def trainer(data='coco',
margin=0.2,
dim=1024,
dim_image=4096,
dim_word=300,
max_epochs=15,
encoder='lstm',
dispFreq=10,
grad_clip=2.0,
maxlen_w=150,
batch_size=128,
saveto='vse/coco',
validFreq=100,
early_stop=20,
lrate=0.0002,
reload_=False):
# Model options
model_options = {}
model_options['data'] = data
model_options['margin'] = margin
model_options['dim'] = dim
model_options['dim_image'] = dim_image
model_options['dim_word'] = dim_word
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['grad_clip'] = grad_clip
model_options['maxlen_w'] = maxlen_w
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['validFreq'] = validFreq
model_options['lrate'] = lrate
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl' % saveto, 'rb') as f:
model_options = pkl.load(f)
# Load training and development sets
print 'loading dataset'
train, dev = load_dataset(data)
# Create and save dictionary
print 'Create dictionary'
worddict = build_dictionary(train[0] + dev[0])[0]
n_words = len(worddict)
model_options['n_words'] = n_words
print 'Dictionary size: ' + str(n_words)
with open('%s.dictionary.pkl' % saveto, 'wb') as f:
pkl.dump(worddict, f)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
model_options['worddict'] = worddict
model_options['word_idict'] = word_idict
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([train[0], train[1]],
batch_size=batch_size,
maxlen=maxlen_w)
img_sen_model = ImgSenRanking(model_options)
img_sen_model = img_sen_model.cuda()
loss_fn = PairwiseRankingLoss(margin=margin)
loss_fn = loss_fn.cuda()
params = filter(lambda p: p.requires_grad, img_sen_model.parameters())
optimizer = torch.optim.Adam(params, lrate)
uidx = 0
curr = 0.0
n_samples = 0
# For Early-stopping
best_r1, best_r5, best_r10, best_medr = 0.0, 0.0, 0.0, 0
best_r1i, best_r5i, best_r10i, best_medri = 0.0, 0.0, 0.0, 0
best_step = 0
for eidx in xrange(max_epochs):
print 'Epoch ', eidx
for x, im in train_iter:
n_samples += len(x)
uidx += 1
x, im = homogeneous_data.prepare_data(x,
im,
worddict,
maxlen=maxlen_w,
n_words=n_words)
if x is None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
x = Variable(torch.from_numpy(x).cuda())
im = Variable(torch.from_numpy(im).cuda())
# Update
x, im = img_sen_model(x, im)
cost = loss_fn(im, x)
optimizer.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm(params, grad_clip)
optimizer.step()
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, '\tUpdate ', uidx, '\tCost ', cost.data.cpu(
).numpy()[0]
if numpy.mod(uidx, validFreq) == 0:
print 'Computing results...'
curr_model = {}
curr_model['options'] = model_options
curr_model['worddict'] = worddict
curr_model['word_idict'] = word_idict
curr_model['img_sen_model'] = img_sen_model
ls, lim = encode_sentences(curr_model, dev[0]), encode_images(
curr_model, dev[1])
r_time = time.time()
(r1, r5, r10, medr) = i2t(lim, ls)
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10,
medr)
(r1i, r5i, r10i, medri) = t2i(lim, ls)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (r1i, r5i,
r10i, medri)
print "Cal Recall@K using %ss" % (time.time() - r_time)
curr_step = uidx / validFreq
currscore = r1 + r5 + r10 + r1i + r5i + r10i
if currscore > curr:
curr = currscore
best_r1, best_r5, best_r10, best_medr = r1, r5, r10, medr
best_r1i, best_r5i, best_r10i, best_medri = r1i, r5i, r10i, medri
best_step = curr_step
# Save model
print 'Saving model...',
pkl.dump(
model_options,
open('%s_params_%s.pkl' % (saveto, encoder), 'wb'))
torch.save(img_sen_model.state_dict(),
'%s_model_%s.pkl' % (saveto, encoder))
print 'Done'
if curr_step - best_step > early_stop:
print 'Early stopping ...'
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (
best_r1, best_r5, best_r10, best_medr)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (
best_r1i, best_r5i, best_r10i, best_medri)
return 0
print 'Seen %d samples' % n_samples
def trainer(X, C, stmodel,
dimctx=4800, #vector dimensionality
dim_word=620, # word vector dimensionality
dim=1600, # the number of GRU units
encoder='gru',
decoder='gru',
doutput=False,
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=40000,
maxlen_w=100,
optimizer='adam',
batch_size = 16,
saveto='adventuremodel.npz',
dictionary='/home/jm7432/tell-tall-tales/decoding/adventure_dict_final.pkl',
embeddings=None,
saveFreq=1000,
sampleFreq=100,
reload_=False):
# Model options
model_options = {}
model_options['dimctx'] = dimctx
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['doutput'] = doutput
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['embeddings'] = embeddings
model_options['saveFreq'] = saveFreq
model_options['sampleFreq'] = sampleFreq
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Load pre-trained embeddings, if applicable
if embeddings != None:
print 'Loading embeddings...'
with open(embeddings, 'rb') as f:
embed_map = pkl.load(f)
dim_word = len(embed_map.values()[0])
model_options['dim_word'] = dim_word
preemb = norm_weight(n_words, dim_word)
pz = defaultdict(lambda : 0)
for w in embed_map.keys():
pz[w] = 1
for w in worddict.keys()[:n_words-2]:
if pz[w] > 0:
preemb[worddict[w]] = embed_map[w]
else:
preemb = None
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options, preemb=preemb)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, inps, cost = build_model(tparams, model_options)
print 'Building sampler'
f_init, f_next = build_sampler(tparams, model_options, trng)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([X,C], batch_size=batch_size, maxlen=maxlen_w)
uidx = 0
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for x, c in train_iter:
n_samples += len(x)
uidx += 1
x, mask, ctx = homogeneous_data.prepare_data(x, c, worddict, stmodel, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, mask, ctx)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
params = unzip(tparams)
numpy.savez(saveto, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
if numpy.mod(uidx, sampleFreq) == 0:
x_s = x
mask_s = mask
ctx_s = ctx
for jj in xrange(numpy.minimum(10, len(ctx_s))):
sample, score = gen_sample(tparams, f_init, f_next, ctx_s[jj].reshape(1, model_options['dimctx']), model_options,
trng=trng, k=1, maxlen=100, stochastic=False, use_unk=False)
print 'Truth ',jj,': ',
for vv in x_s[:,jj]:
if vv == 0:
break
if vv in word_idict:
print word_idict[vv],
else:
print 'UNK',
print
for kk, ss in enumerate([sample[0]]):
print 'Sample (', kk,') ', jj, ': ',
for vv in ss:
if vv == 0:
break
if vv in word_idict:
print word_idict[vv],
else:
print 'UNK',
print
print 'Seen %d samples'%n_samples
def trainer(
X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
decoder='gru',
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=474000,
maxlen_w=30,
optimizer='adam',
batch_size=64,
saveto='/data/embeddingModel.npz',
dictionary='dictionary.pkl',
saveFreq=1000,
reload_=False):
# Model options
model_options = {}
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_
print(model_options)
# reload options
if reload_ and os.path.exists(saveto):
print('reloading...' + saveto)
with open('%s.pkl' % saveto, 'rb') as f:
models_options = pkl.load(f)
# load dictionary
print('Loading dictionary...')
worddict = load_dictionary(dictionary)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.items():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print('Building model')
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, x, x_mask, y, y_mask, z, z_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask, z, z_mask]
# before any regularizer
print('Building f_log_probs...')
f_log_probs = theano.function(inps, cost, profile=False)
print('Done')
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.items():
weight_decay += (vv**2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print('Building f_cost...')
f_cost = theano.function(inps, cost, profile=False)
print('Done')
print('Done')
print('Building f_grad...')
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads],
profile=False)
f_weight_norm = theano.function([],
[(t**2).sum() for k, t in tparams.items()],
profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(
tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip, g))
grads = new_grads
lr = tensor.scalar(name='lr')
print('Building optimizers...', end='')
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print('Optimization')
# Each sentence in the minibatch have same length (for encoder)
trainX = homogeneous_data.grouper(X)
train_iter = homogeneous_data.HomogeneousData(trainX,
batch_size=batch_size,
maxlen=maxlen_w)
uidx = 0
lrate = 0.01
for eidx in range(max_epochs):
n_samples = 0
print('Epoch ', eidx)
for x, y, z in train_iter:
n_samples += len(x)
uidx += 1
x, x_mask, y, y_mask, z, z_mask = homogeneous_data.prepare_data(
x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
if x is None:
print('Minibatch with zero sample under length ', maxlen_w)
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask, z, z_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print('NaN detected')
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print('Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ',
ud)
if numpy.mod(uidx, saveFreq) == 0:
print('Saving...', end='')
params = unzip(tparams)
numpy.savez(saveto, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl' % saveto, 'wb'))
print('Done')
print('Seen %d samples' % n_samples)
def trainer(data='coco', #f8k, f30k, coco
margin=0.2,
dim=1024,
dim_image=4096,
dim_word=300,
encoder='gru', # gru OR bow
max_epochs=15,
dispFreq=10,
decay_c=0.,
grad_clip=2.,
maxlen_w=100,
optimizer='adam',
batch_size = 128,
saveto='/ais/gobi3/u/rkiros/uvsmodels/coco.npz',
validFreq=100,
lrate=0.0002,
reload_=False):
# Model options
model_options = {}
model_options['data'] = data
model_options['margin'] = margin
model_options['dim'] = dim
model_options['dim_image'] = dim_image
model_options['dim_word'] = dim_word
model_options['encoder'] = encoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['validFreq'] = validFreq
model_options['lrate'] = lrate
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# Load training and development sets
print 'Loading dataset'
train, dev = load_dataset(data)[:2]
# Create and save dictionary
print 'Creating dictionary'
worddict = build_dictionary(train[0]+dev[0])[0]
n_words = len(worddict)
model_options['n_words'] = n_words
print 'Dictionary size: ' + str(n_words)
with open('%s.dictionary.pkl'%saveto, 'wb') as f:
pkl.dump(worddict, f)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, inps, cost = build_model(tparams, model_options)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Building sentence encoder'
trng, inps_se, sentences = build_sentence_encoder(tparams, model_options)
f_senc = theano.function(inps_se, sentences, profile=False)
print 'Building image encoder'
trng, inps_ie, images = build_image_encoder(tparams, model_options)
f_ienc = theano.function(inps_ie, images, profile=False)
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([train[0], train[1]], batch_size=batch_size, maxlen=maxlen_w)
uidx = 0
curr = 0.
n_samples = 0
for eidx in xrange(max_epochs):
print 'Epoch ', eidx
for x, im in train_iter:
n_samples += len(x)
uidx += 1
x, mask, im = homogeneous_data.prepare_data(x, im, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
# Update
ud_start = time.time()
cost = f_grad_shared(x, mask, im)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, validFreq) == 0:
print 'Computing results...'
curr_model = {}
curr_model['options'] = model_options
curr_model['worddict'] = worddict
curr_model['word_idict'] = word_idict
curr_model['f_senc'] = f_senc
curr_model['f_ienc'] = f_ienc
ls = encode_sentences(curr_model, dev[0])
lim = encode_images(curr_model, dev[1])
(r1, r5, r10, medr) = i2t(lim, ls)
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr)
(r1i, r5i, r10i, medri) = t2i(lim, ls)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri)
currscore = r1 + r5 + r10 + r1i + r5i + r10i
if currscore > curr:
curr = currscore
# Save model
print 'Saving...',
params = unzip(tparams)
numpy.savez(saveto, **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
print 'Seen %d samples'%n_samples
def trainer(data='coco', #f8k, f30k, coco
margin=0.2,
dim=1024,
dim_image=4096,
dim_word=300,
encoder='gru', # gru OR bow
max_epochs=15,
dispFreq=10,
decay_c=0.,
grad_clip=2.,
maxlen_w=100,
optimizer='adam',
batch_size = 128,
saveto='/ais/gobi3/u/rkiros/uvsmodels/coco.npz',
validFreq=100,
lrate=0.0002,
reload_=False):
# Model options
model_options = {}
model_options['data'] = data
model_options['margin'] = margin
model_options['dim'] = dim
model_options['dim_image'] = dim_image
model_options['dim_word'] = dim_word
model_options['encoder'] = encoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['validFreq'] = validFreq
model_options['lrate'] = lrate
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# Load training and development sets
print 'Loading dataset'
train, dev = load_dataset(data)[:2]
# Create and save dictionary
print 'Creating dictionary'
worddict = build_dictionary(train[0]+dev[0])[0]
n_words = len(worddict)
model_options['n_words'] = n_words
print 'Dictionary size: ' + str(n_words)
with open('%s.dictionary.pkl'%saveto, 'wb') as f:
pkl.dump(worddict, f)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, inps, cost = build_model(tparams, model_options)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Building sentence encoder'
trng, inps_se, sentences = build_sentence_encoder(tparams, model_options)
f_senc = theano.function(inps_se, sentences, profile=False)
print 'Building image encoder'
trng, inps_ie, images = build_image_encoder(tparams, model_options)
f_ienc = theano.function(inps_ie, images, profile=False)
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([train[0], train[1]], batch_size=batch_size, maxlen=maxlen_w)
uidx = 0
curr = 0.
n_samples = 0
for eidx in xrange(max_epochs):
print 'Epoch ', eidx
for x, im in train_iter:
n_samples += len(x)
uidx += 1
x, mask, im = homogeneous_data.prepare_data(x, im, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
# Update
ud_start = time.time()
cost = f_grad_shared(x, mask, im)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, validFreq) == 0:
print 'Computing results...'
curr_model = {}
curr_model['options'] = model_options
curr_model['worddict'] = worddict
curr_model['word_idict'] = word_idict
curr_model['f_senc'] = f_senc
curr_model['f_ienc'] = f_ienc
ls = encode_sentences(curr_model, dev[0])
lim = encode_images(curr_model, dev[1])
(r1, r5, r10, medr) = i2t(lim, ls)
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr)
(r1i, r5i, r10i, medri) = t2i(lim, ls)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri)
currscore = r1 + r5 + r10 + r1i + r5i + r10i
if currscore > curr:
curr = currscore
# Save model
print 'Saving...',
params = unzip(tparams)
numpy.savez(saveto, **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
print 'Seen %d samples'%n_samples
def trainer(Xs,
Xs_val,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
decoder='gru',
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=20000,
maxlen_w=30,
optimizer='adam',
batch_size = 64,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
embeddings=None,
saveFreq=1000,
reload_=False):
# Model options
model_options = {}
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['embeddings'] = embeddings
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Load pre-trained embeddings, if applicable
if embeddings:
print 'Loading embeddings...'
from gensim.models import Word2Vec as word2vec
embed_map = word2vec.load_word2vec_format(embeddings, binary=True)
model_options['dim_word'] = dim_word = embed_map.vector_size
preemb = norm_weight(n_words, dim_word)
preemb_mask = numpy.ones((n_words, 1), dtype='float32')
for w,i in worddict.items()[:n_words-2]:
if w in embed_map:
preemb[i] = embed_map[w]
preemb_mask[i] = 0 # don't propagate gradients into pretrained embs
else:
preemb = None
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options, preemb=preemb)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, x, x_mask, y, y_mask, z, z_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask, z, z_mask]
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
if embeddings:
param_preemb_mask = theano.shared(preemb_mask, name='preemb_mask', broadcastable=(False, True))
grads[0] *= param_preemb_mask
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
if type(Xs[0]) is not list: Xs = [Xs]
if type(Xs_val[0]) is not list: Xs_val = [Xs_val]
trainXs = map(hd.grouper, Xs)
valXs = map(hd.grouper, Xs_val)
train_iters = [hd.HomogeneousData(trainX, batch_size=batch_size, maxlen=maxlen_w) for trainX in trainXs]
val_iters = [hd.HomogeneousData(valX, batch_size=batch_size, maxlen=maxlen_w) for valX in valXs]
f_progress = open('%s_progress.txt' % saveto, 'w', 1)
uidx = 0
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for train_iter in train_iters:
for x, y, z in train_iter:
n_samples += len(x)
uidx += 1
x, x_mask, y, y_mask, z, z_mask = hd.prepare_data(x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask, z, z_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
val_logprob = n_val_samples = 0
for val_iter in val_iters:
for x, y, z in val_iter:
n_val_samples += len(x)
x, x_mask, y, y_mask, z, z_mask = hd.prepare_data(x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
val_logprob += f_log_probs(x, x_mask, y, y_mask, z, z_mask)
val_logprob /= n_val_samples
print 'LOGPROB: %s' % val_logprob
f_progress.write('%s\n' % val_logprob)
print 'Saving...',
params = unzip(tparams)
numpy.savez('%s_%.3f' % (saveto, val_logprob), history_errs=[], **params)
pkl.dump(model_options, open('%s_%.3f.pkl'%(saveto, val_logprob), 'wb'))
print 'Done'
print 'Seen %d samples'%n_samples
def main():
model_config = configuration.ModelConfig()
model_config.data = FLAGS.input_dataset_name
#loading dataset
print('Loading dataset ...')
(train_caps,
train_ims), (test_caps,
test_ims), _ = load_dataset(name=model_config.data,
load_train=True)
train_nic_ims = train_ims[:, 1536:]
test_nic_ims = test_ims[:, 1536:]
train_ims[:, 1536:] = preprocessing.scale(train_nic_ims)
test_ims[:, 1536:] = preprocessing.scale(test_nic_ims)
test_vgg_feature = test_ims[:, :1536]
test_NIC_feature = test_ims[:, 1536:]
#create and save dictionary
print('creating dictionary')
worddict = build_dictionary(train_caps + test_caps)[0]
n_words = len(worddict)
model_config.n_words = n_words
model_config.worddict = worddict
print('dictionary size: ' + str(n_words))
with open('f8k.dictionary.pkl', 'wb') as f:
pkl.dump(worddict, f)
#Building the model
print('Building the model ...')
model = LTS(model_config)
model.build()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
saver = tf.train.Saver(max_to_keep=model_config.max_checkpoints_to_keep)
#sess = tf.Session(config=config)
print('start embedding training')
curr = 0.
uidx = 0.
train_iter = homogeneous_data.HomogeneousData(
data=[train_caps, train_ims],
batch_size=model_config.batch_size,
maxlen=model_config.maxlen_w)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(model_config.max_epochs):
# Train G
print('Epoch ', epoch)
if epoch == 15:
model_config.lrate = model_config.lrate / 10
for x, im in train_iter:
uidx += 1
ls, mask, im = homogeneous_data.prepare_data(
caps=x,
features=im,
worddict=worddict,
maxlen=model_config.maxlen_w,
n_words=model_config.n_words)
vgg_feature = im[:, :1536]
NIC_feature = im[:, 1536:]
#embedding training
_, cost = sess.run(
[model.updates, model.embedding_loss],
feed_dict={
model.VGG_pred_data: vgg_feature,
model.NIC_pred_data: NIC_feature,
model.ls_pred_data: ls.T,
model.input_mask: mask.T,
model.keep_prob: 0.5,
model.phase: 1,
model.learning_rate: model_config.lrate
})
if np.mod(uidx, 10) == 0:
print('Epoch ', epoch, 'Update ', uidx, 'Cost ', cost)
if np.mod(uidx, 100) == 0:
print('test ...')
# encode images into the text embedding space
images = getTestImageFeature(sess, model, test_vgg_feature,
test_NIC_feature)
features = getTestTextFeature(sess, model, model_config,
test_caps)
(r1, r5, r10, medr) = recall.i2t(images, features)
print("Image to text: %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr))
(r1i, r5i, r10i, medri) = recall.t2i(images, features)
print("Text to image: %.1f, %.1f, %.1f, %.1f" %
(r1i, r5i, r10i, medri))
currscore = r1 + r5 + r10 + r1i + r5i + r10i
if currscore > curr:
curr = currscore
# Save model
print('Saving...')
saver.save(sess,
"checkpoint_files/model.ckpt",
global_step=int(uidx + 1))
print('done.')
sess = tf.Session()
model_path = tf.train.latest_checkpoint("checkpoint_files/")
if not model_path:
print("Skipping testing. No checkpoint found in: %s",
FLAGS.checkpoint_dir)
return
print("Loading model from checkpoint: %s", model_path)
saver.restore(sess, model_path)
print("Successfully loaded checkpoint: %s", model_path)
images = getTestImageFeature(sess, model, test_vgg_feature,
test_NIC_feature)
# encode sentences into the text embedding space
features = getTestTextFeature(sess, model, model_config, test_caps)
(r1, r5, r10, medr) = recall.i2t(images, features)
print("Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr))
(r1i, r5i, r10i, medri) = recall.t2i(images, features)
print("Text to image: %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri))
def trainer(X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
decoder='gru',
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=20000,
maxlen_w=30,
optimizer='adam',
batch_size = 64,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
saveFreq=1000,
reload_=False):
# Model options
model_options = {}
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_
print model_options
# reload options
# TODO: if loading old parameters you need to make sure you are using them
# in the rest of the code
# if reload_ and os.path.exists(saveto):
# print 'reloading...' + saveto
# with open('%s.pkl'%saveto, 'rb') as f:
# model_options = pkl.load(f)
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto + '.npz', params)
tparams = init_tparams(params)
trng, x, x_mask, y, y_mask, z, z_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask, z, z_mask]
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
trainX = homogeneous_data.grouper(X)
train_iter = homogeneous_data.HomogeneousData(trainX, batch_size=batch_size, maxlen=maxlen_w)
uidx = 0
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for x, y, z in train_iter:
n_samples += len(x)
uidx += 1
x, x_mask, y, y_mask, z, z_mask = homogeneous_data.prepare_data(x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask, z, z_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
params = unzip(tparams)
numpy.savez(saveto, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
print 'Seen %d samples'%n_samples
def trainer(X, C, stmodel,
dimctx=4800, #vector dimensionality
dim_word=620, # word vector dimensionality
dim=1600, # the number of GRU units
encoder='gru',
decoder='gru',
doutput=False,
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=40000,
maxlen_w=100,
optimizer='adam',
batch_size = 16,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
embeddings=None,
saveFreq=1000,
sampleFreq=100,
reload_=False):
# Model options
model_options = {}
model_options['dimctx'] = dimctx
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['doutput'] = doutput
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['embeddings'] = embeddings
model_options['saveFreq'] = saveFreq
model_options['sampleFreq'] = sampleFreq
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Load pre-trained embeddings, if applicable
if embeddings != None:
print 'Loading embeddings...'
with open(embeddings, 'rb') as f:
embed_map = pkl.load(f)
dim_word = len(embed_map.values()[0])
model_options['dim_word'] = dim_word
preemb = norm_weight(n_words, dim_word)
pz = defaultdict(lambda : 0)
for w in embed_map.keys():
pz[w] = 1
for w in worddict.keys()[:n_words-2]:
if pz[w] > 0:
preemb[worddict[w]] = embed_map[w]
else:
preemb = None
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options, preemb=preemb)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, inps, cost = build_model(tparams, model_options)
print 'Building sampler'
f_init, f_next = build_sampler(tparams, model_options, trng)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([X,C], batch_size=batch_size, maxlen=maxlen_w)
uidx = 0
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for x, c in train_iter:
n_samples += len(x)
uidx += 1
x, mask, ctx = homogeneous_data.prepare_data(x, c, worddict, stmodel, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, mask, ctx)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
params = unzip(tparams)
numpy.savez(saveto, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
if numpy.mod(uidx, sampleFreq) == 0:
x_s = x
mask_s = mask
ctx_s = ctx
for jj in xrange(numpy.minimum(10, len(ctx_s))):
sample, score = gen_sample(tparams, f_init, f_next, ctx_s[jj].reshape(1, model_options['dimctx']), model_options,
trng=trng, k=1, maxlen=100, stochastic=False, use_unk=False)
print 'Truth ',jj,': ',
for vv in x_s[:,jj]:
if vv == 0:
break
if vv in word_idict:
print word_idict[vv],
else:
print 'UNK',
print
for kk, ss in enumerate([sample[0]]):
print 'Sample (', kk,') ', jj, ': ',
for vv in ss:
if vv == 0:
break
if vv in word_idict:
print word_idict[vv],
else:
print 'UNK',
print
print 'Seen %d samples'%n_samples
def trainer(data='coco',
margin=0.2,
dim=1024,
dim_image=4096,
dim_word=300,
encoder='gru',
max_epochs=15,
dispFreq=10,
decay_c=0.0,
grad_clip=2.0,
maxlen_w=150,
batch_size=128,
saveto='vse/coco',
validFreq=100,
lrate=0.0002,
concat=True,
reload_=False):
hyper_params = {
'data': data,
'encoder': encoder,
'batch_size': batch_size,
'time': cur_time,
'lrate': lrate,
'concat': concat,
}
i2t_r1 = dict([('i2t_recall', 'r1')] + hyper_params.items())
i2t_r5 = dict([('i2t_recall', 'r5')] + hyper_params.items())
i2t_r10 = dict([('i2t_recall', 'r10')] + hyper_params.items())
t2i_r1 = dict([('t2i_recall', 'r1')] + hyper_params.items())
t2i_r5 = dict([('t2i_recall', 'r5')] + hyper_params.items())
t2i_r10 = dict([('t2i_recall', 'r10')] + hyper_params.items())
i2t_med = dict([('i2t_med', 'i2t_med')] + hyper_params.items())
t2i_med = dict([('t2i_med', 't2i_med')] + hyper_params.items())
agent = Agent(port=5020)
i2t_r1_agent = agent.register(i2t_r1, 'recall', overwrite=True)
i2t_r5_agent = agent.register(i2t_r5, 'recall', overwrite=True)
i2t_r10_agent = agent.register(i2t_r10, 'recall', overwrite=True)
t2i_r1_agent = agent.register(t2i_r1, 'recall', overwrite=True)
t2i_r5_agent = agent.register(t2i_r5, 'recall', overwrite=True)
t2i_r10_agent = agent.register(t2i_r10, 'recall', overwrite=True)
i2t_med_agent = agent.register(i2t_med, 'median', overwrite=True)
t2i_med_agent = agent.register(t2i_med, 'median', overwrite=True)
# Model options
model_options = {}
model_options['data'] = data
model_options['margin'] = margin
model_options['dim'] = dim
model_options['dim_image'] = dim_image
model_options['dim_word'] = dim_word
model_options['encoder'] = encoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['maxlen_w'] = maxlen_w
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['validFreq'] = validFreq
model_options['lrate'] = lrate
model_options['reload_'] = reload_
model_options['concat'] = concat
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl' % saveto, 'rb') as f:
model_options = pkl.load(f)
# Load training and development sets
print 'loading dataset'
train, dev = load_dataset(data)[:2]
# Create and save dictionary
print 'Create dictionary'
worddict = build_dictionary(train[0] + dev[0])[0]
n_words = len(worddict)
model_options['n_words'] = n_words
print 'Dictionary size: ' + str(n_words)
with open('%s.dictionary.pkl' % saveto, 'wb') as f:
pkl.dump(worddict, f)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
model_options['worddict'] = worddict
model_options['word_idict'] = word_idict
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData([train[0], train[1]],
batch_size=batch_size,
maxlen=maxlen_w)
img_sen_model = ImgSenRanking(model_options)
img_sen_model = img_sen_model.cuda()
loss_fn = PairwiseRankingLoss(margin=margin)
loss_fn = loss_fn.cuda()
params = filter(lambda p: p.requires_grad, img_sen_model.parameters())
optimizer = torch.optim.Adam(params, lrate)
uidx = 0
curr = 0.0
n_samples = 0
for eidx in xrange(max_epochs):
print 'Epoch ', eidx
for x, im in train_iter:
n_samples += len(x)
uidx += 1
x_id, im = homogeneous_data.prepare_data(x,
im,
worddict,
maxlen=maxlen_w,
n_words=n_words)
if x_id is None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
x_id = Variable(torch.from_numpy(x_id).cuda())
im = Variable(torch.from_numpy(im).cuda())
# Update
ud_start = time.time()
x, im = img_sen_model(x_id, im, x)
cost = loss_fn(im, x)
optimizer.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm(params, grad_clip)
optimizer.step()
ud = time.time() - ud_start
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost.data.cpu(
).numpy()[0], 'UD ', ud
if numpy.mod(uidx, validFreq) == 0:
print 'Computing results...'
curr_model = {}
curr_model['options'] = model_options
curr_model['worddict'] = worddict
curr_model['word_idict'] = word_idict
curr_model['img_sen_model'] = img_sen_model
ls, lim = encode_sentences(curr_model, dev[0]), encode_images(
curr_model, dev[1])
r1, r5, r10, medr = 0.0, 0.0, 0.0, 0
r1i, r5i, r10i, medri = 0.0, 0.0, 0.0, 0
r_time = time.time()
if data == 'arch' or data == 'arch_small':
(r1, r5, r10, medr) = i2t_arch(lim, ls)
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5,
r10, medr)
(r1i, r5i, r10i, medri) = t2i_arch(lim, ls)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (
r1i, r5i, r10i, medri)
else:
(r1, r5, r10, medr) = i2t(lim, ls)
print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5,
r10, medr)
(r1i, r5i, r10i, medri) = t2i(lim, ls)
print "Text to image: %.1f, %.1f, %.1f, %.1f" % (
r1i, r5i, r10i, medri)
print "Cal Recall@K using %ss" % (time.time() - r_time)
record_num = uidx / validFreq
agent.append(i2t_r1_agent, record_num, r1)
agent.append(i2t_r5_agent, record_num, r5)
agent.append(i2t_r10_agent, record_num, r10)
agent.append(t2i_r1_agent, record_num, r1i)
agent.append(t2i_r5_agent, record_num, r5i)
agent.append(t2i_r10_agent, record_num, r10i)
agent.append(i2t_med_agent, record_num, medr)
agent.append(t2i_med_agent, record_num, medri)
currscore = r1 + r5 + r10 + r1i + r5i + r10i
if currscore > curr:
curr = currscore
# Save model
print 'Saving model...',
pkl.dump(
model_options,
open('%s_params_%s.pkl' % (saveto, encoder), 'wb'))
torch.save(img_sen_model.state_dict(),
'%s_model_%s.pkl' % (saveto, encoder))
print 'Done'
print 'Seen %d samples' % n_samples
# Inverse dictionary word_idict = dict() for kk, vv in worddict.iteritems(): word_idict[vv] = kk word_idict[0] = '<eos>' word_idict[1] = 'UNK'
print 'Building model' params = init_params(model_options) # reload parameters if reload_ and os.path.exists(saveto): params = load_params(saveto, params)
tparams = init_tparams(params)
trng, inps, cost = build_model(tparams, model_options)
# before any regularizer print 'Building f_log_probs...', f_log_probs = theano.function(inps, cost, profile=False) print 'Done'
# weight decay, if applicable if decay_c > 0.: decay_c = theano.shared(numpy.float32(decay_c), name='decay_c') weight_decay = 0. for kk, vv in tparams.iteritems(): weight_decay += (vv ** 2).sum() weight_decay *= decay_c cost += weight_decay
# after any regularizer print 'Building f_cost...', f_cost = theano.function(inps, cost, profile=False) print 'Done'
print 'Building sentence encoder' trng, inps_se, sentences = build_sentence_encoder(tparams, model_options) f_senc = theano.function(inps_se, sentences, profile=False)
print 'Building image encoder' trng, inps_ie, images = build_image_encoder(tparams, model_options) f_ienc = theano.function(inps_ie, images, profile=False)
print 'Building f_grad...', grads = tensor.grad(cost, wrt=itemlist(tparams)) f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False) f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.: g2 = 0. for g in grads: g2 += (g**2).sum() new_grads = [] for g in grads: new_grads.append(tensor.switch(g2 > (grad_clip**2), g / tensor.sqrt(g2) * grad_clip, g)) grads = new_grads
lr = tensor.scalar(name='lr') print 'Building optimizers...', # (compute gradients), (updates parameters) f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder) train_iter = homogeneous_data.HomogeneousData([train[0], train[1]], batch_size=batch_size, maxlen=maxlen_w)
uidx = 0 curr = 0. n_samples = 0 for eidx in xrange(max_epochs):
print 'Epoch ', eidx
for x, im in train_iter: n_samples += len(x) uidx += 1
x, mask, im = homogeneous_data.prepare_data(x, im, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None: print 'Minibatch with zero sample under length ', maxlen_w uidx -= 1 continue
# Update ud_start = time.time() cost = f_grad_shared(x, mask, im) f_update(lrate) ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost): print 'NaN detected' return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0: print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, validFreq) == 0:
print 'Computing results...' curr_model = {} curr_model['options'] = model_options curr_model['worddict'] = worddict curr_model['word_idict'] = word_idict curr_model['f_senc'] = f_senc curr_model['f_ienc'] = f_ienc
ls = encode_sentences(curr_model, dev[0]) lim = encode_images(curr_model, dev[1])
(r1, r5, r10, medr) = i2t(lim, ls) print "Image to text: %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr) (r1i, r5i, r10i, medri) = t2i(lim, ls) print "Text to image: %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri)
currscore = r1 + r5 + r10 + r1i + r5i + r10i if currscore > curr: curr = currscore
# Save model print 'Saving...', params = unzip(tparams) numpy.savez(saveto, **params) pkl.dump(model_options, open('%s.pkl'%saveto, 'wb')) print 'Done'
print 'Seen %d samples'%n_samples
if __name__ == '__main__': pass
def trainer(X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
num_neg=4,
gamma=1.0,
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=20000,
maxlen_w=30,
optimizer='adam',
batch_size = 64,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
saveFreq=1000,
reload_=False):
# Model options
model_options = {}
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['num_neg'] = num_neg
model_options['gamma'] = gamma
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_
print model_options
# reload options
if reload_ and os.path.exists(saveto):
print 'reloading...' + saveto
with open('%s.pkl'%saveto, 'rb') as f:
models_options = pkl.load(f)
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, x, x_mask, p_f, p_f_mask, p_b, p_b_mask, \
ns_list, ns_masks, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, p_f, p_f_mask, p_b, p_b_mask] + ns_list + ns_masks
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads], profile=False)
f_weight_norm = theano.function([], [(t**2).sum() for k,t in tparams.iteritems()], profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
trainX = homogeneous_data.grouper(X)
train_iter = homogeneous_data.HomogeneousData(trainX, batch_size=batch_size, num_neg=num_neg, maxlen=maxlen_w)
uidx = 0
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for x, p_f, p_b, ns in train_iter:
n_samples += len(x)
uidx += 1
# ns input is list of num_neg negative sentences,
# output ns is list of num_neg (batchsize, neg_len) negative sentences
x, x_mask, p_f, p_f_mask, p_b, p_b_mask, ns_list, ns_masks = homogeneous_data.prepare_data(x, p_f, p_b, ns,
worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
args = [x, x_mask, p_f, p_f_mask, p_b, p_b_mask] + ns_list + ns_masks
cost = f_grad_shared(*args)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost_p1) or numpy.isinf(cost_p1) or numpy.isnan(cost_p2) or numpy.isinf(cost_p2):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost_p1 + cost_p2, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
saveto_idx = saveto.format(uidx)
params = unzip(tparams)
numpy.savez(saveto_idx, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl'%saveto_idx, 'wb'))
print 'Done'
print 'Seen %d samples'%n_samples
def trainer(data='f30k',
margin=0.2,
dim=1024,
dim_image=4096,
dim_word=300,
max_epochs=15,
encoder='lstm',
dispFreq=10,
grad_clip=2.0,
maxlen_w=150,
batch_size=128,
saveto='vse/f30K',
validFreq=100,
early_stop=20,
lrate=1e-3,
reload_=False):
# Model options
model_options = {}
model_options['data'] = data
model_options['margin'] = margin
model_options['dim'] = dim
model_options['dim_image'] = dim_image
model_options['dim_word'] = dim_word
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['grad_clip'] = grad_clip
model_options['maxlen_w'] = maxlen_w
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['validFreq'] = validFreq
model_options['lrate'] = lrate
model_options['reload_'] = reload_
logging.info(model_options)
# reload options
if reload_ and os.path.exists(saveto):
logging.info('reloading...' + saveto)
with open('%s.pkl' % saveto, 'rb') as f:
model_options = pkl.load(f)
# Load training and development sets
logging.info('loading dataset')
titles, album_ims, artist, genre = load_dataset(data)
artist_string = artist
genre_string = genre
# Create and save dictionary
if os.path.exists('%s.dictionary.pkl' % saveto):
logging.info('loading dict from...' + saveto)
with open('%s.dictionary.pkl' % saveto, 'rb') as wdict:
worddict = pkl.load(wdict)
n_words = len(worddict)
model_options['n_words'] = n_words
logging.info('Dictionary size: ' + str(n_words))
else:
logging.info('Create dictionary')
worddict = build_dictionary(titles + artist + genre)[0]
n_words = len(worddict)
model_options['n_words'] = n_words
logging.info('Dictionary words: ' + str(n_words))
with open('%s.dictionary.pkl' % saveto, 'wb') as f:
pkl.dump(worddict, f)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.items():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
model_options['worddict'] = worddict
model_options['word_idict'] = word_idict
# Each sentence in the minibatch have same length (for encoder)
train_iter = homogeneous_data.HomogeneousData(
[titles, album_ims, artist, genre],
batch_size=batch_size,
maxlen=maxlen_w)
img_sen_model = Img_Sen_Artist_Ranking(model_options)
# todo code to load saved model dict
if os.path.exists('%s_model_%s.pkl' % (saveto, encoder)):
logging.info('Loading model...')
# pkl.dump(model_options, open('%s_params_%s.pkl' % (saveto, encoder), 'wb'))
img_sen_model.load_state_dict(
torch.load('%s_model_%s.pkl' % (saveto, encoder)))
logging.info('Done')
img_sen_model = img_sen_model.cuda()
loss_fn = PairwiseRankingLoss(margin=margin).cuda()
params = filter(lambda p: p.requires_grad, img_sen_model.parameters())
optimizer = torch.optim.Adam(params, lr=lrate)
scheduler = ReduceLROnPlateau(optimizer,
factor=0.1,
patience=40,
mode='min',
verbose=True,
threshold=1e-8)
uidx = 0
curr = 0.0
n_samples = 0
# For Early-stopping
best_r1, best_r5, best_r10, best_medr = 0.0, 0.0, 0.0, 0
best_step = 0
writer = SummaryWriter()
for eidx in range(max_epochs):
for x, im, artist, genre in train_iter:
n_samples += len(x)
uidx += 1
x, im, artist, genre = homogeneous_data.prepare_data(
x,
im,
artist,
genre,
worddict,
maxlen=maxlen_w,
n_words=n_words)
if x is None:
logging.info('Minibatch with zero sample under length ',
maxlen_w)
uidx -= 1
continue
x = Variable(torch.from_numpy(x).cuda())
im = Variable(torch.from_numpy(im).cuda())
artist = Variable(torch.from_numpy(artist).cuda())
genre = Variable(torch.from_numpy(genre).cuda())
# Update
x1, im1, artist, genre = img_sen_model(x, im, artist, genre)
#make validation on inout before trainer see it
if numpy.mod(uidx, validFreq) == 0:
img_sen_model.eval()
with torch.no_grad():
print('Epoch ', eidx, '\tUpdate@ ', uidx, '\tCost ',
cost.data.item())
writer.add_scalar('Evaluation/Validation_Loss',
cost.data.item(), uidx)
(r1, r5, r10, medr) = i2t(im1, x) #distances with l2norm
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr))
(r1g, r5g, r10g, medrg) = i2t(im1, genre)
logging.info("Image to genre: %.1f, %.1f, %.1f, %.1f" %
(r1g, r5g, r10g, medrg))
(r1a, r5a, r10a, medra) = i2t(im1, artist)
logging.info("Image to Artist: %.1f, %.1f, %.1f, %.1f" %
(r1a, r5a, r10a, medra))
logging.info("Cal Recall@K ")
writer.add_scalars('Validation Recal/Image2Album', {
'r@1': r1,
'r@5': r5,
'r@10': r10
}, uidx)
writer.add_scalars('Validation Recal/Image2Genres', {
'r@1': r1g,
'r@5': r5g,
'r@10': r10g
}, uidx)
writer.add_scalars('Validation Recal/Image2Artist', {
'r@1': r1a,
'r@5': r5a,
'r@10': r5a
}, uidx)
curr_step = uidx / validFreq
currscore = r1 + r5 + r10 + r1a + r5a + r10a + r1g + r5g + r10g - medr - medrg - medra
if currscore > curr:
curr = currscore
best_r1, best_r5, best_r10, best_medr = r1, r5, r10, medr
best_r1g, best_r5g, best_r10g, best_medrg = r1, r5, r10, medrg
best_step = curr_step
# Save model
logging.info('Saving model...')
pkl.dump(
model_options,
open('%s_params_%s.pkl' % (saveto, encoder), 'wb'))
torch.save(img_sen_model.state_dict(),
'%s_model_%s.pkl' % (saveto, encoder))
logging.info('Done')
if curr_step - best_step > early_stop:
logging.info('early stopping, jumping now...')
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f" %
(best_r1, best_r5, best_r10, best_medr))
logging.info(
"Image to genre: %.1f, %.1f, %.1f, %.1f" %
(best_r1g, best_r5g, best_r10g, best_medrg))
#return 0
lrate = 1e-4
for param_group in optimizer.param_groups:
param_group['lr'] = lrate
img_sen_model.train()
cost = loss_fn(im1, x1, artist, genre)
writer.add_scalar('Evaluation/training_Loss', cost, uidx)
optimizer.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(params, grad_clip)
scheduler.step(cost.data.item())
optimizer.step()
#scheduler.step(cost.data.item())
logging.info('Seen %d samples' % n_samples)
def trainer(
X,
dim_word=620, # word vector dimensionality
dim=2400, # the number of GRU units
encoder='gru',
decoder='gru',
max_epochs=5,
dispFreq=1,
decay_c=0.,
grad_clip=5.,
n_words=20000,
maxlen_w=30,
optimizer='adam',
batch_size=512,
saveto='/u/rkiros/research/semhash/models/toy.npz',
dictionary='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl',
saveFreq=5000,
reload_=False,
reload_path='output_books_full/model_ae_full_bsz_64_iter_313000.npz',
SICK_eval=False):
# Model options
model_options = {}
model_options['dim_word'] = dim_word
model_options['dim'] = dim
model_options['encoder'] = encoder
model_options['decoder'] = decoder
model_options['max_epochs'] = max_epochs
model_options['dispFreq'] = dispFreq
model_options['decay_c'] = decay_c
model_options['grad_clip'] = grad_clip
model_options['n_words'] = n_words
model_options['maxlen_w'] = maxlen_w
model_options['optimizer'] = optimizer
model_options['batch_size'] = batch_size
model_options['saveto'] = saveto
model_options['dictionary'] = dictionary
model_options['saveFreq'] = saveFreq
model_options['reload_'] = reload_
model_options['reload_path'] = reload_path
print model_options
# reload options
if reload_ and os.path.exists(reload_path):
print 'reloading...' + reload_path
with open('%s.pkl' % reload_path, 'rb') as f:
models_options = pkl.load(f)
reload_idx = int(reload_path.split('_')[-1].split('.')[0])
# load dictionary
print 'Loading dictionary...'
worddict = load_dictionary(dictionary)
# Inverse dictionary
word_idict = dict()
for kk, vv in worddict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(reload_path):
params = load_params(reload_path, params)
tparams = init_tparams(params)
trng, x, x_mask, y, y_mask, z, z_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask, z, z_mask]
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=False)
print 'Done'
# weight decay, if applicable
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv**2).sum()
weight_decay *= decay_c
cost += weight_decay
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=False)
print 'Done'
print 'Done'
print 'Building f_grad...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
f_grad_norm = theano.function(inps, [(g**2).sum() for g in grads],
profile=False)
f_weight_norm = theano.function([], [(t**2).sum()
for k, t in tparams.iteritems()],
profile=False)
if grad_clip > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(
tensor.switch(g2 > (grad_clip**2),
g / tensor.sqrt(g2) * grad_clip, g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
# (compute gradients), (updates parameters)
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Optimization'
# Each sentence in the minibatch have same length (for encoder)
trainX = homogeneous_data.grouper(X)
train_iter = homogeneous_data.HomogeneousData(trainX,
batch_size=batch_size,
maxlen=maxlen_w)
if not reload_:
uidx = 0
else:
uidx = reload_idx
lrate = 0.01
for eidx in xrange(max_epochs):
n_samples = 0
print 'Epoch ', eidx
for x, y, z in train_iter:
n_samples += len(x)
uidx += 1
x, x_mask, y, y_mask, z, z_mask = homogeneous_data.prepare_data(
x, y, z, worddict, maxlen=maxlen_w, n_words=n_words)
if x == None:
print 'Minibatch with zero sample under length ', maxlen_w
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask, z, z_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
saveto_iternum = saveto.format(uidx)
params = unzip(tparams)
numpy.savez(saveto_iternum, history_errs=[], **params)
pkl.dump(model_options, open('%s.pkl' % saveto_iternum, 'wb'))
print 'Done'
if SICK_eval:
print "Evaluating SICK Test performance"
embed_map = tools.load_googlenews_vectors()
model = tools.load_model(path_to_model=saveto_iternum,
embed_map=embed_map)
yhat, pr, sr, mse = eval_sick.evaluate(model,
evaltest=True)
del (model)
del (embed_map)
print pr, sr, mse
res_save_file = saveto.format('ALL').split(
'.')[0] + '_SICK_EVAL.txt'
with open(res_save_file, 'a') as rsf:
cur_time = strftime("%a, %d %b %Y %H:%M:%S +0000",
gmtime())
rsf.write('\n \n {}'.format(cur_time))
rsf.write('\n{}, {}, {}, {}'.format(uidx, pr, se, mse))
print "Done"
print 'Seen %d samples' % n_samples
