变量名写错了 (#2249)

莫拉古

2024-11-29 ae49b2a8e1bc676e6014d8a12ebeec947b655e3e

 funasr/models/paraformer/cif_predictor.py

@@ -497,6 +497,63 @@




@torch.jit.script

def cif_v1_export(hidden, alphas, threshold: float):

    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)



    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)

    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)



    # prefix_sum = torch.cumsum(alphas, dim=1)

    prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(

        torch.float32

    )  # cumsum precision degradation cause wrong result in extreme

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    fire_idxs = dislocation_diff > 0

    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor



    # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)

    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)

    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]

    remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]



    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    # max_label_len = batch_len.max()

    max_label_len = alphas.sum(dim=-1)

    max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)



    # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames

    return frame_fires, fires





@torch.jit.script

def cif_export(hidden, alphas, threshold: float):

    batch_size, len_time, hidden_size = hidden.size()

    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)

@@ -608,6 +665,76 @@
    return torch.stack(list_ls, 0), fires





def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):

    batch_size, len_time = alphas.size()

    device = alphas.device

    dtype = alphas.dtype



    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)



    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)



    # prefix_sum = torch.cumsum(alphas, dim=1)

    prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(

        torch.float32

    )  # cumsum precision degradation cause wrong result in extreme

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    fire_idxs = dislocation_diff > 0

    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor

    if return_fire_idxs:

        return fires, fire_idxs

    return fires





def cif_v1(hidden, alphas, threshold):

    fires, fire_idxs = cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=True)



    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    # frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)

    # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)

    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)

    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)



    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]

    remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]



    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    # max_label_len = batch_len.max()

    max_label_len = (

        torch.round(alphas.sum(-1)).int().max()

    )  # torch.round to calculate the max length



    # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames

    return frame_fires, fires





def cif_wo_hidden(alphas, threshold):

    batch_size, len_time = alphas.size()