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/gather_record.py (3260B)

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

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

class GatherRecord(ModelLayer):
    """
    Given 1-D `indices` tensor, gather elements at `i` in `indices` from all the
    blobs in `record`. If a blob is a values blob of a list, all the elements
    included by the list's lengths blob are gathered. For example,

Input:
        indices = [0, 2]
        record:a = [[0, 1], [2, 3], [4, 5], [6, 7]]
        record:b:lengths = [0, 1, 2, 3]
        record:b:items = [0, 1, 2, 3, 4, 5]

Output:
        a = [[0, 1], [4, 5]]
        b:lengths = [0, 2]
        b:items = [1, 2]

This supports nested list.
    """

def __init__(self, model, input_record, name='gather_record', **kwargs):
        super(GatherRecord, self).__init__(model, name, input_record, **kwargs)

assert 'indices' in input_record
        assert 'record' in input_record

self.output_schema = schema.NewRecord(
            model.net, input_record.record.clone_schema())

self._indices = self.input_record.indices()

def _gather_scalar(self, net, record, lengths_blob, output_record):
        if lengths_blob is None:
            net.Gather([record(), self._indices], output_record())
        else:
            net.LengthsGather([record(), lengths_blob, self._indices],
                              output_record())

def _gather_struct(self, net, record, lengths_blob, output_record):
        for name, field in record.get_children():
            self._dispatch(net, field, lengths_blob, output_record[name])

def _gather_list(self, net, record, lengths_blob, output_record):
        self._gather_scalar(
            net, record.lengths, lengths_blob, output_record.lengths)
        if lengths_blob is None:
            lengths_blob = record.lengths()
        else:
            # TODO(kittipat): This is a hacky solution until LengthsSum for int
            # is implemented
            lengths_float = net.Cast(
                record.lengths(),
                net.NextScopedBlob(str(record.lengths()) + '_float'),
                to=core.DataType.FLOAT,
            )
            lengths_blob_float = net.LengthsSum(
                [lengths_float, lengths_blob],
                net.NextScopedBlob(str(record.lengths()) + "_nested_float")
            )
            lengths_blob = net.Cast(
                lengths_blob_float,
                net.NextScopedBlob(str(record.lengths()) + "_nested"),
                to=core.DataType.INT32,
            )
        self._dispatch(net, record._items, lengths_blob, output_record._items)

def _dispatch(self, net, record, lengths_blob, output_record):
        if isinstance(record, schema.Scalar):
            self._gather_scalar(net, record, lengths_blob, output_record)
        elif isinstance(record, schema.Struct):
            self._gather_struct(net, record, lengths_blob, output_record)
        elif isinstance(record, schema.List):
            self._gather_list(net, record, lengths_blob, output_record)
        else:
            raise NotImplementedError

def add_ops(self, net):
        self._dispatch(net, self.input_record.record, None, self.output_schema)

## @package gather_record # Module caffe2.python.layers.gather_record from caffe2.python import core, schema from caffe2.python.layers.layers import ModelLayer class GatherRecord(ModelLayer): """ Given 1-D `indices` tensor, gather elements at `i` in `indices` from all the blobs in `record`. If a blob is a values blob of a list, all the elements included by the list's lengths blob are gathered. For example, Input: indices = [0, 2] record:a = [[0, 1], [2, 3], [4, 5], [6, 7]] record:b:lengths = [0, 1, 2, 3] record:b:items = [0, 1, 2, 3, 4, 5] Output: a = [[0, 1], [4, 5]] b:lengths = [0, 2] b:items = [1, 2] This supports nested list. """ def __init__(self, model, input_record, name='gather_record', **kwargs): super(GatherRecord, self).__init__(model, name, input_record, **kwargs) assert 'indices' in input_record assert 'record' in input_record self.output_schema = schema.NewRecord( model.net, input_record.record.clone_schema()) self._indices = self.input_record.indices() def _gather_scalar(self, net, record, lengths_blob, output_record): if lengths_blob is None: net.Gather([record(), self._indices], output_record()) else: net.LengthsGather([record(), lengths_blob, self._indices], output_record()) def _gather_struct(self, net, record, lengths_blob, output_record): for name, field in record.get_children(): self._dispatch(net, field, lengths_blob, output_record[name]) def _gather_list(self, net, record, lengths_blob, output_record): self._gather_scalar( net, record.lengths, lengths_blob, output_record.lengths) if lengths_blob is None: lengths_blob = record.lengths() else: # TODO(kittipat): This is a hacky solution until LengthsSum for int # is implemented lengths_float = net.Cast( record.lengths(), net.NextScopedBlob(str(record.lengths()) + '_float'), to=core.DataType.FLOAT, ) lengths_blob_float = net.LengthsSum( [lengths_float, lengths_blob], net.NextScopedBlob(str(record.lengths()) + "_nested_float") ) lengths_blob = net.Cast( lengths_blob_float, net.NextScopedBlob(str(record.lengths()) + "_nested"), to=core.DataType.INT32, ) self._dispatch(net, record._items, lengths_blob, output_record._items) def _dispatch(self, net, record, lengths_blob, output_record): if isinstance(record, schema.Scalar): self._gather_scalar(net, record, lengths_blob, output_record) elif isinstance(record, schema.Struct): self._gather_struct(net, record, lengths_blob, output_record) elif isinstance(record, schema.List): self._gather_list(net, record, lengths_blob, output_record) else: raise NotImplementedError def add_ops(self, net): self._dispatch(net, self.input_record.record, None, self.output_schema)