(sys.argv) == 1:
conf = getConfigs(-1)
else:
conf = getConfigs(int(sys.argv[1]))
logger = Logger(LogType.FILE, conf.solver['_solver_log_path'])
threshold = 0.6
db = conf.db_settings
model_prototxt_path = conf.model['model_prototxt_path']
solver_prototxt_path = conf.solver['_solver_prototxt_path']
test_interval = conf.solver['test_interval'] #10000
niter = conf.solver[
'max_iter'] #max(100000, conf.solver['max_iter']) #500000
train_interval = conf.solver['_train_interval'] #1000
termination_threshold = conf.solver['_termination_threshold']
print "net"
from Embedding import *
from Annotation import JHMDBAnnotator as JA, Evaluator as EVAL
import numpy as np
import random
import time
from configs import getConfigs
conf = getConfigs(43)
snapshot_path = conf.solver['snapshot_prefix']
db = conf.db_settings[conf.db]
action = 'pour'
video = db['video_name'][action][0]
level = db['level']
dataset_path = db['pickle_path'].format(action_name='pour', video_name=video, level=level)
#dataset_path = '/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/dataset/{name}'
net_path = conf.model['model_prototxt_path']
#net_path = {name}'
annotation_path = db['annotation_path'].format(action_name=action, video_name=video)
#annotation_path = '/cs/vml3/mkhodaba/cvpr16/dataset/{name}'
#snapshot_name = '_iter_870000.caffemodel'
snapshot_name = '_iter_25000.caffemodel'
#model_name = 'model.prototxt'
test_model_path = '/cs/vml2/mkhodaba/cvpr16/expriments/8/test.prototxt'
snapshot_path = conf.solver['snapshot_prefix']#'/cs/vml3/mkhodaba/cvpr16/code/embedding_segmentation/snapshot/vml_gpu/256bins/'
print net_path
print dataset_path
print
