def feature_detector_blk(max_depth=2):
"""Input: node dict
Output: TensorType([hyper.conv_dim, ])
Single patch of the conv. Depth is max_depth
"""
blk = td.Composition()
with blk.scope():
nodes_in_patch = collect_node_for_conv_patch_blk(
max_depth=max_depth).reads(blk.input)
# map from python object to tensors
mapped = td.Map(
td.Record((coding_blk(), td.Scalar(), td.Scalar(), td.Scalar(),
td.Scalar()))).reads(nodes_in_patch)
# mapped = [(feature, idx, depth, max_depth), (...)]
# compute weighted feature for each elem
weighted = td.Map(weighted_feature_blk()).reads(mapped)
# weighted = [fea, fea, fea, ...]
# add together
added = td.Reduce(td.Function(tf.add)).reads(weighted)
# added = TensorType([hyper.conv_dim, ])
# add bias
biased = td.Function(tf.add).reads(added,
td.FromTensor(param.get('Bconv')))
# biased = TensorType([hyper.conv_dim, ])
# tanh
tanh = td.Function(tf.nn.tanh).reads(biased)
# tanh = TensorType([hyper.conv_dim, ])
blk.output.reads(tanh)
return blk
def composed_embed_blk():
leaf_case = direct_embed_blk()
nonleaf_case = td.Composition(name='composed_embed_nonleaf')
with nonleaf_case.scope():
children = td.GetItem('children').reads(nonleaf_case.input)
clen = td.Scalar().reads(td.GetItem('clen').reads(nonleaf_case.input))
cclens = td.Map(td.GetItem('clen') >> td.Scalar()).reads(children)
fchildren = td.Map(direct_embed_blk()).reads(children)
initial_state = td.Composition()
with initial_state.scope():
initial_state.output.reads(
td.FromTensor(tf.zeros(hyper.word_dim)),
td.FromTensor(tf.zeros([])),
)
summed = td.Zip().reads(fchildren, cclens, td.Broadcast().reads(clen))
summed = td.Fold(continous_weighted_add_blk(),
initial_state).reads(summed)[0]
added = td.Function(tf.add, name='add_bias').reads(
summed, td.FromTensor(param.get('B')))
normed = clip_by_norm_blk().reads(added)
act_fn = tf.nn.relu if hyper.use_relu else tf.nn.tanh
relu = td.Function(act_fn).reads(normed)
nonleaf_case.output.reads(relu)
return td.OneOf(lambda node: node['clen'] == 0, {
True: leaf_case,
False: nonleaf_case
})
def test_tri_combined(self, idx, pclen, depth, max_depth):
"""Test linear_combine_blk on data"""
Wconvl = self.sess.run(tbcnn.param.get('Wconvl'))
Wconvr = self.sess.run(tbcnn.param.get('Wconvr'))
Wconvt = self.sess.run(tbcnn.param.get('Wconvt'))
actual = (td.Scalar(), td.Scalar(), td.Scalar(), td.Scalar()) >> tbcnn.tri_combined_blk()
actual = actual.eval((idx, pclen, depth, max_depth), session=self.sess)
desired = tri_combined_np(idx, pclen, depth, max_depth, Wconvl, Wconvr, Wconvt)
nptest.assert_allclose(actual, desired)
def test_linear_combine(self, clen, pclen, idx):
"""Test linear_combine_blk on data"""
Wl = self.sess.run(embedding.param.get('Wl'))
Wr = self.sess.run(embedding.param.get('Wr'))
actual = (td.Scalar(), td.Scalar(),
td.Scalar()) >> embedding.linear_combine_blk()
actual = actual.eval((clen, pclen, idx), session=self.sess)
desired = linear_combine_np(clen, pclen, idx, Wl, Wr)
nptest.assert_allclose(actual, desired)
def embed_tree(logits_and_state, is_root):
"""Creates a block that embeds trees; output is tree LSTM state."""
return td.InputTransform(tokenize) >> td.OneOf(
key_fn=lambda pair: pair[0] == '2', # label 2 means neutral
case_blocks=(add_metrics(is_root, is_neutral=False),
add_metrics(is_root, is_neutral=True)),
pre_block=(td.Scalar('int32'), logits_and_state))
def train(batch_size=100):
losses = []
net_block = reduce_net_block()
compiler = td.Compiler.create((net_block, td.Scalar()))
y, y_ = compiler.output_tensors
loss = tf.nn.l2_loss(y - y_)
train = tf.train.AdamOptimizer().minimize(loss)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
validation_fd = compiler.build_feed_dict(random_example() for _ in range(1000))
for i in range(10000):
sess.run(train, compiler.build_feed_dict(random_example() for _ in range(batch_size)))
loss_val = sess.run(loss, validation_fd)
losses.append(loss_val)
if i % 100 == 0:
# print (i, loss_val)
saver.save(sess, save_dir, global_step=i)
fig = plt.figure()
plt.plot(losses)
plt.title('Training Loss')
plt.xlabel('Batch')
plt.ylabel('Loss')
fig.savefig('./imgs/avg.png', dpi=fig.dpi)
return net_block
def logits_and_state(self):
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(self.vocab)
def lookup_word(word):
return self.vocab.get(word, unknown_idx)
#(GetItem(key) >> block).eval(inp) => block.eval(inp[key])
# InputTransform(funk): A Python function, lifted to a block.
# Scalar - input to scalar
word2vec = (td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> self.word_embedding)
#
pair2vec = (self.embed_subtree(), self.embed_subtree())
# Trees are binary, so the tree layer takes two states as its
# input_state.
zero_state = td.Zeros((self.tree_lstm_cell.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(self.word_embedding.output_type.shape[0])
# AllOf(a, b, c).eval(inp) => (a.eval(inp), b.eval(inp), c.eval(inp))
word_case = td.AllOf(word2vec, zero_state)
pair_case = td.AllOf(zero_inp, pair2vec)
# OneOf(func, [(key, block),(key,block)])) where funk(input) => key and
# OneOf returns one of blocks
tree2vec = td.OneOf(len, [(1, word_case), (2, pair_case)])
return tree2vec >> self.tree_lstm_cell
def test_weighted_feature(self):
root, _ = self._load_test_data()
Wconvl = self.sess.run(tbcnn.param.get('Wconvl'))
Wconvr = self.sess.run(tbcnn.param.get('Wconvr'))
Wconvt = self.sess.run(tbcnn.param.get('Wconvt'))
idx, pclen, depth, max_depth = (1., 1., 0., 2.)
feature = tbcnn.coding_blk().eval(root, session=self.sess)
actual = (td.Vector(feature.size), td.Scalar(),
td.Scalar(), td.Scalar(), td.Scalar()) >> tbcnn.weighted_feature_blk()
actual = actual.eval((feature, idx, pclen, depth, max_depth), session=self.sess)
desired = np.matmul(feature,
tri_combined_np(idx, pclen, depth, max_depth, Wconvl, Wconvr, Wconvt))
nptest.assert_allclose(actual, desired)
def linearLSTM_over_TreeLstm(self, num_classes, sent_lstm_num_units):
self.sent_cell = td.ScopedLayer(tf.contrib.rnn.BasicLSTMCell(
num_units=sent_lstm_num_units), name_or_scope = self._sent_lstm_default_scope_name)
sent_lstm = (td.Map(self.tree_lstm.tree_lstm()
>> td.Concat()) >> td.RNN(self.sent_cell))
self.output_layer = td.FC(
num_classes, activation=None, name=self._output_layer_default_scope_name)
return (td.Scalar('int32'), sent_lstm >> td.GetItem(1)
>> td.GetItem(0) >> self.output_layer) \
>> self.set_metrics()
def create_compiler():
expr_left_sentence, expr_right_sentence = buid_sentence_expression(
), buid_sentence_expression()
expr_label = td.InputTransform(lambda label: int(label)) >> td.OneHot(
2, dtype=tf.float32)
id = td.Scalar(dtype=tf.int32)
one_record = td.InputTransform(lambda record: json.loads(record)) >> \
td.Record((expr_left_sentence, expr_right_sentence, expr_label, id), name='instance')
compiler = td.Compiler().create(one_record)
return compiler
def buid_sentence_expression():
sentence_tree = td.InputTransform(
lambda sentence_json: WordNode(sentence_json))
tree_rnn = td.ForwardDeclaration(td.PyObjectType())
leaf_case = td.GetItem(
'word_id', name='leaf_in') >> td.Scalar(dtype=tf.int32) >> embedding
index_case = td.Record({'left': tree_rnn(), 'right': tree_rnn()}) \
>> td.Concat(name='concat_root_child') \
>> fc
expr_sentence = td.OneOf(td.GetItem('leaf'), {
True: leaf_case,
False: index_case
},
name='recur_in')
tree_rnn.resolve_to(expr_sentence)
return sentence_tree >> expr_sentence
def logits_and_state():
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(word_idx)
lookup_word = lambda word: word_idx.get(
word) # unknown_idx is the default return value
word2vec = (
td.GetItem(0) >> td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> word_embedding
) # <td.Pipe>: None -> TensorType((200,), 'float32')
context2vec1 = td.GetItem(1) >> td.InputTransform(
makeContextMat) >> td.Vector(10)
context2vec2 = td.GetItem(1) >> td.InputTransform(
makeContextMat) >> td.Vector(10)
ent1posit1 = td.GetItem(2) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent1posit2 = td.GetItem(2) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent2posit1 = td.GetItem(3) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent2posit2 = td.GetItem(3) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
pairs2vec = td.GetItem(0) >> (embed_subtree(), embed_subtree())
# our binary Tree can have two child nodes, therefore, we assume the zero state have two child nodes.
zero_state = td.Zeros((tree_lstm.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(word_embedding.output_type.shape[0]
) # word_embedding.output_type.shape[0] == 200
word_case = td.AllOf(word2vec, zero_state, context2vec1, ent1posit1,
ent2posit1)
children_case = td.AllOf(zero_inp, pairs2vec, context2vec2, ent1posit2,
ent2posit2)
# if leaf case, go to word case...
tree2vec = td.OneOf(lambda x: 1
if len(x[0]) == 1 else 2, [(1, word_case),
(2, children_case)])
# tree2vec = td.OneOf(lambda pair: len(pair[0]), [(1, word_case), (2, children_case)])
# logits and lstm states
return tree2vec >> tree_lstm >> (output_layer, td.Identity())
def logits_and_state():
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(word_idx)
lookup_word = lambda word: word_idx.get(word, unknown_idx)
word2vec = (td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> word_embedding)
pair2vec = (embed_subtree(), embed_subtree())
# Trees are binary, so the tree layer takes two states as its input_state.
zero_state = td.Zeros((tree_lstm.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(word_embedding.output_type.shape[0])
word_case = td.AllOf(word2vec, zero_state)
pair_case = td.AllOf(zero_inp, pair2vec)
tree2vec = td.OneOf(len, [(1, word_case), (2, pair_case)])
return tree2vec >> tree_lstm >> (output_layer, td.Identity())
def build_model():
# create model variables
param.initialize_tbcnn_weights()
# Compile the block and append fc layers
tree_pooling = dynamic_pooling_blk()
compiler = td.Compiler.create((tree_pooling, td.Scalar(dtype='int64')))
(pooled, batched_labels) = compiler.output_tensors
fc1 = tf.nn.relu(
tf.add(tf.matmul(pooled, param.get('FC1/weight')),
param.get('FC1/bias')))
fc2 = tf.nn.relu(
tf.add(tf.matmul(fc1, param.get('FC2/weight')), param.get('FC2/bias')))
# our prediction output with accuracy calc
logits = tf.nn.softmax(fc2)
correct_prediction = tf.equal(tf.argmax(logits, 1), batched_labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
batch_size_op = tf.unstack(tf.shape(batched_labels))[0]
return compiler, fc2, logits, batched_labels, accuracy, batch_size_op
back_dir = (td.RNN(bw_cell) >> td.GetItem(0)).reads(bw_seq)
back_to_leftright = td.Slice(step=-1).reads(back_dir)
output_transform = td.FC(1, activation=None)
bidir_common = (td.ZipWith(
td.Concat() >> output_transform >> td.Metric('logits'))).reads(
forward_dir, back_to_leftright)
bidir_conv_lstm.output.reads(bidir_common)
return bidir_conv_lstm
CONV_data = td.Record((td.Map(
td.Vector(vsize) >> td.Function(lambda x: tf.reshape(x, [-1, vsize, 1]))),
td.Map(td.Scalar())))
CONV_model = (CONV_data >> bidirectional_dynamic_CONV(
multi_convLSTM_cell([vsize, vsize, vsize], [100, 100, 100]),
multi_convLSTM_cell([vsize, vsize, vsize], [100, 100, 100])) >> td.Void())
FC_data = td.Record((td.Map(td.Vector(vsize)), td.Map(td.Scalar())))
FC_model = (FC_data >> bidirectional_dynamic_FC(multi_FC_cell(
[1000] * 5), multi_FC_cell([1000] * 5), 1000) >> td.Void())
store = data(FLAGS.data_dir + FLAGS.data_type, FLAGS.truncate)
if FLAGS.model == "lstm":
model = FC_model
elif FLAGS.model == "convlstm":
model = CONV_model
else:
def reduce_net_block():
net_block = td.Concat() >> td.FC(20) >> td.FC(20) >> td.FC(1, activation=None) >> td.Function(lambda xs: tf.squeeze(xs, axis=1))
return td.Map(td.Scalar()) >> td.Reduce(net_block)
def embed_tree(is_root):
return td.InputTransform(tokenize) >> (
td.Scalar('int32'), logits_and_state()) >> add_metrics(is_root)
def direct_embed_blk():
return (td.GetItem('name') >> td.Scalar('int32') >>
td.Function(lambda x: tf.nn.embedding_lookup(param.get('We'), x))
>> clip_by_norm_blk())
def _compile(self):
with self.sess.as_default():
import tensorflow_fold as td
output_size = len(self.labels)
self.keep_prob = tf.placeholder_with_default(tf.constant(1.0),shape=None)
fshape = (self.window_size * (self.char_embedding_size + self.char_feature_embedding_size), self.num_filters)
filt_w3 = tf.Variable(tf.random_normal(fshape, stddev=0.05))
def CNN_Window3(filters):
return td.Function(lambda a, b, c: cnn_operation([a,b,c],filters))
def cnn_operation(window_sequences,filters):
windows = tf.concat(window_sequences,axis=-1)
products = tf.multiply(tf.expand_dims(windows,axis=-1),filters)
return tf.reduce_sum(products,axis=-2)
char_emb = td.Embedding(num_buckets=self.char_buckets,
num_units_out=self.char_embedding_size)
cnn_layer = (td.NGrams(self.window_size)
>> td.Map(CNN_Window3(filt_w3))
>> td.Max())
# --------- char features
def charfeature_lookup(c):
if c in string.lowercase:
return 0
elif c in string.uppercase:
return 1
elif c in string.punctuation:
return 2
else:
return 3
char_input = td.Map(td.InputTransform(lambda c: ord(c.lower()))
>> td.Scalar('int32') >> char_emb)
char_features = td.Map(td.InputTransform(charfeature_lookup)
>> td.Scalar(dtype='int32')
>> td.Embedding(num_buckets=4,
num_units_out=self.char_feature_embedding_size))
charlevel = (td.InputTransform(lambda s: ['~'] + [ c for c in s ] + ['~'])
>> td.AllOf(char_input,char_features) >> td.ZipWith(td.Concat())
>> cnn_layer)
# --------- word features
word_emb = td.Embedding(num_buckets=len(self.word_vocab),
num_units_out=self.embedding_size,
initializer=self.word_embeddings)
wordlookup = lambda w: (self.word_vocab.index(w.lower())
if w.lower() in self.word_vocab else 0)
wordinput = (td.InputTransform(wordlookup)
>> td.Scalar(dtype='int32')
>> word_emb)
def wordfeature_lookup(w):
if re.match('^[a-z]+$',w):
return 0
elif re.match('^[A-Z][a-z]+$',w):
return 1
elif re.match('^[A-Z]+$',w):
return 2
elif re.match('^[A-Za-z]+$',w):
return 3
else:
return 4
wordfeature = (td.InputTransform(wordfeature_lookup)
>> td.Scalar(dtype='int32')
>> td.Embedding(num_buckets=5,
num_units_out=32))
#-----------
rnn_fwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(
num_units=self.rnn_dim), 'lstm_fwd')
fwdlayer = td.RNN(rnn_fwdcell) >> td.GetItem(0)
rnn_bwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(
num_units=self.rnn_dim), 'lstm_bwd')
bwdlayer = (td.Slice(step=-1) >> td.RNN(rnn_bwdcell)
>> td.GetItem(0) >> td.Slice(step=-1))
rnn_layer = td.AllOf(fwdlayer, bwdlayer) >> td.ZipWith(td.Concat())
output_layer = td.FC(output_size,
input_keep_prob=self.keep_prob,
activation=None)
wordlevel = td.AllOf(wordinput,wordfeature) >> td.Concat()
network = (td.Map(td.AllOf(wordlevel,charlevel) >> td.Concat())
>> rnn_layer
>> td.Map(output_layer)
>> td.Map(td.Metric('y_out'))) >> td.Void()
groundlabels = td.Map(td.Vector(output_size,dtype=tf.int32)
>> td.Metric('y_true')) >> td.Void()
self.compiler = td.Compiler.create((network, groundlabels))
self.y_out = self.compiler.metric_tensors['y_out']
self.y_true = self.compiler.metric_tensors['y_true']
self.y_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.y_out,labels=self.y_true))
self.y_prob = tf.nn.softmax(self.y_out)
self.y_true_idx = tf.argmax(self.y_true,axis=-1)
self.y_pred_idx = tf.argmax(self.y_prob,axis=-1)
self.y_pred = tf.one_hot(self.y_pred_idx,depth=output_size,dtype=tf.int32)
epoch_step = tf.Variable(0, trainable=False)
self.epoch_step_op = tf.assign(epoch_step, epoch_step+1)
lrate_decay = tf.train.exponential_decay(self.lrate, epoch_step, 1, self.decay)
if self.optimizer == 'adam':
self.opt = tf.train.AdamOptimizer(learning_rate=lrate_decay)
elif self.optimizer == 'adagrad':
self.opt = tf.train.AdagradOptimizer(learning_rate=lrate_decay,
initial_accumulator_value=1e-08)
elif self.optimizer == 'rmsprop':
self.opt = tf.train.RMSPropOptimizer(learning_rate=lrate_decay,
epsilon=1e-08)
else:
raise Exception(('The optimizer {} is not in list of available '
+ 'optimizers: default, adam, adagrad, rmsprop.')
.format(self.optimizer))
# apply learning multiplier on on embedding learning rate
embeds = [word_emb.weights]
grads_and_vars = self.opt.compute_gradients(self.y_loss)
found = 0
for i, (grad, var) in enumerate(grads_and_vars):
if var in embeds:
found += 1
grad = tf.scalar_mul(self.embedding_factor, grad)
grads_and_vars[i] = (grad, var)
assert found == len(embeds) # internal consistency check
self.train_step = self.opt.apply_gradients(grads_and_vars)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=100)
def __init__(self,
config,
kb,
text_seq_batch,
seq_length_batch,
num_vocab_txt,
num_vocab_nmn,
EOS_idx,
num_choices,
decoder_sampling,
use_gt_layout=None,
gt_layout_batch=None,
scope='neural_module_network',
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Part 1: Seq2seq RNN to generate module layout tokens
embedding_mat = tf.get_variable(
'embedding_mat', [num_vocab_txt, config.embed_dim_txt],
initializer=tf.contrib.layers.xavier_initializer())
with tf.variable_scope('layout_generation'):
att_seq2seq = netgen_att.AttentionSeq2Seq(
config, text_seq_batch, seq_length_batch, num_vocab_txt,
num_vocab_nmn, EOS_idx, decoder_sampling, embedding_mat,
use_gt_layout, gt_layout_batch)
self.att_seq2seq = att_seq2seq
predicted_tokens = att_seq2seq.predicted_tokens
token_probs = att_seq2seq.token_probs
word_vecs = att_seq2seq.word_vecs
neg_entropy = att_seq2seq.neg_entropy
self.atts = att_seq2seq.atts
self.predicted_tokens = predicted_tokens
self.token_probs = token_probs
self.word_vecs = word_vecs
self.neg_entropy = neg_entropy
# log probability of each generated sequence
self.log_seq_prob = tf.reduce_sum(tf.log(token_probs), axis=0)
# Part 2: Neural Module Network
with tf.variable_scope('layout_execution'):
modules = Modules(config, kb, word_vecs, num_choices,
embedding_mat)
self.modules = modules
# Recursion of modules
att_shape = [len(kb)]
# Forward declaration of module recursion
att_expr_decl = td.ForwardDeclaration(td.PyObjectType(),
td.TensorType(att_shape))
# _key_find
case_key_find = td.Record([
('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))
])
case_key_find = case_key_find >> td.ScopedLayer(
modules.KeyFindModule, name_or_scope='KeyFindModule')
# _key_filter
case_key_filter = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))
])
case_key_filter = case_key_filter >> td.ScopedLayer(
modules.KeyFilterModule, name_or_scope='KeyFilterModule')
recursion_cases = td.OneOf(td.GetItem('module'), {
'_key_find': case_key_find,
'_key_filter': case_key_filter
})
att_expr_decl.resolve_to(recursion_cases)
# _val_desc: output scores for choice (for valid expressions)
predicted_scores = td.Record([('input_0', recursion_cases),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))
])
predicted_scores = predicted_scores >> td.ScopedLayer(
modules.ValDescribeModule,
name_or_scope='ValDescribeModule')
# For invalid expressions, define a dummy answer
# so that all answers have the same form
INVALID = assembler.INVALID_EXPR
dummy_scores = td.Void() >> td.FromTensor(
np.zeros(num_choices, np.float32))
output_scores = td.OneOf(td.GetItem('module'), {
'_val_desc': predicted_scores,
INVALID: dummy_scores
})
# compile and get the output scores
self.compiler = td.Compiler.create(output_scores)
self.scores = self.compiler.output_tensors[0]
# Regularization: Entropy + L2
self.entropy_reg = tf.reduce_mean(neg_entropy)
module_weights = [
v for v in tf.trainable_variables()
if (scope in v.op.name and v.op.name.endswith('weights'))
]
self.l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in module_weights])
def __init__(self,
image_batch,
text_seq_batch,
seq_length_batch,
T_decoder,
num_vocab_txt,
embed_dim_txt,
num_vocab_nmn,
embed_dim_nmn,
lstm_dim,
num_layers,
EOS_idx,
encoder_dropout,
decoder_dropout,
decoder_sampling,
num_choices,
use_gt_layout=None,
gt_layout_batch=None,
scope='neural_module_network',
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Part 0: Visual feature from CNN
with tf.variable_scope('image_feature_cnn'):
image_feat_grid = shapes_convnet(image_batch)
self.image_feat_grid = image_feat_grid
# Part 1: Seq2seq RNN to generate module layout tokens
with tf.variable_scope('layout_generation'):
att_seq2seq = nmn3_netgen_att.AttentionSeq2Seq(
text_seq_batch, seq_length_batch, T_decoder, num_vocab_txt,
embed_dim_txt, num_vocab_nmn, embed_dim_nmn, lstm_dim,
num_layers, EOS_idx, encoder_dropout, decoder_dropout,
decoder_sampling, use_gt_layout, gt_layout_batch)
self.att_seq2seq = att_seq2seq
predicted_tokens = att_seq2seq.predicted_tokens
token_probs = att_seq2seq.token_probs
word_vecs = att_seq2seq.word_vecs
neg_entropy = att_seq2seq.neg_entropy
self.atts = att_seq2seq.atts
self.predicted_tokens = predicted_tokens
self.token_probs = token_probs
self.word_vecs = word_vecs
self.neg_entropy = neg_entropy
# log probability of each generated sequence
self.log_seq_prob = tf.reduce_sum(tf.log(token_probs), axis=0)
# Part 2: Neural Module Network
with tf.variable_scope('layout_execution'):
modules = Modules(image_feat_grid, word_vecs, num_choices)
self.modules = modules
# Recursion of modules
att_shape = image_feat_grid.get_shape().as_list()[1:-1] + [1]
# Forward declaration of module recursion
att_expr_decl = td.ForwardDeclaration(td.PyObjectType(),
td.TensorType(att_shape))
# _Find
case_find = td.Record([('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))
])
case_find = case_find >> \
td.ScopedLayer(modules.FindModule, name_or_scope='FindModule')
# _Transform
case_transform = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_transform = case_transform >> \
td.ScopedLayer(modules.TransformModule, name_or_scope='TransformModule')
# _And
case_and = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_and = case_and >> \
td.ScopedLayer(modules.AndModule, name_or_scope='AndModule')
recursion_cases = td.OneOf(
td.GetItem('module'), {
'_Find': case_find,
'_Transform': case_transform,
'_And': case_and
})
att_expr_decl.resolve_to(recursion_cases)
# _Answer: output scores for choice (for valid expressions)
predicted_scores = td.Record([('input_0', recursion_cases),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))
])
predicted_scores = predicted_scores >> \
td.ScopedLayer(modules.AnswerModule, name_or_scope='AnswerModule')
# For invalid expressions, define a dummy answer
# so that all answers have the same form
INVALID = nmn3_assembler.INVALID_EXPR
dummy_scores = td.Void() >> td.FromTensor(
np.zeros(num_choices, np.float32))
output_scores = td.OneOf(td.GetItem('module'), {
'_Answer': predicted_scores,
INVALID: dummy_scores
})
# compile and get the output scores
self.compiler = td.Compiler.create(output_scores)
self.scores = self.compiler.output_tensors[0]
# Regularization: Entropy + L2
self.entropy_reg = tf.reduce_mean(neg_entropy)
module_weights = [
v for v in tf.trainable_variables()
if (scope in v.op.name and v.op.name.endswith('weights'))
]
self.l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in module_weights])
def _compile(self):
with self.sess.as_default():
import tensorflow_fold as td
output_size = len(self.labels)
self.keep_prob = tf.placeholder_with_default(tf.constant(1.0),shape=None)
char_emb = td.Embedding(num_buckets=self.char_buckets,
num_units_out=self.embedding_size)
#initializer=tf.truncated_normal_initializer(stddev=0.15))
char_cell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'char_cell')
char_lstm = (td.InputTransform(lambda s: [ord(c) for c in s])
>> td.Map(td.Scalar('int32') >> char_emb)
>> td.RNN(char_cell) >> td.GetItem(1) >> td.GetItem(1))
rnn_fwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_fwd')
fwdlayer = td.RNN(rnn_fwdcell) >> td.GetItem(0)
rnn_bwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_bwd')
bwdlayer = (td.Slice(step=-1) >> td.RNN(rnn_bwdcell)
>> td.GetItem(0) >> td.Slice(step=-1))
pos_emb = td.Embedding(num_buckets=300,
num_units_out=32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
pos_x = (td.InputTransform(lambda x: x + 150)
>> td.Scalar(dtype='int32')
>> pos_emb)
pos_y = (td.InputTransform(lambda x: x + 150)
>> td.Scalar(dtype='int32')
>> pos_emb)
input_layer = td.Map(td.Record((char_lstm,pos_x,pos_y)) >> td.Concat())
maxlayer = (td.AllOf(fwdlayer, bwdlayer)
>> td.ZipWith(td.Concat())
>> td.Max())
output_layer = (input_layer >>
maxlayer >> td.FC(output_size,
input_keep_prob=self.keep_prob,
activation=None))
self.compiler = td.Compiler.create((output_layer,
td.Vector(output_size,dtype=tf.int32)))
self.y_out, self.y_true = self.compiler.output_tensors
self.y_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.y_out,labels=self.y_true))
self.y_prob = tf.nn.softmax(self.y_out)
self.y_true_idx = tf.argmax(self.y_true,axis=1)
self.y_pred_idx = tf.argmax(self.y_prob,axis=1)
self.y_pred = tf.one_hot(self.y_pred_idx,depth=output_size,dtype=tf.int32)
epoch_step = tf.Variable(0, trainable=False)
self.epoch_step_op = tf.assign(epoch_step, epoch_step+1)
lrate_decay = tf.train.exponential_decay(self.lrate, epoch_step, 1, self.decay)
if self.optimizer == 'adam':
self.opt = tf.train.AdamOptimizer(learning_rate=lrate_decay)
elif self.optimizer == 'adagrad':
self.opt = tf.train.AdagradOptimizer(learning_rate=lrate_decay,
initial_accumulator_value=1e-08)
elif self.optimizer == 'rmsprop' or self.optimizer == 'default':
self.opt = tf.train.RMSPropOptimizer(learning_rate=lrate_decay,
epsilon=1e-08)
else:
raise Exception(('The optimizer {} is not in list of available '
+ 'optimizers: default, adam, adagrad, rmsprop.')
.format(self.optimizer))
# apply learning multiplier on on embedding learning rate
embeds = [pos_emb.weights, char_emb.weights]
grads_and_vars = self.opt.compute_gradients(self.y_loss)
found = 0
for i, (grad, var) in enumerate(grads_and_vars):
if var in embeds:
found += 1
grad = tf.scalar_mul(self.embedding_factor, grad)
grads_and_vars[i] = (grad, var)
assert found == len(embeds) # internal consistency check
self.train_step = self.opt.apply_gradients(grads_and_vars)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=100)
def __init__(self, image_feat_grid, text_seq_batch, seq_length_batch,
T_decoder, num_vocab_txt, embed_dim_txt, num_vocab_nmn,
embed_dim_nmn, lstm_dim, num_layers, assembler,
encoder_dropout, decoder_dropout, decoder_sampling,
num_choices, use_qpn, qpn_dropout, reduce_visfeat_dim=False, new_visfeat_dim=256,
use_gt_layout=None, gt_layout_batch=None,
scope='neural_module_network', reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Part 0: Visual feature from CNN
self.reduce_visfeat_dim = reduce_visfeat_dim
if reduce_visfeat_dim:
# use an extrac linear 1x1 conv layer (without ReLU)
# to reduce the feature dimension
with tf.variable_scope('reduce_visfeat_dim'):
image_feat_grid = conv('conv_reduce_visfeat_dim',
image_feat_grid, kernel_size=1, stride=1,
output_dim=new_visfeat_dim)
print('visual feature dimension reduced to %d' % new_visfeat_dim)
self.image_feat_grid = image_feat_grid
# Part 1: Seq2seq RNN to generate module layout tokensa
with tf.variable_scope('layout_generation'):
att_seq2seq = AttentionSeq2Seq(text_seq_batch,
seq_length_batch, T_decoder, num_vocab_txt,
embed_dim_txt, num_vocab_nmn, embed_dim_nmn, lstm_dim,
num_layers, assembler, encoder_dropout, decoder_dropout,
decoder_sampling, use_gt_layout, gt_layout_batch)
self.att_seq2seq = att_seq2seq
predicted_tokens = att_seq2seq.predicted_tokens
token_probs = att_seq2seq.token_probs
word_vecs = att_seq2seq.word_vecs
neg_entropy = att_seq2seq.neg_entropy
self.atts = att_seq2seq.atts
self.predicted_tokens = predicted_tokens
self.token_probs = token_probs
self.word_vecs = word_vecs
self.neg_entropy = neg_entropy
# log probability of each generated sequence
self.log_seq_prob = tf.reduce_sum(tf.log(token_probs), axis=0)
# Part 2: Neural Module Network
with tf.variable_scope('layout_execution'):
modules = Modules(image_feat_grid, word_vecs, None, num_choices)
self.modules = modules
# Recursion of modules
att_shape = image_feat_grid.get_shape().as_list()[1:-1] + [1]
# Forward declaration of module recursion
att_expr_decl = td.ForwardDeclaration(td.PyObjectType(), td.TensorType(att_shape))
# _Scene
case_scene = td.Record([('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))])
case_scene = case_scene >> td.Function(modules.SceneModule)
# _Find
case_find = td.Record([('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))])
case_find = case_find >> td.Function(modules.FindModule)
# _Filter
case_filter = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))])
case_filter = case_filter >> td.Function(modules.FilterModule)
# _FindSameProperty
case_find_same_property = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))])
case_find_same_property = case_find_same_property >> \
td.Function(modules.FindSamePropertyModule)
# _Transform
case_transform = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_transform = case_transform >> td.Function(modules.TransformModule)
# _And
case_and = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_and = case_and >> td.Function(modules.AndModule)
# _Or
case_or = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_or = case_or >> td.Function(modules.OrModule)
# _Exist
case_exist = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_exist = case_exist >> td.Function(modules.ExistModule)
# _Count
case_count = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_count = case_count >> td.Function(modules.CountModule)
# _EqualNum
case_equal_num = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_equal_num = case_equal_num >> td.Function(modules.EqualNumModule)
# _MoreNum
case_more_num = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_more_num = case_more_num >> td.Function(modules.MoreNumModule)
# _LessNum
case_less_num = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_less_num = case_less_num >> td.Function(modules.LessNumModule)
# _SameProperty
case_same_property = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_same_property = case_same_property >> \
td.Function(modules.SamePropertyModule)
# _Describe
case_describe = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_describe = case_describe >> \
td.Function(modules.DescribeModule)
recursion_cases = td.OneOf(td.GetItem('module'), {
'_Scene': case_scene,
'_Find': case_find,
'_Filter': case_filter,
'_FindSameProperty': case_find_same_property,
'_Transform': case_transform,
'_And': case_and,
'_Or': case_or})
att_expr_decl.resolve_to(recursion_cases)
# For invalid expressions, define a dummy answer
# so that all answers have the same form
dummy_scores = td.Void() >> td.FromTensor(np.zeros(num_choices, np.float32))
output_scores = td.OneOf(td.GetItem('module'), {
'_Exist': case_exist,
'_Count': case_count,
'_EqualNum': case_equal_num,
'_MoreNum': case_more_num,
'_LessNum': case_less_num,
'_SameProperty': case_same_property,
'_Describe': case_describe,
INVALID_EXPR: dummy_scores})
# compile and get the output scores
self.compiler = td.Compiler.create(output_scores)
self.scores_nmn = self.compiler.output_tensors[0]
# Add a question prior network if specified
self.use_qpn = use_qpn
self.qpn_dropout = qpn_dropout
if use_qpn:
self.scores_qpn = question_prior_net(att_seq2seq.encoder_states,
num_choices, qpn_dropout)
self.scores = self.scores_nmn + self.scores_qpn
else:
self.scores = self.scores_nmn
# Regularization: Entropy + L2
self.entropy_reg = tf.reduce_mean(neg_entropy)
module_weights = [v for v in tf.trainable_variables()
if (scope in v.op.name and
v.op.name.endswith('weights'))]
self.l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in module_weights])
def __init__(self,
image_data_batch,
image_mean,
text_seq_batch,
seq_length_batch,
T_decoder,
num_vocab_txt,
embed_dim_txt,
num_vocab_nmn,
embed_dim_nmn,
lstm_dim,
num_layers,
assembler,
encoder_dropout,
decoder_dropout,
decoder_sampling,
num_choices,
use_qpn,
qpn_dropout,
reduce_visfeat_dim=False,
new_visfeat_dim=128,
use_gt_layout=None,
gt_layout_batch=None,
map_dim=1024,
scope='neural_module_network',
reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Part 0: Visual feature from CNN
with tf.variable_scope('image_feature_cnn'):
image_data_batch = image_data_batch / 255.0 - image_mean
image_feat_grid = nlvr_convnet(image_data_batch)
self.image_feat_grid = image_feat_grid
# Part 1: Seq2seq RNN to generate module layout tokensa
with tf.variable_scope('layout_generation'):
att_seq2seq = AttentionSeq2Seq(
text_seq_batch, seq_length_batch, T_decoder, num_vocab_txt,
embed_dim_txt, num_vocab_nmn, embed_dim_nmn, lstm_dim,
num_layers, assembler, encoder_dropout, decoder_dropout,
decoder_sampling, use_gt_layout, gt_layout_batch)
self.att_seq2seq = att_seq2seq
predicted_tokens = att_seq2seq.predicted_tokens
token_probs = att_seq2seq.token_probs
word_vecs = att_seq2seq.word_vecs
neg_entropy = att_seq2seq.neg_entropy
self.atts = att_seq2seq.atts
self.predicted_tokens = predicted_tokens
self.token_probs = token_probs
self.word_vecs = word_vecs
self.neg_entropy = neg_entropy
# log probability of each generated sequence
self.log_seq_prob = tf.reduce_sum(tf.log(token_probs), axis=0)
# Part 2: Neural Module Network
with tf.variable_scope('layout_execution'):
modules = Modules(image_feat_grid, word_vecs, None,
num_choices, map_dim)
self.modules = modules
# Recursion of modules
att_shape = image_feat_grid.get_shape().as_list()[1:-1] + [1]
# Forward declaration of module recursion
att_expr_decl = td.ForwardDeclaration(td.PyObjectType(),
td.TensorType(att_shape))
# _Find
case_find = td.Record([('time_idx', td.Scalar(dtype='int32')),
('batch_idx', td.Scalar(dtype='int32'))
])
case_find = case_find >> td.Function(modules.FindModule)
# _Transform
case_transform = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_transform = case_transform >> td.Function(
modules.TransformModule)
# _And
case_and = td.Record([('input_0', att_expr_decl()),
('input_1', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_and = case_and >> td.Function(modules.AndModule)
# _Describe
case_describe = td.Record([('input_0', att_expr_decl()),
('time_idx', td.Scalar('int32')),
('batch_idx', td.Scalar('int32'))])
case_describe = case_describe >> \
td.Function(modules.DescribeModule)
recursion_cases = td.OneOf(
td.GetItem('module'), {
'_Find': case_find,
'_Transform': case_transform,
'_And': case_and
})
att_expr_decl.resolve_to(recursion_cases)
# For invalid expressions, define a dummy answer
# so that all answers have the same form
dummy_scores = td.Void() >> td.FromTensor(
np.zeros(num_choices, np.float32))
output_scores = td.OneOf(td.GetItem('module'), {
'_Describe': case_describe,
INVALID_EXPR: dummy_scores
})
# compile and get the output scores
self.compiler = td.Compiler.create(output_scores)
self.scores_nmn = self.compiler.output_tensors[0]
# Add a question prior network if specified
self.use_qpn = use_qpn
self.qpn_dropout = qpn_dropout
if use_qpn:
self.scores_qpn = question_prior_net(
att_seq2seq.encoder_states, num_choices, qpn_dropout)
self.scores = self.scores_nmn + self.scores_qpn
#self.scores = self.scores_nmn
else:
self.scores = self.scores_nmn
# Regularization: Entropy + L2
self.entropy_reg = tf.reduce_mean(neg_entropy)
#tf.check_numerics(self.entropy_reg, 'entropy NaN/Inf ')
#print(self.entropy_reg.eval())
module_weights = [
v for v in tf.trainable_variables()
if (scope in v.op.name and v.op.name.endswith('weights'))
]
self.l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in module_weights])
