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How to use _no_scope method in Slash
Best Python code snippet using slash
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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