mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
ac7663b18c
Summary: This diff allows to export a model partially, filtering layers by tags. Reviewed By: kittipatv Differential Revision: D4885610 fbshipit-source-id: 65394c5c9119d57a4d0703aa67ad8e79e4370e3b
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
## @package layer_model_instantiator
|
|
# Module caffe2.python.layer_model_instantiator
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python import core
|
|
from caffe2.python.layers.layers import InstantiationContext
|
|
from caffe2.python.layers.tags import Tags
|
|
|
|
|
|
def _filter_layers(layers, include_tags):
|
|
if include_tags is None:
|
|
return layers
|
|
include_tags = set(include_tags)
|
|
return filter(lambda l: not include_tags.isdisjoint(l.tags), layers)
|
|
|
|
|
|
def generate_predict_net(model, include_tags=None):
|
|
predict_net = core.Net('predict_net')
|
|
|
|
for layer in _filter_layers(model.layers, include_tags):
|
|
if Tags.TRAIN_ONLY not in layer.tags:
|
|
layer.add_operators(
|
|
predict_net, context=InstantiationContext.PREDICTION)
|
|
return predict_net
|
|
|
|
|
|
def generate_eval_net(model, include_tags=None):
|
|
eval_net = core.Net('eval_net')
|
|
|
|
for layer in _filter_layers(model.layers, include_tags):
|
|
layer.add_operators(eval_net, context=InstantiationContext.EVAL)
|
|
|
|
input_schema = model.input_feature_schema + model.trainer_extra_schema
|
|
output_schema = model.output_schema + model.metrics_schema
|
|
eval_net.set_input_record(input_schema)
|
|
eval_net.set_output_record(output_schema)
|
|
return eval_net
|
|
|
|
|
|
def _generate_training_net_only(model, include_tags=None):
|
|
train_net = core.Net('train_net')
|
|
train_init_net = model.create_init_net('train_init_net')
|
|
|
|
for layer in _filter_layers(model.layers, include_tags):
|
|
layer.add_operators(train_net, train_init_net)
|
|
|
|
input_schema = model.input_feature_schema + model.trainer_extra_schema
|
|
output_schema = model.output_schema + model.metrics_schema
|
|
train_net.set_input_record(input_schema)
|
|
train_net.set_output_record(output_schema)
|
|
return train_init_net, train_net
|
|
|
|
|
|
def generate_training_nets_forward_only(model, include_tags=None):
|
|
train_init_net, train_net = _generate_training_net_only(model, include_tags)
|
|
return train_init_net, train_net
|
|
|
|
|
|
def generate_training_nets(model, include_tags=None):
|
|
train_init_net, train_net = _generate_training_net_only(model, include_tags)
|
|
|
|
loss = model.loss
|
|
grad_map = train_net.AddGradientOperators(loss.field_blobs())
|
|
model.apply_optimizers(train_net, train_init_net, grad_map)
|
|
return train_init_net, train_net
|