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/constant_weight.py (1208B)

# @package constant_weight
# Module caffe2.fb.python.layers.constant_weight

from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np

class ConstantWeight(ModelLayer):
    def __init__(
        self,
        model,
        input_record,
        weights=None,
        name='constant_weight',
        **kwargs
    ):
        super(ConstantWeight,
              self).__init__(model, name, input_record, **kwargs)
        self.output_schema = schema.Scalar(
            np.float32, self.get_next_blob_reference('constant_weight')
        )
        self.data = self.input_record.field_blobs()
        self.num = len(self.data)
        weights = (
            weights if weights is not None else
            [1. / self.num for _ in range(self.num)]
        )
        assert len(weights) == self.num
        self.weights = [
            self.model.add_global_constant(
                '%s_weight_%d' % (self.name, i), float(weights[i])
            ) for i in range(self.num)
        ]

def add_ops(self, net):
        net.WeightedSum(
            [b for x_w_pair in zip(self.data, self.weights) for b in x_w_pair],
            self.output_schema()
        )

# @package constant_weight # Module caffe2.fb.python.layers.constant_weight from caffe2.python import schema from caffe2.python.layers.layers import ModelLayer import numpy as np class ConstantWeight(ModelLayer): def __init__( self, model, input_record, weights=None, name='constant_weight', **kwargs ): super(ConstantWeight, self).__init__(model, name, input_record, **kwargs) self.output_schema = schema.Scalar( np.float32, self.get_next_blob_reference('constant_weight') ) self.data = self.input_record.field_blobs() self.num = len(self.data) weights = ( weights if weights is not None else [1. / self.num for _ in range(self.num)] ) assert len(weights) == self.num self.weights = [ self.model.add_global_constant( '%s_weight_%d' % (self.name, i), float(weights[i]) ) for i in range(self.num) ] def add_ops(self, net): net.WeightedSum( [b for x_w_pair in zip(self.data, self.weights) for b in x_w_pair], self.output_schema() )