# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Modified from Deformable-DETR (https://github.com/fundamentalvision/Deformable-DETR) # Copyright (c) 2020 SenseTime. All Rights Reserved. from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from ppdet.core.workspace import register from ..layers import MultiHeadAttention from .position_encoding import PositionEmbedding from .utils import _get_clones, get_valid_ratio from ..initializer import linear_init_, constant_, xavier_uniform_, normal_ __all__ = ['DeformableTransformer'] class MSDeformableAttention(nn.Layer): def __init__(self, embed_dim=256, num_heads=8, num_levels=4, num_points=4, lr_mult=0.1): """ 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, weight_attr=ParamAttr(learning_rate=lr_mult), bias_attr=ParamAttr(learning_rate=lr_mult)) 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) try: # use cuda op from deformable_detr_ops import ms_deformable_attn except: # use paddle func from .utils import deformable_attention_core_func as ms_deformable_attn self.ms_deformable_attn_core = ms_deformable_attn self._reset_parameters() def _reset_parameters(self): # sampling_offsets constant_(self.sampling_offsets.weight) thetas = paddle.arange( self.num_heads, dtype=paddle.float32) * (2.0 * math.pi / self.num_heads) grid_init = paddle.stack([thetas.cos(), thetas.sin()], -1) grid_init = grid_init / grid_init.abs().max(-1, keepdim=True) grid_init = grid_init.reshape([self.num_heads, 1, 1, 2]).tile( [1, self.num_levels, self.num_points, 1]) scaling = paddle.arange( 1, self.num_points + 1, dtype=paddle.float32).reshape([1, 1, -1, 1]) grid_init *= scaling self.sampling_offsets.bias.set_value(grid_init.flatten()) # attention_weights constant_(self.attention_weights.weight) constant_(self.attention_weights.bias) # proj xavier_uniform_(self.value_proj.weight) constant_(self.value_proj.bias) xavier_uniform_(self.output_proj.weight) constant_(self.output_proj.bias) def forward(self, query, reference_points, value, value_spatial_shapes, value_level_start_index, 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 (Tensor): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] value_level_start_index (Tensor(int64)): [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] assert int(value_spatial_shapes.prod(1).sum()) == Len_v 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, -1).reshape( [bs, Len_q, self.num_heads, self.num_levels, self.num_points]) offset_normalizer = value_spatial_shapes.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 output = self.ms_deformable_attn_core( value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) output = self.output_proj(output) return output class DeformableTransformerEncoderLayer(nn.Layer): def __init__(self, d_model=256, n_head=8, dim_feedforward=1024, dropout=0.1, activation="relu", n_levels=4, n_points=4, weight_attr=None, bias_attr=None): super(DeformableTransformerEncoderLayer, self).__init__() # self attention self.self_attn = MSDeformableAttention(d_model, n_head, n_levels, n_points) self.dropout1 = nn.Dropout(dropout) self.norm1 = nn.LayerNorm(d_model) # ffn self.linear1 = nn.Linear(d_model, dim_feedforward, weight_attr, bias_attr) self.activation = getattr(F, activation) self.dropout2 = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr, bias_attr) self.dropout3 = nn.Dropout(dropout) self.norm2 = nn.LayerNorm(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, src): src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) src = src + self.dropout3(src2) src = self.norm2(src) return src def forward(self, src, reference_points, spatial_shapes, level_start_index, src_mask=None, pos_embed=None): # self attention src2 = self.self_attn( self.with_pos_embed(src, pos_embed), reference_points, src, spatial_shapes, level_start_index, src_mask) src = src + self.dropout1(src2) src = self.norm1(src) # ffn src = self.forward_ffn(src) return src class DeformableTransformerEncoder(nn.Layer): def __init__(self, encoder_layer, num_layers): super(DeformableTransformerEncoder, self).__init__() self.layers = _get_clones(encoder_layer, num_layers) self.num_layers = num_layers @staticmethod def get_reference_points(spatial_shapes, valid_ratios, offset=0.5): valid_ratios = valid_ratios.unsqueeze(1) reference_points = [] for i, (H, W) in enumerate(spatial_shapes): ref_y, ref_x = paddle.meshgrid( paddle.arange(end=H) + offset, paddle.arange(end=W) + offset) ref_y = ref_y.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 1] * H) ref_x = ref_x.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 0] * W) reference_points.append(paddle.stack((ref_x, ref_y), axis=-1)) reference_points = paddle.concat(reference_points, 1).unsqueeze(2) reference_points = reference_points * valid_ratios return reference_points def forward(self, src, spatial_shapes, level_start_index, src_mask=None, pos_embed=None, valid_ratios=None): output = src if valid_ratios is None: valid_ratios = paddle.ones( [src.shape[0], spatial_shapes.shape[0], 2]) reference_points = self.get_reference_points(spatial_shapes, valid_ratios) for layer in self.layers: output = layer(output, reference_points, spatial_shapes, level_start_index, src_mask, pos_embed) return output class DeformableTransformerDecoderLayer(nn.Layer): def __init__(self, d_model=256, n_head=8, dim_feedforward=1024, dropout=0.1, activation="relu", n_levels=4, n_points=4, weight_attr=None, bias_attr=None): super(DeformableTransformerDecoderLayer, self).__init__() # self attention self.self_attn = MultiHeadAttention(d_model, n_head, dropout=dropout) 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, weight_attr, bias_attr) self.activation = getattr(F, activation) self.dropout3 = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr, bias_attr) self.dropout4 = nn.Dropout(dropout) self.norm3 = nn.LayerNorm(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): tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) tgt = tgt + self.dropout4(tgt2) tgt = self.norm3(tgt) return tgt def forward(self, tgt, reference_points, memory, memory_spatial_shapes, memory_level_start_index, memory_mask=None, query_pos_embed=None): # self attention q = k = self.with_pos_embed(tgt, query_pos_embed) tgt2 = self.self_attn(q, k, value=tgt) tgt = tgt + self.dropout1(tgt2) tgt = self.norm1(tgt) # cross attention tgt2 = self.cross_attn( self.with_pos_embed(tgt, query_pos_embed), reference_points, memory, memory_spatial_shapes, memory_level_start_index, memory_mask) tgt = tgt + self.dropout2(tgt2) tgt = self.norm2(tgt) # ffn tgt = self.forward_ffn(tgt) return tgt class DeformableTransformerDecoder(nn.Layer): def __init__(self, decoder_layer, num_layers, return_intermediate=False): super(DeformableTransformerDecoder, self).__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.return_intermediate = return_intermediate def forward(self, tgt, reference_points, memory, memory_spatial_shapes, memory_level_start_index, memory_mask=None, query_pos_embed=None): output = tgt intermediate = [] for lid, layer in enumerate(self.layers): output = layer(output, reference_points, memory, memory_spatial_shapes, memory_level_start_index, memory_mask, query_pos_embed) if self.return_intermediate: intermediate.append(output) if self.return_intermediate: return paddle.stack(intermediate) return output.unsqueeze(0) @register class DeformableTransformer(nn.Layer): __shared__ = ['hidden_dim'] def __init__(self, num_queries=300, position_embed_type='sine', return_intermediate_dec=True, backbone_num_channels=[512, 1024, 2048], num_feature_levels=4, num_encoder_points=4, num_decoder_points=4, hidden_dim=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1, activation="relu", lr_mult=0.1, weight_attr=None, bias_attr=None): super(DeformableTransformer, self).__init__() assert position_embed_type in ['sine', 'learned'], \ f'ValueError: position_embed_type not supported {position_embed_type}!' assert len(backbone_num_channels) <= num_feature_levels self.hidden_dim = hidden_dim self.nhead = nhead self.num_feature_levels = num_feature_levels encoder_layer = DeformableTransformerEncoderLayer( hidden_dim, nhead, dim_feedforward, dropout, activation, num_feature_levels, num_encoder_points, weight_attr, bias_attr) self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers) decoder_layer = DeformableTransformerDecoderLayer( hidden_dim, nhead, dim_feedforward, dropout, activation, num_feature_levels, num_decoder_points, weight_attr, bias_attr) self.decoder = DeformableTransformerDecoder( decoder_layer, num_decoder_layers, return_intermediate_dec) self.level_embed = nn.Embedding(num_feature_levels, hidden_dim) self.tgt_embed = nn.Embedding(num_queries, hidden_dim) self.query_pos_embed = nn.Embedding(num_queries, hidden_dim) self.reference_points = nn.Linear( hidden_dim, 2, weight_attr=ParamAttr(learning_rate=lr_mult), bias_attr=ParamAttr(learning_rate=lr_mult)) self.input_proj = nn.LayerList() for in_channels in backbone_num_channels: self.input_proj.append( nn.Sequential( nn.Conv2D( in_channels, hidden_dim, kernel_size=1, weight_attr=weight_attr, bias_attr=bias_attr), nn.GroupNorm(32, hidden_dim))) in_channels = backbone_num_channels[-1] for _ in range(num_feature_levels - len(backbone_num_channels)): self.input_proj.append( nn.Sequential( nn.Conv2D( in_channels, hidden_dim, kernel_size=3, stride=2, padding=1, weight_attr=weight_attr, bias_attr=bias_attr), nn.GroupNorm(32, hidden_dim))) in_channels = hidden_dim self.position_embedding = PositionEmbedding( hidden_dim // 2, normalize=True if position_embed_type == 'sine' else False, embed_type=position_embed_type, offset=-0.5) self._reset_parameters() def _reset_parameters(self): normal_(self.level_embed.weight) normal_(self.tgt_embed.weight) normal_(self.query_pos_embed.weight) xavier_uniform_(self.reference_points.weight) constant_(self.reference_points.bias) for l in self.input_proj: xavier_uniform_(l[0].weight) constant_(l[0].bias) @classmethod def from_config(cls, cfg, input_shape): return {'backbone_num_channels': [i.channels for i in input_shape], } def forward(self, src_feats, src_mask=None, *args, **kwargs): srcs = [] for i in range(len(src_feats)): srcs.append(self.input_proj[i](src_feats[i])) if self.num_feature_levels > len(srcs): len_srcs = len(srcs) for i in range(len_srcs, self.num_feature_levels): if i == len_srcs: srcs.append(self.input_proj[i](src_feats[-1])) else: srcs.append(self.input_proj[i](srcs[-1])) src_flatten = [] mask_flatten = [] lvl_pos_embed_flatten = [] spatial_shapes = [] valid_ratios = [] for level, src in enumerate(srcs): bs, _, h, w = paddle.shape(src) spatial_shapes.append(paddle.concat([h, w])) src = src.flatten(2).transpose([0, 2, 1]) src_flatten.append(src) if src_mask is not None: mask = F.interpolate(src_mask.unsqueeze(0), size=(h, w))[0] else: mask = paddle.ones([bs, h, w]) valid_ratios.append(get_valid_ratio(mask)) pos_embed = self.position_embedding(mask).flatten(1, 2) lvl_pos_embed = pos_embed + self.level_embed.weight[level] lvl_pos_embed_flatten.append(lvl_pos_embed) mask = mask.flatten(1) mask_flatten.append(mask) src_flatten = paddle.concat(src_flatten, 1) mask_flatten = None if src_mask is None else paddle.concat(mask_flatten, 1) lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1) # [l, 2] spatial_shapes = paddle.to_tensor( paddle.stack(spatial_shapes).astype('int64')) # [l], 每一个level的起始index level_start_index = paddle.concat([ paddle.zeros( [1], dtype='int64'), spatial_shapes.prod(1).cumsum(0)[:-1] ]) # [b, l, 2] valid_ratios = paddle.stack(valid_ratios, 1) # encoder memory = self.encoder(src_flatten, spatial_shapes, level_start_index, mask_flatten, lvl_pos_embed_flatten, valid_ratios) # prepare input for decoder bs, _, c = memory.shape query_embed = self.query_pos_embed.weight.unsqueeze(0).tile([bs, 1, 1]) tgt = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1]) reference_points = F.sigmoid(self.reference_points(query_embed)) reference_points_input = reference_points.unsqueeze( 2) * valid_ratios.unsqueeze(1) # decoder hs = self.decoder(tgt, reference_points_input, memory, spatial_shapes, level_start_index, mask_flatten, query_embed) return (hs, memory, reference_points)