""" 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) ]