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Python resnet_utils.subsample方法代码示例

本文整理汇总了Python中tensorflow.contrib.slim.python.slim.nets.resnet_utils.subsample方法的典型用法代码示例。如果您正苦于以下问题:Python resnet_utils.subsample方法的具体用法?Python resnet_utils.subsample怎么用?Python resnet_utils.subsample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.subsample方法的10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: testAtrousFullyConvolutionalValues

# 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 subsample [as 别名]
def testAtrousFullyConvolutionalValues(self):
    """Verify dense feature extraction with atrous convolution."""
    nominal_stride = 32
    for output_stride in [4, 8, 16, 32, None]:
      with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
        with ops.Graph().as_default():
          with self.test_session() as sess:
            random_seed.set_random_seed(0)
            inputs = create_test_input(2, 81, 81, 3)
            # Dense feature extraction followed by subsampling.
            output, _ = self._resnet_small(
                inputs, None, global_pool=False, output_stride=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._resnet_small(inputs, None, global_pool=False)
            sess.run(variables.global_variables_initializer())
            self.assertAllClose(
                output.eval(), expected.eval(), atol=1e-4, rtol=1e-4) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:26,代码来源:resnet_v2_test.py

示例2: subsample

# 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 subsample [as 别名]
def subsample(inputs, factor, scope=None):
  """Subsamples the input along the spatial dimensions.

  Args:
    inputs: A `Tensor` of size [batch, height_in, width_in, channels].
    factor: The subsampling factor.
    scope: Optional variable_scope.

  Returns:
    output: A `Tensor` of size [batch, height_out, width_out, channels] with the
      input, either intact (if factor == 1) or subsampled (if factor > 1).
  """
  if factor == 1:
    return inputs
  else:
    return layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) 
开发者ID:HiKapok,项目名称:X-Detector,代码行数:18,代码来源:slim_resnet_utils.py

示例3: testSubsampleThreeByThree

# 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 subsample [as 别名]
def testSubsampleThreeByThree(self):
    x = array_ops.reshape(math_ops.to_float(math_ops.range(9)), [1, 3, 3, 1])
    x = resnet_utils.subsample(x, 2)
    expected = array_ops.reshape(
        constant_op.constant([0, 2, 6, 8]), [1, 2, 2, 1])
    with self.test_session():
      self.assertAllClose(x.eval(), expected.eval()) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:9,代码来源:resnet_v2_test.py

示例4: testSubsampleFourByFour

# 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 subsample [as 别名]
def testSubsampleFourByFour(self):
    x = array_ops.reshape(math_ops.to_float(math_ops.range(16)), [1, 4, 4, 1])
    x = resnet_utils.subsample(x, 2)
    expected = array_ops.reshape(
        constant_op.constant([0, 2, 8, 10]), [1, 2, 2, 1])
    with self.test_session():
      self.assertAllClose(x.eval(), expected.eval()) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:9,代码来源:resnet_v2_test.py

示例5: 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 subsample [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6: 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 subsample [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7: 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 subsample [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8: 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 subsample [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9: 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 subsample [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

示例10: conv2d_same

# 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 subsample [as 别名]
def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):
  """Strided 2-D convolution with 'SAME' padding.

  When stride > 1, then we do explicit zero-padding, followed by conv2d with
  'VALID' padding.

  Note that

     net = conv2d_same(inputs, num_outputs, 3, stride=stride)

  is equivalent to

     net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1,
     padding='SAME')
     net = subsample(net, factor=stride)

  whereas

     net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride,
     padding='SAME')

  is different when the input's height or width is even, which is why we add the
  current function. For more details, see ResnetUtilsTest.testConv2DSameEven().

  Args:
    inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
    num_outputs: An integer, the number of output filters.
    kernel_size: An int with the kernel_size of the filters.
    stride: An integer, the output stride.
    rate: An integer, rate for atrous convolution.
    scope: Scope.

  Returns:
    output: A 4-D tensor of size [batch, height_out, width_out, channels] with
      the convolution output.
  """
  if stride == 1:
    return layers_lib.conv2d(
        inputs,
        num_outputs,
        kernel_size,
        stride=1,
        rate=rate,
        padding='SAME',
        scope=scope)
  else:
    kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
    pad_total = kernel_size_effective - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg
    inputs = array_ops.pad(
        inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
    return layers_lib.conv2d(
        inputs,
        num_outputs,
        kernel_size,
        stride=stride,
        rate=rate,
        padding='VALID',
        scope=scope) 
开发者ID:HiKapok,项目名称:X-Detector,代码行数:62,代码来源:slim_resnet_utils.py


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