527 lines
21 KiB
Python
527 lines
21 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Modified from Deformable-DETR (https://github.com/fundamentalvision/Deformable-DETR)
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Modified from detrex (https://github.com/IDEA-Research/detrex)
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# Copyright 2022 The IDEA Authors. All rights reserved.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.regularizer import L2Decay
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from ppdet.core.workspace import register
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from ..layers import MultiHeadAttention
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from .position_encoding import PositionEmbedding
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from .deformable_transformer import (MSDeformableAttention,
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DeformableTransformerEncoderLayer,
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DeformableTransformerEncoder)
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from ..initializer import (linear_init_, constant_, xavier_uniform_, normal_,
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bias_init_with_prob)
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from .utils import (_get_clones, get_valid_ratio,
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get_contrastive_denoising_training_group,
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get_sine_pos_embed, inverse_sigmoid, MLP)
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__all__ = ['DINOTransformer']
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class DINOTransformerDecoderLayer(nn.Layer):
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def __init__(self,
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d_model=256,
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n_head=8,
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dim_feedforward=1024,
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dropout=0.,
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activation="relu",
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n_levels=4,
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n_points=4,
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lr_mult=1.0,
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weight_attr=None,
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bias_attr=None):
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super(DINOTransformerDecoderLayer, self).__init__()
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# self attention
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self.self_attn = MultiHeadAttention(d_model, n_head, dropout=dropout)
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self.dropout1 = nn.Dropout(dropout)
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self.norm1 = nn.LayerNorm(
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d_model, weight_attr=weight_attr, bias_attr=bias_attr)
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# cross attention
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self.cross_attn = MSDeformableAttention(d_model, n_head, n_levels,
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n_points, lr_mult)
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self.dropout2 = nn.Dropout(dropout)
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self.norm2 = nn.LayerNorm(
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d_model, weight_attr=weight_attr, bias_attr=bias_attr)
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# ffn
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.activation = getattr(F, activation)
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self.dropout3 = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.dropout4 = nn.Dropout(dropout)
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self.norm3 = nn.LayerNorm(
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d_model, weight_attr=weight_attr, bias_attr=bias_attr)
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self._reset_parameters()
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def _reset_parameters(self):
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linear_init_(self.linear1)
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linear_init_(self.linear2)
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xavier_uniform_(self.linear1.weight)
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xavier_uniform_(self.linear2.weight)
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def with_pos_embed(self, tensor, pos):
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return tensor if pos is None else tensor + pos
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def forward_ffn(self, tgt):
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return self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
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def forward(self,
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tgt,
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reference_points,
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memory,
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memory_spatial_shapes,
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memory_level_start_index,
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attn_mask=None,
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memory_mask=None,
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query_pos_embed=None):
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# self attention
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q = k = self.with_pos_embed(tgt, query_pos_embed)
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if attn_mask is not None:
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attn_mask = paddle.where(
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attn_mask.astype('bool'),
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paddle.zeros(attn_mask.shape, tgt.dtype),
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paddle.full(attn_mask.shape, float("-inf"), tgt.dtype))
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tgt2 = self.self_attn(q, k, value=tgt, attn_mask=attn_mask)
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tgt = tgt + self.dropout1(tgt2)
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tgt = self.norm1(tgt)
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# cross attention
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tgt2 = self.cross_attn(
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self.with_pos_embed(tgt, query_pos_embed), reference_points, memory,
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memory_spatial_shapes, memory_level_start_index, memory_mask)
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tgt = tgt + self.dropout2(tgt2)
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tgt = self.norm2(tgt)
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# ffn
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tgt2 = self.forward_ffn(tgt)
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tgt = tgt + self.dropout4(tgt2)
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tgt = self.norm3(tgt)
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return tgt
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class DINOTransformerDecoder(nn.Layer):
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def __init__(self,
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hidden_dim,
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decoder_layer,
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num_layers,
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weight_attr=None,
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bias_attr=None):
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super(DINOTransformerDecoder, self).__init__()
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self.layers = _get_clones(decoder_layer, num_layers)
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self.hidden_dim = hidden_dim
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self.num_layers = num_layers
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self.norm = nn.LayerNorm(
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hidden_dim, weight_attr=weight_attr, bias_attr=bias_attr)
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def forward(self,
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tgt,
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ref_points_unact,
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memory,
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memory_spatial_shapes,
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memory_level_start_index,
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bbox_head,
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query_pos_head,
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valid_ratios=None,
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attn_mask=None,
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memory_mask=None):
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if valid_ratios is None:
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valid_ratios = paddle.ones(
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[memory.shape[0], memory_spatial_shapes.shape[0], 2])
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output = tgt
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intermediate = []
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inter_bboxes = []
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ref_points = F.sigmoid(ref_points_unact)
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for i, layer in enumerate(self.layers):
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reference_points_input = ref_points.detach().unsqueeze(
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2) * valid_ratios.tile([1, 1, 2]).unsqueeze(1)
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query_pos_embed = get_sine_pos_embed(
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reference_points_input[..., 0, :], self.hidden_dim // 2)
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query_pos_embed = query_pos_head(query_pos_embed)
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output = layer(output, reference_points_input, memory,
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memory_spatial_shapes, memory_level_start_index,
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attn_mask, memory_mask, query_pos_embed)
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ref_points = F.sigmoid(bbox_head[i](output) + inverse_sigmoid(
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ref_points.detach()))
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intermediate.append(self.norm(output))
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inter_bboxes.append(ref_points)
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return paddle.stack(intermediate), paddle.stack(inter_bboxes)
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@register
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class DINOTransformer(nn.Layer):
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__shared__ = ['num_classes', 'hidden_dim']
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def __init__(self,
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num_classes=80,
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hidden_dim=256,
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num_queries=900,
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position_embed_type='sine',
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in_feats_channel=[512, 1024, 2048],
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num_levels=4,
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num_encoder_points=4,
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num_decoder_points=4,
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nhead=8,
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num_encoder_layers=6,
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num_decoder_layers=6,
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dim_feedforward=1024,
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dropout=0.,
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activation="relu",
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lr_mult=1.0,
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pe_temperature=10000,
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pe_offset=-0.5,
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num_denoising=100,
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label_noise_ratio=0.5,
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box_noise_scale=1.0,
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learnt_init_query=True,
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eps=1e-2):
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super(DINOTransformer, self).__init__()
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assert position_embed_type in ['sine', 'learned'], \
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f'ValueError: position_embed_type not supported {position_embed_type}!'
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assert len(in_feats_channel) <= num_levels
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self.hidden_dim = hidden_dim
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self.nhead = nhead
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self.num_levels = num_levels
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self.num_classes = num_classes
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self.num_queries = num_queries
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self.eps = eps
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self.num_decoder_layers = num_decoder_layers
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weight_attr = ParamAttr(regularizer=L2Decay(0.0))
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bias_attr = ParamAttr(regularizer=L2Decay(0.0))
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# backbone feature projection
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self._build_input_proj_layer(in_feats_channel, weight_attr, bias_attr)
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# Transformer module
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encoder_layer = DeformableTransformerEncoderLayer(
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hidden_dim, nhead, dim_feedforward, dropout, activation, num_levels,
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num_encoder_points, lr_mult, weight_attr, bias_attr)
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self.encoder = DeformableTransformerEncoder(encoder_layer,
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num_encoder_layers)
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decoder_layer = DINOTransformerDecoderLayer(
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hidden_dim, nhead, dim_feedforward, dropout, activation, num_levels,
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num_decoder_points, lr_mult, weight_attr, bias_attr)
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self.decoder = DINOTransformerDecoder(hidden_dim, decoder_layer,
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num_decoder_layers, weight_attr,
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bias_attr)
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# denoising part
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self.denoising_class_embed = nn.Embedding(
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num_classes,
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hidden_dim,
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weight_attr=ParamAttr(initializer=nn.initializer.Normal()))
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self.num_denoising = num_denoising
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self.label_noise_ratio = label_noise_ratio
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self.box_noise_scale = box_noise_scale
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# position embedding
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self.position_embedding = PositionEmbedding(
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hidden_dim // 2,
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temperature=pe_temperature,
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normalize=True if position_embed_type == 'sine' else False,
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embed_type=position_embed_type,
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offset=pe_offset)
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self.level_embed = nn.Embedding(num_levels, hidden_dim)
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# decoder embedding
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self.learnt_init_query = learnt_init_query
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if learnt_init_query:
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self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
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self.query_pos_head = MLP(2 * hidden_dim,
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hidden_dim,
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hidden_dim,
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num_layers=2)
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# encoder head
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self.enc_output = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(
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hidden_dim, weight_attr=weight_attr, bias_attr=bias_attr))
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self.enc_score_head = nn.Linear(hidden_dim, num_classes)
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self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, num_layers=3)
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# decoder head
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self.dec_score_head = nn.LayerList([
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nn.Linear(hidden_dim, num_classes)
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for _ in range(num_decoder_layers)
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])
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self.dec_bbox_head = nn.LayerList([
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MLP(hidden_dim, hidden_dim, 4, num_layers=3)
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for _ in range(num_decoder_layers)
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])
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self._reset_parameters()
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def _reset_parameters(self):
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# class and bbox head init
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bias_cls = bias_init_with_prob(0.01)
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linear_init_(self.enc_score_head)
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constant_(self.enc_score_head.bias, bias_cls)
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constant_(self.enc_bbox_head.layers[-1].weight)
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constant_(self.enc_bbox_head.layers[-1].bias)
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for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
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linear_init_(cls_)
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constant_(cls_.bias, bias_cls)
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constant_(reg_.layers[-1].weight)
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constant_(reg_.layers[-1].bias)
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linear_init_(self.enc_output[0])
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xavier_uniform_(self.enc_output[0].weight)
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normal_(self.level_embed.weight)
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if self.learnt_init_query:
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xavier_uniform_(self.tgt_embed.weight)
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xavier_uniform_(self.query_pos_head.layers[0].weight)
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xavier_uniform_(self.query_pos_head.layers[1].weight)
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for l in self.input_proj:
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xavier_uniform_(l[0].weight)
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constant_(l[0].bias)
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@classmethod
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def from_config(cls, cfg, input_shape):
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return {'in_feats_channel': [i.channels for i in input_shape], }
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def _build_input_proj_layer(self,
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in_feats_channel,
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weight_attr=None,
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bias_attr=None):
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self.input_proj = nn.LayerList()
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for in_channels in in_feats_channel:
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self.input_proj.append(
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nn.Sequential(
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('conv', nn.Conv2D(
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in_channels, self.hidden_dim, kernel_size=1)), (
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'norm', nn.GroupNorm(
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32,
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self.hidden_dim,
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weight_attr=weight_attr,
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bias_attr=bias_attr))))
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in_channels = in_feats_channel[-1]
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for _ in range(self.num_levels - len(in_feats_channel)):
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self.input_proj.append(
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nn.Sequential(
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('conv', nn.Conv2D(
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in_channels,
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self.hidden_dim,
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kernel_size=3,
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stride=2,
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padding=1)), ('norm', nn.GroupNorm(
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32,
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self.hidden_dim,
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weight_attr=weight_attr,
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bias_attr=bias_attr))))
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in_channels = self.hidden_dim
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def _get_encoder_input(self, feats, pad_mask=None):
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# get projection features
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proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)]
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if self.num_levels > len(proj_feats):
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len_srcs = len(proj_feats)
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for i in range(len_srcs, self.num_levels):
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if i == len_srcs:
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proj_feats.append(self.input_proj[i](feats[-1]))
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else:
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proj_feats.append(self.input_proj[i](proj_feats[-1]))
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# get encoder inputs
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feat_flatten = []
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mask_flatten = []
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lvl_pos_embed_flatten = []
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spatial_shapes = []
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valid_ratios = []
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for i, feat in enumerate(proj_feats):
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bs, _, h, w = paddle.shape(feat)
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spatial_shapes.append(paddle.stack([h, w]))
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# [b,c,h,w] -> [b,h*w,c]
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feat_flatten.append(feat.flatten(2).transpose([0, 2, 1]))
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if pad_mask is not None:
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mask = F.interpolate(pad_mask.unsqueeze(0), size=(h, w))[0]
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else:
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mask = paddle.ones([bs, h, w])
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valid_ratios.append(get_valid_ratio(mask))
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# [b, h*w, c]
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pos_embed = self.position_embedding(mask).flatten(1, 2)
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lvl_pos_embed = pos_embed + self.level_embed.weight[i]
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lvl_pos_embed_flatten.append(lvl_pos_embed)
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if pad_mask is not None:
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# [b, h*w]
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mask_flatten.append(mask.flatten(1))
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# [b, l, c]
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feat_flatten = paddle.concat(feat_flatten, 1)
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# [b, l]
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mask_flatten = None if pad_mask is None else paddle.concat(mask_flatten,
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1)
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# [b, l, c]
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lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1)
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# [num_levels, 2]
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spatial_shapes = paddle.to_tensor(
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paddle.stack(spatial_shapes).astype('int64'))
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# [l] start index of each level
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level_start_index = paddle.concat([
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paddle.zeros(
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[1], dtype='int64'), spatial_shapes.prod(1).cumsum(0)[:-1]
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])
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# [b, num_levels, 2]
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valid_ratios = paddle.stack(valid_ratios, 1)
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return (feat_flatten, spatial_shapes, level_start_index, mask_flatten,
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lvl_pos_embed_flatten, valid_ratios)
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def forward(self, feats, pad_mask=None, gt_meta=None):
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# input projection and embedding
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(feat_flatten, spatial_shapes, level_start_index, mask_flatten,
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lvl_pos_embed_flatten,
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valid_ratios) = self._get_encoder_input(feats, pad_mask)
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# encoder
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memory = self.encoder(feat_flatten, spatial_shapes, level_start_index,
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mask_flatten, lvl_pos_embed_flatten, valid_ratios)
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# prepare denoising training
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if self.training:
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denoising_class, denoising_bbox_unact, attn_mask, dn_meta = \
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get_contrastive_denoising_training_group(gt_meta,
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self.num_classes,
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self.num_queries,
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self.denoising_class_embed.weight,
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self.num_denoising,
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self.label_noise_ratio,
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self.box_noise_scale)
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else:
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denoising_class, denoising_bbox_unact, attn_mask, dn_meta = None, None, None, None
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target, init_ref_points_unact, enc_topk_bboxes, enc_topk_logits = \
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self._get_decoder_input(
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memory, spatial_shapes, mask_flatten, denoising_class,
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denoising_bbox_unact)
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# decoder
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inter_feats, inter_bboxes = self.decoder(
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target, init_ref_points_unact, memory, spatial_shapes,
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level_start_index, self.dec_bbox_head, self.query_pos_head,
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valid_ratios, attn_mask, mask_flatten)
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out_bboxes = []
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out_logits = []
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for i in range(self.num_decoder_layers):
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out_logits.append(self.dec_score_head[i](inter_feats[i]))
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if i == 0:
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out_bboxes.append(
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F.sigmoid(self.dec_bbox_head[i](inter_feats[i]) +
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init_ref_points_unact))
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else:
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out_bboxes.append(
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F.sigmoid(self.dec_bbox_head[i](inter_feats[i]) +
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inverse_sigmoid(inter_bboxes[i - 1])))
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out_bboxes = paddle.stack(out_bboxes)
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out_logits = paddle.stack(out_logits)
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return (out_bboxes, out_logits, enc_topk_bboxes, enc_topk_logits,
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dn_meta)
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def _get_encoder_output_anchors(self,
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memory,
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spatial_shapes,
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memory_mask=None,
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grid_size=0.05):
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output_anchors = []
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idx = 0
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for lvl, (h, w) in enumerate(spatial_shapes):
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if memory_mask is not None:
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mask_ = memory_mask[:, idx:idx + h * w].reshape([-1, h, w])
|
|
valid_H = paddle.sum(mask_[:, :, 0], 1)
|
|
valid_W = paddle.sum(mask_[:, 0, :], 1)
|
|
else:
|
|
valid_H, valid_W = h, w
|
|
|
|
grid_y, grid_x = paddle.meshgrid(
|
|
paddle.arange(end=h), paddle.arange(end=w))
|
|
grid_xy = paddle.stack([grid_x, grid_y], -1).astype(memory.dtype)
|
|
|
|
valid_WH = paddle.stack([valid_W, valid_H], -1).reshape(
|
|
[-1, 1, 1, 2]).astype(grid_xy.dtype)
|
|
grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH
|
|
wh = paddle.ones_like(grid_xy) * grid_size * (2.0**lvl)
|
|
output_anchors.append(
|
|
paddle.concat([grid_xy, wh], -1).reshape([-1, h * w, 4]))
|
|
idx += h * w
|
|
|
|
output_anchors = paddle.concat(output_anchors, 1)
|
|
valid_mask = ((output_anchors > self.eps) *
|
|
(output_anchors < 1 - self.eps)).all(-1, keepdim=True)
|
|
output_anchors = paddle.log(output_anchors / (1 - output_anchors))
|
|
if memory_mask is not None:
|
|
valid_mask = (valid_mask * (memory_mask.unsqueeze(-1) > 0)) > 0
|
|
output_anchors = paddle.where(valid_mask, output_anchors,
|
|
paddle.to_tensor(float("inf")))
|
|
|
|
memory = paddle.where(valid_mask, memory, paddle.to_tensor(0.))
|
|
output_memory = self.enc_output(memory)
|
|
return output_memory, output_anchors
|
|
|
|
def _get_decoder_input(self,
|
|
memory,
|
|
spatial_shapes,
|
|
memory_mask=None,
|
|
denoising_class=None,
|
|
denoising_bbox_unact=None):
|
|
bs, _, _ = memory.shape
|
|
# prepare input for decoder
|
|
output_memory, output_anchors = self._get_encoder_output_anchors(
|
|
memory, spatial_shapes, memory_mask)
|
|
enc_outputs_class = self.enc_score_head(output_memory)
|
|
enc_outputs_coord_unact = self.enc_bbox_head(
|
|
output_memory) + output_anchors
|
|
|
|
_, topk_ind = paddle.topk(
|
|
enc_outputs_class.max(-1), self.num_queries, axis=1)
|
|
# extract region proposal boxes
|
|
batch_ind = paddle.arange(end=bs).astype(topk_ind.dtype)
|
|
batch_ind = batch_ind.unsqueeze(-1).tile([1, self.num_queries])
|
|
topk_ind = paddle.stack([batch_ind, topk_ind], axis=-1)
|
|
reference_points_unact = paddle.gather_nd(enc_outputs_coord_unact,
|
|
topk_ind) # unsigmoided.
|
|
enc_topk_bboxes = F.sigmoid(reference_points_unact)
|
|
if denoising_bbox_unact is not None:
|
|
reference_points_unact = paddle.concat(
|
|
[denoising_bbox_unact, reference_points_unact], 1)
|
|
enc_topk_logits = paddle.gather_nd(enc_outputs_class, topk_ind)
|
|
|
|
# extract region features
|
|
if self.learnt_init_query:
|
|
target = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1])
|
|
else:
|
|
target = paddle.gather_nd(output_memory, topk_ind).detach()
|
|
if denoising_class is not None:
|
|
target = paddle.concat([denoising_class, target], 1)
|
|
|
|
return target, reference_points_unact.detach(
|
|
), enc_topk_bboxes, enc_topk_logits |