onnx

游雁

2023-02-25 9ccbadc8be2b52add807c421aa766a94e9176e44

onnx

 funasr/export/export_model.py

@@ -58,7 +58,7 @@
        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 @@
            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()

 funasr/export/models/predictor/cif.py

@@ -76,108 +76,6 @@
        

        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):


 scan.py