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def test_demo_start_subprocess_patched():
from returnn.util.basic import get_patch_atfork_lib
from subprocess import check_call
env = os.environ.copy()
env['LD_PRELOAD'] = get_patch_atfork_lib()
print('LD_PRELOAD:', get_patch_atfork_lib())
check_call([sys.executable, __file__, 'patched_check_d... |
def test_hdf_dataset_init():
hdf_filename = tempfile.mktemp(suffix='.hdf', prefix='nose-dataset-init')
hdf_dataset_init(hdf_filename)
assert os.path.exists(hdf_filename)
os.remove(hdf_filename)
|
def test_hdf_create():
hdf_filename = tempfile.mktemp(suffix='.hdf', prefix='nose-dataset-create')
hdf_dataset = hdf_dataset_init(hdf_filename)
assert os.path.exists(hdf_filename)
dataset = DummyDataset(input_dim=2, output_dim=3, num_seqs=4)
dataset.init_seq_order(epoch=1)
hdf_dump_from_datase... |
def test_hdf_create_and_load():
hdf_filename = tempfile.mktemp(suffix='.hdf', prefix='nose-dataset-load')
hdf_dataset = hdf_dataset_init(hdf_filename)
assert os.path.exists(hdf_filename)
dataset = DummyDataset(input_dim=2, output_dim=3, num_seqs=4)
dataset.init_seq_order(epoch=1)
hdf_dump_from... |
def test_hdf_create_unicode_labels():
hdf_filename = tempfile.mktemp(suffix='.hdf', prefix='nose-dataset-create')
hdf_dataset = hdf_dataset_init(hdf_filename)
assert os.path.exists(hdf_filename)
dataset = DummyDataset(input_dim=2, output_dim=3, num_seqs=4)
assert ('classes' in dataset.get_target_l... |
def test_pack_padded():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Dim)]:
... |
def test_reshape():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return rf.r... |
def test_expand_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
expand_dim = Dim(3, name='expand')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: ... |
def test_concat():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Dim)]:
... |
def test_pad():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Tuple[(Dim, Dim)])]:... |
def test_pad_time():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Tuple[(Dim, Dim... |
def test_gather():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return rf.ga... |
def test_gather_2d_indices():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [batch_dim, time_dim], dtype='int32', sparse_dim=in_dim)})
... |
def test_gather_feature_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], feature_dim=in_dim, dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> ... |
def test_slice():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Dim)]:
... |
def test_shift_right():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim], sparse_dim=in_dim, dtype='int32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
... |
def test_reverse_sequence():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
re... |
def test_where():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'cond': Tensor('cond', [batch_dim, time_dim], dtype='bool'), 'true': Tensor('true', [batch_dim, time_dim, in_dim], dtype='float32'), 'false': Tensor('false', [batch_dim, in_dim... |
def test_where_int():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'cond': Tensor('cond', [batch_dim, time_dim], dtype='bool'), 'true': Tensor('true', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Mo... |
def test_copy_masked():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Conv1d, extern_data: TensorDict):
x = extern_data['da... |
def test_cast_sparse():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Conv1d, extern_data: TensorDict):
x = rf.reduce_argma... |
def test_dot_attention():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
key_dim = Dim(7, name='key')
value_dim = Dim(13, name='value')
extern_data = TensorDict({'q': Tensor('q', [batch_dim, time_dim, key_dim], dtype='float32'), 'k': Tensor('k', [batch_dim, time_dim, key_dim], dtype='float... |
def test_self_attention():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_causal_self_attention():
from returnn.tensor import single_step_dim
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def... |
def test_relative_positional_encoding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(8, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor, *, axis: Dim)... |
def test_rel_pos_self_attention():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(8, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__(... |
def test_sinusoidal_positional_encoding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
feat_dim = Dim(8, name='feat')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, feat_dim], dtype='float32')})
def _forward_step(**_kwargs):
out = rf.sinusoidal_positiona... |
def test_CausalSelfAttention():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
feat_dim = Dim(8, name='feat')
key_dim = Dim(6, name='key')
value_dim = Dim(10, name='value')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, feat_dim], dtype='float32')})
def _fo... |
def test_linear_direct():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
(in_dim, out_dim) = (Dim(7, name='in'), Dim(13, name='out'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [batch_dim, time_dim], dtype='int... |
def test_linear():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
(in_dim, out_dim) = (Dim(7, name='in'), Dim(13, name='out'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [batch_dim, time_dim], dtype='int32', sp... |
def test_state():
import tree
s = rf.LstmState(h=Tensor('h', (), 'float32'), c=Tensor('c', (), 'float32'))
res = tree.map_structure((lambda x: x), s)
assert isinstance(res, rf.LstmState)
assert (res is not s)
assert ((res.h is s.h) and (res.c is s.c))
|
def test_2layers():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
(in_dim, hidden_dim, out_dim) = (Dim(7, name='in'), Dim(11, name='hidden'), Dim(13, name='out'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [ba... |
def test_linear_same_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
(in_dim, out_dim) = (Dim(7, name='in'), Dim(13, name='out'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [batch_dim, time_dim], dtype='i... |
def test_linear_cross_entropy():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
(in_dim, out_dim) = (Dim(7, name='in'), Dim(13, name='out'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32'), 'classes': Tensor('classes', [batch_dim, time_dim], dty... |
def test_linear_ctc():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
target_time_dim = Dim(Tensor('target_time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
hidden_dim = Dim(13, name='hidden')
out_dim = Dim(11, name='classes')
out_wb_dim = (out_dim + 1)
extern_data... |
def test_dropout():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return rf.d... |
def test_dim_value():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
res = rf.... |
def test_dim_mask():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
mask1 = ti... |
def test_param_assign():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_loss_normalized():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _train_step(*, model: rf.Module, extern_data: TensorDict):
model
x =... |
def test_loss_normalization():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
use_normalized = False
use_custom_inv_norm_factor = False
def _train_step(*, ... |
def test_rf_range_over_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Module, extern_data: TensorDict):
rf.range_over... |
def test_cond():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
s... |
def test_cond_via_time_even():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):... |
def test_cond_shared_params():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):... |
def test_cond_twice_shared_params():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(... |
def test_cond_param_assign():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_cond_param_assign2():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_cond_param_assign3():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_constant_bool():
class _Net(rf.Module):
def __call__(self) -> Tuple[(Tensor, Dim)]:
dim = Dim(3, name='dim')
return (rf.constant(False, dims=[dim]), dim)
def _forward_step(*, model: _Net, extern_data: TensorDict):
extern_data
(out, dim) = model()
... |
def test_module_list():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_module_slice_set_del():
rf.select_backend_torch()
base_dim = Dim(3, name='linear-out')
dims = [(base_dim + i) for i in range(4)]
in_dim = Dim(7, name='in')
in_dims = ([in_dim] + dims[:(- 1)])
layers = rf.ModuleList([rf.Linear(in_dim_, out_dim_) for (in_dim_, out_dim_) in zip(in_dims, ... |
def test_sequential_base_case():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_sequential_named_case():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()... |
def test_parameter_list():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_conv1d():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
... |
def test_functional_conv1d_same_padding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __c... |
def test_conv1d_same_padding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self)... |
def test_functional_conv1d_stride_same_padding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(1, name='in')
out_dim = Dim(1, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
d... |
def test_conv1d_stride_same_padding():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim(13, name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init_... |
def test_conv1d_same_out():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_conv1d_depthwise():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
out_dim = Dim((7 * 3), name='out')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(sel... |
def test_maxpool1d_padding_valid():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spatial_d... |
def test_maxpool1d_padding_same():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spatial_di... |
def test_maxpool1d_stride_padding_same():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spa... |
def test_maxpool1d_stride_border_cond():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spat... |
def test_maxpool1d_stride1_padding_same():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_sp... |
def test_avgpool1d_stride1_padding_same():
time_dim = Dim(10, name='time')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spatial_dim: Dim) -> Tuple[(Tensor, Dim)]:
return rf.poo... |
def test_conformer():
import resource
import sys
try:
resource.setrlimit(resource.RLIMIT_STACK, ((2 ** 29), (- 1)))
except Exception as exc:
print(f'resource.setrlimit {type(exc).__name__}: {exc}')
sys.setrecursionlimit((10 ** 6))
time_dim = Dim(Tensor('time', [batch_dim], dtyp... |
def test_scaled_gradient():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Module, extern_data: TensorDict):
model
d... |
def test_label_smoothed_log_prob_gradient():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
vocab_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, vocab_dim], dtype='float32', feature_dim=vocab_dim), 'targets': Tensor('targets', [batch_dim, time_di... |
def test_while_loop_simple():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
def _forward_step(*, model: rf.Module, extern_data: TensorDict):
(model, exter... |
def test_while_loop_two_state():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32', feature_dim=in_dim)})
def _forward_step(*, model: rf.Module, extern_data: TensorDict)... |
def test_while_loop():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
def _co... |
def test_scan_unknown_len():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Dim)]:
... |
def test_scan_existing_spatial_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
... |
def test_scan_changing_dim():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Dim)]:... |
def test_neg():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return (- x)
... |
def test_squared_difference():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'a': Tensor('a', [batch_dim, time_dim, in_dim], dtype='float32'), 'b': Tensor('b', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
... |
def test_abs_complex():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='complex64')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
retur... |
def test_relu():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return rf.relu... |
def test_batch_norm():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_batch_norm_masking():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def test_reduce_max():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
return r... |
def test_reduce_argmax():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
retur... |
def test_reduce_mean_dyn_time():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
... |
def test_reduce_mean_dyn_batch_time():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tensor:
... |
def test_top_k():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: Tensor) -> Tuple[(Tensor, Tensor, Dim)]:
... |
def test_top_k_beam_search():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
vocab_dim = Dim(7, name='vocab')
beam_in_dim = Dim(3, name='beam_in')
beam_out_dim = Dim(5, name='beam_out')
extern_data = TensorDict({'log_probs': Tensor('log_probs', [batch_dim, beam_in_dim, time_dim, vocab_... |
def test_stft():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, x: rf.Tensor, *, in_spatial_dim: Dim) -> Tuple[(Tensor, Dim, Dim)]:
return ... |
def test_mel_filterbank():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
feat_dim = Dim(10, name='mel')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim], dtype='float32')})
class _Net(rf.Module):
def __call__(self, source: rf.Tensor, *, in_spatial_dim: Dim)... |
def test_audio_specaugment():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32', feature_dim=in_dim)})
def _forward_step(*, model: rf.Module, extern_data: TensorDict):
... |
def test_tensor():
batch_dim = Dim(name='batch', dimension=None)
time_dim = Dim(name='time', dimension=None)
feat_dim = Dim(10)
x = Tensor('x', (batch_dim, time_dim, feat_dim), 'float32')
print(x)
|
def run(*args):
args = list(args)
print('run:', args)
global _run_count
if (_run_count == 0):
_run_count += 1
from returnn.util.basic import generic_import_module
mod = generic_import_module(os.path.join(base_dir, args[0]))
mod.main(args)
return
_run_count +... |
def test_compile_tf_graph_basic():
tmp_dir = tempfile.mkdtemp()
with open(os.path.join(tmp_dir, 'returnn.config'), 'wt') as config:
config.write(rec_encoder_decoder_simple_config)
args = ['tools/compile_tf_graph.py', '--output_file', os.path.join(tmp_dir, 'graph.metatxt'), os.path.join(tmp_dir, 'r... |
def test_compile_tf_graph_basic_second_run():
test_compile_tf_graph_basic()
|
def test_compile_tf_graph_enc_dec_simple_recurrent_step():
tmp_dir = tempfile.mkdtemp()
with open(os.path.join(tmp_dir, 'returnn.config'), 'wt') as config:
config.write(rec_encoder_decoder_simple_config)
args = ['tools/compile_tf_graph.py', '--output_file', os.path.join(tmp_dir, 'graph.metatxt'), ... |
def test_compile_tf_graph_enc_dec_att_recurrent_step():
tmp_dir = tempfile.mkdtemp()
with open(os.path.join(tmp_dir, 'returnn.config'), 'wt') as config:
config.write(rec_encoder_decoder_att_config)
args = ['tools/compile_tf_graph.py', '--output_file', os.path.join(tmp_dir, 'graph.metatxt'), '--rec... |
def test_compile_tf_graph_transducer_time_sync_recurrent_step():
tmp_dir = tempfile.mkdtemp()
with open(os.path.join(tmp_dir, 'returnn.config'), 'wt') as config:
config.write(rec_transducer_time_sync_config)
args = ['tools/compile_tf_graph.py', '--output_file', os.path.join(tmp_dir, 'graph.metatxt... |
def test_compile_tf_graph_transducer_time_sync_delayed_recurrent_step():
tmp_dir = tempfile.mkdtemp()
with open(os.path.join(tmp_dir, 'returnn.config'), 'wt') as config:
config.write(rec_transducer_time_sync_delayed_config)
args = ['tools/compile_tf_graph.py', '--output_file', os.path.join(tmp_dir... |
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