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https://github.com/zebrajr/pytorch.git
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e05173a476
Summary: This diff is creating new type of Initializer - ExternalInitializer. This initializer is supposed to be used in cases when the parameter blob is already expected to exist in the workspace. Reviewed By: dzhulgakov Differential Revision: D5171322 fbshipit-source-id: d27861f0f80afdea93c235d49f63da19adccc92c
51 lines
1.9 KiB
Python
51 lines
1.9 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 unittest
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from caffe2.python import brew, model_helper
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from caffe2.python.modeling.initializers import (
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Initializer, pFP16Initializer)
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class InitializerTest(unittest.TestCase):
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def test_fc_initializer(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=1, dim_out=1)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=1, dim_out=1,
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WeightInitializer=Initializer)
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# no operator name set, will use custom
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fc3 = brew.fc(model, fc2, "fc3", dim_in=1, dim_out=1,
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WeightInitializer=Initializer,
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weight_init=("ConstantFill", {}),
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)
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# operator name set, no initializer class set
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fc4 = brew.fc(model, fc3, "fc4", dim_in=1, dim_out=1,
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WeightInitializer=None,
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weight_init=("ConstantFill", {})
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)
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# default operator, pFP16Initializer
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fc5 = brew.fc(model, fc4, "fc5", dim_in=1, dim_out=1,
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WeightInitializer=pFP16Initializer
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)
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# specified operator, pFP16Initializer
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fc6 = brew.fc(model, fc4, "fc5", dim_in=1, dim_out=1,
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weight_init=("ConstantFill", {}),
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WeightInitializer=pFP16Initializer
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)
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def test_fc_external_initializer(self):
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model = model_helper.ModelHelper(name="test", init_params=False)
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=1, dim_out=1) # noqa
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self.assertEqual(len(model.net.Proto().op), 1)
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self.assertEqual(len(model.param_init_net.Proto().op), 0)
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