pytorch

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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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from hypothesis import given
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import numpy as np
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class TestCastOp(hu.HypothesisTestCase):
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    @given(**hu.gcs)
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    def test_cast_int_float(self, gc, dc):
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        data = np.random.rand(5, 5).astype(np.int32)
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        # from int to float
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        op = core.CreateOperator('Cast', 'data', 'data_cast', to=1, from_type=2)
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        self.assertDeviceChecks(dc, op, [data], [0])
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        # This is actually 0
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        self.assertGradientChecks(gc, op, [data], 0, [0])
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    @given(**hu.gcs)
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    def test_cast_int_float_empty(self, gc, dc):
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        data = np.random.rand(0).astype(np.int32)
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        # from int to float
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        op = core.CreateOperator('Cast', 'data', 'data_cast', to=1, from_type=2)
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        self.assertDeviceChecks(dc, op, [data], [0])
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        # This is actually 0
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        self.assertGradientChecks(gc, op, [data], 0, [0])
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    @given(data=hu.tensor(dtype=np.int32), **hu.gcs_cpu_only)
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    def test_cast_int_to_string(self, data, gc, dc):
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        op = core.CreateOperator(
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            'Cast', 'data', 'data_cast', to=core.DataType.STRING)
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        def ref(data):
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            ret = data.astype(dtype=str)
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            # the string blob will be fetched as object, we feed and re-fetch
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            # to mimic this.
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            with hu.temp_workspace('tmp_ref_int_to_string'):
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                workspace.FeedBlob('tmp_blob', ret)
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                fetched_ret = workspace.FetchBlob('tmp_blob')
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            return (fetched_ret, )
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        self.assertReferenceChecks(gc, op, inputs=[data], reference=ref)
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