244 lines
7.7 KiB
Python
244 lines
7.7 KiB
Python
# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/11/10 10:46
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@Usage :
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@Desc : convLSTM 2D基本实现
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'''
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import torch.nn as nn
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import torch
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from RUL.baseModel.dctAttention import dct_channel_block
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class dctLSTMCell(nn.Module):
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def __init__(self, input_dim, hidden_dim, bias):
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"""
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Initialize ConvLSTM cell.
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Parameters
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----------
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input_dim: int
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Number of channels of input tensor.
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hidden_dim: int
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Number of channels of hidden state.
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kernel_size: int
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Size of the convolutional kernel.
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bias: bool
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Whether or not to add the bias.
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Input:
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A tensor of size B, T, C
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B: bacth_size
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T: timestamp
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C: channel
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"""
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super(dctLSTMCell, self).__init__()
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self.input_dim = input_dim
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self.hidden_dim = hidden_dim
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self.bias = bias
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self.hidden = nn.Linear(in_features=self.input_dim + self.hidden_dim,
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out_features=4 * self.hidden_dim,
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bias=self.bias)
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self.attention = dct_channel_block(channel=self.input_dim + self.hidden_dim)
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def forward(self, input_tensor, cur_state):
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# shape :b,c
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h_cur, c_cur = cur_state
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combined = torch.cat([input_tensor, h_cur], dim=-1) # concatenate along channel axis
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# 增加一个channelAttention
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combined = self.attention(combined)
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combined_linear = self.hidden(combined)
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cc_i, cc_f, cc_o, cc_g = torch.split(combined_linear, self.hidden_dim, dim=-1)
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i = torch.sigmoid(cc_i)
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f = torch.sigmoid(cc_f)
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o = torch.sigmoid(cc_o)
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g = torch.tanh(cc_g)
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c_next = f * c_cur + i * g
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h_next = o * torch.tanh(c_next)
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return h_next, c_next
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def init_hidden(self, batch_size):
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return (torch.zeros(batch_size, self.hidden_dim, device=self.hidden.weight.device),
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torch.zeros(batch_size, self.hidden_dim, device=self.hidden.weight.device))
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class LSTM(nn.Module):
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"""
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Parameters:
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input_dim: Number of channels in input
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hidden_dim: Number of hidden channels
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kernel_size: Size of kernel in convolutions
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num_layers: Number of LSTM layers stacked on each other
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batch_first: Whether or not dimension 0 is the batch or not
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bias: Bias or no bias in Convolution
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return_all_layers: Return the list of computations for all layers
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Note: Will do same padding.
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Input:
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A tensor of size B, T, C or T, B, C
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Output:
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A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
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0 - layer_output_list is the list of lists of length T of each output
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1 - last_state_list is the list of last states
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each element of the list is a tuple (h, c) for hidden state and memory
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Example:
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>> x = torch.rand((32, 10, 64))
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>> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
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>> _, last_states = convlstm(x)
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>> h = last_states[0][0] # 0 for layer index, 0 for h index
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"""
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def __init__(self, input_dim, hidden_dim, num_layers,
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batch_first=False, bias=True, return_all_layers=False):
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super(LSTM, self).__init__()
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# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
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hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
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if not len(hidden_dim) == num_layers:
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raise ValueError('Inconsistent list length.')
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self.input_dim = input_dim
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self.hidden_dim = hidden_dim
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self.num_layers = num_layers
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self.batch_first = batch_first
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self.bias = bias
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self.return_all_layers = return_all_layers
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cell_list = []
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for i in range(0, self.num_layers):
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cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]
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cell_list.append(
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dctLSTMCell(input_dim=cur_input_dim,
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hidden_dim=self.hidden_dim[i],
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bias=self.bias),
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)
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self.cell_list = nn.ModuleList(cell_list)
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def forward(self, input_tensor, hidden_state=None):
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"""
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Parameters
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----------
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input_tensor: todo
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5-D Tensor either of shape (t, b, c) or (b, t, c)
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hidden_state: todo
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None. todo implement stateful
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Returns
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-------
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last_state_list, layer_output
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"""
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if not self.batch_first:
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# 等同于transpose
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# (t, b, c, h, w) -> (b, t, c, h, w)
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input_tensor = input_tensor.permute(1, 0, 2)
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b, _, _ = input_tensor.size()
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# Implement stateful ConvLSTM
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if hidden_state is not None:
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raise NotImplementedError()
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else:
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# Since the init is done in forward. Can send image size here
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hidden_state = self._init_hidden(batch_size=b)
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layer_output_list = []
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last_state_list = []
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timestamp = input_tensor.size(1)
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cur_layer_input = input_tensor
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for layer_idx in range(self.num_layers):
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h, c = hidden_state[layer_idx]
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output_inner = []
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for t in range(timestamp):
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h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :],
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cur_state=[h, c])
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output_inner.append(h)
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layer_output = torch.stack(output_inner, dim=1)
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# TODO 每层之间增加一个dct_attention
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# layer_output = self.attention_list[layer_idx](layer_output)
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cur_layer_input = layer_output
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layer_output_list.append(layer_output)
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last_state_list.append([h, c])
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if not self.return_all_layers:
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layer_output_list = layer_output_list[-1:]
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last_state_list = last_state_list[-1:]
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return layer_output_list, last_state_list
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def _init_hidden(self, batch_size):
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init_states = []
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for i in range(self.num_layers):
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init_states.append(self.cell_list[i].init_hidden(batch_size))
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return init_states
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@staticmethod
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def _extend_for_multilayer(param, num_layers):
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if not isinstance(param, list):
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param = [param] * num_layers
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return param
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class PredictModel(nn.Module):
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def __init__(self, input_dim):
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super(PredictModel, self).__init__()
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self.lstm = LSTM(input_dim=input_dim, hidden_dim=[512, 256], num_layers=2, batch_first=True, bias=True,
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return_all_layers=False)
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self.backbone = nn.Sequential(
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nn.Linear(in_features=256, out_features=128),
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nn.ReLU(),
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nn.Linear(in_features=128, out_features=64),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.BatchNorm1d(64),
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nn.Linear(in_features=64, out_features=32),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.BatchNorm1d(32),
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nn.ReLU(),
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nn.Linear(in_features=32, out_features=16),
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nn.Linear(in_features=16, out_features=1)
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)
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def forward(self, input_tensor):
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input_tensor = input_tensor.to(torch.float32)
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layer_output_list, last_states = self.lstm(input_tensor)
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last_timestamp = last_states[0][0]
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predict = self.backbone(last_timestamp)
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return predict
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if __name__ == '__main__':
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x = torch.rand((32, 10, 64))
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lstm = LSTM(input_dim=64, hidden_dim=16, num_layers=1, batch_first=True, bias=True,
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return_all_layers=False)
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layer_output_list, last_states = lstm(x)
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all = layer_output_list[0]
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h = last_states[0][0]
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print(all.size())
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print(h.size())
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