mirror of
https://github.com/zebrajr/pytorch.git
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d0621a2449
Summary: Remove the use of `NextName` in layer model helper, so that the same function return `model_helper` that should construct identical `Net`, when under the same NameScope. The `NextScopedBlob` should only take effect when there is real name conflicting, otherwise it returns ScopedBlobReference. This is critical for parameter blobs. In long run, we need to be able to specify parameter blobs more explicitly. (kennyhorror is working on this). This solution works in short term for e.g., two tower sparse nn models. Reviewed By: kennyhorror Differential Revision: D4555423 fbshipit-source-id: 2c4b99a61392e5d51aa878f7346466a8f14be187
64 lines
2.1 KiB
Python
64 lines
2.1 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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from caffe2.python.schema import (
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Struct, FetchRecord, NewRecord, FeedRecord, InitEmptyRecord)
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from caffe2.python import core, workspace
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from caffe2.python.session import LocalSession
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from caffe2.python.dataset import Dataset
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from caffe2.python.pipeline import pipe
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from caffe2.python.task import TaskGroup
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from caffe2.python.test_util import TestCase
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import numpy as np
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class TestLocalSession(TestCase):
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def test_local_session(self):
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init_net = core.Net('init')
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src_values = Struct(
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('uid', np.array([1, 2, 6])),
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('value', np.array([1.4, 1.6, 1.7])))
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expected_dst = Struct(
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('uid', np.array([2, 4, 12])),
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('value', np.array([0.0, 0.0, 0.0])))
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with core.NameScope('init'):
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src_blobs = NewRecord(init_net, src_values)
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dst_blobs = InitEmptyRecord(init_net, src_values.clone_schema())
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def proc1(rec):
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net = core.Net('proc1')
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with core.NameScope('proc1'):
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out = NewRecord(net, rec)
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net.Add([rec.uid(), rec.uid()], [out.uid()])
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out.value.set(blob=rec.value())
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return [net], out
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def proc2(rec):
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net = core.Net('proc2')
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with core.NameScope('proc2'):
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out = NewRecord(net, rec)
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out.uid.set(blob=rec.uid())
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net.Sub([rec.value(), rec.value()], [out.value()])
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return [net], out
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src_ds = Dataset(src_blobs)
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dst_ds = Dataset(dst_blobs)
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with TaskGroup() as tg:
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out1 = pipe(src_ds.reader(), processor=proc1)
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out2 = pipe(out1, processor=proc2)
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pipe(out2, dst_ds.writer())
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ws = workspace.C.Workspace()
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FeedRecord(src_blobs, src_values, ws)
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session = LocalSession(ws)
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session.run(init_net)
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session.run(tg)
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output = FetchRecord(dst_blobs, ws=ws)
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for a, b in zip(output.field_blobs(), expected_dst.field_blobs()):
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np.testing.assert_array_equal(a, b)
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