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| # ------------------------------------------------------------------------ | |
| # Grounding DINO | |
| # url: https://github.com/IDEA-Research/GroundingDINO | |
| # Copyright (c) 2023 IDEA. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| DETR Transformer class. | |
| Copy-paste from torch.nn.Transformer with modifications: | |
| * positional encodings are passed in MHattention | |
| * extra LN at the end of encoder is removed | |
| * decoder returns a stack of activations from all decoding layers | |
| """ | |
| from typing import Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import Tensor, nn | |
| from .utils import ( | |
| MLP, | |
| _get_activation_fn, | |
| _get_clones, | |
| gen_encoder_output_proposals, | |
| gen_sineembed_for_position, | |
| sigmoid_focal_loss, | |
| ) | |
| class TextTransformer(nn.Module): | |
| def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1): | |
| super().__init__() | |
| self.num_layers = num_layers | |
| self.d_model = d_model | |
| self.nheads = nheads | |
| self.dim_feedforward = dim_feedforward | |
| self.norm = None | |
| single_encoder_layer = TransformerEncoderLayer( | |
| d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout | |
| ) | |
| self.layers = _get_clones(single_encoder_layer, num_layers) | |
| def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor): | |
| """ | |
| Args: | |
| text_attention_mask: bs, num_token | |
| memory_text: bs, num_token, d_model | |
| Raises: | |
| RuntimeError: _description_ | |
| Returns: | |
| output: bs, num_token, d_model | |
| """ | |
| output = memory_text.transpose(0, 1) | |
| for layer in self.layers: | |
| output = layer(output, src_key_padding_mask=text_attention_mask) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| return output.transpose(0, 1) | |
| class TransformerEncoderLayer(nn.Module): | |
| def __init__( | |
| self, | |
| d_model, | |
| nhead, | |
| dim_feedforward=2048, | |
| dropout=0.1, | |
| activation="relu", | |
| normalize_before=False, | |
| ): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| self.nhead = nhead | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward( | |
| self, | |
| src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| ): | |
| # repeat attn mask | |
| if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]: | |
| # bs, num_q, num_k | |
| src_mask = src_mask.repeat(self.nhead, 1, 1) | |
| q = k = self.with_pos_embed(src, pos) | |
| src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0] | |
| # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.dropout1(src2) | |
| src = self.norm1(src) | |
| src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) | |
| src = src + self.dropout2(src2) | |
| src = self.norm2(src) | |
| return src | |