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""" |
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DETR Transformer class. |
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Copy-paste from torch.nn.Transformer with modifications: |
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* positional encodings are passed in MHattention |
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* extra LN at the end of encoder is removed |
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* decoder returns a stack of activations from all decoding layers |
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""" |
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import copy |
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from typing import Optional, List |
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import torch |
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import torch.nn.functional as F |
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from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ |
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from torch import nn, Tensor |
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class Transformer(nn.Module): |
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""" |
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Modified based on deformable transformer to enable multi-scale. |
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""" |
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def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, |
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num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False, num_feature_levels=1, |
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return_intermediate_dec=False): |
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super().__init__() |
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encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, |
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dropout, activation, normalize_before) |
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encoder_norm = nn.LayerNorm(d_model) if normalize_before else None |
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self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) |
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decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, |
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dropout, activation, normalize_before) |
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decoder_norm = nn.LayerNorm(d_model) |
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self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, |
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return_intermediate=return_intermediate_dec) |
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self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) |
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self._reset_parameters() |
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self.d_model = d_model |
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self.nhead = nhead |
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def _reset_parameters(self): |
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for p in self.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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normal_(self.level_embed) |
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def forward(self, src, mask, pos_embed, query_embed=None, lang_feat=None): |
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src_flatten = [] |
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mask_flatten = [] |
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lvl_pos_embed_flatten = [] |
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for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): |
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bs, c, h, w = src.shape |
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src = src.flatten(2).transpose(1, 2) |
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mask = mask.flatten(1) |
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pos_embed = pos_embed.flatten(2).transpose(1, 2) |
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lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) |
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lvl_pos_embed_flatten.append(lvl_pos_embed) |
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src_flatten.append(src) |
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mask_flatten.append(mask) |
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src_flatten = torch.cat(src_flatten, 1).transpose(0, 1) |
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mask_flatten = torch.cat(mask_flatten, 1).transpose(0, 1) |
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lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1).transpose(0, 1) |
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query_embed, tgt = torch.split(query_embed, c, dim=1) |
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query_embed = query_embed.unsqueeze(1).expand(-1, bs, -1) |
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tgt = tgt.unsqueeze(1).expand(-1, bs, -1) |
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lang_feat = lang_feat.transpose(0, 1) |
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query_embed = query_embed + lang_feat |
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memory = self.encoder(src_flatten, src_key_padding_mask=mask_flatten, pos=lvl_pos_embed_flatten) |
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hs = self.decoder(tgt, memory, memory_key_padding_mask=mask_flatten, |
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pos=lvl_pos_embed_flatten, query_pos=query_embed) |
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return hs.transpose(1, 2), |
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class TransformerEncoder(nn.Module): |
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def __init__(self, encoder_layer, num_layers, norm=None): |
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super().__init__() |
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self.layers = _get_clones(encoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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def forward(self, src, |
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mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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output = src |
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for layer in self.layers: |
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output = layer(output, src_mask=mask, |
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src_key_padding_mask=src_key_padding_mask, pos=pos) |
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if self.norm is not None: |
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output = self.norm(output) |
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return output |
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class TransformerDecoder(nn.Module): |
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def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): |
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super().__init__() |
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self.layers = _get_clones(decoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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self.return_intermediate = return_intermediate |
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def forward(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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output = tgt |
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intermediate = [] |
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for layer in self.layers: |
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output = layer(output, memory, tgt_mask=tgt_mask, |
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memory_mask=memory_mask, |
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tgt_key_padding_mask=tgt_key_padding_mask, |
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memory_key_padding_mask=memory_key_padding_mask, |
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pos=pos, query_pos=query_pos) |
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if self.return_intermediate: |
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intermediate.append(self.norm(output)) |
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if self.norm is not None: |
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output = self.norm(output) |
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if self.return_intermediate: |
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intermediate.pop() |
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intermediate.append(output) |
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if self.return_intermediate: |
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return torch.stack(intermediate) |
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return output.unsqueeze(0) |
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class TransformerEncoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, |
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src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(src, pos) |
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src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, |
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key_padding_mask=src_key_padding_mask)[0] |
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src = src + self.dropout1(src2) |
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src = self.norm1(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) |
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src = src + self.dropout2(src2) |
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src = self.norm2(src) |
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return src |
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def forward_pre(self, src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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src2 = self.norm1(src) |
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q = k = self.with_pos_embed(src2, pos) |
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src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, |
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key_padding_mask=src_key_padding_mask)[0] |
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src = src + self.dropout1(src2) |
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src2 = self.norm2(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) |
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src = src + self.dropout2(src2) |
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return src |
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def forward(self, src, |
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src_mask: Optional[Tensor] = None, |
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src_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(src, src_mask, src_key_padding_mask, pos) |
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return self.forward_post(src, src_mask, src_key_padding_mask, pos) |
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class TransformerDecoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.norm3 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.dropout3 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(tgt, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout1(tgt2) |
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tgt = self.norm1(tgt) |
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask)[0] |
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tgt = tgt + self.dropout2(tgt2) |
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tgt = self.norm2(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) |
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tgt = tgt + self.dropout3(tgt2) |
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tgt = self.norm3(tgt) |
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return tgt |
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def forward_pre(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2 = self.norm1(tgt) |
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q = k = self.with_pos_embed(tgt2, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout1(tgt2) |
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tgt2 = self.norm2(tgt) |
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask)[0] |
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tgt = tgt + self.dropout2(tgt2) |
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tgt2 = self.norm3(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
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tgt = tgt + self.dropout3(tgt2) |
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return tgt |
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def forward(self, tgt, memory, |
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tgt_mask: Optional[Tensor] = None, |
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memory_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(tgt, memory, tgt_mask, memory_mask, |
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) |
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return self.forward_post(tgt, memory, tgt_mask, memory_mask, |
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) |
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def _get_clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
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def build_transformer(args): |
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return Transformer( |
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d_model=args.hidden_dim, |
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nhead=args.nheads, |
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num_encoder_layers=args.enc_layers, |
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num_decoder_layers=args.dec_layers, |
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dim_feedforward=args.dim_feedforward, |
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dropout=args.dropout, |
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activation="relu", |
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return_intermediate_dec=True, |
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num_feature_levels=args.num_feature_levels) |
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def _get_activation_fn(activation): |
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"""Return an activation function given a string""" |
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if activation == "relu": |
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return F.relu |
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if activation == "gelu": |
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return F.gelu |
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if activation == "glu": |
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return F.glu |
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |
