/
usr
/
local
/
lib64
/
python3.6
/
site-packages
/
caffe2
/
python
/
layers
/
/usr/local/lib64/python3.6/site-packages/caffe2/python/layers
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__pycache__/
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adaptive_weight.py
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add_bias.py
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arc_cosine_feature_map.py
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batch_huber_loss.py
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batch_lr_loss.py
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batch_mse_loss.py
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batch_normalization.py
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batch_sigmoid_cross_entropy_loss.py
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batch_softmax_loss.py
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blob_weighted_sum.py
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bpr_loss.py
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bucket_weighted.py
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build_index.py
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concat.py
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constant_weight.py
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conv.py
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dropout.py
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fc.py
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fc_without_bias.py
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fc_with_bootstrap.py
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feature_sparse_to_dense.py
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functional.py
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gather_record.py
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homotopy_weight.py
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label_smooth.py
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last_n_window_collector.py
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layers.py
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layer_normalization.py
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margin_rank_loss.py
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merge_id_lists.py
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pairwise_similarity.py
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position_weighted.py
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random_fourier_features.py
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reservoir_sampling.py
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sampling_train.py
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sampling_trainable_mixin.py
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select_record_by_context.py
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semi_random_features.py
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sparse_dropout_with_replacement.py
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sparse_feature_hash.py
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sparse_itemwise_dropout_with_replacement.py
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sparse_lookup.py
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split.py
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tags.py
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uniform_sampling.py
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__init__.py
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Edit:
/usr/local/lib64/python3.6/site-packages/caffe2/python/layers/merge_id_lists.py
(1500B)
from caffe2.python import schema from caffe2.python.layers.layers import ( get_categorical_limit, ModelLayer, IdList ) import numpy as np class MergeIdLists(ModelLayer): """Merge multiple ID_LISTs into a single ID_LIST Args: model: A layer model instance input_record: Tuple (Struct) of ID_LIST features to be merged Returns: the merged ID_LIST feature """ def __init__(self, model, input_record, name='merged'): super(MergeIdLists, self).__init__(model, name, input_record) assert all(schema.equal_schemas(x, IdList) for x in input_record), \ "Inputs to MergeIdLists should all be IdLists." assert all(record.items.metadata is not None for record in self.input_record), \ "Features without metadata are not supported" merge_dim = max(get_categorical_limit(record) for record in self.input_record) assert merge_dim is not None, "Unbounded features are not supported" self.output_schema = schema.NewRecord( model.net, schema.List( schema.Scalar( np.int64, blob=model.net.NextBlob(name), metadata=schema.Metadata(categorical_limit=merge_dim) ))) def add_ops(self, net): return net.MergeIdLists(self.input_record.field_blobs(), self.output_schema.field_blobs())
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