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游雁 2023-02-25 18:27:31 +08:00
parent 1f85cf3aa4
commit 9ccbadc8be
3 changed files with 2 additions and 104 deletions
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4
funasr/export/export_model.py
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@ -58,7 +58,7 @@ class ASRModelExportParaformer:
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
else:
dummy_input = model.get_dummy_inputs_txt()
dummy_input = model.get_dummy_inputs()
# model_script = torch.jit.script(model)
model_script = torch.jit.trace(model, dummy_input)
@ -111,7 +111,7 @@ class ASRModelExportParaformer:
dummy_input,
os.path.join(path, f'{model.model_name}.onnx'),
verbose=verbose,
opset_version=12,
opset_version=14,
input_names=model.get_input_names(),
output_names=model.get_output_names(),
dynamic_axes=model.get_dynamic_axes()

102
funasr/export/models/predictor/cif.py
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@ -76,108 +76,6 @@ class CifPredictorV2(nn.Module):
return hidden, alphas, token_num_floor
# @torch.jit.script
# def cif(hidden, alphas, threshold: float):
# batch_size, len_time, hidden_size = hidden.size()
# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
#
# # loop varss
# integrate = torch.zeros([batch_size], device=hidden.device)
# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
# # intermediate vars along time
# list_fires = []
# list_frames = []
#
# for t in range(len_time):
# alpha = alphas[:, t]
# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
#
# integrate += alpha
# list_fires.append(integrate)
#
# fire_place = integrate >= threshold
# integrate = torch.where(fire_place,
# integrate - torch.ones([batch_size], device=hidden.device),
# integrate)
# cur = torch.where(fire_place,
# distribution_completion,
# alpha)
# remainds = alpha - cur
#
# frame += cur[:, None] * hidden[:, t, :]
# list_frames.append(frame)
# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
# remainds[:, None] * hidden[:, t, :],
# frame)
#
# fires = torch.stack(list_fires, 1)
# frames = torch.stack(list_frames, 1)
# list_ls = []
# len_labels = torch.round(alphas.sum(-1)).int()
# max_label_len = len_labels.max().item()
# # print("type: {}".format(type(max_label_len)))
# for b in range(batch_size):
# fire = fires[b, :]
# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
# pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], dtype=l.dtype, device=hidden.device)
# list_ls.append(torch.cat([l, pad_l], 0))
# return torch.stack(list_ls, 0), fires
# @torch.jit.script
# def cif(hidden, alphas, threshold: float):
# batch_size, len_time, hidden_size = hidden.size()
# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
#
# # loop varss
# integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
# frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
# # intermediate vars along time
# list_fires = []
# list_frames = []
#
# for t in range(len_time):
# alpha = alphas[:, t]
# distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
#
# integrate += alpha
# list_fires.append(integrate)
#
# fire_place = integrate >= threshold
# integrate = torch.where(fire_place,
# integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
# integrate)
# cur = torch.where(fire_place,
# distribution_completion,
# alpha)
# remainds = alpha - cur
#
# frame += cur[:, None] * hidden[:, t, :]
# list_frames.append(frame)
# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
# remainds[:, None] * hidden[:, t, :],
# frame)
#
# fires = torch.stack(list_fires, 1)
# frames = torch.stack(list_frames, 1)
# len_labels = torch.floor(torch.sum(alphas, dim=1)).int()
# max_label_len = torch.max(len_labels)
# pad_num = max_label_len - len_labels
# pad_num_max = torch.max(pad_num).item()
# frames_pad_tensor = torch.zeros([int(batch_size), int(pad_num_max), int(hidden_size)], dtype=frames.dtype,
# device=frames.device)
# fires_pad_tensor = torch.ones([int(batch_size), int(pad_num_max)], dtype=fires.dtype, device=fires.device)
# fires_pad_tensor_mask = sequence_mask_scripts(pad_num, maxlen=int(pad_num_max))
# fires_pad_tensor *= fires_pad_tensor_mask
# frames_pad = torch.cat([frames, frames_pad_tensor], dim=1)
# fires_pad = torch.cat([fires, fires_pad_tensor], dim=1)
# index_bool = fires_pad >= threshold
# frames_fire = frames_pad[index_bool]
# frames_fire = torch.reshape(frames_fire, (int(batch_size), -1, int(hidden_size)))
# frames_fire_mask = sequence_mask_scripts(len_labels, maxlen=int(max_label_len))
# frames_fire *= frames_fire_mask[:, :, None]
#
# return frames_fire, fires
@torch.jit.script
def cif(hidden, alphas, threshold: float):

0
scan.py Normal file
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