帮我详细解释一下这段代码 grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat( batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor) grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat( batch_size * len(self.anchors_mask[i]), 1, 1).view(y.shape).type(FloatTensor)

torch-1.10.2-cp36-cp36m-manylinux1_x86_64.whl

torch-1.10.2-cp36-cp36m-manylinux1_x86_64.whltorch-1.10.2-cp36-cp36m-manylinux1_x86_64.whltorch-1.10.2-cp36-cp36m-manylinux1_x86_64.whltorch-1.10.2-cp36-cp36m-manylinux1_x86_64.whltorch-1.10.2-cp36-cp...

Python库 | torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl

资源分类：Python库所属语言：Python 资源全名：torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl 资源来源：官方安装方法：https://lanzao.blog.csdn.net/article/details/101784059

torch-1.8.0-cp38-cp38-manylinux1_x86_64.whl

以下代码存在无法求导反向传播的bug，请你修改：# 反向LSTM input_backward = torch.flip(input, [0]) outputs_backward = [] for t in range(input_backward.size(0)): x = input_backward[t] i = torch.sigmoid(torch.matmul(x, self.W_i_backward) + torch.matmul(h_backward[-1], self.U_i_backward) + self.b_i_backward) f = torch.sigmoid(torch.matmul(x, self.W_f_backward) + torch.matmul(h_backward[-1], self.U_f_backward) + self.b_f_backward) c_tilde = torch.tanh(torch.matmul(x, self.W_c_backward) + torch.matmul(h_backward[-1], self.U_c_backward) + self.b_c_backward) c_backward[-1] = f * c_backward[-1] + i * c_tilde o = torch.matmul(x, self.W_o_backward) + torch.matmul(h_backward[-1], self.U_o_backward) + self.b_o_backward o = torch.sigmoid(o) h_backward[-1] = o * torch.tanh(c_backward[-1]) outputs_backward.append(h_backward[-1]) outputs_backward = torch.flip(torch.stack(outputs_backward), [0])

这段代码看起来没有明显的无法求导反向传播的 bug，但是如果存在问题的话可能是因为某些操作的输入没有进行 requires_grad=True 的设置。可以尝试添加以下代码，保证所有涉及到反向传播的张量都设置了 requires_grad...

解释一下这段代码gen_input = torch.cat((self.label_emb(labels), noise), -1) 中-1的意思

在这段代码中，torch.cat() 函数会将两个张量按照指定维度进行拼接。-1 表示将两个张量在最后一个维度上进行拼接。具体来说，self.label_emb(labels) 和 noise 张量的最后一个维度的大小必须相同，才能在最后一个...

def create_frustum(self): # Create grid in image plane h, w = self.cfg.IMAGE.FINAL_DIM downsampled_h, downsampled_w = h // self.encoder_downsample, w // self.encoder_downsample # Depth grid depth_grid = torch.arange(*self.cfg.LIFT.D_BOUND, dtype=torch.float) depth_grid = depth_grid.view(-1, 1, 1).expand(-1, downsampled_h, downsampled_w) n_depth_slices = depth_grid.shape[0] # x and y grids x_grid = torch.linspace(0, w - 1, downsampled_w, dtype=torch.float) x_grid = x_grid.view(1, 1, downsampled_w).expand(n_depth_slices, downsampled_h, downsampled_w) y_grid = torch.linspace(0, h - 1, downsampled_h, dtype=torch.float) y_grid = y_grid.view(1, downsampled_h, 1).expand(n_depth_slices, downsampled_h, downsampled_w) # Dimension (n_depth_slices, downsampled_h, downsampled_w, 3) # containing data points in the image: left-right, top-bottom, depth frustum = torch.stack((x_grid, y_grid, depth_grid), -1) return nn.Parameter(frustum, requires_grad=False)这段代码产生了什么？

最后，torch.stack((x_grid, y_grid, depth_grid), -1) 将这三个张量按照最后一个维度进行堆叠，得到形状为 (n_depth_slices, downsampled_h, downsampled_w, 3) 的张量 frustum，它包含了所有像素在图像平面上的...

x=torch.unsqueeze(torch.linspace(-1,1,100),dim=1) x=torch.unsqueezex=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)(torch.linspace(-1,1,100),dim=1)

对于你的代码片段，我注意到你想用torch.unsqueeze函数将一维的张量torch.linspace(-1,1,100)转换为二维的张量。这个函数的作用是在指定的维度上增加一个维度。在你的代码中，你指定了dim=1，所以张量的列数将...

def forward(self, input_question, input_answer): input_question.requires_grad = True question_embed = torch.nn.Parameter(self.embedding(input_question), requires_grad=True) answer_embed = torch.nn.Parameter(self.embedding(input_answer), requires_grad=True) _, question_hidden = self.encoder(question_embed) answer_outputs, _ = self.encoder(answer_embed, question_hidden) attention_weights = self.attention(answer_outputs).squeeze(dim=-1) attention_weights = torch.softmax(attention_weights, dim=1) context_vector = torch.bmm(attention_weights.unsqueeze(dim=1), answer_outputs).squeeze(dim=1) logits = self.decoder(context_vector) return logits

在你的代码中，你已经正确地将input_question和input_answer设置为需要梯度计算的张量。然而，在使用torch.nn.Parameter时，你不需要再次设置requires_grad=True，因为torch.nn.Parameter默认会将其设置...

torch.linspace(0, w - 1, downsampled_w, dtype=torch.float)是干什么？

在这段代码中，torch.linspace(0, w - 1, downsampled_w, dtype=torch.float) 返回一个一维的tensor，包含了downsampled_w个值，这些值是在区间[0, w-1]上均匀分布的。这些值被赋给了变量x_grid，然后通过x_grid....

def create_grid(self, input_size): w, h = input_size[1], input_size[0] # generate grid cells ws, hs = w // self.stride, h // self.stride grid_y, grid_x = torch.meshgrid([torch.arange(hs), torch.arange(ws)]) grid_xy = torch.stack([grid_x, grid_y], dim=-1).float() grid_xy = grid_xy.view(1, hs*ws, 2).to(self.device) return grid_xy

这是一个名为create_grid的函数，它是一个模型类中的方法。下面是对这个函数的解释： python def create_grid(self, input_size): w, h = input_size[1], input_size[0] # generate grid cells ws, hs = w ...

def forward(self, input_question, input_answer): question_embed = self.embedding(input_question) answer_embed = self.embedding(input_answer) _, question_hidden = self.encoder(question_embed) answer_outputs, _ = self.encoder(answer_embed, question_hidden) attention_weights = self.attention(answer_outputs).squeeze(dim=-1) attention_weights = torch.softmax(attention_weights, dim=1) context_vector = torch.bmm(attention_weights.unsqueeze(dim=1), answer_outputs).squeeze(dim=1) logits = self.decoder(context_vector) top_100_values, _ = torch.topk(logits, self.topk, dim=1) # 在第1个维度上获取前100名的值 mask = torch.zeros_like(logits) # 创建与 input_question 相同形状的全零张量 # 对于每一行，将前100名的值设为1 for i in range(logits.size(0)): top_100_indices = torch.argsort(logits[i])[-self.topk:] # 获取前100名的索引 mask[i, top_100_indices] = 1 return mask 无法求导

根据你提供的代码，无法直接对mask进行求导是因为你在forward函数中返回了mask张量，而mask张量并没有设置requires_grad=True。要使得mask张量可以进行求导，你需要将其设置为可求导的张量。以下是...

classification loss, Equation (4) of the paper cls_criterion = nn.NLLLoss() input_res = torch.FloatTensor(opt.batch_size, opt.resSize) input_att = torch.FloatTensor(opt.batch_size, opt.attSize) noise = torch.FloatTensor(opt.batch_size, opt.nz) one = torch.FloatTensor([1]) mone = one * -1 input_label = torch.LongTensor(opt.batch_size)

这段代码用于定义用于分类任务的损失函数以及创建一些输入变量。首先，代码创建了一个用于分类任务的损失函数cls_criterion，采用的是负对数似然损失函数（Negative Log Likelihood Loss，简称NLLLoss）。NLL...

x=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)

根据你的代码，你使用了torch.unsqueeze函数将一维的张量torch.linspace(-1,1,100)转换为二维的张量，并且将其保存在变量x中。这个函数的作用是在指定的维度上增加一个维度。在你的代码中，你正确地使用了...

这段代码哪里错了 class my_BiGRU(torch.nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )

这段代码存在两个问题： 1.函数 _setup_BiGRU_layers 中的代码应该是在类的构造函数 __init__ 中进行初始化，而不是在另外一个函数中进行初始化。 2.在调用 my_BiGRU 类时，参数名称有误，应该将 apsule_...

以下代码存在不可以自动反向传播的bug，为什么： # 前向LSTM outputs_forward = [] for t in range(input.size(0)): x = input[t] i = torch.sigmoid(torch.matmul(x, self.W_i_forward) + torch.matmul(h_forward[-1], self.U_i_forward) + self.b_i_forward) f = torch.sigmoid(torch.matmul(x, self.W_f_forward) + torch.matmul(h_forward[-1], self.U_f_forward) + self.b_f_forward) c_tilde = torch.tanh(torch.matmul(x, self.W_c_forward) + torch.matmul(h_forward[-1], self.U_c_forward) + self.b_c_forward) c_forward[-1] = f * c_forward[-1] + i * c_tilde o = torch.matmul(x, self.W_o_forward) + torch.matmul(h_forward[-1], self.U_o_forward) + self.b_o_forward o = torch.sigmoid(o) h_forward[-1] = o * torch.tanh(c_forward[-1]) outputs_forward.append(h_forward[-1])

这段代码存在一个不能自动反向传播的bug，原因是在循环中对h_forward和c_forward的值进行了修改。在PyTorch中，只有被标记为requires_grad=True的张量才能够自动求梯度，而对张量进行in-place操作会导致梯度信息丢失...

下面代码转化为paddle2.2.2代码： gt_batch_list.append(gt_pack) input_batch = np.concatenate(input_batch_list, axis=0) gt_batch = np.concatenate(gt_batch_list, axis=0) in_data = torch.from_numpy(input_batch.copy()).permute(0,3,1,2).cuda() gt_data = torch.from_numpy(gt_batch.copy()).permute(0,3,1,2).cuda()

in_data = paddle.to_tensor(input_batch.copy()).transpose((0, 3, 1, 2)).astype("float32") gt_data = paddle.to_tensor(gt_batch.copy()).transpose((0, 3, 对于更大的状态空间，可能需要使用其他搜索算法或优化...

x_test = torch.tensor([[4,3,7,2,9],[1,2,0,7,3],[10,12,21,11,23]]) ids_shuffle = torch.argsort(x_test, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) ids_keep = ids_shuffle[:, :3] x_masked = torch.gather(x_test, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, 3)) 报错 RuntimeError: Index tensor must have the same number of dimensions as input tensor

x_test = torch.tensor([[4,3,7,2,9],[1,2,0,7,3],[10,12,21,11,23]]) ids_shuffle = torch.argsort(x_test, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) ...

grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat( batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)是什么意思

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grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat( batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)是什么意思

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Python库 | torch-1.2.0-cp37-cp37m-manylinux1_x86_64.whl

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解释一下这段代码gen_input = torch.cat((self.label_emb(labels), noise), -1) 中-1的意思

x=torch.unsqueeze(torch.linspace(-1,1,100),dim=1) x=torch.unsqueezex=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)(torch.linspace(-1,1,100),dim=1)

torch.linspace(0, w - 1, downsampled_w, dtype=torch.float)是干什么？

x=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)

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