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/concat.py (4849B)

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

from caffe2.python import schema
from caffe2.python.layers.layers import (
    ModelLayer,
)
from future.utils import viewitems
import numpy as np
from collections import defaultdict

import logging
logger = logging.getLogger(__name__)

def get_concatenated_feature_to_index(blobs_to_concat):
    concat_feature_to_index = defaultdict(list)
    start_pos = 0
    for scalar in blobs_to_concat:
        num_dims = scalar.dtype.shape[0]
        if hasattr(scalar, 'metadata') \
            and hasattr(scalar.metadata, 'feature_specs') \
            and hasattr(scalar.metadata.feature_specs, 'feature_to_index') \
                and isinstance(scalar.metadata.feature_specs.feature_to_index, dict):  # noqa B950
            for k, v in scalar.metadata.feature_specs.feature_to_index.items():
                concat_feature_to_index[k].extend([start_pos + vi for vi in v])
        start_pos += num_dims
    return dict(concat_feature_to_index) if concat_feature_to_index.keys() else None

class Concat(ModelLayer):
    """
    Construct Concat layer
    Assume that first dimension is batch,

Example:

embedding_dim = 64
        input_record = self.new_record(schema.Struct(
            ('input1', schema.Scalar((np.float32, (embedding_dim, )))),
            ('input2', schema.Scalar((np.float32, (embedding_dim, )))),
            ('input3', schema.Scalar((np.float32, (embedding_dim, )))),
        ))

output = self.model.Concat(input_record)
        self.assertEqual(
            schema.Scalar((np.float32, ((len(input_record.fields) * embedding_dim, )))),
            output
        )

# Note that in Concat layer we assume first dimension is batch.
        # so input is B * embedding_dim
        # add_axis=1 make it B * 1 * embedding_dim
        # Concat on axis=1 make it B * N * embedding_dim

output = self.model.Concat(input_record, axis=1, add_axis=1)
        self.assertEqual(
            schema.Scalar((np.float32, ((len(input_record.fields), embedding_dim)))),
            output
        )
    """

def __init__(self, model, input_record, axis=1, add_axis=0,
                 name='concat', **kwargs):
        super(Concat, self).__init__(model, name, input_record, **kwargs)
        self.axis = axis
        self.add_axis = add_axis
        assert not (axis == 0 and add_axis == 1), \
            "It's not allowed to add axis=0"
        assert isinstance(input_record, schema.Struct),\
            "Incorrect input type. Expected Struct, but received: {0}".\
            format(input_record)

shapes = []
        for field_name, field_type in viewitems(input_record.fields):
            assert isinstance(field_type, schema.Scalar),\
                "Incorrect input type for {}. Expected Scalar, but got: {}".\
                format(field_name, field_type)
            # Assume that first dimension is batch, so actual axis in shape is
            # axis - 1
            shape = list(field_type.field_type().shape)
            if add_axis:
                shape.insert(axis - 1, 1)
            assert len(shape) >= axis,\
                "Concat expects that limited dimensions of the input tensor"
            shapes.append(shape)
        logger.info('Concat Layer input shapes: ' + str(shapes))

if axis == 0:
            self.output_schema = schema.from_blob_list(
                input_record[0],
                [self.get_next_blob_reference('output')]
            )
            return

concat_dim = 0
        for shape in shapes:
            concat_dim += shape[axis - 1]
            shape[axis - 1] = 0
            assert shape == shapes[0],\
                "Shapes {0} and {1} are not compatible for Concat".\
                format(shape, shapes[0])
        output_dims = shapes[0]
        output_dims[axis - 1] = concat_dim

logger.info('Concat Layer output_dims: ' + str(output_dims))
        self.output_schema = schema.Scalar(
            (np.float32, output_dims),
            self.get_next_blob_reference('output'))

record_to_concat = input_record.fields.values()
        concated_feature_to_index = get_concatenated_feature_to_index(
            record_to_concat
        )
        if concated_feature_to_index:
            metadata = schema.Metadata(
                feature_specs=schema.FeatureSpec(
                    feature_to_index=concated_feature_to_index
                )
            )
            self.output_schema.set_metadata(metadata)

def add_ops(self, net):
        net.Concat(
            self.input_record.field_blobs(),
            [
                self.output_schema.field_blobs()[0],
                self.output_schema.field_blobs()[0] + "_concat_dims"
            ],
            axis=self.axis,
            add_axis=self.add_axis,
        )

cmd: