Delete src/zoo/rtdetr/rtdetr_decoder.py

src/zoo/rtdetr/rtdetr_decoder.py

@@ -1,627 +0,0 @@
-"""by lyuwenyu
-"""
-
-import math 
-import copy 
-from collections import OrderedDict
-from typing import Optional, Tuple
-
-import torch 
-import torch.nn as nn 
-import torch.nn.functional as F 
-import torch.nn.init as init 
-from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
-from torch.nn.parameter import Parameter
-
-from .denoising import get_contrastive_denoising_training_group
-from .utils import deformable_attention_core_func, get_activation, inverse_sigmoid
-from .utils import bias_init_with_prob
-from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
-
-from src.core import register
-
-import numpy as np
-
-import scipy.linalg as sl
-
-__all__ = ['RTDETRTransformer']
-
-
-
-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 = nn.Identity() if act is None else 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 CoPE(nn.Module):
-    def __init__(self,npos_max,head_dim):
-        super(CoPE, self).__init__()
-        self.npos_max = npos_max                  #?
-        self.pos_emb = nn.parameter.Parameter(torch.zeros(1,head_dim,npos_max))
-
-    def forward(self,query,attn_logits):
-        #compute positions
-        gates = torch.sigmoid(attn_logits)                     #sig(qk)
-        pos = gates.flip(-1).cumsum(dim=-1).flip(-1)
-        pos = pos.clamp(max=self.npos_max-1)
-        #interpolate from integer positions
-        pos_ceil = pos.ceil().long()
-        pos_floor = pos.floor().long()                  
-        logits_int = torch.matmul(query,self.pos_emb)
-        logits_ceil = logits_int.gather(-1,pos_ceil)
-        logits_floor = logits_int.gather(-1,pos_floor)
-        w = pos-pos_floor
-        return logits_ceil*w+logits_floor*(1-w)
-
-
-
-
-class MSDeformableAttention(nn.Module):
-    def __init__(self, embed_dim=256, num_heads=8, num_levels=4, num_points=4,):
-        """
-        Multi-Scale Deformable Attention Module
-        """
-        super(MSDeformableAttention, self).__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.num_levels = num_levels
-        self.num_points = num_points
-        self.total_points = num_heads * num_levels * num_points
-
-        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.value_proj = nn.Linear(embed_dim, embed_dim)
-        self.output_proj = nn.Linear(embed_dim, embed_dim)
-
-        self.ms_deformable_attn_core = deformable_attention_core_func
-
-        self._reset_parameters()
-
-
-    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, 1, 2).tile([1, self.num_levels, self.num_points, 1])
-        scaling = torch.arange(1, self.num_points + 1, dtype=torch.float32).reshape(1, 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)
-
-        # proj
-        init.xavier_uniform_(self.value_proj.weight)
-        init.constant_(self.value_proj.bias, 0)
-        init.xavier_uniform_(self.output_proj.weight)
-        init.constant_(self.output_proj.bias, 0)
-
-
-    def forward(self,
-                query,
-                reference_points,
-                value,
-                value_spatial_shapes,
-                value_mask=None):
-        """
-        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})]
-            value_level_start_index (List): [n_levels], [0, H_0*W_0, H_0*W_0+H_1*W_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]
-
-        value = self.value_proj(value)
-        if value_mask is not None:
-            value_mask = value_mask.astype(value.dtype).unsqueeze(-1)
-            value *= value_mask
-        value = value.reshape(bs, Len_v, self.num_heads, self.head_dim)
-
-        sampling_offsets = self.sampling_offsets(query).reshape(
-            bs, Len_q, self.num_heads, self.num_levels, self.num_points, 2)
-        attention_weights = self.attention_weights(query).reshape(
-            bs, Len_q, self.num_heads, self.num_levels * self.num_points)
-        attention_weights = F.softmax(attention_weights, dim=-1).reshape(
-            bs, Len_q, self.num_heads, self.num_levels, self.num_points)
-
-        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:
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :2] + sampling_offsets /
-                self.num_points * reference_points[:, :, None, :, None, 2:] * 0.5)
-        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)
-
-        output = self.output_proj(output)
-
-        return output
-
-
-class TransformerDecoderLayer(nn.Module):
-    def __init__(self,
-                 d_model=256,
-                 n_head=8,
-                 dim_feedforward=1024,
-                 dropout=0.,
-                 activation="relu",
-                 n_levels=4,
-                 n_points=4,):
-        super(TransformerDecoderLayer, self).__init__()
-
-        # 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)
-        self.dropout2 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.activation = getattr(F, 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.cope = CoPE(12,d_model)
-        
-        # self._reset_parameters()
-
-    # def _reset_parameters(self):
-    #     linear_init_(self.linear1)
-    #     linear_init_(self.linear2)
-    #     xavier_uniform_(self.linear1.weight)
-    #     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,
-                tgt,
-                reference_points,
-                memory,
-                memory_spatial_shapes,
-                memory_level_start_index,
-                attn_mask=None,
-                memory_mask=None,
-                query_pos_embed=None):
-        # self attention
-        #print(query_pos_embed.shape)
-        qk = torch.bmm (tgt ,tgt.transpose(-1 ,-2))
-        mask = torch.tril(torch.ones_like(qk),diagonal=0)
-        mask = torch.log(mask)
-        query_pos_embed = self.cope(tgt,qk+mask)                                         #position_embedding
- 
-        
-        n_tgt = tgt.cpu().detach().numpy()
-        
-        itgt = tgt.new_tensor(np.array([sl.pinv(i) for i in n_tgt]))                        #inv_tgt
-        
-#        print('qk:',qk.shape)
-#        print('tgt:',tgt.shape)
-#        print(([email protected](-1,-2)).shape)
-#        print('ik:',itgt.shape)
-
-     #   print(torch.round(itgt@tgt))
-     #   print([email protected](-1,-2))
-
-        k = tgt 
-        q = tgt + ([email protected](-1,-2))
-        
-     #   print((q@(k.transpose(-1,-2))-query_pos_embed))
-        
-        # if attn_mask is not None:
-        #     attn_mask = torch.where(
-        #         attn_mask.to(torch.bool),
-        #         torch.zeros_like(attn_mask),
-        #         torch.full_like(attn_mask, float('-inf'), dtype=tgt.dtype))
-      
-       # q = k = self.with_pos_embed(tgt, query_pos_embed)
-        tgt2, _ = self.self_attn(q, k, value=tgt, attn_mask=attn_mask)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # cross attention
-        tgt2 = self.cross_attn(\
-            self.with_pos_embed(tgt, ([email protected](-1,-2))),  #self.with_pos_embed(tgt, query_pos_embed),
-            reference_points, 
-            memory, 
-            memory_spatial_shapes, 
-            memory_mask)
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
-
-        # ffn
-        tgt2 = self.forward_ffn(tgt)
-        tgt = tgt + self.dropout4(tgt2)
-        tgt = self.norm3(tgt)
-
-        return tgt
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, hidden_dim, decoder_layer, num_layers, eval_idx=-1):
-        super(TransformerDecoder, self).__init__()
-        self.layers = nn.ModuleList([copy.deepcopy(decoder_layer) for _ in range(num_layers)])
-        self.hidden_dim = hidden_dim
-        self.num_layers = num_layers
-        self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
-
-    def forward(self,
-                tgt,
-                ref_points_unact,
-                memory,
-                memory_spatial_shapes,
-                memory_level_start_index,
-                bbox_head,
-                score_head,
-                query_pos_head,
-                attn_mask=None,
-                memory_mask=None):
-        output = tgt
-        dec_out_bboxes = []
-        dec_out_logits = []
-        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)
-
-            output = layer(output, ref_points_input, memory,
-                           memory_spatial_shapes, memory_level_start_index,
-                           attn_mask, memory_mask, query_pos_embed)
-
-            inter_ref_bbox = F.sigmoid(bbox_head[i](output) + inverse_sigmoid(ref_points_detach))
-
-            if self.training:
-                dec_out_logits.append(score_head[i](output))
-                if i == 0:
-                    dec_out_bboxes.append(inter_ref_bbox)
-                else:
-                    dec_out_bboxes.append(F.sigmoid(bbox_head[i](output) + inverse_sigmoid(ref_points)))
-
-            elif i == self.eval_idx:
-                dec_out_logits.append(score_head[i](output))
-                dec_out_bboxes.append(inter_ref_bbox)
-                break
-
-            ref_points = inter_ref_bbox
-            ref_points_detach = inter_ref_bbox.detach(
-            ) if self.training else inter_ref_bbox
-
-        return torch.stack(dec_out_bboxes), torch.stack(dec_out_logits)
-
-
-@register
-class RTDETRTransformer(nn.Module):
-    __share__ = ['num_classes']
-    def __init__(self,
-                 num_classes=80,
-                 hidden_dim=256,
-                 num_queries=300,
-                 position_embed_type='sine',
-                 feat_channels=[512, 1024, 2048],
-                 feat_strides=[8, 16, 32],
-                 num_levels=3,
-                 num_decoder_points=4,
-                 nhead=8,
-                 num_decoder_layers=6,
-                 dim_feedforward=1024,
-                 dropout=0.,
-                 activation="relu",
-                 num_denoising=100,
-                 label_noise_ratio=0.5,
-                 box_noise_scale=1.0,
-                 learnt_init_query=False,
-                 eval_spatial_size=None,
-                 eval_idx=-1,
-                 eps=1e-2, 
-                 aux_loss=True):
-
-        super(RTDETRTransformer, self).__init__()
-        assert position_embed_type in ['sine', 'learned'], \
-            f'ValueError: position_embed_type not supported {position_embed_type}!'
-        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
-        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_decoder_layers = num_decoder_layers
-        self.eval_spatial_size = eval_spatial_size
-        self.aux_loss = aux_loss
-
-        # backbone feature projection
-        self._build_input_proj_layer(feat_channels)
-
-        # Transformer module
-        decoder_layer = TransformerDecoderLayer(hidden_dim, nhead, dim_feedforward, dropout, activation, num_levels, num_decoder_points)
-        self.decoder = TransformerDecoder(hidden_dim, decoder_layer, num_decoder_layers, eval_idx)
-
-        self.num_denoising = num_denoising
-        self.label_noise_ratio = label_noise_ratio
-        self.box_noise_scale = box_noise_scale
-        # denoising part
-        if num_denoising > 0: 
-            # self.denoising_class_embed = nn.Embedding(num_classes, hidden_dim, padding_idx=num_classes-1) # TODO for load paddle weights
-            self.denoising_class_embed = nn.Embedding(num_classes+1, hidden_dim, padding_idx=num_classes)
-
-        # decoder embedding
-        self.learnt_init_query = learnt_init_query
-        if learnt_init_query:
-            self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
-        self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, num_layers=2)
-
-        # encoder head
-        self.enc_output = nn.Sequential(
-            nn.Linear(hidden_dim, hidden_dim),
-            nn.LayerNorm(hidden_dim,)
-        )
-        self.enc_score_head = nn.Linear(hidden_dim, num_classes)
-        self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, num_layers=3)
-
-        # decoder head
-        self.dec_score_head = nn.ModuleList([
-            nn.Linear(hidden_dim, num_classes)
-            for _ in range(num_decoder_layers)
-        ])
-        self.dec_bbox_head = nn.ModuleList([
-            MLP(hidden_dim, hidden_dim, 4, num_layers=3)
-            for _ in range(num_decoder_layers)
-        ])
-
-        # init encoder output anchors and valid_mask
-        if self.eval_spatial_size:
-            self.anchors, self.valid_mask = self._generate_anchors()
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        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)
-
-        for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
-            init.constant_(cls_.bias, bias)
-            init.constant_(reg_.layers[-1].weight, 0)
-            init.constant_(reg_.layers[-1].bias, 0)
-        
-        # linear_init_(self.enc_output[0])
-        init.xavier_uniform_(self.enc_output[0].weight)
-        if self.learnt_init_query:
-            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)
-
-
-    def _build_input_proj_layer(self, feat_channels):
-        self.input_proj = nn.ModuleList()
-        for in_channels in feat_channels:
-            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)):
-            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):
-        # 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 = []
-        level_start_index = [0, ]
-        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])
-            # [l], start index of each level
-            level_start_index.append(h * w + level_start_index[-1])
-
-        # [b, l, c]
-        feat_flatten = torch.concat(feat_flatten, 1)
-        level_start_index.pop()
-        return (feat_flatten, spatial_shapes, level_start_index)
-
-    def _generate_anchors(self,
-                          spatial_shapes=None,
-                          grid_size=0.05,
-                          dtype=torch.float32,
-                          device='cpu'):
-        if spatial_shapes is None:
-            spatial_shapes = [[int(self.eval_spatial_size[0] / s), int(self.eval_spatial_size[1] / s)]
-                for s in self.feat_strides
-            ]
-        anchors = []
-        for lvl, (h, w) in enumerate(spatial_shapes):
-            grid_y, grid_x = torch.meshgrid(\
-                torch.arange(end=h, dtype=dtype), \
-                torch.arange(end=w, dtype=dtype), indexing='ij')
-            grid_xy = torch.stack([grid_x, grid_y], -1)
-            valid_WH = torch.tensor([w, h]).to(dtype)
-            grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH
-            wh = torch.ones_like(grid_xy) * grid_size * (2.0 ** lvl)
-            anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, h * w, 4))
-
-        anchors = torch.concat(anchors, 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, float('inf'))
-        # anchors[valid_mask] = torch.inf # valid_mask [1, 8400, 1]
-        anchors = torch.where(valid_mask, anchors, torch.inf)
-
-        return anchors, valid_mask
-
-
-    def _get_decoder_input(self,
-                           memory,
-                           spatial_shapes,
-                           denoising_class=None,
-                           denoising_bbox_unact=None):
-        bs, _, _ = memory.shape
-        # 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, valid_mask = self.anchors.to(memory.device), self.valid_mask.to(memory.device)
-
-        # memory = torch.where(valid_mask, memory, 0)
-        memory = valid_mask.to(memory.dtype) * memory  # TODO fix type error for onnx export 
-
-        output_memory = self.enc_output(memory)
-
-        enc_outputs_class = self.enc_score_head(output_memory)
-        enc_outputs_coord_unact = self.enc_bbox_head(output_memory) + anchors
-
-        _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.num_queries, dim=1)
-        
-        reference_points_unact = enc_outputs_coord_unact.gather(dim=1, \
-            index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_unact.shape[-1]))
-
-        enc_topk_bboxes = F.sigmoid(reference_points_unact)
-        if denoising_bbox_unact is not None:
-            reference_points_unact = torch.concat(
-                [denoising_bbox_unact, reference_points_unact], 1)
-        
-        enc_topk_logits = enc_outputs_class.gather(dim=1, \
-            index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]))
-
-        # extract region features
-        if self.learnt_init_query:
-            target = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1])
-        else:
-            target = output_memory.gather(dim=1, \
-                index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1]))
-            target = target.detach()
-
-        if denoising_class is not None:
-            target = torch.concat([denoising_class, target], 1)
-
-        return target, reference_points_unact.detach(), enc_topk_bboxes, enc_topk_logits
-
-
-    def forward(self, feats, targets=None):
-
-        # input projection and embedding
-        (memory, spatial_shapes, level_start_index) = self._get_encoder_input(feats)
-        
-        # prepare denoising training
-        if self.training and self.num_denoising > 0:
-            denoising_class, 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=self.box_noise_scale, )
-        else:
-            denoising_class, denoising_bbox_unact, attn_mask, dn_meta = None, None, None, None
-
-        target, init_ref_points_unact, enc_topk_bboxes, enc_topk_logits = \
-            self._get_decoder_input(memory, spatial_shapes, denoising_class, denoising_bbox_unact)
-
-        # decoder
-        out_bboxes, out_logits = self.decoder(
-            target,
-            init_ref_points_unact,
-            memory,
-            spatial_shapes,
-            level_start_index,
-            self.dec_bbox_head,
-            self.dec_score_head,
-            self.query_pos_head,
-            attn_mask=attn_mask)
-
-        if self.training and dn_meta is not None:
-            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)
-
-        out = {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1]}
-
-        if self.training and self.aux_loss:
-            out['aux_outputs'] = self._set_aux_loss(out_logits[:-1], out_bboxes[:-1])
-            out['aux_outputs'].extend(self._set_aux_loss([enc_topk_logits], [enc_topk_bboxes]))
-            
-            if self.training and dn_meta is not None:
-                out['dn_aux_outputs'] = self._set_aux_loss(dn_out_logits, dn_out_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)]