mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
83437853ad
Summary: The current optimizer code in c2/python has the following issues: (1) the optimizers in sgd.py cannot config per param-blob optimizer; (2) sgd.py is a bad file name. optimizer.py is a better name; (3) layer_model_helper.py has another set of optimizer code (which supports per param-blob optimizer) This diff did the following (1) create optimizer objects so that we can config per param-blob optimizer and that are also compatible to the existing optimizer code (2) the new optimizer code are much more modulized (3) move the optimizer code to file with better name (optimizer.py) (4) replace the optimizer imports in the existing code will do in next diffs (1) optimizers with structured parameters for dper2 (2) get rid of the optimizer code in layer_model_helper.py Reviewed By: salexspb Differential Revision: D4609013 fbshipit-source-id: 2e2d6dfa8685d10498f89069157453d9feca3f27
96 lines
3.7 KiB
Python
96 lines
3.7 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 __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 TestBase(object):
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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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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)
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for i 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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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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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)
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for i 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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