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"""
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement
Copyright (c) 2024 The D-FINE Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
Copyright (c) 2023 lyuwenyu. All Rights Reserved.
"""
import copy
import functools
import math
from collections import OrderedDict
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from ...core import register
from .denoising import get_contrastive_denoising_training_group
from .dfine_utils import distance2bbox, weighting_function
from .utils import (
bias_init_with_prob,
deformable_attention_core_func_v2,
get_activation,
inverse_sigmoid,
)
__all__ = ["DFINETransformer"]
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers, act="relu"):
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])
)
self.act = get_activation(act)
def forward(self, x):
for i, layer in enumerate(self.layers):
x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
class MSDeformableAttention(nn.Module):
def __init__(
self,
embed_dim=256,
num_heads=8,
num_levels=4,
num_points=4,
method="default",
offset_scale=0.5,
):
"""Multi-Scale Deformable Attention"""
super(MSDeformableAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.num_levels = num_levels
self.offset_scale = offset_scale
if isinstance(num_points, list):
assert len(num_points) == num_levels, ""
num_points_list = num_points
else:
num_points_list = [num_points for _ in range(num_levels)]
self.num_points_list = num_points_list
num_points_scale = [1 / n for n in num_points_list for _ in range(n)]
self.register_buffer(
"num_points_scale", torch.tensor(num_points_scale, dtype=torch.float32)
)
self.total_points = num_heads * sum(num_points_list)
self.method = method
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.sampling_offsets = nn.Linear(embed_dim, self.total_points * 2)
self.attention_weights = nn.Linear(embed_dim, self.total_points)
self.ms_deformable_attn_core = functools.partial(
deformable_attention_core_func_v2, method=self.method
)
self._reset_parameters()
if method == "discrete":
for p in self.sampling_offsets.parameters():
p.requires_grad = False
def _reset_parameters(self):
# sampling_offsets
init.constant_(self.sampling_offsets.weight, 0)
thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
2.0 * math.pi / self.num_heads
)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = grid_init / grid_init.abs().max(-1, keepdim=True).values
grid_init = grid_init.reshape(self.num_heads, 1, 2).tile([1, sum(self.num_points_list), 1])
scaling = torch.concat([torch.arange(1, n + 1) for n in self.num_points_list]).reshape(
1, -1, 1
)
grid_init *= scaling
self.sampling_offsets.bias.data[...] = grid_init.flatten()
# attention_weights
init.constant_(self.attention_weights.weight, 0)
init.constant_(self.attention_weights.bias, 0)
def forward(
self,
query: torch.Tensor,
reference_points: torch.Tensor,
value: torch.Tensor,
value_spatial_shapes: List[int],
):
"""
Args:
query (Tensor): [bs, query_length, C]
reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
bottom-right (1, 1), including padding area
value (Tensor): [bs, value_length, C]
value_spatial_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
Returns:
output (Tensor): [bs, Length_{query}, C]
"""
bs, Len_q = query.shape[:2]
sampling_offsets: torch.Tensor = self.sampling_offsets(query)
sampling_offsets = sampling_offsets.reshape(
bs, Len_q, self.num_heads, sum(self.num_points_list), 2
)
attention_weights = self.attention_weights(query).reshape(
bs, Len_q, self.num_heads, sum(self.num_points_list)
)
attention_weights = F.softmax(attention_weights, dim=-1)
if reference_points.shape[-1] == 2:
offset_normalizer = torch.tensor(value_spatial_shapes)
offset_normalizer = offset_normalizer.flip([1]).reshape(1, 1, 1, self.num_levels, 1, 2)
sampling_locations = (
reference_points.reshape(bs, Len_q, 1, self.num_levels, 1, 2)
+ sampling_offsets / offset_normalizer
)
elif reference_points.shape[-1] == 4:
# reference_points [8, 480, None, 1, 4]
# sampling_offsets [8, 480, 8, 12, 2]
num_points_scale = self.num_points_scale.to(dtype=query.dtype).unsqueeze(-1)
offset = (
sampling_offsets
* num_points_scale
* reference_points[:, :, None, :, 2:]
* self.offset_scale
)
sampling_locations = reference_points[:, :, None, :, :2] + offset
else:
raise ValueError(
"Last dim of reference_points must be 2 or 4, but get {} instead.".format(
reference_points.shape[-1]
)
)
output = self.ms_deformable_attn_core(
value, value_spatial_shapes, sampling_locations, attention_weights, self.num_points_list
)
return output
class TransformerDecoderLayer(nn.Module):
def __init__(
self,
d_model=256,
n_head=8,
dim_feedforward=1024,
dropout=0.0,
activation="relu",
n_levels=4,
n_points=4,
cross_attn_method="default",
layer_scale=None,
):
super(TransformerDecoderLayer, self).__init__()
if layer_scale is not None:
dim_feedforward = round(layer_scale * dim_feedforward)
d_model = round(layer_scale * d_model)
# self attention
self.self_attn = nn.MultiheadAttention(d_model, n_head, dropout=dropout, batch_first=True)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# cross attention
self.cross_attn = MSDeformableAttention(
d_model, n_head, n_levels, n_points, method=cross_attn_method
)
self.dropout2 = nn.Dropout(dropout)
# gate
self.gateway = Gate(d_model)
# ffn
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.activation = get_activation(activation)
self.dropout3 = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.dropout4 = nn.Dropout(dropout)
self.norm3 = nn.LayerNorm(d_model)
self._reset_parameters()
def _reset_parameters(self):
init.xavier_uniform_(self.linear1.weight)
init.xavier_uniform_(self.linear2.weight)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, tgt):
return self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
def forward(
self, target, reference_points, value, spatial_shapes, attn_mask=None, query_pos_embed=None
):
# self attention
q = k = self.with_pos_embed(target, query_pos_embed)
target2, _ = self.self_attn(q, k, value=target, attn_mask=attn_mask)
target = target + self.dropout1(target2)
target = self.norm1(target)
# cross attention
target2 = self.cross_attn(
self.with_pos_embed(target, query_pos_embed), reference_points, value, spatial_shapes
)
target = self.gateway(target, self.dropout2(target2))
# ffn
target2 = self.forward_ffn(target)
target = target + self.dropout4(target2)
target = self.norm3(target.clamp(min=-65504, max=65504))
return target
class Gate(nn.Module):
def __init__(self, d_model):
super(Gate, self).__init__()
self.gate = nn.Linear(2 * d_model, 2 * d_model)
bias = bias_init_with_prob(0.5)
init.constant_(self.gate.bias, bias)
init.constant_(self.gate.weight, 0)
self.norm = nn.LayerNorm(d_model)
def forward(self, x1, x2):
gate_input = torch.cat([x1, x2], dim=-1)
gates = torch.sigmoid(self.gate(gate_input))
gate1, gate2 = gates.chunk(2, dim=-1)
return self.norm(gate1 * x1 + gate2 * x2)
class Integral(nn.Module):
"""
A static layer that calculates integral results from a distribution.
This layer computes the target location using the formula: `sum{Pr(n) * W(n)}`,
where Pr(n) is the softmax probability vector representing the discrete
distribution, and W(n) is the non-uniform Weighting Function.
Args:
reg_max (int): Max number of the discrete bins. Default is 32.
It can be adjusted based on the dataset or task requirements.
"""
def __init__(self, reg_max=32):
super(Integral, self).__init__()
self.reg_max = reg_max
def forward(self, x, project):
shape = x.shape
x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
x = F.linear(x, project.to(x.device)).reshape(-1, 4)
return x.reshape(list(shape[:-1]) + [-1])
class LQE(nn.Module):
def __init__(self, k, hidden_dim, num_layers, reg_max):
super(LQE, self).__init__()
self.k = k
self.reg_max = reg_max
self.reg_conf = MLP(4 * (k + 1), hidden_dim, 1, num_layers)
init.constant_(self.reg_conf.layers[-1].bias, 0)
init.constant_(self.reg_conf.layers[-1].weight, 0)
def forward(self, scores, pred_corners):
B, L, _ = pred_corners.size()
prob = F.softmax(pred_corners.reshape(B, L, 4, self.reg_max + 1), dim=-1)
prob_topk, _ = prob.topk(self.k, dim=-1)
stat = torch.cat([prob_topk, prob_topk.mean(dim=-1, keepdim=True)], dim=-1)
quality_score = self.reg_conf(stat.reshape(B, L, -1))
return scores + quality_score
class TransformerDecoder(nn.Module):
"""
Transformer Decoder implementing Fine-grained Distribution Refinement (FDR).
This decoder refines object detection predictions through iterative updates across multiple layers,
utilizing attention mechanisms, location quality estimators, and distribution refinement techniques
to improve bounding box accuracy and robustness.
"""
def __init__(
self,
hidden_dim,
decoder_layer,
decoder_layer_wide,
num_layers,
num_head,
reg_max,
reg_scale,
up,
eval_idx=-1,
layer_scale=2,
):
super(TransformerDecoder, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.layer_scale = layer_scale
self.num_head = num_head
self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
self.up, self.reg_scale, self.reg_max = up, reg_scale, reg_max
self.layers = nn.ModuleList(
[copy.deepcopy(decoder_layer) for _ in range(self.eval_idx + 1)]
+ [copy.deepcopy(decoder_layer_wide) for _ in range(num_layers - self.eval_idx - 1)]
)
self.lqe_layers = nn.ModuleList(
[copy.deepcopy(LQE(4, 64, 2, reg_max)) for _ in range(num_layers)]
)
def value_op(self, memory, value_proj, value_scale, memory_mask, memory_spatial_shapes):
"""
Preprocess values for MSDeformableAttention.
"""
value = value_proj(memory) if value_proj is not None else memory
value = F.interpolate(memory, size=value_scale) if value_scale is not None else value
if memory_mask is not None:
value = value * memory_mask.to(value.dtype).unsqueeze(-1)
value = value.reshape(value.shape[0], value.shape[1], self.num_head, -1)
split_shape = [h * w for h, w in memory_spatial_shapes]
return value.permute(0, 2, 3, 1).split(split_shape, dim=-1)
def convert_to_deploy(self):
self.project = weighting_function(self.reg_max, self.up, self.reg_scale, deploy=True)
self.layers = self.layers[: self.eval_idx + 1]
self.lqe_layers = nn.ModuleList(
[nn.Identity()] * (self.eval_idx) + [self.lqe_layers[self.eval_idx]]
)
def forward(
self,
target,
ref_points_unact,
memory,
spatial_shapes,
bbox_head,
score_head,
query_pos_head,
pre_bbox_head,
integral,
up,
reg_scale,
attn_mask=None,
memory_mask=None,
dn_meta=None,
):
output = target
output_detach = pred_corners_undetach = 0
value = self.value_op(memory, None, None, memory_mask, spatial_shapes)
dec_out_bboxes = []
dec_out_logits = []
dec_out_pred_corners = []
dec_out_refs = []
if not hasattr(self, "project"):
project = weighting_function(self.reg_max, up, reg_scale)
else:
project = self.project
ref_points_detach = F.sigmoid(ref_points_unact)
for i, layer in enumerate(self.layers):
ref_points_input = ref_points_detach.unsqueeze(2)
query_pos_embed = query_pos_head(ref_points_detach).clamp(min=-10, max=10)
# TODO Adjust scale if needed for detachable wider layers
if i >= self.eval_idx + 1 and self.layer_scale > 1:
query_pos_embed = F.interpolate(query_pos_embed, scale_factor=self.layer_scale)
value = self.value_op(
memory, None, query_pos_embed.shape[-1], memory_mask, spatial_shapes
)
output = F.interpolate(output, size=query_pos_embed.shape[-1])
output_detach = output.detach()
output = layer(
output, ref_points_input, value, spatial_shapes, attn_mask, query_pos_embed
)
if i == 0:
# Initial bounding box predictions with inverse sigmoid refinement
pre_bboxes = F.sigmoid(pre_bbox_head(output) + inverse_sigmoid(ref_points_detach))
pre_scores = score_head[0](output)
ref_points_initial = pre_bboxes.detach()
# Refine bounding box corners using FDR, integrating previous layer's corrections
pred_corners = bbox_head[i](output + output_detach) + pred_corners_undetach
inter_ref_bbox = distance2bbox(
ref_points_initial, integral(pred_corners, project), reg_scale
)
if self.training or i == self.eval_idx:
scores = score_head[i](output)
# Lqe does not affect the performance here.
scores = self.lqe_layers[i](scores, pred_corners)
dec_out_logits.append(scores)
dec_out_bboxes.append(inter_ref_bbox)
dec_out_pred_corners.append(pred_corners)
dec_out_refs.append(ref_points_initial)
if not self.training:
break
pred_corners_undetach = pred_corners
ref_points_detach = inter_ref_bbox.detach()
output_detach = output.detach()
return (
torch.stack(dec_out_bboxes),
torch.stack(dec_out_logits),
torch.stack(dec_out_pred_corners),
torch.stack(dec_out_refs),
pre_bboxes,
pre_scores,
)
@register()
class DFINETransformer(nn.Module):
__share__ = ["num_classes", "eval_spatial_size"]
def __init__(
self,
num_classes=80,
hidden_dim=256,
num_queries=300,
feat_channels=[512, 1024, 2048],
feat_strides=[8, 16, 32],
num_levels=3,
num_points=4,
nhead=8,
num_layers=6,
dim_feedforward=1024,
dropout=0.0,
activation="relu",
num_denoising=100,
label_noise_ratio=0.5,
box_noise_scale=1.0,
learn_query_content=False,
eval_spatial_size=None,
eval_idx=-1,
eps=1e-2,
aux_loss=True,
cross_attn_method="default",
query_select_method="default",
reg_max=32,
reg_scale=4.0,
layer_scale=1,
):
super().__init__()
assert len(feat_channels) <= num_levels
assert len(feat_strides) == len(feat_channels)
for _ in range(num_levels - len(feat_strides)):
feat_strides.append(feat_strides[-1] * 2)
self.hidden_dim = hidden_dim
scaled_dim = round(layer_scale * hidden_dim)
self.nhead = nhead
self.feat_strides = feat_strides
self.num_levels = num_levels
self.num_classes = num_classes
self.num_queries = num_queries
self.eps = eps
self.num_layers = num_layers
self.eval_spatial_size = eval_spatial_size
self.aux_loss = aux_loss
self.reg_max = reg_max
assert query_select_method in ("default", "one2many", "agnostic"), ""
assert cross_attn_method in ("default", "discrete"), ""
self.cross_attn_method = cross_attn_method
self.query_select_method = query_select_method
# backbone feature projection
self._build_input_proj_layer(feat_channels)
# Transformer module
self.up = nn.Parameter(torch.tensor([0.5]), requires_grad=False)
self.reg_scale = nn.Parameter(torch.tensor([reg_scale]), requires_grad=False)
decoder_layer = TransformerDecoderLayer(
hidden_dim,
nhead,
dim_feedforward,
dropout,
activation,
num_levels,
num_points,
cross_attn_method=cross_attn_method,
)
decoder_layer_wide = TransformerDecoderLayer(
hidden_dim,
nhead,
dim_feedforward,
dropout,
activation,
num_levels,
num_points,
cross_attn_method=cross_attn_method,
layer_scale=layer_scale,
)
self.decoder = TransformerDecoder(
hidden_dim,
decoder_layer,
decoder_layer_wide,
num_layers,
nhead,
reg_max,
self.reg_scale,
self.up,
eval_idx,
layer_scale,
)
# denoising
self.num_denoising = num_denoising
self.label_noise_ratio = label_noise_ratio
self.box_noise_scale = box_noise_scale
if num_denoising > 0:
self.denoising_class_embed = nn.Embedding(
num_classes + 1, hidden_dim, padding_idx=num_classes
)
init.normal_(self.denoising_class_embed.weight[:-1])
# decoder embedding
self.learn_query_content = learn_query_content
if learn_query_content:
self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, 2)
# if num_select_queries != self.num_queries:
# layer = TransformerEncoderLayer(hidden_dim, nhead, dim_feedforward, activation='gelu')
# self.encoder = TransformerEncoder(layer, 1)
self.enc_output = nn.Sequential(
OrderedDict(
[
("proj", nn.Linear(hidden_dim, hidden_dim)),
(
"norm",
nn.LayerNorm(
hidden_dim,
),
),
]
)
)
if query_select_method == "agnostic":
self.enc_score_head = nn.Linear(hidden_dim, 1)
else:
self.enc_score_head = nn.Linear(hidden_dim, num_classes)
self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, 3)
# decoder head
self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
self.dec_score_head = nn.ModuleList(
[nn.Linear(hidden_dim, num_classes) for _ in range(self.eval_idx + 1)]
+ [nn.Linear(scaled_dim, num_classes) for _ in range(num_layers - self.eval_idx - 1)]
)
self.pre_bbox_head = MLP(hidden_dim, hidden_dim, 4, 3)
self.dec_bbox_head = nn.ModuleList(
[
MLP(hidden_dim, hidden_dim, 4 * (self.reg_max + 1), 3)
for _ in range(self.eval_idx + 1)
]
+ [
MLP(scaled_dim, scaled_dim, 4 * (self.reg_max + 1), 3)
for _ in range(num_layers - self.eval_idx - 1)
]
)
self.integral = Integral(self.reg_max)
# init encoder output anchors and valid_mask
if self.eval_spatial_size:
anchors, valid_mask = self._generate_anchors()
self.register_buffer("anchors", anchors)
self.register_buffer("valid_mask", valid_mask)
# init encoder output anchors and valid_mask
if self.eval_spatial_size:
self.anchors, self.valid_mask = self._generate_anchors()
self._reset_parameters(feat_channels)
def convert_to_deploy(self):
self.dec_score_head = nn.ModuleList(
[nn.Identity()] * (self.eval_idx) + [self.dec_score_head[self.eval_idx]]
)
self.dec_bbox_head = nn.ModuleList(
[
self.dec_bbox_head[i] if i <= self.eval_idx else nn.Identity()
for i in range(len(self.dec_bbox_head))
]
)
def _reset_parameters(self, feat_channels):
bias = bias_init_with_prob(0.01)
init.constant_(self.enc_score_head.bias, bias)
init.constant_(self.enc_bbox_head.layers[-1].weight, 0)
init.constant_(self.enc_bbox_head.layers[-1].bias, 0)
init.constant_(self.pre_bbox_head.layers[-1].weight, 0)
init.constant_(self.pre_bbox_head.layers[-1].bias, 0)
for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
init.constant_(cls_.bias, bias)
if hasattr(reg_, "layers"):
init.constant_(reg_.layers[-1].weight, 0)
init.constant_(reg_.layers[-1].bias, 0)
init.xavier_uniform_(self.enc_output[0].weight)
if self.learn_query_content:
init.xavier_uniform_(self.tgt_embed.weight)
init.xavier_uniform_(self.query_pos_head.layers[0].weight)
init.xavier_uniform_(self.query_pos_head.layers[1].weight)
for m, in_channels in zip(self.input_proj, feat_channels):
if in_channels != self.hidden_dim:
init.xavier_uniform_(m[0].weight)
def _build_input_proj_layer(self, feat_channels):
self.input_proj = nn.ModuleList()
for in_channels in feat_channels:
if in_channels == self.hidden_dim:
self.input_proj.append(nn.Identity())
else:
self.input_proj.append(
nn.Sequential(
OrderedDict(
[
("conv", nn.Conv2d(in_channels, self.hidden_dim, 1, bias=False)),
(
"norm",
nn.BatchNorm2d(
self.hidden_dim,
),
),
]
)
)
)
in_channels = feat_channels[-1]
for _ in range(self.num_levels - len(feat_channels)):
if in_channels == self.hidden_dim:
self.input_proj.append(nn.Identity())
else:
self.input_proj.append(
nn.Sequential(
OrderedDict(
[
(
"conv",
nn.Conv2d(
in_channels, self.hidden_dim, 3, 2, padding=1, bias=False
),
),
("norm", nn.BatchNorm2d(self.hidden_dim)),
]
)
)
)
in_channels = self.hidden_dim
def _get_encoder_input(self, feats: List[torch.Tensor]):
# get projection features
proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)]
if self.num_levels > len(proj_feats):
len_srcs = len(proj_feats)
for i in range(len_srcs, self.num_levels):
if i == len_srcs:
proj_feats.append(self.input_proj[i](feats[-1]))
else:
proj_feats.append(self.input_proj[i](proj_feats[-1]))
# get encoder inputs
feat_flatten = []
spatial_shapes = []
for i, feat in enumerate(proj_feats):
_, _, h, w = feat.shape
# [b, c, h, w] -> [b, h*w, c]
feat_flatten.append(feat.flatten(2).permute(0, 2, 1))
# [num_levels, 2]
spatial_shapes.append([h, w])
# [b, l, c]
feat_flatten = torch.concat(feat_flatten, 1)
return feat_flatten, spatial_shapes
def _generate_anchors(
self, spatial_shapes=None, grid_size=0.05, dtype=torch.float32, device="cpu"
):
if spatial_shapes is None:
spatial_shapes = []
eval_h, eval_w = self.eval_spatial_size
for s in self.feat_strides:
spatial_shapes.append([int(eval_h / s), int(eval_w / s)])
anchors = []
for lvl, (h, w) in enumerate(spatial_shapes):
grid_y, grid_x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
grid_xy = torch.stack([grid_x, grid_y], dim=-1)
grid_xy = (grid_xy.unsqueeze(0) + 0.5) / torch.tensor([w, h], dtype=dtype)
wh = torch.ones_like(grid_xy) * grid_size * (2.0**lvl)
lvl_anchors = torch.concat([grid_xy, wh], dim=-1).reshape(-1, h * w, 4)
anchors.append(lvl_anchors)
anchors = torch.concat(anchors, dim=1).to(device)
valid_mask = ((anchors > self.eps) * (anchors < 1 - self.eps)).all(-1, keepdim=True)
anchors = torch.log(anchors / (1 - anchors))
anchors = torch.where(valid_mask, anchors, torch.inf)
return anchors, valid_mask
def _get_decoder_input(
self, memory: torch.Tensor, spatial_shapes, denoising_logits=None, denoising_bbox_unact=None
):
# prepare input for decoder
if self.training or self.eval_spatial_size is None:
anchors, valid_mask = self._generate_anchors(spatial_shapes, device=memory.device)
else:
anchors = self.anchors
valid_mask = self.valid_mask
if memory.shape[0] > 1:
anchors = anchors.repeat(memory.shape[0], 1, 1)
# memory = torch.where(valid_mask, memory, 0)
# TODO fix type error for onnx export
memory = valid_mask.to(memory.dtype) * memory
output_memory: torch.Tensor = self.enc_output(memory)
enc_outputs_logits: torch.Tensor = self.enc_score_head(output_memory)
enc_topk_bboxes_list, enc_topk_logits_list = [], []
enc_topk_memory, enc_topk_logits, enc_topk_anchors = self._select_topk(
output_memory, enc_outputs_logits, anchors, self.num_queries
)
enc_topk_bbox_unact: torch.Tensor = self.enc_bbox_head(enc_topk_memory) + enc_topk_anchors
if self.training:
enc_topk_bboxes = F.sigmoid(enc_topk_bbox_unact)
enc_topk_bboxes_list.append(enc_topk_bboxes)
enc_topk_logits_list.append(enc_topk_logits)
# if self.num_select_queries != self.num_queries:
# raise NotImplementedError('')
if self.learn_query_content:
content = self.tgt_embed.weight.unsqueeze(0).tile([memory.shape[0], 1, 1])
else:
content = enc_topk_memory.detach()
enc_topk_bbox_unact = enc_topk_bbox_unact.detach()
if denoising_bbox_unact is not None:
enc_topk_bbox_unact = torch.concat([denoising_bbox_unact, enc_topk_bbox_unact], dim=1)
content = torch.concat([denoising_logits, content], dim=1)
return content, enc_topk_bbox_unact, enc_topk_bboxes_list, enc_topk_logits_list
def _select_topk(
self,
memory: torch.Tensor,
outputs_logits: torch.Tensor,
outputs_anchors_unact: torch.Tensor,
topk: int,
):
if self.query_select_method == "default":
_, topk_ind = torch.topk(outputs_logits.max(-1).values, topk, dim=-1)
elif self.query_select_method == "one2many":
_, topk_ind = torch.topk(outputs_logits.flatten(1), topk, dim=-1)
topk_ind = topk_ind // self.num_classes
elif self.query_select_method == "agnostic":
_, topk_ind = torch.topk(outputs_logits.squeeze(-1), topk, dim=-1)
topk_ind: torch.Tensor
topk_anchors = outputs_anchors_unact.gather(
dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_anchors_unact.shape[-1])
)
topk_logits = (
outputs_logits.gather(
dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_logits.shape[-1])
)
if self.training
else None
)
topk_memory = memory.gather(
dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, memory.shape[-1])
)
return topk_memory, topk_logits, topk_anchors
def forward(self, feats, targets=None):
# input projection and embedding
memory, spatial_shapes = self._get_encoder_input(feats)
# prepare denoising training
if self.training and self.num_denoising > 0:
denoising_logits, denoising_bbox_unact, attn_mask, dn_meta = (
get_contrastive_denoising_training_group(
targets,
self.num_classes,
self.num_queries,
self.denoising_class_embed,
num_denoising=self.num_denoising,
label_noise_ratio=self.label_noise_ratio,
box_noise_scale=1.0,
)
)
else:
denoising_logits, denoising_bbox_unact, attn_mask, dn_meta = None, None, None, None
init_ref_contents, init_ref_points_unact, enc_topk_bboxes_list, enc_topk_logits_list = (
self._get_decoder_input(memory, spatial_shapes, denoising_logits, denoising_bbox_unact)
)
# decoder
out_bboxes, out_logits, out_corners, out_refs, pre_bboxes, pre_logits = self.decoder(
init_ref_contents,
init_ref_points_unact,
memory,
spatial_shapes,
self.dec_bbox_head,
self.dec_score_head,
self.query_pos_head,
self.pre_bbox_head,
self.integral,
self.up,
self.reg_scale,
attn_mask=attn_mask,
dn_meta=dn_meta,
)
if self.training and dn_meta is not None:
dn_pre_logits, pre_logits = torch.split(pre_logits, dn_meta["dn_num_split"], dim=1)
dn_pre_bboxes, pre_bboxes = torch.split(pre_bboxes, dn_meta["dn_num_split"], dim=1)
dn_out_bboxes, out_bboxes = torch.split(out_bboxes, dn_meta["dn_num_split"], dim=2)
dn_out_logits, out_logits = torch.split(out_logits, dn_meta["dn_num_split"], dim=2)
dn_out_corners, out_corners = torch.split(out_corners, dn_meta["dn_num_split"], dim=2)
dn_out_refs, out_refs = torch.split(out_refs, dn_meta["dn_num_split"], dim=2)
if self.training:
out = {
"pred_logits": out_logits[-1],
"pred_boxes": out_bboxes[-1],
"pred_corners": out_corners[-1],
"ref_points": out_refs[-1],
"up": self.up,
"reg_scale": self.reg_scale,
}
else:
out = {"pred_logits": out_logits[-1], "pred_boxes": out_bboxes[-1]}
if self.training and self.aux_loss:
out["aux_outputs"] = self._set_aux_loss2(
out_logits[:-1],
out_bboxes[:-1],
out_corners[:-1],
out_refs[:-1],
out_corners[-1],
out_logits[-1],
)
out["enc_aux_outputs"] = self._set_aux_loss(enc_topk_logits_list, enc_topk_bboxes_list)
out["pre_outputs"] = {"pred_logits": pre_logits, "pred_boxes": pre_bboxes}
out["enc_meta"] = {"class_agnostic": self.query_select_method == "agnostic"}
if dn_meta is not None:
out["dn_outputs"] = self._set_aux_loss2(
dn_out_logits,
dn_out_bboxes,
dn_out_corners,
dn_out_refs,
dn_out_corners[-1],
dn_out_logits[-1],
)
out["dn_pre_outputs"] = {"pred_logits": dn_pre_logits, "pred_boxes": dn_pre_bboxes}
out["dn_meta"] = dn_meta
return out
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [{"pred_logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)]
@torch.jit.unused
def _set_aux_loss2(
self,
outputs_class,
outputs_coord,
outputs_corners,
outputs_ref,
teacher_corners=None,
teacher_logits=None,
):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [
{
"pred_logits": a,
"pred_boxes": b,
"pred_corners": c,
"ref_points": d,
"teacher_corners": teacher_corners,
"teacher_logits": teacher_logits,
}
for a, b, c, d in zip(outputs_class, outputs_coord, outputs_corners, outputs_ref)
]
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