def main():
start_time = time.time()
parser = argparse.ArgumentParser(prog='trainMemNN.py',
description='Train MemmNN model for visual question answering')
parser.add_argument('--mlp-hidden-units', type=int, default=1024, metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers', type=int, default=3, metavar='<mlp-hidden-layers>')
parser.add_argument('--mlp-activation', type=str, default='tanh', metavar='<activation-function>')
parser.add_argument('--emb-dimension', type=int, default=50, metavar='<embedding-dimension>')
parser.add_argument('--batch-size', type=int, default=128, metavar='<batch-size>')
parser.add_argument('--hops', type=int, default=3, metavar='<memnet-hops>')
#parser.add_argument('--model-path', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weight-path', type=str, required=True, metavar='<weight-path>')
parser.add_argument('--output-path', type=str, required=True, metavar='<output-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 300
max_len = 30
img_feature_num = 125
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
#dev_q_ids, dev_image_ids = LoadIds('dev', data_dir)
test_q_ids,test_image_ids = LoadIds('test', data_dir)
#dev_questions = LoadQuestions('dev', data_dir)
test_questions = LoadQuestions('test', data_dir)
#dev_choices = LoadChoices('dev', data_dir)
test_choices = LoadChoices('test', data_dir)
caption_map = LoadCaptions('test')
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
######################
# Model Descriptions #
######################
print('Loading and compiling model...')
model = CreateGraph(args.emb_dimension, args.hops, args.mlp_activation, args.mlp_hidden_units, args.mlp_hidden_layers, word_vec_dim, img_dim, img_feature_num)
#model = model_from_json(open(args.model_path,'r').read())
# loss and optimizer
model.compile(loss={'output':Loss}, optimizer='rmsprop')
model.load_weights(args.weight_path)
print('Model and weights loaded.')
print('Time: %f s' % (time.time()-start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
'''
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
'''
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
# dev_question_batches = [ b for b in MakeBatches(dev_questions, args.batch_size, fillvalue=dev_questions[-1]) ]
# dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], args.batch_size, fillvalue=dev_answers['labs'][-1]) ]
# dev_choice_batches = [ b for b in MakeBatches(dev_choices, args.batch_size, fillvalue=dev_choices[-1]) ]
# dev_image_batches = [ b for b in MakeBatches(dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
test_question_batches = [ b for b in MakeBatches(test_questions, args.batch_size, fillvalue=test_questions[-1]) ]
test_choice_batches = [ b for b in MakeBatches(test_choices, args.batch_size, fillvalue=test_choices[-1]) ]
test_image_batches = [ b for b in MakeBatches(test_image_ids, args.batch_size, fillvalue=test_image_ids[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Testing #
######################
# start predicting
pbar = generic_utils.Progbar(len(test_question_batches)*args.batch_size)
predictions = []
# feed forward
for i in range(len(test_question_batches)):
X_question_batch = GetQuestionsTensor(test_question_batches[i], word_embedding, word_map)
X_caption_batch = GetCaptionsTensor2(test_image_batches[i], word_embedding, word_map, caption_map)
prob = model.predict_on_batch({'question':X_question_batch, 'image':X_caption_batch})
prob = prob[0]
# get word vecs of choices
choice_feats = GetChoicesTensor(test_choice_batches[i], word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred)
pbar.add(args.batch_size)
SavePredictions(args.output_path, predictions, test_q_ids)
print('Testing finished.')
print('Time: %f s' % (time.time()-start_time))
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='trainMemNN.py',
description='Train MemmNN model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--mlp-activation',
type=str,
default='tanh',
metavar='<activation-function>')
parser.add_argument('--emb-dimension',
type=int,
default=50,
metavar='<embedding-dimension>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
parser.add_argument('--hops', type=int, default=3, metavar='<memnet-hops>')
#parser.add_argument('--model-path', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weight-path',
type=str,
required=True,
metavar='<weight-path>')
parser.add_argument('--output-path',
type=str,
required=True,
metavar='<output-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 300
max_len = 30
img_feature_num = 125
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
#dev_q_ids, dev_image_ids = LoadIds('dev', data_dir)
test_q_ids, test_image_ids = LoadIds('test', data_dir)
#dev_questions = LoadQuestions('dev', data_dir)
test_questions = LoadQuestions('test', data_dir)
#dev_choices = LoadChoices('dev', data_dir)
test_choices = LoadChoices('test', data_dir)
caption_map = LoadCaptions('test')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
######################
# Model Descriptions #
######################
print('Loading and compiling model...')
model = CreateGraph(args.emb_dimension, args.hops, args.mlp_activation,
args.mlp_hidden_units, args.mlp_hidden_layers,
word_vec_dim, img_dim, img_feature_num)
#model = model_from_json(open(args.model_path,'r').read())
# loss and optimizer
model.compile(loss={'output': Loss}, optimizer='rmsprop')
model.load_weights(args.weight_path)
print('Model and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
'''
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
'''
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
# dev_question_batches = [ b for b in MakeBatches(dev_questions, args.batch_size, fillvalue=dev_questions[-1]) ]
# dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], args.batch_size, fillvalue=dev_answers['labs'][-1]) ]
# dev_choice_batches = [ b for b in MakeBatches(dev_choices, args.batch_size, fillvalue=dev_choices[-1]) ]
# dev_image_batches = [ b for b in MakeBatches(dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
test_question_batches = [
b for b in MakeBatches(
test_questions, args.batch_size, fillvalue=test_questions[-1])
]
test_choice_batches = [
b for b in MakeBatches(
test_choices, args.batch_size, fillvalue=test_choices[-1])
]
test_image_batches = [
b for b in MakeBatches(
test_image_ids, args.batch_size, fillvalue=test_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
# start predicting
pbar = generic_utils.Progbar(len(test_question_batches) * args.batch_size)
predictions = []
# feed forward
for i in range(len(test_question_batches)):
X_question_batch = GetQuestionsTensor(test_question_batches[i],
word_embedding, word_map)
X_caption_batch = GetCaptionsTensor2(test_image_batches[i],
word_embedding, word_map,
caption_map)
prob = model.predict_on_batch({
'question': X_question_batch,
'image': X_caption_batch
})
prob = prob[0]
# get word vecs of choices
choice_feats = GetChoicesTensor(test_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred)
pbar.add(args.batch_size)
SavePredictions(args.output_path, predictions, test_q_ids)
print('Testing finished.')
print('Time: %f s' % (time.time() - start_time))
def main():
start_time = time.time()
# argument parser
parser = argparse.ArgumentParser(prog='test_sent.py',
description='Test MemNN-wordvec model for ABSA sentiment classification')
parser.add_argument('--mlp-hidden-units', type=int, default=256, metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers', type=int, default=2, metavar='<mlp-hidden-layers>')
parser.add_argument('--dropout', type=float, default=0.3, metavar='<dropout-rate>')
parser.add_argument('--mlp-activation', type=str, default='relu', metavar='<activation-function>')
parser.add_argument('--batch-size', type=int, default=32, metavar='<batch-size>')
parser.add_argument('--learning-rate', type=float, default=0.001, metavar='<learning-rate>')
parser.add_argument('--aspects', type=int, required=True, metavar='<number of aspects>')
parser.add_argument('--domain', type=str, required=True, choices=['rest','lapt'], metavar='<domain>')
parser.add_argument('--cross-val-index', type=int, required=True, choices=range(0,10), metavar='<cross-validation-index>')
parser.add_argument('--weights', type=str, required=True, metavar='<weights-path>')
parser.add_argument('--output', type=str, required=True, metavar='<prediction-path>')
args = parser.parse_args()
args = parser.parse_args()
word_vec_dim = 300
aspect_dim = args.aspects
polarity_num = 3
emb_dim = 75
emb_size = 100
img_dim = word_vec_dim
hops = 2
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
model = CreateGraph(emb_dim, hops, 'relu', args.mlp_hidden_units, args.mlp_hidden_layers, word_vec_dim, aspect_dim, img_dim, emb_size, polarity_num)
# loss and optimizer
adagrad = Adagrad(lr=args.learning_rate)
model.compile(loss={'output':'categorical_crossentropy'}, optimizer=adagrad)
model.load_weights(args.weights)
print('Compilation finished.')
print('Time: %f s' % (time.time()-start_time))
######################
# Load Data #
######################
print('Loading data...')
# aspect mapping
asp_map = LoadAspectMap(args.domain)
# sentences
te_sents = LoadSentences(args.domain, 'te', args.cross_val_index)
# aspects
te_asps = LoadAspects(args.domain, 'te', args.cross_val_index, asp_map)
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
#####################
# GloVe #
#####################
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
#####################
# Encoders #
#####################
asp_encoder = GetAspectEncoder(asp_map)
lab_encoder = joblib.load('models/'+args.domain+'_labelencoder_'+str(args.cross_val_index)+'.pkl')
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
te_sent_batches = [ b for b in MakeBatches(te_sents, args.batch_size, fillvalue=te_sents[-1]) ]
te_asp_batches = [ b for b in MakeBatches(te_asps, args.batch_size, fillvalue=te_asps[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Testing #
######################
# start testing
print('Testing started...')
pbar = generic_utils.Progbar(len(te_sent_batches)*args.batch_size)
predictions = []
# testing feedforward
for i in range(len(te_sent_batches)):
X_sent_batch = GetSentenceTensor(te_sent_batches[i], word_embedding, word_map)
X_asp_batch = GetAspectFeatures(te_asp_batches[i], asp_encoder)
pred = model.predict_on_batch({'sentence': X_sent_batch, 'aspect': X_asp_batch})
pred = pred[0]
pred = np.argmax(pred, axis=1)
pol = lab_encoder.inverse_transform(pred).tolist()
predictions.extend(pol)
pbar.add(args.batch_size)
SavePredictions(args.output, predictions, len(te_sents))
print('Testing finished.')
print('Time: %f s' % (time.time()-start_time))
