def test_sortagrad_trainable_with_batch_frames(module):
args = make_arg(sortagrad=1)
idim = 6
odim = 5
dummy_json = make_dummy_json_mt(8, [100, 200], [100, 200],
idim=idim,
odim=odim)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
import espnet.nets.chainer_backend.e2e_mt as m
batch_frames_in = 200
batch_frames_out = 200
batchset = make_batchset(dummy_json,
batch_frames_in=batch_frames_in,
batch_frames_out=batch_frames_out,
shortest_first=True,
mt=True)
for batch in batchset:
i = 0
o = 0
for uttid, info in batch:
i += int(info['output'][1]['shape'][0])
o += int(info['output'][0]['shape'][0])
assert i <= batch_frames_in
assert o <= batch_frames_out
model = m.E2E(6, 5, args)
for batch in batchset:
attn_loss = model(*convert_batch(batch, module, idim=6, odim=5))
attn_loss.backward()
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)
def test_sortagrad_trainable_with_batch_bins(module):
args = make_arg(sortagrad=1)
idim = 6
odim = 5
dummy_json = make_dummy_json_mt(4, [10, 20], [10, 20], idim=idim, odim=odim)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
import espnet.nets.chainer_backend.e2e_mt as m
batch_elems = 2000
batchset = make_batchset(dummy_json, batch_bins=batch_elems, shortest_first=True, mt=True, iaxis=1, oaxis=0)
for batch in batchset:
n = 0
for uttid, info in batch:
ilen = int(info['output'][1]['shape'][0])
olen = int(info['output'][0]['shape'][0])
n += ilen * idim + olen * odim
assert olen < batch_elems
model = m.E2E(6, 5, args)
for batch in batchset:
attn_loss = model(*convert_batch(batch, module, idim=6, odim=5))
attn_loss.backward()
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)
def test_sortagrad_trainable(module):
args = make_arg(sortagrad=1)
dummy_json = make_dummy_json_mt(4, [10, 20], [10, 20], idim=6, odim=5)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
import espnet.nets.chainer_backend.e2e_mt as m
batchset = make_batchset(dummy_json,
2,
2**10,
2**10,
shortest_first=True,
mt=True,
iaxis=1,
oaxis=0)
model = m.E2E(6, 5, args)
for batch in batchset:
loss = model(*convert_batch(batch, module, idim=6, odim=5))
if isinstance(loss, tuple):
# chainer return several values as tuple
loss[0].backward() # trainable
else:
loss.backward() # trainable
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)
def test_sortagrad_trainable(module):
args = make_arg(sortagrad=1)
dummy_json = make_dummy_json_mt(4, [10, 20], [10, 20], idim=6, odim=5)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
import espnet.nets.chainer_backend.e2e_mt as m
batchset = make_batchset(dummy_json, 2, 2 ** 10, 2 ** 10, shortest_first=True, mt=True)
model = m.E2E(6, 5, args)
for batch in batchset:
attn_loss = model(*convert_batch(batch, module, idim=6, odim=5))
attn_loss.backward()
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)
def test_context_residual(module):
args = make_arg(context_residual=True)
dummy_json = make_dummy_json_mt(8, [1, 100], [1, 100], idim=6, odim=5)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
raise NotImplementedError
batchset = make_batchset(dummy_json,
2,
2**10,
2**10,
shortest_first=True,
mt=True)
model = m.E2E(6, 5, args)
for batch in batchset:
attn_loss = model(*convert_batch(batch, module, idim=6, odim=5))
attn_loss.backward()
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)
def test_sortagrad_trainable_with_batch_bins(module):
args = make_arg(sortagrad=1)
idim = 6
odim = 5
dummy_json = make_dummy_json_mt(4, [10, 20], [10, 20],
idim=idim,
odim=odim)
if module == "pytorch":
import espnet.nets.pytorch_backend.e2e_mt as m
else:
raise NotImplementedError
batch_elems = 2000
batchset = make_batchset(
dummy_json,
batch_bins=batch_elems,
shortest_first=True,
mt=True,
iaxis=1,
oaxis=0,
)
for batch in batchset:
n = 0
for uttid, info in batch:
ilen = int(info["output"][1]["shape"][0])
olen = int(info["output"][0]["shape"][0])
n += ilen * idim + olen * odim
assert olen < batch_elems
model = m.E2E(6, 5, args)
for batch in batchset:
loss = model(*convert_batch(batch, module, idim=6, odim=5))
if isinstance(loss, tuple):
# chainer return several values as tuple
loss[0].backward() # trainable
else:
loss.backward() # trainable
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randint(0, 5, (1, 100))
model.translate(in_data, args, args.char_list)