Python resnet_utils.conv2d_same方法代碼示例

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def testConv2DSameEven(self):
    n, n2 = 4, 2

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    w = create_test_input(1, 3, 3, 1)
    w = array_ops.reshape(w, [3, 3, 1, 1])

    variable_scope.get_variable('Conv/weights', initializer=w)
    variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
    variable_scope.get_variable_scope().reuse_variables()

    y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = math_ops.to_float([[14, 28, 43, 26], [28, 48, 66, 37],
                                     [43, 66, 84, 46], [26, 37, 46, 22]])
    y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = math_ops.to_float([[14, 43], [43, 84]])
    y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])

    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
    y4_expected = math_ops.to_float([[48, 37], [37, 22]])
    y4_expected = array_ops.reshape(y4_expected, [1, n2, n2, 1])

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def testConv2DSameOdd(self):
    n, n2 = 5, 3

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    w = create_test_input(1, 3, 3, 1)
    w = array_ops.reshape(w, [3, 3, 1, 1])

    variable_scope.get_variable('Conv/weights', initializer=w)
    variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
    variable_scope.get_variable_scope().reuse_variables()

    y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = math_ops.to_float([[14, 28, 43, 58, 34],
                                     [28, 48, 66, 84, 46],
                                     [43, 66, 84, 102, 55],
                                     [58, 84, 102, 120, 64],
                                     [34, 46, 55, 64, 30]])
    y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = math_ops.to_float([[14, 43, 34],
                                     [43, 84, 55],
                                     [34, 55, 30]])
    y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])

    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
    y4_expected = y2_expected

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def testConv2DSameOdd(self):
    n, n2 = 5, 3

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    w = create_test_input(1, 3, 3, 1)
    w = array_ops.reshape(w, [3, 3, 1, 1])

    variable_scope.get_variable('Conv/weights', initializer=w)
    variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
    variable_scope.get_variable_scope().reuse_variables()

    y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = math_ops.to_float([[14, 28, 43, 58, 34], [28, 48, 66, 84, 46],
                                     [43, 66, 84, 102, 55],
                                     [58, 84, 102, 120, 64],
                                     [34, 46, 55, 64, 30]])
    y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = math_ops.to_float([[14, 43, 34], [43, 84, 55], [34, 55, 30]])
    y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])

    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
    y4_expected = y2_expected

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def _build_base(self):
        with tf.variable_scope(self._scope, self._scope):
            net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

        return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def _build_base(self):
        with tf.variable_scope(self._resnet_scope):
            net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

        return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def build_base(self):
    #with tf.variable_scope('noise'):
      ##kernel = tf.get_variable('weights',
                            #shape=[5, 5, 3, 3],
                            #initializer=tf.constant_initializer(Wcnn))
      #conv = tf.nn.conv2d(self.noise, Wcnn, [1, 1, 1, 1], padding='SAME',name='srm')
      #conv = tf.nn.conv2d(self.noise, kernel, [1, 1, 1, 1], padding='SAME',name='srm')
      #srm_conv = tf.nn.tanh(conv, name='tanh')
    with tf.variable_scope(self._resnet_scope, self._resnet_scope):
      net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
      net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
      net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

    return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def build_base(self):
    with tf.variable_scope(self._resnet_scope, self._resnet_scope):
      net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
      net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
      net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

    return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def _build_base(self):
    with tf.variable_scope(self._scope, self._scope):
      net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
      net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
      net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

    return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def build_base(self):
        with tf.variable_scope(self.scope, self.scope):
            net = resnet_utils.conv2d_same(self.image, 64, 7, stride=2, scope='conv1')
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

        return net 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def build_base(self):
        with tf.variable_scope(self.scope, self.scope):
            net = resnet_utils.conv2d_same(self.image, 64, 7, stride=2, scope='conv1') # conv2d + subsample
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')

        return net

# Number of fixed blocks during training, by default ***the first of all 4 blocks*** is fixed (Resnet-50 block)
# Range: 0 (none) to 3 (all)
# __C.RESNET.FIXED_BLOCKS = 1

    # feature extractor 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
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 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               rate=1,
               outputs_collections=None,
               scope=None):
  """Bottleneck residual unit variant with BN after convolutions.

  This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
  its definition. Note that we use here the bottleneck variant which has an
  extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with variable_scope.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
    depth_in = utils.last_dimension(inputs.get_shape(), min_rank=4)
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = layers.conv2d(
          inputs,
          depth, [1, 1],
          stride=stride,
          activation_fn=None,
          scope='shortcut')

    residual = layers.conv2d(
        inputs, depth_bottleneck, [1, 1], stride=1, scope='conv1')
    residual = resnet_utils.conv2d_same(
        residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
    residual = layers.conv2d(
        residual, depth, [1, 1], stride=1, activation_fn=None, scope='conv3')

    output = nn_ops.relu(shortcut + residual)

    return utils.collect_named_outputs(outputs_collections, sc.name, output) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 別名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               rate=1,
               outputs_collections=None,
               scope=None):
  """Bottleneck residual unit variant with BN before convolutions.

  This is the full preactivation residual unit variant proposed in [2]. See
  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
  variant which has an extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with variable_scope.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
    depth_in = utils.last_dimension(inputs.get_shape(), min_rank=4)
    preact = layers.batch_norm(
        inputs, activation_fn=nn_ops.relu, scope='preact')
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = layers_lib.conv2d(
          preact,
          depth, [1, 1],
          stride=stride,
          normalizer_fn=None,
          activation_fn=None,
          scope='shortcut')

    residual = layers_lib.conv2d(
        preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
    residual = resnet_utils.conv2d_same(
        residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
    residual = layers_lib.conv2d(
        residual,
        depth, [1, 1],
        stride=1,
        normalizer_fn=None,
        activation_fn=None,
        scope='conv3')

    output = shortcut + residual

    return utils.collect_named_outputs(outputs_collections, sc.name, output)