def begin(self):
if not self.is_chief:
return
#pylint: disable=W0201
features = graph_utils.get_dict_from_collection("features")
labels = graph_utils.get_dict_from_collection("labels")
self._num_tokens_tensor = tf.constant(0)
if "source_len" in features:
self._num_tokens_tensor += tf.reduce_sum(features["source_len"])
if "target_len" in labels:
self._num_tokens_tensor += tf.reduce_sum(labels["target_len"])
self._tokens_last_step = 0
self._global_step_tensor = tf.train.get_global_step()
# Create a variable that stores how many tokens have been processed
# Should be global for distributed training
with tf.variable_scope("tokens_counter"):
self._tokens_processed_var = tf.get_variable(
name="count",
shape=[],
dtype=tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32))
self._tokens_processed_add = tf.assign_add(
self._tokens_processed_var, self._num_tokens_tensor)
def begin(self):
self._iter_count = 0
self._global_step = tf.train.get_global_step()
self._pred_dict = graph_utils.get_dict_from_collection("predictions")
##self._logits_infer = graph_utils.get_dict_from_collection("logits_infer")
self._logits_softmax = graph_utils.get_dict_from_collection(
"logits_softmax")
# Create the sample directory
if self._sample_dir is not None:
gfile.MakeDirs(self._sample_dir)
def begin(self):
self._global_step = tf.train.get_global_step()
self._pred_dict = graph_utils.get_dict_from_collection("predictions")
self._features = graph_utils.get_dict_from_collection("features")
self._iter_count = 0
self._eval_str = ""
self._current_global_step = None
# Create the sample directory
if self._evalution_result_dir is not None:
if os.path.exists(self._evalution_result_dir) is False:
gfile.MakeDirs(self._evalution_result_dir)
os.chmod(self._evalution_result_dir, 777)
def _create_predictions(self, decoder_output, features, labels, losses=None):
"""Creates the dictionary of predictions that is returned by the model.
"""
predictions = {}
# Add features and, if available, labels to predictions
predictions.update(_flatten_dict({"features": features}))
if labels is not None:
predictions.update(_flatten_dict({"labels": labels}))
if losses is not None:
predictions["losses"] = _transpose_batch_time(losses)
# Decoders returns output in time-major form [T, B, ...]
# Here we transpose everything back to batch-major for the user
output_dict = collections.OrderedDict(
zip(decoder_output._fields, decoder_output))
decoder_output_flat = _flatten_dict(output_dict)
decoder_output_flat = {
k: _transpose_batch_time(v)
for k, v in decoder_output_flat.items()
}
predictions.update(decoder_output_flat)
# If we predict the ids also map them back into the vocab and process them
if "predicted_ids" in predictions.keys():
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
target_id_to_vocab = vocab_tables["target_id_to_vocab"]
predicted_tokens = target_id_to_vocab.lookup(
tf.to_int64(predictions["predicted_ids"]))
# Raw predicted tokens
predictions["predicted_tokens"] = predicted_tokens
return predictions
def _create_predictions(self, decoder_output, features, labels, losses=None):
"""Creates the dictionary of predictions that is returned by the model.
"""
predictions = {}
# Add features and, if available, labels to predictions
predictions.update(_flatten_dict({"features": features}))
if labels is not None:
predictions.update(_flatten_dict({"labels": labels}))
if losses is not None:
predictions["losses"] = _transpose_batch_time(losses)
# Decoders returns output in time-major form [T, B, ...]
# Here we transpose everything back to batch-major for the user
output_dict = collections.OrderedDict(
zip(decoder_output._fields, decoder_output))
decoder_output_flat = _flatten_dict(output_dict)
decoder_output_flat = {
k: _transpose_batch_time(v)
for k, v in decoder_output_flat.items()
}
predictions.update(decoder_output_flat)
# If we predict the ids also map them back into the vocab and process them
if "predicted_ids" in predictions.keys():
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
target_id_to_vocab = vocab_tables["target_id_to_vocab"]
predicted_tokens = target_id_to_vocab.lookup(
tf.to_int64(predictions["predicted_ids"]))
# Raw predicted tokens
predictions["predicted_tokens"] = predicted_tokens
return predictions
def compute_loss(self, decoder_output, features, labels):
"""Computes the loss for this model.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
"""
#pylint: disable=R0201
# Calculate loss per example-timestep of shape [B, T]
# Get the original loss from the model
losses, loss = super(DiscriminatorSeq2Seq, self).compute_loss(
decoder_output, features, labels)
# Add the Discriminator
encoder_output = graph_utils.get_dict_from_collection("encoder_output")
with tf.variable_scope("discriminator"):
discriminator_loss, disc_context = self._discriminator(
encoder_output=encoder_output,
features=features,
labels=labels,
num_classes=2,
num_units=self.params["discriminator_units"])
discriminator_loss *= self.params["discriminator_loss_multiplier"]
tf.summary.scalar("loss/discriminator", discriminator_loss)
tf.summary.scalar("loss/generator", loss)
# Add discriminator loss to original loss (training only)
if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
loss = loss + discriminator_loss
tf.summary.scalar("loss/total", loss)
return losses, loss
def begin(self):
self._iter_count = 0
self._global_step = tf.train.get_global_step()
self._pred_dict = graph_utils.get_dict_from_collection("predictions")
# Create the sample directory
if self._sample_dir is not None:
gfile.MakeDirs(self._sample_dir)
def begin(self):
self._iter_count = 0
self._global_step = training_util.get_global_step()
self._pred_dict = graph_utils.get_dict_from_collection("predictions")
# Create the sample directory
if self._sample_dir is not None:
gfile.MakeDirs(self._sample_dir)
def _set_special_vocab_ids(self):
# Find out the IDs for special vocab
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
target_vocab_to_id = vocab_tables["target_vocab_to_id"]
self.copy_word_id = target_vocab_to_id.lookup(
tf.convert_to_tensor("COPY_WORD", dtype=tf.string))
self.copy_schema_id = target_vocab_to_id.lookup(
tf.convert_to_tensor("COPY_SCHEMA", dtype=tf.string))
def _create_decoder(self, encoder_output, features, _labels):
# TODO: This whole method is copied from schema_attention_seq2seq.py.
# Use multiple inheritance to avoid this?
attention_class = locate(self.params["attention.class"]) or \
getattr(decoders.attention,
self.params["attention.class"])
attention_layer = attention_class(
params=self.params["attention.params"], mode=self.mode)
schema_attention_class = locate(self.params["schema.attention.class"]) or \
getattr(decoders.attention,
self.params["schema.attention.class"])
schema_attention_layer = schema_attention_class(
params=self.params["schema.attention.params"], mode=self.mode)
# If the input sequence is reversed we also need to reverse
# the attention scores.
reverse_scores_lengths = None
if self.params["source.reverse"]:
reverse_scores_lengths = features["source_len"]
if self.use_beam_search:
reverse_scores_lengths = tf.tile(
input=reverse_scores_lengths,
multiples=[self.params["inference.beam_search.beam_width"]])
schema_tables = graph_utils.get_dict_from_collection("schema_tables")
schema_locs = features['schema_loc']
table = schema_tables["schema_file_lookup_table"]
ids = table.lookup(schema_locs)
all_schema_embeddings = schema_tables["all_schema_embeddings"]
schema_embeddings_3d = tf.squeeze(tf.gather(all_schema_embeddings, ids), [1])
schema_lengths = schema_tables["schema_lengths"]
schema_attn_values_length = tf.squeeze(tf.gather(schema_lengths, ids), [1])
# schema_embeddings_file = self.params["schema.location"]
# print("Loading schema embeddings from %s" % schema_embeddings_file)
# schema_embeddings_matrix_np = np.load(schema_embeddings_file)
# schema_embeddings_matrix = tf.constant(schema_embeddings_matrix_np,
# dtype=tf.float32)
# batch_size = tf.shape(encoder_output.attention_values_length)[0]
# schema_embeddings_3d = tf.tile(tf.expand_dims(schema_embeddings_matrix, 0),
# tf.stack([batch_size, 1, 1]))
# schema_attn_values_length = tf.tile(tf.expand_dims(
# tf.shape(schema_embeddings_matrix)[1], 0), [batch_size])
return self.decoder_class(
params=self.params["decoder.params"],
mode=self.mode,
vocab_size=self.target_vocab_info.total_size,
attention_values=encoder_output.attention_values,
attention_values_length=encoder_output.attention_values_length,
attention_keys=encoder_output.outputs,
attention_fn=attention_layer,
reverse_scores_lengths=reverse_scores_lengths,
schema_attention_keys=schema_embeddings_3d,
schema_attention_values=schema_embeddings_3d,
schema_attention_values_length=schema_attn_values_length,
schema_attention_fn=schema_attention_layer
)
def _get_predicted_tokens(self, predictions):
if "predicted_tokens" in predictions.keys():
output_predicted_tokens = predictions["predicted_tokens"]
else:
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
target_id_to_vocab = vocab_tables["target_id_to_vocab"]
output_predicted_tokens = target_id_to_vocab.lookup(
tf.to_int64(predictions["predicted_ids"]))
return output_predicted_tokens
def begin(self):
self._predictions = graph_utils.get_dict_from_collection("predictions")
self.write_cnt = 0
self.sample_cnt = 0
self.infer_outs = []
self.attn_scores_list = []
self.run_cnt = 0
if self._save_pred_path is not None:
self._pred_fout = codecs.open(self._save_pred_path, "w", "utf-8")
if self._attn_path is not None:
self._attn_fout = codecs.open(self._attn_path, "wb")
def __init__(self,
delimiter=" ",
tokens_feature_name="tokens",
length_feature_name="length",
schema_copy_feature_name="schema_copy_indices",
prepend_token=None,
append_token=None):
super(SchemaCopyingDecoder,
self).__init__(delimiter=delimiter,
tokens_feature_name=tokens_feature_name,
length_feature_name=length_feature_name,
prepend_token=prepend_token,
append_token=append_token)
self.schema_copy_feature_name = schema_copy_feature_name
schema_tables = graph_utils.get_dict_from_collection("schema_tables")
self.schema_lookup_table = schema_tables["schema_file_lookup_table"]
self.schema_strings_table = schema_tables["all_schema_strings"]
def source_embedding(self):
"""Returns the embedding used for the source sequence.
"""
if self.params["embedding.source_embedding"] is not None:
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
source_vocab_to_id = vocab_tables["source_vocab_to_id"]
source_vocab = vocab_tables["source_vocab"]
return self.load_embedding(
self.params["embedding.source_embedding"], source_vocab,
source_vocab_to_id)
return tf.get_variable(name="W",
shape=[
self.source_vocab_info.total_size,
self.params["embedding.dim"]
],
initializer=tf.random_uniform_initializer(
-self.params["embedding.init_scale"],
self.params["embedding.init_scale"]))
def create_predictions(self,
decoder_output,
features,
labels,
losses=None):
"""Creates the dictionary of predictions that is returned by the model.
"""
predictions = {}
# Add features and, if available, labels to predictions
predictions.update(_flatten_dict({"features": features}))
if labels is not None:
predictions.update(_flatten_dict({"labels": labels}))
if losses is not None:
predictions["losses"] = losses
# Decoders returns output in time-major form [T, B, ...]
# Here we transpose everything back to batch-major for the user
output_dict = collections.OrderedDict(
zip(decoder_output._fields, decoder_output))
decoder_output_flat = _flatten_dict(output_dict)
predictions.update(decoder_output_flat)
# If we predict the ids also map them back into the vocab and process them
if "predicted_ids" in predictions.keys():
predicted_tk_id = predictions["predicted_ids"]
vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
target_id_to_vocab = vocab_tables["target_id_to_vocab"]
predicted_tokens = target_id_to_vocab.lookup(
tf.to_int64(predicted_tk_id))
# Raw predicted tokens
# Force to reshape to [batch_size, 1, 1] so that it has same shape as regular seq2seq,
# and all post process code can be reused.
#predicted_tokens = tf.reshape(predicted_tokens, [tf.shape(predicted_tokens)[0], 1, 1])
predictions["predicted_tokens"] = predicted_tokens
return predictions
def _create_predictions(self,
decoder_output,
features,
labels,
losses=None):
predictions = super(SchemaAttentionCopyingSeq2Seq,
self)._create_predictions(decoder_output, features,
labels, losses)
if "predicted_ids" in predictions.keys():
prediction_ids = predictions["predicted_ids"]
output_predicted_tokens = self._get_predicted_tokens(predictions)
if self.copy_schema_id is None:
self._set_special_vocab_ids()
schema_attention_copy_vals = predictions[
"schema_attention_copy_vals"]
# Build the schema_tokens structure from the list of schema locations.
schema_tables = graph_utils.get_dict_from_collection(
"schema_tables")
schema_locs = features['schema_loc']
table = schema_tables["schema_file_lookup_table"]
ids = table.lookup(schema_locs)
all_schema_strings = schema_tables["all_schema_strings"]
schema_tokens = tf.squeeze(all_schema_strings.lookup(ids), axis=1)
schema_tokens = tf.sparse_tensor_to_dense(
tf.string_split(schema_tokens),
default_value="UNK",
name="desparsifying_schema_tokens")
# Figure out copy_schema_predicted_tokens
predicted_tokens = self._fill_in_copies(
prediction_ids, output_predicted_tokens,
schema_attention_copy_vals, schema_tokens, self.copy_schema_id)
predictions["predicted_tokens"] = predicted_tokens
return predictions
def _create_decoder(self, encoder_output, features, _labels):
attention_class = locate(self.params["attention.class"]) or \
getattr(decoders.attention,
self.params["attention.class"])
attention_layer = attention_class(
params=self.params["attention.params"],
mode=self.mode,
name="attention_to_input_layer")
# If the input sequence is reversed we also need to reverse
# the attention scores.
reverse_scores_lengths = None
if self.params["source.reverse"]:
reverse_scores_lengths = features["source_len"]
if self.use_beam_search:
reverse_scores_lengths = tf.tile(
input=reverse_scores_lengths,
multiples=[self.params["inference.beam_search.beam_width"]])
if "schema" in self.params["decoder.class"].lower():
schema_attention_class = locate(
self.params["schema.attention.class"]) or \
getattr(decoders.attention,
self.params["schema.attention.class"])
schema_attention_layer = schema_attention_class(
params=self.params["schema.attention.params"],
mode=self.mode,
name="attention_to_schema_layer")
schema_tables = graph_utils.get_dict_from_collection(
"schema_tables")
schema_locs = features['schema_loc']
table = schema_tables["schema_file_lookup_table"]
ids = table.lookup(schema_locs)
all_schema_embeddings = schema_tables["all_schema_embeddings"]
schema_embeddings_3d = tf.squeeze(
tf.gather(all_schema_embeddings, ids), [1])
schema_lengths = schema_tables["schema_lengths"]
schema_attn_values_length = tf.squeeze(
tf.gather(schema_lengths, ids), [1])
return self.decoder_class(
params=self.params["decoder.params"],
mode=self.mode,
vocab_size=self.target_vocab_info.total_size,
attention_values=encoder_output.attention_values,
attention_values_length=encoder_output.attention_values_length,
attention_keys=encoder_output.outputs,
attention_fn=attention_layer,
reverse_scores_lengths=reverse_scores_lengths,
schema_attention_keys=schema_embeddings_3d,
schema_attention_values=schema_embeddings_3d,
schema_attention_values_length=schema_attn_values_length,
schema_attention_fn=schema_attention_layer)
return self.decoder_class(
params=self.params["decoder.params"],
mode=self.mode,
vocab_size=self.target_vocab_info.total_size,
attention_values=encoder_output.attention_values,
attention_values_length=encoder_output.attention_values_length,
attention_keys=encoder_output.outputs,
attention_fn=attention_layer,
reverse_scores_lengths=reverse_scores_lengths)
def _get_tables_and_ids(self, features):
schema_tables = graph_utils.get_dict_from_collection("schema_tables")
schema_locs = features['schema_loc']
table = schema_tables["schema_file_lookup_table"]
ids = table.lookup(schema_locs)
return (schema_tables, ids)
# A hacky way to retrieve prediction result from the task hook...
prediction_dict = {}
def _save_prediction_to_dict(source_tokens, predicted_tokens):
prediction_dict[_tokens_to_str(source_tokens)] = _tokens_to_str(
predicted_tokens)
# print("create session")
# sess = tf.train.MonitoredSession(
# session_creator=session_creator,
# hooks=[DecodeOnce({}, callback_func=_save_prediction_to_dict)])
_predictions = graph_utils.get_dict_from_collection("predictions")
fetches = {}
fetches["predicted_tokens"] = _predictions["predicted_tokens"]
fetches["predicted_ids"] = _predictions["predicted_ids"]
fetches["features.source_tokens"] = _predictions["features.source_tokens"]
fetches["features.source_candidate_tokens"] = _predictions[
"features.source_candidate_tokens"]
#fetches["beam_parent_ids"] = _predictions["beam_search_output.beam_parent_ids"]
tf.train.SessionRunArgs(fetches)
sess = tf.Session()
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer(),
tf.tables_initializer()
])
saver.restore(sess, checkpoint_path)
def begin(self):
self._predictions = graph_utils.get_dict_from_collection("predictions")
