def load_model(self):
"""
Load everything we need for generating
"""
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(self.config.vsemodel)
# VGG-19
print 'Loading and initializing ConvNet...'
net = self.build_convnet(self.config.vgg)
# Captions
print 'Loading captions...'
cap = []
with open(self.config.captions, 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Pack up
z = {}
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
return z
def load_all():
"""
Load everything we need for generating
"""
print config.paths['decmodel']
# Skip-thoughts
print 'Loading skip-thoughts...'
stv = skipthoughts.load_model(config.paths['skmodels'],
config.paths['sktables'])
# Decoder
print 'Loading decoder...'
dec = decoder.load_model(config.paths['decmodel'],
config.paths['dictionary'])
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(config.paths['vsemodel'])
# VGG-19
print 'Loading and initializing ConvNet...'
if config.FLAG_CPU_MODE:
sys.path.insert(0, config.paths['pycaffe'])
import caffe
caffe.set_mode_cpu()
net = caffe.Net(config.paths['vgg_proto_caffe'],
config.paths['vgg_model_caffe'], caffe.TEST)
else:
net = build_convnet(config.paths['vgg'])
# Captions
print 'Loading captions...'
cap = []
with open(config.paths['captions'], 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Biases
print 'Loading biases...'
bneg = numpy.load(config.paths['negbias'])
bpos = numpy.load(config.paths['posbias'])
# Pack up
z = {}
z['stv'] = stv
z['dec'] = dec
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
z['bneg'] = bneg
z['bpos'] = bpos
return z
def load_all():
"""
Load everything we need for generating
"""
print config.paths['decmodel']
# Skip-thoughts
print 'Loading skip-thoughts...'
stv = skipthoughts.load_model(config.paths['skmodels'],
config.paths['sktables'])
# Decoder
print 'Loading decoder...'
dec = decoder.load_model(config.paths['decmodel'],
config.paths['dictionary'])
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(config.paths['vsemodel'])
# Captions
print 'Loading captions...'
cap = []
with open(config.paths['captions'], 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Biases
print 'Loading biases...'
bneg = numpy.load(config.paths['negbias'])
bpos = numpy.load(config.paths['posbias'])
# VGG Net.
net = load_vgg()
# Pack up
z = {}
z['stv'] = stv
z['dec'] = dec
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
z['bneg'] = bneg
z['bpos'] = bpos
return z
def load_all():
"""
Load everything we need for generating
"""
print path_to_decmodel
# Skip-thoughts
print 'Loading skip-thoughts...'
stv = skipthoughts.load_model(path_to_skmodels, path_to_sktables)
# Decoder
print 'Loading decoder...'
dec = decoder.load_model(path_to_decmodel, path_to_dictionary)
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(path_to_vsemodel)
# VGG-19
print 'Loading and initializing ConvNet...'
net = build_convnet(path_to_vgg)
# Captions
print 'Loading captions...'
cap = []
with open(path_to_captions, 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Biases
print 'Loading biases...'
bneg = numpy.load(path_to_negbias)
bpos = numpy.load(path_to_posbias)
# Pack up
z = {}
z['stv'] = stv
z['dec'] = dec
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
z['bneg'] = bneg
z['bpos'] = bpos
return z
def load_all():
"""
Load everything we need for generating
"""
print path_to_decmodel
# Skip-thoughts
print 'Loading skip-thoughts...'
stv = skipthoughts.load_model(path_to_skmodels, path_to_sktables)
# Decoder
print 'Loading decoder...'
dec = decoder.load_model(path_to_decmodel, path_to_dictionary)
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(path_to_vsemodel)
# VGG-19
print 'Loading and initializing ConvNet...'
net = build_convnet(path_to_vgg)
# Captions
print 'Loading captions...'
cap = []
with open(path_to_captions, 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Biases
print 'Loading biases...'
bneg = numpy.load(path_to_negbias)
bpos = numpy.load(path_to_posbias)
# Pack up
z = {}
z['stv'] = stv
z['dec'] = dec
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
z['bneg'] = bneg
z['bpos'] = bpos
return z
def load_all(c, conn):
"""
Load everything we need for generating
"""
def load(field_name, create_field):
c.execute("SELECT value FROM neural WHERE name = ?", (field_name, ))
cache_field = c.fetchone()
if not cache_field:
print 'Creating field'
field = create_field()
c.execute("INSERT INTO neural VALUES (?, ?)",
(field_name, pkl.dumps(field)))
conn.commit()
return field
return pkl.loads(str(cache_field[0]))
print config.paths['decmodel']
z = {}
print 'Loading skip-thoughts...'
z['stv'] = skipthoughts.load_model(config.paths['skmodels'],
config.paths['sktables'])
print 'Loading decoder...'
z['dec'] = decoder.load_model(config.paths['decmodel'],
config.paths['dictionary'])
print 'Loading image-sentence embedding...'
z['vse'] = embedding.load_model(config.paths['vsemodel'])
print 'Loading and initializing ConvNet (VGG-19)...'
z['net'] = create_covnet()
print 'Loading captions...'
z['cap'] = create_captions()
print 'Embedding captions...'
z['cvec'] = embedding.encode_sentences(z['vse'], z['cap'], verbose=False)
print 'Loading biases...'
z['bneg'] = numpy.load(config.paths['negbias'])
z['bpos'] = numpy.load(config.paths['posbias'])
return z
def load_caption():
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(config.paths['vsemodel'])
# Captions
print 'Loading captions...'
cap = []
with open(config.paths['captions'], 'rb') as f:
for line in f:
cap.append(line.strip())
# cap = cap[:100]
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
return {'cap': cap, 'cvec': cvec, 'vse': vse}
def load_all():
"""
Load everything we need for generating
"""
print config.paths['decmodel']
# Skip-thoughts
print 'Loading skip-thoughts...'
stv = skipthoughts.load_model(config.paths['skmodels'],
config.paths['sktables'])
# Decoder
print 'Loading decoder...'
dec = decoder.load_model(config.paths['decmodel'],
config.paths['dictionary'])
# Image-sentence embedding
print 'Loading image-sentence embedding...'
vse = embedding.load_model(config.paths['vsemodel'])
# VGG-19
print 'Loading and initializing ConvNet...'
if config.FLAG_CPU_MODE:
sys.path.insert(0, config.paths['pycaffe'])
import caffe
caffe.set_mode_cpu()
net = caffe.Net(config.paths['vgg_proto_caffe'],
config.paths['vgg_model_caffe'],
caffe.TEST)
else:
net = build_convnet(config.paths['vgg'])
# Captions
print 'Loading captions...'
cap = []
with open(config.paths['captions'], 'rb') as f:
for line in f:
cap.append(line.strip())
# Caption embeddings
print 'Embedding captions...'
cvec = embedding.encode_sentences(vse, cap, verbose=False)
# Biases
print 'Loading biases...'
bneg = numpy.load(config.paths['negbias'])
bpos = numpy.load(config.paths['posbias'])
# Pack up
z = {}
z['stv'] = stv
z['dec'] = dec
z['vse'] = vse
z['net'] = net
z['cap'] = cap
z['cvec'] = cvec
z['bneg'] = bneg
z['bpos'] = bpos
return z
