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/split.py (2257B)

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

from caffe2.python import schema
from caffe2.python.layers.layers import (
    ModelLayer,
)

class Split(ModelLayer):

def __init__(self, model, input_record, num_splits=1, axis=1,
                 name='split', split=None, **kwargs):
        super(Split, self).__init__(model, name, input_record, **kwargs)
        self.axis = axis
        # Assume that first dimension is batch, so actual axis in shape is
        # axis - 1
        axis -= 1
        assert axis >= 0

assert isinstance(input_record, schema.Scalar),\
            "Incorrect input type. Expected Scalar, but received: {0}".\
            format(input_record)

input_shape = input_record.field_type().shape
        assert len(input_shape) >= axis
        if split is None:
            assert input_shape[axis] % num_splits == 0
        else:
            num_splits = len(split)
            assert input_shape[axis] == sum(split)

if split is None:
            output_shape = list(input_shape)
            output_shape[axis] = int(output_shape[axis] / num_splits)
        else:
            output_shape = []
            for i in range(num_splits):
                output_shape_i = list(input_shape)
                output_shape_i[axis] = split[i]
                output_shape.append(output_shape_i)

data_type = input_record.field_type().base

if split is None:
            output_scalars = [
                schema.Scalar(
                    (data_type, output_shape),
                    self.get_next_blob_reference('output_{}'.format(i)),
                )
                for i in range(num_splits)
            ]
        else:
            output_scalars = [
                schema.Scalar(
                    (data_type, output_shape[i]),
                    self.get_next_blob_reference('output_{}'.format(i)),
                )
                for i in range(num_splits)
            ]
        self.output_schema = schema.Tuple(*output_scalars)
        self.split = split

def add_ops(self, net):
        net.Split(
            self.input_record.field_blobs(),
            self.output_schema.field_blobs(),
            split=self.split,
            axis=self.axis,
        )

## @package split # Module caffe2.python.layers.split from caffe2.python import schema from caffe2.python.layers.layers import ( ModelLayer, ) class Split(ModelLayer): def __init__(self, model, input_record, num_splits=1, axis=1, name='split', split=None, **kwargs): super(Split, self).__init__(model, name, input_record, **kwargs) self.axis = axis # Assume that first dimension is batch, so actual axis in shape is # axis - 1 axis -= 1 assert axis >= 0 assert isinstance(input_record, schema.Scalar),\ "Incorrect input type. Expected Scalar, but received: {0}".\ format(input_record) input_shape = input_record.field_type().shape assert len(input_shape) >= axis if split is None: assert input_shape[axis] % num_splits == 0 else: num_splits = len(split) assert input_shape[axis] == sum(split) if split is None: output_shape = list(input_shape) output_shape[axis] = int(output_shape[axis] / num_splits) else: output_shape = [] for i in range(num_splits): output_shape_i = list(input_shape) output_shape_i[axis] = split[i] output_shape.append(output_shape_i) data_type = input_record.field_type().base if split is None: output_scalars = [ schema.Scalar( (data_type, output_shape), self.get_next_blob_reference('output_{}'.format(i)), ) for i in range(num_splits) ] else: output_scalars = [ schema.Scalar( (data_type, output_shape[i]), self.get_next_blob_reference('output_{}'.format(i)), ) for i in range(num_splits) ] self.output_schema = schema.Tuple(*output_scalars) self.split = split def add_ops(self, net): net.Split( self.input_record.field_blobs(), self.output_schema.field_blobs(), split=self.split, axis=self.axis, )