def get_content_module(prefix, dshape, ctx, params):
sym = vgg.get_vgg_symbol(prefix, True)
init = PretrainedInit(prefix, params)
mod = mx.mod.Module(symbol=sym,
data_names=("%s_data" % prefix, ),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)], for_training=False)
mod.init_params(init)
return mod
def get_content_module(prefix, dshape, ctx, params):
sym = vgg.get_vgg_symbol(prefix, True)
init = PretrainedInit(prefix, params)
mod = mx.mod.Module(symbol=sym,
data_names=("%s_data" % prefix,),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)], for_training=False)
mod.init_params(init)
return mod
def get_style_module(prefix, dshape, ctx, params):
input_shape = {"%s_data" % prefix: dshape}
style, content = vgg.get_vgg_symbol(prefix)
gram, gscale = style_gram_symbol(input_shape, style)
init = PretrainedInit(prefix, params)
mod = mx.mod.Module(symbol=gram,
data_names=("%s_data" % prefix, ),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)], for_training=False)
mod.init_params(init)
return mod
def get_style_module(prefix, dshape, ctx, params):
input_shape = {"%s_data" % prefix : dshape}
style, content = vgg.get_vgg_symbol(prefix)
gram, gscale = style_gram_symbol(input_shape, style)
init = PretrainedInit(prefix, params)
mod = mx.mod.Module(symbol=gram,
data_names=("%s_data" % prefix,),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)], for_training=False)
mod.init_params(init)
return mod
def get_loss_module(prefix, dshape, ctx, params):
input_shape = {"%s_data" % prefix : dshape}
style, content = vgg.get_vgg_symbol(prefix)
gram, gscale = style_gram_symbol(input_shape, style)
style_loss, content_loss = get_loss(gram, content)
sym = mx.sym.Group([style_loss, content_loss])
init = PretrainedInit(prefix, params)
gram_size = len(gram.list_outputs())
mod = mx.mod.Module(symbol=sym,
data_names=("%s_data" % prefix,),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)],
for_training=True, inputs_need_grad=True)
mod.init_params(init)
return mod, gscale
def get_loss_module(prefix, dshape, ctx, params):
input_shape = {"%s_data" % prefix : dshape}
style, content = vgg.get_vgg_symbol(prefix)
gram, gscale = style_gram_symbol(input_shape, style)
style_loss, content_loss = get_loss(gram, content)
sym = mx.sym.Group([style_loss, content_loss])
init = PretrainedInit(prefix, params)
gram_size = len(gram.list_outputs())
mod = mx.mod.Module(symbol=sym,
data_names=("%s_data" % prefix,),
label_names=None,
context=ctx)
mod.bind(data_shapes=[("%s_data" % prefix, dshape)],
for_training=True, inputs_need_grad=True)
mod.init_params(init)
return mod, gscale
