def __init__(self,
layer,
layer_name,
rank_selection,
rank=None,
pretrained=None,
vbmf_weaken_factor=None,
param_reduction_rate=None):
"""
rank_selection: str, 'vbmf'/'param_reduction'/'manual'
"""
self.layer_name = layer_name
self.layer = layer
self.pretrained = pretrained
if isinstance(self.layer, nn.Sequential):
self.in_features = self.layer[0].in_features
self.out_features = self.layer[1].out_features
else:
if not isinstance(self.layer, nn.Linear):
raise AttributeError('only linear layer can be decomposed')
self.in_features = self.layer.in_features
self.out_features = self.layer.out_features
self.weight, self.bias = self.get_weights_to_decompose()
if rank_selection == 'vbmf':
self.rank = estimate_vbmf_ranks(self.weight, vbmf_weaken_factor)
elif rank_selection == 'manual':
self.rank = rank
elif rank_selection == 'param_reduction':
if isinstance(self.layer, nn.Sequential):
self.rank = self.layer[0].out_features // param_reduction_rate
else:
self.rank = estimate_rank_for_compression_rate(
(self.out_features, self.in_features),
rate=param_reduction_rate,
key='svd')
##### create decomposed layers
self.new_layers = nn.Sequential()
for j, l in enumerate(self.create_new_layers()):
self.new_layers.add_module('{}-{}'.format(self.layer_name, j), l)
weights, biases = self.get_svd_factors()
for j, (w, b) in enumerate(zip(weights, biases)):
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).weight.data = w
if b is not None:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias.data = b
else:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias = None
self.layer = None
self.weight = None
self.bias = None
def __init__(self,
layer,
layer_name,
rank_selection,
ranks=None,
pretrained=None,
vbmf_weaken_factor=None,
param_reduction_rate=None):
"""
rank_selection: str, 'vbmf'/'param_reduction'/'manual'
"""
self.layer_name = layer_name
self.layer = layer
self.pretrained = pretrained
if isinstance(self.layer, nn.Sequential):
self.cin = self.layer[0].in_channels
self.cout = self.layer[2].out_channels
self.kernel_size = self.layer[1].kernel_size
self.padding = self.layer[1].padding
self.stride = self.layer[1].stride
# print('Sequential, cin:{}, cout: {}'.format(self.cin, self.cout))
else:
if not isinstance(self.layer, nn.Conv2d):
raise AttributeError(
'only convolution layer can be decomposed')
self.cin = self.layer.in_channels
self.cout = self.layer.out_channels
self.kernel_size = self.layer.kernel_size
self.padding = self.layer.padding
self.stride = self.layer.stride
# print('Conv, cin:{}, cout: {}'.format(self.cin, self.cout))
self.weight, self.bias = self.get_weights_to_decompose()
if rank_selection == 'vbmf':
self.ranks = estimate_vbmf_ranks(self.weight, vbmf_weaken_factor)
elif rank_selection == 'manual':
# self.ranks = [rank_cout, rank_cin]
self.ranks = ranks
elif rank_selection == 'param_reduction':
if isinstance(self.layer, nn.Sequential):
self.ranks = estimate_rank_for_compression_rate((self.layer[1].out_channels,\
self.layer[1].in_channels,\
*self.kernel_size),\
rate = param_reduction_rate,\
key = 'tucker2')
else:
self.ranks = estimate_rank_for_compression_rate((self.cout,\
self.cin,\
*self.kernel_size),\
rate = param_reduction_rate,\
key = 'tucker2')
#print(self.ranks)
##### create decomposed layers
self.new_layers = nn.Sequential()
for j, l in enumerate(self.create_new_layers()):
self.new_layers.add_module('{}-{}'.format(self.layer_name, j), l)
weights, biases = self.get_tucker_factors()
for j, (w, b) in enumerate(zip(weights, biases)):
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).weight.data = w
if b is not None:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias.data = b
else:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias = None
self.layer = None
self.weight = None
self.bias = None
def __init__(self,
layer,
layer_name,
rank_selection,
rank=None,
pretrained=None,
vbmf_weaken_factor=None,
param_reduction_rate=None):
self.layer_name = layer_name
self.layer = layer
self.pretrained = pretrained
self.min_rank = 2
#print(layer)
if isinstance(self.layer, nn.Sequential):
self.in_channels = self.layer[0].in_channels
self.out_channels = self.layer[1].out_channels
self.padding = self.layer[1].padding
self.stride = self.layer[1].stride
else:
if not isinstance(self.layer, nn.Conv2d):
raise AttributeError('only conv layer can be decomposed')
self.in_channels = self.layer.in_channels
self.out_channels = self.layer.out_channels
self.padding = self.layer.padding
self.stride = self.layer.stride
self.weight, self.bias = self.get_weights_to_decompose()
if rank_selection == 'vbmf':
self.rank = estimate_vbmf_ranks(self.weight,
vbmf_weaken_factor,
min_rank=self.min_rank)
elif rank_selection == 'manual':
self.rank = rank
elif rank_selection == 'param_reduction':
if isinstance(self.layer, nn.Sequential):
prev_rank = self.layer[0].out_channels
else:
prev_rank = None
self.rank = estimate_rank_for_compression_rate(
(self.out_channels, self.in_channels),
rate=param_reduction_rate,
key='svd',
prev_rank=prev_rank,
min_rank=self.min_rank)
##### create decomposed layers
self.new_layers = nn.Sequential()
for j, l in enumerate(self.create_new_layers()):
self.new_layers.add_module('{}-{}'.format(self.layer_name, j), l)
weights, biases = self.get_svd_factors()
for j, (w, b) in enumerate(zip(weights, biases)):
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).weight.data = w
if b is not None:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias.data = b
else:
self.new_layers.__getattr__('{}-{}'.format(self.layer_name,
j)).bias = None
self.layer = None
self.weight = None
self.bias = None