Python tensorflow.contrib.slim.python.slim.nets.resnet_utils.stack_blocks_dense() Examples

def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
    """A plain ResNet without extra layers before or after the ResNet blocks."""
    with variable_scope.variable_scope(scope, values=[inputs]):
      with arg_scope([layers.conv2d], outputs_collections='end_points'):
        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
        end_points = utils.convert_collection_to_dict('end_points')
        return net, end_points 

def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
    """A plain ResNet without extra layers before or after the ResNet blocks."""
    with variable_scope.variable_scope(scope, values=[inputs]):
      with arg_scope([layers.conv2d], outputs_collections='end_points'):
        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
        end_points = utils.convert_collection_to_dict('end_points')
        return net, end_points 

def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
    """A plain ResNet without extra layers before or after the ResNet blocks."""
    with variable_scope.variable_scope(scope, values=[inputs]):
      with arg_scope([layers.conv2d], outputs_collections='end_points'):
        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
        end_points = utils.convert_collection_to_dict('end_points')
        return net, end_points 

def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
    """A plain ResNet without extra layers before or after the ResNet blocks."""
    with variable_scope.variable_scope(scope, values=[inputs]):
      with arg_scope([layers.conv2d], outputs_collections='end_points'):
        net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
        end_points = utils.convert_collection_to_dict('end_points')
        return net, end_points 

def resnet_v1_backbone(inputs,
              blocks,
              is_training=True,
              output_stride=None,
              include_root_block=True,
              reuse=None,
              scope=None):
  with variable_scope.variable_scope(
      scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    with arg_scope(
        [layers.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
        outputs_collections=end_points_collection):
      with arg_scope([layers.batch_norm], is_training=is_training):
        net = inputs
        if include_root_block:
          if output_stride is not None:
            if output_stride % 4 != 0:
              raise ValueError('The output_stride needs to be a multiple of 4.')
            output_stride /= 4
          net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
          net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope='pool1')
        net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
        # Convert end_points_collection into a dictionary of end_points.
        end_points = utils.convert_collection_to_dict(end_points_collection)

        return net, end_points 

def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
      for output_stride in [1, 2, 4, 8, None]:
        with ops.Graph().as_default():
          with self.test_session() as sess:
            random_seed.set_random_seed(0)
            inputs = create_test_input(1, height, width, 3)
            # Dense feature extraction followed by subsampling.
            output = resnet_utils.stack_blocks_dense(inputs, blocks,
                                                     output_stride)
            if output_stride is None:
              factor = 1
            else:
              factor = nominal_stride // output_stride

            output = resnet_utils.subsample(output, factor)
            # Make the two networks use the same weights.
            variable_scope.get_variable_scope().reuse_variables()
            # Feature extraction at the nominal network rate.
            expected = self._stack_blocks_nondense(inputs, blocks)
            sess.run(variables.global_variables_initializer())
            output, expected = sess.run([output, expected])
            self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) 

def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
      for output_stride in [1, 2, 4, 8, None]:
        with ops.Graph().as_default():
          with self.test_session() as sess:
            random_seed.set_random_seed(0)
            inputs = create_test_input(1, height, width, 3)
            # Dense feature extraction followed by subsampling.
            output = resnet_utils.stack_blocks_dense(inputs, blocks,
                                                     output_stride)
            if output_stride is None:
              factor = 1
            else:
              factor = nominal_stride // output_stride

            output = resnet_utils.subsample(output, factor)
            # Make the two networks use the same weights.
            variable_scope.get_variable_scope().reuse_variables()
            # Feature extraction at the nominal network rate.
            expected = self._stack_blocks_nondense(inputs, blocks)
            sess.run(variables.global_variables_initializer())
            output, expected = sess.run([output, expected])
            self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def construct_embedding(self):
    """Builds an embedding function on top of images.

    Method to be overridden by implementations.

    Returns:
      embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
        holding the embedded images.
    """
    with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
      self._adaptation_scope = vs.name
      net = self._pretrained_output

      # Define some adaptation blocks on top of the pre-trained resnet output.
      adaptation_blocks = []
      adaptation_block_params = [map(
          int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
      for i, (depth, num_units) in enumerate(adaptation_block_params):
        block = resnet_v2.resnet_v2_block(
            'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
            stride=1)
        adaptation_blocks.append(block)

      # Stack them on top of the resent output.
      net = resnet_utils.stack_blocks_dense(
          net, adaptation_blocks, output_stride=None)

      # Average pool the output.
      net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)

      if self._config.emb_connection == 'fc':
        # Use fully connected layer to project to embedding layer.
        fc_hidden_sizes = self._config.fc_hidden_sizes
        if fc_hidden_sizes == 'None':
          fc_hidden_sizes = []
        else:
          fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
        fc_hidden_keep_prob = self._config.dropout.keep_fc
        net = tf.squeeze(net)
        for fc_hidden_size in fc_hidden_sizes:
          net = slim.layers.fully_connected(net, fc_hidden_size)
          if fc_hidden_keep_prob < 1.0:
            net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
                               is_training=self._is_training)

        # Connect last FC layer to embedding.
        embedding = slim.layers.fully_connected(net, self._embedding_size,
                                                activation_fn=None)
      else:
        # Use 1x1 conv layer to project to embedding layer.
        embedding = slim.conv2d(
            net, self._embedding_size, [1, 1], activation_fn=None,
            normalizer_fn=None, scope='embedding')
        embedding = tf.squeeze(embedding)

      # Optionally L2 normalize the embedding.
      if self._embedding_l2:
        embedding = tf.nn.l2_normalize(embedding, dim=1)

      return embedding 

def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
      for output_stride in [1, 2, 4, 8, None]:
        with ops.Graph().as_default():
          with self.test_session() as sess:
            random_seed.set_random_seed(0)
            inputs = create_test_input(1, height, width, 3)
            # Dense feature extraction followed by subsampling.
            output = resnet_utils.stack_blocks_dense(inputs, blocks,
                                                     output_stride)
            if output_stride is None:
              factor = 1
            else:
              factor = nominal_stride // output_stride

            output = resnet_utils.subsample(output, factor)
            # Make the two networks use the same weights.
            variable_scope.get_variable_scope().reuse_variables()
            # Feature extraction at the nominal network rate.
            expected = self._stack_blocks_nondense(inputs, blocks)
            sess.run(variables.global_variables_initializer())
            output, expected = sess.run([output, expected])
            self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) 

def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
      for output_stride in [1, 2, 4, 8, None]:
        with ops.Graph().as_default():
          with self.test_session() as sess:
            random_seed.set_random_seed(0)
            inputs = create_test_input(1, height, width, 3)
            # Dense feature extraction followed by subsampling.
            output = resnet_utils.stack_blocks_dense(inputs, blocks,
                                                     output_stride)
            if output_stride is None:
              factor = 1
            else:
              factor = nominal_stride // output_stride

            output = resnet_utils.subsample(output, factor)
            # Make the two networks use the same weights.
            variable_scope.get_variable_scope().reuse_variables()
            # Feature extraction at the nominal network rate.
            expected = self._stack_blocks_nondense(inputs, blocks)
            sess.run(variables.global_variables_initializer())
            output, expected = sess.run([output, expected])
            self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)