- self.len_target = len_target
-
- def generate_sequences(self, nb):
- k = torch.arange(self.len_total)[None, :]
- l = torch.randint(self.len_total, (2, nb))[:, :, None] + 1
- i = torch.randint(10, (2, nb))[:, :, None]
- a = l[0]
- b = l[0] + 1 + l[1]
- c = l[0] + 1 + l[1] + 1 + l[0]
- sequences = (
- (k < a) * i[0]
- + (k == a) * 10
- + (k > a) * (k < b) * i[1]
- + (k == b) * 11
- + (k > b) * (k < c) * i[1]
- + (k >= c) * 12
- )
- ar_mask = (sequences == 11).long()
- ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
- return sequences, ar_mask
+ self.nb_values = nb_values
+ self.global_constraint = global_constraint
+
+ def generate_sequences_internal(self, nb):
+ return u,v,a,b,c
+
+ def generate_sequences(self,nb):
+
+ u = torch.randint(self.len_total, (nb,))
+ v = torch.randint(self.len_total, (nb,))
+
+ a = torch.randint(self.nb_values, (nb,))
+ b = torch.randint(self.nb_values, (nb,))
+ c = torch.randint(self.nb_values, (nb,))
+
+ while True:
+ to_compute = torch.logical_or(u>=v-self.len_total//10,u<v-self.len_total//5)
+ to_compute =torch.logical_or(to_compute, u == 0)
+ to_compute =torch.logical_or(to_compute, v == self.len_total)
+ n = to_compute.long().sum()
+ if n == 0:
+ break
+ else:
+ u[to_compute] = torch.randint(self.len_total, (n,))
+ v[to_compute] = torch.randint(self.len_total, (n,))
+
+ while True:
+ to_compute = a==b
+ to_compute = torch.logical_or(to_compute,b==c)
+ to_compute = torch.logical_or(to_compute,a==c)
+
+ if self.global_constraint:
+ to_compute = torch.logical_or(to_compute,(a*u+b*(v-u)+c*(self.len_total-v)) // self.len_total != self.nb_values//2)
+
+ n = to_compute.long().sum()
+ if n == 0:
+ break
+ else:
+ a[to_compute] = torch.randint(self.nb_values, (n,))
+ b[to_compute] = torch.randint(self.nb_values, (n,))
+ c[to_compute] = torch.randint(self.nb_values, (n,))
+
+ assert (u>=v).long().sum() == 0
+ assert (a==b).long().sum() == 0
+ assert (a==c).long().sum() == 0
+ assert (c==b).long().sum() == 0
+
+ t = torch.arange(self.len_total)
+ seq = (t[None,:] < u[:,None]).long() * a[:,None] + \
+ (t[None,:] >= u[:,None]).long() * (t[None,:] < v[:,None]).long() * b[:,None] + \
+ (t[None,:] >= v[:,None]).long() * c[:,None]
+
+ return seq,seq.new_full(seq.size(), 1, dtype=torch.int64)
+
+ def compute_nb_correct(self, input, ar_mask, result):
+ nb_total = result.size(0)
+ nb_correct = 0
+ i = torch.arange(result.size(1), device=result.device)
+
+ for k in range(nb_total):
+ s = result[k]
+ a = s[0]
+ uu = (s != a).nonzero()
+ if uu.size(0) > 0:
+ u = uu.min()
+ b = s[u]
+ vv = torch.logical_and(s != b, i >= u).nonzero()
+ if vv.size(0) > 0:
+ v = vv.min()
+ c = s[v]
+ ww = torch.logical_and(s != c, i >= v).nonzero()
+ if ww.size(0) == 0:
+ if not self.global_constraint or (a*u+b*(v-u)+c*(self.len_total-v)) // self.len_total == self.nb_values//2:
+ nb_correct += 1
+
+ return nb_total, nb_correct