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Summary: Layer to allow model to follow different paths for each instantiation context and join later. Together with tagging system cleanup (this is a separate issue), this should reduce the need to write a layer to differentiate between context. Re: tagging system clean up, we should make exclusion more explicit: EXCLUDE_FROM_<CONTEXT>. This would simplify instation code. TRAIN_ONLY should become a set of all EXCLUDE_FROM_*, except EXCLUDE_FROM_TRAIN. Reviewed By: kennyhorror Differential Revision: D4964949 fbshipit-source-id: ba6453b0deb92d1989404efb9d86e1ed25297202
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
## @package layer_model_instantiator
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# Module caffe2.python.layer_model_instantiator
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from caffe2.python.layers.layers import InstantiationContext
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from caffe2.python.layers.tags import Tags
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def _filter_layers(layers, include_tags):
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if include_tags is None:
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return layers
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include_tags = set(include_tags)
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return filter(lambda l: not include_tags.isdisjoint(l.tags), layers)
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def generate_predict_net(model, include_tags=None):
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predict_net = core.Net('predict_net')
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for layer in _filter_layers(model.layers, include_tags):
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if Tags.EXCLUDE_FROM_PREDICTION not in layer.tags:
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layer.add_operators(
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predict_net, context=InstantiationContext.PREDICTION)
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predict_net.set_input_record(model.input_feature_schema.clone())
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predict_net.set_output_record(model.output_schema.clone())
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return predict_net
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def generate_eval_net(model, include_tags=None):
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eval_net = core.Net('eval_net')
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for layer in _filter_layers(model.layers, include_tags):
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if Tags.EXCLUDE_FROM_EVAL not in layer.tags:
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layer.add_operators(eval_net, context=InstantiationContext.EVAL)
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input_schema = model.input_feature_schema + model.trainer_extra_schema
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output_schema = model.output_schema + model.metrics_schema
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eval_net.set_input_record(input_schema)
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eval_net.set_output_record(output_schema)
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return eval_net
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def _generate_training_net_only(model, include_tags=None):
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train_net = core.Net('train_net')
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train_init_net = model.create_init_net('train_init_net')
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for layer in _filter_layers(model.layers, include_tags):
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if Tags.EXCLUDE_FROM_TRAIN not in layer.tags:
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layer.add_operators(train_net, train_init_net)
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input_schema = model.input_feature_schema + model.trainer_extra_schema
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output_schema = model.output_schema + model.metrics_schema
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train_net.set_input_record(input_schema)
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train_net.set_output_record(output_schema)
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return train_init_net, train_net
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def generate_training_nets_forward_only(model, include_tags=None):
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train_init_net, train_net = _generate_training_net_only(model, include_tags)
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return train_init_net, train_net
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def generate_training_nets(model, include_tags=None):
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train_init_net, train_net = _generate_training_net_only(model, include_tags)
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loss = model.loss
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grad_map = train_net.AddGradientOperators(loss.field_blobs())
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model.apply_optimizers(train_net, train_init_net, grad_map)
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return train_init_net, train_net
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