mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
## @package dot_product
|
|
# Module caffe2.python.layers.dot_product
|
|
|
|
|
|
|
|
|
|
|
|
from caffe2.python import schema
|
|
from caffe2.python.layers.layers import (
|
|
ModelLayer,
|
|
)
|
|
|
|
|
|
class PairwiseSimilarity(ModelLayer):
|
|
|
|
def __init__(self, model, input_record, output_dim, pairwise_similarity_func='dot',
|
|
name='pairwise_similarity', **kwargs):
|
|
super(PairwiseSimilarity, self).__init__(model, name, input_record, **kwargs)
|
|
assert isinstance(input_record, schema.Struct), (
|
|
"Incorrect input type. Expected Struct, but received: {0}".
|
|
format(input_record))
|
|
assert (
|
|
('all_embeddings' in input_record) ^
|
|
('x_embeddings' in input_record and 'y_embeddings' in input_record)
|
|
), (
|
|
"either (all_embeddings) xor (x_embeddings and y_embeddings) " +
|
|
"should be given."
|
|
)
|
|
self.pairwise_similarity_func = pairwise_similarity_func
|
|
if 'all_embeddings' in input_record:
|
|
x_embeddings = input_record['all_embeddings']
|
|
y_embeddings = input_record['all_embeddings']
|
|
else:
|
|
x_embeddings = input_record['x_embeddings']
|
|
y_embeddings = input_record['y_embeddings']
|
|
|
|
assert isinstance(x_embeddings, schema.Scalar), (
|
|
"Incorrect input type for x. Expected Scalar, " +
|
|
"but received: {0}".format(x_embeddings))
|
|
assert isinstance(y_embeddings, schema.Scalar), (
|
|
"Incorrect input type for y. Expected Scalar, " +
|
|
"but received: {0}".format(y_embeddings)
|
|
)
|
|
|
|
if 'indices_to_gather' in input_record:
|
|
indices_to_gather = input_record['indices_to_gather']
|
|
assert isinstance(indices_to_gather, schema.Scalar), (
|
|
"Incorrect type of indices_to_gather. "
|
|
"Expected Scalar, but received: {0}".format(indices_to_gather)
|
|
)
|
|
self.indices_to_gather = indices_to_gather
|
|
else:
|
|
self.indices_to_gather = None
|
|
|
|
self.x_embeddings = x_embeddings
|
|
self.y_embeddings = y_embeddings
|
|
|
|
dtype = x_embeddings.field_types()[0].base
|
|
|
|
self.output_schema = schema.Scalar(
|
|
(dtype, (output_dim,)),
|
|
self.get_next_blob_reference('output')
|
|
)
|
|
|
|
def add_ops(self, net):
|
|
if self.pairwise_similarity_func == "cosine_similarity":
|
|
x_embeddings_norm = net.Normalize(self.x_embeddings(), axis=1)
|
|
y_embeddings_norm = net.Normalize(self.y_embeddings(), axis=1)
|
|
Y = net.BatchMatMul(
|
|
[x_embeddings_norm, y_embeddings_norm],
|
|
[self.get_next_blob_reference(x_embeddings_norm + '_matmul')],
|
|
trans_b=1,
|
|
)
|
|
elif self.pairwise_similarity_func == "dot":
|
|
Y = net.BatchMatMul(
|
|
[self.x_embeddings(), self.y_embeddings()],
|
|
[self.get_next_blob_reference(self.x_embeddings() + '_matmul')],
|
|
trans_b=1,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"pairwise_similarity_func={} is not valid".format(
|
|
self.pairwise_similarity_func
|
|
)
|
|
)
|
|
|
|
if self.indices_to_gather:
|
|
flattened = net.Flatten(
|
|
Y, Y + '_flatten',
|
|
)
|
|
net.BatchGather(
|
|
[flattened, self.indices_to_gather()],
|
|
self.output_schema(),
|
|
)
|
|
else:
|
|
net.Flatten(Y, self.output_schema())
|