def upgrade_multipath_vggnet(old_model):
# Make sure we have the right model
node_names = {
'rgb_conv', 'flow_conv', 'shared_layers', 'fc_regr_left',
'fc_regr_right', 'fc_regr_head', 'fc_clas'
}
assert set(old_model.nodes.keys()) == node_names
rv = Graph()
# Upgrade RGB path first (weights handled by upgrade_sequential)
print('Upgrading RGB path')
rgb_shape = old_model.inputs['images'].input_shape[1:]
rv.add_input(
input_shape=rgb_shape, name='images'
)
upgraded_rgb_conv = upgrade_sequential(old_model.nodes['rgb_conv'])
rv.add_node(upgraded_rgb_conv, input='images', name='rgb_conv')
# Upgrade flow path
print('Upgrading flow path')
flow_shape = old_model.inputs['flow'].input_shape[1:]
rv.add_input(
input_shape=flow_shape, name='flow'
)
upgraded_flow_conv = upgrade_sequential(old_model.nodes['flow_conv'])
rv.add_node(upgraded_flow_conv, input='flow', name='flow_conv')
# Upgrade shared path
print('Upgrading shared path')
upgraded_share = upgrade_sequential(old_model.nodes['shared_layers'])
rv.add_node(
upgraded_share, inputs=['rgb_conv', 'flow_conv'], merge_mode='concat',
concat_axis=1, name='shared_layers'
)
# Upgrade dense outputs (weights handled by dense_to_conv)
print('Upgrading regression outputs')
for out_name in {'left', 'right', 'head'}:
dense_name = 'fc_regr_' + out_name
old_dense = old_model.nodes[dense_name]
new_dense = dense_to_conv(old_dense, (old_dense.input_shape[1], 1, 1))
rv.add_node(new_dense, input='shared_layers', name=dense_name)
rv.add_output(input=dense_name, name=out_name)
print('Upgrading classification output')
old_clas_dense = old_model.nodes['fc_clas']
# We're using a custom activation, so we have to register it
register_activation(convolution_softmax, 'convolution_softmax')
new_clas_dense = dense_to_conv(
old_clas_dense, (old_clas_dense.input_shape[1], 1, 1),
activation='convolution_softmax'
)
rv.add_node(new_clas_dense, input='shared_layers', name='fc_clas')
rv.add_output(input='fc_clas', name='class')
# Plan is to save weights to HDF5 and architecture to JSON, then a small
# wrapper can load the data for use from Matlab
print('All done!')
return rv
def upgrade_multipath_vggnet(old_model):
# Make sure we have the right model
node_names = {
'rgb_conv', 'flow_conv', 'shared_layers', 'fc_regr_left',
'fc_regr_right', 'fc_regr_head', 'fc_clas'
}
assert set(old_model.nodes.keys()) == node_names
rv = Graph()
# Upgrade RGB path first (weights handled by upgrade_sequential)
print('Upgrading RGB path')
rgb_shape = old_model.inputs['images'].input_shape[1:]
rv.add_input(input_shape=rgb_shape, name='images')
upgraded_rgb_conv = upgrade_sequential(old_model.nodes['rgb_conv'])
rv.add_node(upgraded_rgb_conv, input='images', name='rgb_conv')
# Upgrade flow path
print('Upgrading flow path')
flow_shape = old_model.inputs['flow'].input_shape[1:]
rv.add_input(input_shape=flow_shape, name='flow')
upgraded_flow_conv = upgrade_sequential(old_model.nodes['flow_conv'])
rv.add_node(upgraded_flow_conv, input='flow', name='flow_conv')
# Upgrade shared path
print('Upgrading shared path')
upgraded_share = upgrade_sequential(old_model.nodes['shared_layers'])
rv.add_node(upgraded_share,
inputs=['rgb_conv', 'flow_conv'],
merge_mode='concat',
concat_axis=1,
name='shared_layers')
# Upgrade dense outputs (weights handled by dense_to_conv)
print('Upgrading regression outputs')
for out_name in {'left', 'right', 'head'}:
dense_name = 'fc_regr_' + out_name
old_dense = old_model.nodes[dense_name]
new_dense = dense_to_conv(old_dense, (old_dense.input_shape[1], 1, 1))
rv.add_node(new_dense, input='shared_layers', name=dense_name)
rv.add_output(input=dense_name, name=out_name)
print('Upgrading classification output')
old_clas_dense = old_model.nodes['fc_clas']
# We're using a custom activation, so we have to register it
register_activation(convolution_softmax, 'convolution_softmax')
new_clas_dense = dense_to_conv(old_clas_dense,
(old_clas_dense.input_shape[1], 1, 1),
activation='convolution_softmax')
rv.add_node(new_clas_dense, input='shared_layers', name='fc_clas')
rv.add_output(input='fc_clas', name='class')
# Plan is to save weights to HDF5 and architecture to JSON, then a small
# wrapper can load the data for use from Matlab
print('All done!')
return rv
def upgrade_multipath_poselet_vggnet(old_model):
# TODO: This is mostly the same as the above code (cut and pasted). This is
# non-ideal, so the above code should be deleted or somehow merged with
# this code.
node_names = {
'rgb_conv', 'flow_conv', 'shared_layers', 'fc_pslt'
}
assert set(old_model.nodes.keys()) == node_names
rv = Graph()
print('Upgrading RGB path')
rgb_shape = old_model.inputs['images'].input_shape[1:]
rv.add_input(
input_shape=rgb_shape, name='images'
)
upgraded_rgb_conv = upgrade_sequential(old_model.nodes['rgb_conv'])
rv.add_node(upgraded_rgb_conv, input='images', name='rgb_conv')
print('Upgrading flow path')
flow_shape = old_model.inputs['flow'].input_shape[1:]
rv.add_input(
input_shape=flow_shape, name='flow'
)
upgraded_flow_conv = upgrade_sequential(old_model.nodes['flow_conv'])
rv.add_node(upgraded_flow_conv, input='flow', name='flow_conv')
print('Upgrading shared path')
upgraded_share = upgrade_sequential(old_model.nodes['shared_layers'])
rv.add_node(
upgraded_share, inputs=['rgb_conv', 'flow_conv'], merge_mode='concat',
concat_axis=1, name='shared_layers'
)
print('Upgrading classification output')
old_clas_dense = old_model.nodes['fc_pslt']
register_activation(convolution_softmax, 'convolution_softmax')
new_clas_dense = dense_to_conv(
old_clas_dense, (old_clas_dense.input_shape[1], 1, 1),
activation='convolution_softmax'
)
rv.add_node(new_clas_dense, input='shared_layers', name='fc_pslt')
rv.add_output(input='fc_pslt', name='poselet')
return rv
def upgrade_multipath_poselet_vggnet(old_model):
# TODO: This is mostly the same as the above code (cut and pasted). This is
# non-ideal, so the above code should be deleted or somehow merged with
# this code.
node_names = {'rgb_conv', 'flow_conv', 'shared_layers', 'fc_pslt'}
assert set(old_model.nodes.keys()) == node_names
rv = Graph()
print('Upgrading RGB path')
rgb_shape = old_model.inputs['images'].input_shape[1:]
rv.add_input(input_shape=rgb_shape, name='images')
upgraded_rgb_conv = upgrade_sequential(old_model.nodes['rgb_conv'])
rv.add_node(upgraded_rgb_conv, input='images', name='rgb_conv')
print('Upgrading flow path')
flow_shape = old_model.inputs['flow'].input_shape[1:]
rv.add_input(input_shape=flow_shape, name='flow')
upgraded_flow_conv = upgrade_sequential(old_model.nodes['flow_conv'])
rv.add_node(upgraded_flow_conv, input='flow', name='flow_conv')
print('Upgrading shared path')
upgraded_share = upgrade_sequential(old_model.nodes['shared_layers'])
rv.add_node(upgraded_share,
inputs=['rgb_conv', 'flow_conv'],
merge_mode='concat',
concat_axis=1,
name='shared_layers')
print('Upgrading classification output')
old_clas_dense = old_model.nodes['fc_pslt']
register_activation(convolution_softmax, 'convolution_softmax')
new_clas_dense = dense_to_conv(old_clas_dense,
(old_clas_dense.input_shape[1], 1, 1),
activation='convolution_softmax')
rv.add_node(new_clas_dense, input='shared_layers', name='fc_pslt')
rv.add_output(input='fc_pslt', name='poselet')
return rv
