layers_per_recurrent_unit = 5 num_recurrent_units = 2 for pass_id in range(num_recurrent_units): # 将上一次生成的得分图和编码信息拼接起来作为这一次输入的数据 x = tf.concat([scoremap_list[-1], encoding], 3) for rec_id in range(layers_per_recurrent_unit): x = ops.conv_relu(x, 'conv%d_%d' % (pass_id+6, rec_id+1), kernel_size=7, stride=1, out_chan=128, trainable=train) x = ops.conv_relu(x, 'conv%d_6' % (pass_id+6), kernel_size=1, stride=1, out_chan=128, trainable=train) scoremap = ops.conv(x, 'conv%d_7' % (pass_id+6), kernel_size=1, stride=1, out_chan=self.num_kp, trainable=train) scoremap_list.append(scoremap) scoremap_list_large = scoremap_list注释

这段代码表示一个循环神经网络（RNN）的训练过程，其中包含两个循环单元（即两个RNN层），每个循环单元内部包含5个卷积层和1个输出层。在训练过程中，每个循环单元内部的卷积层和输出层都会被训练以优化模型的性能。在每次循环中，上一次生成的得分图和编码信息会被拼接起来作为这一次输入的数据，经过一系列卷积层后，生成一个新的得分图。最终，得分图列表会被保存并返回。

相关问题

nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional)

This is a PyTorch module that implements a Long Short-Term Memory (LSTM) layer. An LSTM is a type of recurrent neural network (RNN) that is designed to handle sequence data by maintaining a memory cell that can selectively forget or remember information over time.

The arguments for this module are:
- input_size: the number of expected features in the input
- hidden_size: the number of features in the hidden state/output
- num_layers: the number of recurrent layers (default is 1)
- bidirectional: if True, the LSTM will be bidirectional (default is False)

The input to this module is a tensor of shape (seq_len, batch, input_size), where seq_len is the length of the input sequence, batch is the batch size, and input_size is the number of expected features in the input.

The output of this module is a tensor of shape (seq_len, batch, num_directions * hidden_size), where num_directions is 2 if bidirectional=True, and 1 otherwise. The output represents the hidden state/output value for each element in the input sequence. The final hidden state is also returned as a tensor of shape (num_layers * num_directions, batch, hidden_size).

self.lstm = torch.nn.LSTM( input_size = 224, hidden_size = 128, num_layer = 2,

batch_first = True)

This code initializes an LSTM module in PyTorch with the following parameters:

- `input_size`: The size of the input tensor at each time step. Here, it is 224.
- `hidden_size`: The number of features in the hidden state at each time step. Here, it is set to 128.
- `num_layers`: The number of recurrent layers in the LSTM module. Here, it is set to 2.
- `batch_first`: Whether the input and output tensors should have batch size as their first dimension. Here, it is set to `True`.

Overall, this code creates an LSTM module with two layers and a hidden state size of 128, which can process input sequences of length 224 with batch size as the first dimension.