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Python resnet_utils.conv2d_same方法代码示例

本文整理汇总了Python中tensorflow.contrib.slim.python.slim.nets.resnet_utils.conv2d_same方法的典型用法代码示例。如果您正苦于以下问题:Python resnet_utils.conv2d_same方法的具体用法?Python resnet_utils.conv2d_same怎么用?Python resnet_utils.conv2d_same使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.contrib.slim.python.slim.nets.resnet_utils的用法示例。


在下文中一共展示了resnet_utils.conv2d_same方法的13个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: testConv2DSameEven

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.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 = 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()) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:38,代码来源:resnet_v2_test.py

示例2: testConv2DSameOdd

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.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 = 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()) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:42,代码来源:resnet_v2_test.py

示例3: testConv2DSameOdd

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.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 = 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()) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:39,代码来源:resnet_v1_test.py

示例4: _build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:wanjinchang,项目名称:SSH-TensorFlow,代码行数:9,代码来源:resnet_v1.py

示例5: _build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:InnerPeace-Wu,项目名称:densecap-tensorflow,代码行数:9,代码来源:resnet_v1.py

示例6: build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:pengzhou1108,项目名称:RGB-N,代码行数:16,代码来源:resnet_fusion_noise.py

示例7: build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:pengzhou1108,项目名称:RGB-N,代码行数:9,代码来源:resnet_fusion.py

示例8: _build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:endernewton,项目名称:tf-faster-rcnn,代码行数:9,代码来源:resnet_v1.py

示例9: build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:vt-vl-lab,项目名称:iCAN,代码行数:9,代码来源:iCAN_ResNet50_VCOCO.py

示例10: build_base

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:DirtyHarryLYL,项目名称:Transferable-Interactiveness-Network,代码行数:15,代码来源:TIN_HICO.py

示例11: resnet_v1_backbone

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import conv2d_same [as 别名]
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 
开发者ID:HiKapok,项目名称:X-Detector,代码行数:29,代码来源:slim_resnet_utils.py

示例12: bottleneck

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.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 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) 
开发者ID:MingtaoGuo,项目名称:Chinese-Character-and-Calligraphic-Image-Processing,代码行数:53,代码来源:resnet_v1.py

示例13: bottleneck

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.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 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) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:61,代码来源:resnet_v2.py


注:本文中的tensorflow.contrib.slim.python.slim.nets.resnet_utils.conv2d_same方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。