def tabular_learning(Qs_t, states_t, actions_t, targets):
reusing_scope = tf.get_variable_scope().reuse
state_action_pairs = tf.stack([states_t, actions_t], 1)
estimates = tf.gather_nd(Qs_t, state_action_pairs)
err_estimates = targets - estimates
loss = tf.reduce_mean(err_estimates)
Nsa = tf.get_variable("Nsa",
shape=Qs_t.get_shape(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
if reusing_scope is False:
tf.summary.histogram('Nsa', Nsa)
update_Nsa = tf.scatter_nd_add(Nsa, state_action_pairs,
tf.ones_like(states_t, dtype=tf.float32))
global_step = tf.Variable(
0,
trainable=False,
name="global_step",
collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])
inc_global_step = global_step.assign_add(1)
with tf.control_dependencies([update_Nsa, inc_global_step]):
epsilon = 1e-7
lr = (1 / (epsilon + tf.gather_nd(Nsa, state_action_pairs)))
updates = lr * err_estimates
train_op = tf.scatter_nd_add(Qs_t, state_action_pairs, updates)
return loss, train_op
def body(i):
nearest_pattern = tf.cast(
tf.argmin(distances[i], axis=0), tf.int32)
tf.scatter_nd_add(self.pattern_weights,
[[label, nearest_pattern]], [1])
update = tf.cast(
(1 / self.pattern_weights[label][nearest_pattern]),
tf.float32) * (
sen_list[i] -
self.patterns[label][nearest_pattern])
tf.scatter_nd_add(self.patterns,
[[label, nearest_pattern]], [update])
return tf.add(i, 1)
def smooth_loss(ver_attrs, ver_neighbors, thres, attr_name):
batch_size, n_ver, n_channels = ver_attrs.get_shape().as_list()
n_ver_neighbor_pair = ver_neighbors.get_shape().as_list()[0]
with tf.variable_scope(attr_name):
var_sum_of_neighbor_attrs = tf.get_variable(
'ver_sum_of_neighbor_attrs',
[batch_size, n_ver, n_channels],
tf.float32,
tf.zeros_initializer(),
trainable=False
)
var_sum_of_neighbor_counts = tf.get_variable(
'ver_sum_of_counts',
[batch_size, n_ver, 1],
tf.float32,
tf.zeros_initializer(),
trainable=False
)
init_sum_of_neighbor_attrs = tf.zeros_like(var_sum_of_neighbor_attrs)
init_sum_of_neighbor_counts = tf.zeros_like(var_sum_of_neighbor_counts)
assign_op = tf.group([
tf.assign(var_sum_of_neighbor_attrs, init_sum_of_neighbor_attrs),
tf.assign(var_sum_of_neighbor_counts, init_sum_of_neighbor_counts)
], name='assign_op')
with tf.control_dependencies([assign_op]):
to_ver_ids, from_ver_ids = tf.split(ver_neighbors, 2, axis=1)
tmp_ver_neighbor_attrs = tf.gather(ver_attrs, tf.squeeze(from_ver_ids), axis=1)
tmp_ver_neighbor_counts = tf.ones([batch_size, n_ver_neighbor_pair, 1], tf.float32)
batch_indices = tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size),axis=1),[1,n_ver_neighbor_pair]),
[batch_size, n_ver_neighbor_pair, 1], name='batch_indices')
to_ver_ids = tf.tile(tf.expand_dims(to_ver_ids, axis=0), [batch_size,1,1])
batch_to_ver_ids = tf.concat([batch_indices, to_ver_ids], axis=2)
var_sum_of_neighbor_attrs = tf.scatter_nd_add(var_sum_of_neighbor_attrs,
batch_to_ver_ids, tmp_ver_neighbor_attrs)
var_sum_of_neighbor_counts = tf.scatter_nd_add(var_sum_of_neighbor_counts,
batch_to_ver_ids, tmp_ver_neighbor_counts)
mean_neighbor_attrs = tf.div(var_sum_of_neighbor_attrs, var_sum_of_neighbor_counts + 1e-8)
error = tf.maximum(tf.square(mean_neighbor_attrs - ver_attrs), thres)
loss = tf.reduce_mean(error , name=attr_name + '_smooth_loss')
return loss
def tabular_UCB(Qs_t, inputs_t):
reusing_scope = tf.get_variable_scope().reuse
timestep = tf.get_variable("timestep", shape=[], dtype=tf.int32, trainable=False, initializer=tf.zeros_initializer())
inc_t = tf.assign_add(timestep, 1)
# State Action count
Nsa_t = tf.get_variable("Nsa", shape=Qs_t.get_shape(), dtype=tf.float32, trainable=False, initializer=tf.ones_initializer())
if reusing_scope is False:
tf.summary.histogram('Nsa', Nsa_t)
with tf.control_dependencies([inc_t]):
qs_t = tf.gather(Qs_t, inputs_t)
nsa_t = tf.gather(Nsa_t, inputs_t)
values_t = qs_t + ( (2 * tf.log(tf.cast(timestep, tf.float32))) / nsa_t )**(1/2)
actions_t = tf.cast(tf.argmax(values_t, 1), dtype=tf.int32)
probs_t = tf.one_hot(actions_t, depth=tf.shape(Qs_t)[1])
state_action_pairs = tf.stack([inputs_t, actions_t], 1)
update_Nsa = tf.scatter_nd_add(Nsa_t, state_action_pairs, tf.ones_like(inputs_t, dtype=tf.float32))
with tf.control_dependencies([update_Nsa]): # Force the update call
actions_t = tf.identity(actions_t)
return actions_t, probs_t
def make_update_op(self, predicted_labels, labels):
with tf.name_scope(self.name), tf.name_scope('update_op'):
flat_labels = tf.reshape(labels, (-1, 1))
flat_predictions = tf.reshape(predicted_labels, (-1, self.depth))
n = tf.shape(flat_labels)[0]
# We are going to update n X depth elements
indices = tf.stack((tf.tile(
tf.reshape(tf.range(self.depth, dtype=self.dtype),
(1, self.depth)),
(n, 1)), tf.tile(flat_labels,
(1, self.depth)), flat_predictions),
axis=-1)
_update_n = tf.assign_add(self.n, tf.cast(n, dtype=self.dtype))
with tf.control_dependencies([_update_n]):
# Use tf.group to avoid any return value
return tf.group(
tf.scatter_nd_add(self.matrix,
indices,
tf.ones((n, self.depth),
dtype=self.dtype),
use_locking=True,
name='update_op'))
def tabular_learning_with_lr(init_lr, decay_steps, Qs_t, states_t, actions_t,
targets):
reusing_scope = tf.get_variable_scope().reuse
state_action_pairs = tf.stack([states_t, actions_t], 1)
estimates = tf.gather_nd(Qs_t, state_action_pairs)
err_estimates = targets - estimates
loss = tf.reduce_mean(err_estimates)
global_step = tf.Variable(
0,
trainable=False,
name="global_step",
collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])
lr = tf.train.exponential_decay(tf.constant(init_lr, dtype=tf.float32),
global_step,
decay_steps,
0.5,
staircase=True)
if reusing_scope is False:
tf.summary.scalar('lr', lr)
inc_global_step = global_step.assign_add(1)
with tf.control_dependencies([inc_global_step]):
updates = lr * err_estimates
train_op = tf.scatter_nd_add(Qs_t, state_action_pairs, updates)
return loss, train_op
def update_centroids(self, centroid):
"""compute updated values for centroids as mean of assigned samples
:param centroid - centroid to be updated
:returns updated centroids Tensor"""
sample = self.data_queue_2.dequeue()
# update per centroid count
per_centroid_count = tf.scatter_nd_add(self.samples_per_centroid,
indices=[[centroid]],
updates=[1],
name="incrementPerCenterCount")
# update per center learning rate
with tf.control_dependencies([per_centroid_count]):
learning_rate = tf.squeeze(
tf.cast(1 / tf.slice(per_centroid_count, [centroid], [1]),
tf.float64))
# learning_rate = tf.Print(learning_rate, [learning_rate], message="learning rate: ")
tf.scatter_nd_update(self.learning_rate, [[centroid]], [learning_rate],
name="updateLearningRate")
# compute new centroids
updated_centroids = tf.scatter_nd_update(
self.centroids,
indices=[centroid],
updates=tf.add(
tf.scalar_mul(scalar=(1 - learning_rate),
x=tf.slice(input_=self.centroids,
begin=[centroid, 0],
size=[1, self.n_features])),
tf.scalar_mul(scalar=learning_rate, x=sample)))
with tf.control_dependencies([updated_centroids]):
return centroid
def calculate_via_todense(nz_values, col_indices, ncol_full):
nslice = nz_values.get_shape()[0]
nrow = nz_values.get_shape()[1]
ncol = nz_values.get_shape()[2]
values = tf.reshape(nz_values, [-1])
for_fetch = tf.concat([tf.zeros([1], dtype=tf.float64), values], axis=0)
slice_indices = tf.tile(tf.reshape(tf.range(nslice), [nslice, 1, 1]), [1, nrow, ncol])
row_indices = tf.tile(tf.reshape(tf.range(nrow), [1, nrow, 1]), [nslice, 1, ncol])
indices = tf.stack([slice_indices, row_indices, col_indices], axis=3)
indices = tf.reshape(indices, [-1, 3])
fetch_ind = tf.Variable(tf.zeros([nslice, nrow, ncol_full], dtype=tf.int32))
assign_zero = tf.assign(fetch_ind, tf.zeros(tf.shape(fetch_ind), dtype=tf.int32))
with tf.control_dependencies([assign_zero]):
fetch_ind = tf.scatter_nd_add(fetch_ind, indices, tf.range(nslice * nrow * ncol, dtype=tf.int32) + 1)
dense = tf.gather(for_fetch, fetch_ind)
matmul = tf.matmul(dense, dense, transpose_b=True)
return matmul
def joint_probability(self, y_nn, labels):
# TODO I don't know if it works properly because I only use it for logging
"""
Create joint probability P(s_k, m_j)
:param y_nn: output of the neural net
:param labels: phonemes or states of the data (coming from the alignments of kaldi)
:return: return P(s_k, m_j)
"""
with tf.variable_scope('MiscNNHelper/joint_probability'):
# determine batch size
batch_size = tf.cast(tf.shape(y_nn)[0], dtype=tf.float32)
# create variable in order to use scatter_nd_add
joint_prob = tf.Variable(tf.zeros([self.num_labels, self.cb_size]),
trainable=False,
dtype=tf.float32)
joint_prob = joint_prob.assign(
tf.fill([self.num_labels, self.cb_size],
0.0)) # reset Variable/floor
# cast labels to int32
labels = tf.cast(labels, dtype=tf.int32)
# create P(s_k, m_j), (check dissertation of Neukirchen, p.61 (5.46))
joint_prob = tf.scatter_nd_add(joint_prob, labels, y_nn)
joint_prob = tf.div(joint_prob, batch_size)
return joint_prob
def test_builder_to_backend_programmatic(self, use_cpu_only, backend,
rankData_rankIndices,
accumulate_mode):
data_rank, indices_rank = rankData_rankIndices
data_shape = np.random.randint(low=2, high=5, size=data_rank)
indices_shape = np.random.randint(low=2, high=5, size=indices_rank)
indices_shape[-1] = np.random.randint(low=1, high=data_rank + 1)
updates_shape = list(indices_shape[:-1]) + list(
data_shape[indices_shape[-1]:])
data = np.random.rand(*data_shape).astype(np.float32)
updates = np.random.rand(*updates_shape).astype(np.float32)
indices_list = []
for i in range(indices_shape[-1]):
indices_list.append(
np.random.randint(0, data_shape[i], size=indices_shape[:-1]))
indices = np.stack(indices_list, axis=-1).astype(np.int32)
def build(data, indices, updates):
return mb.scatter_nd(data=data,
indices=indices,
updates=updates,
mode=accumulate_mode)
with tf.Graph().as_default(), tf.Session() as sess:
tf_output = tf.Variable(data)
sess.run(tf.global_variables_initializer())
if accumulate_mode == "update":
sess.run(tf.scatter_nd_update(tf_output, indices, updates))
if accumulate_mode == "add":
sess.run(tf.scatter_nd_add(tf_output, indices, updates))
if accumulate_mode == "sub":
sess.run(tf.scatter_nd_sub(tf_output, indices, updates))
expected_output = sess.run(tf_output)
input_placeholders = {
"data": mb.placeholder(shape=data.shape),
"indices": mb.placeholder(shape=indices.shape, dtype=types.int32),
"updates": mb.placeholder(shape=updates.shape),
}
input_values = {"data": data, "indices": indices, "updates": updates}
expected_output_types = tuple(data_shape[:]) + (types.fp32, )
run_compare_builder(
build,
input_placeholders,
input_values,
expected_output_types,
expected_output,
use_cpu_only=use_cpu_only,
frontend_only=False,
backend=backend,
)
def test_scatter_nd_add(self):
indices = tf.constant([[4], [3], [1], [7], [1]])
updates = tf.constant([9, 10, 11, 12, 13])
shape = tf.constant([8])
ref = tf.Variable(tf.zeros(shape, dtype=tf.int32), trainable=False)
scatter = tf.scatter_nd_add(ref, indices, updates)
expected = np.array([0, 24, 0, 10, 9, 0, 0, 12])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
actual = sess.run(scatter)
self.assertTrue(np.allclose(expected, actual))
def permutohedral_compute(data_vectors, barycentric, blurNeighbours1,
blurNeighbours2, indices, name, reverse):
batch_size = tf.shape(data_vectors)[0]
numSimplexCorners = int(barycentric.get_shape()[-1])
nCh = numSimplexCorners - 1
nChData = tf.shape(data_vectors)[-1]
data_vectors = tf.reshape(data_vectors, [-1, nChData])
data_vectors = tf.concat(
[data_vectors, tf.ones_like(data_vectors[:, 0:1])],
1) # Convert to homogenous coordinates
## Splatting
initialSplat = tf.zeros(
[tf.shape(blurNeighbours1[0])[0] + 1, batch_size, nChData + 1])
with tf.variable_scope(name):
# WARNING: we use local variables so the graph must initialize local variables with tf.local_variables_initializer()
splat = tf.contrib.framework.local_variable(tf.ones([0, 0]),
validate_shape=False,
name='splatbuffer')
with tf.control_dependencies([splat.initialized_value()]):
resetSplat = tf.assign(splat,
initialSplat,
validate_shape=False,
name='assign')
# This is needed to force tensorflow to update the cache
with tf.control_dependencies([resetSplat]):
uncachedSplat = splat.read_value()
for scit in range(numSimplexCorners):
data = data_vectors * barycentric[:, scit:scit + 1]
with tf.control_dependencies([uncachedSplat]):
splat = tf.scatter_nd_add(splat, indices[scit], data)
## Blur
with tf.control_dependencies([splat]):
blurred = [splat]
order = range(nCh, -1, -1) if reverse else range(nCh + 1)
for dit in order:
with tf.control_dependencies([blurred[-1]]):
b1 = 0.5 * tf.gather(blurred[-1], blurNeighbours1[dit])
b2 = blurred[-1][1:, :, :]
b3 = 0.5 * tf.gather(blurred[-1], blurNeighbours2[dit])
blurred.append(
tf.concat([blurred[-1][0:1, :, :], b2 + b1 + b3], 0))
# Alpha is a magic scaling constant from CRFAsRNN code
alpha = 1. / (1. + numpy.power(2., -nCh))
normalized = blurred[-1][:, :, :-1] / blurred[-1][:, :, -1:]
## Slice
sliced = tf.gather_nd(normalized, indices[0]) * barycentric[:, 0:1] * alpha
for scit in range(1, numSimplexCorners):
sliced = sliced + tf.gather_nd(
normalized, indices[scit]) * barycentric[:, scit:scit + 1] * alpha
return sliced
def get_q_tensor_update(self, ind_heads, ind_best_heads, q_tensor,
discount, lr, current_states, actions, rewards,
next_states, apply_actions):
num_models = self.config['num_models']
num_heads = self.config['num_heads']
heads_per_sample = self.config['heads_per_sample']
model_range = tf.range(0, num_models, dtype=tf.int64)
# we have to modify the states and actions a little bit
ind_models = self.duplicate_each_element(model_range, heads_per_sample)
ind_states = self.duplicate_each_element(current_states,
heads_per_sample)
ind_best_heads_duplic = tf.cast(
self.duplicate_each_element(ind_best_heads, heads_per_sample),
tf.int64)
ind_actions = self.duplicate_each_element(actions, heads_per_sample)
ind_next_states = self.duplicate_each_element(next_states,
heads_per_sample)
if ind_heads == None:
ind_heads = tf.tile(tf.range(0, num_heads, dtype=tf.int64),
[num_models])
# obtain current q values
ind_current_q_values = tf.stack(
[ind_models, ind_heads, ind_states, ind_actions], axis=1)
current_q_values = tf.gather_nd(q_tensor, ind_current_q_values)
# retrieve the best model
ind_best_q_values = tf.stack(
[ind_models, ind_best_heads_duplic, ind_states], axis=1)
best_q_values = tf.gather_nd(q_tensor, ind_best_q_values)
actions = tf.argmax(best_q_values, axis=1)
# obtain the best q function available for the next state
ind_next_q_vectors = tf.stack(
[ind_models, ind_heads, ind_next_states,
tf.squeeze(actions)],
axis=1)
next_q_values = tf.gather_nd(q_tensor, ind_next_q_vectors)
# duplicate the rewards as well
mod_shaped_rewards = self.duplicate_each_element(
rewards, heads_per_sample)
td_errors = mod_shaped_rewards + discount * next_q_values - current_q_values
# add dependencies
with tf.control_dependencies([apply_actions]):
# define the q tensor update for the q values
return tf.scatter_nd_add(q_tensor, ind_current_q_values,
lr * td_errors)
def remove(self, src: int, dst: int) -> None:
""" Remove an edge to the graph.
This method process an input edge deleting it to the graph updating all the
variables necessaries to maintain the graph in correct state.
Args:
src (int): The id of the source vertex of the edge.
dst (int): The id of the destination vertex of the edge.
Returns:
This method returns nothing.
"""
if src and dst is None:
raise ValueError(
"tf_G and dst must not be None ")
self.run_tf([tf.scatter_nd_add(self.A_tf, [[src, dst]], [-1.0]),
tf.scatter_nd_add(self.out_degrees_tf, [[src, 0]], [-1.0]),
tf.scatter_nd_add(self.in_degrees_tf, [[0, dst]], [-1.0])])
self.m -= 1
self._notify(np.array([src, dst]), -1)
def move(self, desire):# desire = the softmax desire where each slot is where you want to move.
slot = tf.cast(tf.argmax(tf.multiply(desire, tf.cast(tf.less(self.height, tf.constant(6, dtype=tf.int8)), tf.float32))), tf.int32)
height = tf.cast(tf.gather(self.height, slot), tf.int32)
# print(tf.shape(height))
# print(tf.shape(slot))
# print(type(tf.stack([slot, height], 0)));exit()
self.grid = tf.scatter_nd_add(self.grid, tf.expand_dims(tf.stack([height, slot], 0), 0), tf.expand_dims(self.player, -1))
tf.scatter_add(self.height, slot, tf.constant(1, dtype=tf.int8))
tf.initialize_all_variables()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
print(sess.run(self.grid))
def update(self, indices, values):
"""Returns op to do sparse update.
To elaborate on this one, lets consider two examples of 5x5x5 patches
with target volume 100x100x100:
1. We have 10 patches batched together into [10, 5, 5, 5] then our indices
need to be [10, 5, 5, 5, 3].
2. We have a single patch of shape [5, 5, 5] and indices [5, 5, 5, 3].
As a third example consider a flattened patch:
3. We have 4 5x5x5 patches flattened into [500] then we need indices of
shape [500, 3].
In other words for the innermost dimension we need to provide an index
into the target shape for every value but the outer dimensions are
arbitrary as long as they are the same for indices and values.
Args:
indices
values
Returns:
op
""" ""
if not self._built:
self._build()
with tf.name_scope(f'{self._name}/patch_aggregator'):
weights = tf.to_float(tf.greater(values, 0))
if self._average is None:
with tf.name_scope('average'):
cond = tf.not_equal(self._weight, 0)
ones = tf.ones_like(self._weight)
weight = tf.where(cond, self._weight, ones)
self._average = self._value / weight
return tf.group(tf.scatter_nd_add(self._value, indices, values),
tf.scatter_nd_add(self._weight, indices, weights))
def permutohedral_compute(data_vectors, barycentric, blur_neighbours1,
blur_neighbours2, indices, name, reverse):
"""
Splat, Gaussian blur, and slice
:param data_vectors: value map to be filtered
:param barycentric: embedding coordinates
:param blur_neighbours1: first neighbours' coordinates relative to indices
:param blur_neighbours2: second neighbours' coordinates relative to indices
:param indices: corresponding locations of data_vectors
:param name: layer name
:param reverse: transpose the Gaussian kernel if True
:return: filtered data_vectors (sliced to the original space)
"""
num_simplex_corners = barycentric.shape.as_list()[-1]
n_ch = num_simplex_corners - 1
batch_size, n_voxels, n_ch_data = data_vectors.shape.as_list()
data_vectors = tf.reshape(data_vectors, [-1, n_ch_data])
# Splatting
with tf.variable_scope(name):
splat = tf.contrib.framework.local_variable(tf.constant(0.0),
validate_shape=False,
name='splatbuffer')
# with tf.control_dependencies([splat.initialized_value()]):
initial_splat = tf.zeros(
[tf.shape(blur_neighbours1[0])[0] + 1, batch_size, n_ch_data])
reset_splat = tf.assign(splat, initial_splat, validate_shape=False)
with tf.control_dependencies([reset_splat]):
for scit in range(num_simplex_corners):
data = data_vectors * barycentric[:, scit:scit + 1]
splat = tf.scatter_nd_add(splat, indices[scit], data)
# Blur with 1D kernels
for dit in range(n_ch, -1, -1) if reverse else range(n_ch + 1):
b1 = tf.gather(splat, blur_neighbours1[dit])
b3 = tf.gather(splat, blur_neighbours2[dit])
splat = tf.concat([splat[:1, ...], splat[1:, ...] + 0.5 * (b1 + b3)],
0)
# Slice
sliced = 0.0
# Alpha is a magic scaling constant from CRFAsRNN code
alpha = 1. / (1. + np.power(2., -n_ch))
for scit in range(0, num_simplex_corners):
sliced += tf.gather_nd(splat, indices[scit]) * \
barycentric[:, scit:scit + 1] * alpha
sliced = tf.reshape(sliced, [batch_size, n_voxels, n_ch_data])
return sliced
def create_mask(var, output_shape, pos_idxs, pos_val, neg_val, zero):
# output_shape is a tensor of shape
# zero is the zero constant with the shape shape as var
var = tf.assign(var, zero)
if isinstance(pos_val, int) or isinstance(pos_val, float):
pos_val = np.array(pos_val, dtype=np.float32)
if isinstance(neg_val, int) or isinstance(neg_val, float):
neg_val = np.array(neg_val, dtype=np.float32)
var_subset = tf.gather_nd(var, pos_idxs) + \
pos_val - neg_val
var = tf.scatter_nd_add(var, pos_idxs, var_subset)
var += neg_val
var = tf.slice(var, [0, 0], output_shape)
return var
def scatter_nd_add_diff(x0, x1, x2):
"""
Custom gradient version of scatter_nd_add
"""
dummy = tf.Variable(x0, name='dummy', use_resource=True)
reset_dummy = dummy.assign(0.0 * x0)
with tf.control_dependencies([reset_dummy]):
f = tf.scatter_nd_add(dummy, x1, x2)
def grad(dy, variables=[dummy]):
g = tf.gather_nd(dy, x1)
return [None, None, g], [None]
return f, grad
def bias_ops(ds: tf.data.Dataset, V):
features, labels = ds.make_one_shot_iterator().get_next()
tokens = features[TEXT] # (N, L)
token_lengths = features[SENTENCE_LENGTH] # (N,)
vocab_tally = tf.get_local_variable(
name='vocab_tally',
dtype=tf.int64,
initializer=tf.initializers.zeros,
shape=(V,)
) # (V,)
word_count = tf.get_local_variable(
name='word_count',
dtype=token_lengths.dtype,
initializer=tf.initializers.zeros,
shape=[]
)
max_length = tf.get_local_variable(
name='max_length',
dtype=token_lengths.dtype,
initializer=tf.initializers.zeros,
shape=[]
)
sentence_count = tf.get_local_variable(
name='sentence_count',
dtype=tf.int32,
initializer=tf.initializers.zeros,
shape=[]
)
mask = tf.sequence_mask(
maxlen=tf.shape(tokens)[1],
lengths=token_lengths
) # (N, L)
valid_tokens = tf.boolean_mask(tensor=tokens, mask=mask) # (Z,)
update_tally = tf.scatter_nd_add(
ref=vocab_tally,
indices=tf.expand_dims(valid_tokens, 1),
updates=tf.ones(shape=tf.shape(valid_tokens), dtype=vocab_tally.dtype)
)
update_sentence_count = tf.assign_add(ref=sentence_count, value=tf.shape(tokens)[0])
update_word_count = tf.assign_add(ref=word_count, value=tf.reduce_sum(token_lengths))
update_max_length = tf.assign(ref=max_length, value=tf.maximum(
max_length,
tf.reduce_max(token_lengths)
))
update = tf.group(update_tally, update_sentence_count, update_word_count, update_max_length)
return vocab_tally, sentence_count, word_count, max_length, update
def testConcurrentUpdates(self):
num_updates = 10000
update_values = np.random.rand(num_updates)
ref = tf.Variable(np.zeros([2, 2]), dtype=tf.float64)
indices = tf.constant([[0, 1]] * num_updates, dtype=tf.int32)
updates = tf.constant(update_values, dtype=tf.float64)
exepected_result = np.zeros([2, 2], dtype=np.float64)
exepected_result[0, 1] = np.sum(update_values)
scatter = tf.scatter_nd_add(ref, indices, updates)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
result = sess.run(scatter)
assert np.allclose(result, exepected_result)
def testConcurrentUpdates(self):
num_updates = 10000
update_values = np.random.rand(num_updates)
ref = tf.Variable(np.zeros([2, 2]), dtype=tf.float64)
indices = tf.constant([[0, 1]] * num_updates, dtype=tf.int32)
updates = tf.constant(update_values, dtype=tf.float64)
exepected_result = np.zeros([2, 2], dtype=np.float64)
exepected_result[0, 1] = np.sum(update_values)
scatter = tf.scatter_nd_add(ref, indices, updates)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
result = sess.run(scatter)
assert np.allclose(result, exepected_result)
def permutohedral_compute(data_vectors,barycentric,blurNeighbours1,blurNeighbours2,indices,name,reverse):
batch_size=tf.shape(data_vectors)[0]
numSimplexCorners=int(barycentric.shape[-1])
nCh=numSimplexCorners-1
nChData=tf.shape(data_vectors)[-1]
data_vectors = tf.reshape(data_vectors,[-1,nChData])
data_vectors = tf.concat([data_vectors,tf.ones_like(data_vectors[:,0:1])],1) # Convert to homogenous coordinates
## Splatting
initialSplat=tf.zeros([tf.shape(blurNeighbours1[0])[0]+1,batch_size,nChData+1])
with tf.variable_scope(name):
# WARNING: we use local variables so the graph must initialize local variables with tf.local_variables_initializer()
splat=tf.contrib.framework.local_variable(tf.ones([0,0]),validate_shape=False,name='splatbuffer')
with tf.control_dependencies([splat.initialized_value()]):
resetSplat=tf.assign(splat,initialSplat,validate_shape=False,name='assign')
# This is needed to force tensorflow to update the cache
with tf.control_dependencies([resetSplat]):
uncachedSplat=splat.read_value()
for scit in range(numSimplexCorners):
data = data_vectors*barycentric[:,scit:scit+1]
with tf.control_dependencies([uncachedSplat]):
splat=tf.scatter_nd_add(splat,indices[scit],data)
## Blur
with tf.control_dependencies([splat]):
blurred=[splat]
order = range(nCh,-1,-1) if reverse else range(nCh+1)
for dit in order:
with tf.control_dependencies([blurred[-1]]):
b1=0.5*tf.gather(blurred[-1],blurNeighbours1[dit])
b2=blurred[-1][1:,:,:]
b3=0.5*tf.gather(blurred[-1],blurNeighbours2[dit])
blurred.append(tf.concat([blurred[-1][0:1,:,:], b2+b1+b3],0))
# Alpha is a magic scaling constant from CRFAsRNN code
alpha = 1. / (1.+numpy.power(2., -nCh))
normalized=blurred[-1][:,:,:-1]/blurred[-1][:,:,-1:]
## Slice
sliced = tf.gather_nd(normalized,indices[0])*barycentric[:,0:1]*alpha
for scit in range(1,numSimplexCorners):
sliced = sliced+tf.gather_nd(normalized,indices[scit])*barycentric[:,scit:scit+1]*alpha
return sliced
def eligibility_dutch_traces(Qs_t, states_t, actions_t, lr, discount, lambda_value):
# Beware this trace has to be used with a different learning rule
et = tf.get_variable(
"eligibilitytraces"
, shape=Qs_t.get_shape()
, dtype=tf.float32
, trainable=False
, initializer=tf.zeros_initializer()
)
tf.summary.histogram('eligibilitytraces', et)
state_action_pairs = tf.stack([states_t, actions_t], 1)
current_trace = tf.gather_nd(et, state_action_pairs)
updates = 1 - lr * discount * lambda_value * current_trace
with tf.control_dependencies([updates]):
dec_et_op = tf.assign(et, discount * lambda_value * et)
with tf.control_dependencies([dec_et_op]):
update_et_op = tf.scatter_nd_add(et, indices=state_action_pairs, updates=updates)
reset_et_op = et.assign(tf.zeros_like(et, dtype=tf.float32))
return (et, update_et_op, reset_et_op)
def create_stats_val(self, y_labels, labels):
with tf.variable_scope('MiscNNHelper/create_stats_val'):
labels = tf.cast(labels, dtype=tf.int32)
# define tf variables to use scatter_nd and scatter_nd_add
# pwtmp = tf.Variable(tf.zeros(self.num_labels), trainable=False, dtype=tf.float32)
# pytmp = tf.Variable(tf.zeros(self.cb_size), trainable=False, dtype=tf.float32)
# pw_y_tmp = tf.Variable(tf.zeros([self.num_labels, self.cb_size]), trainable=False, dtype=tf.float32)
pwtmp = tf.get_default_graph().get_tensor_by_name('p_w:0')
pytmp = tf.get_default_graph().get_tensor_by_name('p_y:0')
pw_y_tmp = tf.get_default_graph().get_tensor_by_name('p_w_y:0')
# self.reset_p_w
# create P(w)
pwtmp = tf.assign(pwtmp, tf.zeros([self.num_labels
])) # reset Variable/floor
# pwtmp = self.reset_variable(pwtmp)
pwtmp = tf.scatter_add(pwtmp, labels, tf.ones(tf.shape(labels)))
# create P(y)
pytmp = tf.assign(pytmp,
tf.zeros([self.cb_size])) # reset Variable/floor
pytmp = tf.scatter_add(pytmp, y_labels,
tf.ones(tf.shape(y_labels)))
# create P(w|y)
pw_y_tmp = tf.assign(pw_y_tmp,
tf.zeros([self.num_labels, self.cb_size
])) # reset Variable/floor
pw_y_tmp = tf.scatter_nd_add(
pw_y_tmp,
tf.concat([
tf.cast(labels, dtype=tf.int64),
tf.expand_dims(y_labels, 1)
],
axis=1), tf.ones(tf.shape(y_labels)))
return pwtmp, pytmp, pw_y_tmp
def main(unused_argv):
ref = tf.Variable([[[1, 2, 3, 4], [5, 6, 7, 8],
[1, 2, 3, 4], [5, 6, 7, 8]],
[[10, 11, 12, 13], [14, 15, 16, 17],
[10, 11, 12, 13], [14, 15, 16, 17]]
])
indices = tf.constant([[1, 1]])
updates = tf.constant([[-14, -15, -16, -17]])
out = tf.scatter_nd_add(ref, indices, updates)
init = tf.global_variables_initializer()
with tf.Session(config=config('cpu')) as session:
session.run(init)
result_cpu = session.run(out)
with tf.Session(config=config('ai_core')) as session:
session.run(init)
result_ai_core = session.run(out)
print('====================================')
cmp_result = np.allclose(result_ai_core, result_cpu, atol, rtol)
print(cmp_result)
print('====================================')
def build_mask2(input):
# row = input.eval()
print(row)
y_extent = tf.range(row[0], row[2])
x_extent = tf.range(row[1], row[3])
print('y_extent', y_extent.eval())
Y,X = tf.meshgrid(y_extent, x_extent)
print(Y.shape, X.shape)
bbox_mask = tf.stack([Y,X],axis=2)
print(' bbox_mask shapoe: ',bbox_mask.shape)
mask_indices = tf.reshape(bbox_mask,[-1,2])
print(' size of mask_indices: ', mask_indices.shape)
mask_size = mask_indices.get_shape()[0]
mask_updates = tf.ones([mask_size], dtype = tf.int32)
print(' size of bbox_mask: ', mask_size)
res = tf.scatter_nd_add(ref2, mask_indices, mask_updates)
print( ' ref shape: ', res.shape)
print( ' indices shape: ', mask_indices.shape)
print( ' updates shape: ', mask_updates.shape)
return res
def scatter_var(
blocks,
bin_counts, # pylint: disable=unused-argument
active_block_indices,
outputs,
*,
bsize,
boffset,
bstride,
add=False):
raise NotImplementedError("no gradient for sparse_lib.scatter_var")
indices = _upsample_block_indices(active_block_indices, bsize, boffset,
bstride) # [M, bsize[0], bsize[1], 3]
if add:
outputs = tf.scatter_nd_add(outputs, indices, blocks)
else:
outputs = tf.scatter_nd_update(outputs, indices, blocks)
return outputs
def _helper_mi_tf(self, labels, alignments, cb_len):
"""
Helper functions in tensorflow to get P(w), P(y) and P(w|y)
:param labels: labels coming e.g. out of a neural network
:param alignments: phonemes from e.g. alignments
:param cb_len: codebook size of the neural network (output dim)
:return: P(w), P(y) and P(w|y)
"""
p = 41
pwtmp = tf.Variable(tf.zeros(p), trainable=False, dtype=tf.float32)
pytmp = tf.Variable(tf.zeros(cb_len),
trainable=False,
dtype=tf.float32)
pw_y_tmp = tf.Variable(tf.zeros([p, cb_len]),
trainable=False,
dtype=tf.float32)
# use input array as indexing array
pwtmp = pwtmp.assign(tf.fill([p], 0.0)) # reset Variable/floor
pwtmp = tf.scatter_add(pwtmp, alignments,
tf.ones(tf.shape(alignments)))
pytmp = pytmp.assign(tf.fill([cb_len], 0.0)) # reset Variable/floor
pytmp = tf.scatter_add(pytmp, labels, tf.ones(tf.shape(labels)))
pw_y_tmp = pw_y_tmp.assign(tf.fill([p, cb_len],
0.0)) # reset Variable/floor
pw_y_tmp = tf.scatter_nd_add(
pw_y_tmp,
tf.concat([
tf.cast(alignments, dtype=tf.int64),
tf.expand_dims(labels, 1)
],
axis=1), tf.ones(tf.shape(labels)))
return pwtmp, pytmp, pw_y_tmp
def vq_data(self, y_nn, labels, nominator, denominator, discrete=True):
"""
Create the nominator and denominator for P(s_k|m_j)
This function is used for using all the training data to create P(s_k|m_j)
:param y_nn: output of the neural net
:param labels: phonemes or states of the data (coming from the alignments of kaldi)
:param nominator: nominator for P(s_k|m_j)
:param denominator: denominator for P(s_k|m_j)
:param discrete: flag for creating P(s_k|m_j) in a discrete way
:return: return nominator and denominator of creating P(s_k|m_j)
"""
with tf.variable_scope('MiscNNHelper/vq_data'):
# cast labels to int32
labels = tf.cast(labels, dtype=tf.int64)
y_labels = tf.argmax(y_nn, axis=1)
# labels_softmax = output_soft
if discrete:
# create nominator
nominator = tf.scatter_nd_add(
nominator,
tf.concat([
tf.cast(labels, dtype=tf.int64),
tf.expand_dims(y_labels, 1)
],
axis=1), tf.ones(tf.shape(y_labels)))
# create dominator
denominator = tf.scatter_add(denominator, y_labels,
tf.ones(tf.shape(y_labels)[0]))
else:
raise NotImplementedError("Not implemented!")
return nominator, denominator
def project_uv_render(
ori_img,
norm_image,
clip_xyzw,
tri,
tri_vt,
vt_list,
imageH,
imageW,
uv_rgb,
uv_mask,
para_illum,
var_scope_name,
):
batch_size, _, _ = clip_xyzw.get_shape().as_list()
# get uv coordinates
V, U = tf.split(vt_list, 2, axis=1)
uv_size = uv_rgb.get_shape().as_list()[1]
U = (1.0 - U) * uv_size
V = V * uv_size
UV = tf.concat([U, V], axis=1)
batch_UV = tf.tile(UV, [batch_size, 1])
# get clip_xyzw for ver_uv (according to the correspondence between tri and tri_vt)
# gather and scatter
EPS = 1e-12
batch_tri_indices = tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), axis=1), [1, len(tri_vt) * 3]),
[-1],
name="batch_tri_indices",
)
tri_inds = tf.stack(
[
batch_tri_indices,
tf.concat([tf.reshape(tri, [len(tri) * 3])] * batch_size, axis=0),
],
axis=1,
)
tri_vt_inds = tf.stack(
[
batch_tri_indices,
tf.concat([tf.reshape(tri_vt, [len(tri_vt) * 3])] * batch_size, axis=0),
],
axis=1,
)
tri_clip_xyzw = tf.gather_nd(clip_xyzw, tri_inds, name="tri_clip_xyzw")
ver_uv_clip_xyzw_sum = tf.get_variable(
shape=[batch_size, len(vt_list), 4],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
name=var_scope_name + "ver_uv_clip_xyzw_sum",
trainable=False,
)
ver_uv_clip_xyzw_cnt = tf.get_variable(
shape=[batch_size, len(vt_list), 4],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
name=var_scope_name + "ver_uv_clip_xyzw_cnt",
trainable=False,
)
init_ver_uv = tf.zeros(shape=[batch_size, len(vt_list), 4], dtype=tf.float32)
assign_op1 = tf.assign(ver_uv_clip_xyzw_sum, init_ver_uv)
assign_op2 = tf.assign(ver_uv_clip_xyzw_cnt, init_ver_uv)
with tf.control_dependencies([assign_op1, assign_op2]):
ver_uv_clip_xyzw_sum = tf.scatter_nd_add(
ver_uv_clip_xyzw_sum, tri_vt_inds, tri_clip_xyzw
)
ver_uv_clip_xyzw_cnt = tf.scatter_nd_add(
ver_uv_clip_xyzw_cnt, tri_vt_inds, tf.ones_like(tri_clip_xyzw)
)
ver_uv_clip_xyzw = tf.div(ver_uv_clip_xyzw_sum, ver_uv_clip_xyzw_cnt + EPS)
uv_image, uv_alphas = rasterize_clip_space(
ver_uv_clip_xyzw, batch_UV, tri_vt, imageW, imageH, -1.0
)
uv_image = tf.clip_by_value(
tf.cast(uv_image, tf.int32), 0, 511
) # should be integer
batch_vt_indices = tf.reshape(
tf.tile(
tf.expand_dims(tf.range(batch_size), axis=1), [1, imageW * imageH]
),
[-1, 1],
name="batch_indices",
)
batch_vt_indices = tf.concat(
[batch_vt_indices, tf.reshape(uv_image, [-1, 2])], axis=1
)
# careful
diffuse_image = tf.reshape(
tf.gather_nd(uv_rgb, batch_vt_indices), [batch_size, imageH, imageW, 3]
)
uv_alphas = (
tf.reshape(
tf.gather_nd(uv_mask[:, :, :, 0], batch_vt_indices),
[batch_size, imageH, imageW, 1],
)
* uv_alphas
)
# Have shading
para_light = para_illum
background = ori_img
rgb_images, shading_image = Shader.sh_shader(
norm_image, uv_alphas, background, para_light, diffuse_image
)
ori_img_remove_shading = ori_img / shading_image
diffuse_image = tf.clip_by_value(diffuse_image, 0, 1)
rgb_images = tf.clip_by_value(rgb_images, 0, 1)
uv_attrs_image = tf.clip_by_value(uv_alphas, 0, 1)
ori_img_remove_shading = tf.clip_by_value(ori_img_remove_shading, 0, 1)
render_image = rgb_images
render_image = render_image * uv_attrs_image + ori_img * (1 - uv_attrs_image)
return render_image, uv_attrs_image, ori_img_remove_shading
def get_ver_norm(ver_xyz, tri, scope_name="normal"):
"""
Compute vertex normals.
:param:
ver_xyz: [batch, N, 3], vertex geometry
tri: [M, 3], mesh triangles definition
:return:
ver_normals: [batch, N, 3], vertex normals
"""
with tf.variable_scope(scope_name):
v1_idx, v2_idx, v3_idx = tf.unstack(tri, 3, axis=-1)
v1 = tf.gather(ver_xyz, v1_idx, axis=1, name="v1_tri")
v2 = tf.gather(ver_xyz, v2_idx, axis=1, name="v2_tri")
v3 = tf.gather(ver_xyz, v3_idx, axis=1, name="v3_tri")
EPS = 1e-8
tri_normals = tf.cross(v2 - v1, v3 - v1)
tri_normals = tf.div(
tri_normals,
(tf.norm(tri_normals, axis=-1, keep_dims=True) + EPS),
name="norm_tri",
)
tri_normals = tf.tile(tf.expand_dims(tri_normals, 2), [1, 1, 3, 1])
tri_normals = tf.reshape(tri_normals, [-1, 3])
tri_votes = tf.cast(tf.greater(tri_normals[:, 2:], float(0.1)), tf.float32)
tri_cnts = tf.ones_like(tri_votes)
B = v1.get_shape().as_list()[0] # batch size
batch_indices = tf.reshape(
tf.tile(tf.expand_dims(tf.range(B), axis=1), [1, len(tri) * 3]),
[-1],
name="batch_indices",
)
tri_inds = tf.stack(
[
batch_indices,
tf.concat([tf.reshape(tri, [len(tri) * 3])] * B, axis=0),
],
axis=1,
)
ver_shape = ver_xyz.get_shape().as_list()
ver_normals = tf.get_variable(
shape=ver_shape,
dtype=tf.float32,
initializer=tf.zeros_initializer(),
name="ver_norm",
trainable=False,
)
init_normals = tf.zeros(shape=ver_shape, dtype=tf.float32)
assign_op = tf.assign(ver_normals, init_normals)
with tf.control_dependencies([assign_op]):
ver_normals = tf.scatter_nd_add(ver_normals, tri_inds, tri_normals)
ver_normals = ver_normals / (
tf.norm(ver_normals, axis=2, keep_dims=True) + EPS
)
votes = tf.reshape(
tf.concat([tri_votes, tri_votes, tri_votes], axis=-1), [-1, 1]
)
cnts = tf.reshape(
tf.concat([tri_cnts, tri_cnts, tri_cnts], axis=-1), [-1, 1]
)
ver_votes = tf.get_variable(
shape=ver_shape[:-1] + [1],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
name="ver_vote",
trainable=False,
)
ver_cnts = tf.get_variable(
shape=ver_shape[:-1] + [1],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
name="ver_cnt",
trainable=False,
)
init_votes = tf.zeros(shape=ver_shape[:-1] + [1], dtype=tf.float32)
assign_op2 = tf.assign(ver_votes, init_votes)
assign_op3 = tf.assign(ver_cnts, init_votes)
with tf.control_dependencies([assign_op2, assign_op3]):
ver_votes = tf.scatter_nd_add(ver_votes, tri_inds, tri_votes)
ver_cnts = tf.scatter_nd_add(ver_cnts, tri_inds, tri_cnts)
ver_votes = ver_votes / (ver_cnts + EPS)
ver_votes1 = tf.less(ver_votes, float(1.0))
ver_votes2 = tf.greater(ver_votes, float(0.0))
ver_votes = tf.cast(tf.logical_and(ver_votes1, ver_votes2), tf.float32)
return ver_normals, ver_votes
