-# This is one order of magnitude more complicated than I expected
-
-
-def flash_back_time_src(N, H, t0, t1, CL, CH, proba, device):
- # starting flash backs
- fb_start = (torch.rand(N, CH, t1 - t0, device=device) <= proba).long()
- fb_start[:, :, -CL:] = 0
- fb_start[:, :, :CL] = 0
-
- # Remove series longer than CL
- fb_body = fb_start.clone()
- fb_body[:, :, CL + 1 :] -= fb_start[:, :, : -(CL + 1)]
- fb_body = fb_body.cumsum(dim=2)
- fb_start = fb_start * (fb_body == 1)
-
- # Set a origin source time (at what time start the chunck to copy
- # here) We set it as a multiple of CL to be consistent with the
- # "rolling" caterpillar
- t = torch.arange(fb_start.size(2), device=fb_start.device)[None, None, :]
- src_time = fb_start * (
- t
- - CL
- * (
- 1
- + (
- torch.rand(fb_start.size(), device=fb_start.device) * (t // CL - 1)
- ).long()
- )
- )
- src_time[:, :, CL:] -= src_time.clone()[:, :, :-CL]
- src_time = src_time.cumsum(dim=2)
-
- src_head = fb_start * torch.randint(H, fb_start.size(), device=fb_start.device)
- src_head[:, :, CL:] -= src_head.clone()[:, :, :-CL]
- src_head = src_head.cumsum(dim=2)
-
- # combine
- src_delta = fb_start.clone()
- src_delta[:, :, CL:] -= fb_start[:, :, :-CL]
- src_delta = src_delta.cumsum(dim=2)
- src_delta[:, :, CL:] -= CL * fb_start[:, :, :-CL]
- src_time += src_delta.cumsum(dim=2) - 1
-
- return src_time, src_head
-
-
-def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba):
- N, H, CH = V.size(0), V.size(1), rec_V.size(1)
-
- fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device)
-
- fbt_V = fbt[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
- fbh_V = fbh[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
- t = fbt_V.clamp(min=0)
- n = torch.arange(V.size(0), device=V.device)[:, None, None, None].expand_as(
- rec_V[:, :, t0:t1]
- )
- d = torch.arange(V.size(3), device=V.device)[None, None, None, :].expand_as(
- rec_V[:, :, t0:t1]
- )
- q = V[:, :, t0:t1][n, fbh_V, t, d]
- rec_V[:, :, t0:t1] = q * (fbt_V >= 0) + rec_V[:, :, t0:t1] * (fbt_V < 0)
-
- fbt_K = fbt[:, :, :, None].expand_as(rec_K[:, :, t0:t1])
- fbh_K = fbh[:, :, :, None].expand_as(rec_K[:, :, t0:t1])
- t = fbt_K.clamp(min=0)
- n = torch.arange(K.size(0), device=K.device)[:, None, None, None].expand_as(
- rec_K[:, :, t0:t1]
- )
- d = torch.arange(K.size(3), device=K.device)[None, None, None, :].expand_as(
- rec_K[:, :, t0:t1]
- )
- q = K[:, :, t0:t1][n, fbh_K, t, d]
- rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0)
-
- # print("SANITY", (fbt_K >=0).float().sum()/fbt_K.numel())
-