def test_dropout(self):
input_shape = (8, 7, 9)
output_shape = (8, 7, 9)
final_shape = base.check_shape_agreement(
core.Dropout(rate=0.1, mode="train"), input_shape)
self.assertEqual(final_shape, output_shape)
final_shape = base.check_shape_agreement(
core.Dropout(rate=0.1, mode="eval"), input_shape)
self.assertEqual(final_shape, output_shape)
def test_serial_no_op_list(self):
layer = cb.Serial([])
input_shape = ((3, 2), (4, 7))
expected_shape = ((3, 2), (4, 7))
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
input_shape = ((3, 2), (4, 7)) + _REST_OF_STACK
expected_shape = ((3, 2), (4, 7)) + _REST_OF_STACK
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_swap(self):
layer = cb.Swap()
input_shape = ((3, 2), (4, 7))
expected_shape = ((4, 7), (3, 2))
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
input_shape = ((3, 2), (4, 7)) + _REST_OF_STACK
expected_shape = ((4, 7), (3, 2)) + _REST_OF_STACK
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_drop(self):
layer = cb.Drop()
input_shape = ((3, 2), )
expected_shape = _EMPTY_STACK
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
input_shape = ((3, 2), ) + _REST_OF_STACK
expected_shape = _REST_OF_STACK
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_flatten_n(self):
input_shape = (29, 87, 10, 20, 30)
actual_shape = base.check_shape_agreement(core.Flatten(), input_shape)
self.assertEqual(actual_shape, (29, 87 * 10 * 20 * 30))
actual_shape = base.check_shape_agreement(
core.Flatten(num_axis_to_keep=2), input_shape)
self.assertEqual(actual_shape, (29, 87, 10 * 20 * 30))
actual_shape = base.check_shape_agreement(
core.Flatten(num_axis_to_keep=3), input_shape)
self.assertEqual(actual_shape, (29, 87, 10, 20 * 30))
actual_shape = base.check_shape_agreement(
core.Flatten(num_axis_to_keep=4), input_shape)
self.assertEqual(actual_shape, (29, 87, 10, 20, 30))
# Not enough dimensions.
with self.assertRaises(base.LayerError):
base.check_shape_agreement(core.Flatten(num_axis_to_keep=5),
input_shape)
with self.assertRaises(base.LayerError):
base.check_shape_agreement(core.Flatten(num_axis_to_keep=6),
input_shape)
def test_flatten_n(self):
input_shape = (29, 87, 10, 20, 30)
layer = core.Flatten()
expected_shape = (29, 87 * 10 * 20 * 30)
actual_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(actual_shape, expected_shape)
layer = core.Flatten(n_axes_to_keep=2)
expected_shape = (29, 87, 10 * 20 * 30)
actual_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(actual_shape, expected_shape)
layer = core.Flatten(n_axes_to_keep=3)
expected_shape = (29, 87, 10, 20 * 30)
actual_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(actual_shape, expected_shape)
layer = core.Flatten(n_axes_to_keep=4)
expected_shape = (29, 87, 10, 20, 30)
actual_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(actual_shape, expected_shape)
# Not enough dimensions.
with self.assertRaises(base.LayerError):
base.check_shape_agreement(core.Flatten(n_axes_to_keep=5),
input_shape)
with self.assertRaises(base.LayerError):
base.check_shape_agreement(core.Flatten(n_axes_to_keep=6),
input_shape)
def test_merged_hashed_causal_attention(self):
qkv_shape = (3, 32, 8)
input_shape = (qkv_shape, qkv_shape, qkv_shape)
layer = attention.MemoryEfficientCausalAttention(
loop_stride=16, dropout=0.1, mode='train')
final_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual((3, 32, 8), final_shape)
def test_layer_decorator_and_shape_agreement(self):
@base.layer()
def add_one(x, **unused_kwargs):
return x + 1
output_shape = base.check_shape_agreement(add_one(), (12, 17)) # pylint: disable=no-value-for-parameter
self.assertEqual(output_shape, (12, 17))
def test_time_bin_causal_attention_n_bins(self):
qkv_shape = (3, 57, 8)
input_shape = (qkv_shape, qkv_shape, qkv_shape)
layer = attention.TimeBinCausalAttention(
n_bins=4, dropout=0.1, mode='train')
final_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual((3, 57, 8), final_shape)
def test_branch_named(self):
input_shape = (2, 3)
expected_shape = {'a': (2, 3), 'b': (2, 3)}
output_shape = base.check_shape_agreement(
combinators.Branch(a=combinators.NoOp(), b=combinators.NoOp()),
input_shape)
self.assertEqual(output_shape, expected_shape)
def test_parallel(self):
input_shape = ((2, 3), (2, 3))
expected_shape = ((2, 3), (2, 3))
output_shape = base.check_shape_agreement(
combinators.Parallel(combinators.NoOp(), combinators.NoOp()),
input_shape)
self.assertEqual(output_shape, expected_shape)
def test_branch(self):
input_shape = (2, 3)
expected_shape = ((2, 3), (2, 3))
output_shape = base.check_shape_agreement(
combinators.Branch(combinators.Copy(), combinators.Copy()),
input_shape)
self.assertEqual(output_shape, expected_shape)
def test_rebatch(self):
input_shape = (29, 5, 5, 20)
result_shape = base.check_shape_agreement(convolution.Conv(30, (3, 3)),
input_shape)
self.assertEqual(result_shape, (29, 3, 3, 30))
input_shape = (29, 5, 5, 20)
result_shape = base.check_shape_agreement(
combinators.Rebatch(convolution.Conv(30, (3, 3)), n_batch_dims=1),
input_shape)
self.assertEqual(result_shape, (29, 3, 3, 30))
input_shape = (19, 29, 5, 5, 20)
result_shape = base.check_shape_agreement(
combinators.Rebatch(convolution.Conv(30, (3, 3)), n_batch_dims=2),
input_shape)
self.assertEqual(result_shape, (19, 29, 3, 3, 30))
def test_ngpu(self):
vocab_size = 2
input_shape = [3, 5, 7]
model = neural_gpu.NeuralGPU(d_feature=30,
steps=4,
vocab_size=vocab_size)
final_shape = base.check_shape_agreement(model,
tuple(input_shape),
integer_inputs=True)
self.assertEqual(tuple(input_shape + [vocab_size]), final_shape)
def test_weighted_mean_shape(self):
input_shape = ((29, 4, 4, 20), (29, 4, 4, 20))
result_shape = base.check_shape_agreement(
metrics.WeightedMean(), input_shape)
self.assertEqual(result_shape, ())
def test_select(self):
input_shape = ((2, 3), (3, 4))
expected_shape = (3, 4)
output_shape = base.check_shape_agreement(combinators.Select(1),
input_shape)
self.assertEqual(output_shape, expected_shape)
def test_select_named(self):
input_shape = {'a': (2, 3), 'b': (3, 4)}
expected_shape = (3, 4)
output_shape = base.check_shape_agreement(combinators.Select('b'),
input_shape)
self.assertEqual(output_shape, expected_shape)
def test_parallel_div_div(self):
layer = cb.Parallel(core.Div(divisor=0.5), core.Div(divisor=3.0))
input_shape = ((3, 2), (4, 7))
expected_shape = ((3, 2), (4, 7))
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_parallel_named(self):
input_shape = {'a': (2, 3), 'b': (2, 3)}
expected_shape = {'a': (2, 3), 'b': (2, 3)}
output_shape = base.check_shape_agreement(
combinators.Parallel(a=combinators.NoOp()), input_shape)
self.assertEqual(output_shape, expected_shape)
def test_div_shapes(self):
layer = core.Div(divisor=2.0)
input_shape = (3, 2)
expected_shape = (3, 2)
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_parallel_no_ops(self):
layer = cb.Parallel([], None)
input_shape = ((3, 2), (4, 7))
expected_shape = ((3, 2), (4, 7))
output_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, expected_shape)
def test_reversible_swap(self): layer = reversible.ReversibleSwap() input_shape = ((2, 3), (3, 3)) final_shape = base.check_shape_agreement(layer, input_shape) self.assertEqual(final_shape, input_shape[::-1])
def test_batch_norm_shape(self):
input_shape = (29, 5, 7, 20)
result_shape = base.check_shape_agreement(
normalization.BatchNorm(), input_shape)
self.assertEqual(result_shape, input_shape)
def test_conv_rebatch(self):
input_shape = (3, 29, 5, 5, 20)
result_shape = base.check_shape_agreement(convolution.Conv(30, (3, 3)),
input_shape)
self.assertEqual(result_shape, (3, 29, 3, 3, 30))
def test_layer_norm(self):
input_shape = (29, 5, 7, 20)
result_shape = base.check_shape_agreement(
normalization.LayerNorm(), input_shape)
self.assertEqual(result_shape, input_shape)
def _test_cell_runs(self, layer, input_shape, output_shape):
final_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual(output_shape, final_shape)
def test_accuracy_scalar(self):
input_shape = ((29, 4, 4, 20), (29, 4, 4))
result_shape = base.check_shape_agreement(
metrics.AccuracyScalar(), input_shape)
self.assertEqual(result_shape, ())
def test_cross_entropy_loss_scalar(self):
input_shape = ((29, 4, 4, 20), (29, 4, 4))
result_shape = base.check_shape_agreement(
metrics.CrossEntropyLossScalar(), input_shape)
self.assertEqual(result_shape, ())
def test_causal_conv(self):
input_shape = (29, 5, 20)
conv = convolution.CausalConv(filters=30, kernel_width=3)
result_shape = base.check_shape_agreement(conv, input_shape)
self.assertEqual(result_shape, (29, 5, 30))
def test_weight_mask(self):
input_shape = (29, 4, 4, 20)
result_shape = base.check_shape_agreement(
metrics.WeightMask(), input_shape)
self.assertEqual(result_shape, input_shape)
