mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
6ca28984c7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/10739 I wanted to assert that the blobs in the workspace of the new session after loading checkpoint are exactly the same as the blobs in the workspace of the old session before saving to a checkpoint. But I found that when calling `task.get_step()`, a dummy task output blob, `task:output/ConstIntFill:0`, is added. Also a dummy net `task:output` was also added along with it. See https://fburl.com/937lf2yk This makes it hard to assert "Equal", forcing me to assert "LessThan" or "GreaterThan". This adding a dummy TaskOutput when user specifies no TaskOutput is a hack. The reason for this is that ZMQ socket can't send empty blob list. As a result, if the Task on the Worker had no output, The master would never stop waiting and hang forever. See https://fburl.com/rd7fhy6p and imagine `socket.recv(net, 0)`. TaskOuput is at user layer. The hack shouldn't be exposed to user layer, polluting user workspaces. Instead, we should move the creating of the dummy blob to some deeper layer, and remove the dummy blob in the workspace afterwards to avoid polluting user workspaces. After this change, the workaround becomes totally transparent and no side-effect to users. Reviewed By: mraway Differential Revision: D9413150 fbshipit-source-id: 51aaf3201e26570b4fcf5738e9b9aa17c58777ac
1169 lines
43 KiB
Python
1169 lines
43 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 inspect import currentframe, getframeinfo
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import unittest
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace, schema, test_util
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from caffe2.python.task import Node, Task
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class TestScopes(test_util.TestCase):
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def testBlobReferenceIsIndependentFromNameScope(self):
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blob_v = core.BlobReference("v")
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with core.NameScope("foo"):
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blob_w = core.BlobReference("w")
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with core.NameScope("bar"):
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blob_x = core.BlobReference("x")
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self.assertEqual(str(blob_v), "v")
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self.assertEqual(str(blob_w), "w")
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self.assertEqual(str(blob_x), "x")
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def testNameScopeWithOp(self):
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global_x = core.BlobReference("x")
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global_y = core.BlobReference("y")
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with core.NameScope("foo"):
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# Raw strings should have namescope prepended.
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/y")
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# BlobReferences should not.
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op = core.CreateOperator("Relu", global_x, global_y)
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "y")
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def testNameScopeWithReset(self):
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with core.NameScope("foo"):
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# foo/
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/y")
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with core.NameScope("bar"):
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# foo/bar/
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/bar/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/bar/y")
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# Back to foo/
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/y")
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with core.NameScope("bar", reset=True):
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# bar/
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "bar/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "bar/y")
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# Back to foo/
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op = core.CreateOperator("Relu", "x", "y")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/y")
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def testDeviceScope(self):
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# No device
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op = core.CreateOperator("Relu", "x", "y")
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self.assertFalse(op.HasField('device_option'))
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# explicitly setting a device
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device_option = caffe2_pb2.DeviceOption()
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device_option.device_type = caffe2_pb2.CUDA
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device_option.cuda_gpu_id = 1
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op = core.CreateOperator("Relu", "x", "y", device_option=device_option)
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self.assertTrue(op.HasField('device_option'))
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self.assertEqual(op.device_option.device_type, caffe2_pb2.CUDA)
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self.assertEqual(op.device_option.cuda_gpu_id, 1)
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with core.DeviceScope(device_option):
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# from device scope
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op = core.CreateOperator("Relu", "x", "y")
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self.assertTrue(op.HasField('device_option'))
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self.assertEqual(op.device_option.device_type, caffe2_pb2.CUDA)
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self.assertEqual(op.device_option.cuda_gpu_id, 1)
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# from an overridden device option
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override_device = caffe2_pb2.DeviceOption()
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override_device.device_type = caffe2_pb2.CPU
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op = core.CreateOperator(
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"Relu", "x", "y", device_option=override_device)
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self.assertTrue(op.HasField('device_option'))
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self.assertEqual(op.device_option.device_type, caffe2_pb2.CPU)
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# back from normal: no device
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op = core.CreateOperator("Relu", "x", "y")
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self.assertFalse(op.HasField('device_option'))
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device_option = caffe2_pb2.DeviceOption()
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def testNameAndDeviceScopeTogether(self):
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device_option = caffe2_pb2.DeviceOption()
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device_option.device_type = caffe2_pb2.CUDA
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device_option.cuda_gpu_id = 1
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with core.DeviceScope(device_option):
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with core.NameScope("foo"):
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op = core.CreateOperator("Relu", "x", "y")
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self.assertTrue(op.HasField('device_option'))
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self.assertEqual(op.device_option.device_type, caffe2_pb2.CUDA)
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self.assertEqual(op.device_option.cuda_gpu_id, 1)
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "foo/x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "foo/y")
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class TestCloneNet(test_util.TestCase):
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def testPartialClone(self):
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params = core.Net('params')
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p1 = params.ConstantFill([], ['p1'])
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workspace.CreateNet(params)
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workspace.RunNetOnce(params)
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n = core.Net('original')
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a1 = n.AddExternalInput('a1')
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a2 = n.AddExternalInput('a2')
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b1, b2 = n.Concat([a1, a2], ['b1', 'b2'], axis=0)
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c1 = n.Sum([b1, p1], ['c1'])
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c2 = n.Sum([b2], ['c2'])
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d = n.Sum([c1, c2], ['d'])
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# test that gradient ops are ignored when partial-cloning
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n.AddGradientOperators([d])
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# test some in-place ops
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k = n.Sum([p1], ['k'])
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e = n.Sum([d], ['e'])
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e = n.Sum([e, k], [e])
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e = n.Sum([e], [e])
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f = n.Sum(e, ['f'])
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def net_assert(net, num_ops, inputs, outputs, internals):
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self.assertEqual(len(net.Proto().op), num_ops)
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self.assertEqual(set(net.Proto().external_input), inputs)
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self.assertEqual(set(net.Proto().external_output), outputs)
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all_blobs = set(net.Proto().external_input)
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all_blobs |= set(net.Proto().external_output)
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for op in net.Proto().op:
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all_blobs |= set(op.input) | set(op.output)
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self.assertEqual(all_blobs, inputs | outputs | internals)
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# create net to make sure its valid
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for input in inputs:
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workspace.FeedBlob(input, np.array([]))
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workspace.CreateNet(net)
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n2, (d22, ) = n.ClonePartial('f1', {a1: 'a11', a2: 'a22'}, [d])
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net_assert(
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n2, 4, {'p1', 'a11', 'a22'}, {'f1/d'},
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{'f1/b1', 'f1/b2', 'f1/c1', 'f1/c2', 'p1'})
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self.assertTrue(isinstance(d22, core.BlobReference))
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self.assertEqual(d22.Net(), n2)
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self.assertEqual(str(d22), 'f1/d')
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n3, (d22, ) = n.ClonePartial('f2', [b1, b2], [d])
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net_assert(
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n3, 3, {'p1', 'b1', 'b2'}, {'f2/d'}, {'f2/c1', 'f2/c2', 'p1'})
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self.assertEqual(str(d22), 'f2/d')
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n4, (c22, ) = n.ClonePartial('f3', [b1], [c1])
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net_assert(n4, 1, {'p1', 'b1'}, {'f3/c1'}, {'p1'})
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self.assertEqual(str(c22), 'f3/c1')
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n5, (c11, c22) = n.ClonePartial('f4', [b1, b2], [c1, c2])
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net_assert(n5, 2, {'p1', 'b1', 'b2'}, {'f4/c1', 'f4/c2'}, {'p1'})
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self.assertEqual(str(c11), 'f4/c1')
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self.assertEqual(str(c22), 'f4/c2')
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with self.assertRaises(AssertionError):
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n.ClonePartial('f4', [a1, a2, c2], [d])
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n6, (e22, ) = n.ClonePartial('f5', [d], [e])
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net_assert(n6, 4, {'p1', 'd'}, {'f5/e'}, {'f5/k', 'p1'})
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self.assertEqual(str(e22), 'f5/e')
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n8, (e22, f22) = n.ClonePartial('f7', [d], [e, f])
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net_assert(n8, 5, {'p1', 'd'}, {'f7/e', 'f7/f'}, {'p1', 'f7/k'})
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self.assertEqual(str(e22), 'f7/e')
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self.assertEqual(str(f22), 'f7/f')
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params._CheckLookupTables()
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n._CheckLookupTables()
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def test_mask_clone_update_external_list(self):
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n = core.Net('original')
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a1 = n.AddExternalInput('a1')
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a2 = n.AddExternalInput('a2')
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p1 = 'p1'
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b1, b2 = n.Concat([a1, a2], ['b1', 'b2'], axis=0)
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c1 = n.Sum([b1, p1], ['c1'])
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c2 = n.Sum([b2], ['c2'])
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n.Sum([c1, c2], ['d'])
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new_net = n.Clone(
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"new", op_id_mask=[0, 1], keep_schema=True, update_external_list=True)
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self.assertEqual(
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sorted(map(str, new_net.external_inputs)),
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["a1", "a2", "p1"],
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"external input not matched",
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)
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self.assertEqual(
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sorted(map(str, new_net.external_outputs)),
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["b2", "c1"],
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"external output not matched",
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)
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new_net = n.Clone(
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"new2", op_id_mask=[2, 3], keep_schema=True, update_external_list=True)
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self.assertEqual(
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sorted(map(str, new_net.external_inputs)),
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["b2", "c1"],
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"external input not matched",
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)
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self.assertEqual(
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sorted(map(str, new_net.external_outputs)),
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["d"],
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"external output not matched",
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)
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class TestExternalInputs(test_util.TestCase):
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def testSetInputRecordWithBlobs(self):
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net = core.Net("test")
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record = schema.NewRecord(net, schema.Struct(
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("x", schema.Scalar(np.float)),
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))
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input_record = net.set_input_record(record)
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self.assertTrue(net.BlobIsDefined(input_record.x()))
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self.assertIn(input_record.x(), net.external_inputs)
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def testSetInputRecordWithoutBlobs(self):
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net = core.Net("test")
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record = schema.Struct(("x", schema.Scalar(np.float)))
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input_record = net.set_input_record(record)
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self.assertTrue(net.BlobIsDefined(input_record.x()))
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self.assertIn(input_record.x(), net.external_inputs)
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class TestCreateOperator(test_util.TestCase):
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def testCreate(self):
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device_option = caffe2_pb2.DeviceOption()
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device_option.device_type = caffe2_pb2.CUDA
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device_option.cuda_gpu_id = 1
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op = core.CreateOperator(
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"Ludicrous", "x", "y", name="ludicrous",
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control_input="z", device_option=device_option,
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engine="WARP", arg1=1, arg2="2", arg3=[1, 2, 3])
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self.assertEqual(op.type, "Ludicrous")
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self.assertEqual(op.name, "ludicrous")
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self.assertEqual(op.engine, "WARP")
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self.assertEqual(len(op.input), 1)
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self.assertEqual(op.input[0], "x")
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self.assertEqual(len(op.output), 1)
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self.assertEqual(op.output[0], "y")
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self.assertEqual(len(op.control_input), 1)
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self.assertEqual(op.control_input[0], "z")
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self.assertTrue(op.HasField('device_option'))
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self.assertEqual(op.device_option.device_type, caffe2_pb2.CUDA)
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self.assertEqual(op.device_option.cuda_gpu_id, 1)
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self.assertTrue(len(op.arg), 3)
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# can't guarantee ordering of kwargs, so generate a set of args
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# to test with
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arg_map = {}
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for arg in op.arg:
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arg_map[arg.name] = arg
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# Check all elements exist that should
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self.assertEqual("arg1" in arg_map, True)
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self.assertEqual("arg2" in arg_map, True)
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self.assertEqual("arg3" in arg_map, True)
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# Now test that all args were initialized correctly
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self.assertEqual(arg_map["arg1"].i, 1)
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self.assertEqual(arg_map["arg2"].s, b"2")
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self.assertEqual(list(arg_map["arg3"].ints), [1, 2, 3])
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class TestAutoNaming(test_util.TestCase):
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def assertOperatorListEqual(self, operatorDefList1, operatorDefList2):
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for op in operatorDefList1:
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op.debug_info = ""
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for op in operatorDefList2:
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op.debug_info = ""
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self.assertEqual(operatorDefList1, operatorDefList2)
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"""
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Test that operators are named with different names, and that automatically
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named blob names don't clash intra or inter networks.
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"""
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def test_next_blob(self):
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def create_net():
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net = core.Net('net')
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with core.NameScope('foo'):
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net.Add(['a', 'b'], net.NextScopedBlob('ab'))
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net.Add(['c', 'd'], net.NextBlob('cd'))
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return net
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net_a = create_net()
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net_b = create_net()
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# created net proto is predicatable.
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self.assertOperatorListEqual(net_a.Proto().op,
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net_b.Proto().op)
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self.assertEqual(net_a.Proto().op[0].output[0], 'foo/ab')
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self.assertEqual(net_a.Proto().op[1].output[0], 'cd')
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net_c = core.Net('net')
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# different calls return different blob names
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self.assertNotEqual(str(net_c.NextBlob('b')), str(net_c.NextBlob('b')))
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def test_auto_naming(self):
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a = core.Net('net')
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b = core.Net('net')
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self.assertNotEqual(a.Proto().name, b.Proto().name)
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a_in1 = a.AddExternalInput('a')
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b_in1 = b.AddExternalInput('b')
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all_outputs_single = []
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all_outputs_list = []
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def add_ops():
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all_outputs_single.append(a.Sum([a_in1, a_in1]))
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all_outputs_single.append(a.Sum([a_in1, a_in1]))
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all_outputs_single.append(b.Sum([b_in1, b_in1]))
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all_outputs_single.append(b.Sum([b_in1, b_in1]))
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all_outputs_list.append(a.Sum([a_in1, a_in1], outputs=2))
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all_outputs_list.append(a.Sum([a_in1, a_in1], outputs=2))
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all_outputs_list.append(b.Sum([b_in1, b_in1], outputs=2))
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all_outputs_list.append(b.Sum([b_in1, b_in1], outputs=2))
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add_ops()
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with core.NameScope('n1'):
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add_ops()
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# Force reset of lookup tables
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a.Proto().name
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with core.NameScope('n2'):
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add_ops()
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all_outputs = []
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for s in all_outputs_single:
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all_outputs.append(str(s))
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for l in all_outputs_list:
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for o in l:
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all_outputs.append(str(o))
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for i, o1 in enumerate(all_outputs):
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for j, o2 in enumerate(all_outputs):
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if i != j:
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self.assertNotEqual(str(o1), str(o2))
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a._CheckLookupTables()
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b._CheckLookupTables()
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class TestAppendNet(test_util.TestCase):
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def test_external_inputs_merged_correctly(self):
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netA = core.Net("A")
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netA.Sum(["in1", "in2"], ["sum1"])
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self.assertTrue("in1" in netA.external_inputs)
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netB = core.Net("B")
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netB.Sum(["in3", "in4"], ["in1"])
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netB.AppendNet(netA)
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self.assertFalse("in1" in netB.external_inputs)
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def test_external_inputs_merged_correctlyB(self):
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netA = core.Net("A")
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netA.Sum(["in1", "in2"], ["sum1"])
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self.assertTrue("in1" in netA.external_inputs)
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netB = core.Net("B")
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netB.Sum(["in3", "in4"], ["in1"])
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netA.AppendNet(netB) # note different order than in prev test
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self.assertTrue("in1" in netA.external_inputs)
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class TestExtractPredictorNet(test_util.TestCase):
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def test_extract_simple(self):
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from caffe2.python import brew
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from caffe2.python.model_helper import ModelHelper, ExtractPredictorNet
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model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
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[data, label] = brew.image_input(
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model,
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"reader", ["xx/data", "label"],
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is_test=1,
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)
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cnv = brew.conv(model, data, 'cnv', 32, 32, 4)
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a = brew.fc(model, cnv, 'a', 100, 200)
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pred = brew.fc(model, a, 'pred', 200, 5)
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brew.softmax(model, [pred, label], "softmax")
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|
|
(predict_net, export_blobs) = ExtractPredictorNet(
|
|
net_proto=model.net.Proto(),
|
|
input_blobs=["xx/data"],
|
|
output_blobs=["pred"],
|
|
renames={"xx/data": "image"},
|
|
)
|
|
export_blobs = set(export_blobs)
|
|
|
|
ops = list(predict_net.Proto().op)
|
|
for op in ops:
|
|
self.assertFalse(op.type == "Softmax")
|
|
self.assertFalse("xx/data" in op.input)
|
|
|
|
# Note: image input should not be included
|
|
self.assertEquals(ops[0].type, "Conv")
|
|
self.assertEquals(ops[1].type, "FC")
|
|
self.assertEquals(ops[2].type, "FC")
|
|
self.assertEquals(len(ops), 3)
|
|
|
|
# test rename happened
|
|
self.assertEquals(ops[0].input[0], "image")
|
|
|
|
# Check export blobs
|
|
self.assertTrue("image" not in export_blobs)
|
|
self.assertTrue("xx/data" not in export_blobs)
|
|
self.assertEqual(set([str(p) for p in model.params]), export_blobs)
|
|
|
|
# Check external inputs/outputs
|
|
self.assertTrue("image" in predict_net.Proto().external_input)
|
|
self.assertEquals(set(["pred"]), set(predict_net.Proto().external_output))
|
|
self.assertEqual(
|
|
set(predict_net.Proto().external_input) -
|
|
set([str(p) for p in model.params]), set(["image"])
|
|
)
|
|
|
|
|
|
class TestOperatorTraceback(test_util.TestCase):
|
|
def op_name_check(self, net, cf, line, func):
|
|
net.PopulateProtoWithFileName()
|
|
filename = getframeinfo(cf).filename
|
|
self.assertEqual(net.Proto().op[0].name, '{}:{}:{}'.format(
|
|
filename, line, func))
|
|
|
|
def test_operator_constructor_traceback(self):
|
|
net = core.Net("test")
|
|
a, b = net.AddExternalInput("a", "b")
|
|
net.Mul([a, b], "c"); cf = currentframe(); line = cf.f_lineno
|
|
func = cf.f_code.co_name
|
|
with self.assertRaises(Exception):
|
|
workspace.RunNetOnce(net)
|
|
with self.assertRaises(Exception):
|
|
workspace.CreateNet(net)
|
|
self.op_name_check(net, cf, line, func)
|
|
|
|
def test_operator_runtime_traceback(self):
|
|
net = core.Net("test")
|
|
a = net.AddExternalInput("a")
|
|
workspace.blobs[a] = np.array([1, 2, 3], dtype=np.float32)
|
|
net.Split(a, ["b", "c"], axis=0); cf = currentframe(); line = cf.f_lineno
|
|
func = cf.f_code.co_name
|
|
with self.assertRaises(Exception):
|
|
workspace.RunNetOnce(net)
|
|
workspace.CreateNet(net)
|
|
with self.assertRaises(Exception):
|
|
workspace.RunNet(net)
|
|
self.op_name_check(net, cf, line, func)
|
|
|
|
def test_c_workspace_constructor(self):
|
|
net = core.Net("test")
|
|
a, b = net.AddExternalInput("a", "b")
|
|
net.Mul([a, b], "c"); cf = currentframe(); line = cf.f_lineno
|
|
func = cf.f_code.co_name
|
|
ws = workspace.C.Workspace()
|
|
with self.assertRaises(Exception):
|
|
ws.run(net)
|
|
with self.assertRaises(Exception):
|
|
ws.create_net(net)
|
|
self.op_name_check(net, cf, line, func)
|
|
|
|
def test_c_workspace_runtime(self):
|
|
net = core.Net("test")
|
|
a = net.AddExternalInput("a")
|
|
net.Split(a, ["b", "c"], axis=0); cf = currentframe(); line = cf.f_lineno
|
|
func = cf.f_code.co_name
|
|
ws = workspace.C.Workspace()
|
|
ws.create_blob(str(a)).feed(np.array([1, 2, 3], dtype=np.float32))
|
|
ws.create_net(net)
|
|
with self.assertRaises(Exception):
|
|
ws.run(net)
|
|
self.op_name_check(net, cf, line, func)
|
|
|
|
def test_async_exception_handling(self):
|
|
net = core.Net("test")
|
|
net.Proto().type = 'dag' # this runs operators on background threads
|
|
a = net.AddExternalInput("a")
|
|
net.Split(a, ["b", "c"], axis=0); cf = currentframe(); line = cf.f_lineno
|
|
func = cf.f_code.co_name
|
|
workspace.FeedBlob(a, np.array([1, 2, 3], dtype=np.float32))
|
|
with self.assertRaises(Exception) as enforceNotMet:
|
|
workspace.RunNetOnce(net)
|
|
self.assertIn('enforce fail', str(enforceNotMet.exception))
|
|
self.op_name_check(net, cf, line, func)
|
|
|
|
|
|
class TestCreatePlan(test_util.TestCase):
|
|
|
|
def test_create_plan_from_proto_correctly(self):
|
|
from caffe2.python.net_builder import ops
|
|
with Node('trainer'), Task(name='my_task', num_instances=2) as task:
|
|
with ops.task_init():
|
|
globl = ops.Const(0)
|
|
with ops.task_instance_init():
|
|
local = ops.Const(0)
|
|
with ops.loop(100):
|
|
ops.Copy(globl, local)
|
|
with ops.task_instance_exit():
|
|
ops.Add([globl, local], [globl])
|
|
with ops.task_exit():
|
|
ops.Mul([globl, globl], [globl])
|
|
|
|
plan = core.Plan(task.get_step())
|
|
test_plan = core.Plan.create_from_proto(plan.Proto())
|
|
|
|
self.assertEqual(len(plan.Steps()), 1)
|
|
self.assertEqual(len(test_plan.Steps()), 1)
|
|
self.assertEqual(len(plan.Proto().network), 8)
|
|
self.assertEqual(len(test_plan.Proto().network), 8)
|
|
self.assertEqual(len(plan.Proto().execution_step), 1)
|
|
self.assertEqual(len(test_plan.Proto().execution_step), 1)
|
|
self.assertEqual(plan.Steps()[0].Name(), test_plan.Steps()[0].Name())
|
|
self.assertEqual(len(plan.Nets()), len(test_plan.Nets()))
|
|
for idx in range(0, len(plan.Nets())):
|
|
# When we create Net for test_plan, we will end up with new Net
|
|
# name with postfix.
|
|
net_1 = plan.Nets()[idx]
|
|
net_2 = test_plan.Nets()[idx]
|
|
trim_size = len(net_1.Name())
|
|
self.assertEqual(net_1.Name(), net_2.Name()[:trim_size])
|
|
|
|
|
|
class TestOpRegistryKey(test_util.TestCase):
|
|
def test_is_operator(self):
|
|
self.assertTrue(core.IsOperator('Relu'))
|
|
self.assertFalse(core.IsOperator('NOEXIST'))
|
|
|
|
def test_is_operator_with_engine(self):
|
|
self.assertTrue(core.IsOperatorWithEngine('Relu', 'DEFAULT'))
|
|
self.assertFalse(core.IsOperatorWithEngine('Relu', 'NOEXIST'))
|
|
|
|
|
|
class TestDeviceOption(test_util.TestCase):
|
|
def test_check_equal_node_name(self):
|
|
opt1 = core.DeviceOption(0)
|
|
opt2 = core.DeviceOption(0)
|
|
self.assertTrue(core.device_option_equal(opt1, opt2))
|
|
opt2.node_name = 'test'
|
|
self.assertTrue(core.device_option_equal(opt1, opt2))
|
|
self.assertFalse(core.device_option_equal(opt1, opt2, ignore_node_name=False))
|
|
opt1.node_name = 'test'
|
|
self.assertTrue(core.device_option_equal(opt1, opt2, ignore_node_name=False))
|
|
|
|
def test_check_equal_default_value(self):
|
|
opt1 = caffe2_pb2.DeviceOption()
|
|
opt2 = caffe2_pb2.DeviceOption()
|
|
opt1.device_type = 0
|
|
self.assertTrue(core.device_option_equal(opt1, opt2))
|
|
opt1.cuda_gpu_id = 5
|
|
# opt1 still is on CPU, so the options should be equal
|
|
self.assertTrue(core.device_option_equal(opt1, opt2))
|
|
opt2.device_type = 0
|
|
self.assertTrue(core.device_option_equal(opt1, opt2))
|
|
opt1.device_type = 1
|
|
self.assertFalse(core.device_option_equal(opt1, opt2))
|
|
|
|
|
|
class TestInferDeviceCpuOnly(test_util.TestCase):
|
|
def test_inject_copy(self):
|
|
'''
|
|
Test inject cross device copies - this is a no-op on CPU only devices.
|
|
'''
|
|
send_node = 'node:0'
|
|
recv_node = 'node:1'
|
|
# Using placeholder ops for send/recv. Placeholder ops are
|
|
# decorator/fake ops that don't have operator schema.
|
|
placeholder_send = 'Placeholder:Dummy:Send'
|
|
placeholder_recv = 'Placeholder:Dummy:Recv'
|
|
|
|
# init_net.
|
|
init_net = core.Net("init_net")
|
|
with core.DeviceScope(0, node_name=send_node):
|
|
init_net.XavierFill([], 'fc_w', shape=[10, 100])
|
|
init_net.ConstantFill([], 'fc_b', shape=[10, ])
|
|
|
|
# train_net.
|
|
train_net = core.Net("train_net")
|
|
train_net.Proto().external_input.extend(['fc_w', 'fc_b'])
|
|
with core.DeviceScope(0, node_name=send_node):
|
|
op = core.CreateOperator(
|
|
placeholder_send, ["fc_w", 'fc_b'], [],
|
|
dst_node=recv_node)
|
|
train_net.Proto().op.extend([op])
|
|
with core.DeviceScope(0, node_name=recv_node):
|
|
# Let's rename the recv blob i.e. fc_w -> fc_w_recv.
|
|
op = core.CreateOperator(
|
|
placeholder_recv, [], ['fc_w_recv', 'fc_b'],
|
|
src_node=send_node)
|
|
train_net.Proto().op.extend([op])
|
|
train_net.FC(["data", 'fc_w_recv', 'fc_b'], "fc1")
|
|
|
|
# Inject cross device copies.
|
|
init_net, x_dev_state = core.InjectCrossDeviceCopies(
|
|
init_net,
|
|
placeHolderOps=[placeholder_send, placeholder_recv])
|
|
train_net, x_dev_state = core.InjectCrossDeviceCopies(
|
|
train_net, x_dev_state,
|
|
placeHolderOps=[placeholder_send, placeholder_recv])
|
|
|
|
# Verify: No Copy operators should be injected since it is CPU only.
|
|
op = train_net.Proto().op[0]
|
|
self.assertEqual(op.type, placeholder_send)
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.input[0], "fc_w")
|
|
self.assertEqual(op.input[1], "fc_b")
|
|
op = train_net.Proto().op[1]
|
|
self.assertEqual(op.type, placeholder_recv)
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.output[0], "fc_w_recv")
|
|
self.assertEqual(op.output[1], "fc_b")
|
|
op = train_net.Proto().op[2]
|
|
self.assertEqual(op.type, "FC")
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.input[1], "fc_w_recv")
|
|
self.assertEqual(op.input[2], "fc_b")
|
|
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, 'No GPU support')
|
|
class TestInferDevice(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
self.cuda_option = device_option
|
|
self.cpu_option = caffe2_pb2.DeviceOption()
|
|
|
|
def _test_op(
|
|
self,
|
|
op_name,
|
|
in_option,
|
|
out_option,
|
|
op_option=None,
|
|
inputs=None,
|
|
outputs=None
|
|
):
|
|
op_option = self.cuda_option if not op_option else op_option
|
|
inputs = ["blob_1"] if not inputs else inputs
|
|
outputs = ["blob_2"] if not outputs else outputs
|
|
with core.DeviceScope(op_option):
|
|
op = core.CreateOperator(op_name, inputs, outputs)
|
|
input_dev, output_dev = core.InferOpBlobDevices(op)
|
|
if isinstance(in_option, list):
|
|
assert len(in_option) == len(input_dev), \
|
|
'Length of input device option should match' \
|
|
'{} vs. {}'.format(in_option, input_dev)
|
|
for in_dev, in_opt in zip(input_dev, in_option):
|
|
self.assertEqual(in_dev, in_opt)
|
|
else:
|
|
for in_dev in input_dev:
|
|
self.assertEqual(in_dev, in_option)
|
|
if isinstance(out_option, list):
|
|
assert len(out_option) == len(output_dev), \
|
|
'Length of output device option should match' \
|
|
'{} vs. {}'.format(out_option, output_dev)
|
|
for out_dev, out_opt in zip(output_dev, out_option):
|
|
self.assertEqual(out_dev, out_opt)
|
|
else:
|
|
for out_dev in output_dev:
|
|
self.assertEqual(out_dev, out_option)
|
|
|
|
def test_infer_device(self):
|
|
self._test_op(
|
|
"FC",
|
|
self.cuda_option,
|
|
self.cuda_option,
|
|
op_option=self.cuda_option,
|
|
inputs=["data", "fc_w", "fc_b"],
|
|
outputs=["fc_1"]
|
|
)
|
|
|
|
def test_infer_device_split_by_lengths(self):
|
|
self._test_op(
|
|
"SplitByLengths",
|
|
[self.cuda_option, self.cpu_option],
|
|
self.cuda_option,
|
|
op_option=self.cuda_option,
|
|
inputs=["data", "fc_w"],
|
|
outputs=["fc_1"]
|
|
)
|
|
|
|
def test_infer_device_cross_device(self):
|
|
self._test_op("CopyGPUToCPU", self.cuda_option, self.cpu_option)
|
|
self._test_op("CopyCPUToGPU", self.cpu_option, self.cuda_option)
|
|
self._test_op("CopyFromCPUInput", self.cpu_option, self.cuda_option)
|
|
self._test_op(
|
|
"CopyFromCPUInput",
|
|
self.cpu_option,
|
|
self.cpu_option,
|
|
op_option=self.cpu_option
|
|
)
|
|
|
|
def test_device_inference_function(self):
|
|
# ConcatOp.
|
|
op_option = self.cuda_option
|
|
with core.DeviceScope(op_option):
|
|
op = core.CreateOperator(
|
|
'Concat',
|
|
['X_{}'.format(i) for i in range(4)],
|
|
['concat_result', 'split_info'],
|
|
axis=1)
|
|
input_dev, output_dev = core.InferOpBlobDevices(op)
|
|
# 2nd output's type is CPU irrespective of Concat op's device option.
|
|
self.assertEqual(output_dev[1], self.cpu_option)
|
|
|
|
#SplitOp.
|
|
op_option = self.cuda_option
|
|
with core.DeviceScope(op_option):
|
|
op = core.CreateOperator(
|
|
'Split',
|
|
['input', 'split'],
|
|
['X_{}'.format(i) for i in range(4)],
|
|
axis=0)
|
|
input_dev, output_dev = core.InferOpBlobDevices(op)
|
|
# 2nd input's type is CPU irrespective of Split op's device option.
|
|
self.assertEqual(input_dev[1], self.cpu_option)
|
|
|
|
def test_inject_copy(self):
|
|
net = core.Net("test")
|
|
init_net = core.Net("init")
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
weight = init_net.XavierFill([], 'fc_w', shape=[10, 100])
|
|
bias = init_net.ConstantFill([], 'fc_b', shape=[10, ])
|
|
|
|
with core.DeviceScope(device_option):
|
|
net.FC(["data", weight, bias], "fc1")
|
|
|
|
_, blob_to_device = core.InjectCrossDeviceCopies(init_net)
|
|
new_net, blob_to_device = core.InjectCrossDeviceCopies(
|
|
net, blob_to_device
|
|
)
|
|
op = new_net._net.op[-1]
|
|
self.assertEqual(op.type, "FC")
|
|
self.assertEqual(op.input[0], "data_cuda_1")
|
|
self.assertEqual(op.input[1], "fc_w_cuda_1")
|
|
self.assertEqual(op.input[2], "fc_b_cuda_1")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(new_net._net.op[-2].type, "CopyCPUToGPU")
|
|
self.assertEqual(new_net._net.op[0].type, "CopyCPUToGPU")
|
|
self.assertNotEqual(blob_to_device["fc_w"], device_option)
|
|
|
|
def test_cross_nets(self):
|
|
net = core.Net("test")
|
|
init_net = core.Net("init")
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
weight = init_net.XavierFill([], 'fc_w', shape=[10, 100])
|
|
bias = init_net.ConstantFill([], 'fc_b', shape=[10, ])
|
|
const = init_net.ConstantFill([], 'const', shape=[], value=1.)
|
|
with core.DeviceScope(device_option):
|
|
const = init_net.Add([const, const], [const])
|
|
fc_out = net.FC(["data", weight, bias], "fc1")
|
|
net.Add([fc_out, const], [fc_out])
|
|
|
|
data_remap = {'data': device_option}
|
|
nets, _ = core.InjectDeviceCopiesAmongNets(
|
|
[init_net, net], blob_to_device_init=data_remap
|
|
)
|
|
op = nets[1]._net.op[0]
|
|
self.assertEqual(op.type, "CopyCPUToGPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.output[0], "fc_w_cuda_1")
|
|
op = nets[1]._net.op[1]
|
|
self.assertEqual(op.type, "CopyCPUToGPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.output[0], "fc_b_cuda_1")
|
|
op = nets[1]._net.op[2]
|
|
self.assertEqual(op.type, "FC")
|
|
self.assertEqual(op.input[0], "data")
|
|
self.assertEqual(op.input[1], "fc_w_cuda_1")
|
|
self.assertEqual(op.input[2], "fc_b_cuda_1")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
op = nets[1]._net.op[3]
|
|
self.assertEqual(op.type, "Add")
|
|
self.assertEqual(op.input[0], "fc1")
|
|
self.assertEqual(op.input[1], "const_cuda_1")
|
|
# check that moved blob is in input to the new net
|
|
for c in ["data", "fc_w", "fc_b", "const_cuda_1"]:
|
|
self.assertTrue(c in nets[1]._net.external_input)
|
|
"""
|
|
For reference, net.Proto() should be like:
|
|
name: ""
|
|
op {
|
|
input: "fc_w"
|
|
output: "fc_w_cuda_1"
|
|
name: ""
|
|
type: "CopyCPUToGPU"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "fc_b"
|
|
output: "fc_b_cuda_1"
|
|
name: ""
|
|
type: "CopyCPUToGPU"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "data"
|
|
input: "fc_w_cuda_1"
|
|
input: "fc_b_cuda_1"
|
|
output: "fc1"
|
|
name: ""
|
|
type: "FC"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "fc1"
|
|
input: "const_cuda_1"
|
|
output: "fc1"
|
|
name: ""
|
|
type: "Add"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
external_input: "data"
|
|
external_input: "fc_w"
|
|
external_input: "fc_b"
|
|
external_input: "const"
|
|
external_input: "const_cuda_1"
|
|
"""
|
|
|
|
def test_cross_nets_no_change(self):
|
|
net = core.Net("test")
|
|
init_net = core.Net("init")
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
|
|
with core.DeviceScope(device_option):
|
|
weight = init_net.XavierFill([], 'fc_w', shape=[10, 100])
|
|
bias = init_net.ConstantFill([], 'fc_b', shape=[10, ])
|
|
net.FC(["data", weight, bias], "fc1")
|
|
|
|
data_remap = {'data': device_option}
|
|
nets = core.InjectDeviceCopiesAmongNetsWithoutB2D(
|
|
[init_net, net], blob_to_device_init=data_remap
|
|
)
|
|
op = nets[1]._net.op[0]
|
|
self.assertEqual(op.type, "FC")
|
|
self.assertEqual(op.input[0], "data")
|
|
self.assertEqual(op.input[1], "fc_w")
|
|
self.assertEqual(op.input[2], "fc_b")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
"""
|
|
For reference, net.Proto() should be like:
|
|
name: ""
|
|
op {
|
|
input: "data"
|
|
input: "fc_w"
|
|
input: "fc_b"
|
|
output: "fc1"
|
|
name: ""
|
|
type: "FC"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
external_input: "data"
|
|
external_input: "fc_w"
|
|
external_input: "fc_b"
|
|
"""
|
|
|
|
def test_inject_copy_multi_use(self):
|
|
net = core.Net("test")
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
|
|
with core.DeviceScope(device_option):
|
|
net.Relu("data", "relu1")
|
|
net.Relu("data", "relu2")
|
|
with core.DeviceScope(device_option):
|
|
net.Relu("data", "relu3")
|
|
net.Relu("data", "relu4")
|
|
device_option.cuda_gpu_id = 0
|
|
with core.DeviceScope(device_option):
|
|
net.Relu("data", "relu5")
|
|
device_option.cuda_gpu_id = 1
|
|
with core.DeviceScope(device_option):
|
|
net.Relu("data", "relu6")
|
|
|
|
new_net, _ = core.InjectCrossDeviceCopies(net)
|
|
op = new_net._net.op[0]
|
|
self.assertEqual(op.type, "CopyCPUToGPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.output[0], "data_cuda_1")
|
|
op = new_net._net.op[1]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.output[0], "relu1")
|
|
op = new_net._net.op[2]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.output[0], "relu2")
|
|
op = new_net._net.op[3]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.input[0], "data_cuda_1")
|
|
self.assertEqual(op.output[0], "relu3")
|
|
op = new_net._net.op[4]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.output[0], "relu4")
|
|
op = new_net._net.op[5]
|
|
self.assertEqual(op.type, "CopyCPUToGPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 0)
|
|
self.assertEqual(op.output[0], "data_cuda_0")
|
|
op = new_net._net.op[6]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 0)
|
|
self.assertEqual(op.input[0], "data_cuda_0")
|
|
self.assertEqual(op.output[0], "relu5")
|
|
op = new_net._net.op[7]
|
|
self.assertEqual(op.type, "Relu")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 1)
|
|
self.assertEqual(op.input[0], "data_cuda_1")
|
|
self.assertEqual(op.output[0], "relu6")
|
|
"""
|
|
For reference, net.Proto() should be like:
|
|
name: ""
|
|
op {
|
|
input: "data"
|
|
output: "data_cuda_1"
|
|
name: ""
|
|
type: "CopyCPUToGPU"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "data_cuda_1"
|
|
output: "relu1"
|
|
name: ""
|
|
type: "Relu"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "data"
|
|
output: "relu2"
|
|
name: ""
|
|
type: "Relu"
|
|
}
|
|
op {
|
|
input: "data_cuda_1"
|
|
output: "relu3"
|
|
name: ""
|
|
type: "Relu"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
op {
|
|
input: "data"
|
|
output: "relu4"
|
|
name: ""
|
|
type: "Relu"
|
|
}
|
|
op {
|
|
input: "data"
|
|
output: "data_cuda_0"
|
|
name: ""
|
|
type: "CopyCPUToGPU"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 0
|
|
}
|
|
}
|
|
op {
|
|
input: "data_cuda_0"
|
|
output: "relu5"
|
|
name: ""
|
|
type: "Relu"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 0
|
|
}
|
|
}
|
|
op {
|
|
input: "data_cuda_1"
|
|
output: "relu6"
|
|
name: ""
|
|
type: "Relu"
|
|
device_option {
|
|
device_type: 1
|
|
cuda_gpu_id: 1
|
|
}
|
|
}
|
|
external_input: "data"
|
|
"""
|
|
|
|
def test_inject_copy_placeholder_ops(self):
|
|
'''
|
|
Test inject cross device copies with placeholder ops. Placeholder ops
|
|
are decorator/fake ops that don't have operator schema.
|
|
'''
|
|
# Create CPU and GPU devices on 2 nodes.
|
|
cpu_device = []
|
|
gpu_device = []
|
|
for i in range(0, 2):
|
|
cpu_device.append(caffe2_pb2.DeviceOption())
|
|
cpu_device[i].node_name = 'node:' + str(i)
|
|
gpu_device.append(caffe2_pb2.DeviceOption())
|
|
gpu_device[i].device_type = caffe2_pb2.CUDA
|
|
gpu_device[i].cuda_gpu_id = 0
|
|
gpu_device[i].node_name = 'node:' + str(i)
|
|
send_node = 'node:0'
|
|
recv_node = 'node:1'
|
|
placeholder_send = 'Placeholder:Dummy:Send'
|
|
placeholder_recv = 'Placeholder:Dummy:Recv'
|
|
|
|
# init_net.
|
|
init_net = core.Net("init_net")
|
|
with core.DeviceScope(gpu_device[0]):
|
|
weight = init_net.XavierFill([], 'fc_w', shape=[10, 100])
|
|
bias = init_net.ConstantFill([], 'fc_b', shape=[10, ])
|
|
with core.DeviceScope(cpu_device[0]):
|
|
op = core.CreateOperator(
|
|
placeholder_send, [weight, bias], [],
|
|
dst_node=recv_node)
|
|
init_net._net.op.extend([op])
|
|
|
|
# train_net
|
|
train_net = core.Net("train_net")
|
|
with core.DeviceScope(cpu_device[1]):
|
|
# XXX. replace hardcoded op name. Move test to net_transforms.
|
|
op = core.CreateOperator(
|
|
placeholder_recv, [], [weight, bias],
|
|
src_node=send_node)
|
|
train_net._net.op.extend([op])
|
|
train_net.FC(["data", weight, bias], "fc1")
|
|
|
|
# Inject cross device copies.
|
|
init_net, x_dev_state = core.InjectCrossDeviceCopies(
|
|
init_net,
|
|
placeHolderOps=[placeholder_send, placeholder_recv])
|
|
train_net, x_dev_state = core.InjectCrossDeviceCopies(
|
|
train_net, x_dev_state,
|
|
placeHolderOps=[placeholder_send, placeholder_recv])
|
|
|
|
# Verify (init_net)
|
|
op = init_net._net.op[2]
|
|
self.assertEqual(op.type, "CopyGPUToCPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 0)
|
|
self.assertEqual(op.output[0], "fc_w_cpu")
|
|
op = init_net._net.op[3]
|
|
self.assertEqual(op.type, "CopyGPUToCPU")
|
|
self.assertEqual(op.device_option.device_type, 1)
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 0)
|
|
self.assertEqual(op.output[0], "fc_b_cpu")
|
|
op = init_net._net.op[4]
|
|
self.assertEqual(op.type, placeholder_send)
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.input[0], "fc_w_cpu")
|
|
self.assertEqual(op.input[1], "fc_b_cpu")
|
|
# Verify (train_net)
|
|
op = train_net._net.op[0]
|
|
self.assertEqual(op.type, placeholder_recv)
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.output[0], "fc_w_cpu")
|
|
self.assertEqual(op.output[1], "fc_b_cpu")
|
|
op = train_net._net.op[3]
|
|
self.assertEqual(op.type, "FC")
|
|
self.assertEqual(op.device_option.device_type, 0)
|
|
self.assertEqual(op.input[1], "fc_w_cpu")
|
|
self.assertEqual(op.input[2], "fc_b_cpu")
|
|
|
|
def test_blob_inplace(self):
|
|
net = core.Net("test")
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CUDA
|
|
device_option.cuda_gpu_id = 1
|
|
|
|
net.Adagrad(['param', 'moment', 'grad', 'lr'], ['param', 'moment'])
|
|
with core.DeviceScope(device_option):
|
|
net.Relu("param", "param_relu_no_sense")
|
|
net, _ = core.InjectCrossDeviceCopies(net)
|
|
op = net._net.op[1]
|
|
self.assertEqual(op.type, 'CopyCPUToGPU')
|
|
self.assertEqual(op.input[0], 'param')
|
|
self.assertEqual(op.output[0], 'param_cuda_1')
|
|
op = net._net.op[2]
|
|
self.assertEqual(op.input[0], 'param_cuda_1')
|
|
|
|
net.Relu('nonsense_input', 'moment')
|
|
# should not raise inplace error
|
|
core.InjectCrossDeviceCopies(net)
|
|
with core.DeviceScope(device_option):
|
|
net.Relu('nonsense_input_gpu', 'moment')
|
|
with self.assertRaises(RuntimeError):
|
|
core.InjectCrossDeviceCopies(net)
|
|
|
|
|
|
class TestRerouteTensor(test_util.TestCase):
|
|
def test_reroute_tensor(self):
|
|
net = core.Net("reroute_tensor")
|
|
net.Conv(["input", "w", "b"], "conv1")
|
|
net.Relu(["conv1"], "conv1_relu")
|
|
new_op = core.CreateOperator("SpatialBN",
|
|
["conv1", "scale", "bias", "mean", "var"],
|
|
["conv1_bn", "mean", "var", "saved_mean", "saved_var"])
|
|
# insert bn between conv and relu
|
|
net.reroute_tensor("conv1", new_op, [net.Proto().op[1]])
|
|
self.assertEqual(new_op, net.Proto().op[1], "insertion failed")
|
|
self.assertEqual(net.Proto().op[2].input[0], "conv1_bn", "reroute failed")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|