/
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/position_weighted.py
(2066B)
## @package position_weighted # Module caffe2.python.layers.position_weighted import logging import numpy as np from caffe2.python import schema from caffe2.python.layers.layers import ( get_categorical_limit, ModelLayer, ) from caffe2.python.layers.tags import Tags logger = logging.getLogger(__name__) class PositionWeighted(ModelLayer): def __init__(self, model, input_record, weight_optim=None, name="position_weights"): super(PositionWeighted, self).__init__(model, name, input_record) assert isinstance(input_record, schema.List), "Incorrect input type" length_metadata = input_record.lengths.metadata max_length = (length_metadata.categorical_limit if length_metadata is not None else None) if max_length is not None: self.shape = max_length else: self.shape = get_categorical_limit(input_record) logger.warning( '{}: categorical_limit of lengths is not available, using ' 'categorical_limit of the keys: {}'.format( str(input_record.lengths()), self.shape)) self.pos_w = self.create_param(param_name='pos_w', shape=[self.shape, ], initializer=('ConstantFill', {'value': 1.0}), optimizer=weight_optim) self.output_schema = schema.Struct( ('position_weights', schema.Scalar((np.float32, self.shape), self.get_next_blob_reference("pos_w_gather"))) ) self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER}) def get_memory_usage(self): return self.shape def add_ops(self, net): inc_seq = net.LengthsRangeFill( [self.input_record.lengths()], self.input_record.lengths() + '_pos_w_seq' ) net.Gather( [self.pos_w, inc_seq], self.output_schema.position_weights.field_blobs())
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