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https://github.com/zebrajr/pytorch.git
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c55be38e63
Summary: Basically takes in a live net and creates an init_net and predict_net which can be written to file and run in Predictor Reviewed By: salexspb Differential Revision: D4989425 fbshipit-source-id: 8052065da9ed763d48bd9e1e19f7697ef60a2829
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
## @package mobile_exporter
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# Module caffe2.python.mobile_exporter
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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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from caffe2.python import core, utils
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from caffe2.proto import caffe2_pb2
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def Export(workspace, net, params):
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"""Returns init_net and predict_net suitable for writing to disk
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and loading into a Predictor"""
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predict_net = caffe2_pb2.NetDef()
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predict_net.CopyFrom(net.Proto())
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init_net = caffe2_pb2.NetDef()
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# Populate the init_net.
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ssa, blob_versions = core.get_ssa(net)
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inputs = []
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for versioned_inputs, _ in ssa:
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inputs += [name for name, _ in versioned_inputs]
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input_blobs = [blob_name for blob_name, version in
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blob_versions.items()
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if version == 0 and blob_name not in params]
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# Blobs that are never used as an input to another layer,
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# i.e. strictly output blobs.
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output_blobs = [blob_name for blob_name, version in
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blob_versions.items()
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if version != 0 and blob_name not in inputs]
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for blob_ref in params:
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blob_name = str(blob_ref)
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blob = workspace.FetchBlob(blob_name)
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init_net.op.extend(
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[
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core.CreateOperator(
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"GivenTensorFill", [], [blob_name],
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arg=[
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utils.MakeArgument("shape", blob.shape),
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utils.MakeArgument("values", blob)
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]
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)
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]
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)
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# We have to make sure the blob exists in the namespace
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# and we can do so with fake data. (Which is immediately overwritten
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# by any typical usage)
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for blob_name in input_blobs:
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init_net.op.extend(
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[
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core.CreateOperator(
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"GivenTensorFill", [], [blob_name],
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arg=[
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utils.MakeArgument("shape", [1, 1]),
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utils.MakeArgument("values", [0.0])
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]
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)
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]
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)
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# Now we make input/output_blobs line up with what Predictor expects.
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del predict_net.external_input[:]
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predict_net.external_input.extend(input_blobs)
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# For populating weights
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predict_net.external_input.extend(net.Proto().external_input)
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# Ensure the output is also consistent with what we want
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del predict_net.external_output[:]
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predict_net.external_output.extend(output_blobs)
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return init_net, predict_net
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