mirror of
https://github.com/zebrajr/pytorch.git
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41bb662d96
Reviewed By: xianjiec Differential Revision: D5617571 fbshipit-source-id: 875d7c8753bdb3b6847d5e3f47ad8568cdf172f8
67 lines
2.3 KiB
Python
67 lines
2.3 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 caffe2.python import schema
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from caffe2.python.regularizer_context import UseRegularizer, RegularizerContext
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from caffe2.python.regularizer import L1Norm
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from caffe2.python.optimizer import SgdOptimizer
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from caffe2.python.layer_test_util import LayersTestCase
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from caffe2.python import layer_model_instantiator
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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class TestRegularizerContext(LayersTestCase):
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@given(
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X=hu.arrays(dims=[2, 5]),
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)
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def test_regularizer_context(self, X):
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weight_reg_out = L1Norm(0.2)
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bias_reg_out = L1Norm(0)
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regularizers = {
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'WEIGHT': weight_reg_out,
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'BIAS': bias_reg_out
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}
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output_dims = 2
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input_record = self.new_record(schema.Scalar((np.float32, (5,))))
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schema.FeedRecord(input_record, [X])
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with UseRegularizer(regularizers):
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weight_reg = RegularizerContext.current().get_regularizer('WEIGHT')
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bias_reg = RegularizerContext.current().get_regularizer('BIAS')
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optim = SgdOptimizer(0.15)
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assert weight_reg == weight_reg_out, \
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'fail to get correct weight reg from context'
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assert bias_reg == bias_reg_out, \
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'fail to get correct bias reg from context'
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fc_output = self.model.FC(
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input_record,
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output_dims,
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weight_optim=optim,
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bias_optim=optim,
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weight_reg=weight_reg,
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bias_reg=bias_reg
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)
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# model.output_schema has to a struct
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self.model.output_schema = schema.Struct((
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'fc_output', fc_output
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))
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self.assertEqual(
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schema.Scalar((np.float32, (output_dims, ))),
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fc_output
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)
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_, train_net = layer_model_instantiator.generate_training_nets(self.model)
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ops = train_net.Proto().op
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ops_type_list = [ops[i].type for i in range(len(ops))]
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assert ops_type_list.count('LpNorm') == 2
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assert ops_type_list.count('Scale') == 4
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assert ops_type_list.count('LpNormGradient') == 2
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