def ResNet101V2(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the ResNet101V2 architecture."""
def stack_fn(x):
x = resnet.stack2(x, 64, 3, name='conv2')
x = resnet.stack2(x, 128, 4, name='conv3')
x = resnet.stack2(x, 256, 23, name='conv4')
return resnet.stack2(x, 512, 3, stride1=1, name='conv5')
return resnet.ResNet(stack_fn, True, True, 'resnet101v2', include_top,
weights, input_tensor, input_shape, pooling, classes)
def ResNet50(include_top=True,
weights=None,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
use_bias=False,
block_fn=bottleneck_block,
**kwargs):
"""Instantiates the ResNet50 architecture."""
def stack_fn(x):
x = stack(x,
64,
3,
block_fn=block_fn,
use_bias=use_bias,
stride1=1,
name='conv2')
x = stack(x,
128,
4,
block_fn=block_fn,
use_bias=use_bias,
name='conv3')
x = stack(x,
256,
6,
block_fn=block_fn,
use_bias=use_bias,
name='conv4')
return stack(x,
512,
3,
block_fn=block_fn,
use_bias=use_bias,
name='conv5')
return resnet.ResNet(stack_fn, False, use_bias, 'custom_resnet50',
include_top, weights, input_tensor, input_shape,
pooling, classes, **kwargs)