Upload rtdetr_decoder.py

src/zoo/rtdetr/rtdetr_decoder.py

@@ -0,0 +1,664 @@
+"""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
+import torch.linalg
+
+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,
+                 cope='none',):
+        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)
+        
+        if cope == '24':
+          self.cope = CoPE(24,d_model)
+        elif cope == '12':
+          self.cope = CoPE(12,d_model)
+        else: 
+          self.cope = None
+        
+        # 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)                                         #position_embedding
+ 
+        
+       # n_tgt = tgt.cpu().detach().numpy()
+        
+        #itgt = tgt.new_tensor(np.array([sl.pinv(i) for i in n_tgt]))                        #inv_tgt
+        
+#        with torch.no_grad():
+#          try:
+#            itgt = torch.linalg.pinv(tgt)
+#          except:
+#            print('wrong!!')
+#            itgt = torch.pinverse(tgt.detach().cpu()).cuda()
+#        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))
+        
+        if self.cope == None:
+          q = k = self.with_pos_embed(tgt, query_pos_embed)
+        else:
+          qk = torch.bmm (tgt ,tgt.transpose(-1 ,-2))
+          query_pos_embed = self.cope(tgt,qk)
+          with torch.no_grad():
+            try:
+              itgt = torch.linalg.pinv(tgt)
+            except:
+              print('wrong!!')
+              itgt = torch.pinverse(tgt.detach().cpu()).cuda()
+          k = tgt 
+          q = tgt + ([email protected](-1,-2))
+          
+          
+        tgt2, _ = self.self_attn(q, k, value=tgt, attn_mask=attn_mask)
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # cross attention
+        if self.cope:
+          tgt2 = self.cross_attn(\
+              self.with_pos_embed(tgt, [email protected](-1,-2)),#([email protected](-1,-2))),  #self.with_pos_embed(tgt, query_pos_embed),
+              reference_points, 
+              memory, 
+              memory_spatial_shapes, 
+              memory_mask)
+        else:
+          tgt2 = self.cross_attn(\
+              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,
+                 cope='None',):
+
+        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,cope)
+        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)]