Python resnet_utils.conv2d_same方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
    """Gets root_block_fn for beta variant.

  ResNet-v1 beta variant modifies the first original 7x7 convolution to three
  3x3 convolutions.

  Args:
    net: A tensor of size [batch, height, width, channels], input to the model.

  Returns:
    A tensor after three 3x3 convolutions.
  """
    net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
    net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
    net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')

    return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
  """Gets root_block_fn for beta variant.

  ResNet-v1 beta variant modifies the first original 7x7 convolution to three
  3x3 convolutions.

  Args:
    net: A tensor of size [batch, height, width, channels], input to the model.

  Returns:
    A tensor after three 3x3 convolutions.
  """
  net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
  net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
  net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')

  return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
    """Gets root_block_fn for beta variant.

    ResNet-v1 beta variant modifies the first original 7x7 convolution to three
    3x3 convolutions.

    Args:
      net: A tensor of size [batch, height, width, channels], input to the model.

    Returns:
      A tensor after three 3x3 convolutions.
    """
    net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
    net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
    net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')

    return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
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 = tf.reshape(w, [3, 3, 1, 1])

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

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

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

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

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

    with self.test_session() as sess:
      sess.run(tf.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()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
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 = tf.reshape(w, [3, 3, 1, 1])

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

    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = tf.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 = tf.reshape(y1_expected, [1, n, n, 1])

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

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

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

    with self.test_session() as sess:
      sess.run(tf.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()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def __call__(self, features):
    """ Define tf graph.
    """
    inputs = features['image']

    with tf.variable_scope('encoder') as vsc:
      with slim.arg_scope(resnet_v2.resnet_arg_scope()):
        # conv1
        with arg_scope(
            [layers_lib.conv2d], activation_fn=None, normalizer_fn=None):
          net = resnet_utils.conv2d_same(inputs, 16, 5, stride=2, scope='conv1')
        tf.add_to_collection(vsc.original_name_scope, net)

        # resnet blocks
        blocks = []
        for i in range(len(self.encoder_params['block_name'])):
          block = resnet_v2.resnet_v2_block(
              scope=self.encoder_params['block_name'][i],
              base_depth=self.encoder_params['base_depth'][i],
              num_units=self.encoder_params['num_units'][i],
              stride=self.encoder_params['stride'][i])
          blocks.append(block)
        net, _ = resnet_v2.resnet_v2(
            net,
            blocks,
            is_training=(self.mode == ModeKeys.TRAIN),
            global_pool=False,
            output_stride=2,
            include_root_block=False,
            scope='resnet')

        tf.add_to_collection(vsc.original_name_scope, net)
    return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def _nas_stem(inputs,
              batch_norm_fn=slim.batch_norm):
  """Stem used for NAS models."""
  net = resnet_utils.conv2d_same(inputs, 64, 3, stride=2, scope='conv0')
  net = batch_norm_fn(net, scope='conv0_bn')
  net = tf.nn.relu(net)
  net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1')
  net = batch_norm_fn(net, scope='conv1_bn')
  cell_outputs = [net]
  net = tf.nn.relu(net)
  net = resnet_utils.conv2d_same(net, 128, 3, stride=2, scope='conv2')
  net = batch_norm_fn(net, scope='conv2_bn')
  cell_outputs.append(net)
  return net, cell_outputs 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def resnet_base(img_batch, scope_name, is_training=True):
    '''
    this code is derived from light-head rcnn.
    https://github.com/zengarden/light_head_rcnn

    It is convenient to freeze blocks. So we adapt this mode.
    '''
    if scope_name == 'resnet_v1_50':
        middle_num_units = 6
    elif scope_name == 'resnet_v1_101':
        middle_num_units = 23
    else:
        raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....yjr')

    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
              resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
              # use stride 1 for the last conv4 layer.

              resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=1)]
              # when use fpn . stride list is [1, 2, 2]

    with slim.arg_scope(resnet_arg_scope(is_training=False)):
        with tf.variable_scope(scope_name, scope_name):
            # Do the first few layers manually, because 'SAME' padding can behave inconsistently
            # for images of different sizes: sometimes 0, sometimes 1
            net = resnet_utils.conv2d_same(
                img_batch, 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')

    not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True]
    # Fixed_Blocks can be 1~3

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))):
        C2, _ = resnet_v1.resnet_v1(net,
                                    blocks[0:1],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape')

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))):
        C3, _ = resnet_v1.resnet_v1(C2,
                                    blocks[1:2],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape')

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))):
        C4, _ = resnet_v1.resnet_v1(C3,
                                    blocks[2:3],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape')
    return C4 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
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 tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
                             normalizer_fn=None, activation_fn=None,
                             scope='shortcut')

    residual = slim.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 = slim.conv2d(residual, depth, [1, 1], stride=1,
                           normalizer_fn=None, activation_fn=None,
                           scope='conv3')

    output = shortcut + residual

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

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               unit_rate=1,
               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.
    unit_rate: An integer, unit rate for atrous convolution.
    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 tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
        if depth == depth_in:
            shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
        else:
            shortcut = slim.conv2d(
                inputs,
                depth,
                [1, 1],
                stride=stride,
                activation_fn=None,
                scope='shortcut')

        residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                               scope='conv1')
        residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                            rate=rate * unit_rate, scope='conv2')
        residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                               activation_fn=None, scope='conv3')
        output = tf.nn.relu(shortcut + residual)

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

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               unit_rate=1,
               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.
    unit_rate: An integer, unit rate for atrous convolution.
    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 tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(
          inputs,
          depth,
          [1, 1],
          stride=stride,
          activation_fn=None,
          scope='shortcut')

    residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate*unit_rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           activation_fn=None, scope='conv3')
    output = tf.nn.relu(shortcut + residual)

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

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def _apply_conv_operation(self, net, operation, stride,
                            is_from_original_input):
    """Applies the predicted conv operation to net."""
    if stride > 1 and not is_from_original_input:
      stride = 1
    input_filters = net.shape[3]
    filter_size = self._filter_size
    if 'separable' in operation:
      num_layers = int(operation.split('_')[-1])
      kernel_size = int(operation.split('x')[0][-1])
      for layer_num in range(num_layers):
        net = tf.nn.relu(net)
        net = separable_conv2d_same(
            net,
            filter_size,
            kernel_size,
            depth_multiplier=1,
            scope='separable_{0}x{0}_{1}'.format(kernel_size, layer_num + 1),
            stride=stride)
        net = self._batch_norm_fn(
            net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, layer_num + 1))
        stride = 1
    elif 'atrous' in operation:
      kernel_size = int(operation.split('x')[0][-1])
      net = tf.nn.relu(net)
      if stride == 2:
        scaled_height = scale_dimension(tf.shape(net)[1], 0.5)
        scaled_width = scale_dimension(tf.shape(net)[2], 0.5)
        net = resize_bilinear(net, [scaled_height, scaled_width], net.dtype)
        net = resnet_utils.conv2d_same(
            net, filter_size, kernel_size, rate=1, stride=1,
            scope='atrous_{0}x{0}'.format(kernel_size))
      else:
        net = resnet_utils.conv2d_same(
            net, filter_size, kernel_size, rate=2, stride=1,
            scope='atrous_{0}x{0}'.format(kernel_size))
      net = self._batch_norm_fn(net, scope='bn_atr_{0}x{0}'.format(kernel_size))
    elif operation in ['none']:
      if stride > 1 or (input_filters != filter_size):
        net = tf.nn.relu(net)
        net = slim.conv2d(net, filter_size, 1, stride=stride, scope='1x1')
        net = self._batch_norm_fn(net, scope='bn_1')
    elif 'pool' in operation:
      pooling_type = operation.split('_')[0]
      pooling_shape = int(operation.split('_')[-1].split('x')[0])
      if pooling_type == 'avg':
        net = slim.avg_pool2d(net, pooling_shape, stride=stride, padding='SAME')
      elif pooling_type == 'max':
        net = slim.max_pool2d(net, pooling_shape, stride=stride, padding='SAME')
      else:
        raise ValueError('Unimplemented pooling type: ', pooling_type)
      if input_filters != filter_size:
        net = slim.conv2d(net, filter_size, 1, stride=1, scope='1x1')
        net = self._batch_norm_fn(net, scope='bn_1')
    else:
      raise ValueError('Unimplemented operation', operation)

    if operation != 'none':
      net = self._apply_drop_path(net)
    return net 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               unit_rate=1,
               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.
      unit_rate: An integer, unit rate for atrous convolution.
      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 tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
        if depth == depth_in:
            shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
        else:
            shortcut = slim.conv2d(
                inputs,
                depth,
                [1, 1],
                stride=stride,
                activation_fn=None,
                scope='shortcut')

        residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                               scope='conv1')
        residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                            rate=rate*unit_rate, scope='conv2')
        residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                               activation_fn=None, scope='conv3')
        output = tf.nn.relu(shortcut + residual)

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