mirror of
https://github.com/saymrwulf/pytorch.git
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ee7b3c9b2b
Summary: RFC. This is a naive implementation of Rebatchin Queue for MultiTask effort. Full disclaimer, I'm very new to Caffe/Machine Learning and I'm doing dodge science here (under Dmytros supervision), so please be extra tough on this review so I can learn best practices :) Differential Revision: D4871970 fbshipit-source-id: 924820ef0fce45b5e2bdabeec9885cbafa23a880
73 lines
2.3 KiB
C++
73 lines
2.3 KiB
C++
#include "rebatching_queue_ops.h"
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namespace caffe2 {
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CAFFE_KNOWN_TYPE(RebatchingQueuePtr);
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namespace {
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REGISTER_CPU_OPERATOR(CreateRebatchingQueue, CreateRebatchingQueueOp);
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REGISTER_CPU_OPERATOR(EnqueueRebatchingQueue, EnqueueRebatchingQueueOp);
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REGISTER_CPU_OPERATOR(DequeueRebatchingQueue, DequeueRebatchingQueueOp);
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REGISTER_CPU_OPERATOR(CloseRebatchingQueue, CloseRebatchingQueueOp);
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NO_GRADIENT(CreateRebatchingQueue);
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NO_GRADIENT(EnqueueRebatchingQueue);
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NO_GRADIENT(DequeueRebatchingQueue);
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NO_GRADIENT(CloseRebatchingQueue);
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OPERATOR_SCHEMA(CreateRebatchingQueue)
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.NumInputs(0)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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Creates the Queue.
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)DOC")
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.Output(0, "queue", "object representing the queue")
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.Arg("num_blobs", "Number of input tensors the queue will support")
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.Arg(
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"capacity",
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"Maximal number of elements the queue can hold at any given point");
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OPERATOR_SCHEMA(CloseRebatchingQueue)
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.NumInputs(1)
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.NumOutputs(0)
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.SetDoc(R"DOC(
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Closes the Queue.
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)DOC")
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.Input(0, "queue", "object representing the queue");
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OPERATOR_SCHEMA(EnqueueRebatchingQueue)
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.NumInputs(2, INT_MAX)
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.NumOutputs(0)
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.SetDoc(R"DOC(
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Enqueues Tensors into the queue.
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Number of input tensors should be equal to the number of components passed
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during creation of the queue.
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If the Queue is closed this operation will fail.
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If enqueue_batch argument is set. We will split the input tensors by the
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first dimension to produce single queue elements.
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)DOC")
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.Input(0, "queue", "object representing the queue")
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.Input(1, "tensor", "First tensor to enque. ")
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.Arg(
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"enqueue_batch",
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"Are we enqueuing a batch or just a single element. \
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By default we enqueue single element.");
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OPERATOR_SCHEMA(DequeueRebatchingQueue)
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.NumInputs(1)
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.NumOutputs(1, INT_MAX)
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.SetDoc(R"DOC(
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Dequeue Tensors from the Queue.
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If the Queue is closed this might return less elements than asked.
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If num_elements > 1 the returned elements will be concatenated into one
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tensor per component.
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)DOC")
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.Input(0, "rebatching_queue", "object representing the queue")
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.Input(1, "tensor", "First tensor to enqueue")
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.Arg(
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"num_elements",
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"Number of elements to dequeue. By default we dequeue one element.");
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}
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}
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