mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
43b303bfc0
Reviewed By: jerryzh168 Differential Revision: D6087316 fbshipit-source-id: d90e20429645391f17f0c56c8a8a60685097f801
74 lines
2.1 KiB
Python
74 lines
2.1 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 unittest
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import numpy as np
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from caffe2.python import workspace, core
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from caffe2.proto import caffe2_pb2
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class TestPredictor(unittest.TestCase):
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def setUp(self):
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np.random.seed(1)
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self.predict_net = self._predict_net
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self.init_net = self._init_net
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@property
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def _predict_net(self):
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net = caffe2_pb2.NetDef()
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net.name = 'test-predict-net'
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net.external_input[:] = ['A', 'B']
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net.external_output[:] = ['C']
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net.op.extend([
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core.CreateOperator(
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'MatMul',
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['A', 'B'],
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['C'],
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)
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])
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return net.SerializeToString()
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@property
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def _init_net(self):
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net = caffe2_pb2.NetDef()
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net.name = 'test-init-net'
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net.external_output[:] = ['A', 'B']
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net.op.extend([
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core.CreateOperator(
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'GivenTensorFill',
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[],
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['A'],
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shape=(2, 3),
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values=np.zeros((2, 3), np.float32).flatten().tolist(),
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),
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core.CreateOperator(
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'GivenTensorFill',
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[],
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['B'],
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shape=(3, 4),
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values=np.zeros((3, 4), np.float32).flatten().tolist(),
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),
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])
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return net.SerializeToString()
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def test_run(self):
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A = np.ones((2, 3), np.float32)
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B = np.ones((3, 4), np.float32)
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predictor = workspace.Predictor(self.init_net, self.predict_net)
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outputs = predictor.run([A, B])
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self.assertEqual(len(outputs), 1)
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np.testing.assert_almost_equal(np.dot(A, B), outputs[0])
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def test_run_map(self):
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A = np.zeros((2, 3), np.float32)
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B = np.ones((3, 4), np.float32)
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predictor = workspace.Predictor(self.init_net, self.predict_net)
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outputs = predictor.run({
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'B': B,
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})
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self.assertEqual(len(outputs), 1)
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np.testing.assert_almost_equal(np.dot(A, B), outputs[0])
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