mirror of
https://github.com/zebrajr/pytorch.git
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3eb3507367
Summary: This layer will be used to sample negative labels for sampled softmax. Differential Revision: D4773444 fbshipit-source-id: 605a979c09d07531293dd9472da9d2fa7439c619
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import LayerParameter, ModelLayer
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class UniformSampling(ModelLayer):
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"""
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Uniform sampling `num_samples - len(input_record)` unique elements from the
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range [0, num_elements). `samples` is the concatenation of input_record and
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the samples. input_record is expected to be unique.
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"""
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def __init__(
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self,
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model,
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input_record,
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num_samples,
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num_elements,
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name='uniform_sampling',
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**kwargs
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):
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super(UniformSampling, self).__init__(
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model, name, input_record, **kwargs
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)
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assert num_elements > 0
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assert isinstance(input_record, schema.Scalar)
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self.num_elements = num_elements
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self.num_samples = model.net.NextScopedBlob(name + "_num_samples")
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self.params.append(
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LayerParameter(
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parameter=self.num_samples,
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initializer=core.CreateOperator(
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"GivenTensorInt64Fill",
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[],
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self.num_samples,
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shape=(1, ),
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values=[num_samples],
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),
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optimizer=model.NoOptim,
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)
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)
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self.sampling_prob = model.net.NextScopedBlob(name + "_prob")
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self.params.append(
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LayerParameter(
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parameter=self.sampling_prob,
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initializer=core.CreateOperator(
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"ConstantFill",
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[],
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self.sampling_prob,
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shape=(num_samples, ),
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value=float(num_samples) / num_elements,
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dtype=core.DataType.FLOAT
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),
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optimizer=model.NoOptim,
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)
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)
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self.output_schema = schema.Struct(
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(
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'samples', schema.Scalar(
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np.int32, model.net.NextScopedBlob(name + "_samples")
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)
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),
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('sampling_prob', schema.Scalar(np.float32, self.sampling_prob)),
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)
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def add_ops(self, net):
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net.StopGradient(self.sampling_prob, self.sampling_prob)
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shape = net.Shape([self.input_record()], net.NextScopedBlob("shape"))
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shape = net.Sub([self.num_samples, shape], shape)
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samples = net.UniqueUniformFill(
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[shape, self.input_record()],
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net.NextScopedBlob("samples"),
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min=0,
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max=self.num_elements - 1,
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input_as_shape=True
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)
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net.Concat(
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[self.input_record(), samples],
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[self.output_schema.samples(), net.NextScopedBlob("split_info")],
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axis=0
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)
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net.StopGradient(
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self.output_schema.samples(), self.output_schema.samples()
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)
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