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 build_train_graph_for_RVAE(rvae_block, look_behind_length=0):
token_emb_size = get_size_of_input_vecotrs(rvae_block)
c = td.Composition()
with c.scope():
padded_input_sequence = td.Map(td.Vector(token_emb_size)).reads(
c.input)
network_output = rvae_block
network_output.reads(padded_input_sequence)
un_normalised_token_probs = td.GetItem(0).reads(network_output)
mus_and_log_sigs = td.GetItem(1).reads(network_output)
input_sequence = td.Slice(
start=look_behind_length).reads(padded_input_sequence)
# TODO: metric that output of rnn is the same as input sequence
cross_entropy_loss = td.ZipWith(
td.Function(softmax_crossentropy)) >> td.Mean()
cross_entropy_loss.reads(un_normalised_token_probs, input_sequence)
kl_loss = td.Function(kl_divergence)
kl_loss.reads(mus_and_log_sigs)
td.Metric('cross_entropy_loss').reads(cross_entropy_loss)
td.Metric('kl_loss').reads(kl_loss)
c.output.reads(td.Void())
return c
def add_metrics(is_root, is_neutral):
"""A block that adds metrics for loss and hits; output is the LSTM state."""
c = td.Composition(name='predict(is_root=%s, is_neutral=%s)' %
(is_root, is_neutral))
with c.scope():
# destructure the input; (labels, (logits, state))
labels = c.input[0]
logits = td.GetItem(0).reads(c.input[1])
state = td.GetItem(1).reads(c.input[1])
# calculate loss
loss = td.Function(tf_node_loss)
td.Metric('all_loss').reads(loss.reads(logits, labels))
if is_root: td.Metric('root_loss').reads(loss)
# calculate fine-grained hits
hits = td.Function(tf_fine_grained_hits)
td.Metric('all_hits').reads(hits.reads(logits, labels))
if is_root: td.Metric('root_hits').reads(hits)
# calculate binary hits, if the label is not neutral
if not is_neutral:
binary_hits = td.Function(tf_binary_hits).reads(logits, labels)
td.Metric('all_binary_hits').reads(binary_hits)
if is_root: td.Metric('root_binary_hits').reads(binary_hits)
# output the state, which will be read by our by parent's LSTM cell
c.output.reads(state)
return c
def add_metrics(is_root):
c = td.Composition(name='predict(is_root=%s)' % (is_root))
with c.scope():
labels = c.input[0]
logits = td.GetItem(0).reads(c.input[1])
state = td.GetItem(1).reads(c.input[1])
loss = td.Function(tf_node_loss)
td.Metric('all_loss').reads(loss.reads(logits, labels))
if is_root:
td.Metric('root_loss').reads(loss)
result_logits = td.Function(tf_logits)
td.Metric('all_logits').reads(result_logits.reads(logits))
if is_root:
td.Metric('root_logits').reads(result_logits)
# reserve pred and labels
pred = td.Function(tf_pred)
td.Metric('all_pred').reads(pred.reads(logits))
if is_root:
td.Metric('root_pred').reads(pred)
answer = td.Function(tf_label)
td.Metric('all_labels').reads(answer.reads(labels))
if is_root:
td.Metric('root_label').reads(answer)
c.output.reads(state)
return c
def tree_sum_blk(loss_blk):
# traverse the tree to sum up the loss
tree_sum_fwd = td.ForwardDeclaration(td.PyObjectType(), td.TensorType([]))
tree_sum = td.Composition()
with tree_sum.scope():
myloss = loss_blk().reads(tree_sum.input)
children = td.GetItem('children').reads(tree_sum.input)
mapped = td.Map(tree_sum_fwd()).reads(children)
summed = td.Reduce(td.Function(tf.add)).reads(mapped)
summed = td.Function(tf.add).reads(summed, myloss)
tree_sum.output.reads(summed)
tree_sum_fwd.resolve_to(tree_sum)
return tree_sum
def set_metrics(self, train=True):
"""A block that adds metrics for loss and hits;
output is the LSTM state."""
c = td.Composition(
name='predict')
with c.scope():
# destructure the input; (labels, logits)
labels = c.input[0]
logits = c.input[1]
# calculate loss
loss = td.Function(self.tf_node_loss)
td.Metric('root_loss').reads(loss.reads(logits, labels))
hits = td.Function(self.tf_fine_grained_hits)
td.Metric('root_hits').reads(hits.reads(logits, labels))
c.output.reads(logits)
return c
def coding_blk():
"""Input: node dict
Output: TensorType([1, hyper.word_dim])
"""
Wcomb1 = param.get('Wcomb1')
Wcomb2 = param.get('Wcomb2')
blk = td.Composition()
with blk.scope():
direct = embedding.direct_embed_blk().reads(blk.input)
composed = embedding.composed_embed_blk().reads(blk.input)
Wcomb1 = td.FromTensor(param.get('Wcomb1'))
Wcomb2 = td.FromTensor(param.get('Wcomb2'))
direct = td.Function(embedding.batch_mul).reads(direct, Wcomb1)
composed = td.Function(embedding.batch_mul).reads(composed, Wcomb2)
added = td.Function(tf.add).reads(direct, composed)
blk.output.reads(added)
return blk
def dynamic_pooling_blk():
"""Input: root node dic
Output: pooled, TensorType([hyper.conv_dim, ])
"""
leaf_case = feature_detector_blk()
pool_fwd = td.ForwardDeclaration(td.PyObjectType(),
td.TensorType([
hyper.conv_dim,
]))
pool = td.Composition()
with pool.scope():
cur_fea = feature_detector_blk().reads(pool.input)
children = td.GetItem('children').reads(pool.input)
mapped = td.Map(pool_fwd()).reads(children)
summed = td.Reduce(td.Function(tf.maximum)).reads(mapped)
summed = td.Function(tf.maximum).reads(summed, cur_fea)
pool.output.reads(summed)
pool = td.OneOf(lambda x: x['clen'] == 0, {True: leaf_case, False: pool})
pool_fwd.resolve_to(pool)
return pool
def l2loss_blk():
# rewrite using metric
leaf_case = td.Composition()
with leaf_case.scope():
leaf_case.output.reads(td.FromTensor(tf.constant(1.)))
nonleaf_case = td.Composition()
with nonleaf_case.scope():
direct = direct_embed_blk().reads(nonleaf_case.input)
com = composed_embed_blk().reads(nonleaf_case.input)
loss = td.Function(batch_nn_l2loss).reads(direct, com)
nonleaf_case.output.reads(loss)
return td.OneOf(lambda node: node['clen'] != 0, {
False: leaf_case,
True: nonleaf_case
})
def continous_weighted_add_blk():
block = td.Composition(name='continous_weighted_add')
with block.scope():
initial = td.GetItem(0).reads(block.input)
cur = td.GetItem(1).reads(block.input)
last = td.GetItem(0).reads(initial)
idx = td.GetItem(1).reads(initial)
cur_fea = td.GetItem(0).reads(cur)
cur_clen = td.GetItem(1).reads(cur)
pclen = td.GetItem(2).reads(cur)
Wi = linear_combine_blk().reads(cur_clen, pclen, idx)
weighted_fea = td.Function(batch_mul).reads(cur_fea, Wi)
block.output.reads(
td.Function(tf.add, name='add_last_weighted_fea').reads(
last, weighted_fea),
# XXX: rewrite using tf.range
td.Function(tf.add, name='add_idx_1').reads(
idx, td.FromTensor(tf.constant(1.))))
return block
def weighted_feature_blk():
"""Input: (feature , idx , pclen, depth, max_depth)
(TensorType([hyper.word_dim, ]), Scalar, Scalar, Scalar, Scalar)
Output: weighted_feature
TensorType([hyper.conv_dim, ])
"""
blk = td.Composition()
with blk.scope():
fea = blk.input[0]
Wi = tri_combined_blk().reads(blk.input[1], blk.input[2], blk.input[3],
blk.input[4])
weighted_fea = td.Function(embedding.batch_mul).reads(fea, Wi)
blk.output.reads(weighted_fea)
return blk
def bidirectional_dynamic_CONV(fw_cell, bw_cell, out_features=64):
bidir_conv_lstm = td.Composition()
with bidir_conv_lstm.scope():
fw_seq = td.Identity().reads(bidir_conv_lstm.input[0])
labels = (
td.GetItem(1) >> td.Map(td.Metric("labels")) >> td.Void()).reads(
bidir_conv_lstm.input)
bw_seq = td.Slice(step=-1).reads(fw_seq)
forward_dir = (td.RNN(fw_cell) >> td.GetItem(0)).reads(fw_seq)
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.Function(lambda x: tf.reshape(x, [-1, vsize * out_features])) >>
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
def linear_combine_blk():
blk = td.Function(linear_combine, infer_output_type=False)
blk.set_output_type(td.TensorType([hyper.word_dim, hyper.word_dim]))
return blk
def tri_combined_blk():
blk = td.Function(tri_combined, infer_output_type=False)
blk.set_output_type(td.TensorType([hyper.word_dim, hyper.conv_dim]))
return blk
def CNN_Window3(filters):
return td.Function(lambda a, b, c: cnn_operation([a,b,c],filters))
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])
forward_dir = (td.RNN(fw_cell) >> td.GetItem(0)).reads(fw_seq)
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
def resampling_block(z_size):
reparam_z = td.Function(resampling, name='resampling')
reparam_z.set_input_type(td.TensorType((2 * z_size, )))
reparam_z.set_output_type(td.TensorType((z_size, )))
return reparam_z
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 clip_by_norm_blk(norm=1.0):
return td.Function(lambda x: tf.clip_by_norm(x, norm, axes=[1]))
def build_VAE(z_size, token_emb_size):
c = td.Composition()
c.set_input_type(td.SequenceType(td.TensorType(([token_emb_size]), 'float32')))
with c.scope():
# input_sequence = td.Map(td.Vector(token_emb_size)).reads(c.input)
input_sequence = c.input
# encoder composition TODO: refactor this out
# rnn_cell = td.ScopedLayer(
# tf.contrib.rnn.LSTMCell(
# num_units=2*z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
# activation=tf.tanh
# ),
# 'encoder'
# )
encoder_rnn_cell = td.ScopedLayer(
tf.contrib.rnn.GRUCell(
num_units=2*z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.tanh
),
'encoder'
)
output_sequence = td.RNN(encoder_rnn_cell) >> td.GetItem(0)
mus_and_log_sigs = output_sequence >> td.GetItem(-1)
# reparam_z = mus_and_log_sigs >> td.Function(resampling)
reparam_z = td.Function(resampling, name='resampling')
reparam_z.set_input_type(td.TensorType((2 * z_size,)))
reparam_z.set_output_type(td.TensorType((z_size,)))
# A list of same length of input_sequence, but with empty values
# this is used for the decoder to map over
list_of_nothing = td.Map(
td.Void() >> td.FromTensor(tf.zeros((0,)))
)
# decoder composition
# TODO: refactor this out
# decoder_rnn = td.ScopedLayer(
# tf.contrib.rnn.LSTMCell(
# num_units=z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
# activation=tf.tanh
# ),
# 'decoder'
# )
decoder_rnn = td.ScopedLayer(
tf.contrib.rnn.GRUCell(
num_units=z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.tanh
),
'decoder'
)
decoder_rnn_output = td.RNN(
decoder_rnn,
initial_state_from_input=True
) >> td.GetItem(0)
fc_layer = td.FC(
token_emb_size,
activation=tf.nn.relu,
initializer=tf.contrib.layers.xavier_initializer()
)
un_normalised_token_probs = decoder_rnn_output >> td.Map(fc_layer)
# reparam_z.reads(input_sequence)
mus_and_log_sigs.reads(input_sequence)
reparam_z.reads(mus_and_log_sigs)
list_of_nothing.reads(input_sequence)
un_normalised_token_probs.reads(list_of_nothing, reparam_z)
c.output.reads(un_normalised_token_probs, mus_and_log_sigs)
return c
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 expand_dim_blk(axis):
return td.Function(lambda tensor: tf.expand_dims(tensor, axis=axis))
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])
c = td.Composition()
with c.scope():
input_sequence = td.Map(td.Vector(54)).reads(c.input)
# net = build_VAE(Z_SIZE, 54)
# un_normalised_token_probs, mus_and_log_sigs = input_sequence >> build_VAE(Z_SIZE, 54)
network_output = build_VAE(Z_SIZE, 54)
network_output.reads(input_sequence)
un_normalised_token_probs = td.GetItem(0).reads(network_output)
mus_and_log_sigs = td.GetItem(1).reads(network_output)
cross_entropy_loss = td.ZipWith(td.Function(softmax_crossentropy)) >> td.Mean()
cross_entropy_loss.reads(
un_normalised_token_probs,
input_sequence
)
kl_loss = td.Function(kl_divergence)
kl_loss.reads(mus_and_log_sigs)
td.Metric('cross_entropy_loss').reads(cross_entropy_loss)
td.Metric('kl_loss').reads(kl_loss)
c.output.reads(td.Void())
# Tokenised version of my code
