emb = self.model(conf.test_transform(img).to(conf.device).unsqueeze(0))
时间: 2024-05-22 21:12:56 浏览: 139
As an AI language model, I cannot provide the complete context or code for this statement. However, based on the syntax, it seems to be a line of code written in Python language.
Here, the "self" object is calling the "model" function with an image "img" after applying some transformation using "conf.test_transform". The "unsqueeze" function is used to add an extra dimension in the tensor, which is required by some deep learning models. Finally, the output of the model is assigned to the "emb" variable.
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def forward(self, text): non_padded_position = text != self.pad_idx lens = non_padded_position.sum(dim=1) # 获取词向量 inputs_emb = self.embedding(text) packed_embedded = nn.utils.rnn.pack_padded_sequence(inputs_emb, lens.cpu(), batch_first=True, enforce_sorted=False) # 使用lstm处理数据 _,sequence_output= self.rnn(packed_embedded ) # # 使用聚合层聚合sequence_output # batch_mean_hidden = self.average_layer(sequence_output,sequence_length) # 输出文本分类output output = self.output(sequence_output) return outputlinear(): argument 'input' (position 1) must be Tensor, not tuple
根据您提供的代码,我发现这段代码可能存在以下问题:
1. `sequence_length`这个变量未定义,可能导致代码运行出错。
2. `self.average_layer`这个函数的输入参数`sequence_output`是一个tuple,但是`average_layer`函数可能只接受tensor类型的输入参数。
3. `self.output`函数的输入参数`sequence_output`是一个tuple,可能需要修改为`batch_mean_hidden`或者`sequence_output[0]`。
建议您检查一下代码,特别是`average_layer`和`self.output`这两个函数的实现和输入参数是否正确。
class Model_Adp(nn.Module): def __init__(self, SE, args, window_size = 3, T = 12, N=None): super(Model_Adp, self).__init__() L = args.L K = args.K d = args.d D = K * d self.num_his = args.num_his self.SE = SE.to(device) emb_dim = SE.shape[1] self.STEmbedding = STEmbedding(D, emb_dim=emb_dim).to(device) self.STAttBlock_1 = nn.ModuleList([ST_Layer(K, d, T=T, window_size = window_size,N=N) for _ in range(L)]) self.STAttBlock_2 = nn.ModuleList([ST_Layer(K, d, T=T, window_size = window_size,N=N) for _ in range(L)]) self.transformAttention = TransformAttention(K, d) self.mlp_1 = CONVs(input_dims=[1, D], units=[D, D], activations=[F.relu, None]) self.mlp_2 = CONVs(input_dims=[D, D], units=[D, 1], activations=[F.relu, None]) def forward(self, X, TE): # input X = torch.unsqueeze(X, -1) X = self.mlp_1(X) # STE STE = self.STEmbedding(self.SE, TE) STE_his = STE[:, :self.num_his] STE_pred = STE[:, self.num_his:] # encoder for net in self.STAttBlock_1: X = net(X, STE_his) X = self.transformAttention(X, STE_his, STE_pred) # decoder for net in self.STAttBlock_2: X = net(X, STE_pred) X = self.mlp_2(X) del STE, STE_his, STE_pred return torch.squeeze(X, 3)
这段代码定义了一个名为Model_Adp的类,继承自nn.Module。在初始化函数中,需要传入SE、args、window_size、T和N等参数。该类包含多个属性和方法,其中包括一个STEmbedding、两个STAttBlock、一个TransformAttention、两个CONVs和一个forward方法。其中STEmbedding是对空间数据做embedding操作,STAttBlock是空间-时间数据的多头注意力机制,TransformAttention是将多头注意力输出转化为特征向量,CONVs是一些卷积神经网络。forward方法用来执行前向传播。具体实现可以参考代码中的实现。
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