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https://github.com/zebrajr/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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import unittest
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from caffe2.python import brew, model_helper, workspace
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from caffe2.python.modeling.initializers import (
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Initializer, PseudoFP16Initializer)
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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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@unittest.skipIf(not workspace.has_gpu_support, 'No GPU support')
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def test_fc_fp16_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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# default operator, PseudoFP16Initializer
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fc2 = brew.fc(model, fc1, "fc2", dim_in=1, dim_out=1,
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WeightInitializer=PseudoFP16Initializer
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)
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# specified operator, PseudoFP16Initializer
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fc3 = brew.fc(model, fc2, "fc3", dim_in=1, dim_out=1,
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weight_init=("ConstantFill", {}),
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WeightInitializer=PseudoFP16Initializer
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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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