new_dl = iter(self._dataloaders[di])什么意思

时间: 2023-05-11 08:02:59 浏览: 55
这段代码是在创建一个新的数据加载器对象，其中self._dataloaders[di]是一个已经存在的数据加载器对象，而new_dl则是对该数据加载器对象进行迭代操作后得到的新的数据加载器对象。具体的实现细节需要查看代码的上下文。
相关问题

def _get_nexts(self): def _get_next_dl_batch(di, dl): try: batch = next(dl) except StopIteration: new_dl = iter(self._dataloaders[di]) self._iterators[di] = new_dl batch = next(new_dl) return batch什么意思

这是一个 Python 函数，用于获取数据集的下一个批次。它接受两个参数：di 表示数据集的索引，dl 表示数据集的迭代器。该函数首先尝试从迭代器中获取下一个批次，如果迭代器已经到达末尾，则重新创建一个新的迭代器并获取下一个批次。最后，该函数返回获取到的批次。

def _get_nexts(self): def _get_next_dl_batch(di, dl): try: batch = next(dl) except StopIteration: new_dl = iter(self._dataloaders[di]) self._iterators[di] = new_dl batch = next(new_dl) return batch return [_get_next_dl_batch(di, dl) for di, dl in enumerate(self._iterators)]什么意思

这段代码是一个方法 _get_nexts，它返回一个列表，列表中的每个元素都是一个方法 _get_next_dl_batch 的调用结果。_get_next_dl_batch 方法接受两个参数 di 和 dl，其中 di 是一个整数，表示数据加载器的索引，dl 是一个数据加载器。方法的作用是从数据加载器中获取下一个批次的数据，如果当前加载器已经没有数据了，则从头开始重新加载。最终返回的列表中，每个元素都是一个批次的数据。

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for batch_idx, (inputs, labels) in enumerate(self.dataloaders[phase]): if phase != 'source_train' or epoch < args.middle_epoch: inputs = inputs.to(self.device) labels = labels.to(self.device) else: source_inputs = inputs target_inputs, target_labels = iter_target.next() inputs = torch.cat((source_inputs, target_inputs), dim=0) inputs = inputs.to(self.device) labels = labels.to(self.device) if (step + 1) % len_target_loader == 0: iter_target = iter(self.dataloaders['target_train'])

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def __init__(self, G, max_iter=20): self._G = G self._n = len(G.nodes(False)) # 节点数目 self._max_iter = max_iter这段代码什么意思

这段代码是一个 Python 类的初始化方法（__init__）。 参数 G 是一个 NetworkX 图对象，表示图的结构。 参数 max_iter 是一个整数，表示 PageRank 算法的最大迭代次数...self._max_iter 存储了最大迭代次数。

给以下代码写注释，要求每行写一句：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

return self.eta_min + (1 + math.cos(math.pi * self.T_cur / self.T_max)) / 2 * (self.optimizer.param_groups[0]['lr'] - self.eta_min) 如果当前T值不等于T_max，则根据公式计算当前的学习率，并返回。

class sampler (Sampler): def u (self, train size, batch_ size): num_ data = train_ size self .num_ per batch = int(num_ data 1 batch_ size) self .batch size = batch_ size self .range = torch.arange(0, batch_ size) .view(1, batch_ size).long() self.leftover flag = False if num_ data % batch_ size: self.leftover = torch.arange(self .num_ per batch * batch_ size, num_ data) . long( )self.leftover flag = True def_ iter_a (self): rand_ num = torch.randperm(self .num_ per_ batch) .view(-1, 1) * self .batch size self .rand_ num = rand_ num. expand(self .num_ per_ batch, self .batch_size) + self .range self .rand num_view = self .rand_ num. view(-1) if self.leftover_ flag: self .rand_ num_ view = torch.cat((self.rand_ num_ view, self.leftover), 0 return iter(self .rand_ num_ view) def Len_ (self): return num_ data 分析上述代码中的错误

8. 在 def_ iter_a() 函数中，self .rand_ num_ view = torch.cat((self.rand_ num_ view, self.leftover), 0 的语法是错误的，应该将其改为 self.rand_num_view = torch.cat((self.rand_num_view, self....

代码解释并给每行代码添加注释:class CosineAnnealingWarmbootingLR: 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 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 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

self.iters = external_iter iters = self.iters - self.warmup_iters # 当前迭代次数减去热身迭代次数 last_epoch = self.last_epoch # 上一个 epoch scale = 1.0 # 缩放因子 for i in range(len(self.steps)...

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- self.learning_rates：学习率列表，根据self.learning_rate计算得到，包括三个值，分别为self.learning_rate、self.learning_rate * 0.5、self.learning_rate * 0.1。 - self.learning_rate_boundaries：学习率...

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解释代码 def partial_fit(self, X,Y, lr): cost, summary, _, Coef = self.sess.run( (self.reconst_cost_x, self.merged_summary_op, self.optimizer, self.Coef_test), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X}) self.summary_writer.add_summary(summary, self.iter) self.iter = self.iter + 1 return cost, Coef def pretrain_step(self, X,Y, lr): cost, summary, _ = self.sess.run( (self.reconst_cost_x, self.merged_summary_op, self.optimizer_pretrain), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X}) self.summary_writer.add_summary(summary, self.iter) self.iter = self.iter + 1 return cost

具体实现中，先使用 TensorFlow 的 run 方法执行 self.reconst_cost_x、self.merged_summary_op、self.optimizer 和 self.Coef_test 四个操作，其中 self.reconst_cost_x 是模型的重构误差，self....

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def init_datasets(self): """ Init self.dataset_train, self.dataset_train_iter, self.dataset_val. """ dataset_parameters = dict(base_folder=self.local_base_folder, image_size=self.image_size, image_spacing=self.image_spacing, normalize_zero_mean_unit_variance=False, cv=self.cv, heatmap_sigma=3.0, generate_spine_heatmap=True, use_variable_image_size=True, valid_output_sizes_x=[32, 64, 96, 128], valid_output_sizes_y=[32, 64, 96, 128], valid_output_sizes_z=[32, 64, 96, 128], output_image_type=np.float16 if self.use_mixed_precision else np.float32, data_format=self.data_format, save_debug_images=self.save_debug_images)

这个方法名为init_datasets，用于初始化self.dataset_train、self.dataset_train_iter和self.dataset_val。 在方法内部，通过一个参数字典dataset_parameters来设置数据集的相关参数。这些参数包括： - ...

class MainLoop(MainLoopBase): def __init__(self, cv, config): """ Initializer. :param cv: The cv fold. 0, 1, 2 for CV; 'train_all' for training on whole dataset. :param config: config dictionary """ super().__init__() self.use_mixed_precision = True if self.use_mixed_precision: policy = mixed_precision.Policy('mixed_float16') mixed_precision.set_policy(policy) self.cv = cv self.config = config self.batch_size = 1 self.learning_rate = config.learning_rate self.learning_rates = [self.learning_rate, self.learning_rate * 0.5, self.learning_rate * 0.1] self.learning_rate_boundaries = [50000, 75000] self.max_iter = 10000 self.test_iter = 5000 self.disp_iter = 100 self.snapshot_iter = 5000 self.test_initialization = False self.reg_constant = 0.0 self.data_format = 'channels_first'

接下来，初始化了一些训练过程中的参数，如batch_size、learning_rate、learning_rates、learning_rate_boundaries、max_iter等。这些参数用来控制训练过程中的学习率、迭代次数、显示间隔、保存模型间隔等。 最后...

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- n_iter：表示训练模型时迭代的次数。 - sparrows：表示麻雀群体，是一种基于鸟群行为的优化算法。 - mutate：表示变异操作，是优化算法中的一种操作，包括对参数进行随机扰动或基于其他参数进行变换，以便生成新的...

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for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps): optimizer.zero_grad() hidden = hidden.detach() output, hidden = model(X, hidden) loss = criterion(output, Y.view(-1))...

给代码添加注释:class CosineAnnealingWarmbootingLR: 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 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 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

这是一个用于实现余弦退火学习率调整的类，可以在训练神经网络时使用。它可以根据训练的迭代次数自动调整学习率，以便更好地训练模型。具体来说，它使用余弦函数来调整学习率，以便在训练过程中逐渐减小学习率，从而...

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