mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
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
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
## @package uniform_sampling
|
|
# Module caffe2.python.layers.uniform_sampling
|
|
|
|
|
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
from caffe2.python import core, schema
|
|
from caffe2.python.layers.layers import ModelLayer
|
|
|
|
|
|
class UniformSampling(ModelLayer):
|
|
"""
|
|
Uniform sampling `num_samples - len(input_record)` unique elements from the
|
|
range [0, num_elements). `samples` is the concatenation of input_record and
|
|
the samples. input_record is expected to be unique.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model,
|
|
input_record,
|
|
num_samples,
|
|
num_elements,
|
|
name='uniform_sampling',
|
|
**kwargs
|
|
):
|
|
super(UniformSampling, self).__init__(
|
|
model, name, input_record, **kwargs
|
|
)
|
|
|
|
assert num_elements > num_samples > 0
|
|
assert isinstance(input_record, schema.Scalar)
|
|
|
|
self.num_elements = num_elements
|
|
|
|
num_examples_init = ('GivenTensorInt64Fill',
|
|
{'values': [num_samples]})
|
|
self.num_samples = self.create_param(param_name='num_examples',
|
|
shape=(1,),
|
|
initializer=num_examples_init,
|
|
optimizer=model.NoOptim)
|
|
|
|
sampling_blob_init = ('ConstantFill',
|
|
{'value': float(num_samples) / num_elements,
|
|
'dtype': core.DataType.FLOAT})
|
|
self.sampling_prob = self.create_param(param_name='prob',
|
|
shape=(num_samples,),
|
|
initializer=sampling_blob_init,
|
|
optimizer=model.NoOptim)
|
|
|
|
self.output_schema = schema.Struct(
|
|
(
|
|
'samples', schema.Scalar(
|
|
np.int32, self.get_next_blob_reference("samples")
|
|
)
|
|
),
|
|
('sampling_prob', schema.Scalar(np.float32, self.sampling_prob)),
|
|
)
|
|
|
|
def add_ops(self, net):
|
|
net.StopGradient(self.sampling_prob, self.sampling_prob)
|
|
|
|
shape = net.Shape([self.input_record()], net.NextScopedBlob("shape"))
|
|
shape = net.Sub([self.num_samples, shape], shape)
|
|
samples = net.UniqueUniformFill(
|
|
[shape, self.input_record()],
|
|
net.NextScopedBlob("samples_before_concat"),
|
|
min=0,
|
|
max=self.num_elements - 1,
|
|
input_as_shape=True
|
|
)
|
|
|
|
net.Concat(
|
|
[self.input_record(), samples],
|
|
[self.output_schema.samples(), net.NextScopedBlob("split_info")],
|
|
axis=0
|
|
)
|
|
net.StopGradient(
|
|
self.output_schema.samples(), self.output_schema.samples()
|
|
)
|