Python resnet_v1.resnet_v1_block方法代碼示例

本文整理匯總了Python中nets.resnet_v1.resnet_v1_block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_block方法的具體用法？Python resnet_v1.resnet_v1_block怎麽用？Python resnet_v1.resnet_v1_block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類nets.resnet_v1的用法示例。

在下文中一共展示了resnet_v1.resnet_v1_block方法的6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_small

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def _resnet_small(self,
                    inputs,
                    num_classes=None,
                    is_training=True,
                    global_pool=True,
                    output_stride=None,
                    include_root_block=True,
                    reuse=None,
                    scope='resnet_v1_small'):
    """A shallow and thin ResNet v1 for faster tests."""
    block = resnet_v1.resnet_v1_block
    blocks = [
        block('block1', base_depth=1, num_units=3, stride=2),
        block('block2', base_depth=2, num_units=3, stride=2),
        block('block3', base_depth=4, num_units=3, stride=2),
        block('block4', base_depth=8, num_units=2, stride=1),
    ]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes,
                               is_training=is_training,
                               global_pool=global_pool,
                               output_stride=output_stride,
                               include_root_block=include_root_block,
                               reuse=reuse,
                               scope=scope)

開發者ID:DetectionTeamUCAS，項目名稱:R2CNN_Faster-RCNN_Tensorflow，代碼行數:26，代碼來源:resnet_v1_test.py

示例2: testEndPointsV1

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def testEndPointsV1(self):
    """Test the end points of a tiny v1 bottleneck network."""
    blocks = [
        resnet_v1.resnet_v1_block(
            'block1', base_depth=1, num_units=2, stride=2),
        resnet_v1.resnet_v1_block(
            'block2', base_depth=2, num_units=2, stride=1),
    ]
    inputs = create_test_input(2, 32, 16, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
    expected = [
        'tiny/block1/unit_1/bottleneck_v1/shortcut',
        'tiny/block1/unit_1/bottleneck_v1/conv1',
        'tiny/block1/unit_1/bottleneck_v1/conv2',
        'tiny/block1/unit_1/bottleneck_v1/conv3',
        'tiny/block1/unit_2/bottleneck_v1/conv1',
        'tiny/block1/unit_2/bottleneck_v1/conv2',
        'tiny/block1/unit_2/bottleneck_v1/conv3',
        'tiny/block2/unit_1/bottleneck_v1/shortcut',
        'tiny/block2/unit_1/bottleneck_v1/conv1',
        'tiny/block2/unit_1/bottleneck_v1/conv2',
        'tiny/block2/unit_1/bottleneck_v1/conv3',
        'tiny/block2/unit_2/bottleneck_v1/conv1',
        'tiny/block2/unit_2/bottleneck_v1/conv2',
        'tiny/block2/unit_2/bottleneck_v1/conv3']
    self.assertItemsEqual(expected, end_points)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:29，代碼來源:resnet_v1_test.py

示例3: _resnet_small

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def _resnet_small(self,
                    inputs,
                    num_classes=None,
                    is_training=True,
                    global_pool=True,
                    output_stride=None,
                    include_root_block=True,
                    spatial_squeeze=True,
                    reuse=None,
                    scope='resnet_v1_small'):
    """A shallow and thin ResNet v1 for faster tests."""
    block = resnet_v1.resnet_v1_block
    blocks = [
        block('block1', base_depth=1, num_units=3, stride=2),
        block('block2', base_depth=2, num_units=3, stride=2),
        block('block3', base_depth=4, num_units=3, stride=2),
        block('block4', base_depth=8, num_units=2, stride=1),
    ]
    return resnet_v1.resnet_v1(inputs, blocks, num_classes,
                               is_training=is_training,
                               global_pool=global_pool,
                               output_stride=output_stride,
                               include_root_block=include_root_block,
                               spatial_squeeze=spatial_squeeze,
                               reuse=reuse,
                               scope=scope)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:28，代碼來源:resnet_v1_test.py

示例4: testEndPointsV1

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def testEndPointsV1(self):
    """Test the end points of a tiny v1 bottleneck network."""
    blocks = [
        resnet_v1.resnet_v1_block(
            'block1', base_depth=1, num_units=2, stride=2),
        resnet_v1.resnet_v1_block(
            'block2', base_depth=2, num_units=2, stride=1),
    ]
    inputs = create_test_input(2, 32, 16, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
    expected = [
        'tiny/block1/unit_1/bottleneck_v1/shortcut',
        'tiny/block1/unit_1/bottleneck_v1/conv1',
        'tiny/block1/unit_1/bottleneck_v1/conv2',
        'tiny/block1/unit_1/bottleneck_v1/conv3',
        'tiny/block1/unit_2/bottleneck_v1/conv1',
        'tiny/block1/unit_2/bottleneck_v1/conv2',
        'tiny/block1/unit_2/bottleneck_v1/conv3',
        'tiny/block2/unit_1/bottleneck_v1/shortcut',
        'tiny/block2/unit_1/bottleneck_v1/conv1',
        'tiny/block2/unit_1/bottleneck_v1/conv2',
        'tiny/block2/unit_1/bottleneck_v1/conv3',
        'tiny/block2/unit_2/bottleneck_v1/conv1',
        'tiny/block2/unit_2/bottleneck_v1/conv2',
        'tiny/block2/unit_2/bottleneck_v1/conv3']
    self.assertItemsEqual(expected, end_points.keys())

開發者ID:leimao，項目名稱:DeepLab_v3，代碼行數:29，代碼來源:resnet_v1_test.py

示例5: testEndPointsV1

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def testEndPointsV1(self):
    """Test the end points of a tiny v1 bottleneck network."""
    blocks = [
        resnet_v1.resnet_v1_block(
            'block1', base_depth=1, num_units=2, stride=2),
        resnet_v1.resnet_v1_block(
            'block2', base_depth=2, num_units=2, stride=1),
    ]
    inputs = create_test_input(2, 32, 16, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
    expected = [
        'tiny/block1/unit_1/bottleneck_v1/shortcut',
        'tiny/block1/unit_1/bottleneck_v1/conv1',
        'tiny/block1/unit_1/bottleneck_v1/conv2',
        'tiny/block1/unit_1/bottleneck_v1/conv3',
        'tiny/block1/unit_2/bottleneck_v1/conv1',
        'tiny/block1/unit_2/bottleneck_v1/conv2',
        'tiny/block1/unit_2/bottleneck_v1/conv3',
        'tiny/block2/unit_1/bottleneck_v1/shortcut',
        'tiny/block2/unit_1/bottleneck_v1/conv1',
        'tiny/block2/unit_1/bottleneck_v1/conv2',
        'tiny/block2/unit_1/bottleneck_v1/conv3',
        'tiny/block2/unit_2/bottleneck_v1/conv1',
        'tiny/block2/unit_2/bottleneck_v1/conv2',
        'tiny/block2/unit_2/bottleneck_v1/conv3']
    self.assertItemsEqual(expected, list(end_points.keys()))

開發者ID:google-research，項目名稱:morph-net，代碼行數:29，代碼來源:resnet_v1_test.py

示例6: GetResnet50Subnetwork

# 需要導入模塊: from nets import resnet_v1 [as 別名]
# 或者: from nets.resnet_v1 import resnet_v1_block [as 別名]
def GetResnet50Subnetwork(self,
                            images,
                            is_training=False,
                            global_pool=False,
                            reuse=None):
    """Constructs resnet_v1_50 part of the DELF model.

    Args:
      images: A tensor of size [batch, height, width, channels].
      is_training: Whether or not the model is in training mode.
      global_pool: If True, perform global average pooling after feature
        extraction. This may be useful for DELF's descriptor fine-tuning stage.
      reuse: Whether or not the layer and its variables should be reused.

    Returns:
      net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
        If global_pool is True, height_out = width_out = 1.
      end_points: A set of activations for external use.
    """
    block = resnet_v1.resnet_v1_block
    blocks = [
        block('block1', base_depth=64, num_units=3, stride=2),
        block('block2', base_depth=128, num_units=4, stride=2),
        block('block3', base_depth=256, num_units=6, stride=2),
    ]
    if self._target_layer_type == 'resnet_v1_50/block4':
      blocks.append(block('block4', base_depth=512, num_units=3, stride=1))
    net, end_points = resnet_v1.resnet_v1(
        images,
        blocks,
        is_training=is_training,
        global_pool=global_pool,
        reuse=reuse,
        scope='resnet_v1_50')
    return net, end_points

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:37，代碼來源:delf_v1.py

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