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/sampling_train.py (2210B)

## @package sampling_train
# Module caffe2.python.layers.sampling_train

from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer, get_layer_class
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin

class SamplingTrain(ModelLayer):
    def __init__(
        self,
        model,
        input_record,
        prediction_layer,
        output_dims,
        subtract_log_odd=True,
        name='sampling_train',
        **kwargs
    ):
        super(SamplingTrain, self).__init__(
            model, name, input_record, **kwargs
        )

layer_class = get_layer_class(prediction_layer)
        assert issubclass(layer_class, SamplingTrainableMixin)

assert 'indices' in input_record
        assert isinstance(input_record.indices, schema.Scalar),\
            "input_record.indices is expected to be a schema.Scalar"
        assert 'input' in input_record

self.subtract_log_odd = subtract_log_odd
        if self.subtract_log_odd:
            assert 'sampling_prob' in input_record

self._prediction_layer = layer_class(
            model,
            input_record.input,
            output_dims=output_dims,
            **kwargs
        )

self._prediction_layer.train_param_blobs = [
            model.net.NextBlob(str(blob) + '_sampled')
            for blob in self._prediction_layer.param_blobs
        ]

self.params = self._prediction_layer.params

self.output_schema = self._prediction_layer.output_schema

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

def add_train_ops(self, net):
        for full_blob, sampled_blob in zip(
            self._prediction_layer.param_blobs,
            self._prediction_layer.train_param_blobs
        ):
            net.Gather([full_blob, self.input_record.indices()], sampled_blob)
        self._prediction_layer.add_train_ops(net)
        if not self.subtract_log_odd:
            return
        log_q = net.Log(self.input_record.sampling_prob(),
                        net.NextScopedBlob("log_q"))
        net.Sub([self.output_schema(), log_q], self.output_schema(),
                broadcast=1, use_grad_hack=1)

cmd: