/usr/local/lib64/python3.6/site-packages/caffe2/python/layers
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,
)