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Summary: In some cases (for example, when include_tags option is used) output_schema contains blobs that aren't produced by the generated net. In this case we want to filter them from output_schema as well. Differential Revision: D5120115 fbshipit-source-id: f98ea3f747589390b039d1e1987becec3980634c
105 lines
3.5 KiB
Python
105 lines
3.5 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, schema
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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 shrink_output_schema(net, out_schema):
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if len(out_schema.field_names()) <= 1:
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return out_schema
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exists = [net.BlobIsDefined(blob) for blob in out_schema.field_blobs()]
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return schema.from_column_list(
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[
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col_name for ok, col_name in
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zip(exists, out_schema.field_names()) if ok
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],
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[
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col_type for ok, col_type in
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zip(exists, out_schema.field_types()) if ok
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],
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[
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col_blob for ok, col_blob in
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zip(exists, out_schema.field_blobs()) if ok
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],
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[
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col_meta for ok, col_meta in
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zip(exists, out_schema.field_metadata()) if ok
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]
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)
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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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output_schema = shrink_output_schema(
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predict_net, model.output_schema.clone()
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)
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predict_net.set_output_record(output_schema)
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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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eval_net.set_input_record(input_schema)
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output_schema = shrink_output_schema(
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eval_net, model.output_schema + model.metrics_schema
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)
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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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train_net.set_input_record(input_schema)
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output_schema = shrink_output_schema(
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train_net, model.output_schema + model.metrics_schema
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)
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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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