| import torch | |
| import torch.nn as nn | |
| class LSTM(nn.Module): | |
| def __init__(self, in_channels=3, width=100, patch_size=8, num_classes=10): | |
| super().__init__() | |
| self.patch_size = patch_size | |
| self.width = width | |
| self.num_classes = num_classes | |
| self.projection = nn.Conv2d( | |
| in_channels, width, kernel_size=patch_size, stride=patch_size) | |
| self.cell_f = nn.LSTMCell(width, width) | |
| self.fc = nn.Linear(width, num_classes) | |
| def forward(self, x): | |
| embed = self.projection(x) | |
| embed = torch.flatten(embed, 2).permute(0, 2, 1) | |
| h_f = torch.zeros(x.shape[0], self.width, device=x.device) | |
| c_f = h_f.clone() | |
| for i in range(embed.shape[1]): | |
| h_f, c_f = self.cell_f(embed[:, i], (h_f, c_f)) | |
| logits = self.fc(h_f) | |
| return logits | |
| def LSTM_patch_16_32(in_ch=3, in_dim=32): | |
| assert in_ch == 3 and in_dim == 32 | |
| return LSTM(width=32, patch_size=16) | |
| def LSTM_patch_16_64(in_ch=3, in_dim=32): | |
| assert in_ch == 3 and in_dim == 32 | |
| return LSTM(width=64, patch_size=16) | |