mirror of
https://github.com/zebrajr/pytorch.git
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44257ea5ed
Summary: hankun is using the optimizer, but having mixed set of of GPU and CPU operators. Currently this won't work with optimizer since it adds optimizers for all parameters in the current device scope. But we can actually infer the device that a param belongs to by looking at the device option in the param_init_net. Added a test as well. Reviewed By: salexspb Differential Revision: D5133652 fbshipit-source-id: ad8689d75ac1f5c78981bae1b6978fe91e40ef0f
66 lines
2.7 KiB
Python
66 lines
2.7 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 caffe2.python.optimizer import build_sgd, build_ftrl, build_adagrad, build_adam
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from caffe2.python.optimizer_test_util import OptimizerTestBase
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from caffe2.python.test_util import TestCase
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from caffe2.python import workspace
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import numpy as np
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class TestSgd(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_sgd(model, base_learning_rate=0.1)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertFalse(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().shared:
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tensor = workspace.FetchBlob(param)
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np.testing.assert_allclose(np.array([1.0]), tensor, atol=1e-5)
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class TestFtrl(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = True
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return build_ftrl(
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model, engine=None, alpha=1.0, beta=0.1, lambda1=0.0, lambda2=0.0)
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def check_optimizer(self, optimizer):
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self.assertFalse(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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class TestAdagrad(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_adagrad(model, base_learning_rate=1.0)
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def check_optimizer(self, optimizer):
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self.assertFalse(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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class TestAdam(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_adam(model, base_learning_rate=0.1)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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self.assertTrue(workspace.HasBlob("optimizer_iteration"))
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iteration_tensor = workspace.FetchBlob("optimizer_iteration")
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np.testing.assert_allclose(np.array([2000]),
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iteration_tensor,
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atol=1e-5)
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for param in optimizer.get_auxiliary_parameters().shared:
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workspace.FetchBlob(param)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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