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Summary: This is from discussion with dzhulgakov : as a step towards revisiting the core.Net autonaming, we will first guard against accidental overwrites of existing networks in the workspace. ajtulloch since we are doing Predictors in mobile, this should be safe right? azzolini - I assume this would be safe, but would love to get your approval. akyrola - would this hurt xray? Reviewed By: dzhulgakov Differential Revision: D4897725 fbshipit-source-id: aa41271927ad6671f07a53b9505283623f8c49e5
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
## @package optimizer_test_util
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# Module caffe2.python.optimizer_test_util
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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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import numpy as np
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from caffe2.python import core, workspace, cnn
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class OptimizerTestBase(object):
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"""
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This is an abstract base class.
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Don't inherit from unittest.TestCase, and don't name it 'Test*'.
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Do, however, do these things in classes which inherit from this.
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"""
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def testDense(self):
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perfect_model = np.array([2, 6, 5, 0, 1]).astype(np.float32)
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np.random.seed(123) # make test deterministic
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data = np.random.randint(
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2,
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size=(20, perfect_model.size)).astype(np.float32)
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label = np.dot(data, perfect_model)[:, np.newaxis]
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model = cnn.CNNModelHelper("NCHW", name="test")
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out = model.FC(
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'data', 'fc', perfect_model.size, 1, ('ConstantFill', {}),
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('ConstantFill', {}), axis=0
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)
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sq = model.SquaredL2Distance([out, 'label'])
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loss = model.AveragedLoss(sq, "avg_loss")
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grad_map = model.AddGradientOperators([loss])
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self.assertIsInstance(grad_map['fc_w'], core.BlobReference)
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optimizer = self.build_optimizer(model)
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workspace.FeedBlob('data', data[0])
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workspace.FeedBlob('label', label[0])
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workspace.RunNetOnce(model.param_init_net)
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workspace.CreateNet(model.net, True)
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for _ in range(2000):
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idx = np.random.randint(data.shape[0])
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workspace.FeedBlob('data', data[idx])
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workspace.FeedBlob('label', label[idx])
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workspace.RunNet(model.net.Proto().name)
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np.testing.assert_allclose(
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perfect_model[np.newaxis, :],
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workspace.FetchBlob('fc_w'),
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atol=1e-2
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)
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self.check_optimizer(optimizer)
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def testSparse(self):
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# to test duplicated indices we assign two indices to each weight and
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# thus each weight might count once or twice
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DUPLICATION = 2
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perfect_model = np.array([2, 6, 5, 0, 1]).astype(np.float32)
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np.random.seed(123) # make test deterministic
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data = np.random.randint(
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2,
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size=(20, perfect_model.size * DUPLICATION)).astype(np.float32)
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label = np.dot(data, np.repeat(perfect_model, DUPLICATION))
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model = cnn.CNNModelHelper("NCHW", name="test")
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# imitate what model wrapper does
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w = model.param_init_net.ConstantFill(
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[], 'w', shape=[perfect_model.size], value=0.0)
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model.params.append(w)
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picked = model.net.Gather([w, 'indices'], 'gather')
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out = model.ReduceFrontSum(picked, 'sum')
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sq = model.SquaredL2Distance([out, 'label'])
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loss = model.AveragedLoss(sq, "avg_loss")
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grad_map = model.AddGradientOperators([loss])
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self.assertIsInstance(grad_map['w'], core.GradientSlice)
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optimizer = self.build_optimizer(model)
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workspace.CreateBlob('indices')
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workspace.CreateBlob('label')
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for indices_type in [np.int32, np.int64]:
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workspace.RunNetOnce(model.param_init_net)
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workspace.CreateNet(model.net, True)
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for _ in range(2000):
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idx = np.random.randint(data.shape[0])
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# transform into indices of binary features
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indices = np.repeat(np.arange(perfect_model.size),
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DUPLICATION)[data[idx] == 1]
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if indices.size == 0:
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continue
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workspace.FeedBlob(
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'indices',
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indices.reshape((indices.size,)).astype(indices_type)
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)
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workspace.FeedBlob('label',
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np.array(label[idx]).astype(np.float32))
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workspace.RunNet(model.net.Proto().name)
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np.testing.assert_allclose(
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perfect_model,
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workspace.FetchBlob('w'),
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atol=1e-2
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)
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self.check_optimizer(optimizer)
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