def test_get_item_pyobject(self): self.assertBuildsConst(2., tdb.GetItem(1) >> tdb.Scalar(), [1, 2, 3])
def test_eval_metrics_different_lengths(self): b = tdb.Record((tdb.Map(tdb.Scalar('int32') >> tdm.Metric('x')), tdb.Map(tdb.Scalar() >> tdm.Metric('y')))) desired = ([None, None], [None]), {'x': [1, 2], 'y': [3.]} self.assertBuilds(desired, b, ([1, 2], [3]))
def test_mean_tuple(self): block = (tdb.Scalar(), tdb.Scalar(), tdb.Scalar()) >> tdb.Mean() self.assertBuildsConst(2., block, [0, 0, 6])
def test_input_transform(self): block = tdb.Map(tdb.InputTransform(lambda x: 1 + ord(x) - ord('a')) >> tdb.Scalar('int32') >> tdb.Function(tf.negative)) self.assertBuilds([-1, -2, -3, -4], block, 'abcd')
def test_map_map_const(self): block = tdb.Map(tdb.Map(tdb.Scalar())) self.assertBuildsConst([[]], block, [[]]) self.assertBuildsConst([[1., 2., 3.], [4., 5.], [6.], []], block, [[1, 2, 3], [4, 5], [6], []])
def test_fold_tuple(self): block = ((tdb.Scalar(), tdb.Scalar()) >> tdb.Fold(tdb.Function(tf.add), tf.ones([]))) self.assertBuilds(6., block, (2, 3), max_depth=2)
def test_composition_connect_raises(self): self.assertRaises(TypeError, tdb.Pipe, tdb.Scalar(), tdb.Concat())
def test_map_const(self): block = tdb.Map(tdb.Scalar()) self.assertBuildsConst([], block, []) self.assertBuildsConst([1.], block, [1]) self.assertBuildsConst([1., 2., 3.], block, [1, 2, 3])
def scalar_all_of(*fns): return tdb.Scalar() >> tdb.AllOf(*[tdb.Function(f) for f in fns])
def test_composition_toposort_output(self): block = tdb.Composition() with block.scope(): s = tdb.Scalar('int32').reads(block.input) block.output.reads(s, s) self.assertBuildsConst((3, 3), block, 3)
def test_function_composition_with_block(self): c = tdb.Composition() with c.scope(): scalar = tdb.Scalar().reads(c.input) c.output.reads(times_scalar_block(2.0).reads(scalar)) self.assertBuilds(42., c, 21)
def test_function_composition(self): sc = tdb.Scalar() fn1 = times_scalar_block(2.0) c = sc >> fn1 self.assertBuilds(42., c, 21)
def test_repr(self): goldens = { tdb.Tensor([]): '<td.Tensor dtype=\'float32\' shape=()>', tdb.Tensor([1, 2], 'int32', name='foo'): '<td.Tensor \'foo\' dtype=\'int32\' shape=(1, 2)>', tdb.Scalar('int64'): '<td.Scalar dtype=\'int64\'>', tdb.Vector(42): '<td.Vector dtype=\'float32\' size=42>', tdb.FromTensor(tf.zeros(3)): '<td.FromTensor \'zeros:0\'>', tdb.Function(tf.negative, name='foo'): '<td.Function \'foo\' tf_fn=\'negative\'>', tdb.Identity(): '<td.Identity>', tdb.Identity('foo'): '<td.Identity \'foo\'>', tdb.InputTransform(ord): '<td.InputTransform py_fn=\'ord\'>', tdb.SerializedMessageToTree('foo'): '<td.SerializedMessageToTree \'foo\' ' 'py_fn=\'serialized_message_to_tree\'>', tdb.GetItem(3, 'mu'): '<td.GetItem \'mu\' key=3>', tdb.Length(): '<td.Length dtype=\'float32\'>', tdb.Slice(stop=2): '<td.Slice key=slice(None, 2, None)>', tdb.Slice(stop=2, name='x'): '<td.Slice \'x\' key=slice(None, 2, None)>', tdb.ForwardDeclaration(name='foo')(): '<td.ForwardDeclaration() \'foo\'>', tdb.Composition(name='x').input: '<td.Composition.input \'x\'>', tdb.Composition(name='x').output: '<td.Composition.output \'x\'>', tdb.Composition(name='x'): '<td.Composition \'x\'>', tdb.Pipe(): '<td.Pipe>', tdb.Pipe(tdb.Scalar(), tdb.Identity()): '<td.Pipe>', tdb.Record({}, name='x'): '<td.Record \'x\' ordered=False>', tdb.Record((), name='x'): '<td.Record \'x\' ordered=True>', tdb.AllOf(): '<td.AllOf>', tdb.AllOf(tdb.Identity()): '<td.AllOf>', tdb.AllOf(tdb.Identity(), tdb.Identity()): '<td.AllOf>', tdb.AllOf(name='x'): '<td.AllOf \'x\'>', tdb.AllOf(tdb.Identity(), name='x'): '<td.AllOf \'x\'>', tdb.AllOf(tdb.Identity(), tdb.Identity(), name='x'): '<td.AllOf \'x\'>', tdb.Map(tdb.Scalar(), name='x'): '<td.Map \'x\' element_block=<td.Scalar dtype=\'float32\'>>', tdb.Fold(tdb.Function(tf.add), tf.ones([]), name='x'): '<td.Fold \'x\' combine_block=<td.Function tf_fn=\'add\'> ' 'start_block=<td.FromTensor \'ones:0\'>>', tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8))): '<td.RNN>', tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)), name='x'): '<td.RNN \'x\'>', tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)), initial_state=tf.ones(8)): '<td.RNN>', tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)), initial_state=tf.ones(8), name='x'): '<td.RNN \'x\'>', tdb.Reduce(tdb.Function(tf.add), name='x'): '<td.Reduce \'x\' combine_block=<td.Function tf_fn=\'add\'>>', tdb.Sum(name='foo'): '<td.Sum \'foo\' combine_block=<td.Function tf_fn=\'add\'>>', tdb.Min(name='foo'): '<td.Min \'foo\' combine_block=<td.Function tf_fn=\'minimum\'>>', tdb.Max(name='foo'): '<td.Max \'foo\' combine_block=<td.Function tf_fn=\'maximum\'>>', tdb.Mean(name='foo'): '<td.Mean \'foo\'>', tdb.OneOf(ord, (tdb.Scalar(), tdb.Scalar()), name='x'): '<td.OneOf \'x\'>', tdb.Optional(tdb.Scalar(), name='foo'): '<td.Optional \'foo\' some_case_block=<td.Scalar dtype=\'float32\'>>', tdb.Concat(1, True, 'x'): '<td.Concat \'x\' concat_dim=1 flatten=True>', tdb.Broadcast(name='x'): '<td.Broadcast \'x\'>', tdb.Zip(name='x'): '<td.Zip \'x\'>', tdb.NGrams(n=42, name='x'): '<td.NGrams \'x\' n=42>', tdb.OneHot(2, 3, name='x'): '<td.OneHot \'x\' dtype=\'float32\' start=2 stop=3>', tdb.OneHot(3): '<td.OneHot dtype=\'float32\' start=0 stop=3>', tdb.OneHotFromList(['a', 'b']): '<td.OneHotFromList>', tdb.OneHotFromList(['a', 'b'], name='foo'): '<td.OneHotFromList \'foo\'>', tdb.Nth(name='x'): '<td.Nth \'x\'>', tdb.Zeros([], 'x'): '<td.Zeros \'x\'>', tdb.Void(): '<td.Void>', tdb.Void('foo'): '<td.Void \'foo\'>', tdm.Metric('foo'): '<td.Metric \'foo\'>'} for block, expected_repr in sorted(six.iteritems(goldens), key=lambda kv: kv[1]): self.assertEqual(repr(block), expected_repr)
def test_record_slice_key(self): b = tdb.Record([ (0, tdb.Scalar()), (slice(1, 3), (tdb.Scalar(), tdb.Scalar()) >> tdb.Concat())]) self.assertBuilds((1., [2., 3.]), b, [1, 2, 3])
def test_composition_no_output_void_type(self): b = tdb.AllOf(tdb.Void(), tdb.Scalar()) >> tdb.GetItem(1) self.assertBuildsConst(42., b, 42)
def test_map(self): block = tdb.Map(tdb.Scalar() >> tdb.Function(tf.abs)) self.assertBuilds([], block, [], max_depth=0) self.assertBuilds([1.], block, [-1]) self.assertBuilds([1., 2., 3.], block, [-1, -2, -3])
def test_composition_rasies_read_output(self): a = tdb.Scalar() c = tdb.Composition([a]) self.assertRaisesWithLiteralMatch( ValueError, 'cannot read from composition output', c.connect, c.output, a)
def test_map_map(self): block = tdb.Map(tdb.Map(tdb.Scalar() >> tdb.Function(tf.abs))) self.assertBuilds([[]], block, [[]], max_depth=0) self.assertBuilds([[1., 2., 3.], [4., 5.], [6.], []], block, [[-1, -2, -3], [-4, -5], [-6], []])
def test_composition_rasies_write_input(self): a = tdb.Scalar() c = tdb.Composition([a]) self.assertRaisesWithLiteralMatch( ValueError, 'cannot write to composition input', c.connect, a, c.input)
def test_map_tuple(self): block = (tdb.Scalar(), tdb.Scalar()) >> tdb.Map(tdb.Function(tf.negative)) self.assertBuilds([-3., -4.], block, (3, 4))
def test_record(self): d = tdb.Record(collections.OrderedDict([('b', tdb.Scalar()), ('a', tdb.Scalar())])) c = d >> tdb.Function(tf.subtract) self.assertBuilds(4.0, c, {'a': 1.0, 'b': 5.0})
def test_fold_pyobject(self): block = tdb.Fold((tdb.Identity(), tdb.Scalar()) >> tdb.Sum(), tdb.Zeros([])) self.assertBuilds(5., block, (2, 3), max_depth=None)
def test_record_one_child(self): self.assertBuildsConst((42,), tdb.Record({0: tdb.Scalar('int32')}), {0: 42})
def test_tuple_of_seq(self): block = tdb.AllOf( tdb.Map(tdb.Scalar() >> tdb.Function(tf.negative)), tdb.Map(tdb.Scalar() >> tdb.Function(tf.identity))) self.assertBuilds(([], []), block, [], max_depth=0) self.assertBuilds(([-1., -2.], [1., 2.]), block, [1, 2])
def test_record_tuple(self): block = (tdb.AllOf(tdb.Scalar(), tdb.OneHot(3, dtype='int32')) >> (tdb.Function(tf.square), tdb.Function(tf.negative))) self.assertBuilds((4., [0, 0, -1]), block, 2)
def test_input_transform_const(self): block = tdb.Map(tdb.InputTransform(lambda x: 1 + ord(x) - ord('a')) >> tdb.Scalar('int32')) self.assertBuildsConst([1, 2, 3, 4], block, 'abcd')
def test_record_raises(self): six.assertRaisesRegex( self, RuntimeError, 'created with an unordered dict cannot take ordered', tdb.Pipe, (tdb.Scalar(), tdb.Scalar()), {'a': tdb.Identity(), 'b': tdb.Identity()})
def test_get_item_sequence(self): block = tdb.Map(tdb.Scalar()) >> tdb.GetItem(-1) self.assertBuildsConst(9., block, range(10))
def test_eval_metrics(self): b = tdb.Map(tdb.Scalar() >> tdb.AllOf(tdm.Metric('x'), tdb.Identity())) self.assertBuilds(([(None, 1.), (None, 2.)], {'x': [1., 2.]}), b, [1, 2,])