def resnet_v1_200(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_200'):
"""ResNet-200 model of [2]. 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)] * 23 + [(512, 128, 2)]),
resnet_utils.Block('block3', bottleneck,
[(1024, 256, 1)] * 35 + [(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)
def resnet_v1_152(inputs,
is_training,
reuse,
num_classes=None,
global_pool=True,
output_stride=None,
name='resnet_v2_152'):
"""ResNet-152 model of [1]. See resnet_v2() 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)] * 7 + [(512, 128, 2)]),
resnet_utils.Block('block3', bottleneck,
[(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
]
return resnet_v1(inputs,
is_training,
reuse,
blocks,
num_classes,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=True,
name=name)
def resnet_v1_50_truncated_16x16(inputs,
num_classes=None,
global_pool=True,
output_stride=None,
reuse=None,
scope='resnet_v1_50'):
"""ResNet-50 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)] * 6)
]
return resnet_v1(inputs, blocks, num_classes, global_pool, output_stride,
include_root_block=True, reuse=reuse, scope=scope)
def resnet_v2_block(scope, base_depth, num_units, stride):
# Helper function for creating a resnet_v2 bottleneck block.
return resnet_utils.Block(scope, bottleneck, [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': 1
}] * (num_units - 1) + [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': stride
}])
def resnet_v1_152(inputs,
num_classes=None,
global_pool=True,
output_stride=None,
reuse=None,
scope='resnet_v1_152'):
"""ResNet-152 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)] * 7 + [(512, 128, 2)]),
resnet_utils.Block('block3', bottleneck,
[(1024, 256, 1)] * 8 + [(1024, 256, 2)]),
resnet_utils.Block('block4', bottleneck,
[(1024, 256, 1)] * 10 + [(1024, 256, 2)]),
resnet_utils.Block('block5', bottleneck,
[(1024, 256, 1)] * 15 + [(1024, 256, 2)]),
resnet_utils.Block('block6', bottleneck,
[(2048, 512, 1)] * 2 + [(2048, 512, 2)]),
]
return resnet_v1(inputs,
blocks,
num_classes,
global_pool,
output_stride,
include_root_block=True,
reuse=reuse,
scope=scope)
def resnet_v2_block(scope, base_depth, num_units, stride):
"""Helper function for creating a resnet_v2 bottleneck block.
Args:
scope: The scope of the block.
base_depth: The depth of the bottleneck layer for each unit.
num_units: The number of units in the block.
stride: The stride of the block, implemented as a stride in the last unit.
All other units have stride=1.
Returns:
A resnet_v2 bottleneck block.
"""
return resnet_utils.Block(scope, bottleneck, [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': 1
}] * (num_units - 1) + [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': stride
}])
