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