mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
1906eaf22f
PyTorch has been Python-3.X+ for ages, so it's a shame to still rely on `future.utils` even in a deprecated Caffe2 codebase For the reference: https://peps.python.org/pep-0469/#migrating-directly-to-python-3 Pull Request resolved: https://github.com/pytorch/pytorch/pull/92596 Approved by: https://github.com/kit1980, https://github.com/orionr
137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
## @package concat
|
|
# Module caffe2.python.layers.concat
|
|
|
|
|
|
|
|
|
|
|
|
from caffe2.python import schema
|
|
from caffe2.python.layers.layers import (
|
|
ModelLayer,
|
|
)
|
|
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 input_record.fields.items():
|
|
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,
|
|
)
|