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monotonic_align/__init__.py ADDED
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+ from numpy import zeros, int32, float32
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+ from torch import from_numpy
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+
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+ from .core import maximum_path_jit
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+
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+
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+ def maximum_path(neg_cent, mask):
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+ """ numba optimized version.
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+ neg_cent: [b, t_t, t_s]
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+ mask: [b, t_t, t_s]
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+ """
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+ device = neg_cent.device
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+ dtype = neg_cent.dtype
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+ neg_cent = neg_cent.data.cpu().numpy().astype(float32)
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+ path = zeros(neg_cent.shape, dtype=int32)
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+
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+ t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
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+ t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
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+ maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
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+ return from_numpy(path).to(device=device, dtype=dtype)
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+
monotonic_align/core.py ADDED
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+ import numba
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+
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+
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+ @numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
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+ nopython=True, nogil=True)
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+ def maximum_path_jit(paths, values, t_ys, t_xs):
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+ b = paths.shape[0]
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+ max_neg_val = -1e9
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+ for i in range(int(b)):
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+ path = paths[i]
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+ value = values[i]
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+ t_y = t_ys[i]
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+ t_x = t_xs[i]
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+
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+ v_prev = v_cur = 0.0
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+ index = t_x - 1
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+
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+ for y in range(t_y):
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+ for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
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+ if x == y:
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+ v_cur = max_neg_val
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+ else:
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+ v_cur = value[y - 1, x]
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+ if x == 0:
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+ if y == 0:
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+ v_prev = 0.
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+ else:
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+ v_prev = max_neg_val
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+ else:
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+ v_prev = value[y - 1, x - 1]
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+ value[y, x] += max(v_prev, v_cur)
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+
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+ for y in range(t_y - 1, -1, -1):
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+ path[y, index] = 1
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+ if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
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+ index = index - 1