def __call__(self, graph):
for node in graph.nodes:
if node.data is None:
continue
if node.kind not in self.reshaped_node_types:
# Check for 2+ dimensional data
if any(len(tensor.shape) > 1 for tensor in node.data):
print_stderr('Warning: parameters not reshaped for node: {}'.format(node))
continue
transpose_order = self.map(node.kind)
weights = node.data[0]
if (node.kind == NodeKind.InnerProduct) and self.has_spatial_parent(node):
# The FC layer connected to the spatial layer needs to be
# re-wired to match the new spatial ordering.
in_shape = node.get_only_parent()[0].output_shape
fc_shape = weights.shape
output_channels = fc_shape[0]
weights = weights.reshape((output_channels, in_shape.channels, in_shape.height,
in_shape.width))
weights = weights.transpose(self.map(NodeKind.Convolution))
node.reshaped_data = weights.reshape(fc_shape[transpose_order[0]],
fc_shape[transpose_order[1]])
else:
node.reshaped_data = weights.transpose(transpose_order)
# node.reshaped_data = weights.transpose(transpose_order)
if self.replace:
for node in graph.nodes:
if hasattr(node, 'reshaped_data'):
# Set the weights
node.data[0] = node.reshaped_data
del node.reshaped_data
return graph
def __call__(self, graph):
for node in graph.nodes:
if node.data is None:
continue
if node.kind not in self.reshaped_node_types:
# Check for 2+ dimensional data
if any(len(tensor.shape) > 1 for tensor in node.data):
print_stderr(
'Warning: parameters not reshaped for node: {}'.format(
node))
continue
transpose_order = self.map(node.kind)
weights = node.data[0]
if (node.kind == NodeKind.InnerProduct
) and self.has_spatial_parent(node):
# The FC layer connected to the spatial layer needs to be
# re-wired to match the new spatial ordering.
in_shape = node.get_only_parent()[0].output_shape
fc_shape = weights.shape
output_channels = fc_shape[0]
weights = weights.reshape((output_channels, in_shape.channels,
in_shape.height, in_shape.width))
weights = weights.transpose(self.map(NodeKind.Convolution))
node.reshaped_data = weights.reshape(
fc_shape[transpose_order[0]], fc_shape[transpose_order[1]])
else:
node.reshaped_data = weights.transpose(transpose_order)
# node.reshaped_data = weights.transpose(transpose_order)
if self.replace:
for node in graph.nodes:
if hasattr(node, 'reshaped_data'):
# Set the weights
node.data[0] = node.reshaped_data
del node.reshaped_data
return graph
def __call__(self, graph):
for layer_name, data in self.params:
if layer_name in graph:
node = graph.get_node(layer_name)
node.data = self.adjust_parameters(node, data)
else:
print_stderr('Ignoring parameters for non-existent layer: %s' % layer_name)
return graph
def __call__(self, graph):
for layer_name, data in self.params:
if layer_name in graph:
node = graph.get_node(layer_name)
node.data = self.adjust_parameters(node, data)
else:
print_stderr('Ignoring parameters for non-existent layer: %s' %
layer_name)
return graph
def __call__(self, graph):
for node in graph.nodes:
if node.data is None:
continue
if node.kind in (NodeKind.Convolution, NodeKind.Deconvolution, NodeKind.InnerProduct):
names = ('weights',)
if node.parameters.bias_term:
names += ('bias',)
elif node.kind == NodeKind.BatchNorm:
names = ('mean', 'var')
if len(node.data) == 4:
names += ('scale', 'bias')
else:
print_stderr('WARNING: Unhandled parameters: {}'.format(node.kind))
continue
assert len(names) == len(node.data)
node.data = dict(zip(names, node.data))
return graph
def __call__(self, graph):
for node in graph.nodes:
if node.data is None:
continue
if node.kind in (NodeKind.Convolution, NodeKind.InnerProduct):
names = ('weights', )
if node.parameters.bias_term:
names += ('bias', )
elif node.kind == NodeKind.BatchNorm:
names = ('mean', 'var')
if len(node.data) == 4:
names += ('scale', 'bias')
else:
print_stderr('WARNING: Unhandled parameters: {}'.format(
node.kind))
continue
assert len(names) == len(node.data)
node.data = dict(zip(names, node.data))
return graph
def __init__(self, def_path, data_path, target_toolkit, input_shape=None, phase='test'):
self.layer_name_map = {}
self.data_injector = None
self.is_train_proto = False
self.input_shape = input_shape
if def_path is None:
if self.input_shape is None:
raise ConversionError('if the graph prototxt is not provided, the input shape should be provided')
self.input_shape = [1] + self.input_shape
def_path, self.data_injector = self.gen_prototxt_from_caffemodel(data_path, self.input_shape)
self.is_train_proto = True
else:
model = get_caffe_resolver().NetParameter()
with open(def_path, 'r') as f:
text_format.Merge(f.read(), model)
layers = model.layers or model.layer
if len([layer for layer in layers if NodeKind.map_raw_kind(layer.type) in LAYER_IN_TRAIN_PROTO]) > 0:
if self.input_shape is None:
raise ConversionError('the train_val.prototxt should be provided with the input shape')
self.input_shape = [1] + self.input_shape
self.is_train_proto = True
graph = GraphBuilder(def_path, self.input_shape, self.is_train_proto, phase).build()
if self.is_train_proto:
def_path = graph.prototxt
if data_path is not None:
graph = graph.transformed([
self.data_injector if self.data_injector else DataInjector(def_path, data_path), # Load and associate learned parameters
BatchNormScaleBiasFuser(),
BatchNormPreprocessor() # Pre-process batch normalization data
])
target_toolkit = target_toolkit.lower()
if target_toolkit not in ('caffe', 'caffe2'):
graph = graph.transformed([DataReshaper({ # Reshape the parameters to TensorFlow's ordering
NodeKind.Convolution: (2, 3, 1, 0), # (c_o, c_i, h, w) -> (h, w, c_i, c_o)
NodeKind.Deconvolution: (2, 3, 1, 0), # (c_o, c_i, h, w) -> (h, w, c_i, c_o)
NodeKind.InnerProduct: (1, 0) # (c_o, c_i) -> (c_i, c_o)
}),
ParameterNamer() # Convert parameters to dictionaries
])
self.graph = graph
# self.graph = NodeRenamer()(graph)
print_stderr(self.graph)
def __init__(self,
def_path,
data_path,
target_toolkit,
input_shape=None,
phase='test'):
self.layer_name_map = {}
self.data_injector = None
self.is_train_proto = False
self.input_shape = input_shape
if def_path is None:
if self.input_shape is None:
raise ConversionError(
'if the graph prototxt is not provided, the input shape should be provided'
)
self.input_shape = [1] + self.input_shape
def_path, self.data_injector = self.gen_prototxt_from_caffemodel(
data_path, self.input_shape)
self.is_train_proto = True
else:
model = get_caffe_resolver().NetParameter()
with open(def_path, 'r') as f:
text_format.Merge(f.read(), model)
layers = model.layers or model.layer
if len([
layer for layer in layers if NodeKind.map_raw_kind(
layer.type) in LAYER_IN_TRAIN_PROTO
]) > 0:
if self.input_shape is None:
raise ConversionError(
'the train_val.prototxt should be provided with the input shape'
)
self.input_shape = [1] + self.input_shape
self.is_train_proto = True
graph = GraphBuilder(def_path, self.input_shape, self.is_train_proto,
phase).build()
if self.is_train_proto:
def_path = graph.prototxt
if data_path is not None:
graph = graph.transformed([
self.data_injector if self.data_injector else DataInjector(
def_path,
data_path), # Load and associate learned parameters
BatchNormScaleBiasFuser(),
BatchNormPreprocessor() # Pre-process batch normalization data
])
target_toolkit = target_toolkit.lower()
if target_toolkit not in ('caffe', 'caffe2'):
graph = graph.transformed([
DataReshaper(
{ # Reshape the parameters to TensorFlow's ordering
NodeKind.Convolution:
(2, 3, 1,
0), # (c_o, c_i, h, w) -> (h, w, c_i, c_o)
NodeKind.InnerProduct:
(1, 0) # (c_o, c_i) -> (c_i, c_o)
}),
ParameterNamer() # Convert parameters to dictionaries
])
self.graph = NodeRenamer()(graph)
print_stderr(self.graph)
