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| import torch | |
| from torch import nn | |
| class TextFcLayer(nn.Module): | |
| """Layers used in mapping text embeddings to visual outputs.""" | |
| def __init__(self, in_dim: int, out_dim: int, num_input_tokens: int = 1, num_output_tokens: int = 1, mode: str = 'linear'): | |
| super().__init__() | |
| self.num_input_tokens = num_input_tokens | |
| self.num_output_tokens = num_output_tokens | |
| self.mode = mode | |
| if mode == 'linear': | |
| self.model = nn.Linear(in_dim, out_dim) | |
| elif mode == 'gill_mapper': # TODO(jykoh): Rename to GILLMapper | |
| hidden_dim = 512 | |
| self.fc = nn.Linear(in_dim, hidden_dim) | |
| self.tfm = nn.Transformer(batch_first=True, norm_first=True, | |
| d_model=hidden_dim, num_encoder_layers=4, num_decoder_layers=4, | |
| dim_feedforward=hidden_dim * 4, dropout=0.0, nhead=4) | |
| self.model = nn.Linear(hidden_dim, out_dim) | |
| self.query_embs = nn.Parameter(torch.randn(1, num_output_tokens, hidden_dim)) | |
| else: | |
| raise NotImplementedError(mode) | |
| def forward(self, x: torch.Tensor, input_embs: torch.Tensor) -> torch.Tensor: | |
| outputs = None | |
| if self.mode == 'gill_mapper': | |
| x = x + input_embs | |
| if isinstance(self.model, nn.ModuleList): | |
| assert len(self.model) == x.shape[1] == self.num_input_tokens, (len(self.model), x.shape, self.num_input_tokens) | |
| outputs = [] | |
| for i in range(self.num_input_tokens): | |
| outputs.append(self.model[i](x[:, i, :])) # (N, D) | |
| outputs = torch.stack(outputs, dim=1) # (N, T, D) | |
| else: | |
| if self.mode == 'gill_mapper': | |
| x = self.fc(x) | |
| x = self.tfm(x, self.query_embs.repeat(x.shape[0], 1, 1)) | |
| outputs = self.model(x) | |
| if outputs.shape[1] != self.num_output_tokens and self.mode == 'linear': | |
| if self.mode == 'linear': | |
| outputs = outputs[:, :self.num_output_tokens, :] | |
| else: | |
| raise NotImplementedError | |
| assert outputs.shape[1] == 1 or (outputs.shape[1] * outputs.shape[2] == self.num_output_tokens * 768), (outputs.shape, self.num_output_tokens) | |
| return outputs # (N, T, D) | |