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Python resnet_v1.resnet_v1_101方法代碼示例

本文整理匯總了Python中tensorflow.contrib.slim.nets.resnet_v1.resnet_v1_101方法的典型用法代碼示例。如果您正苦於以下問題:Python resnet_v1.resnet_v1_101方法的具體用法?Python resnet_v1.resnet_v1_101怎麽用?Python resnet_v1.resnet_v1_101使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在tensorflow.contrib.slim.nets.resnet_v1的用法示例。


在下文中一共展示了resnet_v1.resnet_v1_101方法的7個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  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),
      resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope) 
開發者ID:ringringyi,項目名稱:DOTA_models,代碼行數:21,代碼來源:resnet_v1.py

示例2: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  store_non_strided_activations=False,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  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),
      resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   store_non_strided_activations=store_non_strided_activations,
                   reuse=reuse, scope=scope) 
開發者ID:leimao,項目名稱:DeepLab_v3,代碼行數:23,代碼來源:resnet_v1.py

示例3: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block(
          'block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block(
          'block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block(
          'block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
      resnet_utils.Block(
          'block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, reuse=reuse, scope=scope) 
開發者ID:CharlesShang,項目名稱:FastMaskRCNN,代碼行數:23,代碼來源:resnet_v1.py

示例4: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block(
          'block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block(
          'block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block(
          'block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
      resnet_utils.Block(
          'block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(inputs, blocks, num_classes, global_pool, output_stride,
                   include_root_block=True, reuse=reuse, scope=scope) 
開發者ID:dvornikita,項目名稱:blitznet,代碼行數:21,代碼來源:resnet_v1.py

示例5: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  store_non_strided_activations=False,
                  reuse=False,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  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),
      resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   store_non_strided_activations=store_non_strided_activations,
                   reuse=reuse, scope=scope) 
開發者ID:alibaba,項目名稱:FastNN,代碼行數:23,代碼來源:resnet_v1.py

示例6: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  store_non_strided_activations=False,
                  reuse=None,
                  scope='resnet_v1_101',
                  attention_module=None):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_v1_block('block1', base_depth=64, num_units=3, stride=2, attention_module=attention_module),
      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2, attention_module=attention_module),
      resnet_v1_block('block3', base_depth=256, num_units=23, stride=2, attention_module=attention_module),
      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1, attention_module=attention_module),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   store_non_strided_activations=store_non_strided_activations,
                   reuse=reuse, scope=scope) 
開發者ID:kobiso,項目名稱:CBAM-tensorflow-slim,代碼行數:24,代碼來源:resnet_v1.py

示例7: resnet_v1_101

# 需要導入模塊: from tensorflow.contrib.slim.nets import resnet_v1 [as 別名]
# 或者: from tensorflow.contrib.slim.nets.resnet_v1 import resnet_v1_101 [as 別名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block(
          'block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block(
          'block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block(
          'block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
      resnet_utils.Block(
          'block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope) 
開發者ID:nicklhy,項目名稱:DLInfBench,代碼行數:25,代碼來源:resnet_v1.py


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