# # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import os import copy import numpy as np import tensorflow as tf from . import utils from os.path import isdir, join tf.NotDifferentiable("Spans") tf.NotDifferentiable("Antecedents") tf.NotDifferentiable("ExtractMentions") tf.NotDifferentiable("DistanceBins") seed = 5 tf.set_random_seed(seed) class CorefModel(object): """ End-to-end neural model for coreference resolution. Class that create model from https://homes.cs.washington.edu/~kentonl/pub/lhlz-emnlp.2017.pdf """ def __init__(self, opt): """Initialize the class and model according to the given parameters in opt."""
from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow.python import pywrap_tensorflow coref_op_library = tf.load_op_library( "/scratch/sanjay/Improved-Coref/coref_kernels.so") extract_spans = coref_op_library.extract_spans tf.NotDifferentiable("ExtractSpans")
"""Builds a DRAGNN graph for local training.""" import collections import tensorflow as tf from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.platform import tf_logging as logging from dragnn.protos import spec_pb2 from dragnn.python import component from dragnn.python import composite_optimizer from dragnn.python import dragnn_ops from syntaxnet.util import check try: tf.NotDifferentiable('ExtractFixedFeatures') except KeyError, e: logging.info(str(e)) def _validate_grid_point(hyperparams, is_sub_optimizer=False): """Validates that a grid point's configuration is reasonable. Args: hyperparams (spec_pb2.GridPoint): Grid point to validate. is_sub_optimizer (bool): Whether this optimizer is a sub-optimizer of a composite optimizer. Raises: ValueError: If the grid point is not valid. """
import os.path import tensorflow as tf __all__ = 'detection_matching' this_path = os.path.dirname(os.path.realpath(__file__)) matching_module = tf.load_op_library(os.path.join(this_path, 'det_matching.so')) tf.NotDifferentiable("DetectionMatching") detection_matching = matching_module.detection_matching
model_proto: The sentencepiece model serialized proto. Either `model_file` or `model_proto` must be set. reverse: Reverses the tokenized sequence (Default = false) name: The name argument that is passed to the op function. Returns: text: A 1D string tensor of decoded string. """ return _gen_sentencepiece_processor_op.sentencepiece_decode( pieces, sequence_length, model_file=model_file, model_proto=model_proto, reverse=reverse, name=name) # Adds an alias for encode_dense. Accepts the `encode` function. encode = encode_dense sparse_encode = encode_sparse dense_encode = encode_dense tf.NotDifferentiable('SentencepieceGetPieceSize') tf.NotDifferentiable('SentencepieceIdToPiece') tf.NotDifferentiable('SentencepiecePieceToId') tf.NotDifferentiable('SentencepieceGetPieceType') tf.NotDifferentiable('SentencepieceEncodeDense') tf.NotDifferentiable('SentencepieceEncodeSparse') tf.NotDifferentiable('SentencepieceDecode')
left_context: Integer, number of preceding frames to attach to each frame. right_context: Integer, number of preceding frames to attach to each frame. frame_stride: Integer, M frames to skip over, where output[n] = frame[n*M]. zero_padding: Bool, if left/right context is out-of-bounds, attach frame of zeroes. Otherwise, frame[0] or frame[size-1] will be copied. out_scale: Integer, divide all filterbanks by this number. out_type: DType, type of the output Tensor, defaults to UINT16. Returns: filterbanks: 2D Tensor, each row is a time frame, each column is a channel. Raises: ValueError: If the audio tensor is not explicitly a vector. """ audio_shape = audio.get_shape() if audio_shape.ndims is None: raise ValueError( "Input to `AudioMicrofrontend` should have known rank.") if len(audio_shape) > 1: audio = tf.reshape(audio, [-1]) return gen_audio_microfrontend_op.audio_microfrontend( audio, sample_rate, window_size, window_step, num_channels, upper_band_limit, lower_band_limit, smoothing_bits, even_smoothing, odd_smoothing, min_signal_remaining, enable_pcan, pcan_strength, pcan_offset, gain_bits, enable_log, scale_shift, left_context, right_context, frame_stride, zero_padding, out_scale, out_type) tf.NotDifferentiable("AudioMicrofrontend")
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Build structured parser models.""" import tensorflow as tf from tensorflow.python.ops import control_flow_ops as cf from tensorflow.python.ops import state_ops from tensorflow.python.ops import tensor_array_ops from syntaxnet import graph_builder from syntaxnet.ops import gen_parser_ops tf.NotDifferentiable('BeamParseReader') tf.NotDifferentiable('BeamParser') tf.NotDifferentiable('BeamParserOutput') def AddCrossEntropy(batch_size, n): """Adds a cross entropy cost function.""" cross_entropies = [] def _Pass(): return tf.constant(0, dtype=tf.float32, shape=[1]) for beam_id in range(batch_size): beam_gold_slot = tf.reshape( tf.strided_slice(n['gold_slot'], [beam_id], [beam_id + 1]), [1])
from graphcnn.helper import * import tensorflow as tf import numpy as np import math import os import os.path from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import sparse_ops import scipy.sparse import pdb tf.NotDifferentiable('Unique') here = os.path.dirname(__file__) + '/util/sparse/' if os.path.isfile(os.path.join(here, 'SparseConv.so')): _graphcnn_conv_sparse_module = tf.load_op_library( os.path.join(here, 'SparseConv.so')) _graphcnn_conv_sparse_grad_module = tf.load_op_library( os.path.join(here, 'SparseConvGrad.so')) if os.path.isfile(os.path.join(here, 'SparseAverageVertexPool.so')): _graphcnn_avg_vertex_pool_sparse_module = tf.load_op_library( os.path.join(here, 'SparseAverageVertexPool.so')) _graphcnn_avg_vertex_pool_sparse_grad_module = tf.load_op_library( os.path.join(here, 'SparseAverageVertexPoolGrad.so')) if os.path.isfile(os.path.join(here, 'SparseMaxVertexPool.so')): _graphcnn_max_vertex_pool_sparse_module = tf.load_op_library( os.path.join(here, 'SparseMaxVertexPool.so')) _graphcnn_max_vertex_pool_sparse_grad_module = tf.load_op_library( os.path.join(here, 'SparseMaxVertexPoolGrad.so'))
def RegisterWeaverOp(op_name): tf.NotDifferentiable(op_name)
import tensorflow as tf import utils extract_patches_module = tf.load_op_library('extract_patches_op/extract_patches.so') extract_patches = extract_patches_module.extract_patches tf.NotDifferentiable('ExtractPatches') def align_reference_shape(reference_shape, reference_shape_bb, im, bb): def norm(x): return tf.sqrt(tf.reduce_sum(tf.square(x - tf.reduce_mean(x, 0)))) ratio = norm(bb) / norm(reference_shape_bb) align_mean_shape = (reference_shape - tf.reduce_mean(reference_shape_bb, 0)) * ratio + tf.reduce_mean(bb, 0) new_size = tf.to_int32(tf.to_float(tf.shape(im)[:2]) / ratio) return tf.image.resize_bilinear(tf.expand_dims(im, 0), new_size)[0, :, :, :], align_mean_shape / ratio, ratio class MDMModel: def __init__( self, images, shapes, inits, batch_size, num_iterations, num_patches, patch_shape, num_channels, is_training=True ): self.in_images = images self.in_shapes = shapes self.in_init_shapes = inits self.num_iterations = num_iterations self.num_patches = num_patches self.patch_shape = patch_shape self.num_channels = num_channels
import tensorflow as tf from tensorflow.python import pywrap_tensorflow coref_op_library = tf.load_op_library("./coref_kernels.so") spans = coref_op_library.spans tf.NotDifferentiable("Spans") regression = coref_op_library.regression tf.NotDifferentiable("Regression") memory = coref_op_library.memory tf.NotDifferentiable("Memory") tagging = coref_op_library.tagging tf.NotDifferentiable("Tagging") antecedents = coref_op_library.antecedents tf.NotDifferentiable("Antecedents") extract_mentions = coref_op_library.extract_mentions tf.NotDifferentiable("ExtractMentions") distance_bins = coref_op_library.distance_bins tf.NotDifferentiable("DistanceBins")
from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf coref_op_library = tf.load_op_library("./coref_kernels.so") extract_spans = coref_op_library.extract_spans tf.NotDifferentiable("ExtractSpans") gold_scores = coref_op_library.gold_scores tf.NotDifferentiable("GoldScores") gold_scores_with_split_antecedents = coref_op_library.gold_scores_with_split_antecedents tf.NotDifferentiable("GoldScoresWithSplitAntecedents") distance_bins = coref_op_library.distance_bins tf.NotDifferentiable("DistanceBins") cluster_width_bins = coref_op_library.cluster_width_bins tf.NotDifferentiable("ClusterWidthBins") extract_antecedent_labels = coref_op_library.extract_antecedent_labels tf.NotDifferentiable("ExtractAntecedentLabels") oracle_clusters = coref_op_library.oracle_clusters tf.NotDifferentiable("OracleClusters")