def test_model_trainable_and_decodable(module, num_encs, model_dict):
args = make_arg(num_encs=num_encs, **model_dict)
batch = prepare_inputs("pytorch", num_encs)
# test trainable
m = importlib.import_module(module)
model = m.E2E([40 for _ in range(num_encs)], 5, args)
loss = model(*batch)
loss.backward() # trainable
# test attention plot
dummy_json = make_dummy_json(num_encs, [10, 20], [10, 20], idim=40, odim=5, num_inputs=num_encs)
batchset = make_batchset(dummy_json, 2, 2 ** 10, 2 ** 10, shortest_first=True)
att_ws = model.calculate_all_attentions(*convert_batch(
batchset[0], "pytorch", idim=40, odim=5, num_inputs=num_encs))
from espnet.asr.asr_utils import PlotAttentionReport
tmpdir = tempfile.mkdtemp()
plot = PlotAttentionReport(model.calculate_all_attentions, batchset[0], tmpdir, None, None, None)
for i in range(num_encs):
# att-encoder
att_w = plot.get_attention_weight(0, att_ws[i][0])
plot._plot_and_save_attention(att_w, '{}/att{}.png'.format(tmpdir, i))
# han
att_w = plot.get_attention_weight(0, att_ws[num_encs][0])
plot._plot_and_save_attention(att_w, '{}/han.png'.format(tmpdir), han_mode=True)
# test decodable
with torch.no_grad(), chainer.no_backprop_mode():
in_data = [np.random.randn(10, 40) for _ in range(num_encs)]
model.recognize(in_data, args, args.char_list) # decodable
if "pytorch" in module:
batch_in_data = [[np.random.randn(10, 40), np.random.randn(5, 40)] for _ in range(num_encs)]
model.recognize_batch(batch_in_data, args, args.char_list) # batch decodable
def test_calculate_plot_attention_ctc(module, num_encs, model_dict):
args = make_arg(num_encs=num_encs, **model_dict)
m = importlib.import_module(module)
model = m.E2E([2 for _ in range(num_encs)], 2, args)
# test attention plot
dummy_json = make_dummy_json(num_encs, [2, 3], [2, 3],
idim=2,
odim=2,
num_inputs=num_encs)
batchset = make_batchset(dummy_json, 2, 2**10, 2**10, shortest_first=True)
att_ws = model.calculate_all_attentions(*convert_batch(
batchset[0], "pytorch", idim=2, odim=2, num_inputs=num_encs))
from espnet.asr.asr_utils import PlotAttentionReport
tmpdir = tempfile.mkdtemp()
plot = PlotAttentionReport(model.calculate_all_attentions, batchset[0],
tmpdir, None, None, None)
for i in range(num_encs):
# att-encoder
att_w = plot.get_attention_weight(0, att_ws[i][0])
plot._plot_and_save_attention(att_w, "{}/att{}.png".format(tmpdir, i))
# han
att_w = plot.get_attention_weight(0, att_ws[num_encs][0])
plot._plot_and_save_attention(att_w,
"{}/han.png".format(tmpdir),
han_mode=True)
# test CTC plot
ctc_probs = model.calculate_all_ctc_probs(*convert_batch(
batchset[0], "pytorch", idim=2, odim=2, num_inputs=num_encs))
from espnet.asr.asr_utils import PlotCTCReport
tmpdir = tempfile.mkdtemp()
plot = PlotCTCReport(model.calculate_all_ctc_probs, batchset[0], tmpdir,
None, None, None)
if args.mtlalpha > 0:
for i in range(num_encs):
# ctc-encoder
plot._plot_and_save_ctc(ctc_probs[i][0],
"{}/ctc{}.png".format(tmpdir, i))