def _construct_model(model_type='resnet_v1_50'):
"""Constructs model for the desired type of CNN.
Args:
model_type: Type of model to be used.
Returns:
end_points: A dictionary from components of the network to the corresponding
activations.
Raises:
ValueError: If the model_type is not supported.
"""
# Placeholder input.
images = array_ops.placeholder(
dtypes.float32, shape=(1, None, None, 3), name=_INPUT_NODE)
# Construct model.
if model_type == 'inception_resnet_v2':
_, end_points = inception.inception_resnet_v2_base(images)
elif model_type == 'inception_resnet_v2-same':
_, end_points = inception.inception_resnet_v2_base(
images, align_feature_maps=True)
elif model_type == 'inception_v2':
_, end_points = inception.inception_v2_base(images)
elif model_type == 'inception_v2-no-separable-conv':
_, end_points = inception.inception_v2_base(
images, use_separable_conv=False)
elif model_type == 'inception_v3':
_, end_points = inception.inception_v3_base(images)
elif model_type == 'inception_v4':
_, end_points = inception.inception_v4_base(images)
elif model_type == 'alexnet_v2':
_, end_points = alexnet.alexnet_v2(images)
elif model_type == 'vgg_a':
_, end_points = vgg.vgg_a(images)
elif model_type == 'vgg_16':
_, end_points = vgg.vgg_16(images)
elif model_type == 'mobilenet_v1':
_, end_points = mobilenet_v1.mobilenet_v1_base(images)
elif model_type == 'mobilenet_v1_075':
_, end_points = mobilenet_v1.mobilenet_v1_base(
images, depth_multiplier=0.75)
elif model_type == 'resnet_v1_50':
_, end_points = resnet_v1.resnet_v1_50(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_101':
_, end_points = resnet_v1.resnet_v1_101(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_152':
_, end_points = resnet_v1.resnet_v1_152(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_200':
_, end_points = resnet_v1.resnet_v1_200(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_50':
_, end_points = resnet_v2.resnet_v2_50(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_101':
_, end_points = resnet_v2.resnet_v2_101(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_152':
_, end_points = resnet_v2.resnet_v2_152(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_200':
_, end_points = resnet_v2.resnet_v2_200(
images, num_classes=None, is_training=False, global_pool=False)
else:
raise ValueError('Unsupported model_type %s.' % model_type)
return end_points
def _construct_model(model_type='resnet_v1_50'):
"""Constructs model for the desired type of CNN.
Args:
model_type: Type of model to be used.
Returns:
end_points: A dictionary from components of the network to the corresponding
activations.
Raises:
ValueError: If the model_type is not supported.
"""
# Placeholder input.
images = array_ops.placeholder(dtypes.float32,
shape=(1, None, None, 3),
name=_INPUT_NODE)
# Construct model.
if model_type == 'inception_resnet_v2':
_, end_points = inception.inception_resnet_v2_base(images)
elif model_type == 'inception_resnet_v2-same':
_, end_points = inception.inception_resnet_v2_base(
images, align_feature_maps=True)
elif model_type == 'inception_v2':
_, end_points = inception.inception_v2_base(images)
elif model_type == 'inception_v2-no-separable-conv':
_, end_points = inception.inception_v2_base(images,
use_separable_conv=False)
elif model_type == 'inception_v3':
_, end_points = inception.inception_v3_base(images)
elif model_type == 'inception_v4':
_, end_points = inception.inception_v4_base(images)
elif model_type == 'alexnet_v2':
_, end_points = alexnet.alexnet_v2(images)
elif model_type == 'vgg_a':
_, end_points = vgg.vgg_a(images)
elif model_type == 'vgg_16':
_, end_points = vgg.vgg_16(images)
elif model_type == 'mobilenet_v1':
_, end_points = mobilenet_v1.mobilenet_v1_base(images)
elif model_type == 'mobilenet_v1_075':
_, end_points = mobilenet_v1.mobilenet_v1_base(images,
depth_multiplier=0.75)
elif model_type == 'resnet_v1_50':
_, end_points = resnet_v1.resnet_v1_50(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_101':
_, end_points = resnet_v1.resnet_v1_101(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_152':
_, end_points = resnet_v1.resnet_v1_152(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_200':
_, end_points = resnet_v1.resnet_v1_200(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_50':
_, end_points = resnet_v2.resnet_v2_50(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_101':
_, end_points = resnet_v2.resnet_v2_101(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_152':
_, end_points = resnet_v2.resnet_v2_152(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_200':
_, end_points = resnet_v2.resnet_v2_200(images,
num_classes=None,
is_training=False,
global_pool=False)
else:
raise ValueError('Unsupported model_type %s.' % model_type)
return end_points
