From 14ebbebc290510b6cfa491349862e6c5aca4200a Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Wed, 7 Feb 2018 16:24:34 -0800 Subject: [PATCH] Remove tf.contrib.ndlstm as it is not maintained and barely used. Users can find an external implementation by the original author at: https://github.com/tmbarchive/tfndlstm PiperOrigin-RevId: 184914822 --- tensorflow/BUILD | 1 - tensorflow/contrib/BUILD | 1 - tensorflow/contrib/__init__.py | 1 - tensorflow/contrib/cmake/python_modules.txt | 2 - tensorflow/contrib/ndlstm/BUILD | 92 ---------- tensorflow/contrib/ndlstm/README.md | 31 ---- tensorflow/contrib/ndlstm/__init__.py | 22 --- tensorflow/contrib/ndlstm/python/__init__.py | 25 --- tensorflow/contrib/ndlstm/python/lstm1d.py | 184 -------------------- tensorflow/contrib/ndlstm/python/lstm1d_test.py | 106 ------------ tensorflow/contrib/ndlstm/python/lstm2d.py | 213 ------------------------ tensorflow/contrib/ndlstm/python/lstm2d_test.py | 98 ----------- tensorflow/contrib/ndlstm/python/misc.py | 99 ----------- tensorflow/contrib/ndlstm/python/misc_test.py | 78 --------- tensorflow/contrib/specs/BUILD | 1 - tensorflow/contrib/specs/README.md | 11 -- tensorflow/contrib/specs/python/specs_ops.py | 20 --- tensorflow/contrib/specs/python/specs_test.py | 30 ---- tensorflow/tools/docs/generate_1_0.py | 1 - tensorflow/tools/docs/generate_lib.py | 1 - tensorflow/tools/pip_package/BUILD | 2 - 21 files changed, 1019 deletions(-) delete mode 100644 tensorflow/contrib/ndlstm/BUILD delete mode 100644 tensorflow/contrib/ndlstm/README.md delete mode 100644 tensorflow/contrib/ndlstm/__init__.py delete mode 100644 tensorflow/contrib/ndlstm/python/__init__.py delete mode 100644 tensorflow/contrib/ndlstm/python/lstm1d.py delete mode 100644 tensorflow/contrib/ndlstm/python/lstm1d_test.py delete mode 100644 tensorflow/contrib/ndlstm/python/lstm2d.py delete mode 100644 tensorflow/contrib/ndlstm/python/lstm2d_test.py delete mode 100644 tensorflow/contrib/ndlstm/python/misc.py delete mode 100644 tensorflow/contrib/ndlstm/python/misc_test.py diff --git a/tensorflow/BUILD b/tensorflow/BUILD index a73e89b..2e71783 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -535,7 +535,6 @@ filegroup( "//tensorflow/contrib/model_pruning:all_files", "//tensorflow/contrib/model_pruning/examples/cifar10:all_files", "//tensorflow/contrib/nccl:all_files", - "//tensorflow/contrib/ndlstm:all_files", "//tensorflow/contrib/nearest_neighbor:all_files", "//tensorflow/contrib/nn:all_files", "//tensorflow/contrib/opt:all_files", diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 3ed8cef..f48c2fe 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -71,7 +71,6 @@ py_library( "//tensorflow/contrib/metrics:metrics_py", "//tensorflow/contrib/model_pruning", "//tensorflow/contrib/nccl:nccl_py", - "//tensorflow/contrib/ndlstm", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_py", "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/opt:opt_py", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 46b579b..4f6f539 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -84,7 +84,6 @@ from tensorflow.contrib import training from tensorflow.contrib import util from tensorflow.contrib.eager.python import tfe as eager from tensorflow.contrib.lite.python import lite -from tensorflow.contrib.ndlstm import python as ndlstm from tensorflow.contrib.receptive_field import receptive_field_api as receptive_field from tensorflow.contrib.remote_fused_graph import pylib as remote_fused_graph from tensorflow.contrib.specs import python as specs diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 57a52bf..2720c43 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -329,8 +329,6 @@ tensorflow/contrib/nccl/kernels tensorflow/contrib/nccl/ops tensorflow/contrib/nccl/python tensorflow/contrib/nccl/python/ops -tensorflow/contrib/ndlstm -tensorflow/contrib/ndlstm/python tensorflow/contrib/nearest_neighbor/kernels tensorflow/contrib/nearest_neighbor/ops tensorflow/contrib/nearest_neighbor/python diff --git a/tensorflow/contrib/ndlstm/BUILD b/tensorflow/contrib/ndlstm/BUILD deleted file mode 100644 index 8403f84..0000000 --- a/tensorflow/contrib/ndlstm/BUILD +++ /dev/null @@ -1,92 +0,0 @@ -# Description: -# Contains classes implementing 1D and 2D LSTMs for image and signal -# processing problems. - -licenses(["notice"]) # Apache 2.0 - -exports_files(["LICENSE"]) - -package(default_visibility = ["//tensorflow:__subpackages__"]) - -load("//tensorflow:tensorflow.bzl", "tf_py_test") - -py_library( - name = "ndlstm", - srcs = [ - "__init__.py", - "python/__init__.py", - "python/lstm1d.py", - "python/lstm2d.py", - "python/misc.py", - ], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/contrib/framework:framework_py", - "//tensorflow/contrib/layers:layers_py", - "//tensorflow/contrib/rnn:rnn_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - "//tensorflow/python:ops", - "//tensorflow/python:platform", - "//tensorflow/python:random_ops", - "//tensorflow/python:rnn", - "//tensorflow/python:rnn_cell", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - ], -) - -tf_py_test( - name = "lstm1d_test", - srcs = ["python/lstm1d_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:variables", - ], -) - -tf_py_test( - name = "lstm2d_test", - srcs = ["python/lstm2d_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:variables", - ], -) - -tf_py_test( - name = "misc_test", - srcs = ["python/misc_test.py"], - additional_deps = [ - ":ndlstm", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:variables", - ], -) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/contrib/ndlstm/README.md b/tensorflow/contrib/ndlstm/README.md deleted file mode 100644 index 7ccb57f..0000000 --- a/tensorflow/contrib/ndlstm/README.md +++ /dev/null @@ -1,31 +0,0 @@ -Library of multidimensional LSTM models and related code. - -# 2D LSTM code - -The 2D LSTM layers take tensors of the form (batch_size, height, width, -depth), compatible with convolutional layers, as inputs. The library -transposes and reshapes these tensors in a way that allows batches of -images to be processed by LSTMs. - -The library currently provides: - - - a separable 2D LSTM layer - - a simple 2D convolutional layer that can be swapped out against 2D LSTM - - layers to reduce images to sequences and images to final state vectors - - layers for sequence classification, pixel-wise classification - -# Other Dimensions - -There is 1D LSTM code in `lstm1d.py`. This code implements 1D LSTM versions -suitable as a basis for higher dimensional LSTMs. It is intended for constant -batch size and uses a different layout. Although the code is perfectly fine for -1D use, you may find other 1D LSTM implementations to be more convenient if you -are interested in sequence problems. - -# Upcoming Changes - - - PyramidLSTM - - support for 3D and 4D - - optional use of native fused LSTM op - - easy-to-use command line drivers and examples - - operators for patch-wise processing diff --git a/tensorflow/contrib/ndlstm/__init__.py b/tensorflow/contrib/ndlstm/__init__.py deleted file mode 100644 index a5dd100..0000000 --- a/tensorflow/contrib/ndlstm/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Library of multidimensional LSTM models and related code.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.ndlstm.python import lstm1d -from tensorflow.contrib.ndlstm.python import lstm2d diff --git a/tensorflow/contrib/ndlstm/python/__init__.py b/tensorflow/contrib/ndlstm/python/__init__.py deleted file mode 100644 index 1aa51a6..0000000 --- a/tensorflow/contrib/ndlstm/python/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Init file, giving convenient access to all ndlstm ops.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# pylint: disable=wildcard-import,g-importing-member -from tensorflow.contrib.ndlstm.python.lstm1d import * -from tensorflow.contrib.ndlstm.python.lstm2d import * -from tensorflow.contrib.ndlstm.python.misc import * -# pylint: enable=wildcard-import diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py deleted file mode 100644 index 2e2e908..0000000 --- a/tensorflow/contrib/ndlstm/python/lstm1d.py +++ /dev/null @@ -1,184 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""LSTM layers for sequences.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.contrib.framework.python.ops import variables -from tensorflow.python.framework import constant_op -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import rnn -from tensorflow.python.ops import rnn_cell -from tensorflow.python.ops import variable_scope - - -def _shape(tensor): - return tensor.get_shape().as_list() - - -def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False): - """Run an LSTM, either forward or backward. - - This is a 1D LSTM implementation using unrolling and the TensorFlow - LSTM op. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - - Returns: - Output sequence (length, batch_size, noutput) - - """ - with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]): - length, batch_size, _ = _shape(inputs) - lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm_cell.state_size]) - output_u = [] - inputs_u = array_ops.unstack(inputs) - if reverse: - inputs_u = list(reversed(inputs_u)) - for i in xrange(length): - if i > 0: - variable_scope.get_variable_scope().reuse_variables() - output, state = lstm_cell(inputs_u[i], state) - output_u += [output] - if reverse: - output_u = list(reversed(output_u)) - outputs = array_ops.stack(output_u) - return outputs - - -def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): - """Run an LSTM, either forward or backward. - - This is a 1D LSTM implementation using dynamic_rnn and - the TensorFlow LSTM op. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - - Returns: - Output sequence (length, batch_size, noutput) - """ - with variable_scope.variable_scope(scope, "SeqLstm", [inputs]): - lstm_cell = rnn_cell.BasicLSTMCell(noutput) - if reverse: - inputs = array_ops.reverse_v2(inputs, [0]) - outputs, _ = rnn.dynamic_rnn( - lstm_cell, inputs, time_major=True, dtype=inputs.dtype) - if reverse: - outputs = array_ops.reverse_v2(outputs, [0]) - return outputs - - -def ndlstm_base(inputs, noutput, scope=None, reverse=False, dynamic=True): - """Implements a 1D LSTM, either forward or backward. - - This is a base case for multidimensional LSTM implementations, which - tend to be used differently from sequence-to-sequence - implementations. For general 1D sequence to sequence - transformations, you may want to consider another implementation - from TF slim. - - Args: - inputs: input sequence (length, batch_size, ninput) - noutput: depth of output - scope: optional scope name - reverse: run LSTM in reverse - dynamic: use dynamic_rnn - - Returns: - Output sequence (length, batch_size, noutput) - - """ - # TODO(tmb) maybe add option for other LSTM implementations, like - # slim.rnn.basic_lstm_cell - if dynamic: - return ndlstm_base_dynamic(inputs, noutput, scope=scope, reverse=reverse) - else: - return ndlstm_base_unrolled(inputs, noutput, scope=scope, reverse=reverse) - - -def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False): - """Run an LSTM across all steps and returns only the final state. - - Args: - inputs: (length, batch_size, depth) tensor - noutput: size of output vector - scope: optional scope name - name: optional name for output tensor - reverse: run in reverse - - Returns: - Batch of size (batch_size, noutput). - """ - with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]): - length, batch_size, _ = _shape(inputs) - lstm = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm.state_size]) - inputs_u = array_ops.unstack(inputs) - if reverse: - inputs_u = list(reversed(inputs_u)) - for i in xrange(length): - if i > 0: - variable_scope.get_variable_scope().reuse_variables() - output, state = lstm(inputs_u[i], state) - outputs = array_ops.reshape(output, [batch_size, noutput], name=name) - return outputs - - -def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None): - """Run a softmax layer over all the time steps of an input sequence. - - Args: - inputs: (length, batch_size, depth) tensor - noutput: output depth - scope: optional scope name - name: optional name for output tensor - linear_name: name for linear (pre-softmax) output - - Returns: - A tensor of size (length, batch_size, noutput). - - """ - length, _, ninputs = _shape(inputs) - inputs_u = array_ops.unstack(inputs) - output_u = [] - with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]): - initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1) - initial_b = constant_op.constant(0.1, shape=[noutput]) - w = variables.model_variable("weights", initializer=initial_w) - b = variables.model_variable("biases", initializer=initial_b) - for i in xrange(length): - with variable_scope.variable_scope(scope, "SequenceSoftmaxStep", - [inputs_u[i]]): - # TODO(tmb) consider using slim.fully_connected(..., - # activation_fn=tf.nn.softmax) - linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name) - output = nn_ops.softmax(linear) - output_u += [output] - outputs = array_ops.stack(output_u, name=name) - return outputs diff --git a/tensorflow/contrib/ndlstm/python/lstm1d_test.py b/tensorflow/contrib/ndlstm/python/lstm1d_test.py deleted file mode 100644 index 49b15cc..0000000 --- a/tensorflow/contrib/ndlstm/python/lstm1d_test.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Tests for 1D LSTM.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import lstm1d as lstm1d_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.ops import gradient_checker -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -lstm1d = lstm1d_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class Lstm1DTest(test.TestCase): - - def testSequenceToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 1, 5)) - outputs = lstm1d.ndlstm_base(inputs, 8) - variables.global_variables_initializer().run() - names = [v.name for v in variables.trainable_variables()] - self.assertEqual(len(names), 2) - result = outputs.eval() - self.assertEqual(tuple(result.shape), (17, 1, 8)) - - def testSequenceToSequenceGradient(self): - with self.test_session(): - size = (17, 1, 15) - output_size = (17, 1, 8) - inputs = constant_op.constant(_rand(*size)) - outputs = lstm1d.ndlstm_base(inputs, 8, dynamic=False) - variables.global_variables_initializer().run() - gradients = gradients_impl.gradients(outputs, inputs) - if 1: # pylint: disable=using-constant-test - gradients = gradients_impl.gradients(outputs, inputs)[0].eval() - self.assertEqual(gradients.shape, size) - else: - # TODO(tmb) tf.test.compute_gradient error is currently broken - # with dynamic_rnn. Enable this test case eventually. - err = gradient_checker.compute_gradient_error( - inputs, size, outputs, output_size, delta=1e-4) - self.assert_(not np.isnan(err)) - self.assert_(err < 0.1) - - def testSequenceToSequenceGradientReverse(self): - with self.test_session(): - size = (17, 1, 15) - output_size = (17, 1, 8) - inputs = constant_op.constant(_rand(*size)) - outputs = lstm1d.ndlstm_base(inputs, 8, reverse=1, dynamic=False) - variables.global_variables_initializer().run() - if 1: # pylint: disable=using-constant-test - gradients = gradients_impl.gradients(outputs, inputs)[0].eval() - self.assertEqual(gradients.shape, size) - else: - # TODO(tmb) tf.test.compute_gradient error is currently broken - # with dynamic_rnn. Enable this test case eventually. - err = gradient_checker.compute_gradient_error( - inputs, size, outputs, output_size, delta=1e-4) - self.assert_(not np.isnan(err)) - self.assert_(err < 0.1) - - def testSequenceToFinalDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 6, 5)) - outputs = lstm1d.sequence_to_final(inputs, 8) - variables.global_variables_initializer().run() - names = [v.name for v in variables.trainable_variables()] - self.assertEqual(len(names), 2) - result = outputs.eval() - self.assertEqual(tuple(result.shape), (6, 8)) - - def testSequenceSoftmaxDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(17, 1, 5)) - outputs = lstm1d.sequence_softmax(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (17, 1, 8)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/ndlstm/python/lstm2d.py b/tensorflow/contrib/ndlstm/python/lstm2d.py deleted file mode 100644 index ebbb4cc..0000000 --- a/tensorflow/contrib/ndlstm/python/lstm2d.py +++ /dev/null @@ -1,213 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""A small library of functions dealing with LSTMs applied to images. - -Tensors in this library generally have the shape (num_images, height, width, -depth). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.ndlstm.python import lstm1d -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import variable_scope - - -def _shape(tensor): - """Get the shape of a tensor as an int list.""" - return tensor.get_shape().as_list() - - -def images_to_sequence(tensor): - """Convert a batch of images into a batch of sequences. - - Args: - tensor: a (num_images, height, width, depth) tensor - - Returns: - (width, num_images*height, depth) sequence tensor - """ - - num_image_batches, height, width, depth = _shape(tensor) - transposed = array_ops.transpose(tensor, [2, 0, 1, 3]) - return array_ops.reshape(transposed, - [width, num_image_batches * height, depth]) - - -def sequence_to_images(tensor, num_image_batches): - """Convert a batch of sequences into a batch of images. - - Args: - tensor: (num_steps, num_batches, depth) sequence tensor - num_image_batches: the number of image batches - - Returns: - (num_images, height, width, depth) tensor - """ - - width, num_batches, depth = _shape(tensor) - height = num_batches // num_image_batches - reshaped = array_ops.reshape(tensor, - [width, num_image_batches, height, depth]) - return array_ops.transpose(reshaped, [1, 2, 0, 3]) - - -def horizontal_lstm(images, num_filters_out, scope=None): - """Run an LSTM bidirectionally over all the rows of each image. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output depth - scope: optional scope name - - Returns: - (num_images, height, width, num_filters_out) tensor, where - num_steps is width and new num_batches is num_image_batches * height - """ - with variable_scope.variable_scope(scope, "HorizontalLstm", [images]): - batch_size, _, _, _ = _shape(images) - sequence = images_to_sequence(images) - with variable_scope.variable_scope("lr"): - hidden_sequence_lr = lstm1d.ndlstm_base(sequence, num_filters_out // 2) - with variable_scope.variable_scope("rl"): - hidden_sequence_rl = (lstm1d.ndlstm_base( - sequence, num_filters_out - num_filters_out // 2, reverse=1)) - output_sequence = array_ops.concat([hidden_sequence_lr, hidden_sequence_rl], - 2) - output = sequence_to_images(output_sequence, batch_size) - return output - - -def get_blocks(images, kernel_size): - """Split images in blocks - - Args: - images: (num_images, height, width, depth) tensor - kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of - of the pooling. Can be an int if both values are the same. - - Returns: - (num_images, height/kernel_height, width/kernel_width, - depth*kernel_height*kernel_width) tensor - """ - with variable_scope.variable_scope("image_blocks"): - batch_size, height, width, chanels = _shape(images) - - if height % kernel_size[0] != 0: - offset = array_ops.zeros([batch_size, - kernel_size[0] - (height % kernel_size[0]), - width, - chanels]) - images = array_ops.concat([images, offset], 1) - batch_size, height, width, chanels = _shape(images) - if width % kernel_size[1] != 0: - offset = array_ops.zeros([batch_size, - height, - kernel_size[1] - (width % kernel_size[1]), - chanels]) - images = array_ops.concat([images, offset], 2) - batch_size, height, width, chanels = _shape(images) - - h, w = int(height / kernel_size[0]), int(width / kernel_size[1]) - features = kernel_size[1] * kernel_size[0] * chanels - - lines = array_ops.split(images, h, axis=1) - line_blocks = [] - for line in lines: - line = array_ops.transpose(line, [0, 2, 3, 1]) - line = array_ops.reshape(line, [batch_size, w, features]) - line_blocks.append(line) - - return array_ops.stack(line_blocks, axis=1) - - -def separable_lstm(images, num_filters_out, - kernel_size=None, nhidden=None, scope=None): - """Run bidirectional LSTMs first horizontally then vertically. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of - of the pooling. Can be an int if both values are the same. Set to None for - not using blocks - nhidden: hidden layer depth - scope: optional scope name - - Returns: - (num_images, height/kernel_height, width/kernel_width, - num_filters_out) tensor - """ - with variable_scope.variable_scope(scope, "SeparableLstm", [images]): - if nhidden is None: - nhidden = num_filters_out - if kernel_size is not None: - images = get_blocks(images, kernel_size) - hidden = horizontal_lstm(images, nhidden) - with variable_scope.variable_scope("vertical"): - transposed = array_ops.transpose(hidden, [0, 2, 1, 3]) - output_transposed = horizontal_lstm(transposed, num_filters_out) - output = array_ops.transpose(output_transposed, [0, 2, 1, 3]) - return output - - -def reduce_to_sequence(images, num_filters_out, scope=None): - """Reduce an image to a sequence by scanning an LSTM vertically. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - scope: optional scope name - - Returns: - A (width, num_images, num_filters_out) sequence. - """ - with variable_scope.variable_scope(scope, "ReduceToSequence", [images]): - batch_size, height, width, depth = _shape(images) - transposed = array_ops.transpose(images, [1, 0, 2, 3]) - reshaped = array_ops.reshape(transposed, - [height, batch_size * width, depth]) - reduced = lstm1d.sequence_to_final(reshaped, num_filters_out) - output = array_ops.reshape(reduced, [batch_size, width, num_filters_out]) - return output - - -def reduce_to_final(images, num_filters_out, nhidden=None, scope=None): - """Reduce an image to a final state by running two LSTMs. - - Args: - images: (num_images, height, width, depth) tensor - num_filters_out: output layer depth - nhidden: hidden layer depth (defaults to num_filters_out) - scope: optional scope name - - Returns: - A (num_images, num_filters_out) batch. - """ - with variable_scope.variable_scope(scope, "ReduceToFinal", [images]): - nhidden = nhidden or num_filters_out - batch_size, height, width, depth = _shape(images) - transposed = array_ops.transpose(images, [1, 0, 2, 3]) - reshaped = array_ops.reshape(transposed, - [height, batch_size * width, depth]) - with variable_scope.variable_scope("reduce1"): - reduced = lstm1d.sequence_to_final(reshaped, nhidden) - transposed_hidden = array_ops.reshape(reduced, - [batch_size, width, nhidden]) - hidden = array_ops.transpose(transposed_hidden, [1, 0, 2]) - with variable_scope.variable_scope("reduce2"): - output = lstm1d.sequence_to_final(hidden, num_filters_out) - return output diff --git a/tensorflow/contrib/ndlstm/python/lstm2d_test.py b/tensorflow/contrib/ndlstm/python/lstm2d_test.py deleted file mode 100644 index f1b37d7..0000000 --- a/tensorflow/contrib/ndlstm/python/lstm2d_test.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Tests for 2D LSTMs.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import lstm2d as lstm2d_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import test_util -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -lstm2d = lstm2d_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class Lstm2DTest(test_util.TensorFlowTestCase): - - def testImagesToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.images_to_sequence(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (11, 14, 5)) - - def testSequenceToImagesDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(11, 14, 5)) - outputs = lstm2d.sequence_to_images(inputs, 2) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7, 11, 5)) - - def testImagesAndSequenceDims(self): - with self.test_session(): - size = (2, 7, 11, 5) - inputs = constant_op.constant(_rand(*size)) - sequence = lstm2d.images_to_sequence(inputs) - outputs = lstm2d.sequence_to_images(sequence, size[0]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), size) - - def testSeparableLstmDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.separable_lstm(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7, 11, 8)) - - def testSeparableLstmDimsBlocks(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.separable_lstm(inputs, 8, kernel_size=[2, 2]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 4, 6, 8)) - - def testReduceToSequenceDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.reduce_to_sequence(inputs, 8) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 11, 8)) - - def testReduceToFinalDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = lstm2d.reduce_to_final(inputs, 8, 12) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 8)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/ndlstm/python/misc.py b/tensorflow/contrib/ndlstm/python/misc.py deleted file mode 100644 index 38eeff8..0000000 --- a/tensorflow/contrib/ndlstm/python/misc.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Miscellaneous functions useful for nD-LSTM models. - -Some of these functions duplicate functionality in tfslim with -slightly different interfaces. - -Tensors in this library generally have the shape (num_images, height, width, -depth). -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.layers.python.layers import layers -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import sparse_ops - - -def _shape(tensor): - """Get the shape of a tensor as an int list.""" - return tensor.get_shape().as_list() - - -def pixels_as_vector(images, scope=None): - """Reduce images to vectors by combining all pixels.""" - with ops.name_scope(scope, "PixelsAsVector", [images]): - batch_size, height, width, depth = _shape(images) - return array_ops.reshape(images, [batch_size, height * width * depth]) - - -def pool_as_vector(images, scope=None): - """Reduce images to vectors by averaging all pixels.""" - with ops.name_scope(scope, "PoolAsVector", [images]): - return math_ops.reduce_mean(images, [1, 2]) - - -def one_hot_planes(labels, num_classes, scope=None): - """Compute 1-hot encodings for planes. - - Given a label, this computes a label image that contains - 1 at all pixels in the plane corresponding to the target - class and 0 in all other planes. - - Args: - labels: (batch_size,) tensor - num_classes: number of classes - scope: optional scope name - - Returns: - Tensor of shape (batch_size, 1, 1, num_classes) with a 1-hot encoding. - """ - with ops.name_scope(scope, "OneHotPlanes", [labels]): - batch_size, = _shape(labels) - batched = layers.one_hot_encoding(labels, num_classes) - return array_ops.reshape(batched, [batch_size, 1, 1, num_classes]) - - -def one_hot_mask(labels, num_classes, scope=None): - """Compute 1-hot encodings for masks. - - Given a label image, this computes the one hot encoding at - each pixel. - - Args: - labels: (batch_size, width, height, 1) tensor containing labels. - num_classes: number of classes - scope: optional scope name - - Returns: - Tensor of shape (batch_size, width, height, num_classes) with - a 1-hot encoding. - """ - with ops.name_scope(scope, "OneHotMask", [labels]): - height, width, depth = _shape(labels) - assert depth == 1 - sparse_labels = math_ops.to_int32(array_ops.reshape(labels, [-1, 1])) - sparse_size, _ = _shape(sparse_labels) - indices = array_ops.reshape(math_ops.range(0, sparse_size, 1), [-1, 1]) - concated = array_ops.concat([indices, sparse_labels], 1) - dense_result = sparse_ops.sparse_to_dense(concated, - [sparse_size, num_classes], 1.0, - 0.0) - result = array_ops.reshape(dense_result, [height, width, num_classes]) - return result diff --git a/tensorflow/contrib/ndlstm/python/misc_test.py b/tensorflow/contrib/ndlstm/python/misc_test.py deleted file mode 100644 index fac9023..0000000 --- a/tensorflow/contrib/ndlstm/python/misc_test.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# 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. -# ============================================================================== -"""Miscellaneous tests.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.ndlstm.python import misc as misc_lib -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import test_util -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -misc = misc_lib - - -def _rand(*size): - return np.random.uniform(size=size).astype("f") - - -class LstmMiscTest(test_util.TensorFlowTestCase): - - def testPixelsAsVectorDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = misc.pixels_as_vector(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 7 * 11 * 5)) - - def testPoolAsVectorDims(self): - with self.test_session(): - inputs = constant_op.constant(_rand(2, 7, 11, 5)) - outputs = misc.pool_as_vector(inputs) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 5)) - - def testOneHotPlanes(self): - with self.test_session(): - inputs = constant_op.constant([0, 1, 3]) - outputs = misc.one_hot_planes(inputs, 4) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (3, 1, 1, 4)) - target = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) - self.assertAllClose(result.reshape(-1), target.reshape(-1)) - - def testOneHotMask(self): - with self.test_session(): - data = np.array([[0, 1, 2], [2, 0, 1]]).reshape(2, 3, 1) - inputs = constant_op.constant(data) - outputs = misc.one_hot_mask(inputs, 3) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (2, 3, 3)) - target = np.array([[[1, 0, 0], [0, 1, 0]], [[0, 1, 0], [0, 0, 1]], - [[0, 0, 1], [1, 0, 0]]]).transpose(1, 2, 0) - self.assertAllClose(result.reshape(-1), target.reshape(-1)) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/specs/BUILD b/tensorflow/contrib/specs/BUILD index 4b68869..084953a 100644 --- a/tensorflow/contrib/specs/BUILD +++ b/tensorflow/contrib/specs/BUILD @@ -23,7 +23,6 @@ py_library( srcs_version = "PY2AND3", deps = [ "//tensorflow/contrib/layers:layers_py", - "//tensorflow/contrib/ndlstm", "//tensorflow/python:array_ops", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:logging_ops", diff --git a/tensorflow/contrib/specs/README.md b/tensorflow/contrib/specs/README.md index b764e6e..bcf34e6 100644 --- a/tensorflow/contrib/specs/README.md +++ b/tensorflow/contrib/specs/README.md @@ -59,17 +59,6 @@ Reshaping: - `Squeeze` = tf.squeeze - `Expand` = tf.expand_dims -Multidimensional LSTM: - -These are intended as alternatives to 2D convolutions. For sequence models, -there will be other modeling primitives. - - - `Lstm2` = Fun(lstm2d.separable_lstm) # 2D-to-2D - - `Lstm2to1` = Fun(lstm2d.reduce_to_sequence) # 2D-to-1D - - `Lstm2to0` = Fun(lstm2d.reduce_to_final) # 2D-to-vector - - `Clstm2(n, m)` is a `Cl(n, [3,3])` followed by `Lstm2(m)` - - `Dws(n)` is a depthwise convolution `Cs(n, [1, 1])` - Other: - `Id` = identity diff --git a/tensorflow/contrib/specs/python/specs_ops.py b/tensorflow/contrib/specs/python/specs_ops.py index a6bd4d1..49b989b 100644 --- a/tensorflow/contrib/specs/python/specs_ops.py +++ b/tensorflow/contrib/specs/python/specs_ops.py @@ -23,8 +23,6 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.layers.python.layers import layers -from tensorflow.contrib.ndlstm.python import lstm1d -from tensorflow.contrib.ndlstm.python import lstm2d from tensorflow.contrib.specs.python import specs_lib from tensorflow.python.ops import array_ops from tensorflow.python.ops import logging_ops @@ -122,17 +120,6 @@ Sig = Fun(math_ops.sigmoid) Tanh = Fun(math_ops.tanh) Smax = Fun(nn_ops.softmax) -# 2D LSTM - -Lstm2 = Fun(lstm2d.separable_lstm) -Lstm2to1 = Fun(lstm2d.reduce_to_sequence) # 2D to 1D -Lstm2to0 = Fun(lstm2d.reduce_to_final) # 2D to depth-only - - -def Clstm2(n, *args, **kw): - """2D LSTM with 3x3 pre-convolution.""" - return Cl(n, [3, 3]) | Lstm2(*args, **kw) - def Dws(n): """Depth-wise convolution + sigmoid (used after LSTM).""" @@ -143,13 +130,6 @@ def Dwm(n): """Depth-wise convolution + softmax (used after LSTM).""" return Cm(n, [1, 1]) - -# 1D LSTM - -Lstm1 = Fun(lstm1d.ndlstm_base) -Lstm1to0 = Fun(lstm1d.sequence_to_final) # 1D to depth-only -Ssm = Fun(lstm1d.sequence_softmax) - # Sharing of Variables diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py index 41782a9..9a4ad36 100644 --- a/tensorflow/contrib/specs/python/specs_test.py +++ b/tensorflow/contrib/specs/python/specs_test.py @@ -149,36 +149,6 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (10, 20)) self.assertEqual(summaries.tf_spec_structure(spec, inputs), "_ sig sig") - def testLstm2(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 64, 64, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 64, 64, 15)) - - def testLstm2to1(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2to1(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 64, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 64, 15)) - - def testLstm2to0(self): - with self.test_session(): - inputs = constant_op.constant(_rand(1, 64, 64, 5)) - spec = "net = Lstm2to0(15)" - outputs = specs.create_net(spec, inputs) - self.assertEqual(outputs.get_shape().as_list(), [1, 15]) - variables.global_variables_initializer().run() - result = outputs.eval() - self.assertEqual(tuple(result.shape), (1, 15)) - def testKeywordRestriction(self): with self.test_session(): inputs = constant_op.constant(_rand(10, 20)) diff --git a/tensorflow/tools/docs/generate_1_0.py b/tensorflow/tools/docs/generate_1_0.py index cdc03fd..f4384e0 100644 --- a/tensorflow/tools/docs/generate_1_0.py +++ b/tensorflow/tools/docs/generate_1_0.py @@ -53,7 +53,6 @@ if __name__ == '__main__': 'factorization', 'grid_rnn', 'labeled_tensor', - 'ndlstm', 'quantization', 'session_bundle', 'slim', diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index 003f972..34dd419 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -215,7 +215,6 @@ def _get_default_do_not_descend_map(): # Block contrib.keras to de-clutter the docs 'keras', 'labeled_tensor', - 'ndlstm', 'quantization', 'session_bundle', 'slim', diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index a9c4a8d..3189bd0 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -70,7 +70,6 @@ py_binary( "//tensorflow/python/eager:eager_pip", "//tensorflow/contrib/summary:summary_test_util", # These targets don't build on Windows yet. Exclude them for now. - # "//tensorflow/contrib/ndlstm", # "//tensorflow/contrib/slim", # "//tensorflow/contrib/slim/python/slim/nets:nets_pip", # "//tensorflow/contrib/specs", @@ -159,7 +158,6 @@ sh_binary( "//tensorflow/contrib/lite/toco:toco", "//tensorflow/contrib/lite/toco/python:toco_wrapper", "//tensorflow/contrib/lite/toco/python:toco_from_protos", - "//tensorflow/contrib/ndlstm:ndlstm", "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/predictor:predictor_pip", "//tensorflow/contrib/py2tf:py2tf", -- 2.7.4