mirror of
https://github.com/zebrajr/pytorch.git
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014d1fe5c4
Summary: These are all essentially no-op changes which allow for nose-style (or pytest-style) test discovery. With this patch, you can use any of these methods to discover and run tests under `caffe2/python`: ``` python -m unittest discover -p '*test*.py' caffe2/python/ python -m nose caffe2/python/ python -m pytest caffe2/python/ ``` Future work: * Get all of the tests to pass * Some seem to be testing operations which don't have GPU implementations * I get a segfault unless I set `CUDA_VISIBLE_DEVICES=0` * Some tests are flaky * Allow test discovery throughout the whole project (e.g. the `experiments/` dir) Closes https://github.com/caffe2/caffe2/pull/199 Reviewed By: pietern Differential Revision: D4704504 Pulled By: Yangqing fbshipit-source-id: 8f5687ec9c8aa873dfaff30dbf44272bc38a206b
102 lines
3.9 KiB
Python
102 lines
3.9 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 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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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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