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| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import torch | |
| import torch.nn as nn | |
| class PatchTransformerEncoder(nn.Module): | |
| def __init__(self, in_channels, patch_size=10, embedding_dim=128, num_heads=4, use_class_token=False): | |
| """ViT-like transformer block | |
| Args: | |
| in_channels (int): Input channels | |
| patch_size (int, optional): patch size. Defaults to 10. | |
| embedding_dim (int, optional): Embedding dimension in transformer model. Defaults to 128. | |
| num_heads (int, optional): number of attention heads. Defaults to 4. | |
| use_class_token (bool, optional): Whether to use extra token at the start for global accumulation (called as "class token"). Defaults to False. | |
| """ | |
| super(PatchTransformerEncoder, self).__init__() | |
| self.use_class_token = use_class_token | |
| encoder_layers = nn.TransformerEncoderLayer( | |
| embedding_dim, num_heads, dim_feedforward=1024) | |
| self.transformer_encoder = nn.TransformerEncoder( | |
| encoder_layers, num_layers=4) # takes shape S,N,E | |
| self.embedding_convPxP = nn.Conv2d(in_channels, embedding_dim, | |
| kernel_size=patch_size, stride=patch_size, padding=0) | |
| def positional_encoding_1d(self, sequence_length, batch_size, embedding_dim, device='cpu'): | |
| """Generate positional encodings | |
| Args: | |
| sequence_length (int): Sequence length | |
| embedding_dim (int): Embedding dimension | |
| Returns: | |
| torch.Tensor SBE: Positional encodings | |
| """ | |
| position = torch.arange( | |
| 0, sequence_length, dtype=torch.float32, device=device).unsqueeze(1) | |
| index = torch.arange( | |
| 0, embedding_dim, 2, dtype=torch.float32, device=device).unsqueeze(0) | |
| div_term = torch.exp(index * (-torch.log(torch.tensor(10000.0, device=device)) / embedding_dim)) | |
| pos_encoding = position * div_term | |
| pos_encoding = torch.cat([torch.sin(pos_encoding), torch.cos(pos_encoding)], dim=1) | |
| pos_encoding = pos_encoding.unsqueeze(1).repeat(1, batch_size, 1) | |
| return pos_encoding | |
| def forward(self, x): | |
| """Forward pass | |
| Args: | |
| x (torch.Tensor - NCHW): Input feature tensor | |
| Returns: | |
| torch.Tensor - SNE: Transformer output embeddings. S - sequence length (=HW/patch_size^2), N - batch size, E - embedding dim | |
| """ | |
| embeddings = self.embedding_convPxP(x).flatten( | |
| 2) # .shape = n,c,s = n, embedding_dim, s | |
| if self.use_class_token: | |
| # extra special token at start ? | |
| embeddings = nn.functional.pad(embeddings, (1, 0)) | |
| # change to S,N,E format required by transformer | |
| embeddings = embeddings.permute(2, 0, 1) | |
| S, N, E = embeddings.shape | |
| embeddings = embeddings + self.positional_encoding_1d(S, N, E, device=embeddings.device) | |
| x = self.transformer_encoder(embeddings) # .shape = S, N, E | |
| return x | |