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vqa_image_dmn_batch.py
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vqa_image_dmn_batch.py
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import random
import numpy as np
import theano
import theano.tensor as T
from theano.compile.nanguardmode import NanGuardMode
import lasagne
from lasagne import layers
from lasagne import nonlinearities
import cPickle as pickle
import utils
import nn_utils
import re
import copy
import json
import h5py
floatX = theano.config.floatX
class VQA_IMAGE_DMN_batch:
def __init__(self,word2vec, word_vector_size, dim,
mode, answer_module, input_mask_mode, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout,h5file,json_dict_file ,num_answers,img_vector_size,img_seq_len,
img_h5file_train,img_h5file_test,**kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.lr=0.001
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.h5file=h5py.File(h5file,"r")
self.img_h5file_train=h5py.File(img_h5file_train,"r")
self.img_h5file_test=h5py.File(img_h5file_test,"r")
self.img_seq_len=img_seq_len
self.img_vector_size=img_vector_size
with open (json_dict_file) as f:
self.json_dict=json.load(f)
#self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask = self._process_input(babi_train_raw)
#self.test_input, self.test_q, self.test_answer, self.test_fact_count, self.test_input_mask = self._process_input(babi_test_raw)
#self.vocab_size = len(self.vocab)
self.vocab_size=num_answers
self.input_var = T.tensor3('input_var') # (batch_size, seq_len, glove_dim)
self.img_input_var=T.tensor3('img_input_var') # (batch_size * img_seq_len , img_vector_size)
self.q_var = T.tensor3('question_var') # as self.input_var
self.answer_var = T.ivector('answer_var') # answer of example in minibatch
self.fact_count_var = T.ivector('fact_count_var') # number of facts in the example of minibatch
self.input_mask_var = T.imatrix('input_mask_var') # (batch_size, indices)
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
input_var_shuffled = self.input_var.dimshuffle(1, 2, 0)
inp_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=input_var_shuffled,
outputs_info=T.zeros_like(inp_dummy))
inp_c_history_shuffled = inp_c_history.dimshuffle(2, 0, 1)
inp_c_list = []
inp_c_mask_list = []
for batch_index in range(self.batch_size):
taken = inp_c_history_shuffled[batch_index].take(self.input_mask_var[batch_index, :self.fact_count_var[batch_index]], axis=0)
inp_c_list.append(T.concatenate([taken, T.zeros((self.input_mask_var.shape[1] - taken.shape[0], self.dim), floatX)]))
inp_c_mask_list.append(T.concatenate([T.ones((taken.shape[0],), np.int32), T.zeros((self.input_mask_var.shape[1] - taken.shape[0],), np.int32)]))
self.inp_c = T.stack(inp_c_list).dimshuffle(1, 2, 0)
inp_c_mask = T.stack(inp_c_mask_list).dimshuffle(1, 0)
###################### Adding the Image Input Module
print "==> building image img_input module"
### Don't Really Need the GRU to reduce the sentences into vectors ###
self.img_input_var=T.reshape(self.img_input_var , ( self.batch_size * self.img_seq_len , self.img_vector_size ))
img_input_layer=layers.InputLayer( shape=(self.batch_size*self.img_seq_len, self.img_vector_size), input_var=self.img_input_var)
## Convert the img_vector_size to self.dim using a MLP ##
img_input_layer=layers.DenseLayer( img_input_layer , num_units=self.dim )
img_input_var_dim=layers.get_output(img_input_layer)
img_input_var_dim=T.reshape(img_input_var_dim ,(self.batch_size , self.img_seq_len , self.dim ) )
#self.img_inp_c = T.stack(img_input_var_dim).dimshuffle(1, 2, 0)
self.img_inp_c = img_input_var_dim.dimshuffle(1,2,0)
###################################################
q_var_shuffled = self.q_var.dimshuffle(1, 2, 0)
q_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
q_q_history, _ = theano.scan(fn=self.input_gru_step,
sequences=q_var_shuffled,
outputs_info=T.zeros_like(q_dummy))
self.q_q = q_q_history[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
################################# Episodic Memory Module for Image
print "==> creating parameters for image memory module"
self.W_img_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_img_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_img_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_img_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic img_memory module (fixed number of steps: %d)" % self.memory_hops
img_memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_img_episode(img_memory[iter - 1])
img_memory.append(self.GRU_update(img_memory[iter - 1], current_episode,
self.W_img_mem_res_in, self.W_img_mem_res_hid, self.b_img_mem_res,
self.W_img_mem_upd_in, self.W_img_mem_upd_hid, self.b_img_mem_upd,
self.W_img_mem_hid_in, self.W_img_mem_hid_hid, self.b_img_mem_hid))
last_img_mem_raw = img_memory[-1].dimshuffle((1, 0))
#######################################################################
### Concatenating The 2 Memory Modules Representations Assuming the representation as self.batch_size x self.dim ###
combined_mem_raw=T.concatenate([last_mem_raw,last_img_mem_raw],axis=1)
#net = layers.InputLayer(shape=(self.batch_size, self.dim), input_var=last_mem_raw)
net = layers.InputLayer(shape=(self.batch_size, self.dim+self.dim), input_var=combined_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
#self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim+self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim, 2*self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim,2*self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim, 2*self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, self.batch_size), dtype=floatX))
results, updates = theano.scan(fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)], #(last_mem, y)
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
self.prediction = self.prediction.dimshuffle(1, 0)
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, #self.W_b
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a, ## Add the parameters of the Image Input Module
self.W_img_mem_res_in, self.W_img_mem_res_hid, self.b_img_mem_res,
self.W_img_mem_upd_in, self.W_img_mem_upd_hid, self.b_img_mem_upd,
self.W_img_mem_hid_in, self.W_img_mem_hid_hid, self.b_img_mem_hid, #self.W_img_b_img
self.W_img_1, self.W_img_2, self.b_img_1, self.b_img_2] ## Add the parameters of the Image Input Module
dim_transform_mlp_params=layers.get_all_params(img_input_layer )
self.params=self.params+ dim_transform_mlp_params
if self.answer_module == 'recurrent':
self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction, self.answer_var).mean()
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
self.learning_rate=T.scalar(name="learning_rate")
updates=lasagne.updates.adam(self.loss,self.params,learning_rate=self.learning_rate)
#updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var,self.img_input_var,self.learning_rate],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var,self.img_input_var,self.learning_rate],on_unused_input='ignore',
outputs=[self.prediction, self.loss])
def GRU_update(self, h, x, W_res_in, W_res_hid, b_res,
W_upd_in, W_upd_hid, b_upd,
W_hid_in, W_hid_hid, b_hid):
""" mapping of our variables to symbols in DMN paper:
W_res_in = W^r
W_res_hid = U^r
b_res = b^r
W_upd_in = W^z
W_upd_hid = U^z
b_upd = b^z
W_hid_in = W
W_hid_hid = U
b_hid = b^h
"""
z = T.nnet.sigmoid(T.dot(W_upd_in, x) + T.dot(W_upd_hid, h) + b_upd.dimshuffle(0, 'x'))
r = T.nnet.sigmoid(T.dot(W_res_in, x) + T.dot(W_res_hid, h) + b_res.dimshuffle(0, 'x'))
_h = T.tanh(T.dot(W_hid_in, x) + r * T.dot(W_hid_hid, h) + b_hid.dimshuffle(0, 'x'))
return z * h + (1 - z) * _h
def _empty_word_vector(self):
return np.zeros((self.word_vector_size,), dtype=floatX)
def input_gru_step(self, x, prev_h):
return self.GRU_update(prev_h, x, self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid)
def new_attention_step(self, ct, prev_g, mem, q_q):
z = T.concatenate([ct, mem, q_q, ct * q_q, ct * mem, (ct - q_q) ** 2, (ct - mem) ** 2], axis=0)
l_1 = T.dot(self.W_1, z) + self.b_1.dimshuffle(0, 'x')
l_1 = T.tanh(l_1)
l_2 = T.dot(self.W_2, l_1) + self.b_2.dimshuffle(0, 'x')
G = T.nnet.sigmoid(l_2)[0]
return G
def new_img_attention_step(self, ct, prev_g, mem, q_q):
z = T.concatenate([ct, mem, q_q, ct * q_q, ct * mem, (ct - q_q) ** 2, (ct - mem) ** 2], axis=0)
l_1 = T.dot(self.W_img_1, z) + self.b_img_1.dimshuffle(0, 'x')
l_1 = T.tanh(l_1)
l_2 = T.dot(self.W_img_2, l_1) + self.b_img_2.dimshuffle(0, 'x')
G = T.nnet.sigmoid(l_2)[0]
return G
def new_episode_step(self, ct, g, prev_h):
gru = self.GRU_update(prev_h, ct,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid)
h = g * gru + (1 - g) * prev_h
return h
def new_episode(self, mem):
g, g_updates = theano.scan(fn=self.new_attention_step,
sequences=self.inp_c,
non_sequences=[mem, self.q_q],
outputs_info=T.zeros_like(self.inp_c[0][0]))
if (self.normalize_attention):
g = nn_utils.softmax(g)
e, e_updates = theano.scan(fn=self.new_episode_step,
sequences=[self.inp_c, g],
outputs_info=T.zeros_like(self.inp_c[0]))
e_list = []
for index in range(self.batch_size):
e_list.append(e[self.fact_count_var[index] - 1, :, index])
return T.stack(e_list).dimshuffle((1, 0))
def new_img_episode_step(self, ct, g, prev_h):
gru = self.GRU_update(prev_h, ct,
self.W_img_mem_res_in, self.W_img_mem_res_hid, self.b_img_mem_res,
self.W_img_mem_upd_in, self.W_img_mem_upd_hid, self.b_img_mem_upd,
self.W_img_mem_hid_in, self.W_img_mem_hid_hid, self.b_img_mem_hid)
h = g * gru + (1 - g) * prev_h
return h
def new_img_episode(self, mem):
g, g_updates = theano.scan(fn=self.new_img_attention_step,
sequences=self.img_inp_c,
non_sequences=[mem, self.q_q],
outputs_info=T.zeros_like(self.img_inp_c[0][0]))
if (self.normalize_attention):
g = nn_utils.softmax(g)
e, e_updates = theano.scan(fn=self.new_img_episode_step,
sequences=[self.img_inp_c, g],
outputs_info=T.zeros_like(self.img_inp_c[0]))
e_list = []
for index in range(self.batch_size):
e_list.append(e[self.img_seq_len - 1, :, index])
return T.stack(e_list).dimshuffle((1, 0))
def save_params(self, file_name, epoch, **kwargs):
with open(file_name, 'w') as save_file:
pickle.dump(
obj = {
'params' : [x.get_value() for x in self.params],
'epoch' : epoch,
'gradient_value': (kwargs['gradient_value'] if 'gradient_value' in kwargs else 0)
},
file = save_file,
protocol = -1
)
def load_state(self, file_name):
print "==> loading state %s" % file_name
with open(file_name, 'r') as load_file:
dict = pickle.load(load_file)
loaded_params = dict['params']
for (x, y) in zip(self.params, loaded_params):
x.set_value(y)
def _process_batch(self, _inputs, _questions, _answers, _fact_counts, _input_masks , _img_feats):
inputs = copy.deepcopy(_inputs)
questions = copy.deepcopy(_questions)
answers = copy.deepcopy(_answers)
fact_counts = copy.deepcopy(_fact_counts)
input_masks = copy.deepcopy(_input_masks)
img_feats=copy.deepcopy(_img_feats)
zipped = zip(inputs, questions, answers, fact_counts, input_masks,img_feats)
max_inp_len = 0
max_q_len = 0
max_fact_count = 0
for inp, q, ans, fact_count, input_mask,img_feat in zipped:
max_inp_len = max(max_inp_len, len(inp))
max_q_len = max(max_q_len, len(q))
max_fact_count = max(max_fact_count, fact_count)
questions = []
inputs = []
answers = []
fact_counts = []
input_masks = []
img_feats= []
for inp, q, ans, fact_count, input_mask , img_feat in zipped:
while(len(inp) < max_inp_len):
inp.append(self._empty_word_vector())
while(len(q) < max_q_len):
q.append(self._empty_word_vector())
while(len(input_mask) < max_fact_count):
input_mask.append(-1)
inputs.append(inp)
questions.append(q)
answers.append(ans)
fact_counts.append(fact_count)
input_masks.append(input_mask)
img_feats.append(img_feat)
inputs = np.array(inputs).astype(floatX)
questions = np.array(questions).astype(floatX)
answers = np.array(answers).astype(np.int32)
fact_counts = np.array(fact_counts).astype(np.int32)
input_masks = np.array(input_masks).astype(np.int32)
img_feats=np.array(img_feats).astype(floatX)
return inputs, questions, answers, fact_counts, input_masks,img_feats
def _process_input(self, data_raw):
questions = []
inputs = []
answers = []
fact_counts = []
input_masks = []
img_features=[]
for x in data_raw:
#inp = x["C"].lower().split(' ')
x["C"]=x["C"].lower()
inp=re.split("[, \-!?:'\/]+",x["C"])
inp = [w for w in inp if len(w) > 0]
x["Q"]=x["Q"].lower()
q = re.split("[, \-!?:'\/]+",x["Q"])
q = [w for w in q if len(w) > 0]
inp_vector = [utils.process_word(word = w,
word2vec = self.word2vec,
vocab = self.vocab,
ivocab = self.ivocab,
word_vector_size = self.word_vector_size,
to_return = "word2vec") for w in inp]
q_vector = [utils.process_word(word = w,
word2vec = self.word2vec,
vocab = self.vocab,
ivocab = self.ivocab,
word_vector_size = self.word_vector_size,
to_return = "word2vec") for w in q]
if (self.input_mask_mode == 'word'):
input_mask = range(len(inp))
elif (self.input_mask_mode == 'sentence'):
input_mask = [index for index, w in enumerate(inp) if w == '.']
else:
raise Exception("unknown input_mask_mode")
fact_count = len(input_mask)
inputs.append(inp_vector)
questions.append(q_vector)
# NOTE: here we assume the answer is one word!
#answers.append(utils.process_word(word = x["A"],
# word2vec = self.word2vec,
# vocab = self.vocab,
# ivocab = self.ivocab,
# word_vector_size = self.word_vector_size,
# to_return = "index"))
answers.append(x["A"])
fact_counts.append(fact_count)
input_masks.append(input_mask)
img_features.append(x["I"])
return inputs, questions, answers, fact_counts, input_masks , img_features
def get_batches_per_epoch(self, mode):
if (mode == 'train'):
num_train,_=self.h5file['ques_train'].shape
return num_train / self.batch_size
elif (mode == 'test'):
num_test,_=self.h5file['ques_test'].shape
return num_test / self.batch_size
else:
raise Exception("unknown mode")
def shuffle_train_set(self):
print "==> Shuffling the train set"
#combined = zip(self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask)
#random.shuffle(combined)
#self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask = zip(*combined)
def convert_index_to_word(self,index):
return self.json_dict["ix_to_word"][str(index)]
def process_vqa_data(self,mode,start,end):
if mode=="train":
inputs=self.h5file['cap_train'][start:end]
qs=self.h5file['ques_train'][start:end]
answers=self.h5file['answers'][start:end]
img_indices=self.h5file['img_pos_train'][start:end]
if mode=="test":
inputs=self.h5file['cap_test'][start:end]
qs=self.h5file['ques_test'][start:end]
answers=self.h5file['ans_test'][start:end]
img_indices=self.h5file['img_pos_test'][start:end]
all_x=[]
for i in range(self.batch_size):
x={}
x["A"]=answers[i]-1
temp_Q=map( self.convert_index_to_word , filter( lambda x: x>0 , qs[i] ) )
x["Q"]=' '.join(temp_Q)
temp_Cs=[]
for j in range(inputs.shape[1]):
temp_C=map(self.convert_index_to_word,filter(lambda x: x>0 , inputs[i][j]))
temp_C_str=' '.join(temp_C)
temp_Cs.append(temp_C_str)
x["C"]=' . '.join(temp_Cs)
if mode=="train":
x["I"]=self.img_h5file_train['images_train'][ img_indices[i]-1].reshape((self.img_seq_len,self.img_vector_size))
if mode=="test":
x["I"]=self.img_h5file_test['images_test'][ img_indices[i]-1].reshape((self.img_seq_len,self.img_vector_size))
all_x.append(x)
return self. _process_input(all_x)
def process_masks(self,captions):
print("captions")
print(captions)
def step(self,batch_index,mode):
if mode== "train" and self.mode== "test":
raise Exception("Cannot train during test mode")
start_index=batch_index * self.batch_size
inputs, qs, answers, fact_counts, input_masks,img_feats =self.process_vqa_data(mode,start_index,start_index+self.batch_size)
if mode=="train":
theano_fn=self.train_fn
# inputs = self.process_vqa_data(self.h5file['cap_train'][start_index:start_index+self.batch_size])
# qs=self.process_vqa_data(self.h5file['ques_train' ][ start_index:start_index+self.batch_size] )
# answers=self.process_vqa_data(self.h5file['answers'][start_index:start_index+self.batch_size] )
# fact_counts=np.zeros(self.batch_size,dtype="int")
# fact_counts.fill(20)
# input_masks= process_masks( inputs ) # figure it out
if mode=="test":
theano_fn=self.test_fn
# inputs=self.process_vqa_data( self.h5file['cap_test'][start_index:start_index+self.batch_size ] )
# qs=self.process_vqa_data( self.h5file['ques_test'][start_index:start_index+self.batch_size ] )
# answers=self.process_vqa_data( self.h5file['ans_test'][start_index:start_index+self.batch_size ] )
# fact_counts=np.zeros(self.batch_size,dtype="int")
# fact_counts.fill(20)
# input_masks= process_masks( inputs ) # figure it out
inp,q,ans,fact_count,input_mask,img_feat=self._process_batch(inputs,qs,answers,fact_counts,input_masks,img_feats )
img_feat=img_feat.reshape((self.batch_size*self.img_seq_len,self.img_vector_size))
ret = theano_fn( inp,q,ans,fact_count,input_mask,img_feat,self.lr)
param_norm=np.max( [ utils.get_norm( x.get_value()) for x in self.params])
return { "prediction":ret[0],
"answers":ans,
"current_loss":ret[1],
"skipped":0,
"log":"pn: %.3f" % param_norm
}