# Path: selfsup/multi/basenets/basenet.py
# class BaseNet:
#     def __init__(self):
#         pass
# 
#     @property
#     def name(self):
#         raise NotImplemented('Abstract base class')
# 
#     @property
#     def canonical_input_size(self):
#         raise NotImplemented('Abstract base class')
# 
#     @property
#     def hypercolumn_layers(self):
#         """Suggested hypercolumn layers"""
#         return []
# 
#     @property
#     def layers(self):
#         return [name for name, _ in self.hypercolumn_layers]
# 
#     def build(self):
#         raise NotImplemented('Abstract base class')
# 
#     def save_caffemodel(self, path, session, verbose=False):
#         raise NotImplemented('Abstract base class')


from .basenet import BaseNet
import selfsup
                    convolutional=settings.get('convolutional', False),
                    final_layer=False,
                    squeezed=True,
                    global_step=global_step,
                    phase_test=phase_test)
        else:
            z = selfsup.model.vgg16.build_network(x, info=info,
                    convolutional=settings.get('convolutional', False),
                    final_layer=False,
                    squeezed=True,
                    phase_test=phase_test)
        info['activations']['x01'] = x01
        info['activations']['x'] = x
        info['activations']['top'] = z
        info['weights']['firstconv:weights'] = info['weights']['conv1_1:weights']
        info['weights']['firstconv:biases'] = info['weights']['conv1_1:biases']
        return info

    def save_caffemodel(self, path, session, verbose=False, prefix=''):
        layers = [
            'conv1_1',
            'conv1_2',
            'conv2_1',
            'conv2_2',
            'conv3_1',
            'conv3_2',
            'conv3_3',
            'conv4_1',
            'conv4_2',
            'conv4_3',

# Path: selfsup/multi/basenets/basenet.py
# class BaseNet:
#     def __init__(self):
#         pass
# 
#     @property
#     def name(self):
#         raise NotImplemented('Abstract base class')
# 
#     @property
#     def canonical_input_size(self):
#         raise NotImplemented('Abstract base class')
# 
#     @property
#     def hypercolumn_layers(self):
#         """Suggested hypercolumn layers"""
#         return []
# 
#     @property
#     def layers(self):
#         return [name for name, _ in self.hypercolumn_layers]
# 
#     def build(self):
#         raise NotImplemented('Abstract base class')
# 
#     def save_caffemodel(self, path, session, verbose=False):
#         raise NotImplemented('Abstract base class')


# Path: selfsup/multi/basenets/vgg16.py
from .basenet import BaseNet
import selfsup

                    convolutional=settings.get('convolutional', False),
                    final_layer=False,
                    squeezed=True,
                    global_step=global_step,
                    phase_test=phase_test)
        else:
            z = selfsup.model.vgg16.build_network(x, info=info,
                    convolutional=settings.get('convolutional', False),
                    final_layer=False,
                    squeezed=True,
                    phase_test=phase_test)
        info['activations']['x01'] = x01
        info['activations']['x'] = x
        info['activations']['top'] = z
        info['weights']['firstconv:weights'] = info['weights']['conv1_1:weights']
        info['weights']['firstconv:biases'] = info['weights']['conv1_1:biases']
        return info

    def save_caffemodel(self, path, session, verbose=False, prefix=''):
        layers = [
            'conv1_1',
            'conv1_2',
            'conv2_1',
            'conv2_2',
            'conv3_1',
            'conv3_2',
            'conv3_3',
            'conv4_1',
            'conv4_2',
            'conv4_3',

            'conv5_1',