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Browse files- monotonic_align/__init__.py +20 -0
- monotonic_align/core.py +36 -0
monotonic_align/__init__.py
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from numpy import zeros, int32, float32
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from torch import from_numpy
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from .core import maximum_path_jit
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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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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
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import numba
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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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v_prev = v_cur = 0.0
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index = t_x - 1
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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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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
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