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How to use _no_scope method in Slash

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net_component.py

Source:net_component.py

1from lib.config import cfg2from lib.utils import print_tensor_shapes, compute_sequence_length, get_trainable_variables_by_scope3import tensorflow as tf4import tensorflow.contrib.slim as slim5class NetComponent(object):6    """A network component.7    Simply an architecture without a loss or placeholders. Heavily modeled after the Network class.8    """9    def __init__(self, is_training, reuse=False, name='network_component', no_scope=False):10        self._name = name11        self._is_training = is_training12        self._reuse = reuse13        self._no_scope = no_scope14        self._outputs = None15        self._vars_to_restore = None16    def get_trainable_variables(self):17        """Return the trainable variables in the specified scope.18        Args:19            scope_name: Name of the scope as a string.20        Returns:21            trainable_vars: TensorFlow variables that are trainable and in the22                specified scope.23        """24        if self._no_scope is True:25            trainable_vars = self._trainable_vars26        else:27            trainable_vars = get_trainable_variables_by_scope(self.name)28        return trainable_vars29    def build_architecture(self, inputs_dict):30        """Build the architecture, not including placeholders and preprocessing.31        """32        raise NotImplementedError('Must be implemented by a subclass.')33    def build_model(self, inputs_dict):34        """Build the network component, without placeholders and loss.35        """36        print('building network:', self.name)37        def _build_model():38            self._outputs = self.build_architecture(inputs_dict)39            # Create summaries for network outputs40            if isinstance(self._outputs, dict):41                for k, v in self._outputs.items():42                    if isinstance(v, tf.Tensor):43                        tf.summary.histogram(k + '_hist_summary', v)44        if self._no_scope is True:45            _build_model()46            cur_variable_scope = tf.get_variable_scope()47            self._vars_to_restore = slim.get_variables_to_restore(include=[cur_variable_scope])48            self._trainable_vars = get_trainable_variables_by_scope(cur_variable_scope.name)49        else:50            with tf.variable_scope(self.name, reuse=self.reuse) as sc:51                _build_model()52                self._vars_to_restore = slim.get_variables_to_restore(include=[sc.name])53        print('--> done building {}'.format(self.name))54    @property55    def name(self):56        return self._name57    @property58    def outputs(self):59        return self._outputs60    @property61    def is_training(self):62        return self._is_training63    @property64    def vars_to_restore(self):65        return self._vars_to_restore66    @property67    def reuse(self):68        return self._reuse69class TextEncoderNetComponent(NetComponent):70    def __init__(self, is_training, reuse=False, name='text_encoder_net_component'):71        super(TextEncoderNetComponent, self).__init__(is_training, reuse=reuse, name=name)72    def preprocess_inputs(self, inputs_dict):73        caption_batch, vocab_size = (inputs_dict['caption_batch'], inputs_dict['vocab_size'])74        input_batch = caption_batch75        print('--> building embedding layer')76        with tf.variable_scope('embedding_layer', reuse=self.reuse):77            embeddings = tf.Variable(78                tf.random_uniform([vocab_size, self.embedding_size], -1.0, 1.0),79                name='embedding_matrix')80            embedding_batch = tf.nn.embedding_lookup(embeddings,81                                                     input_batch,82                                                     name='input_embedding_batch')83            # print shapes84            print_tensor_shapes([embedding_batch], prefix='----> ')85        seq_length = compute_sequence_length(input_batch)86        return {'embedding_batch': embedding_batch,87                'seq_length': seq_length}88    def build_model(self, inputs_dict):89        """Build the network component, without placeholders and loss.90        """91        print('building network:', self.name)92        with tf.variable_scope(self.name, reuse=self.reuse) as sc:93            processed_inputs = self.preprocess_inputs(inputs_dict)94            arch_inputs_dict = {**inputs_dict, **processed_inputs}  # Combine dicts95            self._outputs = self.build_architecture(arch_inputs_dict)96            # Create summaries for network outputs97            if isinstance(self._outputs, dict):98                for k, v in self._outputs.items():99                    if isinstance(v, tf.Tensor):100                        tf.summary.histogram(k + '_hist_summary', v)101            self._vars_to_restore = slim.get_variables_to_restore(include=[sc.name])102        print('--> done building {}'.format(self.name))103    @property104    def embedding_size(self):105        raise NotImplementedError('Must be implemented by a subclass.')106class LBANetComponent(NetComponent):107    def __init__(self, is_training, reuse=False, name='lba_net_component', no_scope=False):108        super(LBANetComponent, self).__init__(is_training, reuse=reuse, name=name,109                                              no_scope=no_scope)110    def build_architecture(self, inputs_dict):111        """Build the architecture, not including placeholders and preprocessing.112        Literally copy and pasted from lib.LBA (sorry!).113        """114        text_inputs_dict = {115            'caption_batch': inputs_dict['raw_embedding_batch'],116            'vocab_size': inputs_dict['vocab_size'],117        }118        self.text_encoder = self.text_encoder_class(self.is_training, self.reuse)119        self.text_encoder.build_model(text_inputs_dict)120        shape_inputs_dict = {'shape_batch': inputs_dict['shape_batch']}121        self.shape_encoder = self.shape_encoder_class(self.is_training, self.reuse)122        self.shape_encoder.build_model(shape_inputs_dict)123        # Change 'encoder_output' to be normalized124        if cfg.LBA.NORMALIZE is True:125            assert 'encoder_output' in self.text_encoder.outputs126            orig_text_encoder_output = self.text_encoder.outputs['encoder_output']127            self.text_encoder.outputs['encoder_output'] = tf.nn.l2_normalize(128                orig_text_encoder_output,129                dim=1,130                name='normalize_text_encoding',131            )132            assert 'encoder_output' in self.shape_encoder.outputs133            orig_shape_encoder_output = self.shape_encoder.outputs['encoder_output']134            self.shape_encoder.outputs['encoder_output'] = tf.nn.l2_normalize(135                orig_shape_encoder_output,136                dim=1,137                name='normalize_shape_encoding',138            )139        return {140            'text_encoder': self.text_encoder.outputs,141            'shape_encoder': self.shape_encoder.outputs,142        }143    @property144    def text_encoder_class(self):145        raise NotImplementedError('Must be implemented by a subclass.')146    @property147    def shape_encoder_class(self):148        raise NotImplementedError('Must be implemented by a subclass.')149class CriticNetComponent(NetComponent):150    def __init__(self, is_training, reuse=False, name='critic_net_component'):...

test_scope_manager.py

Source:test_scope_manager.py

...54        def session_validation():55            assert slash.get_current_scope() == 'session'56        @gossip.register('slash.configure', token=TOKEN)57        @gossip.register('slash.app_quit', token=TOKEN)58        def _no_scope():59            assert slash.get_current_scope() is None60        def test_something():61            assert slash.get_current_scope() == 'test'62        gossip.unregister_token(TOKEN)63    suite_builder.build().run().assert_success(1)64    assert get_current_scope() is None65@pytest.fixture66def scope_manager(dummy_fixture_store, forge):67    session = slash.Session()68    forge.replace_with(session, 'fixture_store', dummy_fixture_store)69    return ScopeManager(session)70@pytest.fixture71def dummy_fixture_store():72    return DummyFixtureStore()...

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