X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=b8fcdb34b32a07e8fd46900c88d889c3cf9761ea;hb=76e62a5782fc2509ce989fcfc0d0aedc17322b3a;hp=68a46b3cdb89dc79f892b5d3339927c039193e6a;hpb=0f4c86c0e7730db4147f136df5aeb5528fc943a0;p=picoclvr.git diff --git a/problems.py b/problems.py index 68a46b3..b8fcdb3 100755 --- a/problems.py +++ b/problems.py @@ -22,91 +22,88 @@ class Problem: nb_correct = ((result == input).long() * ar_mask).sum().item() return nb_total, nb_correct -#################### +#################### +class ProblemDegradation(Problem): + def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False): + assert value_max // nb_state_tokens >= 2 + self.nb_state_tokens = nb_state_tokens + self.nb_time_steps = nb_time_steps + self.value_max = value_max + self.hard = hard -class ProblemTwoCuts(Problem): - def __init__(self, len_total=50, nb_values=100, global_constraint=True): - self.len_total = len_total - 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).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 generate_sequences(self, nb): + x = ( + torch.rand(nb, self.nb_state_tokens).sort(dim=-1).indices == 0 + ).long() * self.value_max + seq = [x] + + for t in range(self.nb_time_steps - 1): + v = (torch.rand(x.size()).sort(dim=-1).indices + 1) * (x >= 2).long() + u = (v.max(dim=-1, keepdim=True).values == v).long() + n = ( + (u * x) + .minimum(2 + torch.randint(self.value_max // 4 - 2, x.size())) + .sum(dim=-1, keepdim=True) + ) + m = 1 + ((n - 1) * torch.rand(n.size())).long() + x = ( + x + + m * u.roll(shifts=-1, dims=-1) + - n * u + + (n - m) * u.roll(shifts=1, dims=-1) + ) + seq.append(x) + + if self.hard: + seq.reverse() + + seq = torch.cat(seq, dim=1) + 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 + e = result.new_zeros(self.nb_state_tokens) + + for seq in result: + states = list(seq.split(self.nb_state_tokens)) + if self.hard: + states.reverse() + + d = states[0] + j = d.sort(descending=True).indices[0] + e.zero_() + e[j] = self.value_max + if (d - e).abs().sum() == 0: + nb_errors = 0 + for k in range(len(states) - 1): + d = states[k + 1] - states[k] + j = d.sort(descending=False).indices[0] + if ( + d[j] == 0 + or d[j] > self.value_max // 4 + or d[(j + 1) % e.size(0)] <= 0 + or d[(j + 1) % e.size(0)] >= -d[j] + ): + nb_errors += 1 + else: + e.zero_() + e[j] = d[j] + e[(j + 1) % e.size(0)] = d[(j + 1) % e.size(0)] + e[(j - 1) % e.size(0)] = -d[(j + 1) % e.size(0)] - d[j] + if (d - e).abs().sum() > 0: + nb_errors += 1 + if nb_errors == 0: + nb_correct += 1 return nb_total, nb_correct def seq2str(self, seq): - return " ".join( [ f"{x:02d}" for x in seq ] ) + return " | ".join( + [" ".join([f"{x:02d}" for x in s]) for s in seq.split(self.nb_state_tokens)] + ) + #################### @@ -286,7 +283,125 @@ class ProblemAddition(Problem): return "".join(self.id2char[x.item()] for x in seq) +#################### + + +class ProblemMixing(Problem): + def __init__(self, height=4, width=4, nb_time_steps=9, hard=False): + self.height = height + self.width = width + self.nb_time_steps = nb_time_steps + self.hard = hard + + def start_random(self, nb): + y = torch.arange(self.height * self.width).reshape(1, -1).expand(nb, -1) + + # m = (torch.rand(y.size()).sort(dim=-1).indices < y.size(1) // 2).long() + + i = torch.arange(self.height).reshape(1,-1,1).expand(nb,self.height,self.width) + j = torch.arange(self.width).reshape(1,1,-1).expand(nb,self.height,self.width) + + ri = torch.randint(self.height, (nb,)).reshape(nb,1,1) + rj = torch.randint(self.width, (nb,)).reshape(nb,1,1) + + m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1) + + y = (y * m + self.height * self.width * (1 - m)).reshape( + nb, self.height, self.width + ) + + return y + + def start_error(self, x): + i = torch.arange(self.height, device=x.device).reshape(1,-1,1).expand_as(x) + j = torch.arange(self.width, device=x.device).reshape(1,1,-1).expand_as(x) + + ri = (x == self.height * self.width).long().sum(dim=-1).argmax(-1).view(-1,1,1) + rj = (x == self.height * self.width).long().sum(dim=-2).argmax(-1).view(-1,1,1) + + m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1) + + x = x.flatten(1) + u = torch.arange(self.height * self.width, device = x.device).reshape(1, -1) + + d = (x - (m * u + (1 - m) * self.height * self.width)).abs().sum(-1) + return d + + def moves(self, x): + y = ( + x[:, None, :, :] + .expand(-1, self.height * 2 + self.width * 2, -1, -1) + .clone() + ) + k = 0 + + for i in range(self.height): + y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=-1) + k += 1 + y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=1) + k += 1 + + for j in range(self.width): + y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=-1) + k += 1 + y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=1) + k += 1 + + return y + + def generate_sequences(self, nb): + x = self.start_random(nb) + + seq = [x.flatten(1)] + + for t in range(self.nb_time_steps - 1): + y = self.moves(x) + x = y[torch.arange(nb), torch.randint(y.size(1), (nb,))] + seq.append(x.flatten(1)) + + if self.hard: + seq.reverse() + + seq = torch.cat(seq, dim=1) + return seq, seq.new_full(seq.size(), 1, dtype=torch.int64) + + def compute_nb_correct(self, input, ar_mask, result): + a = [ + x.reshape(result.size(0), self.height, self.width) + for x in result.split(self.height * self.width, dim=1) + ] + if self.hard: + a.reverse() + + x = a[0] + + d = self.start_error(x) + + for t in range(self.nb_time_steps - 1): + x0, x = a[t], a[t + 1] + y = self.moves(x0) + d = d + (x[:, None] - y).abs().sum((-1, -2)).min(dim=-1).values + + nb_total, nb_correct = result.size(0), (d == 0).long().sum().item() + + return nb_total, nb_correct + + def seq2str(self, seq): + return " | ".join( + [ + " ".join( + ["-".join([f"{x:02d}" if x < self.height * self.width else "**" for x in s]) for s in r.split(self.width)] + ) + for r in seq.split(self.height * self.width) + ] + ) + + +#################### + if __name__ == "__main__": - p = ProblemTwoCuts(12) + p = ProblemMixing() s, m = p.generate_sequences(10000) + for x in s[:5]: + print(p.seq2str(x)) print(p.compute_nb_correct(None, None, s))