if self.shift_size > 0: # calculate attention mask for SW-MSA H, W = self.input_resolution img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) else: attn_mask = None
时间: 2023-06-19 10:07:05 浏览: 96
这段代码是 ChitGPT 中的一部分,用于实现基于滑动窗口的多尺度自注意力机制(SW-MSA)。主要是计算用于掩盖不相关像素的注意力掩码。如果 shift_size 大于 0,就会生成一个大小为 H x W 的图像掩码,然后将其分成若干个大小为 window_size x window_size 的窗口。对于每对窗口,将它们的编号相减,并用 -100.0 填充非零元素的位置,用 0.0 填充零元素的位置,生成一个注意力掩码。如果 shift_size 等于 0,则不需要掩码。
相关问题
for i in range(self.n_iter): for j in range(self.n_sparrows): # 移动 params = self.sparrows[j] params += np.random.uniform(self.lb, self.ub, size=params.shape) params /= np.sum(params) params = np.clip(params, self.lb, self.ub) # 变异 params = self.mutate(params) # 计算分数 score = self.calculate_score(params) # 更新最优解 if score > self.best_score: self.best_score = score self.best_params = params # 更新麻雀群体 if score > self.scores[j]: params = self.sparrows[j] self.scores[j] = score # 适应度修正 self.fitness_replacement() # 选择最优解 self.select_best_params()
根据你提供的代码,可以看出这是一个麻雀算法的实现。根据错误信息,很有可能是在访问数组时,索引超出了数组的范围。
具体来说,可能是在第8行的 `params += np.random.uniform(self.lb, self.ub, size=params.shape)` 中,`params` 的长度小于 `self.lb` 或 `self.ub` 的长度。你可以尝试打印一下 `params`、`self.lb`、`self.ub` 和 `size` 的值,检查一下它们的维度是否一致。
另外,也有可能是在第14行的 `params = self.sparrows[j]` 中,`j` 的取值范围超出了 `self.sparrows` 的索引范围。你可以检查一下 `self.sparrows` 数组的长度和 `j` 的取值范围是否一致。
希望这些提示能够帮助你找到问题所在。如果还有其他疑问,欢迎随时提出。
将代码转化为paddlepaddle框架可以使用的代码:class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None
将代码转化为PaddlePaddle框架可以使用的代码:
class CosineAnnealingWarmbootingLR(paddle.optimizer.lr.CosineAnnealingDecay):
def __init__(self, T_max, T_warmup, eta_min=0, last_epoch=-1, verbose=False):
super(CosineAnnealingWarmbootingLR, self).__init__(T_max=T_max, eta_min=eta_min, last_epoch=last_epoch, verbose=verbose)
self.T_warmup = T_warmup
def get_lr(self):
if self.last_epoch < self.T_warmup:
return [(self.base_lr * self.last_epoch) / self.T_warmup for _ in self.base_lrs]
else:
return super(CosineAnnealingWarmbootingLR, self).get_lr()
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