FM / layers

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers/

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adaptive_weight.py	5687	0644	edit dl rm
add_bias.py	1396	0644	edit dl rm
arc_cosine_feature_map.py	7345	0644	edit dl rm
batch_huber_loss.py	3523	0644	edit dl rm
batch_lr_loss.py	11577	0644	edit dl rm
batch_mse_loss.py	2333	0644	edit dl rm
batch_normalization.py	3823	0644	edit dl rm
batch_sigmoid_cross_entropy_loss.py	1483	0644	edit dl rm
batch_softmax_loss.py	4580	0644	edit dl rm
blob_weighted_sum.py	2219	0644	edit dl rm
bpr_loss.py	1499	0644	edit dl rm
bucket_weighted.py	2355	0644	edit dl rm
build_index.py	1937	0644	edit dl rm
concat.py	4849	0644	edit dl rm
constant_weight.py	1208	0644	edit dl rm
conv.py	5050	0644	edit dl rm
dropout.py	1410	0644	edit dl rm
fc.py	9296	0644	edit dl rm
fc_without_bias.py	1954	0644	edit dl rm
fc_with_bootstrap.py	12788	0644	edit dl rm
feature_sparse_to_dense.py	14361	0644	edit dl rm
functional.py	4875	0644	edit dl rm
gather_record.py	3260	0644	edit dl rm
homotopy_weight.py	4306	0644	edit dl rm
label_smooth.py	3507	0644	edit dl rm
last_n_window_collector.py	2392	0644	edit dl rm
layers.py	17412	0644	edit dl rm
layer_normalization.py	4291	0644	edit dl rm
margin_rank_loss.py	1951	0644	edit dl rm
merge_id_lists.py	1500	0644	edit dl rm
pairwise_similarity.py	3549	0644	edit dl rm
position_weighted.py	2066	0644	edit dl rm
random_fourier_features.py	3187	0644	edit dl rm
reservoir_sampling.py	3013	0644	edit dl rm
sampling_train.py	2210	0644	edit dl rm
sampling_trainable_mixin.py	1366	0644	edit dl rm
select_record_by_context.py	2381	0644	edit dl rm
semi_random_features.py	5809	0644	edit dl rm
sparse_dropout_with_replacement.py	3943	0644	edit dl rm
sparse_feature_hash.py	4618	0644	edit dl rm
sparse_itemwise_dropout_with_replacement.py	3944	0644	edit dl rm
sparse_lookup.py	22170	0644	edit dl rm
split.py	2257	0644	edit dl rm
tags.py	4114	0644	edit dl rm
uniform_sampling.py	2779	0644	edit dl rm
__init__.py	943	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/layers/select_record_by_context.py (2381B)

import logging

from caffe2.python import schema
from caffe2.python.layers.layers import (
    InstantiationContext,
    ModelLayer,
)

logger = logging.getLogger(__name__)

class SelectRecordByContext(ModelLayer):
    """
    Allowing model to follow different paths for each instantiation context and
    join later at some point. The implementation use `Alias` because schema
    sometimes clone fields internally so we need static blob name for output
    """

def __init__(
        self,
        model,
        input_record,
        name='select_record_by_context',
        check_field_metas=True,
        use_copy=False,
        default_output_record_field=None,
        **kwargs
    ):
        super(SelectRecordByContext, self).__init__(model, name, input_record,
                                                    **kwargs)

assert isinstance(input_record, schema.Struct)
        assert len(input_record) > 1

self.use_copy = use_copy
        self.default_output_record = (
            input_record[default_output_record_field]
            if (default_output_record_field is not None) else None
        )
        ref_record = input_record[0]
        for record in input_record:
            assert schema.equal_schemas(record, ref_record,
                                        check_field_metas=check_field_metas)

self.output_schema = schema.NewRecord(model.net, ref_record)

def _set_output_blobs(self, net, context):
        record = self.input_record.get(context, self.default_output_record)
        assert record is not None, (
            "{} context is not in input record without providing default"
            " output".format(context)
        )
        for in_blob, out_blob in zip(
                record.field_blobs(), self.output_schema.field_blobs()
        ):
            if self.use_copy:
                net.Copy(in_blob, out_blob)
            else:
                net.Alias(in_blob, out_blob)

def add_ops(self, net):
        self._set_output_blobs(net, InstantiationContext.PREDICTION)

def add_eval_ops(self, net):
        self._set_output_blobs(net, InstantiationContext.EVAL)

def add_train_ops(self, net):
        self._set_output_blobs(net, InstantiationContext.TRAINING)

def add_ops_to_accumulate_pred(self, net):
        self._set_output_blobs(net, InstantiationContext.ACCUMULATE_PRED)

import logging from caffe2.python import schema from caffe2.python.layers.layers import ( InstantiationContext, ModelLayer, ) logger = logging.getLogger(__name__) class SelectRecordByContext(ModelLayer): """ Allowing model to follow different paths for each instantiation context and join later at some point. The implementation use `Alias` because schema sometimes clone fields internally so we need static blob name for output """ def __init__( self, model, input_record, name='select_record_by_context', check_field_metas=True, use_copy=False, default_output_record_field=None, **kwargs ): super(SelectRecordByContext, self).__init__(model, name, input_record, **kwargs) assert isinstance(input_record, schema.Struct) assert len(input_record) > 1 self.use_copy = use_copy self.default_output_record = ( input_record[default_output_record_field] if (default_output_record_field is not None) else None ) ref_record = input_record[0] for record in input_record: assert schema.equal_schemas(record, ref_record, check_field_metas=check_field_metas) self.output_schema = schema.NewRecord(model.net, ref_record) def _set_output_blobs(self, net, context): record = self.input_record.get(context, self.default_output_record) assert record is not None, ( "{} context is not in input record without providing default" " output".format(context) ) for in_blob, out_blob in zip( record.field_blobs(), self.output_schema.field_blobs() ): if self.use_copy: net.Copy(in_blob, out_blob) else: net.Alias(in_blob, out_blob) def add_ops(self, net): self._set_output_blobs(net, InstantiationContext.PREDICTION) def add_eval_ops(self, net): self._set_output_blobs(net, InstantiationContext.EVAL) def add_train_ops(self, net): self._set_output_blobs(net, InstantiationContext.TRAINING) def add_ops_to_accumulate_pred(self, net): self._set_output_blobs(net, InstantiationContext.ACCUMULATE_PRED)