def rank_coco(self=None):
# Get 1000 images / captions to test rank
stream = DataETL.getFinalStream(teX, teY, sources=sources,
sources_k=sources_k, batch_size=test_size,
shuffle=True)
print "COCO test"
rank_function(stream)
def trainencoder(
sources = ("image_vects", "word_vects")
, sources_k = ("image_vects_k", "word_vects_k")
, batch_size=128
, embedding_dim=300
, n_captions=5
, n_sbu=None
, separate_emb=False
, test_size=1000 # per dataset
, mode='dev'
):
if mode=="coco120k+flickr38k":
XYsplit_cum = ([], [], [], [])
xyloaders = [
"cocoXYFilenames(dataType='train2014')"
, "cocoXYFilenames(dataType='val2014')"
, "flickrXYFilenames(dataType='8k')"
, "flickrXYFilenames(dataType='30k')"
]
ntrains = [80000, 40000, 8000, 30000]
for xyloader, ntrain in zip(xyloaders, ntrains):
X, Y, _ = eval(xyloader)
XYsplit = train_test_split(X, Y, train_size=ntrain)
for i in range(len(XYsplit)):
XYsplit_cum[i].extend(XYsplit[i])
trX, teX, trY, teY = XYsplit_cum
else:
trX, teX, trY, teY = coco(mode=mode, n_captions=n_captions, test_size=test_size)
if n_sbu:
sbutrX, sbuteX, sbutrY, sbuteY = sbu(mode=mode, test_size=test_size)
pairs = (
(trX, sbutrX)
, (teX, sbuteX)
, (trY, sbutrY)
, (teY, sbuteY)
)
for coco_data, sbu_data in pairs:
if isinstance(coco_data, list):
coco_data.extend(sbu_data)
print("n_train: %d" % len(trX))
print("n_test: %d" % len(teX))
# # # # # # # # # # #
# Modeling Building #
# # # # # # # # # # #
s = Encoder(
image_feature_dim=4096
, embedding_dim=embedding_dim
, biases_init=Constant(0.)
, weights_init=Uniform(width=0.08)
)
s.initialize()
image_vects = tensor.matrix(sources[0]) # named to match the source name
word_vects = tensor.tensor3(sources[1]) # named to match the source name
image_vects_k = tensor.matrix(sources_k[0]) # named to match the contrastive source name
word_vects_k = tensor.tensor3(sources_k[1]) # named to match the contrastive source name
# image_vects.tag.test_value = np.zeros((2, 4096), dtype='float32')
# word_vects.tag.test_value = np.zeros((2, 15, 50), dtype='float32')
# image_vects_k.tag.test_value = np.zeros((2, 4096), dtype='float32')
# word_vects_k.tag.test_value = np.zeros((2, 15, 50), dtype='float32')
# learned image embedding, learned sentence embedding
lim, ls = s.apply(image_vects, word_vects)
# learned constrastive im embedding, learned contrastive s embedding
lcim, lcs = s.apply(image_vects_k, word_vects_k)
# identical cost code thanks to Ryan Kiros
# https://github.com/youralien/skip-thoughts/blob/master/eval_rank.py
lim = l2norm(lim)
lcim = l2norm(lcim)
ls = l2norm(ls)
lcs = l2norm(lcs)
margin = 0.2 # alpha term should not be more than 1
cost_im = margin - (lim * ls).sum(axis=1) + (lim * lcs).sum(axis=1)
cost_im = cost_im * (cost_im > 0.) # this is like the max(0, pairwise-ranking-loss)
cost_im = cost_im.sum(0)
cost_s = margin - (ls * lim).sum(axis=1) + (ls * lcim).sum(axis=1)
cost_s = cost_s * (cost_s > 0.) # this is like max(0, pairwise-ranking-loss)
cost_s = cost_s.sum(0)
cost = cost_im + cost_s
cost.name = "pairwise_ranking_loss"
# function(s) to produce embedding
if separate_emb:
img_encoder = theano.function([image_vects], lim)
txt_encoder = theano.function([word_vects], ls)
f_emb = theano.function([image_vects, word_vects], [lim, ls])
if n_sbu:
sbuname = "sbu%d+" % n_sbu
else:
sbuname = ''
name = "%sproject1.%s.jointembedder" % (sbuname, mode)
savename = MODEL_FILES_DIR + name
def save_function(self):
if separate_emb:
ModelIO.save(
img_encoder
, savename + "_Img")
ModelIO.save(
txt_encoder
, savename + "_Txt")
ModelIO.save(f_emb, savename)
print "Similarity Embedding function(s) saved while training"
def rank_function(stream):
images, captions, _0, _1 = stream.get_epoch_iterator().next()
image_embs, caption_embs = f_emb(images, captions)
ModelEval.ImageSentenceRanking(image_embs, caption_embs)
def rank_coco(self=None):
# Get 1000 images / captions to test rank
stream = DataETL.getFinalStream(teX, teY, sources=sources,
sources_k=sources_k, batch_size=test_size,
shuffle=True)
print "COCO test"
rank_function(stream)
def rank_sbu(self=None):
stream = DataETL.getFinalStream(sbuteX, sbuteY, sources=sources,
sources_k=sources_k, batch_size=test_size,
shuffle=True)
print "SBU test"
rank_function(stream)
def rank_em(self=None):
rank_coco()
if n_sbu:
rank_sbu()
cg = ComputationGraph(cost)
# # # # # # # # # # #
# Modeling Training #
# # # # # # # # # # #
algorithm = GradientDescent(
cost=cost
, parameters=cg.parameters
, step_rule=Adam(learning_rate=0.0002)
)
main_loop = MainLoop(
model=Model(cost)
, data_stream=DataETL.getFinalStream(trX, trY, sources=sources,
sources_k=sources_k, batch_size=batch_size)
, algorithm=algorithm
, extensions=[
DataStreamMonitoring(
[cost]
, DataETL.getFinalStream(trX, trY, sources=sources,
sources_k=sources_k, batch_size=batch_size, shuffle=True)
, prefix='train')
, DataStreamMonitoring(
[cost]
, DataETL.getFinalStream(teX, teY, sources=sources,
sources_k=sources_k, batch_size=batch_size, shuffle=True)
, prefix='test')
, UserFunc(save_function, after_epoch=True)
, UserFunc(rank_em, after_epoch=True)
, Printing()
, LogToFile('logs/%s.csv' % name)
]
)
main_loop.run()
def rank_sbu(self=None):
stream = DataETL.getFinalStream(sbuteX, sbuteY, sources=sources,
sources_k=sources_k, batch_size=test_size,
shuffle=True)
print "SBU test"
rank_function(stream)
