# Ultralytics YOLO 🚀, AGPL-3.0 license """ Transformer modules """ import math import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.init import constant_, xavier_uniform_ from .conv import Conv from .utils import _get_clones, inverse_sigmoid, multi_scale_deformable_attn_pytorch __all__ = ('TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI', 'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP') class TransformerEncoderLayer(nn.Module): """Transformer Encoder.""" def __init__(self, c1, cm=2048, num_heads=8, dropout=0.0, act=nn.GELU(), normalize_before=False): super().__init__() self.ma = nn.MultiheadAttention(c1, num_heads, dropout=dropout, batch_first=True) # Implementation of Feedforward model self.fc1 = nn.Linear(c1, cm) self.fc2 = nn.Linear(cm, c1) self.norm1 = nn.LayerNorm(c1) self.norm2 = nn.LayerNorm(c1) self.dropout = nn.Dropout(dropout) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.act = act self.normalize_before = normalize_before def with_pos_embed(self, tensor, pos=None): """Add position embeddings if given.""" return tensor if pos is None else tensor + pos def forward_post(self, src, src_mask=None, src_key_padding_mask=None, pos=None): q = k = self.with_pos_embed(src, pos) src2 = self.ma(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.fc2(self.dropout(self.act(self.fc1(src)))) src = src + self.dropout2(src2) src = self.norm2(src) return src def forward_pre(self, src, src_mask=None, src_key_padding_mask=None, pos=None): src2 = self.norm1(src) q = k = self.with_pos_embed(src2, pos) src2 = self.ma(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src2 = self.norm2(src) src2 = self.fc2(self.dropout(self.act(self.fc1(src2)))) src = src + self.dropout2(src2) return src def forward(self, src, src_mask=None, src_key_padding_mask=None, pos=None): """Forward propagates the input through the encoder module.""" if self.normalize_before: return self.forward_pre(src, src_mask, src_key_padding_mask, pos) return self.forward_post(src, src_mask, src_key_padding_mask, pos) class AIFI(TransformerEncoderLayer): def __init__(self, c1, cm=2048, num_heads=8, dropout=0, act=nn.GELU(), normalize_before=False): super().__init__(c1, cm, num_heads, dropout, act, normalize_before) def forward(self, x): c, h, w = x.shape[1:] pos_embed = self.build_2d_sincos_position_embedding(w, h, c) # flatten [B, C, H, W] to [B, HxW, C] x = super().forward(x.flatten(2).permute(0, 2, 1), pos=pos_embed.to(device=x.device, dtype=x.dtype)) return x.permute((0, 2, 1)).view([-1, c, h, w]) @staticmethod def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.): grid_w = torch.arange(int(w), dtype=torch.float32) grid_h = torch.arange(int(h), dtype=torch.float32) grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing='ij') assert embed_dim % 4 == 0, \ 'Embed dimension must be divisible by 4 for 2D sin-cos position embedding' pos_dim = embed_dim // 4 omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim omega = 1. / (temperature ** omega) out_w = grid_w.flatten()[..., None] @ omega[None] out_h = grid_h.flatten()[..., None] @ omega[None] return torch.concat([torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], axis=1)[None, :, :] class TransformerLayer(nn.Module): """Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance).""" def __init__(self, c, num_heads): """Initializes a self-attention mechanism using linear transformations and multi-head attention.""" super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): """Apply a transformer block to the input x and return the output.""" x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x class TransformerBlock(nn.Module): """Vision Transformer https://arxiv.org/abs/2010.11929.""" def __init__(self, c1, c2, num_heads, num_layers): """Initialize a Transformer module with position embedding and specified number of heads and layers.""" super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) # learnable position embedding self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) self.c2 = c2 def forward(self, x): """Forward propagates the input through the bottleneck module.""" if self.conv is not None: x = self.conv(x) b, _, w, h = x.shape p = x.flatten(2).permute(2, 0, 1) return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) class MLPBlock(nn.Module): def __init__(self, embedding_dim, mlp_dim, act=nn.GELU): super().__init__() self.lin1 = nn.Linear(embedding_dim, mlp_dim) self.lin2 = nn.Linear(mlp_dim, embedding_dim) self.act = act() def forward(self, x: torch.Tensor) -> torch.Tensor: return self.lin2(self.act(self.lin1(x))) class MLP(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x # From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa # Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa class LayerNorm2d(nn.Module): def __init__(self, num_channels, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(num_channels)) self.bias = nn.Parameter(torch.zeros(num_channels)) self.eps = eps def forward(self, x): u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / torch.sqrt(s + self.eps) x = self.weight[:, None, None] * x + self.bias[:, None, None] return x class MSDeformAttn(nn.Module): """ Original Multi-Scale Deformable Attention Module. https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py """ def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): super().__init__() if d_model % n_heads != 0: raise ValueError(f'd_model must be divisible by n_heads, but got {d_model} and {n_heads}') _d_per_head = d_model // n_heads # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation assert _d_per_head * n_heads == d_model, '`d_model` must be divisible by `n_heads`' self.im2col_step = 64 self.d_model = d_model self.n_levels = n_levels self.n_heads = n_heads self.n_points = n_points self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) self.value_proj = nn.Linear(d_model, d_model) self.output_proj = nn.Linear(d_model, d_model) self._reset_parameters() def _reset_parameters(self): constant_(self.sampling_offsets.weight.data, 0.) thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat( 1, self.n_levels, self.n_points, 1) for i in range(self.n_points): grid_init[:, :, i, :] *= i + 1 with torch.no_grad(): self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) constant_(self.attention_weights.weight.data, 0.) constant_(self.attention_weights.bias.data, 0.) xavier_uniform_(self.value_proj.weight.data) constant_(self.value_proj.bias.data, 0.) xavier_uniform_(self.output_proj.weight.data) constant_(self.output_proj.bias.data, 0.) def forward(self, query, refer_bbox, value, value_shapes, value_mask=None): """ https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py Args: query (torch.Tensor): [bs, query_length, C] refer_bbox (torch.Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area value (torch.Tensor): [bs, value_length, C] value_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements Returns: output (Tensor): [bs, Length_{query}, C] """ bs, len_q = query.shape[:2] len_v = value.shape[1] assert sum(s[0] * s[1] for s in value_shapes) == len_v value = self.value_proj(value) if value_mask is not None: value = value.masked_fill(value_mask[..., None], float(0)) value = value.view(bs, len_v, self.n_heads, self.d_model // self.n_heads) sampling_offsets = self.sampling_offsets(query).view(bs, len_q, self.n_heads, self.n_levels, self.n_points, 2) attention_weights = self.attention_weights(query).view(bs, len_q, self.n_heads, self.n_levels * self.n_points) attention_weights = F.softmax(attention_weights, -1).view(bs, len_q, self.n_heads, self.n_levels, self.n_points) # N, Len_q, n_heads, n_levels, n_points, 2 num_points = refer_bbox.shape[-1] if num_points == 2: offset_normalizer = torch.as_tensor(value_shapes, dtype=query.dtype, device=query.device).flip(-1) add = sampling_offsets / offset_normalizer[None, None, None, :, None, :] sampling_locations = refer_bbox[:, :, None, :, None, :] + add elif num_points == 4: add = sampling_offsets / self.n_points * refer_bbox[:, :, None, :, None, 2:] * 0.5 sampling_locations = refer_bbox[:, :, None, :, None, :2] + add else: raise ValueError(f'Last dim of reference_points must be 2 or 4, but got {num_points}.') output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights) output = self.output_proj(output) return output class DeformableTransformerDecoderLayer(nn.Module): """ https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/deformable_transformer.py """ def __init__(self, d_model=256, n_heads=8, d_ffn=1024, dropout=0., act=nn.ReLU(), n_levels=4, n_points=4): super().__init__() # self attention self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) self.dropout1 = nn.Dropout(dropout) self.norm1 = nn.LayerNorm(d_model) # cross attention self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) self.dropout2 = nn.Dropout(dropout) self.norm2 = nn.LayerNorm(d_model) # ffn self.linear1 = nn.Linear(d_model, d_ffn) self.act = act self.dropout3 = nn.Dropout(dropout) self.linear2 = nn.Linear(d_ffn, d_model) self.dropout4 = nn.Dropout(dropout) self.norm3 = nn.LayerNorm(d_model) @staticmethod def with_pos_embed(tensor, pos): return tensor if pos is None else tensor + pos def forward_ffn(self, tgt): tgt2 = self.linear2(self.dropout3(self.act(self.linear1(tgt)))) tgt = tgt + self.dropout4(tgt2) tgt = self.norm3(tgt) return tgt def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None): # self attention q = k = self.with_pos_embed(embed, query_pos) tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1), attn_mask=attn_mask)[0].transpose(0, 1) embed = embed + self.dropout1(tgt) embed = self.norm1(embed) # cross attention tgt = self.cross_attn(self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes, padding_mask) embed = embed + self.dropout2(tgt) embed = self.norm2(embed) # ffn embed = self.forward_ffn(embed) return embed class DeformableTransformerDecoder(nn.Module): """ https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py """ def __init__(self, hidden_dim, decoder_layer, num_layers, eval_idx=-1): super().__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.hidden_dim = hidden_dim self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx def forward( self, embed, # decoder embeddings refer_bbox, # anchor feats, # image features shapes, # feature shapes bbox_head, score_head, pos_mlp, attn_mask=None, padding_mask=None): output = embed dec_bboxes = [] dec_cls = [] last_refined_bbox = None refer_bbox = refer_bbox.sigmoid() for i, layer in enumerate(self.layers): output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox)) # refine bboxes, (bs, num_queries+num_denoising, 4) refined_bbox = torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(refer_bbox)) if self.training: dec_cls.append(score_head[i](output)) if i == 0: dec_bboxes.append(refined_bbox) else: dec_bboxes.append(torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(last_refined_bbox))) elif i == self.eval_idx: dec_cls.append(score_head[i](output)) dec_bboxes.append(refined_bbox) break last_refined_bbox = refined_bbox refer_bbox = refined_bbox.detach() if self.training else refined_bbox return torch.stack(dec_bboxes), torch.stack(dec_cls)