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/build_index.py (1937B)

import numpy as np

from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer

class MapToRange(ModelLayer):
    """
    This layer aims to build a mapping from raw keys to indices within [0, max_index).
    The mapping is continuously built during training. The mapping will be frozen during
    evaluation and prediction. Unseen keys will be assigned to index 0.
    """

def __init__(
        self, model,
        input_record,
        max_index,
        name='map_to_range',
        **kwargs
    ):
        super(MapToRange, self).__init__(model, name, input_record, **kwargs)

assert max_index > 0
        assert isinstance(input_record, schema.Scalar)

self.max_index = max_index

self.handler = self.create_param(
            param_name='handler',
            shape=[],
            initializer=('LongIndexCreate', {'max_elements': self.max_index}),
            optimizer=model.NoOptim
        )

self.output_schema = schema.Struct(
            ('indices', schema.Scalar(
                np.int64, self.get_next_blob_reference("indices")
            )),
            ('handler', schema.Scalar(
                np.void, self.handler
            )),
        )

def add_train_ops(self, net):
        if self.input_record.field_type().base != np.int64:
            keys = net.Cast(
                self.input_record(),
                net.NextScopedBlob("indices_before_mapping"),
                to=core.DataType.INT64
            )
        else:
            keys = self.input_record()

# Load keys into indices
        indices = net.IndexGet([self.handler, keys],
                                self.output_schema.indices())

net.StopGradient(indices, indices)

def add_eval_ops(self, net):
        net.IndexFreeze(self.handler, self.handler)
        self.add_train_ops(net)

def add_ops(self, net):
        self.add_eval_ops(net)

import numpy as np from caffe2.python import core, schema from caffe2.python.layers.layers import ModelLayer class MapToRange(ModelLayer): """ This layer aims to build a mapping from raw keys to indices within [0, max_index). The mapping is continuously built during training. The mapping will be frozen during evaluation and prediction. Unseen keys will be assigned to index 0. """ def __init__( self, model, input_record, max_index, name='map_to_range', **kwargs ): super(MapToRange, self).__init__(model, name, input_record, **kwargs) assert max_index > 0 assert isinstance(input_record, schema.Scalar) self.max_index = max_index self.handler = self.create_param( param_name='handler', shape=[], initializer=('LongIndexCreate', {'max_elements': self.max_index}), optimizer=model.NoOptim ) self.output_schema = schema.Struct( ('indices', schema.Scalar( np.int64, self.get_next_blob_reference("indices") )), ('handler', schema.Scalar( np.void, self.handler )), ) def add_train_ops(self, net): if self.input_record.field_type().base != np.int64: keys = net.Cast( self.input_record(), net.NextScopedBlob("indices_before_mapping"), to=core.DataType.INT64 ) else: keys = self.input_record() # Load keys into indices indices = net.IndexGet([self.handler, keys], self.output_schema.indices()) net.StopGradient(indices, indices) def add_eval_ops(self, net): net.IndexFreeze(self.handler, self.handler) self.add_train_ops(net) def add_ops(self, net): self.add_eval_ops(net)