Removing research/community models

research/attention_ocr/python/sequence_layers_test.py

-# 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.
-# ==============================================================================
-
-"""Tests for sequence_layers."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.contrib import slim
-
-import model
-import sequence_layers
-
-
-def fake_net(batch_size, num_features, feature_size):
-  return tf.convert_to_tensor(
-      np.random.uniform(size=(batch_size, num_features, feature_size)),
-      dtype=tf.float32)
-
-
-def fake_labels(batch_size, seq_length, num_char_classes):
-  labels_np = tf.convert_to_tensor(
-      np.random.randint(
-          low=0, high=num_char_classes, size=(batch_size, seq_length)))
-  return slim.one_hot_encoding(labels_np, num_classes=num_char_classes)
-
-
-def create_layer(layer_class, batch_size, seq_length, num_char_classes):
-  model_params = model.ModelParams(
-      num_char_classes=num_char_classes,
-      seq_length=seq_length,
-      num_views=1,
-      null_code=num_char_classes)
-  net = fake_net(
-      batch_size=batch_size, num_features=seq_length * 5, feature_size=6)
-  labels_one_hot = fake_labels(batch_size, seq_length, num_char_classes)
-  layer_params = sequence_layers.SequenceLayerParams(
-      num_lstm_units=10, weight_decay=0.00004, lstm_state_clip_value=10.0)
-  return layer_class(net, labels_one_hot, model_params, layer_params)
-
-
-class SequenceLayersTest(tf.test.TestCase):
-  def test_net_slice_char_logits_with_correct_shape(self):
-    batch_size = 2
-    seq_length = 4
-    num_char_classes = 3
-
-    layer = create_layer(sequence_layers.NetSlice, batch_size, seq_length,
-                         num_char_classes)
-    char_logits = layer.create_logits()
-
-    self.assertEqual(
-        tf.TensorShape([batch_size, seq_length, num_char_classes]),
-        char_logits.get_shape())
-
-  def test_net_slice_with_autoregression_char_logits_with_correct_shape(self):
-    batch_size = 2
-    seq_length = 4
-    num_char_classes = 3
-
-    layer = create_layer(sequence_layers.NetSliceWithAutoregression,
-                         batch_size, seq_length, num_char_classes)
-    char_logits = layer.create_logits()
-
-    self.assertEqual(
-        tf.TensorShape([batch_size, seq_length, num_char_classes]),
-        char_logits.get_shape())
-
-  def test_attention_char_logits_with_correct_shape(self):
-    batch_size = 2
-    seq_length = 4
-    num_char_classes = 3
-
-    layer = create_layer(sequence_layers.Attention, batch_size, seq_length,
-                         num_char_classes)
-    char_logits = layer.create_logits()
-
-    self.assertEqual(
-        tf.TensorShape([batch_size, seq_length, num_char_classes]),
-        char_logits.get_shape())
-
-  def test_attention_with_autoregression_char_logits_with_correct_shape(self):
-    batch_size = 2
-    seq_length = 4
-    num_char_classes = 3
-
-    layer = create_layer(sequence_layers.AttentionWithAutoregression,
-                         batch_size, seq_length, num_char_classes)
-    char_logits = layer.create_logits()
-
-    self.assertEqual(
-        tf.TensorShape([batch_size, seq_length, num_char_classes]),
-        char_logits.get_shape())
-
-
-if __name__ == '__main__':
-  tf.test.main()